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California farmers who use reduced-disturbance tillage and winter cover cropping can boost production and improve soil health. However, some farmers are hesitant to try these conservation practices due to uncertainty about whether planting winter cover crops will deplete soil moisture in already drought-stricken regions. Our study addresses these concerns by looking at how long-term reduced-disturbance tillage and winter cover cropping, compared to fallowed soils with standard tillage, affected soil moisture. Although we found a statistical difference in total soil water content, the difference was only about 0.3 inches of water per foot of soil. On average, the soil water content of the top 0-96 inches was highest for the reduced-disturbance fields with winter cover crops. This was especially evident during our driest field season, from November 1, 2017, to March 15, 2018, when cumulative rainfall was only 1.9 inches. Our findings show that winter cover cropping and reduced-disturbance tillage can improve soil without depleting soil water levels in row crops. [ABSTRACT FROM AUTHOR]

Water resources management is threatened by climatic, economic, and political pressures, and these challenges are on particular display in Latin America and the Caribbean. To assess the region's ability to manage water resources, we conducted an unprecedented literature review of over 20,000 multilingual research articles using machine learning and an understanding of the socio-hydrologic landscape. Results reveal that the region's vulnerability to water-related stresses, and drivers such as climate change, is compounded by research blind spots in niche topics (reservoirs and risk assessment) and sub-regions (Caribbean nations), and by its reliance on an individual country (Brazil). A regional bright spot, Brazil produces well-rounded water-related research but its regional dominance suggests that funding cuts there would impede scientifically-informed water management in the entire region. [ABSTRACT FROM AUTHOR]

Hydrologic and geomorphic classifications have gained traction in response to the increasing need for basin-wide water resources management. Regardless of the selected classification scheme, an open scientific challenge is how to extend information from limited field sites to classify tens of thousands to millions of channel reaches across a basin. To address this spatial scaling challenge, this study leverages machine learning to predict reach-scale geomorphic channel types using publicly available geospatial data. A bottom-up machine learning approach selects the most accurate and stable model among ~20,000 combinations of 287 coarse geospatial predictors, preprocessing methods, and algorithms in a three-tiered framework to (i) define a tractable problem and reduce predictor noise, (ii) assess model performance in statistical learning, and (iii) assess model performance in prediction. This study also addresses key issues related to the design, interpretation, and diagnosis of machine learning models in hydrologic sciences. In an application to the Sacramento River basin (California, USA), the developed framework selects a Random Forest model to predict 10 channel types previously determined from 290 field surveys over 108,943 two hundred-meter reaches. Performance in statistical learning is reasonable with a 61% median cross-validation accuracy, a sixfold increase over the 10% accuracy of the baseline random model, and the predictions coherently capture the large-scale geomorphic organization of the landscape. Interestingly, in the study area, the persistent roughness of the topography partially controls channel types and the variation in the entropy-based predictive performance is explained by imperfect training information and scale mismatch between labels and predictors. [ABSTRACT FROM AUTHOR]

Enhanced socioeconomic criteria and temporal changes in the topology of the system often require waste load reallocation (WLRA) in a river-reservoir system to sustain long-term water quality standards. In addition to climate and hydrological changes, hydrologic fragmentation and dam construction may significantly affect the waste-accepting capacity of the water body through changes in its physical, chemical, and even biological characteristics. Deterministic waste load allocation optimization designs are often bounded with a set of rigid constraints. These constraints do not allow any flexibility to account for uncertainties and the possibility of system failure. This paper presents a reliability-based waste load reallocation model in a complex river-reservoir system. We have linked a physical and surrogate simulation model with the Particle Swarm Optimization algorithm to present an efficient methodology for reallocating waste loads in a river-reservoir system with reliability constraints. Reliability requirements are addressed by different sets of constraints in three different formulations for the entire planning horizon. The problem, as defined, contains real and integer variables, and is formulated as a mixed-integer nonlinear programming problem. It finds the maximum values of monthly waste loads that may be discharged into the river-reservoir system under predefined reliability constraints. The surrogate model itself is refined using an online dynamic routine which makes it suitable for planning waste load allocation in multiperiod and high-dimensional system optimization under reliability-based water quality constraints. The proposed model is applied to the Karkheh river-reservoir system to illustrate its performance under various reliabilities. [ABSTRACT FROM AUTHOR]

