Journal of Hydrology最新文献

{"title":"LSTM-FKAN coupled with feature extraction technique for Precipitation–Runoff modeling","authors":"Tongfang Li, Kairong Lin, Tian Lan, Yuanhao Xu","doi":"10.1016/j.jhydrol.2025.132705","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.132705","url":null,"abstract":"Precipitation-runoff modeling is an essential non-engineering measure for assessing water resources and ensuring their sustainable use. For the complex hydrological cycle, Long Short-Term Memory (LSTM) networks have proven effective in handling hydrological time series data. However, LSTM’s compatibility with various data forms and its accuracy still require further improvement. This study aims to propose a high-accuracy modeling method that directly processes time series data and raster data. A Long Short-Term Memory-Fourier Kolmogorov-Arnold Networks coupled with feature extraction technique (CNN-LSTM-FKAN) was developed based on traditional LSTM. By incorporating feature extraction techniques, the model enhances its ability to capture spatial information, while the coupling of Fourier Kolmogorov-Arnold Networks (FKAN) improves simulation accuracy. The model was applied to three basins located in the water source region of the middle route of the South-to-North Water Transfer Project in China, using meteorological data from stations and raster data to simulate runoff processes. The results indicate that the CNN-LSTM-FKAN model consistently outperforms the LSTM model across all study areas, with improvements in the Nash Sutcliffe Efficiency (NSE) during the test period ranging from 0.04 to 0.17. The model generally achieved optimal results at a 60-day time step, with NSE values exceeding 0.89. The optimal hyperparameter combinations varied significantly depending on the time step. The model demonstrated strong applicability across different basins, with the best simulation results yielding NSE values of 0.902, 0.926, and 0.895 in the respective basins. The CNN-LSTM-FKAN model’s capability to extract spatial information further enhanced its performance, with NSE improvements of up to 0.121 compared to the LSTM-FKAN model. In the Hanzhong basin and Xun River basin, the NSE of CNN-LSTM-FKAN improved by more than 0.1 compared to Random Forest. Additionally, the NSE of CNN-LSTM-FKAN increased by up to 0.11 compared to Informer at longer time steps. Furthermore, CNN-LSTM-FKAN demonstrated significantly higher accuracy in capturing flood peaks than both Random Forest and Informer. The CNN-LSTM-FKAN model performed well in simulating runoff during both high-flow and low-flow periods, showcasing significant potential for precipitation-runoff modeling.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"6 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990561","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":"Geochemical behavior of high-level radium contamination in representative coastal saltworks","authors":"Zhe Zhang, Lixin Yi, Hongwei Ren, Tianxue Lyu, Chenyi Liu, Shucheng Li, Haizhen Bian, Cong Wang, Lu Ren, Nan Liu, Honghao Wang, Yingchun Dong, Ruotong Li","doi":"10.1016/j.jhydrol.2025.132716","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.132716","url":null,"abstract":"The coexistence of salt production with coastal and residential areas presents significant environmental pollution risks. This study explores the hydrochemical characteristics and radium isotopic signatures of saline water through case studies of groundwater and surface water in representative coastal salt fields, supplemented by additional data. The water in salt field regions is predominantly of the Na-Cl type, significantly influenced by evaporation and seawater intrusion, with elevated levels of ammonium-nitrogen pollution. Compared to non-salt field areas, groundwater in salt field regions exhibits significantly higher radium radiation levels and annual effective doses, exceeding WHO standards and industrial emission limits, which threaten the limited freshwater resources and nearby ecosystems. Distinct variations in radium activities and their ratios reveal multiple supply and removal mechanisms, including adsorption–desorption, recoil, decay, and co-precipitation. Salinity primarily controls the mobilization of exchangeable radium through constrained desorption and co-precipitation. Seawater intrusion significantly increases groundwater salinity, facilitating the desorption of radium from particles and sediments, thereby elevating associated risks. Evaporation in surface water within salt ponds further promotes the coprecipitation of all four radium isotopes with potential host mineral (barite), at consistent salinity threshold, partially mitigating radiological risks. However, poor management practices observed in salt field operations substantially increase the risk of radioactive water leakage into surrounding environments. Geochemical modeling suggests that short-lived radium isotopes are more effectively incorporated into precipitated minerals compared to their long-lived counterparts. A limitation of this study is the inability to exclude the potential influence of fine colloids on radium transport in high-salinity solutions. These findings provide critical insights for the sustainable management of salt field operations, particularly in regions where freshwater resources are scarce.