Eignungsanalyse für Smart-SWS in Interpore Journal veröffentlicht
Abstract
The hydrological conditions a changing climate imposes require updated measures to address extreme water events sustainably. These must evolve from isolated solutions, such as water retention basins, to holistic management strategies that consider at least two situations simultaneously, such as floods and droughts. One strategy for sustainable groundwater management is managed aquifer recharge (MAR), which copes with decreasing groundwater levels by a targeted recharge of aquifers. Using high water as a recharge water source (Flood-MAR) can additionally cover the aspect of flood protection, resulting in a multi-beneficial solution for the region. This study evaluated the site suitability for subsurface floodwater storage schemes, which must focus more on assessing the existing aquifer characteristics and the specific flood dynamics in nearby rivers compared to known MAR schemes. Potential sites for underground flood storage are characterized by decreasing groundwater levels and, thus, frequent water scarcity, combined with eminent risks of flooding by a nearby river. Suitability, on the other hand, must take the aquifer, surface, and water source characteristics into account. In this study, we present a workflow for generating suitability maps for implementing subsurface floodwater storage systems with a geographic information system-based multi-criteria decision analysis (MCDA). The workflow was intentionally and exclusively based on publicly available data, was implemented in Python, and provided as open-source software. The resulting suitability maps spatially depict the feasibility of underground flood storage, and thus form the basis for the implementation planning of such projects. The approach was demonstrated for the administrative district of Swabia, Germany, where approximately 35% of the area was identified as suitable at varying levels. A sensitivity analysis of the assigned weights was applied to show the high robustness of the underlying data. The results highlighted the enormous potential of implementing such sustainable co-management schemes, which needs to be further concretized by on-site observations.
