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a b s t r a c tLimitations on water resources for agriculture in places such as Colorado, USA, have caused farmers to consider limited irrigation as an alternative to full irrigation practices, where the crop is intentionally stressed during specific growth stages in an effort to maximize yield per unit water consumed, or evapotranspiration (ET). While crop growth models such as CERES-Maize provide the ability to evaluate numerous management scenarios without the costs associated with multiyear field experiments, recent studies have shown that CERES-Maize performs well under full irrigation but overestimates ET of corn under limited irrigation management. The primary objective of this study was to improve CERES-Maize ET simulation under limited irrigation management while maintaining accuracy of other important model output responses. Field experiments with corn were performed in northern Colorado, USA from 2006 to 2010, where four replicates each of full (ET requirement supplied by irrigation throughout the season) and limited (no irrigation before the V12 growth stage unless necessary for emergence, then full irrigation afterwards) irrigation treatments were analyzed. The local sensitivity of model input parameters affecting ET was evaluated, prompting changes to the model code with a new dynamic crop coefficient (K CD ) as a function of the crop leaf area index. The modified CERES-Maize model more accurately represented ET under full and limited irrigation, for example reducing late-season ET potential from a plant with reduced canopy and more closely matched FAO-56 crop coefficient curves under full irrigation. Using the limited irrigation data for evaluation, the modified model showed significant decreases in model error for seasonal cumulative ET (root mean square deviation RMSD from 80.9 mm to 49.9 mm) and water productivity (RMSD from 5.97 kg ha −1 mm −1 to 2.86 kg ha −1 mm −1 ) as compared to the original model. The modified model was subsequently applied to several hypothetical irrigation management strategies, indicating that reducing weekly vegetative stage water applications from 20 mm to 2.5 mm can increase simulated water productivity by over 15%. While these synthetic water production functions may not be feasible in a production field with natural climate variability, the modified ET model indicates promise for limited irrigation management increasing water productivity.Published by Elsevier B.V.

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A new methodology, which integrates field data, geographic information systems, remote sensing, and spatial modeling, was developed to accurately model soil salinity using statistical tools. Ground data from four alfalfa (Medicago sativa L.) fields in the lower Arkansas River basin in Colorado were compared with data derived from Ikonos satellite images with a 4‐m resolution and Landsat satellite images with a 30‐m resolution. For each image, the combination of satellite image bands that had the best correlation with soil salinity was used. Three statistical models were applied to estimate soil salinity from remote sensing: the ordinary least squares (OLS) model, the spatial autoregressive (spatial AR) model, and a modified residual kriging model. A number of criteria were evaluated to select the best model. The results show that both the Ikonos and Landsat satellite images can be used to estimate soil salinity, and regardless of the source of the satellite image used, the modified kriging model provided the best estimates of soil salinity. Although the OLS model met most of the model selection criteria, in most cases it involved some autocorrelation among the residuals. When the same data were tested using the spatial AR model, most of the autocorrelation among the residuals was removed, but the R2 was reduced. In the modified kriging model, where the kriged residuals were combined with the results of the OLS model, there were significant improvements in the R2 for all cases tested in this study. Thus, this study shows that combining field data, geographic information systems, and remote sensing with strong statistical measures can significantly enhance the development of high‐quality soil salinity maps.

Forecasting the Nile River flows is of vital interest for many African nations such as Sudan and Egypt. Any improvement in the forecast accuracy and/or the prediction horizon will have a significant influence on improving the water management in these nations. The idea of this research stems from previous studies that have identified that certain large scale climatic oscillations, such as the El Niño Southern Oscillation (ENSO), as being important factors in long-range hydro-climatic forecasting. The mechanism by which the Pacific ENSO is transmitted to the Nile basin hydrology is not fully understood, although its effects on the flow have been identified. In addition, other large-scale oceanic-atmospheric systems, such as those related to the Atlantic and the Indian Oceans, may exert a significant influence on the climate of the Nile basin. Thus, other predictors need to be identified to find signals that may contribute to explaining the variability of the Nile River flows. The aim of this study is to further identify Oceanic regions and hydro-climatic variables of strong connection with the Nile Basin hydrology, particularly Nile River streamflows. The study will also focus on extending the prediction horizon beyond the previously used time scales.

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