Long-lead flood forecasting for India: challenges, opportunities, outline Tom Hopson

Overview:

ChallengesNaturalObservational limitationsTechnological OpportunitiesOverview of this weeks course

Primary challenge in forecasting river flow:

estimating and forecasting precipitationAndII. measurement of upstream river conditions

*Natural Challenge: TopographyComplete river basin monitoring difficult in Northern sections of major watersheds:

Rain gauge installation and monitoring

River gauging location

Snow gauging location

*Monitoring basins available soil moisture not done in real-time!=> Data collection problem!

*Natural Challenge: TopographyWeather precipitation radar for future monitoring and instrumentation needs (predominantly used in the US):=> Topography causes radar signal blockage, limiting coverageDoppler radar (e.g. Calcutta) providing adequate coverage in places?

*Natural Challenge: TopographyUse of numerical weather prediction forecast output to fill in the instrumentation gaps or for advanced lead-time flood forecasting

but has own set of challenges in mountainous environments

=> Use caution with numerical weather prediction outputs

Trans-boundary challenges:

Parts of watersheds in other countriesQ: Data sharing of both rain and river gauge? How reliable and how quickly? Opportunities for further engagement?

Current method: lagged correlation of stage with border Q (8hr forecast?)

*Parts of basins snow dominated:

-- complicated variable to model and measureQ: significant? Perhaps only for the Kosi in early season?

Historical challenges:

Low density of-rain gauges-river gaugesLack of telemetric reporting=> Basis of (US) traditional flood forecasting approachesQ: what is the density in your basin?How many develop rating curves?

more Historical challenges:

Maintaining updated rating curves--- important for hydrologic (watershed) model calibration and state proper variable for river routing (e.g. not stage)(sediment load issues)sufficient radars (basis of US monitoring)

Opportunities:

Snow covered basins-- latent predictability

*-- latent predictability for snow dominated basins

Opportunities:Snow covered basins-- latent predictabilityRemotely-sensed (satellite) dataDischargeRainSnow

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

Example: Wabash River near Mount Carmel, Indiana, USABlack square showsMeasurement pixel(blue line in next plot)

White square iscalibration pixel(green line in next plot)

Dark blue colors:mapped flooding

New: latency of 6-8hr!Dartmouth Flood Observatory ApproachDischarge Q: preliminary analysis done?Useful for estimating Nepal flows?

Satellite Precipitation Products

Monsoon season (Aug 1, 2004)Indian subcontinentTRMMRainfall Q: data 6hr-delayed. What are typical flood-wave travel times for some of Northern Bihars rivers?

MODIS in the West-- snow covered areaYampa Basin, ColoradoSnow covered area

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

GRACE catchment-integrated soil moisture estimates useful for:1) Hydrologic model calibration and validation2) Seasonal forecasting3) Data assimilation for medium-range (1-2 week) forecastsSlide from Sean Swenson, NCAR

Opportunities:Snow covered basins-- latent predictabilityRemotely-sensed (satellite) dataLarge-scale features of the monsoon-- predictability ENSO, MJO

slide from Peter Webster

(Peter Webster)

Opportunities:Snow covered basins-- latent predictabilityRemotely-sensed (satellite) dataLarge-scale features of the monsoon-- predictability ENSO, MJOModeling developments

Numerical Weather Prediction continues to improve - ECMWF GCM or NCARs WRF

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

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

-- Logarithmic increaseRule of Thumb:

Opportunities:Snow covered basins-- latent predictabilityRemotely-sensed (satellite) dataLarge-scale features of the monsoon-- predictability ENSO, MJOModeling developmentsBlending models with local and remotely-sensed data sets

Data Assimilation: The BasicsImprove knowledge of Initial conditionsAssimilate observations at time t Model relocated to new position

