Effects of the Structure of Water Rights on Agricultural Production During Drought: A Spatiotemporal Analysis of California's Central Valley

Abstract

California's Central Valley region has been called the “bread‐basket” of the United States. The region is home to one of the most productive agricultural systems on the planet. Such high levels of agricultural productivity require large amounts of fresh water for irrigation. However, the long‐term availability of water required to sustain high levels of agricultural production is being called into question following the latest drought in California. In this paper, we use Bayesian multilevel spatiotemporal modeling techniques to examine the influence of the structure of surface water rights in the Central Valley on agricultural production during the recent drought. California is an important place to study these dynamics as it is the only state to recognize the two dominant approaches to surface water management in the United States: riparian and appropriative rights. In this study, Bayesian spatiotemporal modeling is employed to account for spatial processes that have the potential to influence the effects of water right structures on agricultural production. Results suggest that, after accounting for spatiotemporal dependencies in the data, seniority in surface water access significantly improves crop health and productivity on cultivated lands but does not independently affect the ability to maintain cultivated extent. In addition, agricultural productivity in watersheds with more junior surface water rights shows less sensitivity to cumulative drought exposure than other watersheds, however the extent of cultivation in these same watersheds is relatively more sensitive to cumulative drought exposure.

Trends and Challenges in Irrigation Scheduling in the Semi-Arid Area of Spain

“…Hence, during droughts periods, reductions in water allocations affect "new" farmers first [82]. Accordingly, in a study carried out in California's Central Valley on the effects of water rights on agricultural production during drought periods, it was shown that watersheds whose farmers have more senior water rights have on average better agricultural productivity outcomes than watersheds with more junior rights [83].…”

Expanding the Scope and Foundation of Sociohydrology as the Science of Coupled Human‐Water Systems

“…Human activities from land use change and irrigation to dam building and water abstraction alter the propagation of drought through the hydrological cycle (Van Loon et al, ). In turn, drought impacts human activities, driving water conservation, shifting withdrawals from surface to groundwater, and prompting creative policy and infrastructure responses (Gonzales & Ajami, ; Gonzales et al, ; Marston & Konar, ; Nelson & Burchfield, ). The papers in this Special Section characterize and quantify both drought propagation and response and the influence of climatic and social factors in shaping these processes.…”

“…While Apurv et al () assess the climatic controls on drought propagation, Nelson and Burchfield (), Marston and Konar (), Gunda et al (), and O'Keeffe et al () evaluate the influence of social factors in agricultural systems during drought. Both Nelson and Burchfield () and Marston and Konar () investigate responses to the 2012–2014 drought in California's Central Valley. Nelson and Burchfield () developed a spatiotemporal Bayesian model to assess the effect of water rights structures on agricultural production during drought.…”

“…Both Nelson and Burchfield () and Marston and Konar () investigate responses to the 2012–2014 drought in California's Central Valley. Nelson and Burchfield () developed a spatiotemporal Bayesian model to assess the effect of water rights structures on agricultural production during drought. They find that watersheds with more senior water rights are associated with higher productivity during drought but that while junior water rights are more likely to fallow, their production is less sensitive to drought.…”

“…For example, while correlation‐based methods (e.g., OLS regression, granger causality, and Bayesian inference) can identify linear couplings and their timescales, they cannot unambiguously identify the direction when relationships are asymmetric (Ruddell & Kumar, ). In this issue, the authors apply a range of statistical methods such as structural equation modeling (Breyer et al, ), hierarchical Bayesian modeling (Nelson & Burchfield, ), and regression with instrumental variables (Dang & Konar, ) to address these challenges. While Gunda et al (), Dadson et al (), Sung et al (), and others continue the tradition of applying system dynamics modeling to gain qualitative understanding of system behavior, they advance this approach through their application of human system dynamics.…”