Machine learning modeling reveals the spatial variations of lake water salinity on the endorheic Tibetan Plateau

Abstract

Study region: The endorheic Tibetan Plateau (TP). Study focus: Water salinity is sensitive indicator for variations of lake hydrologic and physicochemical characteristics. Due to the heterogeneous influences from geographical and climatic factors, lake water salinity is highly sensitive to environmental diversity and changes. The TP hosts a wide distribution of lakes, the majority of which belong to endorheic drainage type and are saline or salty lakes. However, the harsh environment on the TP poses great challenges for the in–site measurements at large scales, impeding the comprehension of the pattern and variations of lake water salinity across the TP. New hydrological insights for the region: Benefiting extensive field surveys and a meta–analysis, this study establishes machine learning models based on measurements from 100 terminal lakes (>1 km2) and related physical variables. The optimal model (R2 = 0.90, MAE = 8.11 g/L, MAPE = 36.40 %, RMSE = 12.51 g/L, RRMSE = 36.96 g/L) is then applied to predict the water salinity of the other 214 unmeasured terminal lakes. The modeling results reveal a spatial variation pattern of increasing water salinity of these terminal lakes from south to north across the endorheic basins. Further classification of water salinity levels indicated that more than half (213) of the terminal lakes are in an oligosaline state. This study contributes to a spatially–explicit understanding of the distribution variations in water salinity of terminal TP lakes and provides a feasible approach for estimating water salinity of unmeasured lakes at large scales.

Keywords

• Lake
• Water salinity
• Water resources
• Tibetan Plateau
• Machine learning model