Integrated modeling of surface water and groundwater systems: a multivariate stochastic perspective

Abstract

The challenge of using short-term climate data series stems from their limited temporal coverage, which often fails to capture the full spectrum of climatic variability and extremes. This limitation hinders the accurate assessment of long-term trends and the modeling of hydrological processes essential for water resource management and agricultural planning. As a result, there is an urgent need to generate synthetic stochastic climate series that can replicate the statistical properties of observed data over extended periods. This study developed an integrated modeling framework that combines stochastic rainfall generation, monthly evapotranspiration estimation (MASHWIN), and the coupling of the rainfall–runoff model HBV with the groundwater-flow model MODFLOW. The proposed MASHWIN–HBV–MODFLOW approach incorporates uncertainty in precipitation patterns and simulates surface-water and groundwater interactions. The framework was applied to the Aguascalientes Valley aquifer. The stochastic model generated 100 synthetic climate series, which were successfully coupled with the hydrologic and subsurface models. The resulting simulations reproduced the statistical behavior of the observed climate data and enabled analysis of recharge and groundwater-head responses under multiple equiprobable scenarios. The integrated framework provides a comprehensive basis for evaluating hydrological uncertainty and supports long-term groundwater-resource assessment and management in data-limited aquifers.