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ASSESSMENT AND PREDICTION OF HYDROLOGIC HAZARDS:

A PHYSICALLY-BASED PERSPECTIVE

Hydrologic Research LaboratoryUniversity of California, Davis,

U.S.A.

HYDROLOGIC HAZARDS

The term “hydrologic hazard”

has the connotation of

a hydrologic event that is capable of

inflicting damage or causing danger to

human and animal life and/or property.

TYPES OF HYDROLOGIC HAZARDS

FloodsDroughts

Change of Hydroclimatic RegimeErosion/Sediment Movement

Mud/Debris FlowsLandslides

Snow avalanchesEnvironmental Pollutants (Nutrients, Pesticides, Salts, other

chemicals)

MITIGATION OF HYDROLOGIC HAZARDS

Society under threat

Local and state governments

Local and state industries

Short-term Efforts Long-term Efforts

Short-term Hazard Forecasting and Warning

EvacuationShelter and FoodSanitationMedical TreatmentSocial Relief

Long-term Hazard Forecastingand Warning

Structural Measures

Long-term Management Strategies for Mitigation

In order to build the appropriate structures for the control of hydrologic hazards,

it is necessary to develop theassessment tools for the quantification of the risks

of the particular hydrologic hazardsthese structures are supposed to control.

For example, the probabilistic assessment of the risk of floods that would overtop a flood control levee that is protecting an urban center.

Similarly, in order to be able to warn the society under threatfor the short and the long terms, it is necessary to develop tools for

the forecasting of the particular hydrologic hazards.

TOOLS FOR RISK ASSESSMENT AND FORECASTING OF HYDROLOGIC HAZARDS

BLACK BOX TOOLS

Frequency AnalysisStochastic Models

CONCEPTUAL MODELS

PHYSICALLY-BASED MODELS

Stanford WSMUSGS ModelNWS ModelAGNPS ModelEPA BASINS Mdl

GCMsRegional-scale

Hydroclimate mdlsIRSHAMWatershed-scale modelsIHDMSHE/SHE-SEDTHALES ModelWEHY Model

BLACK BOX APPROACH TO THE PROBABILISTIC ASSESSMENT OF HYDROLOGIC HAZARDS

In this approach, mostly known as frequency analysis,

the historical record (usually time-series) of the hydrologic variable of interest to the particular hydrologic hazard (such as annual maximum streamflow discharge for floods, reservoir storage

volume/rainfall depths for droughts, solute loads/concentrations for various environmental substances, etc.)

is analyzed in order to fit a probability distribution to the historical record of the variable.

From the tails of this constructed probability distribution, then inferences are made concerning the risks of exceeding certain

structural design values for the considered hydrologic variable.

BLACK BOX APPROACH TO FORECASTING HYDROLOGIC HAZARDS

In this approach stochastic models (time-series models, Markov models, random walk models, etc.) are fitted to historical records in

order to forecast the near-term and long-term behavior of the hydrologic variables that represent the states of the hydrologic hazards.

For example, the time-series models for daily/weekly/monthly streamflow volumes, hourly/daily rainfall depths, etc.

SOME ISSUES WITH THE BLACK BOX APPROACH

1. Frequency analysis or stochastic models are totally dependent upon the statistical analysis of existing hydrologic records. These records may be too short and may not be representative of the extreme hydrologic conditions related to hydrologic hazards.

2. This approach assumes that the hydroclimatic system at the geographical location of interest is at statistical equilibrium during the duration of the historical record, and this state of statistical equilibrium will stay the same during the future planning/design period. However, the hydroclimatic system is changing under the influence of natural and human-induced causes. Therefore, the equilibrium conditions, if they were to exist for the future planning/design period could be drastically different from thoseof the historical record.

10 different realizations of the standardized hydrologic water storage (regional water balance volume) over Western USA, obtained from a simplified HCM for 72 months.

3. Hydrologic records at few point locations within a watershed at theoutlet of the watershed, may not be informative with respect to the spatial evolution of various hydrologic hazards of interest.

For example, the sediment, nutrient and pesticide loads at the production regions of a watershed may not have high correlation with their values at the watershed outlet.

Yet, for the effective control of such hydrologic hazards, it would be necessary to quantify their values at their production locations. These values may vary significantly from one spatial location to the next.

CONCEPTUAL MODELS FOR HYDROLOGIC HAZARDS

Conceptualizes the hydrologic system (usually, a watershed) in terms of some homogeneous runoff areas (eg.hydrologic response units in USGS PRMS) within each of which the runoff domain is comprised of a number of storages.

