JIANG Runhua, HUANG Xinhui, DONG Xiaohua, MA Yaoming, HU Xue’er, WEI Dibo, WEI Chong, YU Dan, LEI Wenfang, SU Zhongbo
Snow Water Equivalent (SWE) is a critical hydrological variable for assessing the water content stored in snowpacks, particularly in alpine and high-altitude regions like the Qinghai-Xizang Plateau. Given the region’s complex topography, harsh climatic conditions, and the scarcity of in-situ snow measurements, SWE estimation remains a major scientific challenge. This study presents a novel hybrid framework that combines physical modeling and deep learning to simulate daily SWE across the Qinghai-Xizang Plateau, offering a new technical pathway for SWE estimation under data-scarce conditions. The proposed methodology integrates two core models. First, the Factorial Snow Model (FSM), a physically based process model, is employed to simulate daily snow depth. FSM uses meteorological inputs including air temperature, precipitation, radiation, humidity, wind speed, and pressure to simulate key snowpack processes such as accumulation, compaction, energy exchange, and melt. Second, snow density is estimated using a CNN-BiLSTM-Attention model, which leverages Convolutional Neural Networks (CNN) to extract local spatiotemporal features, Bidirectional Long Short-Term Memory networks (BiLSTM) to capture forward and backward temporal dependencies, and an attention mechanism to dynamically emphasize the most influential features across time steps. Meteorological and snow density data were obtained from ERA5 reanalysis datasets spanning 1979 to 2014. Six key input variables were selected via Pearson correlation analysis: longwave radiation, snowfall, rainfall, temperature, wind speed, and relative humidity. The CNN-BiLSTM-Attention model was trained on data from 1979 to 2003 and tested on data from 2004 to 2014. The model achieved strong predictive performance, with MSE=71.66 kg⋅m-3, RMSE=8.465 kg⋅m-3, MAE=6.378 kg⋅m-3, MAPE=4.556, and R 2=0.732, indicating its high accuracy in modeling snow density over long timescales. SWE was calculated by multiplying simulated snow depth from FSM with snow density predicted by the deep learning model. The daily SWE time series from 2006 to 2014 revealed clear seasonal patterns. SWE begins accumulating in October, peaks between December and February, and melts rapidly from March to May. The average daily SWE across the historical period was 0.278 cm, with a maximum of 0.838 cm observed in late December, reflecting the seasonal snow accumulation and melt dynamics typical of the region. The modeled SWE was further validated against two reference datasets: a high-resolution 0.01° SWE dataset and a 0.25° national fused SWE product. Comparisons showed that the proposed model closely tracked observed seasonal and interannual SWE trends, particularly during the critical accumulation and melt periods. It exhibited better agreement with high-resolution data than with coarser products, especially in representing peak values and transitional dynamics. This study introduces an effective and scalable method for SWE estimation in regions lacking dense observational networks. By decoupling the estimation of snow depth and snow density and applying specialized models to each, the framework combines the physical interpretability of FSM with the pattern recognition strength of deep learning. This hybrid modeling approach captures both the mechanistic and statistical characteristics of snowpack evolution, providing a reliable basis for snow resource evaluation. The CNN-BiLSTM-Attention model, which has rarely been applied to snow density modeling before, demonstrated a strong ability to model complex spatiotemporal interactions. When integrated with FSM, it forms a robust and adaptable modeling system that can be generalized to other alpine or cryospheric environments. The results provide valuable support for snow hydrology, water resource planning, and climate change impact assessment on the Qinghai-Xizang Plateau and similar high-mountain regions.