第七届青年地学论坛

Lake Hulun is the fifth-largest lake in China, playing a substantial role in maintaining the balance of the grassland ecosystem of the Mongolia Plateau, which is a crucial ecological barrier in North China. To better understand the changing characteristics of Lake Hulun and the driving mechanisms, it is necessary to investigate the water storage changes on extended timescales. The main objective of this study is to reconstruct the water storage time series of Lake Hulun over the past century. We employed a machine learning approach termed the extreme gradient boosting tree (XGBoost) to reconstruct the water storage changes over a one-century timescale based on the generated bathymetry and satellite altimetry data and investigated the relationships with hydrological and climatic variables in long term. Results show that the water storage changes from 1961 to 2019 were featured by four fluctuation phases, with the highest water storage observed in 1991 (14.02 Gt) and the lowest point in 2012 (5.18 Gt). The century-scale reconstruction result reveals that the water storage reached the highest point in the 1960s within the period of 1910–2019. The lowest stage occurred in the sub-period of the 1930s–1940s, which was even lower than the alerted shrinkage stage in 2012. The predictive model results indicate the effective performance of the XGBoost model in reconstructing century-scale water storage variations, with the MAE of 0.68, NRMSE of 0.11, NSE of 0.97, and CC of 0.94. The annual fluctuations of water storage were mostly affected by precipitation, followed by vapor pressure, temperature, potential evapotranspiration, and wet day frequency. The atmospheric circulations of the AO, ENSO, PDO, and NAO have tight associations with the water storage variations, which change with different study periods.
 

Lake Hulun, water storage, climate change, machine learning, atmospheric circulation