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The sustainability of irrigated agriculture in many arid and semiarid areas of the world is at risk because of a combination of several interrelated factors, including lack of fresh water, lack of drainage, the presence of high water tables, and salinization of soil and groundwater resources. Nowhere in the United States are these issues more apparent than in the San Joaquin Valley of California. A solid understanding of salinization processes at regional spatial and decadal time scales is required to evaluate the sustainability of irrigated agriculture. A hydro-salinity model was developed to integrate subsurface hydrology with reactive salt transport for a 1,400-km 2 study area in the San Joaquin Valley. The model was used to reconstruct historical changes in salt storage by irrigated agriculture over the past 60 years. We show that patterns in soil and groundwater salinity were caused by spatial variations in soil hydrology, the change from local groundwater to snowmelt water as the main irrigation water supply, and by occasional droughts. Gypsum dissolution was a critical component of the regional salt balance. Although results show that the total salt input and output were about equal for the past 20 years, the model also predicts salinization of the deeper aquifers, thereby questioning the sustainability of irrigated agriculture.regional hydrology ͉ salinization ͉ vadose zone

Young, Charles A., Marisa I. Escobar‐Arias, Martha Fernandes, Brian Joyce, Michael Kiparsky, Jeffrey F. Mount, Vishal K. Mehta, David Purkey, Joshua H. Viers, and David Yates, 2009. Modeling the Hydrology of Climate Change in California’s Sierra Nevada for Subwatershed Scale Adaptation. Journal of the American Water Resources Association (JAWRA) 45(6):1409‐1423. Abstract:  The rainfall‐runoff model presented in this study represents the hydrology of 15 major watersheds of the Sierra Nevada in California as the backbone of a planning tool for water resources analysis including climate change studies. Our model implementation documents potential changes in hydrologic metrics such as snowpack and the initiation of snowmelt at a finer resolution than previous studies, in accordance with the needs of watershed‐level planning decisions. Calibration was performed with a sequence of steps focusing sequentially on parameters of land cover, snow accumulation and melt, and water capacity and hydraulic conductivity of soil horizons. An assessment of the calibrated streamflows using goodness of fit statistics indicate that the model robustly represents major features of weekly average flows of the historical 1980‐2001 time series. Runs of the model for climate warming scenarios with fixed increases of 2°C, 4°C, and 6°C for the spatial domain were used to analyze changes in snow accumulation and runoff timing. The results indicated a reduction in snowmelt volume that was largest in the 1,750‐2,750 m elevation range. In addition, the runoff center of mass shifted to earlier dates and this shift was non‐uniformly distributed throughout the Sierra Nevada. Because the hydrologic model presented here is nested within a water resources planning system, future research can focus on the management and adaptation of the water resources system in the context of climate change.

[1] Computational capabilities have evolved to a point where it is possible to use multidimensional physically based hydrologic models to study spatial and temporal patterns of water flow in the vadose zone. However, models based on multidimensional governing equations have only received limited attention, in particular because of their computational, distributed input, and parameter estimation requirements. The aim of the present paper is to explore the usefulness and applicability of the inverse method to estimate vadose zone properties using the solution of a physically based, distributed three-dimensional model combined with spatially distributed measured tile drainage data from the 3880-ha Broadview Water District (BWD) in the San Joaquin Valley of California. The inverse problem is posed within a single-criterion Bayesian framework and solved by means of the computerized Shuffled Complex Evolution Metropolis global optimization algorithm. To study the benefits of using a spatially distributed three-dimensional vadose zone model, the results of the 3-D model were compared with those obtained using a simple storage-based bucket model and a spatially averaged one-dimensional unsaturated water flow model for a 2-year period. District-wide results demonstrate that measured spatially distributed patterns of drainage data contain only limited information for the identification of vadose zone model parameters and are particularly inadequate to identify the soil hydraulic properties. In contrast, the drain conductance and a soil matrix bypass coefficient were well determined, indicating that the dominant hydrology of the BWD was determined by drain system properties and preferential flow. Despite the significant CPU time needed for model calibration, results suggest that there are advantages in using physically based hydrologic models to study spatial and temporal patterns of water flow at the scale of a watershed. These models not only generate consistent forecasts of spatially distributed drainage data during the calibration and validation period but also possess unbiased predictive capabilities with respect to measured groundwater table depths not included in the calibration.

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