Predicting groundwater vulnerability to pollution in countries with sparse data is difficult. Using Zimbabwe as a case study, this study presents a simple expert-based GIS model that incorporates seven natural and anthropogenic factors for modelling groundwater vulnerability at a national scale. The predicted groundwater vulnerability was tested against three water quality parameters: pH, total nitrates and Total Dissolved Solids (TDS). The agreement between predicted vulnerability and measured groundwater quality was reasonably high for pH and total nitrates (R² ≥ 0.54, P < 0.05). For the dissolved solids, the relationship between vulnerability predicted by the model and measured TDS was weak (R² = 0.33). Model predictions indicate spatial variability of groundwater vulnerability to pollution, with high pollution in Mazowe and Manyame catchments dominated by high urban land use and commercial agriculture. Moderately high vulnerability of groundwater to pollution was predicted for Mzingwane and Runde catchments, whereas the lowest vulnerability of groundwater to pollution were predicted for the lower Sanyati sub-catchment and Gwayi catchment. The advantage of the expert-based GIS model presented here is that it requires only a limited and widely available input data set. Hence, it can be used in other countries with limited hydrogeological data to generate results showing spatial variability of groundwater vulnerability to pollution to guide sustainable groundwater management. We, however, recommend further research with case studies focusing on the most vulnerable catchments such as Mazowe, and more extensive validation of parameters, especially TDS, to improve the proposed model.

在数据稀疏的国家很难预测地下水对污染的脆弱性。本研究以津巴布韦为例，提出了一个基于专家的简单GIS模型，该模型结合了七个自然和人为因素，可以在全国范围内对地下水脆弱性进行建模。针对三个水质参数测试了预测的地下水脆弱性：pH，总硝酸盐和总溶解固体（TDS）。预测脆弱性和测量的地下水质量之间的协议是相当高的pH值和总的硝酸盐（R ²  ≥0.54，P  <0.05）。对于溶解的固体，模型预测的易损性与测得的TDS之间的关系很弱（R ² = 0.33）。模型预测表明，地下水对污染的脆弱性具有空间变异性，其中马佐韦和曼雅梅流域的高污染主要是城市土地利用和商业农业的支配。预测Mzingwane和Runde流域的地下水对污染的敏感性较高，而较低的Sanyati子流域和Gwayi流域的地下水对污染的敏感性最低。此处介绍的基于专家的GIS模型的优点在于，它仅需要有限的且广泛可用的输入数据集。因此，它可以在水文地质数据有限的其他国家使用，以产生显示地下水对污染的脆弱性的空间变异性的结果，以指导可持续的地下水管理。但是，我们