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

Overview: I.Challenges i.Natural ii.Observational limitations II.Technological Opportunities III.Overview of this week’s course Primary challenge in forecasting river flow: I.estimating and forecasting precipitation And II. measurement of upstream river conditions

3 Natural Challenge: Topography Complete river basin monitoring difficult in Northern sections of major watersheds: –Rain gauge installation and monitoring –River gauging location –Snow gauging location

4 Monitoring basin’s available soil moisture not done in “real-time”! => Data collection problem!

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

6 Natural Challenge: Topography Use 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 countries Q: 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?)

9 Parts of basins snow dominated: -- complicated variable to model and measure Q: significant? Perhaps only for the Kosi in early season?

“Historical challenges”: Low density of -rain gauges -river gauges Lack of telemetric reporting => Basis of (US) traditional flood forecasting approaches Q: 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

13 -- latent predictability … for snow dominated basins

Opportunities: Snow covered basins -- latent predictability Remotely-sensed (satellite) data -Discharge -Rain -Snow

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 Objective Monitoring of River Status: The Microwave Solution

Example: Wabash River near Mount Carmel, Indiana, USA Black square shows Measurement pixel (blue line in next plot) White square is calibration pixel (green line in next plot) Dark blue colors: mapped flooding New: latency of 6-8hr! Dartmouth Flood Observatory Approach Discharge … Q: preliminary analysis done? Useful for estimating Nepal flows?

Satellite Precipitation Products Monsoon season (Aug 1, 2004) Indian subcontinent TRMM Rainfall … Q: data 6hr-delayed. What are typical flood-wave travel times for some of Northern Bihar’s rivers?

MODIS in the West -- snow covered area Yampa Basin, Colorado Missing Cloud Snow Snow-Free Snow 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 validation 2) Seasonal forecasting 3) Data assimilation for medium-range (1-2 week) forecasts Slide from Sean Swenson, NCAR

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

slide from Peter Webster

(Peter Webster)

Opportunities: Snow covered basins -- latent predictability Remotely-sensed (satellite) data Large-scale features of the monsoon -- predictability ENSO, MJO Modeling developments

Numerical Weather Prediction continues to improve … - ECMWF GCM or NCAR’s WRF

-- 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:

Opportunities: Snow covered basins -- latent predictability Remotely-sensed (satellite) data Large-scale features of the monsoon -- predictability ENSO, MJO Modeling developments Blending models with local and remotely-sensed data sets

Data Assimilation: The Basics Improve knowledge of Initial conditions Assimilate observations at time t Model “relocated” to new position

Bangladesh Flood Forecasting

Opportunities: Snow covered basins -- latent predictability Remotely-sensed (satellite) data Large-scale features of the monsoon -- predictability ENSO, MJO Modeling developments Blending models with local data sets Institutional 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 Outline Day2 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: 1)Introduction (brief) on advanced techniques being implemented 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.