Frontline communities of California experience disproportionate social, economic, and environmental injustices, and climate change is exacerbating the root causes of inequity in those areas. Yet, climate adaptation and mitigation strategies often fail to meaningfully address the experience of frontline community stakeholders. Here, we present three challenges, three errors, and three solutions to better integrate frontline communities' needs in climate change research and to create more impactful policies. We base our perspective on our collective firsthand experiences and on scholarship to bridge local knowledge with hydroclimatic research and policymaking. Unawareness of local priorities (Challenge 1) is a consequence of Ignoring local knowledge (Error 1) that can be, in part, resolved with Information exchange and expansion of community-based participatory research (Solution 1). Unequal access to natural resources (Challenge 2) is often due to Top-down decision making (Error 2), but Buffer zones for environmental protection, green areas, air quality, and water security can help achieve environmental justice (Solution 2). Unequal access to public services (Challenge 3) is a historical issue that persists because of System abuse and tokenism (Error 3), and it may be partially resolved with Multi-benefit projects to create socioeconomic and environmental opportunities within frontline communities that include positive externalities for other stakeholders and public service improvements (Solution 3). The path forward in climate change policy decision-making must be grounded in collaboration with frontline community members and practitioners trained in working with vulnerable stakeholders. Addressing co-occurring inequities exacerbated by climate change requires transdisciplinary efforts to identify technical, policy, and engineering solutions.
Assessing impacts on coupled food-water systems that may emerge from water policies, changes in economic drivers and crop productivity requires an understanding of dominant uncertainties. This paper assesses how a candidate groundwater pumping restriction and crop prices, crop yields, surface water price, electricity price, and parametric uncertainties shape economic and groundwater performance metrics from a coupled hydro-economic model (HEM) through a diagnostic global sensitivity analysis (GSA). The HEM used in this study integrates a groundwater depth response, modeled by an Artificial Neural Network (ANN), into a calibrated Positive Mathematical Programming (PMP) agricultural production model. Results show that in addition to a groundwater pumping restriction, performance metrics are highly sensitive to prices and yields of perennial tree crops. These sensitivities become salient during dry years when there is a higher reliance on groundwater. Furthermore, results indicate that performing a GSA for two different water baseline conditions used to calibrate the production model, dry and wet, result in different sensitivity indices magnitudes and factor prioritization. Diagnostic GSA results are used to understand key factors that affect the performance of a groundwater pumping restriction policy. This research is applied to the Wheeler Ridge-Maricopa Water Storage District located in Kern County, California, region reliant on groundwater and vulnerable to surface water shortages.
<p>Groundwater is a major source for irrigated agriculture yet often managed unsustainably. Groundater overdraft compromises future viability of irrigated agriculture, water for cities, streams baseflows and groundwater dependent ecosystems. The recent 2012-2016 California drought heightened the role of groundwater as a buffer resource and catalyzed the 2014 Sustainable Groundwater Management Act (SGMA). Under this regulation, by 2040 all groundwater basins need to achieve balance in recharge and extractions. Groundwater overdraft in California&#8217;s Central Valley accounts for roughly 15 percent of the total agricultural use. The greater Kern region within California&#8217;s Central Valley, the most productive region for fruits, nuts and vegetables in the USA, suffers from chronic overdraft and demand hardening due to a rapid increase in perennial crops. This paper presents an integrated multi-objective framework to analyze agricultural production in the greater Kern region as it achieves groundwater sustainability at the irrigation district level by 2040. The model employs a programing model approach with a selection of open access components to predict cropping decisions that maximize net economic returns, using a 1997-2015 calibration period. The agricultural production model bundles with a groundwater module based on the Integrated Water Model Flow model (IWFM) from the California Department of Water Resources to meet sustainability objectives. &#160;Modeling scenarios include SGMA groundwater restrictions, water shortages under climate change and environmental regulations, with and without markets, managed aquifer recharge and infrastructure enhancements. Results show that more flexible water allocations using markets and managed recharge can help mitigate the economic impacts from SGMA and also improve prospects for managing financial risk under economic uncertainty at the irrigation district level.</p>
The modeling of coupled food-water systems to represent the effect of water supply variability as well as shocks that may emerge from changes in policies, economic drivers, and productivity requires an understanding of dominant uncertainties. These uncertainties cascade into forecasts of impacts of water management policies, such as groundwater pumping restrictions. This paper assesses how parametric, crop price, crop yields, surface water price, and electricity price uncertainties shape hydro-economic model estimates for agricultural production through a diagnostic global sensitivity analysis (GSA).The diagnostic GSA explores how the uncertainties in combination with a candidate groundwater pumping restriction influence three metrics of concern: total economic revenue, total land use and groundwater depth change. The hydro-economic model integrates a Groundwater Response Function (GRF) by integrating an Artificial Neural Network (ANN) into a calibrated Positive Mathematical Programming (PMP) production model for the Wheeler Ridge-Maricopa Water Storage District located in Kern County, California. Our results show that in addition to groundwater pumping restriction, performance metrics of the system are highly sensitive to prices and yields particularly of profitable crops. These sensitivities become salient during dry years when there is a higher reliance on groundwater.
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