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Agriculture is a major water user, especially in dry and drought-prone areas that rely on irrigation to support agricultural production. In recent years, the over-extraction of groundwater, exacerbated by climate change, population growth, and intensive agricultural irrigation, has led to a drop in water levels and influenced the hydrological cycle. Understanding changes in hydrological processes is essential for pursuing water sustainability. This study aims to estimate the amount and impact of irrigation on hydrological processes in two breadbasket regions, Jing-Jin-Ji (JJJ), China, and northern Texas (NTX), US. We used the Soil and Water Assessment Tool (SWAT) to explore spatiotemporal variations of irrigation from 2008 to 2013 and compared changes in hydrological processes caused by irrigation. The results indicated that deficit irrigation is more common in JJJ than in NTX and can reduce approximately 50 % of irrigation water use in areas with intensively irrigated cropland. The applied irrigation varies less over time in NTX but fluctuates in JJJ. Compared with NTX, the higher irrigation intensity in JJJ results in a more significant change in downstream peak streamflow of around 6 m3/s. Moreover, the difference in crop growing seasons can lead to different impacts of irrigation on hydrological processes. For example, the percentage change of surface runoff under real-world relative to the no-irrigation scenario was the greatest, around 40 %, in JJJ and NTX. However, the peak change occurred at different times, with the nearing maturity of winter wheat in May in JJJ and corn in August in NTX. The great potential to reduce groundwater extraction by adopting water conservation irrigation techniques calls for policies and regulations to help farmers shift towards more sustainable water management practices. 

Weather data are the key forces that drive hydrological processes so that their accuracy in watershed modeling is fundamentally important. For large-scale watershed modeling, weather data are either generated by using interpolation methods or derived from assimilated datasets. In the present study, we compared model performances of the Soil and Water Assessment Tool (SWAT), as driven by interpolation weather data, and NASA North American Land Data Assimilation System Phase Two (NLDAS2) weather dataset in the Upper Mississippi River Basin (UMRB). The SWAT model fed with different weather datasets were used to simulate monthly stream flow at 11 United States Geological Survey (USGS) monitoring stations in the UMRB. Model performances were evaluated based on three metrics: coefficient of determination (R2), Nash–Sutcliffe coefficient (NS), and percent bias (Pbias). The results show that, after calibration, the SWAT model compared well at all monitoring stations for monthly stream flow using different weather datasets indicating that the SWAT model can adequately produce long-term water yield in UMRB. The results also show that using NLDAS2 weather dataset can improve SWAT prediction of monthly stream flow with less prediction uncertainty in the UMRB. We concluded that NLDAS2 dataset could be used by the SWAT model for large-scale watersheds like UMRB as a surrogate of the interpolation weather data. Further analyses results show that NLDAS2 daily solar radiation data was about 2.5 MJ m−2 higher than the interpolation data. As such, the SWAT model driven by NLDAS2 dataset tended to underestimate stream flow in the UMRB due to the overestimation in evapotranspiration in uncalibrated conditions. Thus, the implication of overestimated solar radiation by NLDAS2 dataset should be considered before using NLDAS2 dataset to drive the hydrological model.