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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. 

Many cities have been suffering from severe water deficiency in recent years due to rapid urban expansion, socioeconomic development, population growth, and climate change. Domestic water use plays an important role in the total urban water use. A framework for estimating domestic water use is highly needed to develop adaptive measures for efficient water use under climate change and urbanization. In this study, we developed an agent-based model (ABM) with two groups of agents to estimate the domestic water use. These two groups include the government agent that determines the income growth rate, adjusts water prices, and promotes water-efficient appliances, and the residential agents who consume water. To better capture the impact of urbanization and climate change on water use, the utility function of residential agents was further divided into base water use related to economic condition and seasonal water use that is sensitive to climate conditions. Moreover, a bass diffusion model was proposed and integrated into the ABM to consider the diffusion of water-efficient appliances. Results show that our ABM can capture the spatiotemporal pattern of domestic water use in different regions. Residents in the central urban area consume more water compared to residents in the suburbs in the study cities in China, but it is opposite in the study counties in the US. The growth of income and water-efficient appliances are two factors affecting domestic water use. The proposed modeling framework is transferrable to other regions to develop strategies for mitigating domestic water use. 

Abstract Tile drainage is one of the dominant agricultural management practices in the United States and has greatly expanded since the late 1990s. It has proven effects on land surface water balance and quantity and quality of streamflow at the local scale. The effect of tile drainage on crop production, hydrology, and the environment on a regional scale is elusive due to lack of high-resolution, spatially-explicit tile drainage area information for the Contiguous United States (CONUS). We developed a 30-m resolution tile drainage map of the most-likely tile-drained area of the CONUS (AgTile-US) from county-level tile drainage census using a geospatial model that uses soil drainage information and topographic slope as inputs. Validation of AgTile-US with 16000 ground truth points indicated 86.03% accuracy at the CONUS-scale. Over the heavily tile-drained midwestern regions of the U.S., the accuracy ranges from 82.7% to 93.6%. These data can be used to study and model the hydrologic and water quality responses of tile drainage and to enhance streamflow forecasting in tile drainage dominant regions.