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"7 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990558","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":"Improved non-stationary SPEI and its application in drought monitoring in China","authors":"Qiang Zhang, Danzhou Wang, Anlan Feng, Gang Wang, Lei Hu, Chong-Yu Xu, Vijay P. Singh","doi":"10.1016/j.jhydrol.2025.132706","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.132706","url":null,"abstract":"Drought index is the first step in drought monitoring and mitigation. However, nonstationarity in the hydrometeorological data series violates the assumption of stationarity in drought indices. Here we propose a nonstationary standardized precipitation-evapotranspiration index (NSPEIt). We adopt the Penalized Splines (PS) and Stochastic Partial Differential Equations (SPDE) to fit the water deficit (Dt), derive a time-varying log-logistic distribution, and develop two versions of NSPEIt, i.e. NSPEIt-PS and NSPEIt-SPDE. We find that hydrometeorological data series in the Qinghai-Tibet Plateau Region (QTR), East Asian Monsoon Region (EMR) are nonstationary. NSPEIt performed satisfactorily in drought monitoring for both stationary and nonstationary hydrometeorological series. Specifically, NSPEIt-PS had higher drought monitoring performance than NSPEIt-SPDE. We compared standardized precipitation index (SPI), self-calibrated Palmer Drought Severity Index (scPDSI), NSPEIt-PS, NSPEIt-SPDE, soil moisture (SM), and normalized difference vegetation index (NDVI) and found that NSPEIt-PS performed better than NSPEIt-SPDE in drought monitoring and other drought indices considered in this study. However, NSPEIt_SPDE performed better in meteorological drought monitoring but NSPEI_PS was better in agricultural drought monitoring. When compared to historical droughts in 2009, the existing drought indices considered in this study tended to underestimate and/or overestimate drought intensity, whereas NSPEIt-PS and NSPEIt-SPDE captured well droughts with higher intensity, closely describing the spatial evolution of historical meteorological droughts. Results of NSPEIt-PS and NSPEIt-SPDE indicated intensifying droughts in the QTR, and regions with intensifying droughts distributed sporadically in the Northwest Arid and semi-arid Region (NAR) and EMR. Besides, droughts with higher frequency, longer duration, and higher intensity can be monitored in southwest China, the Pearl River Basin and the Yellow River basin.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"37 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990563","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":"Harnessing ensemble Machine learning models for improved salinity prediction in large river basin scales","authors":"Mohamed F. Mahmoud, Mazdak Arabi, Shrideep Pallickara","doi":"10.1016/j.jhydrol.2025.132691","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.132691","url":null,"abstract":"This study develops a robust ensemble machine learning methodology for predicting average annual salinity by combining multiple machine learning algorithms. Salt concentration is a crucial water quality indicator, and salinity issues cost $300 million annually in the U.S. Irrigated agricultural lands in the Upper Colorado River Basin contribute excessively to dissolved solid loads despite covering less than 2% of the basin area. The economic impact and complex relationship between irrigation practices, groundwater dynamics, and salinity levels necessitate improved predictive capabilities at river basin scales. Using twenty years of data from 150 watersheds, eleven machine learning algorithms were evaluated through both random and spatial cross-validation approaches, with Extreme Gradient Boosting, Gradient Boosting, and Random Forest emerging as top performers. Bayesian Model Averaging and stacked generalization were employed to create ensemble models, demonstrating enhanced performance validity. The BMA ensemble achieved better spatial generalization compared to individual models while requiring significantly less computational resources than stacking. Model uncertainty analysis revealed that BMA provided the most stable predictions among all approaches. Soil electrical conductivity and calcium carbonate content emerged as the most important predictors, followed by river flow. The resulting spatially distributed predictions revealed distinct patterns in sulfate loads and concentrations across sub-basins, providing insights for targeted salinity management. This study demonstrates the effectiveness of ensemble machine learning approaches for robust salinity prediction while highlighting the importance of comprehensive uncertainty assessment and spatial validation in environmental modeling applications.