Combining groundwater numerical modelling and social sciences to assess water access in developing countries rural environments

Abstract

<p>Shallow groundwater is usually more accessible than surface water in remote and rural areas due to the infrastructure cost to collect and allocate surface water on dispersed communities. However, the absence of a proper hydrogeological characterization of the aquifer system added to the lack of groundwater infrastructure and maintenance, technical capacity, and governance has not allowed the development of sustainable use of local groundwater resources in different territories worldwide.</p><p>We propose an interdisciplinary approach to determine the risk of a household experiencing water shortage due to depletion of the aquifer, degradation of the water quality, not access to the water point, or sustainable functionality. Three main parameters were defined: Closeness (determined by geographical parameters and easily computed using GIS), Availability (determined by hydrogeological parameters that can be assessed from a groundwater model), and Sustainability (differentiating between software functionality and hardware functionality (Bonsor, MacDonald, Casey, Carter, & Wilson, 2018), the former analyzed through Multiple Factor Analysis. Each of these three factors range between 0 and 1, and their product provides an index that can be used to map the risk of individual households.</p><p>An application case in Kwale County, southeast coast of Kenya, is presented, where community handpumps are the main water supply system. The novelty of the index relies on the combination of groundwater model outputs with household data, which allows the generation of time-dependent risk indexes that can be calculated for several scenarios depending on the data available. In this case, we present three scenarios, one involving the potential malfunctioning of a percentage of the existing handpumps, and two other ones dealing with extreme climate scenarios, all of them designed to test the resilience and applicability of the proposed index and their applicability for decision making.</p><p>Acknowledgements: This work was funded by the Centre of Cooperation for Development of the Universitat Politècnica de Catalunya. We want to thank UPGRO and Gro For Good projects for their support and collaboration in acquiring available data.</p><p>