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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]

Winter cover crops could contribute to more sustainable agricultural production and increase resiliency to climate change; however, their adoption remains low in California. This paper seeks to understand barriers to winter cover crop adoption by monetizing their long-term economic and agronomic impacts on farm profitability in two of California's specialty crop systems: processing tomatoes and almonds. Our modeling effort provides a present, discounted valuation of the long-term use of winter cover crops through a cost-benefit analysis. A net present value model estimates the cumulative economic value of this practice. We then explore how the long-term trade-offs associated with winter cover crops can affect an operation's profits under a spectrum of hypothetical changes in California's agricultural landscape. Our analysis sheds light on the barriers to adoption by reporting benefit-cost ratios that indicate profitability across several scenarios; however, benefits and costs accrue differently over time and with long planning horizons. At the same time, a small portion of gained benefits are external to the grower. Findings from this study reveal that winter cover crops in California can be profitable in the long-term, but the extent of profit depends on the cropping system, extent of irrigation savings due to improved soil function, access to financial subsidies and climate change. Winter cover crops can return positive net benefits to growers who have flexible contractual obligations, can wait for the long-term return on investment and manage cover crops as closely as cash crops. This analysis contributes to the study of conservation agriculture practices by explaining possible reasons for low adoption through an economic valuation of the implications of soil management choices and policy counterfactuals. • Net present value models were used to understand winter cover crop adoption in California. • Winter cover cropping can increase baseline profits in specialty crop systems sometimes. • The value of winter cover crops accrues over many planting seasons. • Reduced irrigation and increased subsidies can increase adoption of cover crops. [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]

Tropical cyclones provide precipitation in continental regions. These natural events can bring rainfall due to the direct impact or close approximation to inland regions (without coast) of Mexico. In recent years, the total precipitation in arid and semi-arid regions of Mexico has been modified due to the increase in the number of hurricanes originated in the Atlantic and Pacific oceans. For the period of analysis (1949 to 2013), this study estimated the share of the total number of tropical cyclones that impacted Mexico's coast from the Atlantic and Pacific Ocean, 6.28% and 8.64, respectively. For the same period, only 10 tropical cyclones directly impacted the Conchos river basin and 142 had a close trajectory to the Conchos river basin (less than 550 kilometers). In summary, there is an increase in the frequency of tropical cyclones generated in the Pacific Ocean. [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]

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]

Substrate facies monitoring is critical for the understanding of fluvial geomorphologic and ecohydraulic patterns and processes. However, direct substrate measurement is time-consuming and subjected to data sparsity because of small sample, size, and limited data collections within an area of interest, which make it difficult to capture facies patterns. Most new experimental studies focus on mapping substrate based on median grain size of a specific grain size class using automatic or semiautomatic photosieving techniques. This study aimed to develop and apply a method to accurately predict size-mixture facies patterns on exposed riverbeds with minimal ground truth plots (100) using airborne lidar and machine learning. The selected testbed river was a 37.5-km stretch of the regulated lower Yuba River in California, USA, mapped at sub-meter resolution in 2017. First, we designed a grid-by-point grain size sampling method and binned grain sizes into representative mixtures, such as fine or large gravel, to assign subaerial facies labels. Second, we classified facies based on a multivariate cluster analysis. Third, we generated 15 lidar-derived topographic and spectral predictors. Six distinct size-mixture facies were identified from field data and a seventh, pure sand facies, from UAV data. A random forest predictive model with an 86% 10-fold cross-validation accuracy was applied to produce a facies map at the 1.54 m pixel scale. The detrended elevation was identified as the most important variable for predicting facies spatial patterning, followed by baseflow, wetted area proximity, and green lidar intensity. We conclude that machine learning combined with intensity lidar data is highly effective for distinguishing mixed classes of substrates. Ultimately, the new substrate mixture-binning approach also provides novel insights into the arrangement of river sediment facies patterns. [Display omitted] • Cluster analysis of field-plot grain-size data yields mixed-size sediment facies to train machine learning. • A supervised random forest machine learning model accurately predicts subaerial facies using only airbone lidar. • The top lidar predictor is detrended elevation. [ABSTRACT FROM AUTHOR]