modelling flood extent with 3b42 satellite rainfall
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THE UNIVERSITY OF HULL
Flood plain modelling based on TRMM 3b42 satellite rainfall.
Lessons learned from the Semois basin
being a Dissertation submitted in partial f~lfilment of the requirements for the-
Degree of Master of Science
GIS & Environmental Modelling
in the University of Hull
by
Albert Grela, Agr Eng
September 2010
Flood plain modelling based on TRMM 3b42 satellite rainfall.
Lessons learned from the Semois basin
Summary
The extensive flood damages sustained worldwide cost approximately 20 billions f: a year.
These natural catastrophes have a certain level of predictability allowing preparedness,
mitigation and defences. The key to minimizing the damages lies in the precision of the
prediction of the flood extent and dynamic. The present study explores the error propagation
from satellite rainfall estimates into the modelling of the flood plain extent in the Semois
basin (Belgium). The area near Villers-sur-Semois (49.69° Lat N, 5.56° Long E, 5.8 ha) has
been modelled with a ID2D software suites (ISIS free) forced with discharge inputs from 3
different sources : the observed gauged flow, the simulated flow based on rain gauges data
and the simulated flow based on the 3b42 v6. multi-satellite product of the Tropical Rain
Monitoring Mission (TRMM) providing a rainfall rate every 3hours for a cell size of 0.25
deg. Precipitations were transformed into dischal'ge with the revitalised FSRlFEH rainfall
runoff method which is part the Flood Estimation Handbook in UK. The flood plain based on
the 3b42 dataappears grossly overestimated (135% of the observed flow flood extent) or
underestimated (50%) depending on the event chosen, c1early dismissing the adequacy of the
dataset. The flood plain modelled with the observed discharge and the one modelled based on
the rain gauge data appear quite similar in their maximum extent but present a different
temporal dynamic. They present a good similarity with the flood risk map pl'epared by the
regional authorities. The modelled flow based on the rain gauges has a Nash Sutcliffe
coefficient of efficiency between 0.23.and 0.33 according to the event modelled.
Additional online presentation of the study :
http://web.me.comlalbert.grelal3b42_flood...J)lain/Welcome.html
Abstract
Abstract
The extensive flood damages sustained worldwide cost approximately 20 billions f a
year. These natural catastrophes have a certain level of predictability allowing
preparedness, mitigation and defences. The key to minimizing the damages lies in
the precision of the prediction of the flood extent and dynamic. The present study
explores the error propagation from satellite rainfall estimates into the modelling of
the flood plain extent in the Semois basin (Belgium). The area near Villers-sur
Semois (49.69° Lat N, 5.56° Long E , 5.8 ,ha) has been modelled with a ID2D
software suites (ISIS free) forced with discharge inputs from 3 different sources: the
observed gauged flow, the simulated flow based on rain gauges data and the
simulated flow based on the 3b42 v6. multi-satellite product of the Tropical Rain
Monitoring Mission (TRMM) providing a rainfall rate every 3hours for a cell size of
0.25 deg. Rain were transformed into discharge with the revitalised FSRlFEH
rainfall-runoff method which is part the Flood EstimatIon Handbook in UK. The
flood plain based on the 3b42 data appears grossly overestimated (135% of the
observed flow flood extent) or underestimated (50%) depending on the event
chosen, clearly dismissing the adequacy of the dataset. The flood plain modelled
with the observed discharge and the one modelled based on the rain gauge data
appear quite similar in their maximum extent but present a different temporal
dynamic. They present a good similarity with the flood risk map prepared by the
regional authorities. The modelled flow based on the rain gauges has a Nash
Sutcliffe coefficient of efficiency between 0.23.and 0.33 according to the event
modelled.
Acknowledgements
Acknowledgements
My deepest gratitude is due to my supervisor Dr. Tim Bellerby, University of Hull who corrected many ofmy misunderstanding regarding the 3b42 rain product and helped me to focus my efforts torward sorne achievable aims.
1 would have remained clueless about flood modelling without the careful tutoring of Dr. Matt Horris and Dr. Mark Bailes from Halcrow. They are great trainers and very experienced modellers.
Professor Keith Beven from Lancaster University de serves a special mention here. His books have been a real guiding light in the complex world of hydrological uncertainties. 1 like to express my admiration for the depth of his knowledge and his very pedagogic writing style. .
1 got excellent response and supply of data and information from Eng. Phillipe Dierickx from "Direction de la Gestion hydrologique intégrée, Service Public de Wallonie" and Eng. Didier de Thysebaert "Direction des Cours d'Eau non navigables - Service Public de Wallonie". They both de serve my sincere thanks.
Marina Thunus from "Direction de la Gestion hydrologique intégrée - Service Public de Wallonie" and Cecile Motte "Cellule CARTO, Service Public de Wallonie" have been very cooperative in the supply of cartographic data. Their contribution is acknowledged.
Dave Brooks, Alex Duke, John Goodman, Alan Wright, Geoff Watson, Martin Burton, Gerry Stephenson from the Cottingham Flood Action group were excellent guide in the
intricate world of flood defence planning in UK. They provided me a lot of inspiration and their courage and resilience was contagious. They kept me sane and focussed.
Professeur Philippe Lejeune from the University of Liège with whom 1 graduated 26 years ago shared his experience with various data set. He provided sorne useful advice and companionship.
Philippe Hellemans from Ge06 is an other classmate tumed GIS expert, he shared his experience with GIS dedicated to crisis management. He helped me to remain practical and realistic.
Last but not least my daughter Shivata and wife Ragini de serve my most profound gratitude for their patience and encouragement.
Table of content
Content
LIST OF TABLES •....•....••••........•...•••..•.•••.....••....•••...•••..•.•.•....••..••...•.•••.•.•...•..•••...•.••.•.••••.•..........•.•.•... v
LIST OF FIGURES ............................................................................................................................. VII
LIST OF ABBREVIATIONS AND ACRONYMS ...................................................................................... IX
1 GENERAL INTRODUCTION ....................................................................................................... 1
1.1 WHO IS TALKING? ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••.• 1
1.2 THE FLOOD PLAIN, THE RUNOFF AND THE SATElLITE ............................................................................. 2
2 LITERATURE REVlEW ............................................................................................................... 4
2.1 THE PRAGMATIC REAlISM AND INSTRUMENTAlISM .............................................................................. 4
2.2 ENVIRONMENTAL MODELlING EQUIFINAlITY ...................................................................................... 4
2.3 FLOOD DAMAGES .......................................................................................................................... 6
2.4 FLOOD MODELlING ....................................................................................................................... 8
2.4.1 The Google Scholar metric .............................................................................................. 8
2.4.2 The hydrological connections ......................................................................................... 9
2.4.3 The hydraulic models .................................................................................................... 11
2.4.3.1
2.4.3.2
2.4.3.3
10 modelling - The de St-Venant equations ..................................................................... 11
20 shallow water equations .............................................................................................. 14
10 versus 20 floodplain inundation modelling ................................................................. 16
2.4.4 2 D flood modelling packages ...................................................................................... 19
2.5 RAINFALL-RUNOFF MODEL ............................................................................................................ 22
2.5.1 The Google Scholar metric ........................................................................................... 22
2.5.2 The rationales of rainfall-runoff modelling .................................................................. 23
2.5.3 The perceptual model ................................................................................................... 23
2.5.4 The conceptual model .................................................................................................. 25
2.5.5 The procedural model .................................................................................................. 26
2.5.6 The calibration ............................................................................................................. 26
Table of content
2.5.7 Validation /performance evaluation ............................................................................ 27
2.5.8 Rainfall-runoff model classification .............................................................................. 28
2.5.9 ReFH package ............................................................................................................... 30
2.5.9.1
2.5.9.2
Introduction ...................................................................................................................... 30
Modelling concepts ........................................................................................................... 32
2.6 SATELLITE RAINFALl. .................................................................................................................... 39
2.6.1 The rain gouges deficiencies ........................................................................................ 39
2.6.2 The satellite rainfall concepts and instruments ........................................................... 42
2.6.3 The Tropical Rainfall Monitoring Mission .................................................................... 47
2.6.3.1
2.6.3.2
2.6.3.3
Concepts and instruments ................................................................................................ 47
TRMM data products ........................................................................................................ 50
3b42 rainfall product ........................................................................................................ 51
2.6.4 3b42 rainfall runoff modelling ...................................................................................... 53
2.6.4.1
2.6.4.2
Google Scholar metric ....................................................................................................... 53
Results of rainfall-runoff modelling with 3642 data ......................................................... 54
2.6.5 3b42 and flood warning system ...................................... : ............................................. 56
3 METHODOLOGY .................................................................................................................... 59
3.1 RESEARCH QUESTION AND RATIONALES ........................................................................................... 59
3.2 THE FLOOD PLAIN OF INTEREST ...................................................................................................... 61
3.3 THE REACH OF INTEREST ............................................................................................................... 62
3.4 THE CATCHMENT OF INTEREST ....................................................................................................... 63
3.5 THE PERIOD STUDIED ................................................................................................................... 63
3.6 THE CHOICE OF PASSIVE-MICROWAVE- AND INFRARED-BASED SATElLITE RAINFALL DATA ........................... 64
3.7 THE CHOICE OF 10-20 FLOOD PLAIN MODElllNG SOFTWARE .............................................................. 64
3.8 THE CHOICE OF RAINFALL-RUNOFF MODELlING PACKAGE .................................................................... 64
3.9 A BRIEF INTRODUCTION TO THE REFH PACKAGE OPERATION & PARAMETERS PREPARATION ....................... 65
3.10 THE RAINFALL DATA SOURCES & PROCESSING .............................................................................. 68
3.10.1 Rainfall Gouge ......................................................................................................... 68
11
Table of content
3.10.2 3b42 data set. .......................................................................................................... 69
3.10.3 Aggregation of rain gauges hourly data to 3 hourly data ....................................... 69
3.10.4 Comparison between 3b42 pixel and individual rain gauges .................................. 69
3.10.5 8ias .......................................................................................................................... 72
3.10.6 Root Mean Square Error .......................................................................................... 72
3.10.7 Probability of detection ........................................................................................... 72
3.10.8 False alarm Rate ...................................................................................................... 72
3.10.9 Heidke Skill Score (HSS) ............................................................................................ 72
3.10.10 Inverse distance weighing (lDW) ............................................................................. 73
3.11 SOURCE OF DISCHARGE DATA ................................................................................................... 74
3.12 SOURCE OF EVAPOTRANSPIRATION DATA .................................................................................... 74
3.13 PREPARATION OF CROSS SECTIONS ............................................................................................. 75
3.13.1
3.13.2
Correction of LJDAR data ......................................................................................... 75
Generation of cross sections with ISIS Mapper , ...................................................... 76
3.14 GENERATION OF FLOOD EVENTS AND ANTECEDENT RAINFALL SETS ..................................................... 77
3.15 CALIBRATION AND VALIDATION SET ............................................................................................ 80
3.16 RUNNING ISIS 10 WITH THE VARIOUS FLOOD EVENTS ................................................................... 80
3.17 RUNNING THE ISIS 10 20 WITH THE VARIOUS FLOOD EVENTS ........................................................ 81
4 RESULTS& DISCUSSIONS •..•...•.........•......•.•..•.••....•••..•....•.••...•.••................•...•..•...•.....•........... 82
4.1 COMPARISONS OF 3842 DATA WITH RAIN GAUGES ............................................................................ 82
4.1.1 8ias ............................................................................................................................... 82
4.1.2 Root Mean Square Error ............................................................................................... 82
4.1.3 Probability of detection ................................................................................................ 83
4.1.4 Fa/se a/arm Rate ........................................................................................................... 83
4.1.5 Heidke Skill Score (HSS) ................................................................................................ 83
4.1.6 Correlations .................................................................................................................. 83
4.1.7 Individual gauges comparison performance review .................................................... 84
4.2 COMPARISONS OF INVERSE DISTANCE WEIGHTED RAINFALL FOR THE WHOLE SEMOIS WATERSHED .............. 85
111
Table of content
4.3 COMPARISONS OF INVERSE DISTANCE WEIGHTED RAINFAll FOR THE ST MARIE-SUR-SEMOIS CATCH MENT ... 85
4.4 COMPARISONS OF RAINFALL DURING FLOOD PERIODS ......................................................................... 86
4.5 COMPARISON OF RAINFALl DURING INTER-flOOD PERIODS ................................................................. 87
4.6 COMPARISON OF MODEllED FlOWS ............................................................................................... 88
4.6.1 Introduction .................................................................................................................. 88
4.6.2 Total, Average, Peak, Minimum& Error ....................................................................... 89
4.6.3 Graphie presentation .................................................................................................... 91
4.7 COMPARISON OF 10 MODElUNG RESUlTS ....................................................................................... 93
4.7.1 Introduction .................................................................................................................. 93
4.7.2 Maximum stage and overfJow depth ........................................................................... 93
4.8 COMPARISON OF THE 10-20 MODElUNG RESUlTS ........................................................................... 99
5 CONCLUSIONS •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 103
5.1 FLOOD PLAIN MODElllNG ........................................................................................................... 103
5.2 REFH ADEQUACY OUTSIDE UK .................................................................................................... 103
5.3 3842 VAUDITV UNDERTHE 50° LAT N ...................................... ; ................................................... l04
5.4 LIDAR DATA PROCESSING .......................................................................................................... 104
5.5 DATA PROCESSING CAPACITV ....................................................................................................... 105
5.6 PROPAGATION OF ERROR AND RECOMMENDATION FOR lOCAL OBSERVATIONS ...................................... 106
5.7 FURTHER WORK ........................................................................................................................ 106
6 BIBLIOGRAPHY ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 108
Online presentation of the study at :
http://web.me.comlalbert.grela/3b42 _flood --p1ain/Welcome.html
lV
List of tables
List of tables
Table 1 : Flood damages since 1985 ............................................................................ 7
Table 2 : Flood & inundation modelling - Google Scholar hits ................................... 8
Table 3 : Most common 2 D packages in UK (Néelz and Great, 2009) ..................... 21
Table 4 : Classification of inundation packages (Néelz and Great, 2009) ................. 22
Table 5 : Rainfall-runoffhits - Google Scholar ......................................................... 23
Table 6 : Meteorological satellites ............................................................................ .46
Table 7 : TRMM data level (NASDA, 2001 ) ............................................................. 50
Table 8 : TRMM Data products (NASDA, 2001) ...................................................... 50
Table 9 : TRMM data flow (NASDA, 2001) ............................................................. 51
Table 10 : Google Scholar hits for 3b42 & hydrological models, flood, runoff ........ 53
Table Il: 3b42 pixel paired with rain gauge .............................................................. 71
Table 12 : Flood and flood antecedent periods ............. , ..... : ...................................... 78
Table 13 : Individual gauges comparison .................................................................. 82
Table 14 : Correlations coefficients with individual rain gauges .............................. 84
Table 15 : Five major flood events ............................................................................. 86
Table 16 : Accumulated rainfall during five floodlhigh flow events ......................... 86
Table 17 : Nbr of 3 hours period > 0 mm of acc. rainfall- Flood periods ................ 87
Table 18 : Inter-floodlhigh flow periods .................................................................... 87
Table 19 : Accumulated rainfall during five inter-flood/inter-high flow events ........ 88
Table 20 : Nbr of 3 hours period > 0 mm of acc. rainfall- inter flood periods ......... 88
Table 21 : Flow descriptors - one hour timestep ........................................................ 89
Table 22 : Flow descriptors 3hours timestep ............................................................. 90
Table 23 : Maximum stages and overflow depth ....................................................... 94
v
List of tables
Table 24 : Flood maximum extent near Villers-sur-Semois (m2) •••••••••••••••••••••••••••••• 99
Table 25 : Ratio ofFLl et FL2 flood extent with a depth superior to 30 cm and area
classified as high risk ............................................................................................... 1 02
Vi
List of figures
List of figures
Figure 1 : Taxonomy ofhydrological models ............................................................ 10
Figure 2 : A schematic outline of the different steps in the modelling process ......... 11
Figure 3 : A classification of process mechanisms in the response of hillslopes to
rainfalls. (a) Infiltration excess overland flow (Horton, 1933). (b) Partial area
infiltration excess overland flow (Betson, 1964). (c) Saturation excess overland
(Cappus~ 1960~ Dunne and Blac~ 1970). (d) Subsurface stormflow (Hursh~ 1936~
Hewlett, 1961). (e) Perched saturation and' throughflow (Weyman, 1970) ............... 25
Figure 4 : ReFH model concept diagram ................................................................... 34
Figure 5 : Cumulative distribution of soil moisture capacity ..................................... 35
Figure 6 : Shape of standard instantaneous unit hydrograph adopted in ReFH ......... 37
Figure 7 : GHCN-Monthly Coverage Map for Precipitation (Burroughs, 2008) ...... .42
Figure 8 : TRMM sens ors chart .................................... , ..... ~ ...................................... 49
Figure 9 : Location of Villers-sur-Semois .................................................................. 62
Figure 10 : Location of the modelled reach ............................................................... 63
Figure Il : Rain gauges localisation .......................................................................... 70
Figure 12 : 3b42 pixel centroid and rain gauges location ......................................... 71
Figure 13 : Localisation of long section ..................................................................... 76
Figure 14 : Cross-section localisation ........................................................................ 76
Figure 15 : Long section before DEM correction ...................................................... 76
Figure 16 : Cross-section before DEM correction ..................................................... 76
Figure 17 : Long section after DEM correction ......................................................... 76
Figure 18 : Cross-section after DEM correction ........................................................ 76
Figure 19 : 3D view before DEM correction ............................................................. 76
vu
List of figures
Figure 20 : 3D view after DEM correction ................................................................ 76
Figure 21 : Generation of cross-sections in ISIS Mapper .......................................... 77
Figure 23 : Maximum stage 5 flood events ................................................................ 79
Figure 23 : FLI rain gauges modelled ........................................................................ 91
Figure 24 : FL1 3b42 modelled .................................................................................. 91
Figure 25 : FL2 rain gauges modelled ........................................................................ 92
Figure 26 : FL2 3b42 modelled .................................................................................. 92
Figure 27: FL1 Maximum stages Rain Gauged Modelled & Gauged Flow ............. 95
Figure 28 : FL 1 Maximum stages: 3b42 Modelled Flow & Gauged Flow .............. 96
Figure 29: FL2 - Maximum stages - Rain gauges modelled & Gauged Flow .......... 97
Figure 31 : FL2 Maximum stages 3b42 modelled & Gauged Flow ........................... 98
Figure 31 : FL 1 flood extent .................................................................................... 100
Figure 32 : FL2 flood extent .................................................................................... 1 00
Figure 33 : FLI Flood extent with a depth of at least 30 cm and official flood risk
map ........................................................................................................................... 101
Figure 34 : FL2 Flood extent with a depth of at least 30 cm and official flood risk
map ........................................................................................................................... 102
Vlll
~:. _0 ..
