Alemu Mikhael G, Zimale Fasikaw A
Department of Climate Change Engineering, Pan African University Institute for Water and Energy Sciences -Including Climate Change (PAUWES), Tlemcen, Algeria.
Action for Human Rights and Development, PO Box 1551, Adama, Ethiopia.
Environ Monit Assess. 2025 Feb 4;197(3):243. doi: 10.1007/s10661-025-13708-0.
Drought remains a menace in the Horn of Africa; as a result, the Ethiopia's Genale Dawa River Basin is one of the most vulnerable to agricultural drought. Hence, this study integrates remote sensing and machine learning algorithm for early warning identification through assessment and prediction of index-based agricultural drought over the basin. To track the severity of the drought in the basin from 2003 to 2023, a range of high-resolution satellite imagery output indexes were used, including the Vegetation Condition Index (VCI), Thermal Condition Index (TCI), and Vegetation Health Index (VHI). Additionally, the Artificial Neural Network machine learning technique was used to predict agricultural drought VHI for the period of 2028 and 2033. Results depict that during the 2023 period, 25% of severe drought and 18% of extreme drought countered at the lower part of the basin at Dolo ado and Chereti regions. A high TCI value was found that around 23.24% under extreme drought and low precipitation countered in areas of Moyale, Dolo ado, Dolobay, Afder, and Bure lower than 3.57 mm per month. Similarly, increment of severe drought from 24.26% to 24.58% and 16.53% to 16.58% of extreme drought value of VHI might be experienced during the 2028 and 2033 period respectively in the area of Mada Wolabu, Dolo ado, Dodola, Gore, Gidir, and Rayitu. The findings of this study are significantly essential for the institutes located particularly in the basin as they will allow them to adapt drought-coping mechanisms and decision-making easily.
干旱仍是非洲之角的一大威胁；因此，埃塞俄比亚的盖纳莱-达瓦河流域是最易遭受农业干旱的地区之一。因此，本研究通过对流域内基于指数的农业干旱进行评估和预测，将遥感和机器学习算法相结合用于早期预警识别。为了追踪2003年至2023年该流域干旱的严重程度，使用了一系列高分辨率卫星图像输出指标，包括植被状况指数（VCI）、热状况指数（TCI）和植被健康指数（VHI）。此外，还使用了人工神经网络机器学习技术来预测2028年和2033年期间的农业干旱VHI。结果表明，在2023年期间，该流域下游的多洛阿多和切雷蒂地区遭遇了25%的严重干旱和18%的极端干旱。发现高TCI值出现在莫亚莱、多洛阿多、多洛贝、阿夫德和布雷等地区，这些地区极端干旱且降水少，每月低于3.57毫米。同样，在马达沃拉布、多洛阿多、多多拉、戈尔、吉迪尔和拉伊图地区，VHI的严重干旱值可能会在2028年和2033年期间分别从24.26%增至24.58%，极端干旱值从16.53%增至16.58%。这项研究的结果对特别是位于该流域的机构极为重要，因为这将使它们能够轻松地采用抗旱机制并进行决策。
