Technological Improvements in Flood ForecastingTechnological Improvements in Flood Forecasting

Thomas HopsonThomas HopsonNational Center for Atmospheric Research (NCAR)National Center for Atmospheric Research (NCAR)

Overview:Technological improvements in flood forecasting

I. New data sets for flood forecasting- satellite-derived precipitation estimates- ensemble weather forecasts

II. Coupling new data sets to hydrological models- case study: Bangladesh CFAB project

III. Future improvements: remotely-sensed river discharge- Dartmouth Flood Observatory

IV. Future improvements: catchment-scale water balance- GRACE satellite system

Satellite-derived Rainfall Estimates

1) Satellite-derived estimates: NASA TRMM (GPCP)0.25º X 0.25º spatial resolution; 3hr temporal resolution6hr reporting delaygeostationary infrared “cold cloud top” estimates calibrated from SSM/I and TMI microwave instruments

2) Satellite-derived estimates: NOAA CPC “CMORPH”0.25º X 0.25º spatial resolution; 3hr temporal resolution18hr reporting delay precipitation rain rates derived from microwave instruments (SSM/I, TMI, AMSU-B), but “cloud tracking” done using infrared satellites

Both centers now producing rapid 8km X 8km spatial resolution; 30min temporal resolution; 3hr latency (roughly)

Other similar products: NRL, CSU, PERSIANN

3) Rain gauge estimates: NOAA CPC and WMO GTS0.5º X 0.5º spatial resolution; 24h temporal resolution24hr reporting delay

Spatial Comparison of Precipitation Products

Monsoon season (Aug 1, 2004)Indian subcontinent

TRMM

Weather Forecasts for Hydrologic ApplicationsECMWF example

• Seasonal -- ECMWF System 3- based on: 1) long predictability of ocean circulation, 2) variability in tropical

SSTs impacts global atmospheric circulation- coupled atmosphere-ocean model integrations- out to 7 month lead-times, integrated 1Xmonth

- 41 member ensembles, 1.125º X 1.125º (TL159L62), 130km• Monthly forecasts -- ECMWF

- “fills in the gaps” -- atmosphere retains some memory with ocean variability impacting atmospheric circulation

- coupled ocean-atmospheric modeling after 10 days- 15 to 32 day lead-times, integrated 1Xweek

- 51 member ensemble, 1.125º X 1.125º (TL159L62), 130km• Medium-range -- ECMWF EPS

- atmospheric initial value problem, SST’s persisted- 6hr - 15 day lead-time forecasts, integrated 2Xdaily

- 51 member ensembles, 0.5º X 0.5º (TL255L40), 80km

Motivation for generating ensemble forecasts (weather or hydrologic): a well-calibrated ensemble forecast provides a prognosis of its own uncertainty

or level of confidence

-- Weather forecast skill (RMS error) increases with spatial (and temporal) scale

=> Utility of weather forecasts in flood forecasting increases for larger catchments

-- Logarithmic increase

Rule of Thumb:

Overview:Technological improvements in flood forecasting

I. New data sets for flood forecasting- satellite-derived precipitation estimates- ensemble weather forecasts

II. Coupling new data sets to hydrological models- case study: Bangladesh CFAB project

III. Future improvements: remotely-sensed river discharge- Dartmouth Flood Observatory

IV. Future improvements: catchment-scale water balance- GRACE satellite system

CFAB Project: Improve Bangladesh flood warning lead time

Problems:1. Limited warning of upstream river discharges2. Precipitation forecasting in tropics difficult

Assets:1. Good data inputs=> ECMWF weather forecasts, satellite rainfall estimates2. Large catchments => weather forecasting skill “integrates” over large spatial and temporal scales3. Partnership with Bangladesh’s Flood Forecasting Warning Centre (FFWC)=> daily border river readings used in data assimilation scheme

