Quantifying Groundwater Recharge Uncertainty: A Multiple-Model Framework and Case Study
Wednesday, 17 December 2014
In practice, it is difficult to estimate groundwater recharge accurately. Despite this challenge, most recharge investigations produce a single, best estimate of recharge. However, there is growing recognition that quantification of natural recharge uncertainty is critical for groundwater management. We present a multiple-model framework for estimating recharge uncertainty. In addition, we show how direct water flux measurements can be used to reduce the uncertainty of estimates of total basin recharge for an arid, closed hydrologic basin in the Atacama Desert, Chile. We first formulated multiple hydrogeologic conceptual models of the basin based on existing data, and implemented each conceptual model for the purpose of conducting numerical simulations. For each conceptual model, groundwater recharge was inversely estimated; then, Null-Space Monte Carlo techniques were used to quantify the uncertainty on the initial estimate of total basin recharge. Second, natural recharge components – including both deep percolation and streambed infiltration – were estimated from field data. Specifically, vertical temperature profiles were measured in monitoring wells and streambeds, and water fluxes were estimated from thermograph analysis. Third, calculated water fluxes were incorporated as prior information to the model calibration and Null-Space Monte Carlo procedures, yielding revised estimates of both total basin recharge and associated uncertainty. The fourth and final component of this study uses value of information analyses to identify potentially informative locations for additional water flux measurements. The uncertainty quantification framework presented here is broadly transferable; furthermore, this research provides an applied example of the extent to which water flux measurements may serve to reduce groundwater recharge uncertainty at the basin scale.