Assessing the impact of a land-use change (LUC) or the change in nutrient management practices on nonpoint source-driven groundwater quality requires complex analysis. 

This study evaluates an efficient screening tool that is available through a user-friendly online web interface. This is not NPSAT, our much more sophisticated nonpoint-source assessment tool, which also has a Central Valley, California, web interface.  This study builds a simpler point-and-click web-based tool, based on the simulation of an archetype groundwater systems simulation that is representative of alluvial aquifer systems in the Central Valley, California. It also applies to similar alluvial aquifer systems in semi-arid and mediterranean agricultural regions around the world.

This paper provides the underlying meta-analytical solution used to evaluate the changes in future nitrate (or other contaminant in contaminant breakthrough curves at extraction wells in response to LUC. The solution uses the concentration percentiles from a reference stochastic simulation of water flow and solute transport in a groundwater system, assuming a reference land-use distribution pattern.

Reference land-use controls the spatially variable rates of both, recharge and contaminant mass loading. The effect of a change in land-use or nutrient management practices is evaluated by scaling the ratio between the reference and the new (post-LUC) average input concentrations. This paper provides a validation of the proposed meta-analysis tool by comparing the results of the meta-analytical solution with those from a full stochastic simulation of the post-LUC scenario.

Simulation results show that the accuracy of the meta-analytical solution is best when the regional average recharge rates for both pre- and post-LUC remain approximately unchanged, despite the LUC and the change in contaminant mass loading. Results also indicate that changes in the spatial variability and in the pattern of the recharge rate do not significantly impact the flow field, travel times, and resulting concentrations, if the magnitude of local recharge remains about the same.

Lastly, the results show large variability among wells of (and—for an individual well—uncertainty about) the time lag between the time of LUC and the time of consequential effective change in concentrations across wells in the affected region, captured here using statistical metrics.