Daily to Seasonal Operational Flood ForecastingDaily to Seasonal Operational Flood Forecasting

Tom Hopson, NCAR and ADPCTom Hopson, NCAR and ADPCPeter Webster, CFAB and Georgia TechPeter Webster, CFAB and Georgia Tech

A. R. Subbiah, ADPCA. R. Subbiah, ADPC

Overview:Bangladesh flood forecasting

I. Overview of daily to seasonal weather forecast productsII. Seasonal forecasting: Bangladesh CFAB exampleIII. Short-term forecasting: Bangladesh CFAB example

1. Where does good predictability derive?1. precipitation forecast bias removal2. multi-model river forecasting3. accounting for all error: weather and hydrologic errors

IV. Future Work: Dartmouth Flood Observatory

Utility of a Three-Tier Forecast System

SEASONAL OUTLOOK: Long term planning of agriculture, water resource management & disaster mitigation especially if high probability of anomalous season (e.g., flood/drought)

30 DAY FORECAST: Broad-scale planning schedules for planting, harvesting, pesticide & fertilizer application and water resource management (e.g., irrigation/hydro-power determination). Major disaster mitigation resource allocation.

1-10 DAY FORECAST: Detailed agriculture, water resource and disaster planning. E.g., fine tuning of reservoir level, planting and harvesting.

forecast products for hydrologic applications• 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, 1.125X1.125 degrees (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, 1.125X1.125 degrees (TL159L62), 130km

• Medium-range -- ECMWF EPS- atmospheric initial value problem, SST’s persisted- 6hr - 15 day lead-time forecasts, integrated 2Xdaily- 51 member ensembles, 0.5X0.5 deg (TL255L40), 80km

• Short-range -- RIMES- 26-member Country Regional Integrated Multi-hazard Early Warning System (RIMES) WRF Precipitation Forecasts- 3hr - 5 day lead-time, integrated 2X daily- 9km resolution

1) Greater accuracy of ensemble mean forecast (half the error variance of single forecast)

2) Likelihood of extremes3) Non-Gaussian forecast PDF’s4) Ensemble spread as a representation of forecast

uncertainty

Motivation for Generating Ensemble Discharge Forecasts (from ensemble weather forecasts)

Overview:Bangladesh flood forecasting

I. Overview of daily to seasonal forecast productsII. Seasonal forecasting: Bangladesh CFAB exampleIII. Short-term forecasting: Bangladesh CFAB example

1. Where does good predictability derive?1. precipitation forecast bias removal2. multi-model river forecasting3. accounting for all error: weather and hydrologic errors

IV. Future Work: Dartmouth Flood Observatory

Seasonal rainfall prediction for 2006Seasonal rainfall prediction for 2006An example of An example of

seasonal seasonal predictions of predictions of precipitation issued precipitation issued in JFMA 2006 (left) in JFMA 2006 (left) and MJJA 2006 and MJJA 2006 (right), to be (right), to be compared with the compared with the observed rainfall observed rainfall (dotted line) and (dotted line) and climatology climatology (dashed line).(dashed line).

The seasonal The seasonal forecasts correctly forecasts correctly indicate months in indicate months in advance ‘higher advance ‘higher than normal’ than normal’ rainfall.rainfall.

Overview:Bangladesh flood forecasting

I. Overview of daily to seasonal forecast productsII. Seasonal forecasting: Bangladesh CFAB exampleIII. Short-term forecasting: Bangladesh CFAB example

1. Where does good predictability derive?1. precipitation forecast bias removal2. multi-model river forecasting3. accounting for all error: weather and hydrologic errors

IV. Future Work: Dartmouth Flood Observatory

CFAB Project: Improve flood warning lead time

Problems:

1. Limited warning of upstream river discharges

2. Precipitation forecasting in tropics difficult

Good forecasting skill derived from:1. good data inputs: ECMWF weather forecasts, satellite rainfall2. 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

1) Rainfall Inputs

1) Rain gauge estimates: NOAA CPC and WMO GTS0.5 X 0.5 spatial resolution; 24h temporal resolutionapproximately 100 gauges reporting over combined catchment24hr reporting delay

