Predictions in a data-sparse region using a regionalized grid-based hydrologic model driven by remotely sensed data

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Courtesy of IWA Publishing

The goal of this study was to assess the feasibility of using Tropical Rainfall Measuring Mission (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS) products to drive a mesoscale hydrologic model (mHM) in a poorly gauged basin. Other remotely sensed products such as LandSat and Shuttle Radar Topography Mission (SRTM) were also used to complement the local geoinformation. For this purpose, three data blending techniques that combine satellite with in situ observations were implemented and evaluated in the Mod basin (512 km2) in India. The climate of the basin is semi-arid and monsoon-dominated. The rainfall gauging network comprised six stations with daily records spanning 9 years. Daily discharge time series was only 4 years long and incomplete. Lumped and distributed versions of mHM were evaluated. Parameters of the lumped version were obtained through calibration. A multiscale regionalization technique was used to parameterize the distributed version using global parameters from other gauged basins. Both mHM versions were evaluated during six monsoon seasons. Results of numerical experiments indicated that driving mHM with satellite-based products is possible and promising. The distributed model with regionalized parameters was at least 20% more efficient than that of its lumped version. Initialization conditions must be carefully considered when the model is only driven by remotely sensed inputs.

Keywords: data-sparse region, mHM model, PUB, regionalization, remote sensing

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