A Streamflow and Water Level Forecasting Model for the Ganges, Brahmaputra and Meghna Rivers with Requisite Simplicity

A Streamflow and Water Level Forecasting Model for the Ganges, Brahmaputra and Meghna Rivers with... AbstractA forecasting lead-time of 5-10 days is desired to increase the flood response and preparedness for large river basins. Large uncertainty in observed and forecasted rainfall appears to be key bottleneck in providing reliable flood forecasting. Significant efforts continue to be devoted to develop mechanistic hydrological models, statistical, and satellite-driven methods to increase the forecasting lead-time without exploring the functional utility of these complicated methods. This paper examines the utility of a data-based modeling framework with requisite simplicity – to paraphrase Einstein ‘simple, but not simpler’ – that identifies key variables and processes and develops ways to track their evolution and performance. Findings suggest that models with requisite simplicity – relying on flow persistence, aggregated upstream rainfall and travel time – can provide reliable flood forecasts comparable to relatively more complicated methods for up to 10-day lead-time for the Ganges, Brahmaputra, and upper Meghna gauging locations inside Bangladesh. Forecasting accuracy improves further by including weather model generated forecasted rainfall into the forecasting scheme. Use of water level in the model provides equally good forecasting accuracy for these rivers. The findings of the study also suggest that large-scale rainfall patterns captured by the satellites or weather models and their ‘predictive ability’ of future rainfall are useful in a data-driven model to obtain skillful flood forecasts up to 10-day for the GBM basins. Ease of operationalization and reliable forecasting accuracy of the proposed framework is of particular importance for large rivers, where access to upstream gauge measured rainfall and flow data are limited and detailed modeling approaches are operationally prohibitive and functionally ineffective. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Hydrometeorology American Meteorological Society

A Streamflow and Water Level Forecasting Model for the Ganges, Brahmaputra and Meghna Rivers with Requisite Simplicity

Loading next page...
 
/lp/ams/a-streamflow-and-water-level-forecasting-model-for-the-ganges-CYnGt3P2RL
Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1525-7541
D.O.I.
10.1175/JHM-D-16-0202.1
Publisher site
See Article on Publisher Site

Abstract

AbstractA forecasting lead-time of 5-10 days is desired to increase the flood response and preparedness for large river basins. Large uncertainty in observed and forecasted rainfall appears to be key bottleneck in providing reliable flood forecasting. Significant efforts continue to be devoted to develop mechanistic hydrological models, statistical, and satellite-driven methods to increase the forecasting lead-time without exploring the functional utility of these complicated methods. This paper examines the utility of a data-based modeling framework with requisite simplicity – to paraphrase Einstein ‘simple, but not simpler’ – that identifies key variables and processes and develops ways to track their evolution and performance. Findings suggest that models with requisite simplicity – relying on flow persistence, aggregated upstream rainfall and travel time – can provide reliable flood forecasts comparable to relatively more complicated methods for up to 10-day lead-time for the Ganges, Brahmaputra, and upper Meghna gauging locations inside Bangladesh. Forecasting accuracy improves further by including weather model generated forecasted rainfall into the forecasting scheme. Use of water level in the model provides equally good forecasting accuracy for these rivers. The findings of the study also suggest that large-scale rainfall patterns captured by the satellites or weather models and their ‘predictive ability’ of future rainfall are useful in a data-driven model to obtain skillful flood forecasts up to 10-day for the GBM basins. Ease of operationalization and reliable forecasting accuracy of the proposed framework is of particular importance for large rivers, where access to upstream gauge measured rainfall and flow data are limited and detailed modeling approaches are operationally prohibitive and functionally ineffective.

Journal

Journal of HydrometeorologyAmerican Meteorological Society

Published: Nov 28, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve Freelancer

DeepDyve Pro

Price
FREE
$49/month

$360/year
Save searches from
Google Scholar,
PubMed
Create lists to
organize your research
Export lists, citations
Read DeepDyve articles
Abstract access only
Unlimited access to over
18 million full-text articles
Print
20 pages/month
PDF Discount
20% off