Acronym
AGPI
ALTBAR
AMSR-E
AM SU
AMSU-A
AMSU-B
ASPBAR
ASPVAR
ASTER
AVHRR
BFIHOST
BL
BR
CEH
CERES
CMax
Cmorph
DK
DMSP
DPLBAR
DPSBAR
DSS
EOS-AM
EOS-PM
ESMA
ESMR
FARL
FC
FD
FEH
FSR
GDEM
GFM
GLUE
GOES
GOMS
GPCP
List of abbreviations and acronyms
List of abbreviations and acronyms
Signification
Adjusted Geostationary Operational Environmental Satellite Precipitation Index (AGPI)]
Mean altitude of catchment (metres above sea level)
Advanced Microwave Scanning Radiometer-EOS
Advanced Microwave Sounding Unit
Advanced Microwave Sounding Unit (on Aqua satellite)
Advanced Microwave Sounding Unit (on NOAA satellite)
Mean direction of aU drainage path slopes (bearing in degrees)
Invariability of slope d~ections
Advanced Spacebome Thermal Emission and Reflection Radiometer
Advanced Very High Resolution Radiometer
BaseFlow Index derived using the HOST (Hydrology Of Soil Types) classification
Baseflow recession constant (lag)
Baseflow recharge
Centre for Ecology and Hydrology
Clouds and the Earth's Radiant Energy System
Maximum Soil Moisture Capacity
NOAA CPC Morphing Technique
Daily Soil Moisture Decay rate
Defense Meteorological Satellite Program
Mean of distances along drainage paths between each 50m grid no de and the outlet
Mean of an inter-nodal slopes, along drainage paths (mIkm)
differential split-sample test
Earth Observing System launched 1998
Earth Observing System launched 2000
explicit soil moisture accounting
Electrically Scanning Microwave Radiometer
Index of flood attenuation due to reservoirs and lakes (1.0 indicates no attenuation)
Field Capacity
Finite difference
Flood Estimation Handbook
Flood Study Report
Global Digital Elevation Model
Global Flood Monitoring
Generalised Likelihood Uncertainty Estimation
Geostationary Operational Environmental Satellites
Geostationary Operational Meteorological Satellite
Global Precipitation Climatology Project (GPCP)
lX
GPCP
GPCP
GPM
GPROF
GSMap_MVK
HEC
HIRS
HMS
INSAT
IR
IUH
JAXA
LDP
LiDAR
LIS
MODIS
MS
MSU
NASA
NASDA
NOAA
NSE
PB
PBDSS
PDM
PERSIANN
POES
PR
PROPWET
QI RD
ReFH
RMEDID
RMEDIH
RMED2D
RMS
RMSE
RW
SAAR
SG
SMMR
SMS
SPRHOST
SPW
List of abbreviations and acronyms
Global Precipitation Climatology Project (GPCP)
Global Precipitation Climatology Project
Global Precipitation Measurement
Graph Execution Profiler
Global Satellite Mapping of Precipitation with Micro Wave sounder
Hydrologic Engineering Center
High Resolution InfraRed Sounder
Hydrologic Modeling System
Indian National Satellite System
Infrared
Instantaneous Unit Hydrograph
National Space Development Agency of Japan
Longest drainage path
Light Detection And Ranging
Lightning Imaging Sensor
Moderate Resolution Imaging Spectroradiometer
multi-satellite
Microwave Sounding Unit
National Aeronautics and Space Administration in USA
National Space Development Agency of Japan
National Oceanic and Atmospheric Administration in USA
Nash Sutcliffe coefficient of efficiency
proxy-basin test
proxy-basin differential split-sample
Probability Distributed Model
Precipitation Estimation from Remote Sensing Information using Artificial Neural Network.
Polar Orbiting Environmental Satellites POES
Precipitation Radar
Proportion of time when soil moi sture deficit was below 6mm
total observed flow at time t
Routing depth
Revitalised Flood Hydrograph
Median annual maximum I-day rainfall (mm)
Median annual maximum I-hour rainfall (mm)
Median annual maximum 2-day rainfall (mm)
Root Mean Square
Root Mean Square Error
Region Wallonne
average annual rainfall
satellite-gauge combination
Scanning Multichannel Microwave
Synchronous Meteorological Satellites
Standard Percentage Runoff derived using the HOST classification
Service Public de Wallonnie
x
SRFE
SRTM
SS
SSMII
SSU
TCI
TIROS-I
TM!
TMPA
TOVAS
Tp
TRMM
UH
UK
Uk
Up
URBEXT
USA
UTC
VIRS
List of abbreviations and acronyms
Satellite Rainfall Estimate
Shuttle Radar Topography Mission
split-sample test
Special Sensor Microwavellmager
Stratospheric Sounding Unit
TRMM combined instrument
Television and Infrared Observation Satellite
Microwave Imager
TRMM multi satellite precipitation analysis
TRMM Online Visualization and Analysis System
Time-to-peak
Tropical Rainfall Measuring Mission
Unit Hydrograph
United Kingdom
KinkofIUH
Height ofIUH
Extent of urban and suburban land coyer
United States of America
Coordinated Univers al Time
Visible and Infrared Scanner
Xl
General introduction
1 General introduction
1.1 Who is talking?
Although the author of the present study is confined by academic rules to present his
work without using the first person, semantic and epistemological demands cannot
avoid the elucidation of the philosophical position used to formulate and credit the
present work. The distortion of discourse is entirely the responsibility of the
University's of Hull Department of Geogniphy. The elucidation of the philosophical
and personal element of a scientific study is recommended by a wide spectrum of
scholars ranging from Edgar Morin(Morin, 1977) and Michel Foucault (Foucault,
1976) to Keith Beven (Beven, 2009) and Noel Castree (Castree et al., 2005).
The Karl Poper's the ory ofverisimilitude (Popper, 1963) is totally embraced by the
present study . The intention of pragmatic realism is admowledged : the modelling
effort aims at representing as weIl as possible the reality but the limitations of
observations and computational capacities restrict the "fitting",- as long as the models
allows sorne useful predictions, they should be granted sorne scientific respectability.
(Beven, 2009)
Although a very minimal treatment of the uncertainty could be provided within the
timeframe of the present study, the environmental modelling and systematic
elucidation of the uncertainty should be pronounced in a same breath.(Beven, 2009).
The equifinality of hydrological modelling is also accepted as a rather reassuring
concept rather than a suggestion of inadequate science. (Beven, 2006, , 2009,
Savenije, 2001) .
1
,, '
General introduction
1.2 The flood plain, the runoff and the satellite
During the decade ending in 2008, the number of people affected by flood reached
1.037 billions according to the World Disaster report 2008(IFRCRCS, 2010). This
year, the flood in China and Pakistan will add 32 millions of displaced
people.(Reliefweb, 2010a, Reliefweb, 2010b). During the last 25 years, the average
annual flood damages exceeds 30 billions USD (Brakenridge, 2010). Prevention and
mitigation are crucial to face this natural disaster only second to food insecurity
(IFRCRCS, 2010). The capacity to model flood plain extent is a key element to
quantify the flood risk, design flood defences and prepare emergency plans. (Néelz
and Great, 2009). This modelling process presuppose the ability to estimate the
discharge in the water course and the surface runoff during a flood event.(Shaw et
al., 2010, Beven, 2004). This estimation is constraint by the availability of weIl
established stream gauges and in the absence of them many plans have been made
based on a simulated discharge through a rainfall-runoffmodel ((Kjeldsen and Jones,
2009, Kjeldsen, 2007, Kjeldsen et al., 2006). The recent availability of satellite
rainfall products and their growing historical depth offer a opportunity to
compensate the deficiencies of rain gauges as a prime of rainfall information
((Vazquez and Feyen, 2010, STISEN et al., 2010, Lohani et al., 2010, Harrison et al.,
2009, Sugiura et al., 2009, Valeriano et al., 2009, Su et al., 2008, Collischonn et al.,
2008a, Magome et al., 2008, Werner and Delft, 2005). Although many researches
highlight the importance of flood forecasting only a very few (Fotopoulos et al.,
2010, Magome et al., 2008, Kuszmaul et al., 2008, Hossain et al., 2007, HOSSAIN
et al., 2004) elaborate a real flood detection method and quantify its error and
uncertainty. The present study appears rather original as the impact of the error in
rainfall estimate will be carry over until the ca1culation of the flood extent. Such
2
General introduction
approach bears a certain level of irreconcilability as the flood plain modelling
requires extremely detailed elevation data while the satellite rainfall products are
meant to coyer large swat of land. The exercise offers the possibility to focus the
analysis of the errors into operational values such as the foreseeable extent of the
flood plain. The non-linearity of the rainfall-runoff relation and the specificity of the
topography of the flood plain prevent the estimation of the flood plain error based on
the rainfall error.(Beven, 2006, Beven, 2004, Beven, 1993, Beven, 1989). The
present study was extremely constrained by time, software availability, local
expertise and word count. The literature review in chapter 2 tries to explore briefly
three huge body of sciences and research : the flood modelling techniques, the
rainfall-runoff models and the satellite rainfall product. The methodology presents in
chapter 3 the selection of location and data sets which was based more on practical
constraints than refinement of scientific approach. The task to assemble and process
all the data required was huge and the experience with hydraulic modelling was nil.
A full year may have been spent on the subject without exhausting it. The results are
satisfactory in tenus of internai consistency but dismiss the opportunity to use the
Tropical Rainfall Monitoring Mission data for flood plain modelling in the 50° lat N.
But using a better rain sources (rain gauges) in a rainfall-runoffmode1 seems a
appropria te option for flood plain modelling. The future satellite rainfall products are
likely to provide much better estimates of rainfall and soi! moisture and should
improve the hydrological modelling particularly for the higher latitude. (NASA,
2010a, Beven, 2009). It is hoped that these improvements will translate in better
management of flood risks and decrease the fatalities observed so sadly during the
summer 2010.
3
Literature review
2 Literature review
2.1 The pragmatic realism and instrumentalism
The Karl Popper (Popper, 1963) states that the most that could be hoped from a
scientific the ory is a certain degree of verisimilitude. He rates the closeness to Truth
by two factors : truth and content. The more truths that a theory entails (other things
being equal) the closer it is to the truth.
The intention of pragmatic realism is quite simpler : the modelling effort aims at
representing as weIl as possible the reality but the limitations of observations and
computational capacities restrict the "fitting", as long as the rnodels allows sorne
useful predictions it can be granted sorne scientific respectability. "AlI models are
wrong but sorne models are usefull" (Box, 1979).
The instrumentalists' views that aIl scientific theories of the past have proven to be
false to sorne extent, convey the idea that the current one will also turn out to be false
too. That does not prevent the enunciations of scientific statements but these are not
the reality. Instrumentalism in that sense is anti-realist especially since it also allows
that sorne statements about the nature ofthings may be subjective. Logically it
derives that the only justification for scientific theorising is empirical adequacy.
(Van Fraassen, 1980, Cartwright, 1983, Haack, 1994, Haack, 2002, Haack, 2003)
2.2 Environmental modelling equifinality
According to Ludwig von Bertalanffy, equifinality is the principle that in open
systems a given end state can be reached by many potential means. He prefers this
term, in contrast to "goal", in describing complex systems' similar or convergent
4
Literature review
behaviour. It emphasizes that the same end state may be achieved via many different
paths or trajectories. In closed systems, a direct cause-and-effect relationship exists
between the initial condition and the fmal state of the system. The idea of
equifinality suggests that similar results may be achieved with different initial
conditions and in many different ways. This phenomenon has also been referred to as
isotelesis (Von Bertalanffy, 1950).
The term equifinality has a long history in geomorphology, indicating that similar
landforms might arise as a result of quite different sets of processes and
histories.(Beven, 1996)
Landscape evolution models have recently been tested about their
equifinality.(Odoni, 2007)
But the most prolific advocate of the equifinality concept i:p. hydrological modelling
is most likely Keith Beven (Beven, 1996, , 1997, , 1998, Schulz et al., 1999, Zak and
Beven, 1999, Brazier et al., 2000, Beven and Freer, 2001, ,2006) . The origin of the
concept lies in purely empirical studies that have found many models giving good
fits to data. But for many modellers, the optimal model is still an aim that should be
pursue and the present "imperfections" of the model are a step toward the optimal
model(Beven, 2006).
The Generalised Likelihood Uncertainty Estimation (GLUE) methodology proposes
an evaluation frame to test the fitting and the uncertainty in a consistent
manner.(Beven and Binley, 1992). It intends to focus the attention on the fact that
there are many acceptable representations that cannot be easily rejected and should
be considered in assessing the uncertainty associated with predictions. The concept
5
Literature review
owes a lot to the Homberger-Speer-Young (HSY) sensitivity analysis of multiple
behavioural models (Homberger and Spear, 1981).
For any particular set. of observation, sorne of the acceptable or behavioural models
will be better in terms of one or more performance measures. But given the sources
of error in the modelling process, the behaviourals models cannot easily be rejected
as feasible representations of the system given the level of error in representing the
system. This can be viewed as a problem of decidability between feasible
descriptions of how the system is working.' (Beven, 2006)
To be able to represent different hypotheses about the processes of a hydrological
system, it is necessary to have sorne parametrisation of these processes. This usually
leads to a growth of complexity and multiplication of parameters quite often without
additional data collection to determine these additional parameters. Equifinality is
therefore rather inevitable because even in case of mathematically perfect model the
errors in the initial conditions, boundary conditions and output measurements leads
to a cloud of equally fitting solutions.
Environmental models are therefore mathematically ill-posed or ill-conditioned. The
information content available to define a modelling problem does not allow a single
or unambiguous mathematical solution to the identification problem.(Beck, 1987)
2.3 Flood damages
The best source regarding recent flood statistics is most likely the Dartmouth Flood
Observatory.(Brakenridge, 2010) The database holds the records of3703 floods
dating back to 1985.
A summary of the available data is presented in Table 1 : Flood damages since 1985
6
Literature review
Table 1 : Flood damages since 1985
Nbrof Sumof Sumof Sum of Damage Years floods Dead Displaced (USD) 1985 69 3,034 5,831,709 5,882,203,782 1986 47 1,554 8,862,982 75,157,098,463 1987 45 3,218 1,452,016 2,667,700,000 1988 111 6,229 20,317,659 11,523,995,494 1989 112 9,838 8,620,018 1,673,442,200 1990 105 4,170 14,932,486 9,554,391,478 1991 122 150,169 17,804,633 82,285,582,000 1992 109 8,720 13,078,525 24,929,269,900 1993 99 7,453 35,102,006 20,759,890,046 1994 107 6,401 8,574,572 13,542,094,000 1995 115 7,625 48,575,631 45,860,002,000 1996 100 5,974 12,857,803 11,586,866,000 1997 159 10,361 6,475,540 11,140,926,000 1998 180 21,331 42,880,501 235,875,681,175 1999 100 33,792 57,106,721 55,222,677,688 2000 102 10,684 50,228,261 13,381,700,000 2001 172 5,624 37,993,069 13,217,061,635 2002 260 4,421 20,516,442 29,694,450,684 2003 297 4,554 21,667,574 7,564,443,015 2004 194 173,123 51,018,052 7,550,518,000 2005 171 10,140 19,367,962 82,249,650,000 2006 232 7,992 18,710,396 9,312,081,480 2007 243 12,506 36,013,671 22,265,410,000
2008 179 107,612 22,260,628 8,279,760,000 2009 158 3,897 8,804,644 387,000,000
2010 115 6,110 21,234,094 20,005,000
Grand Total 3703 626,532 610,287,595 801,583,900,041 Average 142 24,097 23,472,600 30,830,150,002
The damages cost of China and Pakistan floods in 2010, is probably not yet
reflected. UN officiaIs at the end of August 2010 report 14.5 millions displaced in
Pakistan (NydailyNews) and Chinese authorities are estimating the direct economic
losses at 3.9 billion USD (Xinhua, 2010).
The figures presented in this table coyer the last 25 years only. The deadliest flood in
history reached an estimated 3.7 millions fatalities in 1931 in China.(Wikipedia,
2010)
... "'~-"".""
7
Literature review
The 2007 flood in UK cost around 6 billions GBP with 13 casualties.(pitt, 2007)
These figures leave no doubt about the social priority that flood defence, flood
warning and flood mitigation may have.
2.4 Flood modelling
2.4.1 The Google Scholar metric
Searching Google Scholar about flood modelling faces two possible spelling for
modelling and a partial synonymy between flood and inundation.
Hydrologists seem to prefer flood while engineers may focus on inundation. The
recent Desktop review of 2D hydraulic modelling packages commissioned by the
UK Environmental Agency employs the words without much discrimination.
Inundation may be more urban, flood plain, flood extent may be more rural.
The number of hits excluding citation retumed by Google Scholar for the three
periods (before 1990, 1991 to 2000, after 2001) is presented in Table 2
Table 2 : Flood & inundation modelling - Google Scholar hits
inundation inundation inundation flood flood flood Year modelling modeling model mode Il ing modeling model
<1990 8,320 8,890 8,700 18,300 18,700 23,600
1991-2000 9,790 9,760 9,430 17,300 17,400 26,300
2001~2010 17,000 17,000 17,300 17,300 17,400 24,200
The inundation model, model(l)ing has a fast growth during the present decade. The
flood model, model(l)ing is rather stagnating with even a slight dec1ine during the
8
Literature review
present decade but this may just reveal a change of vocabulary rather than a decline
in the number of publication related to the "unintended submersion with water".
2 .4.2 The hydrological connections
The flood modelling is a sub-division of a vast domain of hydrological modelling.
Hydrologie models are simplified, conceptual representations of a part of the
hydrologie cycle. They are primarily used for hydrologie prediction and for
understanding hydrologie processes. (Shaw et al., 2010).
The methods and concepts relating the movement of water segment the hydrological
modelling in : groundwater modelling, surface water modelling and composite
models.(Beven, 2004, Shaw et al., 2010).
Groundwater models represent groundwater flow systems, and are used by
hydrogeologists. Groundwater models are employed to si~ulate and predict aquifer
conditions. Surface water modelling are subdivided in rainfall-runoffmodel and
hydrological transport model. The composite model combine water movements
above and underground.
The flood modelling usually involve two type of modellings : the rainfall-runoff
model helps to derive a discharge from the rainfall (tbis particularly applies to
ungauged catchement) and the hydraulic model use the hydraulic laws to calculate
velocity and water level along the flow path.(O'Connell et al., 1970, Beven, 1993,
AI-Sabhan et al., 2003, Lai, 2005, Bastin et al., 2009, Sugiura et al., 2009, Shaw et
al., 2010).
9
, " ,
::.
~ ~"
= ""1 ~
~ ~ ~ 0
= 0 3 ~
0 .... =-~ Co ""1 0 0'
(JQ
r;" ~ 3 0 Co ~ (;;"
...... 0
.':;
System f (randomness, space, time)
no yes Randomness?
Spatial Variation?
Temporal Variation? r &' ~ (t)
~ ~'
.:".~~.':"~'.
""
• ~ ~-:: 1.1 ~
Literature review
Chow presents a taxonomy of hydrological models based on randomness, space and
time. Figure 1 : Taxonomy ofhydrological models.
Revise perceptions The Perceptual Model: 3" 0
deciding on the pro cesses m Pl en 5 "
Revise equations The Conceptual Model: co 1»
deciding on the equations "0 '0 a X
Debug code The Procedural Model: 3" ~
getting the code to run on a computer 0 " ::J
~ Revise parameter Model Calibration:
values getting values of parameters
Model Validation: good idea but difficult in practice
No
Figure 2 : A schematic outline of the different steps in the modelling process
In the modelling process outlined in Figure 2, Beven insists on the initial perceptual
model that he considers more complex that the conceptual model, but he views that
the reduction of complexity is often essential to reach mathematically supported
predictions.
2.4.3 The hydraulic models
The most important concepts of mathematical modelling of shallow water flows are introduced first in one dimension (St-Venant equations), then in two dimensions (shallow water equations). Note that the following sections are partly reproduced from "Desktop review of 2D hydraulic modelling packages" (Néelz and Great, 2009)
2.4.3.1 1D modelling - The de St-Venant equations
The St-Venant equations can be expressed as follows:
dQ + dA = 0 dx dt
Il
Equation 1
Literature review
!:. dQ + !:.~ (Q2 ) + 0 dh - o(So - S.) = 0 A dt A dx A dx }
Equation 2
(i) (ii) (iii) (iv) (V)
where Equation (1) is referred to as the continuity or mass conservation
equation, and Equation (2) is the momentum conservation equation. In this,
Q is the flow discharge (Q = U.A where U is the cross-sectional averaged
velocity and A in the cross-section surface area), g is the acceleration of
gravity, h is the cross-sectional averaged water depth, So is the bed slope in
the longitudinal direction and SI is the friction slope (the slope of the energy
line).(Néelz and Great, 2009)
The various terms in the momentum conservation equation are as follows:
(i) local acce1eration term;
(ii) advective acceleration term;
(iii) pressure term;
(iv) bed slope term
(v) friction slope term
Here the momentum equation is expressed in conservative form. It is possible
to substitute UA for Q in Equations (1) and (2), expand Equation (2) and
simplify it using Equation (1) to yie1d the mathematically correct non
conservative form of the momentum equation. Use of the non-conservative
form may, however, lead to practical difficulties in its numerical
solution.(Néelz and Great, 2009, Sleigh and Goodwill, 2000)
The equations are referred as a set ofhyperbolic partial differential equations.
