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The uncertainty of water resources availability is a growing problem in California as agricultural industrialization, population growth, and climate change affect water resources. The intense manipulation of the hydrological regime has led to the depletion of the water resources in the state and the subsequent use of various adaptive management strategies to cope with environmental conditions and social concerns. The historical imbalance between water pumping and replenishment in Pajaro Valley has led to overdrafted aquifers, seawater intrusion, and salinization. The objective of this study is to estimate the sustainable carrying capacity of agricultural land in Pajaro Valley while preventing groundwater overdraft. A groundwater box model was built and calibrated using historical data to represent current and future hydrology and water management strategies. An optimization model maximized the economic profit using the agricultural acreage as the decision variable with a set of constraints aimed at determining the sustainable carrying capacity of the groundwater basin. Model constraints include total land and water availability, crop acreage, agricultural water use, and historical demand. In the Pajaro Valley, agricultural operations must use less water more efficiently, which means changes in crop types, size of activities, and fallowing land in parts of the basin. Results of the optimal scenario over 25 years show a 15% reduction of total agricultural acreage, 8.5% reduction in food production, average profit loss of 4%, and a 79% reduction in aquifer depletion. This study provides an overall vision of what can be accomplished with coordinated land use planning using strategies that harmonize individual decisions and shared natural resources.

Leaching is an important aspect of irrigation water management, as it must be minimal to save available irrigation water resources, prevent shallow groundwater tables, and reduce nutrient loadings to the groundwater. However, at the same time, leaching should be sufficient to maintain root zone salinity levels below the threshold to prevent yield reduction. Therefore, monitoring leaching is the key component in evaluation and optimization of irrigation water management practices. Water balance (WB) is a common approach used to estimate leaching in agricultural fields and was applied in this study to assess field-scale leaching and the associated uncertainties for an almond orchard under drip and micro-sprinkler irrigation systems. In this study, we showed that change is soil water storage (ΔS) is highly influenced by the extent of monitoring depth, the location and number of monitoring points. Local measurement of WB parameters showed that leaching is highly variable across the field, thereby introducing considerable uncertainty on estimated leaching using WB approach. It was also shown that unknown input of water through fog interception added to the complexity of closing water balance at field scale.

Treated wastewater irrigation began two decades ago in the Salinas Valley of California and provides a unique opportunity to evaluate the long-term effects of this strategy on soil salinization. We used data from a long-term field experiment that included application of a range of blended water salinity on vegetables, strawberries and artichoke crops using surface and pressurized irrigation systems to calibrate and validate a root zone salinity model. We first applied the method of Morris to screen model parameters that have negligible influence on the output (soil-water electrical conductivity (EC sw )), and then the variance-based method of Sobol to select parameter values and complete model calibration and validation. While model simulations successfully captured long-term trends in soil salinity, model predictions underestimated EC sw for high EC sw samples. The model prediction error for the validation case ranged from 2.6% to 39%. The degree of soil salinization due to continuous application of water with electrical conductivity (EC w ) of 0.57 dS/m to 1.76 dS/m depends on multiple factors; EC w and actual crop evapotranspiration had a positive effect on EC sw , while rainfall amounts and fallow had a negative effect. A 50-year simulation indicated that soil water equilibrium (EC sw ≤ 2dS/m, the initial EC sw ) was reached after 8 to 14 years for vegetable crops irrigated with EC w of 0.95 to 1.76. Annual salt output loads for the 50-year simulation with runoff was a magnitude greater (from 305 to 1028 kg/ha/year) than that in deep percolation (up to 64 kg/ha/year). However, for all sites throughout the 50-year simulation, seasonal root zone salinity (saturated paste extract) did not exceed thresholds for salt tolerance for the selected crop rotations for the range of blended applied water salinities.

Precision mobile drip irrigation (MDI) describes the application of water through surface drip irrigation lines that are dragged by center pivot or linear move. MDI has the potential to greatly reduce water losses due to wind drift, soil water evaporation, and canopy evaporation. Two studies were conducted with the following objectives: (1) compare soil water evaporation under MDI and LESA; (2) assess soil water redistribution under MDI; (3) compare end-of-season profile soil water under MDI and LESA at two irrigation capacities 3.1 and 6.2 mm/day and to investigate design objectives that were implemented to overcome problems of earlier MDI designs. The experiments were conducted in western Kansas. Soil water evaporation was 35% lower under MDI. There was adequate redistribution of soil water in the subsurface. End-of-season soil water was significantly higher (p = 0.001) under MDI compared to LESA at the 3.1 mm/day irrigation capacity. Statistical uniformity of MDI was good ranging between 85 and 90%. The MDI system prevented deep wheel tracks and its redesign eliminated emitter clogging and reduced the frequency of the drip lines moving into the crop. The ability to better manage MDI due to design changes and its demonstration of superior performance in reducing soil water evaporation under limited canopy cover suggests that the system has the potential to enhance crop water productivity in a water-limited environment.

