ABSTRACT:

Irrigated agriculture depends on surface water and groundwater, but we do not have a clear picture of how much water is consumed from these sources by different crops across the US over time. Current estimates of crop water requirements are insufficient in providing the spatial granularity and temporal depth required for comprehensive long-term analysis. To fill this data gap, we utilized crop growth models to quantify the monthly crop water consumption - distinguishing between rainwater, surface water, and groundwater - of 30 of the most widely irrigated annual crops in the US from 1981 to 2019 at 2.5 arc minutes. These 30 crops represent approximately 95% of US irrigated cropland. We found that the average annual total crop water consumption for these 30 irrigated crops in the US was 154.2 km3 (70% blue, 30% green). Corn and alfalfa accounted for approximately 16.7 km3 and 24.8 km3 of average annual blue crop water consumption, respectively, which is nearly two-fifths of the blue crop water consumed in the US. Surface water consumption decreased by 41.2%, while groundwater consumption increased by 6.8%, resulting in a 17.3% decline in blue water consumption between 1981 and 2019. We find good agreement between our results and existing modeled evapotranspiration (ET) products, remotely sensed ET estimates (OpenET), and water use data from the U.S. Geological Survey and U.S. Department of Agriculture. Our dataset and model can help assess the impact of irrigation practices and water scarcity on crop production and sustainability.

ABSTRACT:

Agriculture plays a vital role in the US, generating billions of dollars in revenue and providing essential resources such as food, fiber, and fuel. At the same time, this sector consumes more water than all other sectors combined, and contributes significantly to greenhouse gas emissions. Accurately mapping cropland areas at a high spatial resolution, and understanding cultivation trends are crucial for promoting sustainable land management. However, it is not always clear where these crops are cultivated at a fine spatial resolution. Existing data sources either provide extensive temporal coverage with low spatial detail or vice versa. To address this, we employed a data-fusion approach, combining strengths of existing data sources. We produced annual irrigated and harvested areas for 30 major crops in the US from 1981 to 2019 at 2.5 arc minutes by combining county level US Department of Agriculture records with gridded land use datasets. We evaluated our dataset by comparing it with existing large-scale gridded cropland data, revealing alignment and divergence across data products depending on the year, area, and crop. Our dataset serves as a critical tool for analyzing and understanding long-term trends in cropland cultivation, thereby contributing to more informed and sustainable agricultural practices