Blackstock Joshua M, Odipo Obembe, Shew Aaron M, Reba Michele L, Massey Joseph M, Owens Phillip R, Delhom Christopher D
USDA ARS Dale Bumpers Small Farms Research Center, Booneville, Arkansas, USA.
Center for Advance Spatial Technologies, University of Arkansas, Fayetteville, Arkansas, USA.
J Environ Qual. 2025 Sep-Oct;54(5):1045-1059. doi: 10.1002/jeq2.70007. Epub 2025 Apr 24.
Expansion of irrigated cropland area in eastern Arkansas has led to the formation of regional cones of depression and creation of critical groundwater areas for the Mississippi River Valley alluvial aquifer (MRVA) in Arkansas. In response, use of surface water reservoirs for irrigation in the Grand Prairie critical groundwater area (GPCGA) has been implemented toward improving groundwater recovery, but effects of this strategy are unclear. We leverage publicly available satellite imagery and geospatial computational resources to estimate in GPCGA: (1) total surface water area on cropland and non-cropland potentially used for irrigation using a supervised classification model and (2) causal effect of surface water area on groundwater depth-to-water measurements using a two-way fixed effects (FE) model. We show persistent surface water area can be accurately predicted with confusion matrix accuracy ranging from 97.1% to 98.7% compared with known surface water reservoirs. Causal effect of cropland surface water reservoirs on depth-to-groundwater shows an approximate 0.4 m or 3.3% decrease in mean depth-to-water measurements for a given growing season for watersheds with a 100 ha increase in surface water area. Greatest reductions in depth-to-water measurements occur in those watersheds overlying regional cones of depression, corroborating previous groundwater simulation experiments. We note that alternate specifications of FE model exhibited similar effects, indicating FE model robustness. While conversion of arable land to surface water reservoirs incurs economic impacts, surface water reservoirs present a viable groundwater conservation strategy and tool for groundwater resource recovery for MRVA and other heavily stressed aquifers.
阿肯色州东部灌溉农田面积的扩大导致了区域降落漏斗的形成,并为阿肯色州的密西西比河谷冲积含水层(MRVA)创造了关键的地下水区域。作为回应,在大草原关键地下水区域(GPCGA)利用地表水库进行灌溉已被实施,以改善地下水恢复,但这一策略的效果尚不清楚。我们利用公开可用的卫星图像和地理空间计算资源,在GPCGA中估计:(1)使用监督分类模型估计农田和非农田上潜在用于灌溉的总地表水面积;(2)使用双向固定效应(FE)模型估计地表水面积对地下水水位测量的因果效应。我们表明,与已知的地表水库相比,利用混淆矩阵准确率在97.1%至98.7%之间,可以准确预测持续的地表水面积。农田地表水库对地下水位的因果效应表明,对于地表水面积增加100公顷的流域,在给定的生长季节,平均水位测量值大约下降0.4米或3.3%。水位测量值下降最大的情况发生在覆盖区域降落漏斗的那些流域,这证实了之前的地下水模拟实验。我们注意到FE模型的其他规格表现出类似的效果,表明FE模型具有稳健性。虽然将耕地转变为地表水库会产生经济影响,但地表水库是一种可行的地下水保护策略,也是MRVA和其他压力较大的含水层进行地下水资源恢复的工具。