Policy-oriented Land-Use and Agricultural Management Scenarios for Groundwater Nitrate Hotspot Mitigation

Nitrate contamination of groundwater is often addressed as a diffuse agricultural management problem, yet monitoring in Denmark depicts that exceedance risk at abstraction and observation wells is spatially structured and closely linked to surrounding land-use composition and configuration. This suggests a land-use policy opportunity: if landscape fractions and fragmentation patterns help drive nitrate vulnerability, interventions could be spatially targeted and tailored rather than uniformly applied. In this study, we present a scenario-based planning framework for policy appraisal, enabling regulators, municipalities, and water utilities to test alternative policy packages and targeting rules and quantify their expected effects on groundwater nitrate hotspot risk. The system operates a predictive pipeline that relates nitrate outcomes to land-use fractions and landscape configuration metrics computed within configurable protection zones. Model outputs are formulated as a binary hotspot classification (hotspot vs. non-hotspot) based on exceedance of a drinking-water nitrate threshold, producing vulnerability maps to prioritize locations for intervention and prevention. The core functionality is a “what-if” engine built on an AI-based ensemble that generates a baseline nitrate-risk probability map and re-predicts risk under user-defined scenarios. Scenario levers are organized into two policy bundles: (i) land-use policy and management, implemented as controlled reallocations among land-cover fractions (e.g., reducing large contiguous cropland blocks, increasing wetland/riparian woodland cover, restricting impervious expansion) while enforcing feasibility constraints; and (ii) agricultural management, implemented as proportional reductions or caps on nitrogen surplus and fertilizer inputs. For each scenario, the system outputs an updated probability map and a different map relative to baseline, supporting spatial prioritization, instrument design, and transparent justification of differential targeting. By combining ex-ante scenario testing with ex-post monitoring of hotspot transitions after implementation, the framework supports adaptive groundwater governance and moves from risk mapping toward operational, spatially explicit nitrate-reduction policy design.