Climate and hydrological analysis of glacial drainage basin based on BP neural network restoration of monitoring data - a case study of Weigeledangxiong Glacier

作者：Wei Kang, Ziyi Nie, Bei Li, Yuxi Zhang, Bing Yi, Dandan Liu · 发表于：Journal of Hydrology Regional Studies · 年份：2025 · DOI：10.1016/j.ejrh.2025.103024 · 研究领域：Cryospheric studies and observations、Hydrology and Watershed Management Studies、Hydrological Forecasting Using AI

The drainage basin of the melting water of the Weigeledangxiong Glacier. Under global climate change, increasing meltwater from Weigeledangxiong Glacier has altered watershed composition in its drainage basin. Continuous in-situ monitoring is crucial for identifying sensitive hydrological processes, yet harsh high-altitude conditions and technological limitations restrict data quality and hinder studies of melt-driven short-term water cycle mechanisms. To address this, we developed a BP neural network-based reconstruction method for generating accurate, continuous, high-temporal-resolution datasets. Using complete monitoring records, we applied Mann-Kendall (M-K) trend analysis, M-K mutation testing, and Spearman correlation to assess meteorological and hydrological variations throughout the hydrological year in the glacial drainage basin. (1) The BP neural network exhibits superior applicability and accuracy (average accuracy: 94.82 %) compared to traditional interpolation methods for restoring short-term continuous monitoring data. (2) June 28th marks a critical threshold when the study area's temperature transitioned from persistent decline to significant increase, leading to substantially enhanced DXG melting intensity under temperature control beginning in July. (3) The total annual meltwater runoff from DXG reaches 20,422,030 m³ , with July accounting for 38.18 % of the annual total, reflecting pronounced seasonal concentration in local hydrology. Additionally, river fr...