12
Literature review
A number of theoretical assumptions must be met for the St-Venant
equations to apply mainly:
• the bed slope is small
• the pressure is hydrostatic, that is, streamline curvature is small
• and vertical accelerations are negligible
• the effects of boundary friction and turbulence can be accounted for
by representations of channel conveyance derived for steady-state
flow.
The St Venant equations cannot be solved explicitly except by making some very large assumptions which are unrealistic for most situations. Therefore numerical techniques have to be used. Three groups of methods are usually used : fmite difference, fmite elements and finite volumes.(Sleigh and Goodwill, 2000)
Finite difference (FD) methods rely on Taylor series expansions to express the value taken by a variable (h, u, v and so on) at a given point, as ~ function of the values at neighbouring points and of local derivatives ofincreasing orders. These Taylor series are then combined to yield approxima te expressions for the derivatives involved in the shallow water equations, as a function of a fmite number of neighbouring point values. The accuracy of the approximations can be controlled by the order to which the Taylor series expansions are developed (the order of the socalled truncation), which is also linked to the number of neighbouring points involved. The implementation of finite difference methods is significantly more straightforward on a structured grid, which is a computational grid that can effectively be represented on a square matrix. This explains to some extent why their popularity is currently in decay in the academic community (Alcrudo, 2004), as unstructured grids lend themselves better to the modelling of environmental flows. Software packages based on FD methods, however, are popular with a number of UK consultants, due mainly to their compatibility with high resolution digital terrain models and digital bathymetric models created from LiDAR and sonar surveys.(Néelz and Great, 2009)
For the finite element methods, the solution space in divided into a number of elements in 2D. In each element, the unknown variables are approximated by a linear combination of piecewise linear functions called trial functions. There are as many such functions as there are vertices defining the element, and each takes the value of one at one vertex and the value of zero at all other vertices. A global function based
13
Literature review
on this approximation is substituted into the governing partial differential equations. This equation is then integrated with weighting functions and the resulting error is minimised to give coefficients for the trial functions that represent an approximate solution (Bates et al., 2005).
Finite element methods benefit from a rigorous mathematical foundation (A1crudo, 2004) that allows a better understanding oftheir accuracy (Hervouet, 2007); however, the technique has not been used as much as other approaches in commercial software, perhaps because it is less accessible conceptually and produces models that result in large run-times. Also, generating meshes can be timeconsuming when a suitable mesh generation tool is not available(Néelz and Great, 2009).
In the finite volume methods, space is divided into so-called fmite volume which are 2D (in this context) regions of any geometric shapes. The shallow water equations (in conservative form) are integrated over each control volume to yield equations in terms of fluxes through the control volume boundaries. Flux values across a given boundary (ca1culated using interpolated variables) are used for both control volumes separated by the boundary, resulting in the theoretically perfect mass and momentum conservativeness of the approach (a flux into a finite volume through a boundary is always equal to a flux out of a neighbouring one through the same boundary). In ID, fmite volume methods are equivalent to fmite difference methods.(Néelz and Great, 2009)
One of the most significant advances in 1 D models is the ability to link 1 D and 2D models (Syme, 1991, Evans et al., 2007). This can be applied in various ways:
• within a channel that one wishes to model partly in ID and partly in 2D;
• between a 1 D drainage network model and a 2D surface flood model;
• between a 1 Driver model and a 2D floodplain model;
• within a mainly 2D model where, for example, cul verts are modelled in ID linking 2D cells between themselves.(Néelz and Great, 2009)
2.4.3.2 2D shallow water equations
The two-dimensional shallow water equations expressed in vector form are:
dU + dF + dG = H Equation 3 dt dx dy
u = [f:l G _ huv
[
hv 1 - h 2
9 2 + hv2 '
14
Literature review
Equation 4
where u and V are the depth-averaged velocities in the x and y directions,
respectively. Sox and Soy are the bed slopes in the x and y directions. The friction
slopes in the x and y directions can be expressed in a manner analogous to the 1 D
formulation, as follows (assuming the use of Manning's n) (Néelz and Great, 2009):
n 2 u .Ju 2+ v 2 n 2 v .Ju 2+ v 2
SIx = - 4 and S,y = - 4
h'J h3 Equation 5
The viscosity is taken into account in more refme formulation but not presented here.
The contribution of the kinematic viscosity to the value of the viscosity coefficient E
is typically at least an order of magnitude smaller than the turbulent eddy viscosity
and for this reason is neglected. The apparent viscosity resulting from non-
uniformity in the horizontal velo city along the vertical direction is recognised as a
much more significant contributor to the value of E (Alcrudo, 2004). However, this
effect is poorly understood and is therefore neglected in most applications. The,
turbulent eddy diffusivity has been the object of more significant research (see Rodi
1980), but in the context of flood modelling it is generally not considered an
important parameter (Alcrudo, 2004). For overland flow conditions, it is unlikely
that the eddy viscosity will have a major effect on model predictions as friction will
dominate. It may however, for flow in and around structures, have a significant
effect upon local high-resolution predictions (Néelz and Great, 2009).
Similarly with the 1 D formulation, it is possible to neglect the acceleration terms in
the 2D shallow-water equations (the terms involving u and v in U, F and G) to yield
15
Literature review
the 2D diffusion wave equations (Bradbrook et al., 2004). This is appropriate where
the flow is predominantly driven by local water surface slope and momentum effects
are less important, as is often the case in the context ofUK fluvial floodplains. Such
modelling approaches and recent practical applications are discussed in Hunter et
al.(2007).
An important mathematical property of the shallow water equations is that they are
non-linear (they do not satisfy the principle of superposition), in accordance with the
true non-linearity of the flow processes being modelled. One of the implications is
that shallow water flows are subject to shock waves, which are understood to be
discontinuous solutions of the shallow water equations (Toro and Toro, 2001).
Shocks on floodplains are mainly encountered in the form ofhydraulic jumps, that
is, transitions from supercritical to subcritical flows. These may be caused by local
changes in terrain topography (diminution of bottom slope, lateral expansion), or by
the effect of bottom friction.
2.4.3.3 1D versus 2D floodplain inundation modelling
The choice between a 1 D and a 2D mode1 is relevant primarily in the context of river
floodplain modelling. The the ory of open channel flow in the form of ID St-Venant
equations is not applicable to urban flood flows where extreme non-uniformity and
spatial variability of flow patterns is common. Flows may happen in sequences of
fast moving shallow flows (possibly supercritical) and large still ponds, rather than
in the form of channe1s that are weIl defined over long distances. The significance of
storage and recirculation areas that clearly do not fit in a 1 D description should not
be underestimated. Besides, urban flows rarely happen along routes that are clearly
identifiable in advance to allow the building of a model and running the simulations
16
Literature review
(unlike rivers). However, a case where ID modelling is as close as possible to being
appropriate can be found, for example, in Lhomme and al. (2006) (deep flooding in a
network of weIl defmed narrow streets). Similarly, in coastal flooding it is not
generally the case that floodplains may be reasonably represented as networks of
well-defined channels and therefore 1 D floodplain modelling is rarely appropriate.
In river flooding contexts, however, ID (that is, ID models ofrivers with cross
section extending over lateral floodplains) are appropriate for narrow floodplains,
typically where their width is not larger than three times the width of the main river channel.
The underlying assumption should be that the contribution of the floodplains to conveyance
can be quantified using recent advances in the estimation of compound channel conveyance.
An additional condition for such models to be valid is that the floodplains should not be
separated from the main channel by embankments, levees or any raised ground, where the
channel floodplain unit effectively behaves as a single channel. It is clear that ID river
models have limitations that can become significant in matiy practical applications.(Néelz
and Great, 2009) The flow is assumed to be unidirectional (generally happening in the
direction parallel to the main channel flow), and where this is not true (recirculation areas)
conveyance predictions can be severely overestimated. Situations where floodplain flow
"makes its own way" are frequent, but perhaps an even more significant issue is the fact that
ID cross-sections will offer a rather crude representation of floodplain storage capacity in
the case of large floodplains. A better balance between the correct representation of
floodplain conveyance and the correct representation of floodplain storage capacity can be
obtained through the use of ID+ models, where large "disconnected" floodplains are
modelled as storage reservoirs (white narrow floodplains can still be mode lied as part of
channel cross sections). This latter modelling approach has its own limitations: exchange
flows between the river and reservoirs and between the reservoirs are typically mode lied
using broad-crested weir equations (Evans et al., 2007), which are not always appropriate.
17
Literature review
Weir equations adapted for drowned (downstream controlled) flows are also used,
but the assumption that water levels are horizontal within each reservoir results in
incorrect water level predictions in the vicinity of reservoir boundaries, often causing
large errors in the predictions of exchange flows. These do not matter if the time
duration of the flood plain filling and draining is srnall cornpared to the duration of
the flood. Lastly, the size and location of floodplain storage cells and links between
thern are user-defined and therefore require sorne a priori understanding of flow
pathways in the floodplain which may result in circular reasoning within models.
The choice of a model type (ID, 2D- or 2D) for surface flow modelling is mostly
relevant in river flooding applications. 2D is the preferred choice in urban and in
coastal environments. 2D modelling of river floodplains can itselfbe divided into
two important classes of approaches, namely the one where only floodplains are
modelled in 2D (as part ofa combined ID/2D model) and the one where floodplain
flow and channel flow are modelled as part of the same 2D grid. The main advantage
of 2D modelling (over any other approach for floodplain modelling) are that local
variations of velo city and water levels and local changes in flow direction can be
represented (SYME, 2006). The approach also does not suffer from the limitations of
the 1 D and 2D- approaches detailed in the previous paragraphs. It allows in principle
a better representation of floodplain conveyance, but a major limitation of combined
1 D/2D models for river and floodplain systems is that the exchange processes
between the river and the floodplains are still modelled crudely (momentum transfer
is not modelled). A major drawback of 2D models is their computational cost. The
approach where the whole river and floodplain system is represented as part of a 2D
unstructured grid deserves special attention (see for example Horritt and Bates
18
Literature review
(2002)). This approach is not particularly common in UK practice, perhaps because
there is a long-established tradition of 1 Driver modelling. Surveyed cross-sections
which are intended primarily for 1 D models exist for a large proportion rivers in the
UK. Numerous existing ID models have been calibrated using measured data, and
ID Manning's n values are well-known for many rivers or river types. There is
therefore a c1ear incentive to make use of these data and knowledge by continuing to
build 1 Driver models or to use existing ones. In addition, the grid resolution needed
to model a river in 2D is significantly fmer than what is typically applied on
floodplains, resulting in significantly increased computation times. These reasons
explain the current enthusiasm for combined 1 D/2D modelling for river and
floodplain systems.(Néelz and Great, 2009)
2.4.4 2 D flood modelling packages
A recent desktop review, commissioned by the Environment Agency' s Science
Department and funded by the joint Environment Agency/Defra Flood and Coastal
Erosion Risk Management Research and Development Programme, has analysed 17
2d modelling packages currently used in UK.(Néelz and Great, 2009)
The vast majority use the shallow water equations and are commercially available.
F ewer (9) provides the possibility to integrate shock capturing. The possibility to
link 1 D models exist for 7 of the packages reviewed.
The theory upon which 2D packages are based suggests that predictions using these
alternative approaches will differ where acceleration terms are significant.
19
Literature review
When simulation of inundation extent for dam break flooding is required, the ability
to model super and subcritical flows may become more crucial and shock wave
modelling may dominate the selection of packages.
TUFLOW, InfoWorks, Mike-Flood and JFLOW are presently the most commonly
used in UK but there was not fundamental cause to reject the other ones. Negotiation
of license, maintenance fees and training cost are open to negotiations ; 10 licences
would cost between ;(15,000 and ;(25,000; the annual maintenance costs are
typically between 10 and 20 per cent of licence costs. The recommended duration of
training courses is two to three days at a cost of around f2,000 for ten participants.
The fastest appears to be MIKE FLOD (DHI, 2010). The only one with a free
version in 2D is ISIS 2D (Halcrow, 2010a).
20
N -
,1 .,' "
Name Physics
FINITE DIFFEREN CE SCHEMES
TU FLOW SWE DIVAST SWE DIVAST-ND SWE ISIS2D SWE MIKE 21 SWE MIKE FLOOD SWE
SIPSONlUIM SWE SOBEK SWE JFLOW Diffusive wave
FINITE ELEMENT SCHEMES
TELEMAC 20 SWE
FINITE VOLUME SCHEMES
TELEMAC 20 SWE MIKE 21 FM SWE MIKE FLOOD SWE
Inf oWorks- RS SV\IE InfoWorks-CS SV\IE HEMAT SWE
BreZo SWE TRENT SWE
OTHERS
USFLooD-FP Norm. Flow in x and y dir.
RFSM G ravity only Flowoute Diffusive wave Grid-2-Grid Floodflow
Further information on Shock numerical scheme ca pturinÇ)
Alternatinq Direct . 1 mplicit No Alternatinq Direct . Implicit No Explicit 1YD- MacCormack Yes Altematina Direct . Imolicit No Alternatinq Direct. Implicit No MIKE21 No
Alternatinq Direct . Implicit No Implicit - Staggered grid Yes Explicit No
No
Tbc Yes Godunov based Yes MIKE 21 FM Yes
Roe's Riemann solver Yes Roe's Riemann solver Yes Roe's Riemann solver Yes
Explicit- R Riemann solver Yes Explicit- R Riemann solver Yes
Explicit No
Volume filling algorithm No
0-3 ~ 0-;;-Vol
~ Developer Status Linkages 0
~ ~ 0 3
BMT-\J\J8M Commercial 0Nn 10 river and pipes solver Cardiff U niv. Re se arch As part of 1 SIS 20
3 0 = N
Cardiff U niv. Re se arch 0 Halcrow Commercial Own 10 river solver "0 OHI Commercial As part of M1KE FLOOD ~
~
OHI Commercial Own 10 river (MIKE 11) an d urban drainage ;J::" ~
(M IKE URBAN) solvers ~ ~
U. of Exeter Research Own multiple linking element OELTARES Commercial Own 10 river solver, vertical l ink
<IJ
:; J8A Internai ~
~ 1
'2 EDF Commercial
1
~,
~
N ~ =
EDF Commercial OHI Commercial As part of MIKE FLOOD
Q.
Ci ""'1
OHI Commercial OWn 10 river (MIKE 11) and urban drainage (MIKE URBAN) solvers + MOUSE (7)
Wal'ford Softw Commercial OWn 10 river solver
~ ~ :t" N <:>
Wal'ford Softw Commercial 0Nn 10 urban dra inage solver Iran Wat. Res. Research Cent. & Cardiff U . of California Research Nottinqham U. Research
~ <:> ~ .... .
~
~
~ (l)
""1 (l)
U . of Bristol Research 10 kinematic wave treatment. Verticallink . <: ..... (l)
HR-Wal'ford Internai Linked ta other components of national FRA ~ Ambiental Internai No technical information published. CEH No technical information published . Microdrainage No technical information published .
Literature review
Table 4 : Classification of inundation packages (Néelz and Great, 2009)
Method Description App licati on Typical Ou:puts Example computation Models times
10 Solution of the one- Design scale modell ng Minutes Water depth, cross-section Mike 11 dime1sional8t- which can be of the Jrder averaged velocity, and HEC-RAS Venant equaticns . of 10sto 100s of km discharge at each cross- ISIS
depending on catchrnent section . InfoWorks size . Inundation extent if RS
floodplains are part of 10 model , orthrough horiIontal projection of water level .
10+ 10 plus a storege Design scale modell ng Minutes Asfor 10 models, pluswater Mike 11 cell approach t) the which can be of the )rder levels and inundation Extent HEC-RAS simu lati on of of 1 Os ta lODs of km in foodplain storage cells ISIS floodJlain flow. depending on catchment 1 nfoWorks
size, al sa has th e RS potential for broa d scal e application if used V\ith sparse cross-sectior data.
20- 20 rrin us the law of 8road scale modelling Hours Inundation extent LlSFLOOD-conservation o! and applications Vllhere Water depths FP morrentum forthe inertial effects are not JFLOW floodJlain flow. important.
20 Sol uti on of th e tvvo- Design scale mode Il ng of Hours or days Inundation e~ent TUFLOW dime1sional shallow t1e ordercf 10s ofkll. Water depths Mike 21 water equations. May have :he potential Depth-averaged velocijes TELEMAC
f)r use in broad scale SOBEK modelling if applied "'vith InfoWorks-very coars~ grids. LD
20+ 20 plus a solution for Predominently coastal Days Inundation extent TELEMAC vertical velociti::s modelling 3pplications Water d epth s :::0 usin~ continuity only. where 3D '/elocity profiles 3D vel ocitie s
are important. Has also been appli~d to reach ~l:al~ riv~r rrlULl~lIilly
problems i1 research projects .
3D Solution of the three- Local predictions of Days Inundation extent CFX dirne1sional t1 ree-d irnension al Water depths Reynolds averaged velo city fields in main 3D velocities Navier Stokes channels end floodp ains. equa:ions.
2.5 Rainfall-runoffmodel
2.5.1 The Google Scholar metric
Searching Google Scholar about rainfall-runoff modelling faces two possible
spelling for modelling.
The number of hits excluding citation returned by Google Scholar for the three
periods (before 1990, 1991 to 2000, after 2001) is presented in Table 5
22
Literature review
Table 5 : Rainfall-runoff hits - Google Scholar
Nbr Rainfall- Rainfall- Rainfall-Rainfall- Runoff Runoff Runoff
Year Runoff modelling modeling model <1990 3,790 2,810 2,810 2,730 1991-2000 5,580 4,950 4,950 4,810
2001-2010 16,400 16,400 16,400 16,300
The present decades shows a 194% increase compare to the previous one (Rainfall-
runoffkeyword), the association with modelling increases from 74% prior to 1990 to
100% posterior to 2000.
2.5.2 The rationales of rainfall-runoff modelling
The main justification for rainfall-runoffmodelling is the limitations ofhydrological
measurements.(Beven, 2004). The available measurements need to be extrapolated
both in time and space particularly for the ungauged catchments (where
measurements are not available) and into the future (where measurements are not
possible) to explore the impact of foreseeable hydrological changes.
A significant proportion of rainfall-runoff models are used to formalize knowledge
about hydrological systems but the ultimate aim of prediction using models must be
to improve the decision-making about a hydrological problem (water resources
planning, flood protection, mitigation of contamination, licensing of abstraction ... )
(Beven, 2004).
2.5.3 The perceptual model
The perceptual model in hydrology undertook a dramatic paradigm shift with the
measurements of the Seine by Pierre Perault in 1668 to 1669 and ms calculations
proving that rainfall was sufficient to account for flow of the Seine. Before him, the
23
Literature review
opinions were that the stream were fed by underground channels whose
contributions out weighted the precipitations.(Linsley, 1967). Nevertheless during
the 17th and 18th centuries, numerous summit reservoirs to supply water to canals
crossing divides were constructed in Europe and England. There must have been
sorne shrewd estirnates of the runoff available in order to assure that these reservoirs
would meet the demand of the canals during protracted dry periods. There is no
evidence in the literature of the techniques used to make these estimates, and one
suspects that there was heavy dependence on the judgment of the planners.(Linsley,
1967). The perceptual model was their main reference. Keith Beven too insists on
the importance of the perceptual model as a summary of one' s perceptions of how
the catchments respond to rainfall under different conditions.(Beven, 2004) A
perceptual model is necessarily personaI. It depends on the training that a hydrologist
has had, what he read, the data and field sites he came in contact with. The
translation of the perceptual model into mathematical descriptions inevitably lead to
simplifications in sorne cases gross simplifications but often preserving sorne
capacity to make quantitative predictions. This process of deciding the equations to
represent the process leads to the conceptual model (see Figure 2 : A schematic
outline of the different steps in the modelling process)
The runoff generation processes involved in the perceptual models are iIIustrated in
Figure 3
24
Literature review
(a) Infiltration excess overland flow
p
~
~f
(b) Partial area infiltration excess overland flow
P
~
~f
(c) Saturation excess overland f10w
P
~
(d) Subsurface stormflow
(e) Perched subsurface stormflow
Figure 3 : A classification of process mechanisms in the response of hillslopes to rainfalls. (a) Infiltration ex cess overland flow (Horton, 1933). (b) Partial area infiltration excess overland flow (Betson, 1964). (c) Saturation excess overland (Cappus, 1960, Dunne and Black, 1970). (d) Subsurface stormflow (Hursh, 1936, Hewlett, 1961). (e) Perched saturation and throughflow (Weyman, 1970).
