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 of Hydrometeorology – American Meteorological Society
Published: Nov 28, 2017
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