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"6 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990568","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":"Spatiotemporal graph convolutional network using sparse monitoring data for accurate water-level reconstruction in urban drainage systems","authors":"Li He, Jun Nan, Lei Chen, Xuesong Ye, Shasha Ji, Zewei Chen, Yibo Zhang, Fangmin Wu, Bohan Liu, Zhencheng Ge, Yanhan Che","doi":"10.1016/j.jhydrol.2025.132681","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.132681","url":null,"abstract":"A comprehensive monitoring of urban drainage network (UDN) is essential for maintenance, management, and sustainable urban development. However, limited sensor deployment hinders the acquisition of sufficient information. Conventional deep learning methodologies can predict and correct monitored data but struggle with unobserved data. Hydraulic models can simulate behaviors but face data collection challenges and low real-time performance. To address these issues, a novel spatiotemporal graph convolutional network (STGCN) model, based on graph neural networks, is proposed to reconstruct a real-time information system for UDNs. By extracting fundamental elements from limited monitoring data and UDN topology, the STGCN model effectively reconstructed unmonitored node data. The experimental results showed that the training efficiency and reconstruction accuracy of the model could be optimized by reducing the spatial data dimensionality to 0.6, adopting a passive-masked training strategy with a ratio of 4:3 for model-training sensors to loss-calculation sensors, and using a historical data input length of 3 h. This approach allowed for the reconstruction of water levels for 527 unmonitored nodes using only seven monitoring nodes, with a median mean absolute error of 0.038 m and an accuracy of 71.3 %. These results demonstrate that the STGCN model can accurately reconstruct unmonitored node data using low monitoring-node density and basic network topology, offering a practical solution to data-driven challenges in intelligent UDNs. The source code is available at <ce:inter-ref xlink:href=\"https://github.com/holylove9412/UDNs_STGCN_model\" xlink:type=\"simple\">https://github.com/holylove9412/UDNs_STGCN_model</ce:inter-ref>.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"27 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990569","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":"Revisiting the composite drought index for improving drought monitoring","authors":"Muhammad Abrar Faiz, Liangliang Zhang, Dong Liu, Ning Ma, Mo Li, Zhou Zhaoqiang, Faisal Baig, Tianxiao Li, Song Cui","doi":"10.1016/j.jhydrol.2025.132707","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.132707","url":null,"abstract":"Well-known natural disasters (i.e., drought) are notably difficult to control and have far-reaching effects. The identification and evaluation of drought are essential for designing water scarcity management plans. Studies have shown that a single drought index has a limited ability for drought monitoring. Therefore, the composite drought index (CDI) was revised by integrating a moisture index that represents the degree of dryness/wetness, reference, and actual and potential evapotranspiration, including advection and radiation component-based water balance and rainfall anomalies. The revised indices were subsequently compared with the original CDI and composite index (CI) developed by the National Climate Center, China, and well-known traditional drought indices (e.g., the standardized precipitation index (SPI) and standardized potential evapotranspiration index (SPEI)). The results revealed that the revised CDIs, especially those with potential and actual evapotranspiration-based moisture and water balance, were well correlated with soil moisture and crop water requirements. The correlation coefficient was statistically significant and greater than 0.5 in most of the grid cells. The indices demonstrated a good ability to determine drought events and characteristics (duration and severity). For example, a one-month drought duration was captured by the CDI (based on actual and potential evapotranspiration); in contrast, the CI presented no drought duration in a similar region. Additionally, the CDIs robustly captured dryness, whereas the CI, SPEI, and SPI reflected a greater amount of wetness. As drought severity has a major impact on crops, these findings emphasize the potential of revised CDIs in drought monitoring and management.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"9 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990562","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":"Sulfate and pH drive microbial assembly and coexistence in hyporheic zone contaminated by acid coal mine drainage","authors":"Liyuan Ma, Lanfang Lin, Xingjie Wang, Zikui Zheng, Xin Zhang, Pallavee Srivastava, Xubo Gao","doi":"10.1016/j.jhydrol.2025.132703","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.132703","url":null,"abstract":"Overflow of Acid Mine Drainage (AMD) from coal mining poses critical ecological restoration challenges to the surrounding water, sediment and soil. However, little is known about the assembly processes and coexistence patterns of microbial communities in AMD-contaminated rivers. Therefore, by using high-throughput sequencing combined with multivariate statistical analysis, the seasonal dynamics of microbial distribution, community assembly, and coexistence patterns in the Shandi River, a river seriously polluted by overflowed AMD were revealed. The results indicated that the patterns of