Geospatial Information Systems and Remote Sensing, Institute of Geomatics, Dedan Kimathi University of Technology, Nyeri, Kenya.
School of Science and Informatics, Taita Taveta University, Voi, Kenya.
Environ Monit Assess. 2021 Mar 24;193(4):213. doi: 10.1007/s10661-021-08989-0.
The study focused on developing a novel socio-economic drought index (SeDI) for monitoring the severity of drought in a dry basin ecosystem dominated by nomadic pastoralists. The study utilized the domestic water deficit index, bareness index, normalized difference vegetation index, and water accessibility index as the input variables. An ensembled stochastic framework that coupled the 3D Euclidean feature space algorithm, least-squares adjustment, and iteration was used to derive the new SeDI. This approach minimized the uncertainties propagated by the stochastic nature of the input variables that has been a major bottleneck exhibited by the existing models. The regression analyses between the simulated SeDI and the observed ground river discharge registered a correlation coefficient (r) of -0.84 and a p-value of 0.02, while the correlation between the Hull's score-derived SeDI and ground river discharge registered a correlation coefficient (r) of -0.75 and a p-value of 0.05. The assessment revealed that the newly derived SeDI was more sensitive to the river discharge than the Hull's score-derived SeDI. The SeDI's classification results for the period between 1986 and 2018 revealed that only January 2009 manifested a significant slight severity level covering about 12.4% of the basin. Additionally, the results indicated that the basin exhibited a moderate severity level ranging between 85 and 96%, a severe level ranging between 2.2 and 13.3%, and an extreme level ranging between 0.73 and 1.17%. The derived SeDI would serve as an early warning tool necessary for increasing the resilience to climate-related risks and offer support in reducing the loss of life and livelihood.
本研究旨在开发一种新的社会经济干旱指数（SeDI），用于监测以游牧牧民为主的干旱流域生态系统的干旱严重程度。该研究利用国内缺水指数、光秃指数、归一化差异植被指数和水可及性指数作为输入变量。采用集成随机框架，耦合三维欧几里得特征空间算法、最小二乘调整和迭代，得出新的 SeDI。这种方法最大限度地减少了输入变量的随机性质所带来的不确定性，这是现有模型存在的主要瓶颈。模拟 SeDI 与观测到的地面河流流量之间的回归分析得到相关系数（r）为-0.84，p 值为 0.02，而 Hull 得分衍生的 SeDI 与地面河流流量之间的相关性得到相关系数（r）为-0.75，p 值为 0.05。评估结果表明，新衍生的 SeDI 对河流流量的变化比 Hull 得分衍生的 SeDI 更为敏感。1986 年至 2018 年期间的 SeDI 分类结果表明，只有 2009 年 1 月表现出轻微严重程度，覆盖了流域的约 12.4%。此外，结果表明，流域表现出中度严重程度，范围在 85%至 96%之间，严重程度在 2.2%至 13.3%之间，极端程度在 0.73%至 1.17%之间。所衍生的 SeDI 将作为一种必要的预警工具，用于提高对气候相关风险的抵御能力，并支持减少生命和生计的损失。