2.5.4 The conceptual model
At this point, the hypotheses and assumptions being made to simplify the
descriptions of the processes need to be made explicit. Keith Beven notes that in
25
r_o-_-_...:--
Literature review
many articles and model users manuals, the goveming equations are presented
prominently but the underlying sirnplifying assumptions are not (Beven, 2004). This
however should be the starting point for the evaluation of a particular rnodel relative
to the perceptual model in mind.
2.5.5 The procedural model
The equations driving the conceptual model rnay not be resolved analytically within
the boundary conditions of the real system. This is often the case for partial
differential equations. A replacement of these equations by finite-difference or fmite
volume equivalents is frequent. Sorne error/approxirnations are often introduced at
this points and great care should be paid to understand the impact of these
approximations.
2.5.6 The calibration
A variety of parameters are often involved relating soil properties and dimensions of
the catchments with the water discharge. Sorne are strongly time dependant, sorne
are constant during a given simulation. Some parameters like the ones related to
catchments size, drainage length and slope are easily available through GIS
techniques. The parameters relating to soil properties are often known at a very
different scale than the one being rnodelled (very limited soil samples representing
the whole catchments with very broad limit of confidence). The optimisation of the
parameters is often done by various techniques of fitting a set of known outputs with
the available inputs in the rnodel. ,(Beven, 1993, Clarke, 1973, SCHUMANN, 1993,
Moradkhani and Sorooshian, 2008, Franks et al., 1997, Beven and Binley, 1992,
Beven, 2004, Beven, 2009).
26
j •• ".--:. . .. _ ... -..,.
Literature review
Parameters values determined by calibration are effectively valid only inside the
model structure used in calibration. Transferring these values to other model
structure or other catchments may generate serious errors.(Beven, 1993, Beven,
2006)
The concept of an optimum parameter set may be ill-founded in hydrological
modelling. The equifinality (cfr 2.2Environmental modelling equifmality) is most
likely inevitable
2.5.7 Validation jperformance evaluation
The validation process as understood by "classic" modellers consist at using a data
set with know results that was not used in the calibration (independent data) and
quantify the divergence between the modelled and observed output. Jens Christian
Refsgaard and Jesper Knudsen (Refsgaard and Knudsen, 1996) propose a testing
frame in 4 steps :
a) The split-sample test (SS) involves calibration of a model based on 3-5 years of
data and validation on another period of a appropriate length.
b) The differential split-sample test (DSS) involves calibration of a model based on
data before catchment change occurs, adjustment of model parameters to
characterize the change, and validation on the subsequent period.
c) In the proxy-basin test (PB) no direct calibration is allowed, but advantage may
be taken of information from other gauged catchments. Rence validation will
comprise identification of a gauged catchment deemed to be of a nature similar
to that of the validation catchment; initial calibration; transfer of model,
27
Literature review
including adjustment of parameters to reflect actual conditions within validation
catchment.
d) With the proxy-basin differential split-sample test (PBDSS), again no direct
calibration is allowed, but information from other catchments may be used.
Renee validation will comprise initial calibration on the other relevant
catchment, transfer of model to validation catchment, selection of two parameter
sets to represent the periods before and after the change, and subsequent
validations on both periods.
But these validation test are based on the idea of optimal model that is more or less
approached with the actual system modelled.(Beven, 2004, Beven, 2006, Anderson
et al., 2001)
The adoption of the equifinality proposition requires a much broader measure of
likelihood and uncertainty. The Generalised Likelihood Uncertainty Estimation
(GLUE) (Beven and Freer, 2001) achieves this objective by running many different
model runs with randomly choosen parameters sets. Each run is evaluated against
observed data by means of a likelihood measure. If a model is rejected it is given a
likelihood value of zero. The likelihood measure are then used to weight the
predictions of the retained models to calculate uncertainty estimates or prediction
limits for the simulation.
2.5.8 Rainfall-runoff model classification
There are many different ways of classifying hydrological models (see (Clarke,
1973, O'Connell, 1991, Singh, 1995). Rere after is a very basic classification adapted
from Beven (2004). The frrst taxon concems the spatial extension of the calculation
28
Literature review
unit: lumped or distributed modelling approach. Lumped models treat the catchment
as a single unit, with state variables that represent averages over the catchment area.
Distributed models make predictions that are distributed in space, with state
variables that represent local averages of storage, flow depths or hydraulic potential,
by discretizing the catchment into a large number of elements or grid squares and solving
the equations for the state variables associated with every element grid square.
Parameter values must also be specified for every element in a distributed model.
There is a general correspondence between lumped models and the 'explicit soil
moi sture accounting' (ESMA) models ofO'Connell (1991), and betweendistributed
models and 'physically based' or process-based models. Even this correspondence is not
exact, however, since some distributed models use ESMA components 10 represent
different subcatchments or parts of the landscape as hydrologïcal response units while
even the most distributed models currently available must use average variables and
parameters at grid or element scales greater than the scare of variation of the processes.
They are consequently, in a sense, lumped conceptual models at the element scale
(Beven, 1989). There is also a range of models that do not make calculations for
every point in the catchment but for a distribution function of characteristics,
TOPMODEL, is a model of this type, but has the feature that the predictions can be
mapped back into space for comparison with any observations of the hydrological
response of the catchment. It could therefore be called, perhaps, a semi-distributed
model.(Beven, 2004)
A second consideration is whether to use a deterministic or stochastic model. Deter
ministic models permit only one outcome from a simulation with one set of inputs and
parameter values. Stochastic models allow for some randomness or uncertainty in the
29
Literature review
possible outcomes due to uncertainty in input variables, boundary conditions or model
parameters. The vast majority of models used in rainfall-runoff modelling are used
in a detenninistic way, although again the distinction is not clear-cut since there are
examples of models which add a stochastic error model to the deterministic predic-
tions of the hydrological model and there are models that use a probability distribution
function of state variables but make predictions in a deterministic way. A working
rule is that if the model output variables are associated with some variance or other
measure of predictive dispersion the model çan he considered stochastic; if the output
values are single valued at any time step the mode! can he considered deterministic,
regardless of the nature of the underlying calculations.
There is one other modelling strategy based on :fuzzy methods that looks highly
promising for the future. In particular, fuzzy models would appear to offer the
potential for a more direct translation from the complexity.ofthe perceptual model
into a procedural model. Lohani and al. have recently compared rainfall-runoff
predictions based on Artificial Neural Network, fuzzy logic and linear transfer
functions on the upper Narmada basin (India). In their study, fuzzy models
outperformed the two other approaches.(Lohani et al., 2010).
2.5.9 ReFH package
2.5.9.1 Introduction
ReFH is a rainfall-runoffmodelling package for estimating design flood hydrographs
and analysis of observed events. It also includes reservoir routing methods for
estimating the attenuation of flood hydrographs by reservoirs. It is developed by
WHS HydroSolutions Ltd. The rationales for its use in the present study are
developed in para. 3.8 The choice of rainfall-runoff modelling package.
30
Literature review
The Revitalised Flood Hydrograph (ReFH) model is the new FEH rainfaII-runoff
method for UK design flood estimation. The methods used is based on the FEH
Supplementary Report No. 1 - The revitalised FSRlFEH rainfaII-runoffmethod.
The method was developed by The Centre for Ecology & Hydrology (CEH ) as a
direct replacement for the Flood Studies Report rainfall -runoff method, and the
software replaces the Micro-FSR software. This new lumped conceptual rainfall
runoff model was calibrated using recent and large flood events and has added
emphasis on seasonal flooding, with the same underlying principles as the previous
method. It is an event based model.
The software is capable to :
1. Generate design flood hydrographs
• use a design rainfaII event of the required return period
• specify both soil moi sture and rainfall on a seasonal basis depending on the
degree of urbanisation within the catchment.
2. Analyse observed events
• visually inspect quality of the data for each event
• estimate model parameters using FEH CD-ROM catchment descriptors
• optimise model parameters through event analysis
o specify sub-daily event rainfall and runoff
o model antecedent soil moi sture conditions using daily rainfaII and
evaporation
ouse regression fitting to select baseflow parameters on the hydrograph
for each event
31
.::,r ... :,-•• -
Literature review
o optimise time-to-peak and maximum soil moisture content
o optionally optimise additional parameters
• use these parameters to generate design flood hydrographs at the site
3. Estimate the attenuation of flood hydrographs by reservoirs through reservoir
routing methods
4. Manage and view observed event data and modelled results
In addition to the software, the ReFH design model has been implemented as a freely
available spreadsheet version.
Note that the following sections are partly reproduced from "The revitalised
FSRlFEH rainfall-runoff method" (Kjeldsen, 2007)
2.5.9.2 Modelling concepts
Rainfall-runoffmodelling for design flood estimation has conventionally been based
on the modelling of individual events. At the most rudimentary level ail that is
required to reproduce the catchment-scale relationship between storm rainfall and the
corresponding stream flow response is :
• a volumetrie loss to account for hydrological processes such as evaporation,
soil moi sture storage, groundwater recharge and interception losses; and
• a time distribution model to represent the various dynamic modes of
catchment response.
However, the specification of the model developed to represent the rainfall-runoff
relationship is very much related to scale, both spatial and temporal. For instance, a
model relating the annual rainfall and runoff for a small homogeneous catchment
32
Literature review
may be very simple, while the relationship between hourly rainfall and runoff on a
large, heterogeneous catchment may be extremely complexe The Revitalised Flood
Hydrograph (ReFH) model has been developed for use in the revitalised FSRlFEH
rainfaIl-runoff method as a parameter-sparse hydrological model, representing the
major rainfall-runoff processes on a catchment scale.
In the following it is important to distinguish whether the ReFH model is used for
modelling an observed flood event or for generating a design flood event. When
modelling an observed event, the ReFH model is used as a deterministic mode)
trying to reproduce a flood event from historical series of observed rainfall and soil
moi sture data. In contrast, a design event is a probabilistic estimate of a flood event
that will be exceeded on average once every T years, where T is the return period
(e.g. T= 100 years). When generating a design event, the input values of rainfall and
antecedent soi! moi sture do not represent a particular historie event but are
generalised vaIues specified so that certain combinations will result in a flood event
of the required return periode
The ReFH model consists of three main components: a loss model converting total
rainfaIl into effective rainfall, a routing model and a baseflow model. The
connections between the three model components are shown in Figure 4 : ReFH
model concept diagram, together with the required input variables and model
parameters. In addition to the three main components, a soi! moi sture accounting
model based on daily data is used to determine the state of the soil at the start of the
flood event based on long-term series of antecedent rainfall.
When simulating a flood event, the 10ss model is used to estimate the fraction of the
total rainfall volume turned into direct runoff. The direct runoff is then routed to the
33
•• -.. ~ ! .... ', .
Literature review
catchment outlet using the unit hydrograph convolution in the routing model and,
finally, the baseflow is added to the direct runoffto obtain total runoff. Each of the
three components, is explained further in the following sections.
Total ra infall
Net rainfall
, , ~ <~-~ --i--'" ,..-_....L.-_----,
Initial soli moisture ..... -.. ~ Loss model {Cmax }
Routing model
(Tp ) Cini ......
Initial baseflow BFo
~ Total f10w
__ 1 Baseflow model 1 1
____ {_BR_,_B_L} __ ---'I Figure 4 : ReFH model concept diagram
The loss model in ReFH is based on the Probability Distributed Model (PDM)
developed by Moore (1985) and widely used for a variety ofhydrological
applications in the UK. The PDM model is being used in a framework for a national
system for flood frequency estimation using continuous simulation modelling in the
UK (Lamb, 1999; Calver et al., 2005). Furthermore, the model has heen used in real-
time flood forecasting (Moore, 1999) and it has been used to investigate the impact
of climate change on runoff from small catchments in the UK (Prudhomme et al.,
2003).
Conceptually, the PDM assumes the catchment to consist of a number of individual
storage elements, each of a random soil moisture capacity C arising from a statistical
distribution. Assuming a uniform distribution of soil moisture capacities, if the
34
Literature review
storage elements are arranged in order from the highest (Cmax ) down to zero
capacity, the resulting PDM distribution of soil moisture capacity is shown in Figure
5 : Cumulative distribution of soil moi sture capacity . It is further assumed that the
storage elements interact such that the soil moisture is redistributed between stores
between rainfall events. Thus, at any time soil moisture is constant for all elements
of capacity greater than Ct and is at full capacity for elements of capacity smaller
than Ct as illustrated by the dotted area in Figure 5 : Cumulative distribution of soil
moisture capacity. During a storm, the depth ofwater in each storage element is
increased by rainfall (horinzontalline are a) and when rainfall exceeds the storage
capacity, direct runoff is generated. For the short duration of the storms under
consideration, the effects of evaporation and drainage out of the soils have not been
inc1uded.
EXeèSS
'-"'-~ ............. ~ ....... "'-. ..... {I ............ ..". ....... ~ ... ~ --~. .. _ ....... .... ..--.. -~. -• • • • • C· ... --... ....... -..,..,.
- . • f •• -li---_ ..... ~ -_ ........ ~ ~,~ • • 1 •• :--: ..
~ . .. o GlJrnul a~v.~ d~l~buUon of so~1 mo~stura capa'~ty
Figure 5 : Cumulative distribution of soil moisture capacity
o
So~1 m(l~ s.1ure capaclty, C t
Thus, a pulse of rain, Pt, on the soil gives 100% runoff from the area already at full
capacity and increases the moisture content in an other areas. The excess amount of
rainfall converted into direct runoff, q t, can be estimated through simple geometric
considerations as
35
Literature review
Equation 6
where the continuity equation Ct+M = Ct + Pt applies and Cilli is the soil moisture
content at the start of an event. The ratio q/P of rainfall transformed into direct
runoff is a measure of the percentage runoff, and Cmax is the only model parameter.
Once Ct exceeds Cmax, the model assumes that 100% of the rainfall is converted into
runoff. The loss model can be applied sequentially, where a loss is ca1culated
for each time step, or it can be applied to calculate a single loss of total rainfall
volume. In the revitalised FSRlFEH rainfall-runoff method, the former option has
been adopted, Le. a loss is calculated for each individual time step. As the soil
becomes increasingly wet during the storm, the loss decreases and the runoff rate
increases. The initial soil moi sture content (Cilli) is an important parameter when
applying the loss model, either for analysing an observed event or for simulating a
design flood event.
F or the routing model, ReFH uses the unit hydrograph (UH) concept for routing the
net rainfall to the catchment outlet (direct runoff). A UH can be estimated directly
for each flood event through simultaneous analysis of the effective rainfall
hyetograph and the direct runoffhydrograph as described by Chow et al. (1988), for
example. The original FSRlFEH model adopted a standard triangular-shaped
instantaneous unit hydrograph (lUH) scaled to each catchment using the time-to-
peak (Tp) parameter, catchment area and the selected time step. The ReFH model
retains the concept of a standard IUH shape scaled to individual catchments, but
introduces a more flexible shape as shown in Figure 6 in the form of a kinked
triangle.
36
Literature review
UI it hyalrogra'ph
Uk ;;:; Unp,l IJ~
U,,;;;; 1 -'> Triangular IUH
Uk ;;:; D ~ 1 nfllrli-te T/3
l ime units .: _______ 2_Tp _ _ - r~ ____ • ______ -.~ .~------------ TB -----------------.~
Figure 6 : Shape of standard instantaneous unit hydr?graph adopted in ReFH
The kinked triangle is described by a time scaling parameter, Tp , and two
dimensionless parameters, Up and Uk , controlling the height and kink., respectively,
of the IUH. The parameter Uk is a multiplier applied to the ordinate Uc of a non-
kinked triangular IUH at 2Tp , i.e.
Equation 7
where TBt= 2 Tp /[h to ensure unit-area under the non-kinked triangular unit
hydrograph, illustrated by the broken line in Figure 6. Thus if Uk= 1 the IUH is a
simple triangle, but as Uk drops towards zero, the 'lost area' is transferred into the
IUH tail by extending the overall time base TB.
Equation 8
Attempts to relate the parameters controlling the height (Up ) and kink (Uk) of the
IUH to readily available catchment descriptors were unsuccessful. Instead, average
values of Up = 0.65 and Uk= 0.8 are recommended for use in the revitalised FSRlFEH
rainfall-runoffmethod. Because of the kink introduced at 2Tp the standard ReFH
IUH has a lower peak and a longer time base than the FSR IUH. To convert the
37
Literature review
dimensionless IUH in Figure 6 into the required units of m3 s -1 mm -1, a scaling factor
of AREA 1 (3.6 Tp) is applied, where AREA is in km2 and Tp is in hours. The IUH can
only be used directly when rainfall is given as a continuous function of time. If
rainfall is given as a sequence of depths in successive time steps I1t, the IUH must
first be converted to an equivalent I1t-hour UH. To transform the IUH in Figure 6into
a unit hydrograph of any given time step I1t, the ReFH model uses the S-curve
method as described in many standard hydrology textbooks such as Chow et al.
(1988). The S-curve method replaces the existing FEH approximation of adding half
the time step to the time-to- peak of the IUH. This approximation only works if I1t is
a small component of Tp and the unit hydrograph is not too skewed.
The baseflow mode} implemented in the ReFH model is based on the linear
reservoir concept, with a characteristic recession defined as an exponential decay.
The approach, discussed by Appleby (1974) allows the· separation of total flow in
baseflow and surface flow without knowing the rainfall input. It is based on the
contributing area concept, and assumes that the saturated area of the catchment that
pro duces surface runoff is the same area that also produces baseflow recharge, and
furthermore that the ratio of recharge to runoff, BR, is fixed. An unsaturated area
produces neither runoff nor recharge because rainfall is retained as soil moisture.
Rainfall that becomes recharge is assumed to pass through a linear storage (with a
lag value of BL ) before emerging into the same channel system that carries the
surface runoff. The baseflow hydrograph at the catchment outIet can be determined
by routing BR times the (as yet undefmed) surface flow at the outlet through the
groundwater store. The observed hydrograph at the outlet is then the sum of the
surface and baseflow hydrographs.
38
Literature review
The resulting baseflow model calculates the baseflow at successive time steps ~t
apart by linking the baseflow to the observed runoff and the estimated baseflow from
the previous time step as
where Qtis total observed flow at time t. For the case where the baseflow model is
being used to analyse an observed flood event, the constants kt, k2 and k3 are given
as :
k1
= BR ( BL (1-k3 )
(l+BR) tH l+BR k3 ) Equation 9
k - BR (1 _ BL (1-k3))
2 - (1+BR) t1t 1+BR Equation 10
k3 = exp ( - ;: (1 + BR) ) Equation Il
2.6 Satellite rainfall . . .
~ '.
2.6.1 The rain gauges deficiencies
People have been measuring rainfall amounts for more than 2000 years, but a lot of
uncertainty remains regarding how much rain falls in remote areas of the globe.
(NASA,2010d).
