Modeling river-aquifer interactions in dryland regions to enhance water management strategies

Abstract

Water security relies on accurately representing hydrological processes in river-aquifer dynamics, including aquifer recharge driven by rainfall, hyporheic flow, and lateral tributary inflow. River–aquifer studies and hedging models often overlook these processes. This study proposes a conceptual model of river-aquifer interactions in data scarce regions (Chapter 2). Building on this conceptual model, a parsimonious numerical model was developed, and these processes were accurately calculated in a data-scarce region at scales relevant to water management (Chapter 3). Furthermore, a numerical hedging model for a dryland reservoir, featuring an early rationing system based on the identification of drought events and river-aquifer dynamics, is presented (Chapter 4). Short-term field-measured hydrological data were analyzed along with secondary data on hydrogeology, water use, water participation, and satellite imagery. The results from Chapter 2 indicate that the average transmission losses ranged from 12% to 28% of river inflow, while the transmission gains ranged from 10% to 34%. Transmission losses were prevalent at the beginning of the rainy season (rainfall ≤ 10 mm/day), whereas transmission gains were dominant following intense rainfall events (> 20 mm/day). The results from Chapter 3 indicate that the numerical model’s performance measures (Kling-Gupta efficiency, Nash–Sutcliffe efficiency, and percent bias) effectively estimate river and groundwater flow using a simple modeling approach, requiring only minimal calibration (three parameters). Notably, the data required for the model are often available in regions with scarce data. Sensitivity analysis of the simplified model parameters showed that riverbed hydraulic conductivity and aquifer width significantly affected outflow in data-scarce environments. Hyporheic flow is crucial for the water balance in dryland rivers. In contrast, subcatchment runoff demonstrated minimal influence compared to inflow and groundwater fluxes, suggesting a greater dependence on river volume and stage than on intraseasonal rainfall. In Chapter 4, the heading model has key advantages, including the optimization of decision variables (trigger volumes and rationing coefficients) using a genetic algorithm (NSGA-II), integration of water user participation, water allocation connected with drought assessments through a simple drought index, streamflow prediction based on river-aquifer dynamics, and the use of short-term field-measured hydrological data. The results show that the proposed hedging rule maintained system vulnerability below 10% using both simulated and measured inflow data, and the objective function (the modified shortage index) was successfully optimized, even when early rationing occurred during the rainy season. The quantitative analysis suggests that for adaptive hedging in data-scarce drylands, the calculation method (the rule itself) is more critical than the availability of on-site inflow measurements. Therefore, operating rules for a dryland reservoir optimized using simulated data may be effective in satisfying water demands and stakeholder requirements, even when integrated with a simple drought index and in the presence of data uncertainties. This study contributes to a more accurate water balance of key hydrological processes in data-scarce regions and provides valuable tools for similar areas that require viable management solutions.