ABSTRACT:

Groundwater forecasts that support sustainable aquifer management often account for climate and hydrologic uncertainty, but they typically assume that human pumping behavior remains stable over time. In intensively irrigated aquifers, this assumption may not hold because pumping decisions can shift with drought, crop choice, energy costs, irrigation technology, regulation, and conservation programs. We examine how non-stationary pumping behavior affects coupled human--groundwater prediction using annual pumping-depth data for 43 county-level agents in the High Plains Aquifer Hydrologic Observatory Area within the Ogallala Aquifer. Using the 1993--2020 pumping record, our workflow identifies where pumping behavior departs from stationarity, localizes when these shifts occur, compares stationary and regime-aware data-driven pumping models, and propagates pumping-prediction uncertainty through the Republican River Compact Administration MODFLOW groundwater model. Results show that non-stationarity occurred in a minority of eight agents and was more clearly detected at the county-agent scale than in aggregated cluster means. Regime-aware modeling better captured post-transition pumping-depth trajectories for seven of the eight non-stationary agents. After propagation through the groundwater model, however, improvements were less consistent: regime-aware simulations better represented groundwater-level trajectories for five agents. The coupled simulations show that uncertainty in changing pumping behavior can widen the range of plausible groundwater outcomes over time. These findings identify behavioral non-stationarity as an important source of groundwater-forecast uncertainty and provide a framework for evaluating when coupled human--water models should update behavioral assumptions and propagate behavioral uncertainty.

ABSTRACT:

This resource provides data related to the irrigation decision making for each county (agent) within the High Plains Aquifer Hydrologic Observatory Area from 1993 to 2020. The data is at a monthly scale, and includes monthly irrigation depth, monthly average prices of corn, wheat, soybean, sorghum and diesel, accumulated precipitation and average temperature from May to October.

ABSTRACT:

Non-point source pollution has been attributed as the cause of significant surface water quality concerns in the Great Lake Region. Over a hundred edge-of-field (EOF) runoff observational sites, which consist of hydrologic and meteorologic instruments are available at the edge of individual agricultural fields across the states in the region, are installed to measure and record runoff timing and magnitude. Conservation partners, such as Discovery Farms (Wisconsin and Minnesota), USGS, and USDA-ARS, provided the observational data. The identities of individual sites are removed to ensure anonymity. The selected model outputs of the National Water Model from the year 2004 - 2018 at the 250m x 250m grid are combined with the EOF measurements of the same location.