Journal of Hydrology最新文献

{"title":"Ultraviolet radiation stimulates the degradability of groundwater-fed DOC during the baseflow period of streams on the Qinghai-Tibet Plateau permafrost region","authors":"Yuhao Li ,&nbsp;Genxu Wang ,&nbsp;Wenzhi Wang ,&nbsp;Xiangyang Sun ,&nbsp;Yang Li ,&nbsp;Jinwang Xiao ,&nbsp;Wentian Xie ,&nbsp;Jiali Ding ,&nbsp;Chunlin Song","doi":"10.1016/j.jhydrol.2025.132751","DOIUrl":"10.1016/j.jhydrol.2025.132751","url":null,"abstract":"<div><div>As the climate warms and permafrost thaws, large quantities of dissolved organic carbon (DOC) enter streams via surface or subsurface flows and undergo biodegradation and photodegradation. Deciphering the transformation mechanisms of riverine DOC are crucial to understand the riverine carbon cycle and carbon-climate feedback. Yet the degradation characteristics of groundwater-fed DOC in streams of the Qinghai-Tibet Plateau (QTP) are not well understood. Here we sampled riverine DOC in a catchment of the QTP during the baseflow period and explored how the concentration, composition, and degradation of DOC respond to microorganisms and UV radiation, by combining laboratory incubations, ultraviolet–visible absorption, fluorescence spectroscopy and parallel factor analyses. Photo-, bio- and photo-bio-degradation experiments were conducted in parallel. Our results showed that riverine DOC concentrations varied from 3.59 mg·L^-1 to 5.61 mg·L^-1 during the baseflow period. After 28-day biodegradation experiments, 53.3 % (41.2 %–60.8 %) of DOC were degraded, which underpins the high biodegradability of DOC. In 3-day photodegradation and photo-biodegradation experiments, 30.6 % (22.4 %–36.7 %) and 42.9 % (32.8 %–53.8 %) of DOC were degraded, respectively. We found that microorganisms and UV radiation degraded non-aromatic compounds and small molecules. The relative abundances of C1 and C2 changed negligibly by microorganisms. In contrast, UV radiation reduced the degree of humification as a result of significant degradation of terrestrial humic-like substances (C2). Synergistic effects were also found in the tandem photo- and microbial degradation experiments within 48 h. Our findings demonstrated that UV radiation enhances the degradability of groundwater-fed DOC by preferentially degrading the terrestrial humic-like substances, and sunlight is a crucial regulator for groundwater-fed DOC degradation in streams of the QTP.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"653 ","pages":"Article 132751"},"PeriodicalIF":5.9,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diverse vegetation response to meteorological drought from propagation perspective using event matching method","authors":"Qianzuo Zhao ,&nbsp;Xuan Zhang ,&nbsp;Chong Li ,&nbsp;Yang Xu ,&nbsp;Junyuan Fei ,&nbsp;Fanghua Hao ,&nbsp;Rulin Song","doi":"10.1016/j.jhydrol.2025.132776","DOIUrl":"10.1016/j.jhydrol.2025.132776","url":null,"abstract":"<div><div>Climate change has led to increased frequency, duration, and severity of meteorological drought (MD) events worldwide, causing significant and irreversible damage to terrestrial ecosystems. Understanding the impact of MD on diverse vegetation types is essential for ecological security and restoration. This study investigated vegetation responses to MD through a drought propagation framework, focusing on the Yangtze River Basin in China, which has been stricken by drought frequently in recent decades. By analyzing propagation characteristics, we assessed the sensitivity and vulnerability of different vegetation types to drought. Using Copula modeling, the occurrence probability of vegetation loss (VL) under varying MD conditions was estimated. Key findings include: (1) The majority of the Yangtze River Basin showed a high rate of MD to VL propagation. (2) Different vegetation types exhibited varied responses: woodlands had relatively low sensitivity and vulnerability, grasslands showed medium sensitivity with high vulnerability, while croplands demonstrated high sensitivity and moderate vulnerability. (3) The risk of extreme VL increased sharply with rising MD intensity. This framework and its findings could provide valuable insights for understanding vegetation responses to drought and inform strategies for managing vegetation loss.