2 November 2017 Calibration and validation of a semi-distributed hydrological model in the Amur River Basin using remote sensing data
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Abstract
As the tenth-largest river basin in the world and one of the largest in the Russian Federation, the Amur River basin’s water resources have changed greatly in the last decades. More comprehensive understanding of hydrological process in the Amur River basin based on hydrological model is needed. With the increased availability of remotely sensed information, some hydrological variables assessed through remote measurements can be used to complement discharge data and a different respect of hydrological observations into the modelling process. In this paper, the calibration and validation of a semi-distributed hydrological model in the Amur River basin using remote sensing data were presented. The long-term hydrological processes of the Amur River basin for 2000-2013 was simulated based on Soil and Water Assessment Tool (SWAT) and the changes of the hydrological variables were analyzed. The total water storage change (TWSC) derived from the Gravity Recovery And Climate Experiment (GRACE), the actual evapotranspiration (ET) calculated using Moderate Resolution Imaging Spectroradiometer (MODIS) and advanced very high resolution radiometer (AVHRR) data, and multi-site river discharge data were used in the model calibration and validation. This study showed that the streamflow, evapotranspiration, surface runoff, soil water content and groundwater discharge into reach had all changed to varying degrees in Amur River basin during the period 2000-2013 under the influence of climate changes and human activities. Remotely sensed information was demonstrated useful in successful application of the model calibration and validation, and especially in reducing the equifinality for different parameters.
Conference Presentation
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Shilun Zhou, Shilun Zhou, Wanchang Zhang, Wanchang Zhang, } "Calibration and validation of a semi-distributed hydrological model in the Amur River Basin using remote sensing data", Proc. SPIE 10421, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX, 1042104 (2 November 2017); doi: 10.1117/12.2278345; https://doi.org/10.1117/12.2278345
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