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Managed aquifer recharge (MAR) can provide long‐term storage of excess surface water for later use. While decades of research have focused on the physical processes of MAR and identifying suitable MAR locations, very little research has been done on how to consider competing factors and tradeoffs in siting MAR facilities. This study proposes the use of a simulation‐optimization (SO) framework to map out a cost‐effectiveness frontier for MAR by combining an evolutionary algorithm with two objective functions that seek to maximize groundwater storage gains while minimizing MAR cost. We present the theoretical framework along with a real‐world application to California's Central Valley. The result of the SO framework is a Pareto front that allows identifying suitable MAR locations for different levels of groundwater storage gain and associated MAR project costs, so stakeholders can evaluate different choices based on cost, benefits, and tradeoffs of MAR sites. Application of the SO framework to the Central Valley shows groundwater can be recharged from high‐magnitude (95th percentile) flows at a marginal cost of $57 to $110 million per km3. If the 10 percent largest flows are recharged the total groundwater storage gain would double and the marginal costs would drop to between $30 and $50 million per km3. If recharge water is sourced from outside local basins (e.g., the Sacramento‐San Joaquin Delta), groundwater storage gain is approximately 25%–80% greater than can be achieved by recharging local flows, but the total cost is about 10%–15% higher because of additional lift cost. Key Points: Combining a simulation‐optimization framework with a groundwater model can identify suitable managed aquifer recharge locationsRecharging water from local rivers within the same basin is the most cost‐efficient managed aquifer recharge approach per unit of groundwater storage gainDiverting more water may not yield the most efficient groundwater storage gain due to higher transportation cost [ABSTRACT FROM AUTHOR]

Airborne electromagnetic (AEM) data can be inverted to recover models of the electrical resistivity of the subsurface; these, in turn, can be transformed to obtain models of sediment type. AEM data were acquired in Butte and Glenn Counties, California, USA to improve the understanding of the aquifer system. Around 800 line‐kilometers of high‐quality data were acquired, imaging to a depth of ∼300 m. We developed a workflow designed to obtain, from the AEM data, information about the large‐scale structure and heterogeneity of the aquifer system to better understand the vertical connectivity. Using six different inversions incorporating various forms of available information and posterior sampling of the recovered resistivity models, we produced 6,006 resistivity models. These models were transformed to models of sediment type and estimates of percentage of sand/gravel. Exploring the model space, containing the resistivity models and the derived models, allowed us to delineate the large‐scale structure of the aquifer system in a way that captures and communicates the uncertainty in the identified sediment type. The uncertainty increased, as expected, with depth, but also served to indicate, as areas of high uncertainty in sediment type, the location of both large‐scale and small‐scale interfaces between sediment types. A plan view map of the integrated percentage of sand/gravel, when compared to existing hydrographs, revealed the extent of lateral changes in vertical connectivity within the aquifer system throughout the study area. Plain Language Summary: In studying and managing groundwater systems, it can be very difficult to get the information needed about the subsurface. The airborne electromagnetic (AEM) method uses a helicopter to move a geophysical system over the land surface to collect this needed information. In this study we acquired ∼800 line‐kilometers of high‐quality AEM data in an area of Butte and Glenn Counties in the Central Valley of California, USA. Acquisition of these data allowed us to obtain three‐dimensional (3D) resistivity models covering the region from the ground surface to a depth of about 300 m. Working with descriptions from wells, we were able to transform the 3D resistivity models into 3D sediment‐type models. These models allowed us to map out the large‐scale structure of the groundwater system and better understand the vertical connectivity within the system. Because of fundamental limitations in the AEM method, we obtained many different resistivity models and corresponding sediment‐type models. Exploring these models allowed us to quantify the uncertainty in our interpretation of the data. This not only assisted in our interpretation, but it also communicated, to the local water agency, our confidence in our interpretation. Key Points: By incorporating various forms of available information in the inversion of airborne electromagnetic data, we designed a workflow that reduces and quantifies uncertainty in the derived sediment‐type modelsModels of sediment type reveal the large‐scale architecture of the aquifer systemModels of the percentage of sand/gravel, when combined with hydrographs, provide information about the lateral variability in vertical connectivity within the aquifer system [ABSTRACT FROM AUTHOR]