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"653 ","pages":"Article 132776"},"PeriodicalIF":5.9,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of groundwater overextraction and agricultural irrigation on hydrological processes in an inland arid basin","authors":"Heng Yan ,&nbsp;Zhenghui Xie ,&nbsp;Binghao Jia ,&nbsp;Ruichao Li ,&nbsp;Longhuan Wang ,&nbsp;Yuhang Tian ,&nbsp;Yanbin You","doi":"10.1016/j.jhydrol.2025.132770","DOIUrl":"10.1016/j.jhydrol.2025.132770","url":null,"abstract":"<div><div>Irrigation accounts for a major proportion of human water usage, exerting significant impacts on the natural environment and regional climate in inland arid basins. Groundwater overextraction and agricultural irrigation can drastically alter the water distribution in terrestrial systems, with potential impacts on hydrological processes. To better understand these risks and improve water resource regulation in inland arid basins, a land surface model was employed to investigate the impact of different groundwater overextraction ratios and irrigation efficiencies on hydrological processes in the Heihe River Basin during 2015–2020. The model integrated daily irrigation water use data that were estimated through the combination of satellite data and machine learning. The results showed a rationality of irrigation water use data between the inter-annual variation of estimated irrigation data and government reported data. When irrigation water was only withdrawn from the surface, it effectively increased evapotranspiration (22.1 %) and soil moisture (11.1 %), with little impact on water table depth (−1.7 %). However, the groundwater balance was seriously impaired when groundwater was extracted for irrigation, increasing water table depth (32.6 %) and depleting groundwater storage throughout the study period. Improving irrigation efficiency is considered to be an effective technique for maintaining groundwater; however, excessively high irrigation efficiency may also lead to an increase in water table depth. This is because irrigated water is unable to return to the water cycle, which reduces the overall amount of water availability in inland arid basins. These results highlight the complexity and significance of irrigation in inland arid basin hydrology, as well as the necessity for more realistic estimates of irrigation water use and efficiency to better understand the impact of human activities on the water cycle.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"653 ","pages":"Article 132770"},"PeriodicalIF":5.9,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Streamflow regime-based classification and hydrologic similarity analysis of catchment behavior using differentiable modeling with multiphysics outputs","authors":"Yuqian Hu ,&nbsp;Heng Li ,&nbsp;Chunxiao Zhang ,&nbsp;Bingli Xu ,&nbsp;Wenhao Chu ,&nbsp;Dingtao Shen ,&nbsp;Rongrong Li","doi":"10.1016/j.jhydrol.2025.132766","DOIUrl":"10.1016/j.jhydrol.2025.132766","url":null,"abstract":"<div><div>Streamflow regime-based catchment classification has been demonstrated to fully exploit the temporal information stored in the data to better reflect seasonal changes and drought/flood behavior. However, the catchment behavior of the clusters generated under this classification framework in terms of water storage and release deserves further exploration. This study quantified the streamflow sequences of 531 catchments in the contiguous United States using a hybrid hydrological model with multiphysics outputs. Based on standardized weekly-step mean annual hydrographs, the catchments were grouped into 14 distinct streamflow regime clusters. Finally, using the snowpack, snowmelt, soil water and evapotranspiration output by hybrid hydrological model, we described the water storage and release behaviors of clusters and analyzed their linkage with catchment attributes. Results shown: (1) most clusters exhibit varying degrees of spatial continuity, but there is considerable variability between catchments within clusters, and the catchments that are far apart can also exhibit similar streamflow regime; (2) most clusters also exhibit similar water storage (snowpack, soil water) and release (evapotranspiration, snowmelt) behaviors, while some more extreme catchments deviate from this trend; (3) catchments with similar streamflow regimes may differ significantly in attributes like vegetation and soil. Classification based on streamflow similarity does not establish a universal link to catchment attributes. From these results, we conclude that streamflow regime-based catchment classification can serve as a useful tool for understanding hydrologic similarity, but is constrained by the compounding influence of different climate conditions and catchment attributes and by relatively rough interpretable variables.