Journal of Water Resources Planning and Management · 2023 · 13 citations · 13 references
Natural ResourcesHydrologyWater Resources ManagementEnvironmental FlowsHydrologic ModelingWatershed HydrologyEnvironmental Flow RequirementsHydrologic Regime RestorationIrrigationIrrigation Water RequirementsRiver Basin HydrologyWatershed ModelingBasin-scale Hydrologic ModelingEnvironmental Flow ConditionsEnvironmental Restoration PlanningFarm Irrigation ManagementHydrologic Model ValidationOn-farm Irrigation InfrastructureRiver Ecosystem Resilience To Flow ChangeRiver Restoration Flow Assessment
The Rio Grande–Rio Bravo’s flow regime has been highly altered for more than 130 years, yet the river ecosystem still supports important biodiversity including numerous endangered species. More than 80% of water consumed in the basin goes to irrigating farms, but in recent decades, farmers have repeatedly experienced severe water shortages. Given this water-scarce condition, any plans for enhancing environmental flows must be carefully designed to minimize impacts or provide benefits to agriculture. This study describes the development of the Rio Grande–Rio Bravo’s first whole-basin hydrologic model—representing both the United States and Mexico portions of the basin—to enable exploration of environmental flow restoration needs and options for meeting these needs. We then demonstrate an analytical process in which environmental flow needs are compared to existing flow conditions to quantify gaps, and then evaluate how those gaps can be filled by reducing farm irrigation needs by shifting to less water-intensive crops and fallowing a portion of existing farmland while maintaining or improving net revenues. In our pilot assessment we find that an improvement of 2.2 m3/s would fill the environmental flow gap for late-summer low-flow conditions at Albuquerque, New Mexico. This flow enhancement is attainable by fallowing 18%–26% of cropland and shifting to more profitable and less water-intensive crops to sustain overall farm revenues.
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SoilGrids250m: Global gridded soil information based on machine learning
Tomislav Hengl, Jorge Mendes de Jesus, G.B.M. Heuvelink et al. · PLoS ONE · 2017 · 4.7K citations · Full text