Technical: Peter Webster (PI), GTA.R. Subbiah, ADPCFunding: USAID, CARE, ECMWF

Merged FFWC-CFAB Hydraulic Model Schematic

Primary forecast boundary conditions shown in gold:

Ganges at Hardinge Bridge

Brahmaputra at Bahadurabad

Benefit: FFWC daily river discharge observations used in forecast data assimilation scheme (Auto-Regressive Integrated Moving Average model [ARIMA] approach)

Daily Operational Flood Forecasting Sequence

Forecast Trigger: ECMWF forecast files

Updated TRMM-CMORPH-CPC precipitation estimates

Updated distributed model parameters

Updated outlet discharge estimates

Above-critical-level forecast probabilities transferred to Bangladesh

Lumped Model Hindcast/Forecast Discharge Generation

Distributed Model Hindcast/Forecast Discharge Generation

Multi-Model Hindcast/Forecast Discharge Generation

Discharge Forecast PDF Generation

Calibrate model

Statistically corrected downscaled forecasts

Generate forecasts Generate hindcasts Generate forecasts Generate hindcasts

Update soil moisture states and in-stream flows

Generate hindcasts

Calibrate AR error model

Calibrate multi-model

Generate forecasts Generate hindcasts

Generate forecasted model error PDF

Convolve multi-model forecast PDF with model error PDF

Generate forecasts

Weather Forecast Ensembles Transformed into Discharge Forecasts Ensembles

3 day 4 day

Precipitation Forecasts

1 day 4 day

7 day 10 day

1 day 4 day

7 day 10 day

Discharge Forecasts

Transforming (Ensemble) Rainfall into Transforming (Ensemble) Rainfall into (Probabilistic) River Flow Forecasts(Probabilistic) River Flow Forecasts

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 1 2 3 4 5 6

Rainfall Probability

Rainfall [mm]

Discharge Probability

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

10,000 30,000 50,000 70,000 90,000

Discharge [m3/s]

Above danger level probability 36%Greater than climatological seasonal risk?

Daily Operational Flood Forecasting Sequence

Forecast Trigger: ECMWF forecast files

Updated TRMM-CMORPH-CPC precipitation estimates

Updated distributed model parameters

Updated outlet discharge estimates

Above-critical-level forecast probabilities transferred to Bangladesh

Lumped Model Hindcast/Forecast Discharge Generation

Distributed Model Hindcast/Forecast Discharge Generation

Multi-Model Hindcast/Forecast Discharge Generation

Discharge Forecast PDF Generation

Calibrate model

Statistically corrected downscaled forecasts

Generate forecasts Generate hindcasts Generate forecasts Generate hindcasts

Update soil moisture states and in-stream flows

Generate hindcasts

Calibrate AR error model

Calibrate multi-model

Generate forecasts Generate hindcasts

Generate forecasted model error PDF

Convolve multi-model forecast PDF with model error PDF

Generate forecasts

2003 Model Comparisons for the Ganges (4-day lead-time)

hydrologic distributed modelhydrologic lumped model

Resultant Hydrologic multi-modelMulti-Model-Ensemble Approach:• Rank models based on historic residual error using current model calibration and “observed” precipitation

•Regress models’ historic discharges to minimize historic residuals with observed discharge

•To avoid over-calibration, evaluate resultant residuals using Akaike Information Criteria (AIC)

•If AIC minimized, use regression coefficients to generate “multi-model” forecast; otherwise use highest-ranked model => “win-win” situation!