Before TRMM's launch, the measurements of the global distribution of rainfall at the
Earth's surface had uncertainties of the order of50%.(NASA, 2010b)
39
Literature review
Traditionally rainfall has been monitored by open networks of raingauges at point
locations. However, such a sampling approach is ill-suited to a parameter which
seems to affect one percent or less of the surface of the Earth at any time (Barrett &
Martin, 1981), and which "typically occurs at only a very small fraction of the time
at any given location" (Theon, 1992). Furthermore, instantaneous precipitation
intensities range from 0 to > 125 mmJh, and characteristically vary over distances as
small as tens of metres, and over periods of minutes or even seconds, by orders of
magnitude of intensity. Meanwhile, because the gradients of rainfall intensity may
instead be very shaIlow, it is intrinsically difficult to establish the rain: no-rain
boundary with precision. It is also widely recognized that natural problems of
rainfall measurement by gauges have been exacerbated by problems which have
arisen in association with the gauges themselves: many different gauge types are in
everyday usage, the time intervals investigated by families of gauges differ, the
raingauges themselves affect the parameter they are designed to measure, and
deficiencies in rainfall station and data record management aIl adversely affect
the timeliness, availability and quality of rain gauge data.(Barrett, 1997)
The main limitations of recording gauges, due to the short intervals between
measurements, are that small rainfall totals may not be accurately recorded, some
rainfall may be missed during the emptying of storage vessels, and the delayed
delivery of water to the measuring container can introduce temporal errors (Linsley
et al., 1949, Sumner, 1988, Mc Anelly and Cotton, 1989, Robinson and Ward, 1990,
Horsfield, 2006, Beven et al., 2010) .
40
Literature review
Measurements from gauges are also subject to systematic errors due to shading and
sheltering, drops splashing in or out, condensation or evaporation of moi sture, and
the occurrence of solid precipitation. Wind turbulence, initiated by local topography
and features but amplified if the gauge rim is above ground level, increases the wind
speed above the opening and carries raindrops downwind, causing rainfall totals to
be underestimated. The impact of this can be minimised by mounting the top of the
gauge level with the ground surface, building a turf wall around it, or placing it in a
pit surrounded by a mesh grid (Arlon and Meisner, 1987, Sumner, 1988, Horsfield,
2006, Beven et al., 2010).
Gauges provide good measurements of rainfall at a single point, but rainfall is highly
variable spatially and it is necessary to interpola te these values for most applications
in order to produce an estimate over an area. The accuracy of areal estimates is
dependent on gauge density and distribution, and on the ability of the available data
to represent patterns over a larger area; sparse point values may not effectively
monitor areas that experience pronounced spatial gradients, particularly in
convective regimes(Hildebrand et al., 1979, Arkin and Meisner, 1987, Sumner,
1988, Horsfield, 2006, Barrett and Beaumont, 1994). Point values are interpolated to
provide areal estimates for unsampled locations, but this can introduce smoothing,
which misses sorne fine-scale features (Hildebrand et al., 1979), and only perfectly
represents the actual situation where rainfall demonstrates spatial homogeneity (Oh
et al., 2002, Horsfield, 2006).
For the land area divided in grids of2.5° * 2.5 0 (= 3378 cells), 42.8 % have no
gauge, 41 % have only one, 10% have two to five gauges, 2.2% have ten and
more.(Bellerby, 2010). The density ofrain gauges is illustrated in Figure 7
41
Literature review
Reliability of recording is often compromised by the inadequate staffing and
protection of the gauges.(Gyawali, 2001).
Figure 7 : GHCN-Monthly Coverage Map for Precipitation (Burroughs, 2008).
2.6.2 The satellite rainfall concepts and instruments
Historically, satellite meteorology may be said to have been launched in April 1960
through the first Television and Infrared Observation Satellite (TIROS-l ).(Barrett,
1997). The foundations of satellite rainfall monitoring were laid by Lethbridge
(1967) who considered general relationships between cloud brightness and coldness
and rainfall, and by Barrett (1970) who prepared monthly rainfall maps for part of
the tropical Far East based on satellite cloud charts (nephanalyses), using climate
station data for calibration (Barrett and Martin, 1981). Since then ever-increasing
efforts have been made to exploit satellite visible, infrared and microwave data for
such purposes (Barrett and Beaumont, 1994). Visible and infrared data provide
information on cloud top characteristics which may be indicative of areas and rates
of rain falling from the bases of the clouds, but passive microwave radiation is
42
Literature review
scattered by, or emitted from the hydrometeors themselves embedded in the clouds.
A fundamental problem with satellite-based techniques relates to the frequencies of
the radiation data, these being characteristically rather low spatially (at best about 0.5
km in the visible and infrared, and as low as about 50 km in the passive microwave)
and infrequent temporally (ranging from every half an hour, or less, from
geostationary satellites to as low as once per day from polar orbiting satellite
systems).
Note that the following paragraphs are adapted from "Satellite rainfall climatology: a
review". (Kidd, 2001).
Perhaps the most important contribution that satellite observations can make to
climatological rainfall estimates is that their observations can be made quasi
globally, a feature that was evident since the launch of the first meteorological
satellite.
Since the launch ofTIROS-l, polar-orbiting (or Low Earth Orbit) satellite sensors
have been a mainstay of global meteorological satellite observations. The current
polar-orbiting satellite series of the National Oceanographic and Atmospheric
Administration (NOAA) are direct descendants of the TIROS-1 satellite and are
characterized by dual satellite coverage of the globe twice daily at a basic resolution
of 1 km. These satellites constantly circle the Earth in an aImost north-south orbit,
passing close to both poles. The sun-synchronous nature of the orbit allows one
satellite to cross the Equator at 07 :30 h local time, with the other at 13 :40 h local
time: two of these satellites ensure that data for any region of the Earth are no more
than 6 hours old. The current NOAA series of satellites started in 1979 with the
43
Literature review
launch ofNOAA-6, with the newest satellite being NOAA-N launched on May 20,
2005(NOAA, 2010). Additional polar-orbiting visible/infrared (VislIR)
meteorological satellites are provided by China and Russia . (See
The primary instrument aboard the NOAA series of satellites is the Advanced Very
High Resolution Radiometer (A VHRR). This instrument senses radiation in the
visible and IR parts of the spectrum with a spatial resolution of 1 km, resulting in
many thousands of global measurements being collected daily. Complementing the
A VHRR : the Microwave Sounding Unit (MSU) and Stratospheric Sounding Unit
(SSU), and, more recently, the Advanced Microwave Sounding Dnit (AMSU), can
be used to help improve the satellite rainfall estimates. In addition, the use of the
High Resolution InfraRed Sounder (HIRS) can be used to detect the transparency of
clouds and, hence, their rainfall potential. However, the main limitation of the polar
orbiting satellites is the poor temporal sampling (about every 6 h) being insufficient
for sorne rneteorological applications. Geostationary meteorological satellites started
in 1974 with the Synchronous Meteorological Satellites (SMS). The geostationary
weather satellites are placed around the globe to provide continuous, repeat coverage
of the Earth. From an altitude of 35,400 km, each satellite can provide data from
about one third of the Earth' s surface, but due to image degradation towards the
edges it is necessary to have at least five geostationary satellites. Imagery can be
captured as frequently as every 30 seconds from the CUITent Geostationary Earth
Orbiting Satellite (GEOS) satellites, but more typically every 30 minutes. The
international cooperation between the participating nations is designed to ensure that
a minimum of 3-hourly data is provided, free of charge, to any other nation.
Although the resolution of the geostationary satellites is coarser than that of the
44
Literature review
polar-orbiting satellites (4 km in the IR at the sub-point), their ability for continuous
monitoring of the atmosphere is crucial for short-term monitoring and forecasting of
weather systems.
Whilst the sensors described above operate in the visible and IR regions of the
spectmm, another set of sensors have been developed for detecting microwave
radiation. Early experiments with passive microwave radiometry started in 1972 with
the Iaunch of the polar-orbiting Electrically Scanning Microwave Radiometer
(ESMR-5) operating at 19 GHz. This was followed by ESMR-6 in 1975, operating at
37 GHz. Results from these two instruments illustrated the benefits to be gained
from passive microwave instruments. The Scanning Multichannei Microwave
Radiometer (SMMR) was launched in 1978: this instrument collected data at five,
duai-polarized frequencies (6.6, 10.69, 18.0,21.0 and 37.0 GHz). Since 1987 the
US' s Defense MeteoroIogicai Satellite Program (DMSP) series of satellites have
carried the Special Sensor Microwave/lmager (SSMIl) providing dual polarized
observations at three frequencies (19.35, 37.0 and 85.5 GHz) and a vertically
polarized channel (22.235 GHz) with the resolution at the highest frequency being
13x15 km.
45
Literature review
Table 6 : Meteorological satellites
Low-Earth orbits Country of origin Sen or Co erage
NOAA USA Vis/IR 90oN- 90oS Meteor Rus ia Vis/IR 90oN- 900S FY-I China Vi /IR 90oN- 900S TRMM USA/Japan Vis/IR/MW /radar 40oN-400S DMSP USA Vi /IR/MW 89°N- 89°S
Geo tationary orbits Country of origin Sensors Sub-satellite point
Meteosat-7 Europe Vis/IR 0° .... --.-~--
Meteosat-5 Europe Vi /IR 63°E GOMS-I Russia Vis/IR 76°E INSAT- ID lndia Vis/IR 74°E INSAT-2E India Vis/IR 83°E FY-2 China Vis/IR 105°E GMS-5 Japan V~ /IR 1400E GOES-IO USA V~ /IR 135°W GOES-8 USA Vis/IR 75°\V
:r': • . - >
Despite the host of Vis/IR techniques developed, many showing great promise
especially over land, aIl have their limitations. The major drawback is that aIl the
techniques have to infer rainfall from the cloud top temperature or brightness. There
is sometimes little difference between the visible and IR signatures of precipitation
and non-precipitating clouds, often resulting in misclassification of rain areas.(Kidd,
2001).
In the meantime, it was established as early as the mid-1970s that the passive
microwave imagery could provide useful evaluation of rainfall over oceans, at least
on a climatological time and space scales (Rao and Abbott, 1976). The frequencies
0.81 cm (37 GHz), 1.43 cm (19.35 GHz) and 1.66 cm (18 GHz) were particularly
useful for delineating atmospheric liquid water content, precipitatable water and
rainfall intensity(Alishouse, 1983). The ability of the microwave to sense these
parameters is due to the capacity of the microwaves to penetrate clouds, precipitation
sized particles being the major source of attenuation at passive microwave
frequencies. The brightness temperatures measured by the satellites are dependent
46
Literature review
upon the radiation emerging from the Earth's surface and from the intervening
atmosphere between the Earth and the satellite (Allison et al., 1974). Early works by
(Adler et al., 1991, Weinman and Wilheit, 1981, Bellerby et al., 1998) all confirmed
that passive microwave radiometry had great potential for surface rain rate
estimation.
Despite the ability of satellites to monitor precipitation effectively, it must be noted
that absolute values are still uncertain: over sea areas there is a lack of adequate
validation data, surfaces in colder climatic iregions pose significant difficulties,
particularly to passive microwave rainfall (or precipitation) estimates, and the
frequency of observations from satellites leads to temporal samp-ling errors.
The various intercomparison exercises have all indicated that there are variations in
the amount and distributions of rainfall that individual algorithms retrieve. Assessing
which algorithm is 'best' is made more difficult by the Jact that, over regions where
greatest differences between the satellite products occur, generally are regions of
little of no surface validation data. Also, in common with existing disadvantages of
surface measurements, sorne error in the intercomparisons is due to the surface data
not being ofhigh enough quality for a direct comparison with satellite data.(Kidd et
al., 2003, Bellerby and Sun, 2005, Feidas, 2010)
2.6.3 The Tropical Rainfall Monitoring Mission
2.6.3.1 Concepts and instruments
The Tropical Rainfall Measuring Mission (TRMM) was launched in 1997,
augmenting the passive microwave capabilities . The TRMM satellite is in a low
Earth, non sun-synchronous orbit and carries sensors designed to enhance our
47
Literature review
understanding of rainfall. The TRMM Microwave Imager (TMI) was developed
from the SSMIl instrument, with the addition of a 10.7 GHz channel, with a best
resolution of 4 km at 85.5 GHz. This, together with the A VHRR derived Visible and
InfraRed Scanner (VIRS) constitutes the passive rainfall sensors(see Figure 8).
(Huffman et al., 2007, NASA, 2010c). The most significant instrument ofthis
satellite is the active microwave Precipitation Radar (PR) capable of providing
information on the horizontal and vertical distribution of rainfall at resolutions of 4
km and 250 m, respectively. It is the combination of these instruments that makes
the satellite quite unique. The co-Iocated, co-temporal sampling of the sens ors
enables a better understanding of the rainfall processes to be achieved, thus leading
to better retrieval algorithms and rainfall estimates.(Kidd, 2001).
48
Literature review
Figure 8 : TRMM sensors chart
49
Literature review
2.6.3.2 TRMM data products
TRMM observed data are processed by NASA and NASDA, and distributed to
users. The definition of the TRMM products is presented in Table 7
Table 7: TRMM data level (NASDA, 2001)
Levet Definition o Un essed instrument drt~ time ocdered, uali checked, no reœndanc . 1
2
3
The different products are presented in Table 8
Table 8 : TRMM Data products (NASDA, 2001)
Estimated Do Sensor Processing Level Proœd Seme Unit"} Volume"'2
(Compressed)
PR IB2l Calibmled Received Power 1 orbit (16/day) 149MB
(6~70 MB)
IC2l Radar Refle ct ivity î orbit (16lday) 149MB
(4()-"SO MS)
2A21 Normalized R..J .... Swface
1 orbit (l61day) 10MB
Cross Section (s'J) (6-7 MB)
2A23 PR Qualihtive 1 orbit (16/day) 13MB
(6---7 MB)
2A25 Rain Profile 1 orbit (16/day) 241 :MS
(13-17 MSl
3A25 Monthly Statistics of Global Map (Monthly) 40MB
Rain Parameter (Grid: 5° x 5°, O.Y x O.S·) (26--27 MB)
3A26 Monthly Rain Rate Global Map (Monthly) 9.3 MB
8smg llStaJ:isticaJ Aletkod (Orid: 5° x 5°) \.~ .. a..tf.LM9;'
TM! IBll Brightness TemperZire 1 orbit (16/day) 14:MB
(14 MB)
2A12 Rain Profile 1 orbit (16/day) 97:MB
(6.7-9 ME)
3A11 Monthly Oceanic Ramfall Global Map (Montbly) 53KB
(Grid: 5" x S") (44 KB) .r.,_-_':~-::.
Vm.S IBOl Radililce 1 orbit (16/day) 92MB
(90MB)
COMB 2B3l Rain Profile 1 orbit (16/day) 151 MB (8MB)
3B3l Monthly Rainfall Global Map (Monthly) 442KB
(Grid: 3° x 5°) (380.-410 KB)
3B42 TRMM &IR Global Map (S/day) 242KB DailyRainfall Grid : 0.25 * 0.25 (1l~115 KBl
3B43 TRMM & Other Sources Global Map (Monthly) 242KB
Monthly Rainfall (Qid: 1° x 1") (242 KB)
50
The data flow is presented in Table 9
Table 9 : TRMM data flow (NASDA, 2001)
LelJel 13
Le'/el2
1 ~IJ~I .~
Lo'/ol3 (Comhin::li P'OcllJct Iisino -RM rv1 g Ot hY dGtG)
2.6.3.3 3b42 rainfall product
Literature review
, . PR
Fllonthly R:ain Rate usln Il a Stalls.cal MetnoCi
IR InfraRed C PI G IDb:a1 P ... eipitaüo n Ind IJ( .
SSMA SlleClalsensor Micrawlve/lma ger
CAM6 Cllm:all! Assessment and Monitoring SY!ium
G PCC G IDb III P recipi1 ëlton Clmltology Center
The 3 b42 rainfall estimates are sometimes referred as TMP A for TRMM
Multisatellite Precipitation Analysis but this is misleading as the TMP A includes the
3b43 product which is a monthlys estimate while the 3b42 is a 3 hourlyestimate.
51
Literature review
The 3b42 (Huffman et al., 2007) is a near real-time precipitation rate product at fine
time and space scales (3-hr, 0.25 degree X 0.25 degree latitude-longitude) over the
latitude band 50 degree N-S. This product makes use ofTRMM's highest quality
observations, along with high quality passive microwave-based rain estimates from
3-7 polar-orbiting satellites (e.g. AMSR, (2) SSMIIDSMP, (2) AMSUIPOES), and
all the geosynchronous IR sensors (Meteosat, GOES, GMS). The combined quasi
global rain map at 3-hr resolution is produced by using TRMM to calibrate, or
adjust, the estimates from aIl the other satellites, and then combining aU the
estimates into the TMP A final products. The technique uses as much microwave
data as possible, and uses the geo-IR estimates to fill in gaps in the three-hour
analysis. The calibrations are computed using monthly accumulations of matched
data to ensure stability.
The primary merged microwave- infrared product is computed at the 3-hourly, 0.25 0
*0.25° latitude-longitude resolution. In common with the opep products, the
TMP A is designed to combine precipitation estimates from various satellite systems,
as weIl as land surface precipitation gauge analyses when possible, with the goal that
the final product will have a calibration traceable back to the single "best" satellite
estimate. In the present implementation, the calibration is based on TRMM
estimates. The TMP A is computed twice as part of the routine processing for
TRMM, first as an experimental best-effort real-time monitoring product about 9 h
after real time, and then as a post-real-time research-quality product about 10- 15
days after the end of each month. (For brevity, these will be referred to as the RT and
research products, respectively.) The fust bas been posted to the Web since February
52
Literature review
2002, while the second is available from January 1998, for a record that totals more
than 10 yr and continues to grow.
For the 3B42RT product (Huffman et al., 2009), files are produced every 3 hours on
synoptic observation hours (00 UTC, 03 UTC, ... , 21 UTC), these rainfall rates
correspond to the 3 hours centred on the nominal hour (ie: from 1 :30 to 4:30 for the
03 UTC). The Version 6 TRMM product 3B42 is being computed with the TMPA
after real time, and constitutes the research-grade archive of TMP A estimates. The
version numbering for the TMPA-RT and official TRMM products are not related,
although both are currently numbered 6.
2.6.4 3b42 raiDfall rUDoff modelliDg
2.6.4.1 Google Scholar metric
The number of hits exc1uding citations returned by Google. Scholar for the various
combination of keyword with 3b42 is presented in Table 10
Table 10 : Google Scholar hits for 3b42 & hydrological models, flood, runoff
3b42 - 3b42 hydrological 3b42 3b42 flood
Year model runoff flood warning
<1990 0 1 2 0 1991-2000 2 0 1 0 2001-2005 19 5 11 5
2006 19 6 5 2 . .; ... .,:..:-.,:- 2007 32 9 16 3
2008 48 21 26 10 2009 71 32 49 12 2010 35 22 30 8
53
Literature review
The surge of interest is linked to the growing historical depth of available data, the
excellent downloading options and the betler delineation of catchment offered by
world DEM such the SRTM(Jarvis et al., 2008) or the Aster GDEM (Jaxa, 2010) .
2.6.4.2 Results ofrainfall-runoffmodelling with 3B42 data
Large basins are often the focus of 3b42 rainfall-runoff modelling because they
present inaccessible areas and transboundary situation. The Mekong River is a case
where very advanced used of 3b42 data have been reported(Magome et al., 2008).
The study used 29 years of data to calibrate YHylBTOP model and obtain a volume
ration of 107% and a Nash-Sutcliffe coefficient of 85%.
Similarly in La Plata basin (Su et al., 2008), the 3B42 based runoff prediction had
an average Nash-Sutcliffe coefficient of 0.71 for the daily discharge.
In the Tapajos river basin (Collischonn et al., 2008b), the Nash-Sutcliffe coefficients
were in the bracket 0.68 to 0.99.
In China, the quality of the TRMM 3B42 rainfall data is validated with the gauged
rainfall data in a 3-year periode Forced by the gauged and TRMM 3B42 rainfall data,
two continuous hydrologic simulation cases are conducted to analyze the temporal
variability and the spatial distribution of the hydrologie process in Huaihe River
basin (32° Lat N) from 1998 to 2003 .The analyses show that the TRMM rainfall is
comparable to the gauged rainfall data in the hydrologic study. The simulated
streamflow hydrograph with the TRMM rainfall is also consistent with the observed
one at Bengbu station(Yang et al., 2009).