2) Satellite-derived estimates: NASA TRMM0.25X0.25 spatial resolution; 3hr temporal resolution6hr reporting delaygeostationary infrared “cold cloud top” estimates calibrated from SSM/I and TMI microwave instruments

3) Satellite-derived estimates: NOAA CPC “CMORPH”0.25X0.25 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

4) Weather forecasts: ECMWF GCM 51-member ensemble weather forecasts at 1-day to 15-day forecast lead-times (nominal resolution about 0.5degree)

Comparison of Precipitation Products:

Rain gauge, GPCP, CMORPH, ECMWF

-- Increase in forecast skill(RMS error) with increasingspatial scale

-- Logarithmic increase

2) Spatial Scale

Merged FFWC-CFAB Hydraulic Model Schematic

Primary forecast boundary conditions shown in gold:

Ganges at Hardinge Bridge

Brahmaputra at Bahadurabad

3) 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

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

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Rainfall [mm]

Discharge Probability

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Discharge [m3/s]

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

ECMWF 51-member Ensemble Precipitation Forecasts

2004 Brahmaputra Catchment-averaged Forecasts-black line satellite observations-colored lines ensemble forecastsBasic structure of catchment rainfall similar for both forecasts and observationsBut large relative over-bias in forecasts

5 Day Lead-time Forecasts=> Lots of variability

Pmax

25th 50th 75th 100th

Pfcst

Pre

cipi

tatio

n

Quantile

Pmax

25th 50th 75th 100th

Padj

Quantile

Forecast Bias Adjustment -done independently for each forecast grid

(bias-correct the whole PDF, not just the median)

Model Climatology CDF “Observed” Climatology CDF

In practical terms …

Precipitation 0 1m

ranked forecasts

Precipitation 0 1m

ranked observations

Bias-corrected Precipitation Forecasts

Brahmaputra Corrected Forecasts Original Forecast

Corrected Forecast

=> Now observed precipitation within the “ensemble bundle”

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

Discharge Multi-Model Forecast

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!

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

hydrologic distributed modelhydrologic lumped model

Resultant Hydrologic multi-model

Multi-Model Forecast Multi-Model Forecast Regression CoefficientsRegression Coefficients

- Lumped model (red)- Lumped model (red)- Distributed model (blue)- Distributed model (blue)

Significant catchment variationCoefficients vary with the forecast lead-timeRepresentative of the each basin’s hydrology

-- Ganges slower time-scale response

-- Brahmaputra “flashier”

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

Significance of Weather Forecast Uncertainty Discharge Forecasts

3 day 4 day

Precipitation Forecasts

1 day 4 day

7 day 10 day

1 day 4 day

7 day 10 day

Discharge Forecasts

What do we mean by “calibration” or “post-processing”?

Pro

babi

lity

calibration

Basin Rainfall [mm]

Pro

babi

lity

Basin Rainfall [mm]

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

Producing a Reliable Probabilistic Discharge Forecast

Step 1: generate discharge ensembles from precipitation forecast ensembles (Qp):

1/51

1

Qp [m3/s]

Prob

abil

ity

PDF

Step 3: combine both uncertainty PDF’s to generate a “new-and-improved” more complete PDF for forecasting (Qf):

Qf [m3/s]

1Pr

obab

ilit

y

Step 2: a) generate multi-model hindcast error time-series using precip estimates;b) conditionally sample and weight to produce empirical forecasted error PDF:

1000

-1000

forecasthorizon

time

PDF 1

-1000 1000Residual [m3/s]

[m3/s]

Residuals

=>

a) b)

Significance of Weather Forecast Uncertainty Discharge Forecasts

3 day 4 day

5 day

7 day 8 day

9 day 10 day

2004 Brahmaputra DischargeForecast Ensembles

Corrected Forecast Ensembles

7 day 8 day

9 day 10 day

2 day

3 day 4 day

5 day

7 day 8 day

9 day 10 day

50% 95%Critical Q black dash

2004 Brahmaputra Forecast Results

Above-Critical-Level Cumulative Probability

7 day 8 day

9 day 10 day

Confidence Intervals

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:Bangladesh flood forecasting