In these models the runoff/solute load computations are based primarily upon mass conservation in terms of water/solute budgets at various storages each of which represents one component hydrologic process (such as soil moisture zone storage, groundwater zone storage, etc.).

The conceptual approach has been adopted by various U.S.Federal agencies for the assessment and prediction of hydrologic hazards(PRMS by USGS, Sacramento Model by NWS, AGNPS by USDA/NRCS, BASINS by EPA, etc.)

Typical Structure of a Conceptual

Watershed Hydrology Model

ISSUES WITH CONCEPTUAL MODELING APPROACH

1. The parameters of conceptual models, due to being empirical, need to be estimated from model fitting to historical rainfall-runoff data.Therefore, it is not possible to use conceptual models in ungauged watersheds where there are no historical rainfall-runoff data.

2. For the assessment of hydrologic hazards under future changed-hydroclimate conditions:The parameters of a conceptual model, calibrated based upon the historical hydrologic record that reflects the historical physical conditions of the region/watershed under study,need to be recalibrated based upon the future regional/watershedconditions corresponding to the future changed hydroclimatic system. However, such data do not exist.

3. Concerning any future planned land restoration and land use management actions for the mitigation of hydrologic hazards

The parameters of a hydrologic model will change with the

changing land use/land cover conditions under different land

restoration and land use management scenarios.

Again, since the parameters of conceptual models are calibrated

based upon current land use/land cover conditions,

they will not be able to reflect the impact of the changes to the

physical features of a region or a watershed under planned land

restoration and land use management scenarios,

to the hydrologic hazard conditions being considered.

PHYSICALLY-BASED MODELS FOR HYDROLOGIC HAZARDS

Physically-based models deal with the conservation of mass, momentum, energy (potential and thermodynamic) and constitutive physical relationships

in order to simulate and predict the evolution of the interactive hydroclimatic (atmosphere-land-ocean) processes and resulting hydrologic hazards

in time and space over the globe/continent/region/watershed of interest.

Global Climate Models (GCMs) simulate the future hydroclimate at the global scale under various scenarios.

Regional hydroclimate models at Continental and Country scales simulate and predict hydroclimatic events which may lead to hydrological hazards such as floods and droughts.

Watershed hydrology models simulate and predict various hydrologic flow and environmental processes in order to simulateand predict the hydrological hazards at specified watersheds.

The parameters of physically-based models are directly measurable from atmospheric, oceanic and land observations.

For example, the parameters of physically-based watershed hydrology models are measurable from features of the land under study (eg. soil hydraulic conductivity estimated from soil texture and soil cover information).

Therefore, it is possible to use physically-based models in ungauged watersheds for the simulation and prediction of hydrologic hazards.

When the physical features of the geographical region under study (such as vegetation type and other land cover and land use

characteristics) change with change in climate,

the parameters of the physically-based modelswhich are determined from the physical features of the region, will adjust to the new physical features of the changed climate.

Therefore, the physically-based modelsare the appropriate tools for

the simulation of hydrologic hazards under future climatic conditions.

Also, since its parameters are fundamentally determined by

the physical features of the land under study,

a physically-based hydrology model will be able to incorporate the fundamental influence of new land surface conditions, corresponding to planned land use management actions,

on

the outcome of hydrologic hazards over the region of study.

Drought Prediction Using A Hydroclimatic Model

• Sources of hydrologic variability– Nonlinear process evolution in the hydroclimatic

system (atmospheric-oceanic-hydrologic systems)

– Variations in system conditions during the initiation and evolution of a hydroclimatic event (e.g. La Niña)

• Variability results in a distribution of hydrologic system response

• A Simplified Hydroclimate Model (SHM) can be used to quantify distributions of simulated hydrologic system responses

• Ensemble forecasting by means of the (SHM), can be used to quantify the distribution of simulated hydrologic system responses.

• Water resources applications can utilize information contained in the hydrologic response distributions

Methodology

Identify HydrologicVariability ForcingMechanism (HFM)

Develop HFM Structure for SHM

Run Monte CarloSimulations with

SHM

Standardize SHMResults Using SeasonalMeans and Variances

Create Relative FrequencyHistograms, Cumulative

Distribution Functions andRisk Maps

Apply Risks to Probabilistic

Management Model

Develop orIncorporateHydrologic

ClassificationScheme

SHM=Simplified Hydroclimate Model

Simplified Hydroclimate Model

Troposphere

Top ofAtm

200 hPa

500 hPa Cloud

Latent Heat FluxesPrecipitation ET

Ground

Stratosphere

SensibleHeat Flux

Outgoing Longwave

H2O and CO2Absorption &

Emission

Ozone Absorption& Emission

Incoming Shortwave

α

SHM State Variables

• Five state variables– Atmospheric Water Content, W– Atmospheric Temperature, T– Quasi-geostrophic Potential Vorticity, q– Land (Sea) Surface Temperature, Tg

