Civil Engineering, Indian Institute of Technology (IIT), Gandhinagar, India.
Sci Data. 2017 Oct 3;4:170145. doi: 10.1038/sdata.2017.145.
Drought in South Asia affect food and water security and pose challenges for millions of people. For policy-making, planning, and management of water resources at sub-basin or administrative levels, high-resolution datasets of precipitation and air temperature are required in near-real time. We develop a high-resolution (0.05°) bias-corrected precipitation and temperature data that can be used to monitor near real-time drought conditions over South Asia. Moreover, the dataset can be used to monitor climatic extremes (heat and cold waves, dry and wet anomalies) in South Asia. A distribution mapping method was applied to correct bias in precipitation and air temperature, which performed well compared to the other bias correction method based on linear scaling. Bias-corrected precipitation and temperature data were used to estimate Standardized precipitation index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) to assess the historical and current drought conditions in South Asia. We evaluated drought severity and extent against the satellite-based Normalized Difference Vegetation Index (NDVI) anomalies and satellite-driven Drought Severity Index (DSI) at 0.05°. The bias-corrected high-resolution data can effectively capture observed drought conditions as shown by the satellite-based drought estimates. High resolution near real-time dataset can provide valuable information for decision-making at district and sub-basin levels.
南亚的干旱影响了粮食和水安全,给数百万人带来了挑战。对于次流域或行政级别的水资源的政策制定、规划和管理,需要高分辨率的降水和气温数据集,且这些数据集需要接近实时。我们开发了一种高分辨率(0.05°)的经过偏差校正的降水和温度数据集,可用于监测南亚的实时干旱状况。此外,该数据集还可用于监测南亚的气候极值(热浪和寒潮、干湿异常)。我们应用分布映射方法来校正降水和气温的偏差,与基于线性缩放的其他偏差校正方法相比,该方法表现良好。我们使用经过偏差校正的降水和温度数据来估计标准化降水指数(SPI)和标准化降水蒸散指数(SPEI),以评估南亚的历史和当前干旱状况。我们在 0.05°的水平上,根据基于卫星的归一化差异植被指数(NDVI)异常和卫星驱动的干旱严重指数(DSI)评估干旱的严重程度和范围。经过偏差校正的高分辨率数据可以有效地捕捉到观测到的干旱情况,正如基于卫星的干旱估计所显示的那样。高分辨率的近实时数据集可以为地区和次流域的决策提供有价值的信息。