Documentation of methods and inventory of irrigation information collected for the 2015 U.S. Geological Survey estimated use of water in the United States

Painter, Jaime A.; Brandt, Justin T.; Caldwell, Rodney R.; Haynes, Jonathan V.; Read, Amy L.

doi:10.3133/sir20205139

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…At this time however, and to the best of our knowledge, a spatially explicit seasonal record of irrigation does not exist for our study area. However, spatially aggregated irrigation data does exist for large areas within our study region (Torak & Painter, 2011) and efforts are being made to increase available data on irrigation water use across the U.S. (Painter et al., 2021). In addition to irrigation data, future work consisting of adding explanatory variables (Liang et al., 2019; Tulbure & Broich, 2019) to improve our understanding of the driver categories should consider including soil type, soil moisture, slope, elevation, stream gage data, and time‐lag factors such as precipitation or surface water area from the proceeding season (Heimhuber et al., 2017; Liang et al., 2019; Tulbure & Broich, 2019; Walker et al., 2020).…”

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confidence: 99%

Effects of Climate and Anthropogenic Drivers on Surface Water Area in the Southeastern United States

Gaines

Tulbure

Perin

2022

Water Resources Research

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Surface water is the most readily accessible water resource and provides an array of ecosystem services, but its availability and access are stressed by changes in climate, land cover, and population size. Understanding drivers of surface water dynamics in space and time is key to better managing our water resources. However, few studies estimating changes in surface water account for climate and anthropogenic drivers both independently and together. We used 19 years (2000–2018) of the newly developed Dynamic Surface Water Extent Landsat Science Product in concert with time series of precipitation, temperature, land cover, and population size to statistically model maximum seasonal percent surface water area as a function of climate and anthropogenic drivers in the southeastern United States. We fitted three statistical models (linear mixed effects, random forests, and mixed effects random forests) and three groups of explanatory variables (climate, anthropogenic, and their combination) to assess the accuracy of estimating percent surface water area at the watershed scale with different drivers. We found that anthropogenic drivers accounted for approximately 37% more of the variance in the percent surface water area than the climate variables. The combination of variables in the mixed effects random forest model produced the smallest mean percent errors (mean −0.17%) and the highest explained variance (R2 0.99). Our results indicate that anthropogenic drivers have greater influence when estimating percent surface water area than climate drivers, suggesting that water management practices and land‐use policies can be highly effective tools in controlling surface water variations in the Southeast.

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