Flood forecas,ng data, models, tools, and sources of predictability
Tom Hopson, NCAR (among others) Charon BirkeB, Univ. of Maryland Daniel Broman, Univ. of Colorado
Robert Brakenridge, Dartmouth Flood Observatory David Yates, NCAR
Large-scale constraints on Extreme Precipitation What we expect – extreme precipitation • individual storms increase 6-10% /degC (scales
with available moisture) • high confidence much greater than mean
precipitation, but varies with time-scale, location, season
South Asian Monsoon Precip increases in: • average • variance • 5-day seasonal max • duration
TS
Technical Summary
107
Figure TS.24 | Future change in monsoon statistics between the present-day (1986–2005) and the future (2080–2099) based on CMIP5 ensemble from RCP2.6 (dark blue; 18 models), RCP4.5 (blue; 24), RCP6.0 (yellow; 14), and RCP8.5 (red; 26) simulations. (a) GLOBAL: Global monsoon area (GMA), global monsoon intensity (GMI), standard deviation of inter-annual variability in seasonal precipitation (Psd), seasonal maximum 5-day precipitation total (R5d) and monsoon season duration (DUR). Regional land monsoon domains determined by 24 multi-model mean precipitation in the present-day. (b)–(h) Future change in regional land monsoon statistics: seasonal average precipitation (Pav), Psd, R5d, and DUR in (b) North America (NAMS), (c) North Africa (NAF), (d) South Asia (SAS), (e) East Asia (EAS), (f) Australia-Maritime continent (AUSMC), (g) South Africa (SAF) and (h) South America (SAMS). Units are % except for DUR (days). Box-and-whisker plots show the 10th, 25th, 50th, 75th and 90th percentiles. All the indices are calculated for the summer season (May to September for the Northern, and November to March for the Southern Hemisphere) over each model’s monsoon domains. {Figures 14.3, 14.4, 14.6, 14.7}
of precipitation even if atmospheric circulation variability remains the same. This applies to ENSO-induced precipitation variability but the possibility of changes in ENSO teleconnections complicates this gener-al conclusion, making it somewhat regional-dependent. {12.4.5, 14.4, 14.8.3–14.8.5, 14.8.7, 14.8.9, 14.8.11–14.8.14}
TS.5.8.4 Cyclones
Projections for the 21st century indicate that it is likely that the global frequency of tropical cyclones will either decrease or remain essentially unchanged, concurrent with a likely increase in both global mean trop-ical cyclone maximum wind speed and rain rates (Figure TS.26). The influence of future climate change on tropical cyclones is likely to vary by region, but there is low confidence in region-specific projections. The frequency of the most intense storms will more likely than not increase in some basins. More extreme precipitation near the centers of tropical cyclones making landfall is projected in North and Central America, East Africa, West, East, South and Southeast Asia as well as in Australia and many Pacific islands (medium confidence). {14.6.1, 14.8.3, 14.8.4, 14.8.7, 14.8.9–14.8.14}
40 N
60 W 0 60 E 120 E 180
20 N
EQ
20 S
40 S
NAMS
SAF
SAS
EASNAF
AUSMCSAMS
Regional land monsoon domain
120 W
90 % tile75 % tile50 % tile25 % tile
10 % tile
6040
0-20-40-60
20
Pav Psd R5d DUR
(e) EAS
6040
0-20-40-60
20
Pav Psd R5d DUR
(f) AUSMC
(a) GLOBAL40
20
0
-20GMA GMI Psd R5d DUR
6040
0-20-40-60
20
Pav Psd R5d DUR
(b) NAMS
Chan
ge (%
or d
ays)
6040
0-20-40-60
20