Multi-Model Forecast Weighting Multi-Model Forecast Weighting (Regression) Coefficients(Regression) Coefficients

- Lumped model (red)- Lumped model (red)

- Distributed model (blue)- Distributed model (blue)

Significant catchment variation

Coefficients vary with the forecast lead-time

Representative of the each basin’s hydrology

Ganges slower time-scale response Brahmaputra “flashier”

Improvements: incorporating 78 multi-Improvements: incorporating 78 multi-model approach (M. Clark, NIWA)model approach (M. Clark, NIWA)

- blending elements from ARNO/VIC, - blending elements from ARNO/VIC,

PRMS, Sacramento, TOPmodelPRMS, Sacramento, TOPmodel

Daily Operational Flood Forecasting Sequence

Forecast Trigger: ECMWF forecast files

Updated TRMM-CMORPH-CPC precipitation estimates

Updated distributed model parameters

Updated outlet discharge estimates

Above-critical-level forecast probabilities transferred to Bangladesh

Lumped Model Hindcast/Forecast Discharge Generation

Distributed Model Hindcast/Forecast Discharge Generation

Multi-Model Hindcast/Forecast Discharge Generation

Discharge Forecast PDF Generation

Calibrate model

Statistically corrected downscaled forecasts

Generate forecasts Generate hindcasts Generate forecasts Generate hindcasts

Update soil moisture states and in-stream flows

Generate hindcasts

Calibrate AR error model

Calibrate multi-model

Generate forecasts Generate hindcasts

Generate forecasted model error PDF

Convolve multi-model forecast PDF with model error PDF

Generate forecasts

Final flood forecast “calibration” or “post-processing”

Pro

babi

lity

calibration

Flow rate [m3/s]

Pro

babi

lity

Post-processing has corrected:• the “on average” bias• as well as under-representation of the 2nd moment of the empirical forecast PDF (i.e. corrected its “dispersion” or “spread”)

“spread” or “dispersion”

“bias”obs

obs

ForecastPDF

ForecastPDF

Flow rate [m3/s]

Our approach:• under-utilized “quantile regression” approach• probability distribution function “means what it says”• daily variation in the ensemble dispersion directly relate to changes in forecast skill

2004 Brahmaputra Ensemble Forecasts and Danger Level Probabilities

3 day 4 day

5 day

3 day 4 day

5 day

7 day 8 day

9 day 10 day

7-10 day Ensemble Forecasts

7 day 8 day

9 day 10 day

7-10 day Danger Levels

Five Pilot Sites chosen in 2006 consultation workshops based on biophysical, social criteria:

Rajpur Union -- 16 sq km-- 16,000 pop.

Uria Union-- 23 sq km-- 14,000 pop.

Kaijuri Union-- 45 sq km-- 53,000 pop.

Gazirtek Union-- 32 sq km-- 23,000 pop.

Bhekra Union-- 11 sq km-- 9,000 pop.

A v e r a g e D a m a g e ( T k . ) p e r H o u s e h o l d i n P i l o t U n i o n

7 , 2 5 5

2 8 , 7 4 5

6 0 , 9 9 3

6 4 , 0 0 0

4 0 5 8

0

1 0 , 0 0 0

2 0 , 0 0 0

3 0 , 0 0 0

4 0 , 0 0 0

5 0 , 0 0 0

6 0 , 0 0 0

7 0 , 0 0 0

U r i a G a z i r t e k K a i j u r i R a j p u r B e k r a

U n i o n

Average Damage (Tk) per

Household

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

Overview:Technological improvements in flood forecasting

I. New data sets for flood forecasting- satellite-derived precipitation estimates- ensemble weather forecasts

II. Coupling new data sets to hydrological models- case study: Bangladesh CFAB project

III. Future improvements: remotely-sensed river discharge- Dartmouth Flood Observatory

IV. Future improvements: catchment-scale water balance- GRACE satellite system

Satellite-based River Discharge EstimationBob Brakenridge, Dartmouth Flood Observatory, Dartmouth College

2000

2200

2400

2600

2800

1-Jan-056-Jan-0511-Jan-0516-Jan-0521-Jan-0526-Jan-0531-Jan-055-Feb-05

10-Feb-0515-Feb-0520-Feb-0525-Feb-052-Mar-057-Mar-05

12-Mar-0517-Mar-0522-Mar-0527-Mar-051-Apr-056-Apr-05

11-Apr-0516-Apr-0521-Apr-0526-Apr-051-May-056-May-0511-May-0516-May-0521-May-0526-May-0531-May-05

5-Jun-0510-Jun-0515-Jun-0520-Jun-0525-Jun-0530-Jun-05

5-Jul-0510-Jul-0515-Jul-0520-Jul-0525-Jul-0530-Jul-054-Aug-059-Aug-05

14-Aug-0519-Aug-0524-Aug-0529-Aug-053-Sep-058-Sep-05

T, degrees K x 10010000200003000040000500006000070000

Discharge, c.f.s.