– Cumulative Regional Water Balance Volume, Wg

• State variables interact through parameterized forcings to the state variable equations

Monte Carlo Runs with the Hydroclimate model of Northern

Hemisphere

• 10 year simulation following 2 year spin up period

• Eighteen month forcing incorporated starting in first year of 10 year simulation period

• 100 realizations run with different initial conditions for the state variables

La Niña Event Description• Cold phase of the El

Niño/Southern Oscillation (ENSO) phenomenon

• Cooler than normal eastern tropical Pacific sea-surface temperatures (SST)

• Modulation of Aleutian Low at the 500 hPa geopotential

• Potential drought-initiator for Southwest U.S.

Dec 1998 La Niña

Dec 1993 “Normal”

Dec 1997 El Niño

Figure Reference:http://www.pmel.noaa.gov/toga-tao/la-nina-story.html#animat

Example - La Niña Event

-180.00 -165.00 -150.00 -135.00 -120.00 -105.00 -90.00

5.0010.0015.00

30.0035.0040.0045.0050.0055.0060.0065.00

Longitude

Latit

ude

Study Regionwithin theNorthern

HemisphereModel Domain

RepresentativeSea SurfaceTemperature& Latent HeatForcings

Representative 500 hPaGeopotential Forcing

0.0

0.2

0.4

0.6

0.8

1.0

-2.0 -1.0 0.0 1.0 2.0 3.0

Standardized Hydrologic Water Storage

Cum

ulat

ive

Freq

uenc

y

0.0

0.2

0.4

0.6

0.8

1.0

-2.0 -1.0 0.0 1.0 2.0 3.0

Standardized Hydrologic Water Storage

Cum

ulat

ive

Freq

uenc

y10 Realizations 20 Realizations

0.0

0.2

0.4

0.6

0.8

1.0

-2.0 -1.0 0.0 1.0 2.0 3.0

Standardized Hydrologic Water Storage

Cu

mu

lati

ve F

req

uen

cy

0.0

0.2

0.4

0.6

0.8

1.0

-2.0 -1.0 0.0 1.0 2.0 3.0

Standardized Hydrologic Water Storage

Cu

mu

lati

ve F

req

uen

cy

50 Realizations 100 Realizations

Cumulative Frequency Diagrams with 95% Confidence Intervals

CA Water Year Classification Relative Frequency Histograms

Historic Record reflects 90 years of CA water year index values.

0.0

0.1

0.2

0.3

0.4

0.5

Critical Dry BelowNormal

AboveNormal

Wet

California Water Year Index Clas s ification

Rel

ativ

e Fr

eque

ncy

La Niña YearsHis toric Record

-125 -120 -115 -110 -105 -100 -95

Longitude (degrees)

30

35

40

45

50

Latit

ude

(deg

rees

)

-125 -120 -115 -110 -105 -100 -95

Longitude (degrees)

30

35

40

45

50

Latit

ude

(deg

rees

)

Characterization of Hydrologic System Response

0.0

0.1

0.2

0.3

0.4

0.5

Rel

ativ

e Fr

eque

ncy

Critical Dry BelowNormal

AboveNormal

Wet

Winter Year 4

45N, 115W 40N,120W CADWR Historical

0.0

0.1

0.2

0.3

0.4

0.5

Rel

ativ

e Fr

eque

ncy

Critical Dry Below Normal AboveNormal

Wet

Winter Year 5

45N, 115W 40N,120W CADWR Historical

Winter During Forcing

Winter After Forcing>50% probability of below mean conditions<50% probability of below mean conditions

FORECASTING FLOODS AND ENVIRONMENTAL SUBSTANCES BY PHYSICALLY-BASED MODELS:

Examples from WEHY (Watershed Environmental Hydrology) Model Applications

WEHY Model was jointly developed by PWRI of Japan and California Hydrologic Research Laboratory (CHRL) of Davis, California during 1995 – 2003 period.

Model development concerning environmental substances still continuing.