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"653 ","pages":"Article 132766"},"PeriodicalIF":5.9,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Twin extreme learning machine model and cooperation search algorithm for multi-step-ahead point and interval runoff prediction","authors":"Zhong-kai Feng ,&nbsp;Pan Liu ,&nbsp;Wen-jing Niu ,&nbsp;Xin-yue Fu ,&nbsp;Yang Xiao ,&nbsp;Tao Yang ,&nbsp;Hai-yan Huang","doi":"10.1016/j.jhydrol.2025.132778","DOIUrl":"10.1016/j.jhydrol.2025.132778","url":null,"abstract":"<div><div>Accurate runoff predictions provide crucial technical supporting information for water resource decision-makers, offering insights into future runoff changes. This study investigates the effectiveness of twin extreme learning machine (TELM) and cooperation search algorithm (CSA) in multi-step-ahead point and interval runoff prediction. Then, three multi-step-ahead forecasting strategies are considered to develop various models: recursive, direct, and direct-recursive. The results show that the developed model consistently delivers superior accuracy and reliability in predicting runoff, while CSA outperforms other evolutionary methods in determining model parameters. However, no single forecasting strategy consistently outshines others across all scenarios, with the recursive strategy showing a slight edge in performance. Besides, the interval runoff predictions confirm the effectiveness of TELM in yielding high-quality prediction intervals across various experiments by incorporating upper and lower boundary estimation and boundary functions. For station A with a 98% confidence level, the proposed method achieves prediction interval coverage probability, prediction interval normalized average width, and coverage width criterion of 0.9904, 0.1138, and 0.6828, respectively, indicating overall high interval prediction quality. Thus, a novel artificial intelligence model is developed for multi-step-ahead point and interval runoff prediction.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"653 ","pages":"Article 132778"},"PeriodicalIF":5.9,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143286840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Framework for short-term hydropower cascade–station–unit integrated multi-objective scheduling: Considering unit safety and economic efficiency","authors":"Jingwei Huang ,&nbsp;Hui Qin ,&nbsp;Xu Yang ,&nbsp;Keyan Shen ,&nbsp;Huaming Yao ,&nbsp;Xinyu Chang ,&nbsp;Gaoge Li ,&nbsp;Yuan Gao","doi":"10.1016/j.jhydrol.2025.132756","DOIUrl":"10.1016/j.jhydrol.2025.132756","url":null,"abstract":"<div><div>Short-term hydropower scheduling primarily focuses on maximizing economic benefits, and the hydroelectric generating unit, as the primary component responsible for power generation, has become increasingly important given the growth in installed capacity. Consequently, current research has focused on hydro-unit commitment, particularly in terms of its safety status. This study addressed the challenges of sudden load changes and multi-session benefit optimization in cascade power stations, developing a short-term multi-objective optimization model that considers both cascade energy storage and unit safety benefits. First, the work proposed methods to evaluate unit operation status and priority, integrating it into the unit safety objectives. Subsequently, a mixed-integer linear programming model was constructed, incorporating a dynamic variable constraint corridor strategy based on the initial state to narrow the solution space and improve the solving efficiency. The model was validated through case studies involving the scheduling of the Three Gorges–Gezhouba and Xiluodu-Xiangjiaba cascade power stations on the Yangtze River in China, comparing the computational effects in four operational scenarios: drawdown, impoundment, maintenance, and emergency. The results show that: (1) The proposed single-period model effectively reduced the units start/stop frequencies under load fluctuations. (2) In multi-period optimization scheduling, the model enhanced the energy storage benefits at the end of scheduling and optimized the safe operation of the units. (3) A competitive relationship exists between cascade economic benefits and unit safety objectives.