Measurement Reach Calibration Target Estimated Discharge Measured Discharge at Piketon

River Watch

Application to the Ganges and Brahmaputra Rivers

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

Utility of River Watch discharge estimates to flood forecasting:1) Calibration of ungauged subcatchments outflow and routing2) Operational improvements through data assimilation

-- blending of enKF, 4DVAR, and “quantile regression”

Ganges River Watch sitesBrahmaputra floodwave isochrons

Overview:Technological improvements in flood forecasting

I. New data sets for flood forecasting- satellite-derived precipitation estimates- ensemble weather forecasts

II. Coupling new data sets to hydrological models- case study: Bangladesh CFAB project

III. Future improvements: remotely-sensed river discharge- Dartmouth Flood Observatory

IV. Future improvements: catchment-scale water balance- GRACE satellite system

Gravity Recovery And Climate Experiment (GRACE)

Slide from Sean Swenson, NCAR

GRACE catchment-integrated soil moisture estimates useful for:GRACE catchment-integrated soil moisture estimates useful for:

1) Hydrologic model calibration and validation1) Hydrologic model calibration and validation

2) Seasonal forecasting2) Seasonal forecasting

3) Data assimilation for medium-range (1-2 week) forecasts3) Data assimilation for medium-range (1-2 week) forecasts

Slide from Sean Swenson, NCAR

ConclusionsConclusionsExciting time for flood forecasting for both developed and developing countries:Exciting time for flood forecasting for both developed and developing countries:-- satellite-based observational sensors provide global and timely estimates of water budget -- satellite-based observational sensors provide global and timely estimates of water budget componentscomponents-- coupling hydrologic forecast models to (ensemble) weather forecasts greatly extends -- coupling hydrologic forecast models to (ensemble) weather forecasts greatly extends forecast time-horizonforecast time-horizon

Case study: CFAB Brahmaputra and Ganges river flow forecasts:Case study: CFAB Brahmaputra and Ganges river flow forecasts:-- 2003: went operational with ECMWF ensemble weather forecasts-- 2003: went operational with ECMWF ensemble weather forecasts-- 2004: 1) forecasts fully-automated; 2) forecasted severe Brahmaputra July flooding events-- 2004: 1) forecasts fully-automated; 2) forecasted severe Brahmaputra July flooding events-- 2007: 5 pilot areas warned citizens many days in-advance during two (July-August, -- 2007: 5 pilot areas warned citizens many days in-advance during two (July-August, September) severe Brahmaputra flooding eventsSeptember) severe Brahmaputra flooding events

Further Advances:Further Advances:Data assimilation of new satellite-derived products:Data assimilation of new satellite-derived products:-- Dartmouth Flood Observatory river discharge estimates-- Dartmouth Flood Observatory river discharge estimates-- GRACE integrated catchment soil moisture-- GRACE integrated catchment soil moisture-- QSCAT and TMI soil moisture estimates (Nghiem, JPL)-- QSCAT and TMI soil moisture estimates (Nghiem, JPL)

Expansion of multi-model approach (78 member multi-model)Expansion of multi-model approach (78 member multi-model)

Daily-updated seamless weather-to-seasonal flood forecasting:Daily-updated seamless weather-to-seasonal flood forecasting:-- utilizing short-, medium-, monthly-, and seasonal ensemble forecasts-- utilizing short-, medium-, monthly-, and seasonal ensemble forecasts

Thank You!hopson@ucar.edu