Pav Psd R5d DUR
(c) NAF6040
0-20-40-60
20
Pav Psd R5d DUR
(d) SAS
6040
0-20-40-60
20
Pav Psd R5d DUR
(h) SAMS6040
0-20-40-60
20
Pav Psd R5d DUR
(g) SAF
Stan
dard
dev
iation
of N
ino3
index
(°C)
1.2
1
0.8
0.6
0.4PI 20C RCP4.5 RCP8.5
Figure TS.25 | Standard deviation in CMIP5 multi-model ensembles of sea surface temperature variability over the eastern equatorial Pacific Ocean (Nino3 region: 5°S to 5°N, 150°W to 90°W), a measure of El Niño amplitude, for the pre-industrial (PI) control and 20th century (20C) simulations, and 21st century projections using RCP4.5 and RCP8.5. Open circles indicate multi-model ensemble means, and the red cross symbol is the observed standard deviation for the 20th century. Box-and-whisker plots show the 16th, 25th, 50th, 75th and 84th percentiles. {Figure 14.14}
%
Yr 2100
AR5
Historical Simulation
River flow
Precipitation Soil moisture
Observed Data
Past Future
SNOW-17 / SAC
Sources of Predictability
1. Run hydrologic model up to the start of the forecast period to estimate basin initial conditions; the flows created can then be “advected” downstream
Model solutions to the streamflow forecasting problem…
Historical Simulation
River flow
Precipitation Soil moisture
Historical Data Forecasts
Past Future
SNOW-17 / SAC
1. Run hydrologic model up to the start of the forecast period to estimate basin initial conditions;
2. Run hydrologic model into the future, using an ensemble of local-scale weather and climate forecasts.
Sources of Predictability Model solutions to the streamflow forecasting problem…
?
Historical Simulation
River flow
Precipitation Soil moisture
Historical Data Forecasts
Past Future
SNOW-17 / SAC
1. Run hydrologic model up to the start of the forecast period to estimate basin initial conditions;
2. Run hydrologic model into the future, using an ensemble of local-scale weather and climate forecasts.
Sources of Predictability Model solutions to the streamflow forecasting problem…
Flood forecas,ng data, models, tools, etc.
Data assimila,on approaches
Rain gages radar
Hydrologic modeling
Hydraulic modeling Remotely-‐sensed
soil moisture Remotely-‐sensed river widths
Remotely-‐sensed river al,metry
Global circula,on model ensemble forecasts
Mesoscale weather forecasts
Physically-‐based hydrologic modeling approaches
Data-‐based hydrologic modeling approaches
Weather sta,on observa,ons
In situ river stage measurements
Snow measurements
Isochrones
Time scale
Spa,al Scale
Time scale
Spa,al Scale
Rain gages
radar In situ river stage
Remotely-‐sensed river al,metry and width
Satellite precipita,on
Time scale
Spa,al Scale
Rain gages
radar In situ river stage
Remotely-‐sensed river al,metry and width
Satellite precipita,on
nowcas,ng
Mesoscale weather forecas,ng with data assimila,on
Global circula,on model Ensemble
Time scale
Spa,al Scale
Rain gages
radar In situ river stage
Remotely-‐sensed river al,metry and width
Satellite precipita,on
nowcas,ng
Mesoscale weather forecas,ng with data assimila,on
Global circula,on model Ensemble
Hydraulic modeling
Data-‐based hydrologic modeling
Physically-‐based hydrologic modeling
Outline I. Precipita,on products
• QPE: Rain gauges telemetric systems, Radar, satellite precipita,on es,mates
• QPE/QPF “nowcas,ng” • QPF NWP: mesoscale and ensemble medium-‐range GCM
II. Global forecast systems • Satellite-‐based systems
• Hydrologic Research Center • NASA GFMS
