资源环境科技发展态势分析平台

Accurate estimation of the magnitude and spatio-temporal variability of rainfall in the Indian Himalaya is difficult because of the sparse and limited network of ground stations located within complex terrain, as well as the difficulty of maintaining the stations over time. Thus, secondary rainfall sources are important to hydrological and hazard studies, if they reproduce the dynamics of rainfall satisfactorily. In this work, we evaluate four secondary products in the Garhwal Himalaya in India, with a focus on their application within the Mandakini River Catchment, the site of a devastating flood and multiple large landslides in 2013. The analysis included two satellite products: from the Tropical Rainfall Measuring Mission (TRMM) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) program, as well as two gridded products: the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) product and the India Meteorological Department (IMD) product. In comparing the four products against data collected at four ground stations (Rudraprayag, Joshimath, Purola, and Mukhim) using a variety of statistical indices, we determined that the IMD and TRMM products were superior to the others. In particular, the IMD product ranked the best for most indices including probability of detection (POD), false alarm ratio (FAR), receiver operating curve (ROC), and root mean squared error (RMSE). The TRMM product performed satisfactorily in terms of bias and detecting daily maximum monsoon rainfall at three of the four stations. The APHRODITE product had POD, FAR and ROC values that were among the best at higher rainfall depths at the Mukhim station. The PERSIANN product generally did not perform well based on these indices, consistently underestimating station rainfall depths. Finally, the IMD product could document the daily rainfall distribution during the June 2013 flood in the Mandakini Catchment and adjoining places. (C) 2017 Elsevier B.V. All rights reserved.