No abstract

In the Western United States, demand for water is often out of balance with limited water supplies. This has led to extensive water rights conflict and litigation. A tool that can reliably forecast natural aquifer discharge months ahead of peak water demand could help water practitioners and managers by providing advanced knowledge of potential water-right mitigation requirements. The timing and magnitude of natural aquifer discharge from the Eastern Snake Plain Aquifer (ESPA) in southern Idaho is accurately forecast 4 months ahead of the peak water demand, which occurs annually in July. An ARIMA time-series model with exogenous predictors (ARIMAX model) was used to develop the forecast. The ARIMAX model fit to a set of training data was assessed using Akaike's information criterion to select the optimal model that forecasts aquifer discharge, given the previous year's discharge and values of the predictor variables. Model performance was assessed by application of the model to a validation subset of data. The Nash-Sutcliffe efficiency for model predictions made on the validation set was 0.57. The predictor variables used in our forecast represent the major recharge and discharge components of the ESPA water budget, including variables that reflect overall water supply and important aspects of water administration and management. Coefficients of variation on the regression coefficients for streamflow and irrigation diversions were all much less than 0.5, indicating that these variables are strong predictors. The model with the highest AIC weight included streamflow, two irrigation diversion variables, and storage.

If the nature of groundwater sources and sinks can be determined or predicted, the data can be used to forecast natural aquifer discharge. We present a procedure to forecast the relative contribution of individual aquifer sources and sinks to natural aquifer discharge. Using these individual aquifer recharge components, along with observed aquifer heads for each January, we generate a 1-year, monthly spring discharge forecast for the upcoming year with an existing numerical model and convolution. The results indicate that a forecast of natural aquifer discharge can be developed using only the dominant aquifer recharge sources combined with the effects of aquifer heads (initial conditions) at the time the forecast is generated. We also estimate how our forecast will perform in the future using a jackknife procedure, which indicates that the future performance of the forecast is good (Nash-Sutcliffe efficiency of 0.81). We develop a forecast and demonstrate important features of the procedure by presenting an application to the Eastern Snake Plain Aquifer in southern Idaho.