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The Mississippi Embayment aquifer is one of the largest alluvial groundwater aquifers in the United States. It is being excessively used, located along the lower Mississippi River covering approximately 202,019 km²(78,000 square miles). Annual average groundwater depletion in the aquifer has been estimated at 5.18 billion cubic meters (Gm³) (4.2 million acre‐feet) in 1981–2000. However, since 2000, annual groundwater depletion has increased abruptly to 8 Gm³(2001–2008). In recent years, multi‐state efforts have been initiated to improve the Mississippi Embayment aquifer sustainability. One management strategy of interest for preserving groundwater resources is managed aquifer recharge (MAR). In this study, we evaluate the impact of different MAR scenarios on land and water use decisions and the overall groundwater system using an economic model able to assess profitability of crop and land use decisions coupled to the Mississippi Embayment Regional Aquifer Study (MERAS) hydrogeologic model. We run the coupled model for 60 years by considering the hydrologic conditions from the MERAS model for the years 2002–2007 and repeating them 10 times. We find MAR is not economically attractive when the water cost is greater than $0.05/m³. Groundwater storage is unlikely to improve when relying solely on MAR as groundwater management strategy but rather should be implemented jointly with other groundwater conservation policies.

 

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.

 

Despite the growing focus on understanding how to build resilience, the interaction between resilience and equity, particularly in the context of power asymmetries like those in communities reliant on resource-based industries, or resource-based communities, is not well understood. Here we present a stylized dynamical systems model of asymmetric resource access and control in resource-based communities that links industrial resource degradation, community well-being, and migration in response to economic and resource conditions. The model reveals a mechanism of collapse due to these dynamics in which over-extraction and resource degradation trigger irreversible population decline. Regulating resource extraction can increase resilience (in the sense of persistence) while also shifting the sustainable equilibrium and the implications for equity. Resilience does not guarantee equity at equilibrium, and this misalignment is more pronounced in the transient interactions between short term equity and long term resilience. The misalignment between resilience and equity demonstrates how equity considerations change the policy design process in important ways.

 

California’s Central Valley is one of the world’s most productive agricultural regions. Its high-value fruit, vegetable, and nut crops rely on surface water imports from a vast network of reservoirs and canals as well as groundwater, which has been substantially overdrafted to support irrigation. The region has undergone a shift to perennial (tree and vine) crops in recent decades, which has increased water demand amid a series of severe droughts and emerging regulations on groundwater pumping. This study quantifies the expansion of perennial crops in the Tulare Lake Basin, the southern region of the Central Valley with limited natural water availability. A gridded crop type dataset is compiled on a 1 mi²spatial resolution from a historical database of pesticide permits over the period 1974–2016 and validated against aggregated county-level data. This spatial dataset is then analyzed by irrigation district, the primary spatial scale at which surface water supplies are determined, to identify trends in planting decisions and agricultural water demand over time. Perennial crop acreage has nearly tripled over this period, and currently accounts for roughly 60% of planted area and 80% of annual revenue. These trends show little relationship with water availability and have been driven primarily by market demand. From this data, we focus on the increasing minimum irrigation needs each year to sustain perennial crops. Results indicate that under a range of plausible future regulations on groundwater pumping ranging from 10% to 50%, water supplies may fail to consistently meet demands, increasing losses by up to 30% of annual revenues. More broadly, the datasets developed in this work will support the development of dynamic models of the integrated water-agriculture system under uncertain climate and regulatory changes to understand the combined impacts of water supply shortages and intensifying irrigation demand.

 

Abstract. The ability to adapt to social and environmental change is an increasinglycritical feature of environmental governance. However, an understandingof how specific features of governance systems influence how theyrespond to change is still limited. Here we focus on how system featureslike diversity, heterogeneity, and connectedness impact stability,which indicates a system's capacity to recover fromperturbations. Through a framework that combines agent-basedmodeling with “generalized”dynamical systems modeling, we model the stability of thousandsof governance structures consisting of groups of resource users and non-government organizations interacting strategically with the decision centers that mediate their access to a shared resource. Stabilizing factors include greater effortdedicated to venue shopping and a greater fraction of non-governmentorganizations in the system. Destabilizing factors include greaterheterogeneity among actors, a greater diversity of decision centers,and greater interdependence between actors. The results suggest thatwhile complexity tends to be destabilizing, there are mitigating factorsthat may help balance adaptivity and stability in complex governance. This study demonstrates the potential inapplying the insights of complex systems theory to managing complexand highly uncertain human–natural systems in the face of rapid socialand environmental change. 

Precipitation occurs in two basic forms defined as liquid state and solid state. Different from rain-fed watershed, modeling snow processes is of vital importance in snow-dominated watersheds. The seasonal snowpack is a natural water reservoir, which stores snow water in winter and releases it in spring and summer. The warmer climate in recent decades has led to earlier snowmelt, a decline in snowpack, and change in the seasonality of river flows. The Soil and Water Assessment Tool (SWAT) could be applied in the snow-influenced watershed because of its ability to simultaneously predict the streamflow generated from rainfall and from the melting of snow. The choice of parameters, reference data, and calibration strategy could significantly affect the SWAT model calibration outcome and further affect the prediction accuracy. In this study, SWAT models are implemented in four upland watersheds in the Tulare Lake Basin (TLB) located across the Southern Sierra Nevada Mountains. Three calibration scenarios considering different calibration parameters and reference datasets are applied to investigate the impact of the Parallel Energy Balance Model (ParBal) snow reconstruction data and snow parameters on the streamflow and snow water-equivalent (SWE) prediction accuracy. In addition, the watershed parameters and lapse rate parameters-led equifinality is also evaluated. The results indicate that calibration of the SWAT model with respect to both streamflow and SWE reference data could improve the model SWE prediction reliability in general. Comparatively, the streamflow predictions are not significantly affected by differently lumped calibration schemes. The default snow parameter values capture the extreme high flows better than the other two calibration scenarios, whereas there is no remarkable difference among the three calibration schemes for capturing the extreme low flows. The watershed and lapse rate parameters-induced equifinality affects the flow prediction more (Nash-Sutcliffe Efficiency (NSE) varies between 0.2–0.3) than the SWE prediction (NSE varies less than 0.1). This study points out the remote-sensing-based SWE reconstruction product as a promising alternative choice for model calibration in ungauged snow-influenced watersheds. The streamflow-reconstructed SWE bi-objective calibrated model could improve the prediction reliability of surface water supply change for the downstream agricultural region under the changing climate.