• NWP Ensemble-‐based systems • Unified systems
• USA System, Mid-‐Atlan,c River Forecas,ng Center • European EFAS, France • Climate Forecas,ng Applica,ons for Bangladesh
II. New river measurement technologies for flood forecas,ng
• Most gauges are placed near permanent seUlements rather than distributed evenly
Measurement of Precipita,on – Limits of Rain Gauges:
Sends and receives horizontal & vertical polarized radiation
Image courtesy Terry Schuur
Dual Polarimetric Radar
• Satellite precipitation estimation useful in areas with poor radar & rain gauge coverage• Although satellite sampling more consistent than radar sampling, generally less accurate, with infrared less accurate than passive microwave sensors
OutlineI. Precipitation products
• QPE: Rain gauges telemetric systems, Radar, satellite precipitation estimates
• QPE/QPF “nowcasting”• QPF NWP: mesoscale and ensemble medium-range GCM
II. Global forecast systems• Satellite-based systems
• Hydrologic Research Center• NASA GFMS
• NWP Ensemble-based systems• Unified systems
• USA System, Mid-Atlantic River Forecasting Center• European EFAS, France• Climate Forecasting Applications for Bangladesh
II. New river measurement technologies for flood forecasting
Nowcas,ng defini,on – descrip,on of the current state of the weather in detail and the predic,on of changes in a few hours
WHAT IS NOWCASTING Originally defined by Browning for the 1st Nowcas,ng Conference in 1981 as:
O-‐6 hr forecas,ng by any method
spa,al scale of no more than a few kilometers (1-‐3 km) with frequent updates (5-‐10 min) Heavy emphasis on observa,ons
Jim Wilson
East
Nor
th
Storm Echo at Time-1
Time-2
Time-3
Time-4Nowcast forTime-5
Storm track
Storm Motion Vector
Extrapolation
Jim Wilson
Increasing Forecast Length
less
more
Nowcast
Schematic Representation of Forecast Skill R
elat
ive
Fore
cast
Ski
ll
Numerical Models
• Nowcast skill decreases rapidly with leadtime
• High-resolution NWP required for predicting storm organization.
• Blending optimally combines Nowcast and NWP
Radar data assimilation
CoSPA Technical Review Panel : May 16, 2011
Blending
Some Blending REFS Golding 1998 Pierce 2001 Lin et al 2005 Bowler 2006 Yeung et al. 2009 Kitzmiller 2010 Atencia et al. 2010 Pinto et al. 2010
James Pinto
Archive Centre
Current Data Provider
NCAR NCEP
CMC
UKMO
ECMWF MeteoFrance
JMA KMA
CMA
BoM CPTEC
IDD/LDM
HTTP
FTP
NCDC
Unique Datasets/Software Created Thorpex-Tigge
Early May 2011, floods in southwestern Africa
Early May 2011, floods in southwestern Africa -‐-‐ examine ens forecasts … ECMWF 5-‐day precip
Outline I. Precipita,on products
• QPE: Rain gauges telemetric systems, Radar, satellite precipita,on es,mates
• QPE/QPF “nowcas,ng” • QPF NWP: mesoscale and ensemble medium-‐range GCM
II. Global forecast systems • Satellite-‐based systems
• Hydrologic Research Center • NASA GFMS
• NWP Ensemble-‐based systems • Unified systems
• USA System, Mid-‐Atlan,c River Forecas,ng Center • European EFAS, France • Climate Forecas,ng Applica,ons for Bangladesh
II. New river measurement technologies for flood forecas,ng
• Established in 1993 as a nonprofit research, technology transfer, and training organization. • HRC was created to help bridge gaps between scientific research in hydrology and applications for the solution of important societal problems that involve water.