Stem water potential (SWP) is a commonly used method for determining plant water status (PWS) but requires a significant amount of time and is tedious to measure. To eliminate the necessity for this fieldwork, artificial neural networks (ANNs) were designed to predict PWS using information that is easier to measure, such as leaf temperature and microclimatic variables including ambient air temperature, relative humidity, incident radiation, and soil water content. To collect these variables, leaf and soil water sensors were placed in a 1.6 ha almond orchard. The sensors were interconnected through a wireless mesh network, which allowed remote data access. SWP values were taken in the field at midday three times a week during the growing season. The ANNs were trained using the Levenberg-Marquardt algorithm with the data divided into 70% training, 15% validation, and 15% test data. Each network contained one hidden layer with one to three hidden neurons. For each unique combination of inputs, the network was retrained five times, and the best network was selected based on the lowest mean squared error for the test data. When compared with multiple linear regression models fitting the same data, the networks consistently resulted in better R2 values, and higher values may be achieved with further optimization. These results suggest that there is potential for machine learning techniques that use ANNs to model the relationship between environmental conditions and PWS, which may be used for predicting acceptable temperature differences from target SWP. [ABSTRACT FROM AUTHOR]

Maize is the dominant irrigated crop in Kansas. In recent years, as a result of declining groundwater levels in the Ogallala aquifer and diminished well capacities, farmers are turning to deficit irrigation strategies. This study demonstrates the potential of model-based optimization for determining adequate soil water depletion levels. CERES-Maize was used as surrogate crop, while the AquaCrop model was used in an optimization procedure that determined the optimal water depletion levels. A multi-objective optimization framework was used to determine several combinations of optimal water depletion levels based on ten years of historical weather, and these combinations were tested using an additional 50 years of historical weather. The results show that, although imperfect modeling and weather fluctuations caused the actual yield to be different from the target yield, the fluctuations around the multi-year averages were not significantly larger when testing the irrigation schedule with the CERES-Maize model than when testing it with the AquaCrop model that had been used to develop the irrigation schedule. [ABSTRACT FROM AUTHOR]

This article introduces the fourteen articles that comprise the "Crop Modeling and Decision Support for Optimizing Use of Limited Water" collection. This collection was developed from a special session on crop modeling applications in agricultural water management held at the 2016 ASABE Annual International Meeting (AIM) in Orlando, Florida. In addition, other authors who were not able to attend the 2016 ASABE AIM were also invited to submit papers. The articles summarized in this introductory article demonstrate a wide array of applications in which crop models can be used to optimize agricultural water management. The following section titles indicate the topics covered in this collection: (1) evapotranspiration modeling (one article), (2) model development and parameterization (two articles), (3) application of crop models for irrigation scheduling (five articles), (4) coordinated water and nutrient management (one article), (5) soil water management (two articles), (6) risk assessment of water-limited irrigation management (one article), and (7) regional assessments of climate impact (two articles). Changing weather and climate, increasing population, and groundwater depletion will continue to stimulate innovations in agricultural water management, and crop models will play an important role in helping to optimize water use in agriculture. [ABSTRACT FROM AUTHOR]

