Sustainability of irrigated agriculture in the San Joaquin Valley, California

Schoups, Gerrit; Hopmans, Jan W.; Young, Charles A.; Vrugt, Jasper A.; Wallender, W. W.; Tanji, Ken K.; Panday, Sorab

doi:10.1073/pnas.0507723102

Cited by 239 publications

(144 citation statements)

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“…equation (A7)). If S was insufficient to fulfill GIR, we only assumed additional water Wd a (mm) to be withdrawn from the neighboring cell with the largest upstream area to represent conveyance systems and transportation of water by trucks over limited distances (i.e., large-scale interbasin transfers or river diversions like in California [e.g., Schoups et al, 2005] are not taken into account). The amount of blue water Irr lim (mm) brought to the field was thus given by:…”

Section: A55 Simulations Of Limited and Potential Irrigationmentioning

confidence: 99%

Agricultural green and blue water consumption and its influence on the global water system

Rost

Gerten

Bondeau

et al. 2008

Water Resources Research

763

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[1] This study quantifies, spatially explicitly and in a consistent modeling framework (Lund-Potsdam-Jena managed Land), the global consumption of both ''blue'' water (withdrawn for irrigation from rivers, lakes and aquifers) and ''green'' water (precipitation) by rainfed and irrigated agriculture and by nonagricultural terrestrial ecosystems. In addition, the individual effects of human-induced land cover change and irrigation were quantified to assess the overall hydrological impact of global agriculture in the past century. The contributions to irrigation of nonrenewable (fossil groundwater) and nonlocal blue water (e.g., from diverted rivers) were derived from the difference between a simulation in which these resources were implicitly considered (IPOT) and a simulation in which they were neglected (ILIM ), whereas irrigation increased evapotranspiration by up to 1.9% and decreased discharge by 0.5% at least (IPOT, 1971(IPOT, -2000. The diverse water fluxes displayed considerable interannual and interdecadal variability due to climatic variations and the progressive increase of the global area under cultivation and irrigation.Citation: Rost, S., D. Gerten, A. Bondeau, W. Luncht, J. Rohwer, and S. Schaphoff (2008), Agricultural green and blue water consumption and its influence on the global water system, Water Resour. Res., 44, W09405,

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Section: A55 Simulations Of Limited and Potential Irrigationmentioning

confidence: 99%

Agricultural green and blue water consumption and its influence on the global water system

Rost

Gerten

Bondeau

et al. 2008

Water Resources Research

763

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“…Typically, groundwater salinity increases with depth (16). Fresh groundwater resources occurring at relatively shallow depths ( 1,000 m) have been studied extensively in terms of groundwater availability (17)(18)(19)(20)(21)(22) and quality (23)(24)(25)(26). In California, water quality data from over 200,000 groundwater wells are available from the State Water Resources Control Board Groundwater Ambient Monitoring and Assessment Program (27).…”

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confidence: 99%

Salinity of deep groundwater in California: Water quantity, quality, and protection

Kang

Jackson

2016

Proc. Natl. Acad. Sci. U.S.A.

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Deep groundwater aquifers are poorly characterized but could yield important sources of water in California and elsewhere. Deep aquifers have been developed for oil and gas extraction, and this activity has created both valuable data and risks to groundwater quality. Assessing groundwater quantity and quality requires baseline data and a monitoring framework for evaluating impacts. We analyze 938 chemical, geological, and depth data points from 360 oil/gas fields across eight counties in California and depth data from 34,392 oil and gas wells. By expanding previous groundwater volume estimates from depths of 305 m to 3,000 m in California’s Central Valley, an important agricultural region with growing groundwater demands, fresh [<3,000 ppm total dissolved solids (TDS)] groundwater volume is almost tripled to 2,700 km3, most of it found shallower than 1,000 m. The 3,000-m depth zone also provides 3,900 km3 of fresh and saline water, not previously estimated, that can be categorized as underground sources of drinking water (USDWs; <10,000 ppm TDS). Up to 19% and 35% of oil/gas activities have occurred directly in freshwater zones and USDWs, respectively, in the eight counties. Deeper activities, such as wastewater injection, may also pose a potential threat to groundwater, especially USDWs. Our findings indicate that California’s Central Valley alone has close to three times the volume of fresh groundwater and four times the volume of USDWs than previous estimates suggest. Therefore, efforts to monitor and protect deeper, saline groundwater resources are needed in California and beyond.

