Local calibration of remotely sensed rainfall from the TRMM satellite for different periods and spatial scales in the Indus Basin
The availability of accurate rainfall data at proper temporal and spatial scales is vital for knowledge of renewable water resources and safe withdrawals for irrigation. Rain gauge networks in mountainous basins such as the Indus are sparse and insufficient to plan withdrawals and water management applications. Satellite rainfall estimates can be used as an alternative source of information but need area-specific calibration and validation due to the indirect nature of the radiation measurements. In this study, a calibration protocol is worked out for rainfall data from the Tropical Rainfall Measuring Mission (TRMM) satellite because uncalibrated TRMM rainfall data are inaccurate for use in rainfall-runoff studies and in soil water balance studies. Two alternative techniques, regression analysis (RA) and geographical differential analysis (GDA), were used to calibrate TRMM rainfall data for different periods and spatial distributions. The validity of these techniques was tested using Nash-Sutcliffe efficiency and the standard error of estimate. The GDA technique proved to be better, with higher efficiency and smaller error in complex mountainous terrains. The deviation between TRMMdata and rain gauge data was decreased considerably from 10.9% (pre-calibration at 625 km2) to 6.1% (post-calibration at 3125 km2) for annual time periods. For monthly periods, the deviation of 34.9% (pre-calibration at 625 km2) was decreased to 15.4% (post-calibration at 3125 km2). Calibration can be improved further if more rain gauges are available. The GDA technique can be applied to calibrate TRMM rainfall data in regions with limited rain gauge data and can provide a sufficiently accurate estimate of the key hydrological process that can be used in water management applications.
| Main Authors: | , |
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| Format: | Journal Article biblioteca |
| Language: | English |
| Published: |
Informa UK Limited
2012-04-20
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| Subjects: | water management, remote sensing, calibration, techniques, rain, river basins, satellite surveys, analytical methods, regression analysis, agriculture, land use, |
| Online Access: | https://hdl.handle.net/10568/40338 https://doi.org/10.1080/01431161.2011.617397 |
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| Summary: | The availability of accurate rainfall data at proper temporal and spatial scales is vital for knowledge of renewable water resources and safe withdrawals for irrigation. Rain gauge networks in mountainous basins such as the Indus are sparse and insufficient to plan withdrawals and water management applications. Satellite rainfall estimates can be used as an alternative source of information but need area-specific calibration and validation due to the indirect nature of the radiation measurements. In this study, a calibration protocol is worked out for rainfall data from the Tropical Rainfall Measuring Mission (TRMM) satellite because uncalibrated TRMM rainfall data are inaccurate for use in rainfall-runoff studies and in soil water balance studies. Two alternative techniques, regression analysis (RA) and geographical differential analysis (GDA), were used to calibrate TRMM rainfall data for different periods and spatial distributions. The validity of these techniques was tested using Nash-Sutcliffe efficiency and the standard error of estimate. The GDA technique proved to be better, with higher efficiency and smaller error in complex mountainous terrains. The deviation between TRMMdata and rain gauge data was decreased considerably from 10.9% (pre-calibration at 625 km2) to 6.1% (post-calibration at 3125 km2) for annual time periods. For monthly periods, the deviation of 34.9% (pre-calibration at 625 km2) was decreased to 15.4% (post-calibration at 3125 km2). Calibration can be improved further if more rain gauges are available. The GDA technique can be applied to calibrate TRMM rainfall data in regions with limited rain gauge data and can provide a sufficiently accurate estimate of the key hydrological process that can be used in water management applications. |
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