But further North in China, in the West Laohahe River basin at latitude of 41 0_
42.75°N, three-Iayer Variable Infiltration Capacity (VIC-3L) model cannot tolerate
54
Literature review
the nonphysical overestimation behaviour of 3B42RT through the hydrologic
integration proeesses, and as sueh the 3B42RT data have almost no hydrologie
utility, even at the monthly sca1e. In contrast, the 3B42 research data can produce
much better hydrologic predictions with reduced error propagation from input to
stream flow at both the daily and monthly sca1es.(Y ong et al., 2010).
In Senegal, a comparison of several satellite rainfall estimates (SRFE) as input into
MIKESHE showed that as a raw input, 3b42 generated a streamflow with a
correlation (R2) = 0.41 with the observed',flow, while the best SRFE (CCD) had R2
= 0.68. After correction and recalibration the best correlation obtained was with
CPC-FEWS(R2 =0.86) , 3b42 ranked last in the 5 SRFEs tested.(STISEN et al.,
2010).
In the Ganges-Bhramaputra- Megna basin, MIKEBASIN was successfully setup
with a combination of gauges and TRMM data. (Nishat and Rahman, 2009).
Each of the studies mentioned above are using different rainfall-runoff models to
generate a discharge from a rainfall. Therefore, they may not be strictly representing
the performance of the 3B42 data (and a sharp difference seem to emerge between
the 3B42RT and 3b42 research(corrected on rain gauges monthly accumulation).
Most of the authors cited request some improvementlloca1 correction in the dataset
and a lot hope is placed in future Global Precipitation Measurement (GPM) to
resolve the present discrepancies.
55
Literature review
2.6.5 3b42 and flood warning system
Three cases of flood warning systems based on 3b42 have been identified : the
Global Flood Monitoring (Hossain et al., 2007), the Mekong Flood Forecasting
(Magome et al., 2008) and the EFLOOD package developed for the Evros
catchment, extending between Greece, Bulgaria and Turkey. (Fotopoulos et al.,
2010).
To offer a cost-effective solution to the ultimate challenge ofbuilding flood alert
systems for the data-sparse regions of the world, a modular-structured Global Flood
Monitoring (GFM) framework that incorporates satellite-based near real-time
rainfall flux into a cost-effective hydrological model for flood modelling quasi
globally has been initiated by NASA. This framework includes four major
components: TRMM-based real-time precipitation, a global land surface database, a
distributed hydrological model, and an open-access web interface. Retrospective
simulations for 1998-2006 demonstrate that the GFM performs consistently at .
catchment levels. The interactive GFM website
(http://trmm.gsfc.nasa.gov/publications_dir/potential_flood_hydro.html) shows
close-up maps of the flood risks overlaid on topography/population or integrated
with the Google-Earth visualization too1. The global DEM is based on the
SRTM,(Jarvis et al., 2008) Global soil properties are extracted from the Digital Soil
of the World (F AO 2003). The MODIS land classification map is used as proxy of
land coyer/uses and a modified version of HEC-HMS is used to generate the runoff
from the 3b42RT dataset.(Hossain et al., 2007).
56
Literature review
In order to achieve effective flood risk forecasting for poorly gauged sub-basins in
the Mekong River Basin, the feasibility of using a currently available distributed
hydrological model and satellite-based precipitation datasets cou pied with a simple
statistical approach was tested. A physically based distributed hydrological model,
the YHyMIBTOP model, was used to simulate at any grid for the whole Mekong
River Basin, including poorly gauged basins. Historical discharge data for the past
29 years were reconstructed and archived with validation using ground-based
observed hydro-meteorological data with other public domain datasets as input.
Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis
(TMP A) data were forced in the model. Simulated discharge by the YHyMIBTOP
model using satellite-based precipitation generally represents the trend of observed
discharge weIl and the possibility of real-time flood risk assessment was weIl
demonstrated.(Magome et al., 2008).
The most ambitious modelling using a automatic forcing of the model with 3b42RT
data is achieved by the Department of Water Resources and Environmental
Engineering, School of Civil Engineering, National Technical University of Athens,
Greece. (Fotopoulos et al., 2010). The system was developed for the Evros
catchment, extending between Greece, Bulgaria and Turkey. The main component is
called EFLOOD and is the core of the system. It retrieves the areal rainfall
estimation for a given time period and location from 3b42RT or 3B42 and computes
the expected runoff by means of a statistical rainfall-runoff model. The estimated
high discharge values are then compared to two pre-defined values that correspond
to the alarm level and to the maximum possible flow rate that can be routed through
the crosssections of Evros river respectively. If these two values are exceeded, then
57
Literature review
EFLOOD issues either an alarm warning or a flood event. During the period 2000-
2009, the system using 3B42 research had a successfui diagnostic of 87 %, faise
alarm 8%, missed flood 1%, false flood event 4%. The 3b42 RT (real time) was less
efficient : successful diagnostic of 77 %, false alarm 13%, missed flood 2%, false
flood event 80/0. But a locally recalibrated 3B42RT reached a better efficiency :
successful diagnostic of 95 0/0, faise alarm 2%, missed flood 2%, faise flood event
1%.
58
... " '. '~-
Methodology
3 Methodology
3.1 Research question and rationales
Is the error introduced by passive-microwave- and infrared-based satellite rainfall
data tolerable for sm aIl catchments flood plain modelling ?
This question arises as a substantial number of research are undertaken to propose
sorne hydrological rnodelling driven by re,motely sensed rainfall.
Many of these studies stops at the estimation of a discharge based on a rainfall
information generated at catchment' s level. This usually where hydrologists stop.
The significance of a river discharge is manifold but one of the crucial consequence
of high flow is the inundation of the flood plain. The para 2.3 presents only a short
summary of flood damages but that should be sufficient to convince about the
requirements of flood defence and mitigation. These actions frorn the population
living in flood prone area require some spatial framework where the flood extent and
flood depth need to be as explicit as possible. This requires a modelling effort with a
proper treatment of the potential errors and uncertainty.
The present study intends clearly to contribute to some engineering questions of data
reliability and the impact of error propagation in modelling flood plain. The
technologies involved in the acquisition of the data discussed here (space borne
radar, passive micro-wave & infrared captors and laser ranging) are in very rapid
development and each technological steps reveals huge potentials but also generate
risks of errors/ misrepresentation. The effort placed in this study is motivated by the
author's experimental tradition developed from an early age in a photo lab where the
59
Methodology
optimal combination of exposure and development duration had to be interpolated
between inadequate attempts (probe and automatic calibration were at their infancy
and unaffordable). The present focus is not to elucidate exhaustively the
performance and error of each instrument and its data processing but to look at a
final pragmatic result : a flood plain delineation processed with different data set.
The error generated by the passive-microwave- and infrared-based satellite rainfall
data will be identified by comparing the flood extent modelled based on stream
gauge data measurements, rain gauge data and passive-microwave- and infrared
based satellite rainfall data. The modelling of the flood plain requires the
transformation of rainfall information into a river discharge which is done through a
rainfall-runoffmodel.(Beven, 2004). The link between the flood plain and the
satellite based estimation of rainfall is the backbone of the present study. Time and
word count constrains limit the investigation of connected· topics such as the
calibration of satellite based data, calibration of rainfall-runoff model, model
structure choice, estimation of errors and uncertainties, mobilisation of flood plain
model in emergency planning and management, and flood defence design but their
point of branching will be noted at the appropria te chapters.
Along the resolution of the central research question, some additional subsidiary
questions emerged while mobilising the various tools and software. One concem, the
adequacy of the ReFH method outside UK, the other is related to the use of LIDAR
data as the only source for river cross-sections.
60
~;-.;.-.--~- . .
Methodology
3.2 The flood plain of interest
The choice of the flood plain to be modelled resulted of practical considerations
emerging during the study rather than planned at its inception. A cruciallimiting
factor was to obtain a software package sufficiently recent, properly debugged and
well documented. These constraints result from the academic timeframe. The other
constraints is related to the access to satellite data, topographical data, river flow and
rain gauge data. A manageable flood plain South of Villers-sur-Semois (49.69° Lat
N, 5.56° Long E see Figure 9 : Location of Villers-sur-Semois) emerges as fulfilling
the following criteria:
a) Being of a size that the 2,500 cells of ISIS 3.3 free could modelled in 2
dimensions(Halcrow, 2010a). The size of the active area is 5.8 ha
b) located in the Semois basin for which river flow, rain gauge data, and LIDAR
data were available
c) Being located within the coverage of the TRMM satellite observation (Lat <
50° N (NASA, 2010b»
61
Methodology
Figure 9 : Location of Villers-sur-Semois
3.3 The reach of interest
The 1 D module of ISIS free provides 250 nodes and withiil this limitation it was
possible to compute 103 sections at 50 meter distance downstream of the stream
gauge ofSt-Marie-sur-Semois (49.68495 °Lat N, 5.5634° Long E).
The mode lIed reach is 5,101 m long, cross two roads with bridges and does not have
any confluence with a tributary. The Figure 10 shows its location.
62
-.,-" --.:~ .. ~.
Methodology
Figure 10 : Location of the modelled reach
3.4 The catchment of interest
The catchment upstream of the stream gauge ofSt-Marie-sur-Semois was delineated
based on the 10 m DEM provided by the regional authority with the ArcHydro
utility (ESRI, 2009). The result returns 141.3 km2 , the official catchment size for the
gauging station is presented as 143 km2 • In the present study the 141.3 km2 value is
used.
3.5 The period studied
It is considered relevant to include the January 2003 flood in the study. At the time
of ordering the ground data, the plan included using the GSMap MVK + which are
available for the 2003-2008 period. Therefore the data requested from the local
authorities included only November 2002 to December 2008. This is considered a
reasonable time span for an MSc dissertation. But the 3b42 data set is available from
January 1998 to present and the whole period could have been included in the study~
The data processed here starts on 01/11/0201:00 AM and ends on 31/11/08 12:00
PM.
63
Methodology
3.6 The choice of passive-microwave- and infrared-based satellite
rainfall da ta
The initial intention was to compare several data set namely : CMORPH (Joyce et
al., 2004), TRMM 3B42 (NASA, 2008) and GSMapK+(USHIO et al., 2009) but the
time constraint made TRMM 3B42 the only possibility. The main advantages of
3b42 is the very comprehensive interface for data extraction: TOVAS(Kempler,
2010). The 3b42 product contains more inputs than the MW and IR results as it
incorporates also the observation from th~ space borne rain radar (see para 2.6.3.3).
The possibility of near real time access (latency of 6 to 9 hours) is an attractive
element for the 3b42RT product although this had no influence in the present study.
The data set used here is the 3b42 ver 6. Re se arch (NASA, 2008, NASDA, 2001).
3.7 The choice of lD-ZD flood plain modelljngo software
A large consensus supports the flood modelling based on an interaction between 1 D
and 2D modelling.(Wallingford, 2010, DHI, 2010, Ernst et al., 2009, Pender, 2009,
Werner, 2004, Horritl and Bates, 2002, Syme, 1991). The Department of Geography
at the University of Hull has no license for any 1D-2D modelling software, ISIS free
was the only affordable option. The cost of the 4 days training in Leeds and London
was around 1,200 f despite a substantial rebate.
3.8 The choice of rainfall-runoff modelling package
The choice ofrainfall-runoffmodel is very open and the lack of experience and tight
deadline led to a choice based on the existing flood modelling recommendation in
UK where the ReFH method is a respected norm (Faulkner and Barber, 2009,
Kjeldsen, 2007). The parameterisation of the mode1looked rather simple at fust sight
64
Methodology
although this is the case for UK only where catchments descriptors are readily
available. The purchase of the software amounted to 294 f..
3.9 A brief introduction to the ReFH package operation &
parameters preparation
The rainfaIl-runoff concept is described in the heading 2.5.9. The parameterization
requires two types of catchment characteristics : the geometrical and the
hydrological.
The geometrical characteristics are :
1. (AREA) : Catchment drainage area derived from the Digital Terrain Model
(km2)
2. (AL TB AR) : Mean altitude of catchment (metres above sea level)
3. (ASPBAR) : Mean direction of aIl drainage path slopes (bearing in degrees)
4. (ASPV AR) : Invariability of slope directions (values approaching 1.0
indicate dominance of one direction)
5. (DPLBAR) : Mean of distances along drainage paths between each 50m grid
node and the outlet (km)
-.:_".- '.~
6. (DPSBAR) : Mean of aIl inter-nodal slopes, along drainage paths (mIkm)
7. (LDP) : Longest drainage path from source to outlet (km)
8. (URBEXT) : Extent ofurban and suburban land coyer
65
Methodology
AlI these parameters were derived from the 10m DEM provided by the regional
authorities.
The hydrological characteristics are :
1. (FARL): Index of flood attenuation due to reservoirs and lakes (1.0
indicates no attenuation)
2. (BFIHOST) : BaseFlow Index derived using the HOST (Hydrology Of Soil
Types) classification
3. (SPRHOST) : Standard Percentage Runoff derived using the HOST
classification
4. (PROPWET) : Proportion of time when soil moi sture deficit was below 6mm
during 1961-90
5. (SAAR) Standard period (1961-1990) average annual rainfall (mm)
6. (SAAR4170) 1941-70 average annuaI rainfaiI (mm)
7. (RMED 1H) Median annual maximum 1-hour rainfall (mm)
8. (RMED1D) Median annuaI maximum 1-day rainfall (mm)
9. (RMED2D) Median annuaI maximum 2-day rainfall (mm)
RMEDIH, RMEDID, RMED2D have been calculated based on the 6 years hourly
data available.
The long term average rainfall (SAAR) have been extracted from F AO Loc _ Clim
database (Grieser et al., 2006).
66
Methodology
The BFI has been ca1culated by extracting the base flow from the 6 years data
available with the software ABSCAN.(Free Software Directory, 2006)
PROPWET has been estimated with a regression using the long term rainfall as
independent variable and the FEH-CD ROM data (WHS, 2010).
SPRHOST has been estimated with a multi-variate regression using as independent
variables: the hydrological properties of the European Soil Database (JRC, 2010),
the Corine Land Coyer 2000 database (Agency, 2000), DPLBAR, DPSBAR, BFI,
PROPWET and the SPRHOST data available from the HiFlows-UK v3.02 data set
(Environment Agency UK, 2010). Twenty four catchments with the same water
regime as the St-Marie-sur-Semois' catchment have been used to establish the multi
variate regression. (R2=O.89) .
URBEXT was estimated based on a regression between the Corine Land Coyer 2000
database and the URBEXT data available from the HiFlows-UK_ v3.02 data set.
The model is calibrated with rainfall-runoff data grouped in flood events. Each event
requires a synchronised set of rainfall-runoff observation. The timestep is variable
but aIl the events need to be presented with the same timestep.
The initial moisture is obtained from antecedent rainfall. For the evapotranspiration,
ReFH generate a sin curve based on an annual daily mean
During the calibration run, the following parameters are optimised :
FC : Field Capacity
RD : Routing depth
67
Methodology
FC scaling factor (SM)
CMax (mm): Maximum Soil Moisture Capacity
DK: Daily Soil Moisture Decay rate
Tp : Time to Peak
Up and Uk : height and kink of hydrograph
BL : Baseflow recession constant (lag)
BR : Baseflow recharge.
Once the model is calibrated, rainfall data and evapotranspiration annual daily mean
are introduced as input for the flood period and the antecedent period; the model
output a simulated discharge.
3.10 The rainfall data sources & processing
3.10.1 Rainfall Gauge
The typing bucket gauges used in the Semois basin are all equipped with processeur
and modem allowing the accumulation on hourI y basis and the transmission to a
central database (Water Control Data system for Hydrology and Water Management)
(RW, 2010) operated under the "Direction générale opérationnelle de la Mobilité et
des Voies hydrauliques". The houri y data were provided by "SPW - DGO. Direction
de la Gestion hydrologique intégrée" based on a request by email. Daily rainfall data
are available online at :
http://voies-hydrauligues.wallonie.be/opencms/opencms/fr/hydro/Archive/annuaires/index.html
68
.r-.-\.W"_'" •
Methodology
A total of 266,640 rain gauges records were used.
3.10.2 3b42 data set
The whole time span was downloaded pixel by pixel using the TRMM Online
Visualization and Analysis System (TOVAS). The URL
(http://disc2.nascom.nasa.gov/Giovanni/tovas/TRMM V6.3B42.shtml) allows a
spatial and temporal selection very convenient for a small area. The data can be
downloaded on Ascii, HDF, or binary format.
A total of 124,425 records were used.
3.10.3 Aggregation of rain gauges hourly data to 3 hourly data
3b42 data are provided as rain rate in mm!h. To transform them into accumulated
rain during the 3h covered, they were simply multiplied by 3. The rain gauge data
were summed during the same period (1.5 hour before, 1.5 hour after the nominal
time). Downscaling the 3b42 to a 1 hour timestep was considered too demanding for
the present study. It is noticing that the GSMap MVK + product (USHIO et al., 2009)
has a one hour timestep and the latest version of CMorph (Joyce et al., 2004)
provides data with a 30 minutes timestep but only as rotating file set for the last 31
days. The archive are also with a 3 hours timestep.
3.10.4 Comparison between 3b42 pixel and individual rain gauges
Four rain gauge stations data were available with a 1 hour time step : Vresse, Sugny,
Bouillon, Bertrix. A fifth station was available with a Iday time step. Bouillon and
Fratin daily data are highly correlated (r=0.75) and it was decided to downscale the
Fratin data using the proportion of daily rainfall for each hourly observation from
Bouillon. As Fratin is the only rainfall station located within the catchment of
69
Methodology
interest (see Figure Il : Rain gauges localisation) and the inclusion of its data
improves the correlation between 3b42 and rain gauges, it was decided to base the
whole study on a data set including the downscaled data from Fratin station.
Rain gauges localisation
legend • Raingauges
- SemorsR;ver
o Catchrrsntof St M.rie-sur-Semots
o Belgium boundaries
Figure Il : Rain gauges localisation
Based on the shortest distance between gauges and pixel centroid, the pairs in Table
Il are used for comparison.
70
Methodology
Table t t: 3b42 pixel paired with rain gauge
Pixel refno Rain Gauge
0 Sugny & Vresse
1 Bouillon
2 Bertrix
5 Fratin
The location of 3b42 pixel centroid and rain gauges is shown in Figure 12
A
o •
Legend
• Rail Gauges
\t~.
• 3M2 pixel centroid
D Semois Basin
3b42 pixel centroids & rain gauges location
1
•
o 3.5 7 14 Kilometres ,! !! I! ! Il
2 •
6 •
Figure 12 : 3b42 pixel centroid and rain gauges location
71
3 •
Frain.
5 •
Methodology
3.10.5 Bias
The bias is the difference between the average of the whole time series of 3b42 data
and the rain gauges. It has been calculated for each pair 3b42-rain gauge. The
relative bias is the difference divided by the average of the rain gauge series.
3.10.6 Root Mean Square Error
The difference between each pair of3b42-rain gauge is squared for every timestep,
the square root of the mean square of these deviation is ca1culated for each pair of
3b42-rain gauge.
3.10.7 Probability of detection
The probability of detection (POD), also known as Hit Rate (HR), measures the
fraction of observed events that were correctly forecast. It has been evaluated for
each pair 3b42 - rain gauge.
3.10.8 False alarm Rate
The false alarm ratio gives the fraction of forecast events that were observed to be
non events.
3.10.9 Heidke Skill Score (HSS)
The HSS measures the fractional improvement of the forecast over the standard
forecast. It is normalized by the total range of possible improvement over the
standard, which me ans Heidke Skill scores can safely be compared on different
datasets. The range of the HSS is -00 to 1. Negative values indicate that the chance
forecast is better, 0 means no skill, and a perfect forecast obtains a HSS of. 1
(Online, 2009). The equation is as follows :
72
Methodology
HSS = _( h_I_"ts_+_co_~_re_c_t_n_e_g_at_iv_e_S_)_-_(_exp_e_c_t_ed_C_O~_/î_ec_t---:)ra;.=..;ndo.:..=..:..:.;;.nt N - (exp ected correct }randont
where
t l 1 [(hits + misses) (hits + false alarms) + -
ex ected correc Jra nt = - .. . ~ P ndo N (correct negatlves + misses) ( correct negatlves + false alarms)_
3.10.10 Inverse distance weighing (IDW)
The weight of each source of rainfall data relative to the catchment area was
calculated by the following process =
1. Create 10m grid in the catchment area
2. Create the centroid point for each cell
3. Calculate the weight of each centroid point using the Shephard formula
-p hi
W i = n -p Equation 12 Lj=l hj
Where p is where p is an arbitrary positive real number called the
power parameter (typically, p=2) and hi is the distance from the
scatter point to the interpolation point.