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"653 ","pages":"Article 132756"},"PeriodicalIF":5.9,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mixture of experts leveraging Informer and LSTM variants for enhanced daily streamflow forecasting","authors":"Zerong Rong ,&nbsp;Wei Sun ,&nbsp;Yutong Xie ,&nbsp;Zexi Huang ,&nbsp;Xinlin Chen","doi":"10.1016/j.jhydrol.2025.132737","DOIUrl":"10.1016/j.jhydrol.2025.132737","url":null,"abstract":"<div><div>Streamflow forecasting is of paramount importance for water resources management and flood prevention. Machine learning, particularly deep learning, has had significant success in hydrological forecasting. However, there is still a desire for newer single-type and integrated architectures to further enhance the accuracy and reliability of forecasts. Recently, Transformer-based models have emerged as promising tools, and their effectiveness in streamflow modeling tasks warrants further investigation. The Mixture of Experts (MoE) model has also demonstrated potential in other fields, but its application in the hydrological domain remains relatively limited. This study presents an innovative streamflow forecasting model for the Quinebaug River Basin in Connecticut, USA, based on the MoE framework. Firstly, the hyperparameters of expert models, including LSTM, GRU, LSTM-Sequence to Sequence-Attention, and Informer, with lead times ranging from 1 to 8 days, are optimized using the grid search method. Subsequently, Random Forest, LSTM, and Transformer are used as routers to construct 4-class and 2-class MoE frameworks. Finally, the classified outputs are integrated to synthesize the streamflow forecasting results. The results indicate that the Informer model outperforms other benchmark models in all forecast periods, especially in shorter ones. Both 4-class and 2-class MoE can improve the streamflow forecasting results of the optimal sub-model to some extent: when the lead time reaches 5 days or more, the NSE increases by nearly or more than 3 %. This study highlights that the MoE framework can improve daily streamflow forecasting accuracy by integrating the strengths of different experts, although the router tends to prioritize models with superior performance during the classification process.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"653 ","pages":"Article 132737"},"PeriodicalIF":5.9,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143286422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causality-Guided Deep learning for streamflow predicting in a mountainous region","authors":"Xuan Tang,&nbsp;Guanghua Qin,&nbsp;Xuemei Wu,&nbsp;Yuting Zhao,&nbsp;Hongxia Li","doi":"10.1016/j.jhydrol.2025.132719","DOIUrl":"10.1016/j.jhydrol.2025.132719","url":null,"abstract":"<div><div>Accurate streamflow predictions in mountainous regions are crucial for water resource management and flood mitigation. Deep learning (DL) models, which have been widely used for streamflow predicting recently, can simulate the nonlinear hydrological relationships but may not capture the underlying laws of physics. This study proposed a Causality-Guided Deep Learning (CGDL) model to enhance the streamflow predicting for mountainous regions by incorporating physics-based causal inference and improved multivariate Transfer Entropy (IMTE) algorithm. We assessed the CGDL model through a case study in a mountainous catchment using in situ hydrometeorological variables (precipitation, temperature, and humidity, etc.). The results demonstrated that CGDL outperformed DL and process-based (PB) models, achieving a higher <em>NSE</em> (Nash-Sutcliffe Efficiency) of 0.805, compared to 0.701 for DL and 0.716 for PB during the testing period. Furthermore, CGDL significantly reduced the <em>EHF</em> (Error of High Flow) to −9.7%, versus −14.9% for DL and –22.0% for the PB model in the testing period, highlighting its efficiency in high flow predictions. The CGDL also showed superior robustness and generalization when extending forecast lead time and simulating beyond the bounds of the training data. Additionally, the SHAP analysis indicated that CGDL provided greater interpretability than the DL model. This study demonstrates that integrating causality knowledge into deep learning models has the potential to enhance streamflow predicting in mountainous regions. It is helpful for improving our understanding of hydrological processes and decision-making to issue flood warnings.