Internal variability can dominate inference of groundwater conservation policy effectiveness in coupled human–environment systems: evidence from the Sheridan-6 LEMA, Kansas

Abstract

Groundwater conservation policies are typically evaluated using historical observations. However, in coupled human-water systems, outcomes can vary substantially even under unchanged mean conditions of the climatic, economic, and land-use inputs due to nonlinear dynamics and spatiotemporal interactions. This phenomenon is referred to as internal variability. Here, we quantify how internal variability shapes inference about policy performance using the Sheridan-6 Local Enhanced Management Area (SD-6 LEMA) in western Kansas, a well-documented irrigator-led conservation program. We employ an agent-based human-hydrologic model to generate large ensembles through alternative temporal sequences of precipitation and crop prices and alternative spatial configurations of initial crop fields, while preserving fixed mean system states. These ensembles are explicitly contrasted with scenarios representing shifts in mean climate, economic, and land-use conditions. A three-way factorial analysis of variance shows that internal variability accounts for more than half of total variance in both policy-relevant indicators (groundwater withdrawal and saturated thickness) and behavioral outcomes (crop choice and farmer behavioral state transitions). The influence of internal variability is further amplified under higher irrigation intensity, where tighter constraints increase system sensitivity to spatiotemporal fluctuations. These findings do not challenge the effectiveness of the SD-6 LEMA policy itself, but instead demonstrate that policy performance inferred from observational data or a single historical realization can be misleading over short to medium timescales. We conclude that robust groundwater policy evaluation requires ensemble-based approaches that characterize outcome distributions under fixed institutional rules, with important implications for policy robustness and transferability in complex adaptive groundwater systems.