[Model outputs] Identifying major hydrologic change drivers in a highly managed transboundary endorheic basin: integrating hydro‐ecological models and time‐series data mining techniques
Cite this dataset
Acero Triana, Juan S.; Ajami, Hoori (2022). [Model outputs] Identifying major hydrologic change drivers in a highly managed transboundary endorheic basin: integrating hydro‐ecological models and time‐series data mining techniques [Dataset]. Dryad. https://doi.org/10.6086/D1ZM33
The fragile balance of endorheic lakes in highly managed semi-arid basins with transboundary water issues has been altered by the intertwined effects of global warming and long-term water mismanagement to support agricultural and industrial demand. The alarming rate of global endorheic lakes’ depletion in recent decades necessitates formulating mitigation strategies for ecosystem restoration. However, detecting and quantifying the relative contribution of causal factors (climate variability and anthropogenic stressors) is challenging. This study developed a diagnostic multivariate framework to identify major hydrologic drivers of lake depletion in a highly managed endorheic basin with a complex water distribution system. The framework integrates the Soil and Water Assessment Tool (SWAT) simulations with time-series decomposition and clustering methods to identify the major drivers of change. This diagnostic framework was applied to the Salton Sea Transboundary Basin (SSTB), the host of the world's most impaired inland lake. The results showed signs of depletion across the SSTB since late 1998 with no significant changes in climate conditions. The time-series data mining of the SSTB water balance components indicated that decreases in lake tributary inflows (-16.4 Mm3 yr-2) in response to decline in Colorado River inflows, associated with state water transfer agreements, are causing the Salton Sea to shrink, not changes in the irrigation operation as commonly believed. The developed multivariate detection and attribution framework is useful for identifying major drivers of change in coupled natural-human systems.
National Science Foundation, Award: 1739977