4. Average the weights relative to each source ofrainfall to obtain the weight
coefficient.
5. The weight coefficients multiply the rainfall data at each timestep.
6. The sum of the weighted rainfall provides the IDW rainfall in the catchment
area for each timestep.
73
Methodology
3.11 Source of discharge data
The stream gauges used in the Semois basin are aH equipped with processor and
modem allowing the averaging hourly basis and the transmission to a central
database (Water Control Data system for Hydrology and Water Management) (RW,
2010) operated under the "Direction générale opérationnelle de la Mobilité et des
Voies hydrauliques". The hourly discharge data were provided by "SPW - DGO.
Direction de la Gestion hydrologique intégrée" based on a request by email. Daily
discharge data are available online at :
http=//voies-hydrauliques.wallonie.be/opencms/opencms/fr/hydro/Archive/annuaires/index.html
The flow is recorded with a float-operated Thalimedes Shaft Encoder with integral
data logger designed for continuous, unatlended monitoring of water level. It
generates a measure every two minutes and calculates on site the hourly average
which is sent to the central database. An alert level can be' set to force a transmission
of data earlier than the routine interval. The precision is around 0.5 cm in water
depth (around 10 l/s).(Dierickx, 2010).
3.12 Source of evapotranspiration data
The evapotranspiration is retrieved from the GLDAS model. The data are available
on 3 hours 0.25 deg basis. The series was downscaled to 1 hour timestep by linear
interpolation. But the ReFH package returned an error while reading the evapo
transpiration data and this could not be corrected. The option to simulate the ETP
based on the annual daily average was chosen.
74
Methodology
3.13 Preparation of cross sections
3.13.1 Correction of LIDAR data
The LIDAR data collected during 2000 to 2002 over the corridor of the main rivers
in Region Wallonne have recorded tirst and last pulse at a density of 8 pulses/m2
averaged to 1 elevation/m2 • The average vertical precision is Il cm. The last pulse is
expected to represent the altitude of the soil under the vegetation but many of the
trees around the river have echoed the signal rather than the river. This resulted in a
considerable distortion of the river profile'as illustrated in the Figure 13 to Figure 20.
As these LIDAR data were communicated very late in the course of the study (end of
July 2010), a rapid correction was deemed necessary. The small software Raster Edit
provided by the Departement of Geography at Hull University was used to
interpolate between points of consistent elevation but operating with the initial 1 m
DEM was too slow. The data were then aggregated to 4m using ArcGIS 9.3 and the
resample tool with bilinear interpolation option. The correction with RasterEdit have
been then tinalised on this 4 m DEM. It took 4 days ofwork to cover the 100 m
corridor along the river centralline on the 5 km reach. For the area outside the
corridor, patches of irrelevant elevation have been replaced by elevation originating
from the 10m DEM (Mokhadem, 2010) resampled to 4 m.
75
Fi re 13 : Localisation of Ion
Figure 17 : Long section after DEM correction
'riaI_~ JI----
--='-~ w. __ ro--- ..... _ .. ~ -w. .. _ -----rOllPoJ'i\n.'flND 'AeW
3)Vel;bDildtyQ:ltcn
J;1 QdsJ3)V~h.t1.mn~~Tet:IrIsnaœ
Figure 19: 3D view before DEM correction
Methodology
Figure 16: Cross-section before DEM correction
- - - - - -Figure 18 : Cross-section after DEM correction
Figure 20: 3D view after DEM correction
3.13.2 Generation of cross sections with ISIS Mapper
A river centralline was elaborated based on the vector data of the topomap available
on the cartographie portal of the Region Wallonne, corrected visually based on the
Google Earth image. This centralline and the corrected 4 m DEM are used as input
in a ISIS Mapper tool to generate the cross-sections as presented in Figure 21
76
er~ect I.,ayer lacis yje-N ~ngs !:ielp
; • ~H 1 ~ ;51 ~ 1 ~ e; ~ ~ ~o<; El 1\ . :!H 0~~ ~ ···· !laD - 1 Iml . 4 ;m Mode.fingTooIBax
Matin Bridg" iiIJl 10 _ a . . . 1-V' L.OlIO"-II
_ M.ninOlMd",,_Simpline.l è-IP' Sbep.'"
i:;:l 0 M.tinatMdem_SimpLine.l III tI' ISIS SpI
~ œ MarieXS2 ~ :::::'ns
-61
~RNerCentrelile t-# l OldRNtfS«OOns
;-t? Oraw RNer SecIj)ns
!,-~ EdIRNerSedioos f-.# .rt RNer Sednns from66 OAJ Fie
~AssignRiterCerrtreÜ'le
~ ::;:,:~= on RNer cen~ U1e
~nt~lateRrlerSe.ebaM f-.:P' Ex1nIdZV.~ .. '--# Save .. ISIS ED Ae
lm 2D _ a :-tI' L • • d06l $-(p loatiUOdeluode:$
":tP Lo!.!t20UDd~Shapeflles 0-11' Cl'eale5S20Fies
~ Run SS20~lerfice !-tP' ..... rtllFAe 9..q. C,. ... TUFLOW.,..
ffrÛ' Export TUFLOW"" To UF/UD Fe""" ~ lOAood ~ :""tI' LoOl106l
:--t!' Lo.dTl.
~# Lo"SilapeAe L..# load Tabular csv Tex! fie. M Shepe fle
'-tP' EdiSll,pe,.. ~TriangulateShllpefJes f-.q> ... TI.
~(""7'; l "=''''-=Ii)'':"'1 ~-, =-I!l =(3'""11 !--# Md B.erylS5 R ..... "'TIl f=-- -----'I j-t? M6Tabu.rCSV Resulstonl
~ ChangeOispio!yedReJul ~ Generlleflooduap '-V' ANna'eRe<U" L#' R..:o<dAVl
!t 2D Aood ~ - lP' l •• dOEU
r-tl' lo, !l20Res:ib
~GenerateFb:)dLlap ~ AniTla1eRes.ul:5 ~ ReCGrdÂV1
Figure 21 : Generation of cross-sections in ISIS Mapper
Methodology
--. '\
\ --- \\ --- -------._---- \
--""-_ 'i .--....-----~
3.14 Generation offlood events and antecedent rainfall sets
ReFH is an event based deterministic model, the separation of peak flow and
baseflow cannot be automatically carried out by the packages. Therefore, the five
largest peaks have been extracted from the data set available. This number is rather
arbitrary based on time constraints. The five peak flows appear to have created sorne
inundations in the modelled reach (see Figure 22 : Maximum stage 5 flood events) .
Once the peaks have been selected, the rising limb and recession limb were based on
the departure from the long term average flow and return to this long term average
flow. The period between the end of the recession limb to the beginning of the next
peak rising limb is considered an inter-flood period or antecedent rainfall. The
various periods are presented in Table 12.
77
Methodology
Table 12 : Flood and flood antecedent periods
Discharge m3/s
Period Start End Average Min Max Duration (days)
Pre FL1 01/11/200200:00 21/12/200207:00 3.59 0.86 19.12 50.29
Pre FL2 10/01/2003 10:00 30/12/2006 18:00 1.47 0.17 19.98 1450.33
Pre FL3 26/01/200708:00 01/12/200708:00 1.75 0.40 19.39 309.00
Pre FL4 15/12/200721:00 01/02/2008 02:00 2.86 0.79 Il.41 47.21
Pre FL5 13/02/200802:00 26/02/2008 18:00 1.41 1.21 1.94 13.67
Pre FL6 09/03/2008 02:00 30/11/200823:00 1.77 0.43 17.56 266.88
FL1 21/12/2002 08:00 10/01/200309:00 . 9.61 1.95 37.76 20.04
FL2 30/12/2006 19:00 26/01/200707:00 7.34 1.95 30.25 26.50
FL3 01/12/200709:00 15/12/2007 20:00 8.92 1.94 24.45 14.46
FL4 01/02/2008 03 :00 13/02/2008 01 :00 7.77 1.96 24.33 Il.92
FL5 26/02/2008 19:00 09/03/200801 :00 5.47 1.95 20.55 Il.25 General average abs min max 1.87 0.17 37.76
The selection of the 5 large st peak as flood events is for practical purpose ; there
were peak flow of magnitude similar to the smallest ·flood flow during the inter flood
period. A more hydrological method would have been to simulate a variety of
discharge in the 1 D model and determine the flood threshold based on the presence
of a flow overtopping the banks at sorne point of the channel. This threshold may be
around 16 m3/s. But the present work intends to focus on the flood plain extent and
minor floods are less significant for planning purpose.
78
f f
i 1
f f
1
f f
f 1
o
,~
~ ,0
i ! ~ ,~ ~ ~ ,0 8
6L
SlU<lt\<l poou S <l~hqs WnW!XBW : ZZ <lJn~!.!I
aL!Xl1 ><20,002 l x20_003 ><20,004 î x2O-'J05 1 -ZOJJ06 1
:=: 7 -ZO,OO9 -ZO,010
:'~:~ ~ -ZO'013 7 -ZO'014 7 -ZO'015 7 .20'016 , 7 -ZO=017 7 -zo 016 x2O'019 7 x2O'020 1 -zo' 021 Î x2O'022 1 x20'023 --, -ZO'024 --, -ZO'025 --,
-ZO=026 --,
x20027 x2O'028 -, .20'029 --, -zo'OOO ,
x2O'031 Î
X2O=032~ x2O_033 t----f ><20,034 x20_035 -ZO,036 -ZO,037 x2O,036 x20,039 " x2O_040 -ZO,041 x2O_042 ><20,043 x2O_044 ><20,045 x2O_046 .20,047
BRU
t.::------------( 'L ____ _
.----_:>-_:_----------L1
-~r> "
'----------------- .. _-- ...
~ :=:
i x2tU>5) x20,054 x20,055 x20,055 .20,057 x20,056 x20_059 -ZO,06O ><20,061 -ZO,062 -ZO,063
x20,066
x20,070 1
-ZO,071 -ZO,072 x20,073 x20 074 .20=075 x2O_078 -ZO,077 x20_078 -ZO,079 -ZO,06O ><20,061 x20_082
-ZO,063 x20_084 .20,065 -ZO,066 ><20,067 x20_088 x2O __
><20,090 --ZO,091 -ZO,092 ><20,093 ><20,094 -ZO,095
~~~I --_:~( ~;%"':::::::-f::;;~:::::;:=~ t ! ><20=100 . :-'--1' - r--,------'~ .. --------------------~~~~::::: __ :::::::.~~"'_ :=:: ---o f /., , ~--------- -::: __ ==_ ..
Â3010PO-ql~W
.i J ~ '§ I~ 3
i ~
i
... -~ -.~~--
Methodology
3.15 Calibration and validation set
Two ensembles of calibration and validation data have been tested. The first one
used the 4 smaller flood events (rain and discharge) for calibration (FL2 to FL5) and
tested the predictive capacity with the rainfalllinked to the FL 1 period. The second
used the FL 1, FL3 to FL5 flood events for calibration and FL2 rainfall for validation.
3.16 Running ISIS ID with the various flood events
The sections described at heading 3.13.2 are imported in ISIS ID, the two bridges
are modelled using approximate measurements based on available levels and basic
construction standards.
A Flow-Time boundary condition is used at the head providing a discharge
hydrograph based on 1 hour or 3hours timestep. A Normal/Critical Depth Boundary
(NCDBDY) unit is specified as a downstream boundary, this automatically generates
a flow-he ad relationship based on section data. (Ha1crow, 201 Ob)
A Manning N value of 0.030 for the channel and 0.015 for the flood plain is adopted
based on the similarity with the Trent River study (McGahey et al., 2006)
Six events are modelled : FLI and FL2 based on gauged flow, rain gauge based
modelled flow and 3b42 modelled flow.
The timestep is fixed at lOs; saving every 1200 s .
. . ;'::..~-.. '~ .:'.'
80
Methodology
3.17 Running the ISIS ID 2D with the various flood events
One major source of delay was that the version 3.4 of ISIS Free installed on a
QuadCore based computer with Windows 7 (the core computer for the present
study) did not process the link 1D-2D while the version 3.3 installed on a dualCore
with Windows XP did. A lot of time was wasted due to this bug.
The 2D domain, computational area and active area went through a succession of
reduction to fmally reach a do main that was computable with the limited number of
cell (2500).
The procedure of linking the 1 D to the 2D model involves the creation of a link line
(z polyline) at the edge of the 2D domain, along the main channel.
The vertices of this link line are supplied with the 1 D model nodes they should use
to pass the flow information to the 2D mode!.
The Water levellinking (type H) is used ; water levels from 1 D nodes are taken by
the model, and imposed as a boundary condition on the 2D model. The 2D part of
the computational calculates the flow at these linked boundaries, and passes this
information back to the 1 D part, where it is addedlremoved from the channel, to
ensure mass conservation. (Halcrow, 201 Ob).
The timestep of the 1 D model is lOs, it is 1 s for the 2D model.
81
Results and discussion
4 Results& discussions
4.1 Comparisons of 3b42 data with rain gauges
4.1.1 Bias
Comparisons pixels by pixels with the five rain gauges in the Semois basin reveals a
considerable negative bias ranging from -0.11 to -0.18 mm /3 hours with an average
of -0.14. The relative bias (bias/mean) is in the bracket -27% to -39%.
The largest bias is observed between the pixel 1 and the rain gauge located in
Bouillon as seen in Table 13.
4.1.2 Root Mean Square Error
The RMSE is ranging between 1.64 and 1.91 with an average of 1.80. The largest
RMSE is observed at Bouillon (Table 13). The relative RMSE is really high: 426%
Table 13 : Individual gauges comparison
Rain Vresse Sugny Avg Bouillon Bertrix Fratin
gauges Sugny
Vresse
3b42 pix 0 0 0 1 2 5
RMSE 1.6942 1.6881 1.6453 1.9005 1.9065 1.8915
Bias -0.1384 -0.1140 -0.1262 -0.1856 -0.1339 -0.1106 :. --- .- _--r.·
Relative bias -34% -29% -32% -39% -31% -27%
82
''''~''-'' , ....
'~".""'~.~ -.
Results and discussion
4.1.3 Probability of detection
The probability of detection (POD) is the likelihood that the event would be forecast,
given that it occurred.(Ramirez-Beltran et al., 2007)
The POD is particularly low, ranging between Il and 15 % with an average of 13 %
4.1.4 False alarm Rate
The false-alarm rate is the proportion of forecast events that fails to
materialize.(Ramirez-Beltran et al., 2007)
The FAR is very low : 3 0/0
4.1.5 Heidke Ski Il Score (HSS)
The HSS measures the fractional improvement of the forecast over the standard
forecast. The range of the HSS is -00 to 1. Negative values indicate that the chance
forecast is better, 0 means no skill, and a perfect forecast obtains a HSS of. 1
(Online, 2009). The equation is presented in the heading 3.10.9.The HHS score of
the four 3b42 pixels ranges between 0.01 and 0.18 with an average of 0.14
4.1.6 Correlations
The correlation coefficient (r) 3b42 pixels with individual rain gauges ranges
between 0.37 and 0.46 with an average of 0.42. The highest correlation coefficient is
observed between the pixel 0 and the average of the gauges located in Vresse and
Sugny as presented in Table 14
83
Results and discussion
Table 14 : Correlations coefficients with individual rain gauges
Pixel Vresse Sugny Avg Vresse Sugny 0 0.45 0.43 0.46
Pixel Bouillon Bertrix Avg Bouillon Bertrix 1 0.42 0.40 0.43
Pixel Bertrix 2 0.40
Pixel Fratin 5 0.37
4.1.7 Individual gauges comparison performance review
The number of 3b42 pixel covering availcible gauges data is very low to draw
conclusion for the whole product. It is a small sample but it is the one operational for
the purpose of the present modelling exercise.
The performance of the 4 pixels of this study is rather poor ; comparing the 3b42
data at 0.5 0 resolution with a network of76 rain gauges in Greece, Feidas (Feidas,
2010) found that the 3b42 data had a low relative bias ( -4.2%), a much lower
relative RMSE (48.4%) and a much higher efficiency (0.76) and a correlation
coefficient of 0.88.
The difference are very like1y linked to the lower spatial resolution (the present study
used 0.25 0 resolution) and to the lower latitude (Greece is around 39 0 Lat N). The
present study is at the very extreme North of the 3b42 data domain.
A validation with rain gauges in Thailand (Chokngamwong and Chiu, 2008) shows
that around ISOLat N the 3b42 data most often overestimate the rain gauges except
for the Northem most region where altitude compounds with higher latitude and
mountainous c1imatology to produce a negative bias for the 3b42 data.
84
Results and discussion
The Northem most Ground Validation sites for the 3b42 data are located at 35° Nin
Nagoya (Japan) and Mesonet (Oklahoma USA) ; the difference of latitude
compounds with a large difference in continentality with the present study.(Hedge,
2010)
It seems relevant to note that a negative correlation exist in the present study
between the altitude of the rain gauges and the gauge to 3b42 correlation (r= -0.42).
This relation is also observed in the Laohahe basin, China around 41 ° lat N (Y ong et
al., 2010).
4.2 Comparisons of Inverse Distance Weighted rainfall for the
whole Semois watershed.
The Shephard IDW was calculated (see heading 3.10.10) and applied for the whole
Semois watershed for both the 3b42 data and the rain gauges. The correlation
coefficient between the 7 pixels and the 5 rain gauges is equal to 0.5. It is 20% better
than the average of individual rain gauges correlation coefficients. The relative bias
is -31 %. The improvement is related to the aggregative nature of the satellite
observations which have a better ability to represent areal rainfall than discrete rain
gauge observations.
4.3 Comparisons of Inverse Distance Weighted rainfall for the St
Marie-sur-Semois catchment.
The Shephard IDW was calculated (see 3.10.10) and applied for catchment upstream
of St Marie-sur-Semois gauging station for both the 3b42 data and the rain gauges.
Three rain gauges stations (Bertrix, Bouillon, Fratin) and three 3b42 pixels (3,4,5
see Figure 12) are evaluated for the correlation and bias. The correlation coefficient
85
Results and discussion
is equal to 0.39 and the relative bias to -34%. The reduction of scale and increase in
the rain gauges elevation seems to increase the error/divergence of the 3b42 data.
Seasonal effect will be discussed while discussing precipitations during flood events
(heading 4.4.)
4.4 Comparisons of rainfall during flood periods
As presented in the heading 3.14, five major flood events have been identified.
Table 15 : Five major flood events
Period Start End Duration (days)
FLI 21/12/200209:00 10/01/2003 09:00 20.0
FL2 30/12/200621:00 26/01/200709:00 26.5
FL3 01/12/200709:00 16/12/200700:00 14.6
FL4 01/02/200803:00 13/02/200803:00 12.0
FL5 26/02/2008 21 :00 09/03/2008 09:00 11.5
During these periods, the cumulated IDW rainfalls in ~t Marie-sur-Semois watershed
are as follows :
Table 16 : Accumulated rainfall du ring five flood events
Rain gauges IDW 3b42 IDW Period (ace. mm) (ace. mm) Bias Relative bias
FL1 204.5 193.0 -11.5 -6%
FL2 261.3 76.5 -184.8 -71%
FL3 157.6 44.3 -113.4 -72%
FL4 83.7 31.2 -52.5 -63%
FL5 76.0 45.0 -30.9 -41%
Sum 783.1 390.1 -393.0 -50%
The relative bias has a wide bracket from -6% to -72%. The average relative bias
during flood periods is quite superior to the relative bias for the whole period of
observation. The fact that the five floods occurred during the winter leads to a
86
Results and discussion
degradation of the 3b42 performance (the algorithm does not differentiate weIl cold
with rain and cold without rain). This is also observed by F eidas in Greece (Feidas,
2010) who notes a -15% relative bias during the winter while during the other
seasons it is close to 0%. But Su (Su et al., 2008) in the La Plata basin (15° to 36°
Lat S) does not observe such a seasonal degradation.