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"653 ","pages":"Article 132719"},"PeriodicalIF":5.9,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bacterial and archaeal community successions in high-salinity groundwater and their potential impact on arsenic cycling","authors":"Chuanshun Zhi ,&nbsp;Xiaonong Hu ,&nbsp;Zhuo Zhang ,&nbsp;Baonan He ,&nbsp;Jing Bai ,&nbsp;Xiancang Wu ,&nbsp;Hui Mu ,&nbsp;Wenbo Chang ,&nbsp;Fan Yang ,&nbsp;Qi Qiu ,&nbsp;Yuzheng Wang","doi":"10.1016/j.jhydrol.2025.132742","DOIUrl":"10.1016/j.jhydrol.2025.132742","url":null,"abstract":"<div><div>Groundwater arsenic (As) contamination is a global issue involving complex biogeochemical processes. However, the arsenic cycling in high-salinity groundwater environments remain poorly understood. In this study, we used hydrogeochemical and microbial techniques to investigate the impact of salinity on bacterial and archaeal community structures and their functional evolution in the Yellow River Delta (YRD), China, and to explore how these dynamics influence arsenic enrichment. The results showed that bacterial richness and evenness decreased significantly with increasing salinity, especially in samples with TDS above 10 g/L, and the decrease was even more pronounced compared to archaea. Bacterial communities were dominated by <em>Proteobacteria</em> and <em>Omnitrophica</em>, while archaeal communities were predominantly composed of <em>Halobacteria</em>. Microbial communities actively mediate As-Fe-C-N-S redox cycling, exhibiting distinct cycling characteristics under varying salinity conditions. Microbe-mediated processes such as organic matter degradation, sulfate reduction, iron reduction, methanotrophy, and methanogenesis potentially contributed to As mobilization in low-salinity groundwater. In contrast, in high-salinity groundwater, sulfur respiration, iron respiration, and nitrate respiration were intensified, while methane oxidation and methanogenesis were inhibited, significantly affecting As cycling. This study highlights the critical role of salinity in shaping microbial community dynamics and their influence on arsenic biogeochemical cycling in the YRD aquifers, providing new insights into As mobilization in high-salinity groundwater.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"653 ","pages":"Article 132742"},"PeriodicalIF":5.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extreme degradation of alpine wet meadow decelerates soil heat transfer by preserving soil organic matter on the Qinghai–Tibet Plateau","authors":"Zeyong Gao ,&nbsp;Chengming Zhang ,&nbsp;Wengyan Liu ,&nbsp;Fujun Niu ,&nbsp;Yibo Wang ,&nbsp;Zhanju Lin ,&nbsp;Guoan Yin ,&nbsp;Zekun Ding ,&nbsp;Yunhu Shang ,&nbsp;Jing Luo","doi":"10.1016/j.jhydrol.2025.132748","DOIUrl":"10.1016/j.jhydrol.2025.132748","url":null,"abstract":"<div><div>Alpine wet meadow (AWM), an important wetland type on the Qinghai–Tibet Plateau (QTP), is sensitive to climate change, which alters the soil hydrothermal regime and impacts ecological and hydrological functions in permafrost regions. The mechanisms underlying extreme AWM degradation in the QTP and hydrothermal factors controlling permafrost degradation remain unclear. In this study, soil hydrothermal processes, soil heat migration, and the permafrost state were measured in AWM and extremely degraded AWM (EDAWM). The results showed that the EDAWM exhibited delayed onset of both soil thawing and freezing, shortened thawing period, and extended freezing period at the lower boundary of the active layer. The lower ground temperatures resulted in a 0.2 m shallower active layer thickness in the EDAWM compared with the AWM. Moreover, the EDAWM altered soil thermal dynamics by redistributing energy, modifying soil moisture, preserving soil organic matter, and adjusting soil thermal properties. As for energy budget, a substantial amount of heat in the EDAWM was consumed by turbulent heat fluxes, particularly latent heat flux, which reduced the amount of heat transferred to the ground. Additionally, the higher soil organic matter content in EDAWM decreased the annual mean soil thermal conductivity from 1.42 W m⁻1 K⁻1 in AWM to 1.26 W m⁻1 K⁻1 in EDAWM, slowing down heat transfer within the active layer and consequently mitigating permafrost degradation. However, with continued climate warming, the soil organic matter content in EDAWM will inevitably decline due to microbial decomposition in the absence of new organic inputs. As the soil organic matter content diminishes, soil heat transfer processes will likely accelerate, and the permafrost warming rate may surpass that in undistributed AWM. These findings enhance our understanding of how alpine ecosystem succession influences regional hydrological cycles and greenhouse gas emissions.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"653 ","pages":"Article 132748"},"PeriodicalIF":5.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}