Saline endorheic lakes play a crucial role in ecological regulation and biodiversity conservation in semi-arid regions. They provide a wide range of ecosystem services such as habitat for aquatic life and migratory birds, water supply, mineral extraction, and recreation. Although saline lakes are critical to ecological health and human wellbeing, they are being shrunk at alarming rates in recent decades, representing some of the most threatened ecosystems in the world. Factors contributing to lake degradation are directly linked to the expansion and intensification of agriculture in the 20th century and its intertwined effect with global warming. However, quantifying the relative contribution of climate variability and anthropogenic stressors in water budget dynamics of the lakes is challenging. Hydrologic processes of highly managed agricultural landscapes are complex and the lack of spatial data in endorheic basins with transboundary water issues further contribute to this problem. In this study, we developed a modeling framework that integrates the Soil and Water Assessment Tool (SWAT) simulations with seasonal trend decomposition by LOESS (STL), residual analysis for anomaly detection, and time series clustering, to identify the major drivers of lake trend and seasonal shrinking patterns. This modeling framework was applied to the Salton Sea Basin (SSB), host of the largest inland lake in California. Salton Sea’s water level has declined by 32% over the last 25 years, causing a massive bird and fish die-off, and a regional asthma crisis due to the spreading of toxic dust from the exposed playa. Results suggest that decreases in Colorado River allocation are causing the lake to shrink, not changes in the irrigation efficiency or the climate regime as commonly believed. Our results are expected to assist decision-makers with a robust modeling tool to evaluate the environmental tradeoffs in formulating and implementing timely adaptation and mitigation strategies across the SSB while minimizing their economic consequences.

Accurate simulation of plant water use across agricultural ecosystems is essential for various applications, including precision agriculture, quantifying groundwater recharge, and optimizing irrigation rates. Previous approaches to integrating plant water use data into hydrologic models have relied on evapotranspiration (ET) observations. Recently, the flux variance similarity approach has been developed to partition ET to transpiration (T) and evaporation, providing an opportunity to use T data to parameterize models. To explore the value of T/ET data in improving hydrologic model performance, we examined multiple approaches to incorporate these observations for vegetation parameterization. We used ET observations from 5 eddy covariance towers located in the San Joaquin Valley, California, to parameterize orchard crops in an integrated land surface – groundwater model. We find that a simple approach of selecting the best parameter sets based on ET and T performance metrics works best at these study sites. Selecting parameters based on performance relative to observed ET creates an uncertainty of 27% relative to the observed value. When parameters are selected using both T and ET data, this uncertainty drops to 24%. Similarly, the uncertainty in potential groundwater recharge drops from 63% to 58% when parameters are selected with ET or T and ET data, respectively. Additionally, using crop type parameters results in similar levels of simulated ET as using site-specific parameters. Different irrigation schemes create high amounts of uncertainty and highlight the need for accurate estimates of irrigation when performing water budget studies.