Table 17 : Nbr of 3 hours period > 0 mm of acc. rainfall- Flood periods
Period Rain gauges IDW 3b42 IDW
FLI 109 16
FL2 123 10
FL3 74 5
FL4 43 7
FL5 49 9
Sum 398 47
The capacity to detect rain during the winter' s flood period seems really poor as shown in Table 17 . The POD falls to 12% during the flood period while it is 14% for the whole period of study.
4.5 Comparison of rainfall during inter-flood periods
As presented in the methodology the inter-flood periods are :
Table 18 : Inter-flood/high flow periods
Period Start End Duration (days)
Pre FLI 01/11/200203:00 21/12/2002 06:00 50.1
Pre FL2 10/01/2003 12:00 30/12/2006 18:00 1,450.2
Pre FL3 26/01/2007 12:00 01/12/200706:00 308.7
Pre FL4 16/12/200703:00 01/02/200800:00 46.9
Pre FL5 13/02/2008 06:00 26/02/2008 18:00 13.5
During these periods, the cumulated IDW rainfalls in St Marie-sur-Semois watershed
are as follows :
87
Results and discussion
Table 19 : Accumulated rainfall during five inter-flood events
Rain gauges Period IDW 3b42 IDW Bias Relative bias
-PreFLI 221.9 133.0 88.9 -40%
-PreFL2 4,064.0 2,924.1 1,139.9 -28%
-PreFL3 1,134.0 738.3 395.7 -35%
-PreFL4 155.2 36.2 119.0 -77%
-PreFL5 22.7 6.6 16.2 -71%
-Sum 5,597.7 3,838.1 1,759.6 -31%
The relative bias is much lower than during the flood period. The winter effect is the
most available explanation.
The same applies to the POD (18%) as observed in Table 20.
Table 20 : Nbr of 3 hours period > 0 mm of ace. rainfall- inter flood periods
Period Rain gauges IDW 3b42 IDW
Pre FLI 236 13
Pre FL2 5238 742
Pre FL3 1217 173
Pre FL4 178 9
Pre FL5 30 2
Sum 961 172
4.6 Comparison ofmodelled flows
4.6.1 Introduction
As explained in the methodology, the revitalised FSRlFEH rainfall-runoff
method(Kjeldsen, 2007) was used with the adequate software package ReFH 1.0
sold by Wallingford HydroSolutions (WHS). The parameterisation of the model is
explained in the heading 3.9. Out of 5 events available, 4 were used for calibration 1
88
Results and discussion
was used for validation. Two combinations of events are reported here : combination
1 use the FL2 to FL5 events for calibration and FL 1 for validation; combination 2
use the FL 1 and FL3 to FL5 events for calibration and FL2 for validation.
Two comparisons are envisaged : a) the comparison of the modelled flow using the
rain gauges IDW with gauged flow measured at St-Marie ... sur-Semois at 1 hour
timestep; b) the comparison of the modelled flow using the 3b42 IDW at 3 hours
timestep with the 3 hours average of the gauged flow measured at St-Marie-sur-
Semois.
4.6.2 Total, Average, Peak, Minimum& Error
The descriptions of the flows is presented Table 21 and Table 22.
Table 21 : Flow descriptors - one hour timestep
One hour timestep (m3/s)
Standard Period Source Total Average deviation Peak Minimum RMSE NSE
FLI Gauged 4538.8 10.3 9.3 37.8 2.0
Rain gauge Mod 3118.1 7.1 8.1 43.4 1.5 5.9 0.33
FL2 Gauged 4673.6 7.3 6.2 30.2 2.0
Rain gauge Mod 4027.9 6.3 7.8 43.5 1.2 5.5 0.23
89
Results and discussion
Table 22 : Flow descriptors 3hours timestep
Three hours timestep m3/s
Standard Period Source Total Average deviation Peak Minimum RMSE NSE
FLI Gauged 1,399.4 11.6 9.8 37.5 2.1 3b42 Mod 3,973.4 32.8 45.7 228.0 0.4 46 <0
FL2 Gauged 1,459 8 6.46 30.1 2.1 3b42 Mod 141 1 2.21 19.7 0.0 9 0.09
The sum of gauged flows differs between the two timesteps because the flow is
averaged on the 3 hours timestep and create a slightly different time period because
the departure and return to baseflow do not coincide exactly between the two
timesteps. The multiplication by 3 of the three hours sum is nearly equal to the sum
of the one hour timestep. The modelled flow based on rain gauges gives a result with
a reasonable error (relative RMSE = 66%) and providè a peak flow within a 50%
bracket ; this contrasts sharply with the 3b42 modelled flow which exhibits a relative
RMSE of258% and a peak flow within a 500 % bracket. The Nash Sutcliffe
coefficient of efficiency (NSE) differs sharply too between the two sources of
rainfall data in ratio of 1 to 25. There are few studies with which to compare the
present one regarding flow modelling with 3b42 data. One has been conducted in the
La Plata basin in South America, (Su et al., 2008), the timestep is daily and the
rainfall runoff model is the Variable Infiltration Capacity (VIC) semidistributed
hydrology model. The size of the basins observed ranges between : 62 k km2 and
1,100 k km2, the NSE with model forced with 3b42 ranges between <0 and 0.71 for
daily flows and <0 and 0.8 for monthly flow. The weighted average are respectively
0.71 and 0.8. But there too, the peak flows are overestimated by the 3b42 product.
90
Results and discussion
4.6.3 Graphie presentation
FL1 - 20/12/2002 - 9/01/2003 50
45
40
35
30
..... 25 '" E
- RG Modelled Flow
20 - Gauged Flow
., -= n· "J'..,
15
10
Figure 23 : FLI rain gauges modelled
FL1 - 31/12/2002 ta 5/01/2003 250 ,-------------------------------------------------------------------
200 +-------------------------------------------------~----------------
150 +--------1~--------------------------------------~r_----_.--------
..... 'ê
- 3b42 Modelled Flow 100 +--------+-r--------------------------------------+-+-----~r_------ - Gauged Flow
50 +-------~_1,_++--------.. ----------------------+_--_+~~~~------
Figure 24 : FLI 3b42 modelled
91
Results and discussion
FL 2 - 29/12/2006 to 28/01/2007 50.0
45.0
40.0
35.0
30.0
.... 25 .0 ë
- RG Modelled Flow
20.0 - GaugedFlow
15.0
10.0
5.0
Figure 25 : FL2 rain gauges modelled
FL 2 - 31/12/2006 to 22/01/2007 35.0
30.0
25.0
20.0
~ 15 .0 - 3b42 Modelled Flow
- Gauged Flow
10.0
5.0
_ ... .- ~ ... f'o.- • . ..,;
0.0
()($~() ()çs~() ()çs~() ()($~() ()çs~() ()().~ ()çs~() çf)ro ()()1 ()()1 ()()1 ()()1 ()()1 ()()1
\'\.'\.\'): \()'\.\'): \()'\.\'): \()'\.\'): \()'\.\'): \()'\.\'): \()'\.\'): '\."'-' ()"? ()'b '\."? '\.'b '\."? '\.'b
Figure 26 : FL2 3b42 modelled
92
Results and discussion
Despite the high number of iterations allowed for the optimisation of the model, the
recession limb of the modelled flow appears faster than the "real" flow. A review of
ReFH methodology (Faulkner and Barber, 2009) notes that the model tends to
overshoot the peak flow and sharpen the recession. The present study notes that
when the model is calibrated using the 3b42 FLI event (the largest flow in the data
set) a slightly smaller flow (FL2) is predicted at a much lower peak value. It is the
only instance ofunderestimation of the peak flow.
4.7 Comparison of 10 modelling'results
4.7.1 Introduction
As presented in the methodology, the ISIS free software ver 3.3 and ver 3.4 were
used to model the flow depth and stage in a 5 km reach downstream for the gauging
station located at St-Marie-sur-Semois. The package uses the St Venant equations
(conservation of mass and momentum) to solve the relation between flow and water
levels. Three hydrographs are available for each flood periods studied = one
originating from the river gauging station at a 1 hour timestep, one from the flow
modelled with rain gauges at a Ihour timestep, one from the flow modelled with
3b42 data at a 3 hours timestep. These 6 flow streams generated 6 different runs for
the 1 D model. One hundred three cross sections (30 m on each side of the central
line) based on a LIDAR DEM were used to compute the flow-stage data.
4.7.2 Maximum stage and overflow depth
The overflow depth is the difference between the flow stage and the elevation of the
lowest bank. It is computed from the maximum stage and analysed on the 103 cross
sections. The results are presented in Table 23 : Maximum stages and overflow depth
93
Results and discussion
Table 23 : Maximum stages and overflow depth
Ratio Ratio Rain 3b42
Rain Gauge Mod Gauged Gauge 3B42 Mod/Gauged /Gauged
Period Descriptor Flow Mod Mod Flow Flow
FLI Average max stage (masl) 329.95 330.05 331.98 1.00 1.01
Average overflow depth (m) 0.26 0.32 2.12 1.24 8.19
Maximum overflow depth (m) 0.92 1.02 3.34 1.11 3.65
FL2 Average max stage (masl) 329.80 330.05 329.47 1.00 1.00
Average overflow depth (m) 0.17 0.32 0.04 1.89 0.26
Maximum overflow depth (m) 0.76 1.02 0.43 1.34 0.57
The divergence between the two modelled flows and the two events is substantial.
The errors between the rain gauge modelled flow and gauged flow are of higher
proportion than the error between the maximum overflow depth (14% vs Il % for
FL1, 44% vs 34 % for FL2). For 3B42 based maximum overflow depth too, the error
is of a lower proportion. That convergence of the two results is related to the
approximations in the resolution of the flow-depth equations. A larger sample may
be required to confmn this trend. The Figure 27, The maximum overflow depth for
4b42 is a misrepresentation of the reality because the cross-section were too narrow
to allow the spread of a such a large volume of water. The ISIS model builds "glass
walls" at the end of the cross-section to allow the continuation of the simulation.
94
Results and discussion
B ~I
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Figure 27 : FLI Maximum stages Rain Gauged Modelled & Gauged Flow
95
1
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Results and discussion
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Figure 30 : FL2 Maximum stages 3b42 modelled & Gauged Flow
98
Results and discussion
4.8 Comparison of the ID-2D modelling results
As ISIS is OpenID compliant package, its 1 D and 2D mode1s are able to exchange
input-output during a synchronous run. The area modelled in 2 D had to be reduced
at a rectangle of81,000 m2 due the 2500 cells limitation of the free version. The
village of Villers-sur-Semois is incIuded in the area as it is the critical asset in the
modelled reach. The area of the maximum flood extent are presented in Table 24
Table 24 : Flood maximum extent near Villers-sur-Semois (m 2)
3b42/ Gauged Rain gauge 3b42 Rain Gauge/ gauged
Period Flow modelled modelled GaugedFlow flow
FL1 40,003 31,752 53,900 0.79 1.35
FL2 36,946 36,946 18,473 1.00 0.50
The 3b42 case may be misrepresented as the extent nearly reached the Northern limit
of the available computing area. But it is a fact that the local topography and the
roads minimize the expansion of the flood plain developed by the 3b42 modelled
flow, the end result is a flood plain much doser to the other sources of rainfall. ·
The Figure 31 and Figure 32 present the delineation of the flood extent on Google
Earth snapshot.
Animations and interactive map are posted online at :
http://web.me.com/albert.grela/3b42_flood~lainlWelcome.htm1
99
-,- ".- .~ .. c::=] Max Flood Extent FI1 - 3b42 Modelled Flow
Max Flood Extent FI1 - Raln gauge<! modellad flow
[.=J Max Flood Extent FI1 - Gauge<! Flow
D 20 modelled area
Figure 31 : FLI flood extent
C:] Max Flood Extent FL2 - Gauge<! Flow
Max Flood Extent FL2 - 3b42 Modelled Flow
C=] Max Flood Extent FL2 - Raln Gauge ModeUe<! Flow
D 20 modelled aree
Figure 32 : FL2 flood extent
Results and discussion
The Regional Authorities (Region Wallonne) have prepared an official mapping of
fluvial flood risk(Region Wallonne, 2010) which is compared with the present
modelling of the flood extent. The official definition of the high risk is :
100
Results and discussion
" an area with a flood return period equal or less than 25 years and a submersion of
at least 30 cm".
For the two flood events of the present study, the flood extent presented in Table 24
is re-delineated to correspond to a submersion by 30 cm ofwater. The results of
these extent with a depth >= 30 cm is plotted on the background of the official flood
risk map in Figure 33 and Figure 34
1 . '. 21/12/02 to 10/01/03
FLl max sup 0.3 m depth : 31>42 mod
FL 1 max sup 0.3 m depth : Rain Gauge mod
FU max sup 0.3 m depth : Gauged Aow mod
Figure 33 : FLI Flood extent with a depth of at least 30 cm and official flood risk map
101
-'-.: ~ .--,,-
Results and discussion
Figure 34 : FL2 Flood extent with a depth of at least 30 cm and official flood risk map
The match is rather good for FL 1 and a bit less for FL2, the two events are in 10 years return period range but FL 1 is the second largest discharge in a 28 years series while FL2 is the 5th
•
The Table 25 provides the ratio between the area classified as high risk and the extent of the 30 cm depth during the two flood events: The convergence of the two assessments (probabilistic from the Region Wallonne,event based for the present study) converge remarkably weIl for the Gauged Flow and Rain Gauge Modelled. The 3b42 based modelled exhibits a sharp divergence which is quite likely underestimated for the reason expressed in para 4.7.2.
Table 25 : Ratio of FLI et FL2 flood extent with a depth superior to 30 cm and area c1assified as high risk
Reg Wal- High Risk area 24,728 m2
Gauged Raingauge 3b42 Flood extent Flow mode lied modelled
FLl>O.3 / RW HR 0.98 1.04 1.78
FL2>0.3 / RW HR 0.76 1.03 0.10
102
Conclusions
5 Conclusions
5.1 Flood plain modelling
The small size of the flood plain limits the extent of the conclusions but the fact that
it was possible to model it given the limited resources in expertise and software
license is a rather reassuring fact. The match with the official flood risk map (Region
Wallonne, 2010) is also a positive element to recommend this approach for assessing
risk with small chunk of area at a time. As ISIS Professional could be rented on
monthly basis, a large variety of cost options could be considered to prepare a flood
map. The annual estimated cost of flood damages reaching 30 billions USD
worldwide (Brakenridge, 2010) clearly suggests that savings on flood modelling
software may not be the most economically efficient. This also advocates that flood
modelling should gain sorne prominence in courses leading to "Environmental
modelling" graduation. Flood limits markers should be promoted and made available
online to maximise the possibility of model validation! verification.
5.2 ReFH adequacy outside UK
The model and optimization algorithm seem to perform weIl outside UK. The
hydrological properties of the European Soil Database (1RC, 2010), the Corine Land
Cover 2000 database (Agency, 2000) provide good correlations to propagate the data
collected in UK. A European rainfall-runoff model may have sorne relevance to
foster transboundaries collaboration. The experienced gained with the ReFH method
is probably worth considering but basin wide methods are probably more likely to
appeal to the pragmatic planner than national standards levelling the actual diversity
of hydrological conditions. The efforts made to provide rationales to the estimations
103
-.~.":_-.~ -
Conclusions
of discharge in ungauged catchments are a disincentive to establish instruments and
protocols to collect actual observations for specific catchments.
5.3 3b42 validity under the 50 ° Lat N
Without any doubt, this is the most disappointing part of the present study. The 3b42
data are clearly inappropriate for winter flood modelling close the 50° Lat N. Many
factors concur in this inadequacy : the precipitation radar does not coyer beyond the
38°, the ground validation station are aU in lower latitude and the ground elevation
does not seem perfectly included in the niin retrieval algorithme The low level of rain
event detection and the monthly adjustment based on rain-gauge tends to
overestimate the rain event reported by 3b42. This distorts the simulated hydrograph
very sharply (cfr Figure 24 ).
This was somehow to be expected as the literature review did not identify any
validation exercise at such latitude. Nevertheless the tRMM mission seems a
success confirmed by its extension and by the preparation of a constellation of
satellites based on a dual frequency rain radar: the Global Precipitation
Measurement Mission (GPM) is expected to provide rain estimation every 2 to 4
hours on a global coverage. The launch is schedule for July 2013.(NASA, 2010a)
5.4 LIDAR data processing
The LIDAR data are getting sorne popularity because the high spatial resolution and
their average elevation accuracy. The present study observed that the last pulse is far
to be always the soillevel. Trees along the river have often masked the river
bed/pound level. The time was too short to investigate the physical causes of this
error and the supplier of the data (Region Wallonne) had no explanation. The
104
Conclusions
interpolation of levels between consistent ones was long and tedious but the small
software RasterEdit permitted a correction leading to a consistent generation of
cross-sections. This program is quite unique and highly relevant in the context of
LIDAR data set. It disserves sorne further development.
5.5 Data processing capa city
The rainfall data at 1 hour timestep were a considerable load (266,640 records) but
MS Excel 2007 absorbed it with a few limitations : automatic calculation had to be
disabled and logical tests had to be based 'on numerical data only.
ISIS 1 D performed the modelling of the 613 hours of FL2 at 10 sec timestep in a
very reasonable time : 235 seconds. This was performed on a computer with Intel
Quad Q9000 @ 2.00 GHz CPU with 4GB memory.
The 1D2D modelling of the same FL2 event took 24.5 minutes on a computer with
Intel Core Duo Mobile T2500 2.Ghz CPU with 2 GB memory.
The calibration for ReFH package took 12 minutes, running the model with the FL2
rain took 4 minutes, on the Quad computer described above.
These were reasonable processing time.
A Python script using the IDW object of ArcGIS 9.3 would have taken 9 days to
compute the whole period of rain data for the ID W of the Semois catchment.
105
Conclusions
5.6 Propagation of error and recommendation for local
observations
The 3b42 data do not provide any usable flood prediction tools at the location of the
present study. The rainfall-runoffmodel ReFH forced with rain gauges data, allows
sorne useful evaluation of the extent of a flood plain but many of the discharge
impact such as the average overflow depth are estimated with a bias of 56%. (see
Table 23 : Maximum stages and overflow depth). This may distort quite significantly
the design of flood defence structures. Th~ cost of flood damages and flood defences
should warrant a significant investment in hydrological data collection in all the
catchments where a flood mitigation investment is planned. The uncertainty of the
rainfall-runoffrelation particularly in ungauged catchments (Beven, 2009, Beven,
2006, Beven, 2004, Beven, 1996, Beven, 1993, Beven and Binley, 1992, Degré et
al., 2008, Faulkner and Barber, 2009) turns the infrastruc~e investment into a
casino's bet if carried out without consistent observation of the actual runoff.
The various attempt to recalibrate the 3b42 data ((Fotopoulos et al., 2010) or to use a
regional algorithm for satellite rainfall (STISEN et al., 2010) have been successful at
reducing the error and are like1y to be emulated in the near future.
5.7 Further work
The 1 D2D mode1 of the present study could be used as a benchmark for a variety of
rainfall products such as CMorph, PERSIANN, GSMapMVK +. Recalibration of
3b42 data could be attempted although for the winter period the POD appears very
weak and is likely to distort the hydrograph beyond utility. Combination of satellite
borne data with ground radar have a very potential prospect as described by
106
Conclusions
Chandrasekar (Chandrasekar et al., 2008). Such attempt is possible at the same
location as it is covered by the rain radar installed at Wideumont (Kunstmaan, 2010).
More attention should be dedicated to the mass balance analysis for the ID2D model
and to the equifinaIity of the rainfall-runoffmodel.
107
. -:"'----; ~~
. ' ."
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