Managed Aquifer Recharge as a Low-Regret Measure for Climate Change Adaptation: Insights from Los Arenales, Spain

Abstract

In view of heightened climate change (CC), adaptation strategies are imperative to diminish the impacts on social and environmental assets. Two approaches are commonly used to formulate adaptation measures, namely bottom-up and top-down, each with inherited limitations. A sound bridge between both approaches is low-regret adaptive measures, which result in win-win scenarios, as they provide solutions to current pressures and contribute to building CC adaptive capacity. Managed aquifer recharge (MAR) is a term that includes a series of techniques that enhance groundwater storage for later use or environmental purposes. MAR is often mentioned in the literature as a CC adaptation measure. Nonetheless, few examples explicitly prove this point. We show through the Los Arenales MAR systems (Central Spain) that MAR is a low-regret CC adaptive measure. We evaluate a series of social and environmental challenges that MAR systems contribute to solving, as well as their attributes that diminish the expected impacts of CC in the study area. MAR in the Los Arenales groundwater body has resulted in an overall increase in groundwater levels; a reduction in groundwater pumping energy and costs and CO2 emissions; restoration of a surface water body; improvement in rural population indexes; and enhanced groundwater demand control and CC adaptive capacity through irrigation communities. To cope with CC, the Los Arenales MAR systems can be operated even if decreasing streamflow precludes the use of river water surpluses; they provide surface storage volume to mitigate flooding; and they decrease the impacts of droughts and water scarcity. This research proves that MAR is a water management tool capable of providing solutions to several pressures simultaneously in the present and future, an attribute particularly useful when dealing with adaptation gaps in developing countries, rural areas, or regions lacking long-term climatic data.