1. Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)

2. 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

3. Satellite-derived Rainfall Estimates 1)Satellite-derived estimates: NASA TRMM (GPCP) 0.25º X 0.25º spatial resolution; 3hr temporal resolution 6hr reporting delay geostationary 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 resolution 18hr 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 GTS 0.5º X 0.5º spatial resolution; 24h temporal resolution 24hr reporting delay

4. Spatial Comparison of Precipitation Products Monsoon season (Aug 1, 2004) Indian subcontinent TRMM

5. Weather Forecasts for Hydrologic Applications ECMWF 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, 1.125 º X 1.125 º (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, 1.125 º X 1.125 º (TL159L62), 130km Medium-range -- ECMWF EPS - atmospheric initial value problem, SST’s persisted - 6hr - 15 day lead-time forecasts, integrated 2Xdaily - 51 member ensembles, 0.5 º X 0.5 º (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

6. -- 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:

7. 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

8. CFAB Project: Improve Bangladesh flood warning lead time Problems: 1. Limited warning of upstream river discharges 2. Precipitation forecasting in tropics difficult Assets: 1.Good data inputs => ECMWF weather forecasts, satellite rainfall estimates 2.Large catchments => weather forecasting skill “integrates” over large spatial and temporal scales 3. Partnership with Bangladesh’s Flood Forecasting Warning Centre (FFWC) => daily border river readings used in data assimilation scheme Technical: Peter Webster (PI), GT A.R. Subbiah, ADPC Funding: USAID, CARE, ECMWF

10. 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)

11. Daily Operational Flood Forecasting Sequence

12. Weather Forecast Ensembles Transformed into Discharge Forecasts Ensembles 3 day 4 day Precipitation Forecasts 1 day4 day 7 day10 day 1 day4 day 7 day 10 day Discharge Forecasts

13. Transforming (Ensemble) Rainfall into (Probabilistic) River Flow Forecasts Rainfall Probability Rainfall [mm] Discharge Probability Discharge [m 3 /s] Above danger level probability 36% Greater than climatological seasonal risk?

14. Daily Operational Flood Forecasting Sequence

15. 2003 Model Comparisons for the Ganges (4-day lead-time) hydrologic distributed modelhydrologic lumped model Resultant Hydrologic multi-model Multi-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!

16. Multi-Model Forecast Weighting (Regression) Coefficients - Lumped model (red) - 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- model approach (M. Clark, NIWA) - blending elements from ARNO/VIC, PRMS, Sacramento, TOPmodel

17. Daily Operational Flood Forecasting Sequence

18. Final flood forecast “calibration” or “post-processing” Probability calibration Flow rate [m 3 /s] Probability 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 Forecast PDF Forecast PDF Flow rate [m 3 /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

19. 2004 Brahmaputra Ensemble Forecasts and Danger Level Probabilities 3 day 4 day 5 day 3 day 4 day 5 day 7 day8 day 9 day10 day 7-10 day Ensemble Forecasts 7 day8 day 9 day10 day 7-10 day Danger Levels

20. 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.

21. 2007 Brahmaputra Ensemble Forecasts and Danger Level Probabilities 7-10 day Ensemble Forecasts7-10 day Danger Levels 7 day 8 day 9 day10 day 7 day8 day 9 day10 day

22. 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

23. Satellite-based River Discharge Estimation Bob Brakenridge, Dartmouth Flood Observatory, Dartmouth College River Watch

24. Application to the Ganges and Brahmaputra Rivers Utility of River Watch discharge estimates to flood forecasting: 1)Calibration of ungauged subcatchments outflow and routing 2)Operational improvements through data assimilation -- blending of enKF, 4DVAR, and “quantile regression” Ganges River Watch sitesBrahmaputra floodwave isochrons

25. 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

26. Gravity Recovery And Climate Experiment (GRACE) Slide from Sean Swenson, NCAR

27. GRACE catchment-integrated soil moisture estimates useful for: 1) Hydrologic model calibration and validation 2) Seasonal forecasting 3) Data assimilation for medium-range (1-2 week) forecasts Slide from Sean Swenson, NCAR

28. Conclusions Exciting time for flood forecasting for both developed and developing countries: -- satellite-based observational sensors provide global and timely estimates of water budget components -- coupling hydrologic forecast models to (ensemble) weather forecasts greatly extends forecast time-horizon Case study: CFAB Brahmaputra and Ganges river flow forecasts: -- 2003: went operational with ECMWF ensemble weather forecasts -- 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, September) severe Brahmaputra flooding events Further Advances: Data assimilation of new satellite-derived products: -- Dartmouth Flood Observatory river discharge estimates -- GRACE integrated catchment soil moisture -- QSCAT and TMI soil moisture estimates (Nghiem, JPL) Expansion of multi-model approach (78 member multi-model) Daily-updated seamless weather-to-seasonal flood forecasting: -- utilizing short-, medium-, monthly-, and seasonal ensemble forecasts

29. Thank You! hopson@ucar.edu