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

Tom Hopson

“Science exists to serve human welfare. It’s wonderful to have the opportunity given us by society to do basic research, but in return, we have a very important moral responsibility to apply that research to benefiting humanity.”

Dr. Walter Orr Roberts (NCAR founder)

NCAR Scientific facilities

2. Supercomputers, data and networks

3. International Collaborative Research Environment

National Science Foundation Research & Development Center- 900 Staff, 500 Scientists/Engineers- Basic Research & Societal Applications- Atmospheric and related sciences

1. Advanced Observational Facilities

universities

-- NCAR board composed exclusively of US universities

Global Climate Models

Overview:I.Challenges

i. Naturalii. Observational limitations

II.Technological OpportunitiesIII.Overview of this week’s course

Primary challenge in forecasting river flow:

I. estimating and forecasting precipitationAndII. measurement of upstream river

conditions

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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

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

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Natural Challenge: Topography

Weather 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?

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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?)

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Parts of basins snow dominated:

-- complicated variable to model and measure

“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

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-- latent predictability … for snow dominated basins

Opportunities:•Snow covered basins

-- latent predictability•Remotely-sensed (satellite) data

-Discharge-Rain-Snow

MODIS in the West

-- snow covered area

• Yampa Basin, Colorado

MissingCloudSnowSnow-Free

Snow covered area …

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, USA

Black 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 …

Satellite Precipitation Products

Monsoon season (Aug 1, 2004)Indian subcontinent

TRMM

Rainfall …

data roughly 6hr-delayed. IR-based data 15min delays

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 India

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 -- running full CFAB codes -- verification

Goals:

1)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.

Next up:

Linux – why learn new OS for flood forecasting? - powerful, with easily automated processes- most-used scientific and engineering tool development and computational environment- efficient- free (sort of)!

R – why?- powerful cutting-edge statistical tools (e.g. post-processing techniques, parametric and

non-parametric tools and regression analysis, verification, extreme-value analysis

- efficient (not so)- free (sort of)!