Wheat is an important crop in the highlands of Northern Ethiopia and climate change is expected to be a major threat to wheat productivity. However, the potential impacts of climate change and adaptation on wheat yield has not been documented for this region. Wheat field experiments were carried out during the 2011–2013 cropping seasons in Northern Ethiopia to: (1) calibrate and evaluate Agricultural Production Systems sIMulator (APSIM)-wheat model for exploring the impacts of climate change and adaptation on wheat yield; (2) explore the response of wheat cultivar/s to possible change in climate and carbon dioxide (CO2) under optimal and sub-optimal fertilizer application and (3) assess the impact of climate change and adaptation practices on wheat yield based on integration of surveyed field data with climate simulations using multi-global climate models (GCMs; for short- and mid-term periods) for the Hintalo-Wajrat areas of Northern Ethiopia. The treatments were two levels of fertilizer (optimal and zero fertilization); treatments were replicated three times and arranged in a randomized complete block design. All required information for model calibration and evaluation were gathered from experimental studies. In addition, a household survey was conducted in 2012 in Northern Ethiopia. Following model calibration and performance testing, response of wheat to various nitrogen (N) fertilizer rates, planting date, temperature and combinations of other climate variables and CO2 were assessed. Crop simulations were conducted with future climate scenarios using 20 different GCMs and compared with a baseline. In addition, simulations were carried out using climate data from five different GCM with and without climate change adaptation practices. The simulated yield showed clear responses to changes in temperature, N fertilizer and CO2. Regardless of choice of cultivar, increasing temperatures alone (by up to 5 °C compared with the baseline) resulted in reduced yield while the addition of other factors (optimal fertilizer with elevated CO2) resulted in increased yield. Considering optimal fertilizer (64 kg/ha N) as an adaptation practice, wheat yield in the short-term (2010–2039) and mid-term (2040–2069) may increase at least by 40%, compared with sub-optimal N levels. Assuming CO2 and present wheat management is unchanged, simulation results based on 20 GCMs showed that median wheat yields will reduce by 10% in the short term and by 11% in the mid-term relative to the baseline data, whereas under changed CO2 with present management, wheat yield will increase slightly, by up to 8% in the short term and by up to 11% in the mid-term period, respectively. Wheat yield will substantially increase, by more than 100%, when simulated based on combined use of optimal planting date and fertilizer applications. Increased temperature in future scenarios will cause yield to decline, whereas CO2 is expected to have positive impacts on wheat yield. [ABSTRACT FROM PUBLISHER]

Increasing irrigated wheat yields is important to the overall profitability of limited-irrigation cropping systems in western Kansas. A simulation study was conducted to (1) validate APSIM's (Agricultural Production Systems sIMulator) ability to simulate wheat growth and yield in Kansas, and (2) apply the model to assess the response of wheat yield, biomass, and water productivity to irrigation allocation based on growth stage. The methodology involved combining short-term experimental data, long-term historical weather data (1950-2013), and mechanistic crop growth simulation to determine optimum irrigation management strategies. The model adequately simulated measured soil water in the profile. The goodness- of-fit test between the observed and simulated values for phenology, yield, biomass, and ET of the experimental cultivar in 2008-2009 in our study site agreed well with the 'Batten winter' wheat cultivar in APSIM. Results indicated that, on average, an irrigation allocation of 100 mm increased wheat yield by 14% to 46% compared to rainfed (dryland) production. Application of an additional 100 mm of irrigation did not improve wheat yield substantially (on average <0.26 t ha-1). Higher water productivity for grain yield was obtained when irrigation was applied at booting and heading. Overall, irrigation water use efficiency for grain decreased with an increase in irrigation allocation. The highest irrigation water use efficiency was simulated for wheat grown with a limited irrigation allocation of 100 mm applied at booting and heading. This study demonstrates that limited irrigation targeted at sensitive growth stages could enhance wheat yields and improve water productivity of water-limited cropping systems. [ABSTRACT FROM AUTHOR]

Declining groundwater levels in the Ogallala aquifer due to withdrawals exceeding annual recharge result in diminished well capacities that eventually become incapable of meeting full crop water needs. Producers need recommendations for deficit irrigation strategies that can maximize net returns in most years under low well capacities. The objectives of this study were to (1) calibrate and validate the CERES-Maize model in DSSAT-CSM v4.6 under southwest Kansas soils and climatic conditions and (2) apply the calibrated model to assess three factors related to irrigation management: (i) the optimum plant-available water threshold to initiate irrigation for maximizing net returns, (ii) the effect of percentage soil water depletion at planting on yield, seasonal transpiration, water productivity, extractable soil water at maturity, and net returns, and (iii) the effect of late irrigation season termination on extractable soil water at physiological maturity, yield, and net returns. The CERES-Maize model in DSSAT-CSM v4.6 in conjunction with short-term experimental data and 63 years (1950 to 2013) of historical weather data were used in this study. The calibrated model was able to predict end of season grain yield with acceptable accuracy (NSE > 0.9, 0.13 < %RMSE < 0.19), indicating that the model could be used for assessing alternative management strategies for optimizing the use of limited water for irrigating corn in southwest Kansas. Irrigation scheduling based on a 50% plant-available water threshold maximized net returns compared to initiating irrigation at greater soil water content at corn prices ranging from $0.10 to $0.26 kg-1. Accounting for inter-annual variations in weather and irrigation downtime due to repairs, 14 to 17 irrigation applications of 25 mm of water each would be needed to maintain soil water at 50% of plant-available water during the season. Having soil water in the top 1.2 m of the soil profile between 0% and 25% depleted at planting maximized net returns, although it also resulted in more extractable soil water at physiological maturity. Terminating irrigation 90 or 95 days after planting depending on corn price maximized net returns and resulted in the lowest amount of extractable soil water at physiological maturity, implying that opportunities exist to mine stored soil water toward the end of the season even under deficit irrigation. We recommend that late season irrigation termination be done in conjunction with soil water monitoring and management- allowable depletion techniques to minimize potential reduction in yields. Before adopting any of the management strategies assessed in this study, producers should consider the unique yield potential constraints for their farm. The concepts explored in this analysis, which combined experimental data, computer simulation, and long-term weather data to generate optimum management recommendations, could be applied in other areas with constrained water supplies for irrigation. [ABSTRACT FROM AUTHOR]

