Annual Irrigation Dynamics in the U.S. Northern High Plains Derived from Landsat Satellite Data

Abstract

Sustainable management of agricultural water resources requires improved understanding of irrigation patterns in space and time. We produced annual, high‐resolution (30 m) irrigation maps for 1999–2016 by combining all available Landsat satellite imagery with climate and soil covariables in Google Earth Engine. Random forest classification had accuracies from 92 to 100% and generally agreed with county statistics (r2 = 0.88–0.96). Two novel indices that integrate plant greenness and moisture information show promise for improving satellite classification of irrigation. We found considerable interannual variability in irrigation location and extent, including a near doubling between 2002 and 2016. Statistical modeling suggested that precipitation and commodity price influenced irrigated extent through time. High prices incentivized expansion to increase crop yield and profit, but dry years required greater irrigation intensity, thus reducing area in this supply‐limited region. Data sets produced with this approach can improve water sustainability by providing consistent, spatially explicit tracking of irrigation dynamics over time.

Quantifying irrigation adaptation strategies in response to stakeholder-driven groundwater management in the US High Plains Aquifer

“…We established the control region by manually demarcating an area analogous to SD-6 during the five years prior to the LEMA (2008-2012, figure 1). We targeted adjacent areas (1.5 km away to reduce direct well effects (Fileccia 2016)) with similar well density and irrigation frequency based on AIM (Deines et al 2017). Working in Google Earth Engine (Gorelick et al 2017), we iteratively adjusted control region boundaries until the 2008-2012 mean control region statistics were within 10% of SD-6 for the following metrics: (a) total area (0.12% difference); (b) crop area based on the USDA Cropland Data Layers (CDL; 1.38%) (USDA-NASS 2017); (c) annual precipitation derived from GRIDMET 4 km gridded daily climate data (0.01%) (Abatzoglou 2013); and (d) total pumped volume divided by total area based on WIMAS well data (7.1%, data described below).…”

“…To calculate the BAU scenario, we used a causal impact analysis which is based on an emerging Bayesian structural time-series method (Brodersen et al 2015) new to agrohydrology. We then combined detailed well records, satellite-derived annual irrigation maps (AIM) (Deines et al 2017), and annual national crop maps to quantify how pumping reductions were achieved to understand land use impacts and farmer adaptation strategies.…”

Utilizing SMAP Soil Moisture Data to Constrain Irrigation in the Community Land Model

“…Further, as in other LSMs (e.g., Lawston et al, ; Pei et al, ), CLM4.5 irrigation scheme uses a constant bulk coefficient (here F irrig ; equation ) globally, which is a major limitation of the existing scheme as described above. Finally, the use of fixed irrigated areas representing approximately 2000 is another structural limitation in CLM4.5 because irrigation location and extent can have significant interannual variability, especially during wet‐dry transitions (Deines et al, ).…”

Transition Pathways to Sustainable Agricultural Water Management: A Review of Integrated Modeling Approaches

“…Some data are widely available, but can be difficult to process or interpret, such as LANDSAT imagery (Deines et al 2017) or social media mentions (Zipper 2018). For example, a complex process-based crop model may incorporate a simple empirical model to estimate solar radiation.…”

“…Often data are collected at a point, but is needed for a larger area, such as precipitation from rain gages. Some data are widely available, but can be difficult to process or interpret, such as LANDSAT imagery (Deines et al 2017) or social media mentions (Zipper 2018). Compounding these challenges, different locations often have different data availability, but similar modeling needs.…”