The accelerated warming in the Arctic poses serious risks to freshwater ecosystems by altering streamflow and river thermal regimes. However, limited research on Arctic River water temperatures exists due to data scarcity and the absence of robust methodologies, which often focus on large, major river basins. To address this, we leveraged the newly released, extensive AKTEMP data set and advanced machine learning techniques to develop a Long Short-Term Memory (LSTM) model. By incorporating ERA5-Land reanalysis data and integrating physical understanding into data-driven processes, our model advanced river water temperature predictions in ungauged, snow- and permafrost-affected basins in Alaska. Our model outperformed existing approaches in high-latitude regions, achieving a median Nash-Sutcliffe Efficiency of 0.95 and root mean squared error of 1.0 degrees C. The LSTM model learned air temperature, soil temperature, solar radiation, and thermal radiation-factors associated with energy balance-were the most important drivers of river temperature dynamics. Soil moisture and snow water equivalent were highlighted as critical factors representing key processes such as thawing, melting, and groundwater contributions. Glaciers and permafrost were also identified as important covariates, particularly in seasonal river water temperature predictions. Our LSTM model successfully captured the complex relationships between hydrometeorological factors and river water temperatures across varying timescales and hydrological conditions. This scalable and transferable approach can be potentially applied across the Arctic, offering valuable insights for future conservation and management efforts.

冻结层上水是寒区冻土水文循环的关键层，揭示其动态演变规律，对认知冻土区地下水运移机制及精准预测具有重要科学意义。然而，由于多年冻土区原位监测数据的匮乏，以及非线性适应型水文过程模型构建的缺失，冻结层上水动态时空预测精度难以满足科学研究和工程实践需求。本研究以青藏高原风火山小流域（海拔4063～5398 m）为典型研究区，基于2021—2023年原位观测气象数据（精度±0.1℃/±0.1 mm）、逐日土壤水热(精度±1℃/±0.03 m3·m-3)及冻结层上水位（精度±0.14 cm）原位监测数据，揭示坡面尺度冻结层上水动态的水热时空协同机制；集成气温、降水、土壤温湿度和初始水位等多要素，构建及评估基于长短期记忆神经网络（LSTM）的冻土水文预测模型的适应性。研究发现：（1）冻结层上水动态具有显著季节分异特征，其水位波动（年变幅0～1.53 m）与活动层土壤温湿度呈现一致性，基于Boltzmann函数的平均拟合优度为0.90。（2）所构建的基于LSTM方法的冻结层上水位预测模型（学习率0.002）在坡面多梯度验证中表现出卓越性能，平均纳什效率系...

【目的】干涉合成孔径雷达测量(InSAR)技术近年来被广泛用于反演活动层厚度（ALT），然而现有研究较少考虑冻融对地表形变和土壤孔隙水热变化的影响，因此，本文构建了考虑土壤水热变化的ALT反演模型。【方法】使用InSAR技术和CNNBiLSTM-AM模型得到地表参数，顾及冻融驱动下活动层的变形和土壤孔隙及水分的变化构建了活动层厚度反演模型。首先，通过SBAS-InSAR技术提取研究区垂直向地表形变。然后，构建CNN-BiLSTM-AM模型，使用卷积神经网络（Convolutional Neural Networks, CNN）对多源遥感数据特征提取，采用双向长短期记忆网络（Bi-directional Long Short-term Memory,BiLSTM）对提取特征进行预测，添加多头自注意力层（Attention Mechanism, AM）提高模型对关键信息的提取，得到多特征约束下的土壤含水量预测值。最后，以垂直向地表形变作为表征活动层的主要参数，构建基于土壤孔隙比和土壤含水量的活动层厚度反演模型，得到兰新高铁冻土区活动层厚度的时空分布。【结果】模型估计值与俄博岭实测数据验证的...

The soil freeze/thaw (FT) state has emerged as a critical role in the ecosystem, hydrological, and biogeochemical processes, but obtaining representative soil FT state datasets with a long time sequence, fine spatial resolution, and high accuracy remains challenging. Therefore, we propose a decision-level spatiotemporal data fusion algorithm based on Convolutional Long Short-Term Memory networks (ConvLSTM) to expand the SMAP-enhanced L3 landscape freeze/thaw product (SMAP_E_FT) temporally. In the algorithm, the Freeze/Thaw Earth System Data Record product (ESDR_FT) is sucked in the ConvLSTM and fused with SMAP_E_FT at the decision level. Eight predictor datasets, i.e., soil temperature, snow depth, soil moisture, precipitation, terrain complexity index, area of open water data, latitude and longitude, are used to train the ConvLSTM. Direct validation using six dense observation networks located in the Genhe, Maqu, Naqu, Pali, Saihanba, and Shandian river shows that the fusion product (ConvLSTM_FT) effectively absorbs the high accuracy characteristics of ESDR_FT and expands SMAP_E_FT with an overall average improvement of 2.44% relative to SMAP_E_FT, especially in frozen seasons (averagely improved by 7.03%). The result from indirect validation based on categorical triple collocation also shows that ConvLSTM_FT performs stable regardless of land cover types, climate types, and terrain complexity. The findings, drawn from preliminary analyses on ConvLSTM_FT from 1980 to 2020 over China, suggest that with global warming, most parts of China suffer from different degrees of shortening of the frozen period. Moreover, in the Qinghai-Tibet region, the higher the permafrost thermal stability, the faster the degradation rate.

冻土覆盖率高的小流域的径流形成受温度因素控制明显,普通水文模型不适用,而常规冻土水文模型因需要较多的气象观测要素而难以应用。考虑冻土流域产流机制,利用青藏高原腹地风火山小流域2017—2018年逐日降水、气温、径流观测数据,以降水、气温为输入,径流为输出,基于长短期记忆神经网络（LSTM）建立了适用于小流域尺度的冻土水文模型,并利用2019年观测数据进行验证。模型得益于LSTM特殊的细胞状态和门结构能够学习、反映活动层冻融过程和土壤含水量变化,具有一定的冻土水文学意义,能很好地模拟冻土区径流过程。模型训练期R2、NSE均为0.93,RMSE为0.63m3·s-1,验证期R2、NSE分别为0.81、0.77,RMSE为0.69m3·s-1。同时,为了验证模型可靠性,将模型应用于邻近的沱沱河流域,模型训练期（1990—2009年）R2、NSE均为0.73,验证期（2010—2019年）R2、NSE分别为0.66、0....