ABSTRACT

In this study, advanced scatterometer (ASCAT) soil moisture data is employed to compute the basin water index (BWI) over six river basins of India for 10 years (2007–2016). The BWI time series is assessed for the development of its relationship with observed streamflow. Further, a popular ensemble learning technique, random forest, is employed to compute the 10-d streamflow using the BWI time series. Moreover, the results are compared with the classical rainfall–runoff model forced with satellite-based precipitation and evapotranspiration, BWI–rainfall–runoff model, and Global Flood Awareness System (GloFAS). The performance of the model is evaluated in terms of multiple efficiency measures, viz. Nash-Sutcliffe efficiency (NSE), correlation coefficient (R) and root mean square error (RMSE). The results reveal the BWI–rainfall–runoff model is the most accurate model for prediction of discharge. The performance of the BWI–rainfall–runoff model is very good over four of six catchments and good to satisfactory over the remaining two catchments.

摘要

在这项研究中，采用了高级散射仪（ASCAT）的土壤湿度数据来计算印度六个河流域10年（2007-2016年）的流域水指数（BWI）。对BWI时间序列进行评估，以了解其与观测到的水流之间关系的发展。此外，一种流行的整体学习技术，即随机森林，被用来使用BWI时间序列来计算10维流。此外，将结果与基于卫星降水和蒸散的经典降雨-径流模型，BWI-降雨-径流模型以及全球洪水意识系统（GloFAS）进行了比较。该模型的性能是根据多种效率衡量标准来评估的。Nash-Sutcliffe效率（NSE），相关系数（R）和均方根误差（RMSE）。结果表明，BWI-降雨-径流模型是预测流量的最准确模型。在六个流域中有四个流域的BWI降雨径流模型的性能非常好，在其余两个流域中的表现令人满意。