Efficient uncertainty quantification for seawater intrusion prediction using optimized sampling and null space Monte Carlo method

Abstract

Uncertainty in environmental modeling predictions, stemming from parameter estimation, is a crucial challenge that must be addressed to ensure effective decision-making. Limited field measurements, high computational costs, and a lack of guidance in estimating measurement uncertainty further compound this challenge, particularly for highly parameterized complex models. In this study, we propose a novel and computationally efficient framework for quantifying predictive uncertainty that can be applied to a range of environmental modeling contexts. The novel components of the framework include efficient parameter space sampling using an Optimized Latin hypercube sampling strategy, and applying the Null Space Monte Carlo method (NSMC) along with a developed filtering technique. The NSMC generates sample sets to calibrate the model while exploring the null space. This space contains parameter combinations that are not sufficiently supported by observations. The filtering technique omits low-potential parameter sets from undergoing model calibration. The framework was tested on the seawater intrusion (SWI) model of Wadi Ham aquifer in the United Arab Emirates (UAE) to investigate aquifer sustainability in 2050. Our results demonstrate the importance of incorporating direct and indirect measurements of heads, salinity, and geophysical survey data into the calibration dataset to reduce uncertainty in salinity predictions. The extent of SWI for multiple calibrated parameter sets varied by 4.5% to 11% relative to their means at two main pumping fields. We conclude, with a moderate to a high degree of certainty, that SWI is a serious threat to these fields, and actions are needed to protect the aquifer from salinization. Additionally, variations in SWI length under different geological conditions illustrate regions of high uncertainty that require further data collection. Our framework effectively reduced and quantified prediction uncertainty and provides decision-makers with critical information to inform risk management strategies.