Bangladesh Flood Forecasting

Opportunities:Snow covered basins-- latent predictabilityRemotely-sensed (satellite) dataLarge-scale features of the monsoon-- predictability ENSO, MJOModeling developmentsBlending models with local data setsInstitutional commitment to capacity build up Scientific and engineering talent of CWC

Day1

Session 1-- overview of course-- Introductions of participants and questionnaire

Session 2-- CFAB example

Session 3-- introduction to linux: shell commands, cron

Session 4-- introduction to R

Course OutlineDay2

Session 1-- QPE products -- rain and snow gauges -- radar -- satellite precip-- QPF products -- NWP -- GCM and mesoscale atmospheric models -- ensemble forecasting

Session 2 -- preprocessing -- bias removal and types/sources of stochastic behavior/uncertainty -- quantile-to-quantile matching -- deterministic processing and particularities of precip/wind speed -- ensemble products and making statistically-equiv

Session 3-- Introduction to IDL

Session 4 -- wget and download satellite precip and cron-- quantile-to-quantile matching

Day3

Session 1 -- hydrologic models and their plusses/minuses -- lumped model -- time-series analysis -- overcalibration and cross-validation and information criteria

Session 2 -- distributed model -- numerical methods -- calibration and over-calibration

Session 3 -- time-series analysis -- AR, ARMA, ARIMA, and other types of models -- overfitting, information criteria, and cross-validation

Session 4 -- numerical methods and 2-layer models -- multi-modeling

Course Outline (cont)Day4

Session 1 -- multi-model -- post-processing -- BMA/KNN/QR/LR

Session 2 -- verification -- user needs

Session 3 -- post-processing algorithms via R

Session 4 -- verification

Goals:

Introduction (brief) on advanced techniques beingimplemented for flood forecasting many are still evolving in their effectiveness, so be discriminating!

2) Awareness of (new) global data sets available for use

3) Awareness of available and relevant software tools

Stress: stay simple and only add complexity *if* needed. Stay focused on your goals. Do you have what you need already, both in terms of data and tools (have you adequately tested them)? If not, prioritize and build from the simple.

e.g. calibrating rainfall at a point versus for the whole watershed.

Only high resolution captures vertical motion, which itself dictates precipitation pattern (due to orographic lifting).**Here is the satellite we are currently using. Similar data are also provided today by the NASA TRRM satellite, with an extensive follow-on constellation of satellites being planned (GPM, or Global Precipitation Measurement System). There is also a long series of Defense Department satellites whose data are publically available: the DMSP SSM/I sensor. Thus, we may be able to extend many river records back to 1985. These satellites were not designed to measure rivers, but their data stream can in fact provide such measurements. We use the 36.5 GHz AMSR-E band that is not affected by cloud cover.*We compare the microwave signal coming from an image pixel centered over the river, and one nearby, not affected itself by the rising and falling of river discharge. AMSR-E is a global-coverage, frequent repeat sensor, with coarse pixels (10 Km and upwards, depending on wavelength). We calibrate the remote sensing signal very precisely, so that it is affected almost entirely only by water area changes. We use a ratio approach (measurement pixel, centered over the river) compared to calibration pixel (nearby, not affected by the river). The ratio has been tested to track discharge very well.********CFAB: Project began in 1999, became fully operational in 2003, with a program to actively disseminate the forecasts down to the household began this year. To our knowledge, first time USAID has funded a university-based research group to produce an operational humanitarian assistance program

Forecasting for Bangladesh for both Brahmaputra and Ganges rivers out to 10-day lead-timesUpper right plot shows the forecasting locations (Bahadurabad for the Brahmaputra and Hardinge Bridge for the Ganges) -- also on the plot are 5 areas (green)that were set up to receive direct flood warning disseminations in 2006.Lower left shows extreme July and September floods of 2007 and the ensemble forecastsLower right shows the above-danger level probability forecasts => upshot, showed good skill out to 10-days and led to days-in-advance warnings for vulnerable people living along the Brahmaputra that year.