www.hrc-‐lab.org
NASA Real-‐,me Global Flood Es,ma,on System (GFMS)
• quasi-‐global (tropics and mid-‐la,tudes) • satellite precipita,on from TRMM Mul,-‐satellitePrecipita,on
Analysis [TMPA]) -‐-‐ IR and microwave instruments used • Univ of Oklahoma hydrologic model • flood es,mates every three hours • calculates water depth and streamflow at each grid (at 0.125
la,tude-‐longitude) • Flood detec,on based on water depth thresholds calculated from a
13-‐year retrospec,ve
Mature ensemble-‐based systems
European Flood Awareness System US Na,onal Weather Service, North Central River Forecast Centre (NCRFC) Climate Forecast Applica,ons in Bangladesh (CFAB) UK Flood Forecast Centre Swedish Meteorological and Hydrological Ins,tute (SMHI) Electricité de France (EDF) Water Management Centre of The Netherlands (WMCN) Meuse forecasts Bonneville Power Authority
River Forecast Centers
Weather Forecast Offices
Radar Sites across the US
Scott Ellis
Above-Critical-Level Cumulative Probability
7 day 8 day
9 day 10 day
3 day 4 day
5 day
7 day 8 day
9 day 10 day
Brahmaputra DischargeForecast Ensembles
2004 Brahmaputra Ensemble Forecasts and Danger Level Probabilities
2007 Brahmaputra Ensemble Forecasts and Danger Level Probabilities
7-10 day Ensemble Forecasts 7-10 day Danger Levels
7 day 8 day
9 day 10 day
7 day 8 day
9 day 10 day
Outline I. Precipita,on products
• QPE: Rain gauges telemetric systems, Radar, satellite precipita,on es,mates
• QPE/QPF “nowcas,ng” • QPF NWP: mesoscale and ensemble medium-‐range GCM
II. Global forecast systems • Satellite-‐based systems
• Hydrologic Research Center • NASA GFMS
• NWP Ensemble-‐based systems • Unified systems
• USA System, Mid-‐Atlan,c River Forecas,ng Center • European EFAS, France • Climate Forecas,ng Applica,ons for Bangladesh
II. New river measurement technologies for flood forecas,ng
-- Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) & NASA TRMM(Future: Global Precipitation Measurement System) - - Utilizing 36-37Ghz (unaffected by cloud)- - pixel size ~20km- - ~2day complete global coverage (night-time brightness temperatures)- - data range: 1997 to present
Objective Monitoring of River Stage and Flow:Satellite-based Passive Microwave Radiometer
Other Approaches: satellite altimeter-derived water level (and discharge derived through rating curve):e.g. Birkett, 1998; Alsdorf et al. 2000; Jung et al. 2010, Papa et al. 2010, Alsdorf et al. 2011, Biancamaria et al. 2011
MODIS sequence of 2006 Winter Flooding
2/24/2006 C/M: 1.004 3/15/2006 C/M: 1.029 3/22/2006 C/M: 1.095
Satellite Altimetry – Jason 2Traditionally used for sea level
Satellite Altimetry – now used for river heights with potential for downstream flood forecasts
for Bangladesh FFWC
37
738
Figure 6. Ground tracks or virtual stations of JASON-2 (J2) altimeter over the GB basin shown 739
in yellow lines. The locations where the track crosses a river and used for deriving forecasting 740
rating curves is shown with a circle and station number. Circles without a station number 741
represent the broader view of sampling by JASON-2 if all the ground tracks on main stem rivers 742
and neighboring tributaries of Ganges and Brahmaputra are considered. 743
744
NCAR
Summary
1. U,lity of flood forecast systems dictated by the precipita,on product at their core
2. Effec,ve flash flood guidance (FFG) dominated by skillful es,mates of local rainfall processes with spa,al precision
3. FFG tradi,onally based on telemetric rain (and stream) gage networks
NCAR
Summary (cont)
1. More recently, FFG u,lizes dual-‐polar radar with greater spa,al sampling and “nowcas,ng” capabili,es – but requiring more “overhead” to maintain
2. River flood forecas,ng (RFF) (medium to large catchments) requires less “local” and more “regional” knowledge of rainfall-‐runoff processes, and upstream catchment condi,ons
NCAR
Summary (cont)
1. RFF also benefit from lower requirements in rainfall spa,al precision, and can thus u,lize numerical weather predic,on (NWP)
2. Larger catchments can benefit from long-‐lead weather forecasts (5-‐15 days), but which are inherently probabilis,c (ensembles)
3. Ensemble RFF must account for uncertain,es introduced throughout the “forecas,ng chain” to truly be effec,ve in user decision making
NCAR
Summary (cont)
1. Indirect satellite measurements of river discharge (changes in river width or height) provide new poten,al for flood warnings by travel ,me lags in upstream water flow
“I have a very strong feeling that science exists to serve human welfare. It’s wonderful to have the opportunity given us by society to do basic research, but in return, we have a very important moral responsibility to apply that research to benefiting humanity.” Dr. Walter Orr Roberts (NCAR founder)