FORECASTING OF FLOODS

BY COUPLED

ATMOSPHERIC (MM5) AND HYDROLOGIC (WEHY) MODELS

48-HOURS IN ADVANCE

Proposed Methodology

• Obtain coarse time-space resolution atmospheric forecasts from a regional/continental scale atmospheric model (eg.Eta or JMAM) 48 hours in advance;

• Downscale the coarse time-space resolution precipitation forecasts of the regional/continental scale atmospheric model (eg. NCEP or JMAM) to fine time (hourly) and space (~2km) resolution over thewatershed of interest by means of MM5 model, 48 hours in advance of the runoff event;

• Utilize the fine time-space resolution precipitation forecasts of MM5 as input to WEHY Model in order to produce 48-hours-ahead runoff forecasts.

Forecasting Procedure

Precipitation Forecasts

Eta/JMAM AtmosphericForecast Data

MM5Large

Domain Simulation

MM5Small

Domain SimulationMM5

Simulations

FormatPrecipitation Data for WEHY Model

WEHYRunoff Forecasts

Download DEMData for Watershed

GIS Routinesto Obtain

Watershed Characteristics

Build WEHYWatershed Model

Calibrate and VerifyWEHY

Watershed Model

IC &BC

BC

IC

WEHY Model

Atmospheric Forecasts by JMAM

• 20km resolution JMAM forecasts are obtained from the internet;

• JMAM provides atmospheric forecasts at 12- hour intervals for 48 hours ahead;

• The 3D forecasts of precipitation, relative humidity, wind and atmospheric pressure by JMAM provide the boundary conditions for MM5 atmospheric model forecasts.

MM5 Atmospheric Model

• Fifth generation regional atmospheric model of NCAR (National Center for Atmospheric Research) and Penn State University;

• Nonhydrostatic dynamic simulation of atmospheric processes (JMAM is hydrostatic);

• Downscaling and upscaling capabilities;• Many modeling options for various atmospheric

processes (eg. at least 5 options for convective modeling of precipitation).

WEHY Model is a watershed hydrology model

with

areally-averaged conservation equations

for

interception;

snow accumulation/snowmelt,

evapotranspiration,

unsaturated flow, subsurface stormflow,

overland flow (with interacting rill flow and sheet flow),

and also for

channel network flow and regional groundwater flow.

Interception and Evapotranspiration Model

Subsurface Stormflow(Horizontal 1-D)

Overland flow (Sheet and Rill Flow) (1-Ds)

Regional Groundwater (2-D)

Stream Network Flow (1-D)

Hillslope Nutrient/Pesticide Transport (1-D)

In-stream Erosion/Sediment Transport (1-D)

In-stream Nutrient/Pesticide Transport (1-D)

Solar Radiation + Snow Accumulation + Snowmelt Models

Unsaturated flow and Infiltration Model(Vertical 1-Ds)

Precipitation

Snowfall, Temperature, Wind Speed, Relative Humidity

Rainfall

StreamflowHydrograph at any desired location within a watershed

Nutrient/Pesticide Load at any desired location within a watershed

Sediment load at any desired location within a watershed

OUTPUT

I. HYDROLOGIC MODULE

INPUT

Hillslope Processes (Hybrid 1-Ds)

Stream and Regional Groundwater Processes (1-Ds and 2-D)

Hillslope Erosion/Sediment Transport (1-D)

II. ENVIRONMENTAL MODULE

Parameter values related to Topography, Soil, Land Use Land Cover characteristics

Hortonian(Infiltration-

excess) overland flow

region

θ

Sr

Sxl

Syl

rillflow

local ridges

saturated interrillarea

unsaturatedinterrill area

y

x

rill rill

Qxl

Qyl

subsurfacestormflow

interrillsheet flow

Sr

Sr

rill

HSaturation overland flow region

Sxl

Syl

Qx

Qr

L1 L2 L2Mr

unsaturated soil water flow

Qy

qz

Impeding soil layer

A A

B

B

A: Overland flowB: Subsurface stormflow C: Groundwater flowD: Channel flow

AB

D

C

Schematic description of hillslope-stream interaction

Schematic description of Subdivisions and Contributing Areas in a Watershed

Since all the parameters of WEHY are physically-based,

they are estimated directly from

the land use/land cover, soil, vegetation, topographic and geologic data

and

not from fitting the model to observed runoff hydrographs.

Therefore, it is possibleto calibrate and apply the WEHY

at ungaged watersheds.

MM5 Precipitation Forecasts at Shiobara Dam Watershed

• Double-nested grid• 34 x 34 outer grid mesh with 6 km resolution = 204km

x 204km outer domain area;• 31 x 31 inner grid mesh with 2 km resolution = 62km x

62km inner domain area; • The initial and boundary conditions are obtained from

the weather forecasts of JMAM.• Computational time increments are 20 seconds.• Precipitation forecasts are given for a 48-hour period

with one-hour intervals.

The simulation started at 1998-08-26_12:00Z.

