Abstract

Previous studies on irrigation impacts on land surface fluxes/states were mainly conducted as sensitivity experiments, with limited analysis of uncertainties from the input data and model irrigation schemes used. In this study, we calibrated and evaluated the performance of irrigation water use s... Show morePrevious studies on irrigation impacts on land surface fluxes/states were mainly conducted as sensitivity experiments, with limited analysis of uncertainties from the input data and model irrigation schemes used. In this study, we calibrated and evaluated the performance of irrigation water use simulated by the Community Land Model version 4 (CLM4) against observations from agriculture census. We investigated the impacts of irrigation on land surface fluxes and states over the conterminous United States (CONUS) and explored possible directions of improvement. Specifically, we found large uncertainty in the irrigation area data from two widely used sources and CLM4 tended to produce unrealistically large temporal variations of irrigation demand for applications at the water resources region scale over CONUS. At seasonal to interannual time scales, the effects of irrigation on surface energy partitioning appeared to be large and persistent, and more pronounced in dry than wet years. Even with model calibration to yield overall good agreement with the irrigation amounts from the National Agricultural Statistics Service, differences between the two irrigation area data sets still dominate the differences in the interannual variability of land surface responses to irrigation. Our results suggest that irrigation amount simulated by CLM4 can be improved by calibrating model parameter values and accurate representation of the spatial distribution and intensity of irrigated areas. Furthermore, through a set of numerical experiments, the deficiency in the current parameterization is evaluated and a critical path forward to a realistic assessment of irrigation impacts using an earth system modeling approach is recommended. Show less