Sustainable management of groundwater resources highly relies on the accurate estimation of recharge. However, accurate recharge estimation is a challenge, especially in data-scarce regions, as the existing models are data-intensive and require extensive parameterization. This study developed a process-based hydrologic model combining local and remotely sensed data for characterizing recharge in data-limited regions using a Basin Characterization Model (BCM). This study was conducted in Raya and Kobo Valleys, a semi-arid region in Northern Ethiopia, considering both the structural basin and the surrounding mountainous recharge areas. Climatic Research Unit monthly datasets for 1991 to 2020 and WaPOR actual evapotranspiration data were used. The model results show that the average annual recharge and surface runoff from 1991 to 2020 were 73 mm and 167 mm, respectively, with a substantial portion contributed along the front of the mountainous parts of the study area. The mountainous recharge occurred along and above the valleys as mountain-block and mountain-front recharge. The long-term estimates of the monthly recharge time series indicated that the water balance components follow the temporal pattern of rainfall amount. However, the relation of recharge to precipitation was nonlinearly related, showing the episodic nature of recharge in semi-arid regions. This study informed the spatial and temporal distribution of recharge and runoff hydrologic variables at fine spatial scales for each grid cell, allowing results to be summarized for various planning units, including farmlands. One third of the precipitation in the drainage basin becomes recharge and runoff, while the remaining is lost through evapotranspiration. The current study’s findings are vital for developing plans for sustainable management of water resources in semi-arid regions. Also, monthly groundwater withdrawals for agriculture should be regulated in relation to spatial and temporal recharge patterns. We conclude that combining scarce local data with global datasets and tools is a useful approach for estimating recharge to manage groundwater resources in data-scarce regions.

Changes in land use and land cover, water systems, and climate are inextricably linked, and their combined stresses have had severe impacts in many regions worldwide. Integrated adaptation planning can support adaptive capacity by helping institutions manage land and water resources at regional to local scales. Linkages between these stressors mean that planners are often faced with potential trade-offs, and how to couple social and environmental sustainability remains a key question. We explore these questions in California’s Central Coast, a region that is already experiencing serious water shortages, housing shortages, rapid expansion of perennial agriculture, and severe droughts that are projected to become worse with climate change. Linked models of land use change (the Land Use and Carbon + Water Simulator [LUCAS-W]), water resources (LUCAS-W), and climate (the Basin Characterization Model [BCM]) produced forecasts of exposure to regional changes at 270-m resolution. We worked with regional stakeholders to develop a matrix of nine vulnerability measures that assessed key sensitivities to these changes. Each vulnerability measure combined one of the three exposure projections with spatial datasets representing one of three sensitivity communities (agricultural, domestic, or ecological). We assessed how five scenarios of land-use and water management strategies under consideration by regional planners could provide institutional, top-down adaptive capacity, and whether there were trade-offs in sustainable development goals for these communities. We found that specific land and water management strategies could greatly reduce regional vulnerability, particularly programs to cap water extractions to sustainable levels. The most dramatic trade-off was between the strategy of water demand caps that increased risk of habitat loss and ecosystem preservation that increased water vulnerability. However, trade-offs were usually limited and spatially localized, suggesting local tailoring of the strategies we assessed could reduce them. Trade-offs were more frequent across exposure classes (land use vs. water vs. climate changes) rather than sensitivity classes (agricultural vs. domestic vs. ecological communities), suggesting win-win opportunities for natural resource management. Our vulnerability maps can inform prioritization efforts for local adaptation planning.

The effective characterization of landscape water balance components—evapotranspiration, runoff, recharge, and soil storage—is critical for understanding the integrated effects of the water balance on vegetation dynamics, water availability, and associated environmental responses to climate change. An improved parameterization of these components can improve assessments of landscape stress and provide useful insights for predicting and managing vegetation responses to climate change. Hydrology models typically are not able to address water availability below the mapped soil profile, but we refined a landscape hydrology model, the Basin Characterization Model, by balancing measures of actual evapotranspiration (AET) with modeled subsurface soil water holding capacity, including bedrock storage. The purpose of this study was to characterize the effective rooting depth (the depth of soil and bedrock storage required to support AET) for 35 native vegetation types in California in order to quantify soil and bedrock water use, which ranged from 0 to 3.1 m for most vegetation types, exceeding mapped soil depths. This resulted in the quantification of bedrock water use, increasing available water 67% over that calculated by mapped soils alone. We found that mid-elevation vegetation types with lower water and energy limitations have the highest evapotranspiration rates and deepest effective rooting depth. We also evaluated the resilience to drought with this more spatially realistic characterization of water and vegetation interactions.

The authors would like to make the following corrections about the published paper