Transformation of spatially-distributed precipitation data from the MM5 grids to the computational units (MCUs) of WEHY Model

0

200

400

600

800

1000

1200

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47

Hours

m3 /s

Gage_OnlyMM5_Modulated

Observed basin average rainfall

versus

48-hour ahead predicted basin average rainfall by MM5

Comparison of runoff forecasts based upon raingage observations,

versus

runoff forecasts based upon MM5 predicted rainfall

0

200

400

600

800

1000

1200

1400

1600

1800

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47

Hours

m3 /s

MM5_ModulatedGage_OnlyObservation at Yunnhara

Comparison of observed runoff at Yuunohara station versusrunoff forecasts based upon raingage observationsandrunoff forecasts based upon MM5 rainfall forecasts.

PREDICTION OF NONPOINT SOURCE POLLUTION

IN WATERSHEDS

BY THE WEHY MODEL

• Spatially distributed Sediment Loading/Transport

• Nonpoint source transport of nutrients

• Nonpoint source transport of pesticides

.

Ward Creek Basin(area : 24.6 km2; elevation:6300-8870ft)

N

EW

S

•Number of MCUs : 18•Number of Channel reaches: 9•Simulation Duration: November 12, 1981 - December 31, 1981

Data requirement : • Rainfall + snowmelt, • DEM,• Soil class, • Land use and land cover

(including vegetation coverage)

CASE 2: Watershed Runoff

Ward Creek Watershed

ft

CHANNEL EROSION/DEPOSITION

RILL INTERRILL INTERRILL RILLFLOW AREA FLOW AREA FLOW FLOW

EROSION IN INTERRILL

AREA

EROSION /DEPOSITIONIN RILLS

EROSION IN INTERRILL AREA

•Number of MCUs : 18•Number of Channel reaches: 9•Simulation Duration: November 12, 1981 - December 31, 1981

Data requirement : • Rainfall + snowmelt,• Rill and interrill area flow data from CASE 2 • DEM,• Soil class, • Land use and land cover (including vegetation coverage)

CASE 3: Spatially Distributed Sediment Loading

Basin Average Sediment Loading in Ward Creek

(11/12/81-12/31/81)

Rainfall+snowmeltObserved total loadingSimulated total loadingContribution from interrill areasContribution from rills

Sediment Loading Distribution in Ward Creek

(11/12/81)

Sediment Loading Distribution in Ward Creek

(11/22/81)

Sediment Loading Distribution in Ward Creek

(12/18/81)

The t ot al suspended sedi ment ( SS) l oad of t he War d Cr eek wat er shed ( Nov. 12,1981- Dec. 31, 1981)

0

10

20

30

40

50

60

70

80

90

0 200 400 600 800 1000 1200Ti me ( hr )

SS (

kg/s

)

Measur edSi mul at ed

t he t ot al sol ubl e r eact i ve phosphor us ( SRP) l oad of t he War d Cr eekwat er shed( Nov. 12, 1981- Dec. 31, 1981)

0

100

200

300

400

500

600

0 200 400 600 800 1000 1200

t i me ( hr )

SRP (mg/s)

Measur edSi mul at ed

The t ot al pest i ci de l oad of t he War d Cr eek wat er shed i n wat ercol umn ( Nov. 12 - Dec. 31, 1981)

0. 00

2. 00

4. 00

6. 00

8. 00

10. 00

12. 00

0 200 400 600 800 1000 1200t i me ( hr )

pest

icid

e lo

ad (

mg/s

)

CONCLUSIONS:

As tools for the assessment of risks and for the forecasting of hydrologic hazards, the physically-based models are emerging as the most promising group.

Their physically-based parameters are determined by and change with the physical features of landscapes.

Hence, their parameters1. Can be estimated even over ungauged geographical

regions/watersheds;2. Will change with the changing land use/land cover conditions

under different land management scenarios for the mitigation of hydrologic hazards;

3. Will change with the change in land features under a changing climate.

Consequently, the physically-based models can avoid some of the

fundamental problems dogging the black box and conceptual models,

such as

a. the lack of or shortness of hydrologic records over study regions;

b. the necessity of assuming fixed land conditions (in order to keep

the black box and conceptual model parameters fixed) within a

changing climate;

c. assuming homogeneous land features (at least over a MCU for

conceptual models) over the study region.

Being fully-spatially distributed with changing parameters over

spatial locations, and having time-evolving dynamical structure under

the physical initial and boundary conditions, the physically-based

models can provide information for the risk assessment and

forecasting of hydrologic hazards over any desired spatial location

over the study region at any time increment.