The southern Central Valley of California is one of the most productive agricultural regions in the world. Yet, decades of groundwater use beyond sustainable yield have left rural communities highly vulnerable to shortages and contamination of their drinking water supply. As state regulation begins to address these issues, a need exists to design adaptive and appropriate management systems to increase resilience of rural communities. Targeted managed aquifer recharge on agricultural land (Ag‐MAR) near rural communities is one such strategy that could potentially stabilize groundwater tables and maintain or improve groundwater quality in domestic supply wells. Here we present a geographic information system‐based multicriteria decision analysis that combines biophysical data (soils, land use, and surface water conveyance) with groundwater modeling and particle tracking to identify suitable agricultural land parcels for multibenefit groundwater recharge within well capture zones of 288 rural communities. Parcels are prioritized using a vulnerability index to change in groundwater supply, derived from well reliance and failures, pesticide applications, land subsidence, and socio‐economic data. Our analysis identifies 2,998 suitable land parcels for Ag‐MAR within the well capture zones of 149 of the 288 communities, of which 144 rely mainly on groundwater for drinking water. The majority of identified Ag‐MAR parcels serve communities ranked as having extreme or very high vulnerability to changes in groundwater supply. Our research produces new understanding of factors contributing to community vulnerability and resilience to changes in drinking water supply and can be used to discuss actions to help achieve a stable and high‐quality water supply. Key Points: Determined vulnerability to changes in groundwater supply for 288 rural communities in southern Central Valley, CaliforniaCombined geographic information system‐based multicriteria decision analysis with groundwater modeling and particle tracking to identify recharge locationsIdentified 2,998 agricultural land parcels for managed aquifer recharge within domestic well capture zone of 149 communities [ABSTRACT FROM AUTHOR]

GRACE satellite data are widely used to estimate groundwater storage (GWS) changes in aquifers globally; however, comparisons with GW monitoring and modeling data are limited. Here we compared GWS changes from GRACE over 15 yr (2002–2017) in 14 major U.S. aquifers with groundwater‐level (GWL) monitoring data in ~23,000 wells and with regional and global hydrologic and land surface models. Results show declining GWS trends from GRACE data in the six southwestern and south‐central U.S. aquifers, totaling −90 km3 over 15 yr, related to long‐term (5–15 yr) droughts, and exceeding Lake Mead volume by ~2.5×. GWS trends in most remaining aquifers were stable or slightly rising. GRACE‐derived GWS changes agree with GWL monitoring data in most aquifers (correlation coefficients, R = 0.52–0.95), showing that GRACE satellites capture groundwater (GW) dynamics. Regional GW models (eight models) generally show similar or greater GWS trends than those from GRACE. Large discrepancies in the Mississippi Embayment aquifer, with modeled GWS decline approximately four times that of GRACE, may reflect uncertainties in model storage parameters, stream capture, pumpage, and/or recharge rates. Global hydrologic models (2003–2014), which include GW pumping, generally overestimate GRACE GWS depletion (total: approximately −172 to −186 km3) in heavily exploited aquifers in southwestern and south‐central U.S. by ~2.4× (GRACE: −74 km3), underscoring needed modeling improvements relative to anthropogenic impacts. Global land surface models tend to track GRACE GWS dynamics better than global hydrologic models. Intercomparing remote sensing, monitoring, and modeling data underscores the importance of considering all data sources to constrain GWS uncertainties. Plain Language Summary: The major U.S. aquifers provide an ideal system to assess GRACE (Gravity Recovery and Climate Experiment) satellite data. We compared GRACE groundwater storage anomalies (GWSAs) with groundwater level anomalies (GWLAs) from ~23,000 wells and with groundwater storage (GWS) from regional and global models in 14 major U.S. aquifers. Results show large GWS declines from GRACE in southwestern (Central Valley and Arizona Alluvial Basins) and south‐central (Central and Southern High Plains and Texas) aquifers from multiyear droughts (5–15 yr). In contrast, GWS trends in aquifers throughout the rest of the U.S. showed mostly stable or rising values. Time series of GRACE GWSAs compared favorably with GWLAs from most aquifers, suggesting that GRACE data track groundwater (GW) dynamics. Regional GW models show similar or greater declines in GWS compared with GRACE data, with the largest discrepancy of a factor of four times greater modeled depletion in the Mississippi Embayment. Global hydrologic models show minimal storage dynamics but greatly overestimated GWS declines by ~2.4× in southwestern and south‐central aquifers with intensive irrigation compared with GRACE data. In contrast, global land surface models show similar GW dynamics to GRACE data but underestimated GWS declines in heavily exploited aquifers because these land surface models do not include human intervention. Key Points: GRACE satellites track GW storage in U.S. aquifers based on good agreement between GRACE and in situ GW level monitoring (23,000 wells)Regional models capture GRACE GW storage in most aquifers, except the Mississippi Embayment, which overestimates GRACE GWS trends by approximately four timesGlobal hydrologic models overestimate GW depletion compared with GRACE by ~2.4× in heavily depleted aquifers in the SW and SC United States [ABSTRACT FROM AUTHOR]