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“…evolutionary search ͉ multiple objectives ͉ optimization problems ͉ Pareto front E volutionary optimization is a subject of intense interest in many fields of study, including computational chemistry, biology, bioinformatics, economics, computational science, geophysics, and environmental science (1)(2)(3)(4)(5)(6)(7)(8). The goal is to determine values for model parameters or state variables that provide the best possible solution to a predefined cost or objective function, or a set of optimal tradeoff values in the case of two or more conflicting objectives.…”

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confidence: 99%

Improved evolutionary optimization from genetically adaptive multimethod search

Vrugt

Robinson

2007

Proc. Natl. Acad. Sci. U.S.A.

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515

379

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In the last few decades, evolutionary algorithms have emerged as a revolutionary approach for solving search and optimization problems involving multiple conflicting objectives. Beyond their ability to search intractably large spaces for multiple solutions, these algorithms are able to maintain a diverse population of solutions and exploit similarities of solutions by recombination. However, existing theory and numerical experiments have demonstrated that it is impossible to develop a single algorithm for population evolution that is always efficient for a diverse set of optimization problems. Here we show that significant improvements in the efficiency of evolutionary search can be achieved by running multiple optimization algorithms simultaneously using new concepts of global information sharing and genetically adaptive offspring creation. We call this approach a multialgorithm, genetically adaptive multiobjective, or AMALGAM, method, to evoke the image of a procedure that merges the strengths of different optimization algorithms. Benchmark results using a set of well known multiobjective test problems show that AMALGAM approaches a factor of 10 improvement over current optimization algorithms for the more complex, higher dimensional problems. The AMALGAM method provides new opportunities for solving previously intractable optimization problems.evolutionary search ͉ multiple objectives ͉ optimization problems ͉ Pareto front E volutionary optimization is a subject of intense interest in many fields of study, including computational chemistry, biology, bioinformatics, economics, computational science, geophysics, and environmental science (1-8). The goal is to determine values for model parameters or state variables that provide the best possible solution to a predefined cost or objective function, or a set of optimal tradeoff values in the case of two or more conflicting objectives. However, locating optimal solutions often turns out to be painstakingly tedious, or even completely beyond current or projected computational capacity (9).Here, we consider a multiobjective minimization problem, with n decision variables (parameters) and m objectives:, where x denotes the decision vector, and y is the objective space. We restrict attention to optimization problems in which the parameter search space X, although perhaps quite large, is bounded: x ϭ (x 1 , . . . , x n ) ʦ X. The presence of multiple objectives in an optimization problem gives rise to a set of Pareto-optimal solutions, instead of a single optimal solution (10, 11). A Pareto-optimal solution is one in which one objective cannot be further improved without causing a simultaneous degradation in at least one other objective. As such, they represent globally optimal solutions to the tradeoff problem.Numerous approaches have been proposed to efficiently find Pareto-optimal solutions for complex multiobjective optimization problems (12-15). In particular, evolutionary algorithms have emerged as the most powerful approach for solving search and optimization pr...

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Sustainability of irrigated agriculture in the San Joaquin Valley, California

Cited by 239 publications

References 20 publications

Agricultural green and blue water consumption and its influence on the global water system

Agricultural green and blue water consumption and its influence on the global water system

Salinity of deep groundwater in California: Water quantity, quality, and protection

Improved evolutionary optimization from genetically adaptive multimethod search

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