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This paper describes a study conducted on a coastal agricultural area of southern Italy to assess the impending risk of aquifer degradation related to intensive groundwater pumping by farmers. It occurs that farmers rely on groundwater pumping to offset the inadequate irrigation delivery service provided by the local water management agency. The study area is intensively farmed by small land-holding growers with high-value horticultural crops, whose irrigation deliveries are supplied by a gravity-fed water distribution system operated by a local water users' organization. The soil and aquifer degradation hazards were appraised using a simplified environmental risk assessment procedure that allowed identifying the risk-generating processes, assessing the magnitude of impacts, and estimating the overall risks significance. The investigations revealed significant aquifer salinity increase during the past years. The stakeholders' perspective on agricultural water use was collected through field interviews, and was framed using a fuzzy cognitive map, which revealed the farmers' propensity to pump groundwater rather than rely on rotational deliveries from the surface distribution system. Finally, some preliminary risk mitigation options were appraised by exploring the growers' response to possible changes of irrigation deliveries by the water users' organization. The presented study consisted of multi-annual observations, data analysis, and modeling efforts, which jointly proved to be useful to analyze the main drivers to stakeholders' decisions and their long-term impacts on water resources use and management. [ABSTRACT FROM AUTHOR]

Groundwater pumping chronically exceeds natural recharge in many agricultural regions in California. A common method of recharging groundwater -- when surface water is available -- is to deliberately flood an open area, allowing water to percolate into an aquifer. However, open land suitable for this type of recharge is scarce. Flooding agricultural land during fallow or dormant periods has the potential to increase groundwater recharge substantially, but this approach has not been well studied. Using data on soils, topography and crop type, we developed a spatially explicit index of the suitability for groundwater recharge of land in all agricultural regions in California. We identified 3.6 million acres of agricultural land statewide as having Excellent or Good potential for groundwater recharge. The index provides preliminary guidance about the locations where groundwater recharge on agricultural land is likely to be feasible. A variety of institutional, infrastructure and other issues must also be addressed before this practice can be implemented widely. [ABSTRACT FROM AUTHOR]

Integrated management of quality and quantity of river-reservoir water can provide comprehensive information to manage river-reservoir water resources. However, high computational bottlenecks have prevented such management from being applicable in real-world systems. Accordingly, in the present study, we proposed a multi-objective optimization algorithm based on modular support vector regression (SVR) in which several small sub-SVR modules trained through an efficient adaptive procedure cooperate to solve a large-scale problem related to integrated management of quality and quantity of river-reservoir. The performance of the proposed approach was evaluated through an adaptive surrogate-based simulation-optimization (ASBSO) framework under reservoir selective withdrawal scheme (SWS) in Ilam integrated River-Reservoir. The ASBSO framework provided a set of non-dominated optimal solutions to alleviate Ilam River water quality standard violations, enhance Ilam Reservoir outflow water quality, and maximize the downstream water supply satisfaction. The analysis of the Pareto-front indicated that the implementation of MPWLA (multi-pollutant waste load allocation) programs at any level could alleviate the water quality problems in Ilam Reservoir. Furthermore, the reservoir water storage in the regular patterns to meet downstream water demands have resulted in water quality deteriorations in Ilam Reservoir. The results obtained from examining the proposed approach, showed that integrated river-reservoir system management improved water quality in the range from 7 to 28% at the checkpoints of the Gol-Gol Branch of Ilam River and in the range from 5 to 21% at Ilam Reservoir outflow. [Display omitted] • Integrated waste load allocation program & reservoir operation strategy are presented. • The eutrophication & water supply management model in river-reservoir is developed. • A multi-objective optimization algorithm based on adaptive modular SVR is applied. • A modular SVR, consists of several small sub-SVR modules, depicts water quality. • Water quality in the river-reservoir system and downstream water supply are enhanced. [ABSTRACT FROM AUTHOR]