Mortenson Center in Global Engineering, University of Colorado Boulder, Boulder, CO, USA.
Mortenson Center in Global Engineering, University of Colorado Boulder, Boulder, CO, USA; Regional Centre for Mapping of Resources for Development, Nairobi, Kenya; Environmental Studies, University of Colorado Boulder, Boulder, CO, USA.
Sci Total Environ. 2022 Jul 20;831:154453. doi: 10.1016/j.scitotenv.2022.154453. Epub 2022 Mar 25.
Groundwater is an important source of water for people, livestock, and agriculture during drought in the Horn of Africa. In this work, areas of high groundwater use and demand in drought-prone Kenya were identified and forecasted prior to the dry season. Estimates of groundwater use were extended from a sentinel network of 69 in-situ sensored mechanical boreholes to the region with satellite data and a machine learning model. The sensors contributed 756 site-month observations from June 2017 to September 2021 for model building and validation at a density of approximately one sensor per 3700 km. An ensemble of 19 parameterized algorithms was informed by features including satellite-derived precipitation, surface water availability, vegetation indices, hydrologic land surface modeling, and site characteristics to dichotomize high groundwater pump utilization. Three operational definitions of high demand on groundwater infrastructure were considered: 1) mechanical runtime of pumps greater than a quarter of a day (6+ hr) and daily per capita volume extractions indicative of 2) domestic water needs (35+ L), and 3) intermediate needs including livestock (75+ L). Gridded interpolation of localized groundwater use and demand was provided from 2017 to 2020 and forecasted for the 2021 dry season, June-September 2021. Cross-validated skill for contemporary estimates of daily pump runtime and daily volume extraction to meet domestic and intermediate water needs was 68%, 69%, and 75%, respectively. Forecasts were externally validated with an accuracy of at least 56%, 70%, or 72% for each groundwater use definition. The groundwater maps are accessible to stakeholders including the Kenya National Drought Management Authority (NDMA) and the Famine Early Warning Systems Network (FEWS NET). These maps represent the first operational spatially-explicit sub-seasonal to seasonal (S2S) estimates of groundwater use and demand in the literature. Knowledge of historical and forecasted groundwater use is anticipated to improve decision-making and resource allocation for a range of early warning early action applications.
地下水是非洲之角干旱期间人们、牲畜和农业的重要水源。在这项工作中，在旱季之前，确定并预测了肯尼亚易受干旱影响地区高地下水利用和需求的区域。利用卫星数据和机器学习模型，将地下水利用的估计范围从 69 个现场传感器机械钻孔的监测网络扩展到该地区。这些传感器从 2017 年 6 月到 2021 年 9 月共贡献了 756 个站点月的观测数据，用于在大约每 3700 公里一个传感器的密度下进行模型构建和验证。一组 19 个参数化算法的集合，通过包括卫星衍生降水、地表水可用性、植被指数、水文陆地表面建模和站点特征等特征来提供信息，将高地下水泵利用的区域进行二分法处理。考虑了三种地下水基础设施高需求的操作定义：1）泵的机械运行时间超过一天的四分之一（6 小时以上），并且每天的人均提取量表明 2）生活用水需求（35 升以上），3）包括牲畜在内的中间需求（75 升以上）。从 2017 年到 2020 年，提供了局部地下水利用和需求的网格化插值，并对 2021 年旱季（2021 年 6 月至 9 月）进行了预测。对满足生活和中间用水需求的每日泵运行时间和每日体积提取的当代估计进行交叉验证的准确性分别为 68%、69%和 75%。对于每个地下水利用定义，预测都通过外部验证，其准确性至少为 56%、70%或 72%。地下水图可供利益相关者使用，包括肯尼亚国家干旱管理局（NDMA）和饥荒预警系统网络（FEWS NET）。这些地图代表了文献中首次对地下水利用和需求进行的季节性到季节性（S2S）的操作空间估计。预计对历史和预测地下水利用的了解将改善一系列早期预警早期行动应用程序的决策制定和资源分配。
