Abstract

In this study, a comparative analysis of three satellite precipitation products including the Tropical Rainfall Measuring Mission (TRMM-3B43 V7), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS V2) with ground-measured Indian Meteorological Department (IMD) precipitation data were performed to estimate the meteorological drought in the Bundelkhand region of Central India. The high-resolution CHIRPS data showed the closest agreement with the IMD precipitation and well captured the drought characteristics. The Standardized Precipitation Index (SPI) identified seven major droughts events during the period of 1981 to 2016. Appropriate calibration and validation were performed for drought forecasting using the Auto-Regressive Integrated Moving Average (ARIMA) model. The forecasting result showed a reasonably good agreement with the observed datasets with the one-month lead time. The outcomes of this study have policy level implications for drought monitoring and preparedness in this region.

摘要

在这项研究中，对热带降雨测量任务 (TRMM-3B43 V7)、使用人工神经网络-气候数据记录 (PERSIANN-CDR) 遥感信息降水估计和气候灾害等三个卫星降水产品的比较分析使用地面测量的印度气象局 (IMD) 降水数据进行群站红外降水 (CHIRPS V2) 以估计印度中部邦德尔坎德地区的气象干旱。高分辨率 CHIRPS 数据显示与 IMD 降水最接近，并很好地捕捉到了干旱特征。标准化降水指数 (SPI) 确定了 1981 年至 2016 年期间的七次重大干旱事件。使用自回归综合移动平均 (ARIMA) 模型对干旱预报进行了适当的校准和验证。预测结果显示与观察到的数据集与 1 个月的提前期有相当好的一致性。这项研究的结果对该地区的干旱监测和防备具有政策层面的意义。