This article is part of a collection of articles that provides a comprehensive description of hydrologic and water quality (H/WQ) model calibration and validation concepts and processes. Sensitivity analysis (SA), which is often used to quantify the strength of relationships between model inputs and outputs, is an essential evaluation of any kind of modeling. SA is crucial in H/WQ models due to various aspects involved in H/WQ modeling processes, such as empiricism, spatiotemporal scales, and complexity, that require an assessment of parameters' influence on the model's prediction. This study synthesized SA applications for 25 H/WQ models in the special collection on model use, calibration, and validation published in 2012 and provides guidance on their future applications. Commonly used SA methods are summarized along with tools to implement them. While SA was not employed for all 25 models in the special collection, a wide range of SA methods (from partial derivatives to variance-based global methods) and sensitivity measures (from scatter plots to variance decomposition measures) were used in the literature. Some model parameters were found to be important in most sensitivity applications performed for the models; however, their relative importance varied from study to study, underscoring the necessity of SA for every new model application. Nevertheless, summarizing important model parameters can still serve as a starting point for model users. Since most studies concentrated on model parameters alone; future SA applications in H/WQ modeling should also consider other inputs (climate data, boundary conditions, etc.) and non-parametric aspects, such as features and processes considered in the model. [ABSTRACT FROM AUTHOR]

The goal of this study was to better characterize parameters influencing the exchange of surface water in south Florida's C-111 canal and Biscayne aquifer using the analytical model STWT1. A three-step model evaluation framework was implemented as follows: (1) qualitative parameter ranking by comparing two Morris method sampling strategies, (2) quantitative variance-based sensitivity analysis using Sobol's method, and (3) estimation of parameter posterior probability distributions and statistics using the Generalized Likelihood Uncertainty Estimator (GLUE) methodology. Results indicated that the original Morris random sampling method underestimated total parameter effects compared to the improved global Morris sampling strategy. However, parameter rankings from the two sampling methods were similar For the STWT1 model, only four out of the six parameters analyzed were important for predicting water table response to canal stage and recharge fluctuations. Morris ranking in order of decreasing importance resulted in specific yield (ASY), aquifer saturated thickness (AB), horizontal hydraulic conductivity (AKX), canal leakance (XAA), vertical hydraulic conductivity (AKZ), and half-width of canal (XZERO). Sobol's sensitivity indices for the four most critical parameters revealed that summation of first-order parameter effects was 1. 0, indicating that STWT1 behaved as an additive model or negligible parameter interactions. We estimated parameter values of 0.07 to 0.14 for ASY, 11,000 to 14,300 m d-1 for AKX, 13.4 to 18.3 m for AB, and 99.8 to 279 m for XAA. The estimated values were within the range of values estimated using more complex methods at nearby sites. The Nash-Sutcliffe coefficient of efficiency and root mean square error for estimated parameters ranged from 0.66 to 0.95 and from 4 to 7 cm, respectively. This study demonstrates a simple and inexpensive way to characterize hydrogeological parameters controlling groundwater-surface interactions in any region with aquifers that are highly permeable without using standard pumping tests or canal drawdown experiments. Hydrogeological parameters estimated using this approach could be used as starting values in large-scale numerical simulations. [ABSTRACT FROM AUTHOR]