I. Overview of daily to seasonal forecast productsII. Seasonal forecasting: Bangladesh exampleIII. Short-term forecasting: Bangladesh example

1. Where does good predictability derive?1. precipitation forecast bias removal2. multi-model river forecasting3. accounting for all error: weather and hydrologic errors

IV. Future Work: Dartmouth Flood Observatory

Satellite-based River Discharge Estimation

Bob Brakenridge, Dartmouth Flood Observatory, Dartmouth College

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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 •Day/Night Flood detection on a near-daily basis regardless of cloud cover.•Measurement of river discharge changes; current flood magnitude assessments•Immediate map-based prediction of what is under water•Access to rapid response detailed mapping as new maps are made•Access to map data base of previous flooding and associated recurrence intervals.

http://www.dartmouth.edu/~floods/http://www.dartmouth.edu/~floods/

Application to the Ganges River Basin

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

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

The Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) is a twelve-channel, six-frequency, passive-microwave radiometer system. It measures horizontally and vertically polarized brightness temperatures at 6.9 GHz, 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz.

Spatial resolution of the individual measurements varies from 5.4 km at 89 GHz to 56 km at 6.9 GHz.AMSR-E was developed by the Japan Aerospace Exploration Agency (JAXA) and launched by the U.S. aboard Aqua in mid-2002.

Objective Monitoring of River Status:The Microwave Solution

One day of data collection(high latitudes revisited most frequently)

Example: Wabash River near Mount Carmel, Indiana, USA

Black square showsMeasurement pixel.White square iscalibration pixel.

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19-Jun-0219-Jul-0218-Aug-0217-Sep-0217-Oct-0216-Nov-0216-Dec-0215-Jan-0314-Feb-0316-Mar-0315-Apr-0315-May-0314-Jun-0314-Jul-0313-Aug-0312-Sep-0312-Oct-0311-Nov-0311-Dec-0310-Jan-049-Feb-0410-Mar-04

9-Apr-049-May-048-Jun-048-Jul-047-Aug-046-Sep-046-Oct-045-Nov-045-Dec-044-Jan-053-Feb-055-Mar-054-Apr-054-May-053-Jun-053-Jul-05

2-Aug-051-Sep-051-Oct-0531-Oct-0530-Nov-0530-Dec-0529-Jan-0628-Feb-0630-Mar-0629-Apr-0629-May-0628-Jun-0628-Jul-0627-Aug-06

AMSR-E radiance,degrees K x 10

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Estimated Discharge (m3/sec)

Site 98, Wabash River at New Harmony, Indiana, USA

2/17/2003 1.18 9/1/2002 1.82 7/24/2004 2.17

Guide to Predicting Inundation Irrawaddy River, Burma

The current hydrologic status and discharge or C/M ratio can be used to determine present inundation extent.

ConclusionsConclusions

2003: CFAB Brahmaputra/Ganges forecasts went operational2003: CFAB Brahmaputra/Ganges forecasts went operational

2004: 2004: -- Forecasts fully-automated-- Forecasts fully-automated

-- forecasted severe Brahmaputra flooding event-- forecasted severe Brahmaputra flooding event

2007: 5 pilot areas warned many days in-advance during two 2007: 5 pilot areas warned many days in-advance during two severe Brahmaputra flooding eventssevere Brahmaputra flooding events

Future WorkFuture Work

Dartmouth Flood Observatory river discharge estimates Dartmouth Flood Observatory river discharge estimates assimilated for improved skillful long-lead forecastsassimilated for improved skillful long-lead forecasts

Fully-automated forecasting scheme relying on global inputs Fully-automated forecasting scheme relying on global inputs (ECMWF forecasts, satellite rainfall) rapidly and cost-effectively (ECMWF forecasts, satellite rainfall) rapidly and cost-effectively applied to other river basins with in-country capacity buildingapplied to other river basins with in-country capacity building

Thank You!