contamination, community assembly and coexistence in high- and low-contaminated areas showed similar patterns in both two seasons. The levels of hydrogen ion, sulfate and heavy metals increased from low-contaminated areas toward high-contaminated areas. Microbial diversity and community structure significantly differed along the river. <ce:italic>Acidiphilium</ce:italic> and <ce:italic>Ferrimicrobium,</ce:italic> which were capable of thriving in environments with high acidity and sulfate levels, were predominant in high-contaminated areas. Stochastic processes predominantly influenced microbial assembly in low-contaminated areas, while deterministic processes were more pronounced in high-contaminated areas. Meanwhile, negative interactions in microbial co-occurrences, were more frequent in high-contaminated areas with increased network modularity. Linear regression analysis results demonstrated that pH and sulfate were significantly correlated with biodiversity indicators, βNTI and closeness centrality, and determined as the primary drivers of microbial community structure and assembly processes. These findings highlight the crucial role of sulfate content and pH in shaping microbial diversity, community assembly, and species coexistence in coal AMD-contaminated areas.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"49 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990564","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":"Conversion of earthen aquaculture ponds to integrated mangrove-aquaculture systems significantly reduced the emissions of CH4 and N2O","authors":"Zhinan Su, Guanglong Qiu, Ping Yang, Hong Yang, Wenjing Liu, Lishan Tan, Linhai Zhang, Dongyao Sun, Jiafang Huang, Kam W. Tang","doi":"10.1016/j.jhydrol.2025.132692","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.132692","url":null,"abstract":"Mangrove ecosystem helps mitigate regional and global climate change, but increasing land reclamation for aquaculture has degraded many mangroves wetlands. Integrated mangrove wetland-aquaculture systems can be a promising way to support both mangrove restoration and aquaculture, but its impacts on greenhouse gas emissions remain largely unknown. In this study, we compared CH<ce:inf loc=\"post\">4</ce:inf> and N<ce:inf loc=\"post\">2</ce:inf>O fluxes between earthen aquaculture ponds (EAPs) and integrated mangrove-aquaculture systems (IMASs) in Beibu Gulf in southern China. Results showed that both EAPs and IMASs were CH<ce:inf loc=\"post\">4</ce:inf> and N<ce:inf loc=\"post\">2</ce:inf>O emission sources with strong temporal variabilities. CH<ce:inf loc=\"post\">4</ce:inf> fluxes were primarily affected by total organic carbon, dissolved oxygen and salinity. These fluxes were significantly larger in EAPs (976.3 ± 146.4µg m<ce:sup loc=\"post\">-2</ce:sup>h<ce:sup loc=\"post\">−1</ce:sup>) than in IMASs (60.3 ± 7.7µg m<ce:sup loc=\"post\">-2</ce:sup>h<ce:sup loc=\"post\">−1</ce:sup>). Ebullition was responsible for 52.9–93.4 % of the total CH<ce:inf loc=\"post\">4</ce:inf> emission. The average N<ce:inf loc=\"post\">2</ce:inf>O flux in EAPs (3.4 ± 0.5µg m<ce:sup loc=\"post\">-2</ce:sup>h<ce:sup loc=\"post\">−1</ce:sup>) was 5.7 times higher than IMASs during the farming period and was mainly driven by nitrogenous substrate availability. The results highlight that integrated mangrove wetland-aquaculture systems can not only promote mangrove restoration and support aquaculture, but also mitigate greenhouse gases emissions from coastal wetlands.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"9 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990566","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":"The spatiotemporal evolution and propagation characteristics of multiple drought types from a three-dimensional perspective","authors":"Dan Li, Yibo Ding, Zhaoqiang Zhou, Tian Wang, Renjuan Wei","doi":"10.1016/j.jhydrol.2025.132702","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.132702","url":null,"abstract":"Climate change will increase drought risk, severity, and economic losses. The spatiotemporal evolution and propagation of droughts could be deeply understood based on three-dimensional perspective. In this study, The Standardized Precipitation Evapotranspiration Index, Standardized Runoff Index, and Standardized Soil Moisture Index was used to describe meteorological, hydrological, and agricultural drought, respectively. We identified drought events by the three-dimensional connectedness recognition method and deeply investigate spatial evolution characteristics of drought patches. Subsequently, we compared recorded and identified drought events, and estimate drought propagation in different climate regions of China with drought propagation rate and correlation analysis. The results indicated that the three-dimensional connectedness recognition method could accurately identify large drought event characteristics (including time and area) by comparing and recording large drought disasters. The meteorological drought events usually had higher area coverage proportion, frequency, intensity, and severity than hydrological and agricultural drought events over different climate regions. The three-dimensional connectedness recognition method could accurately recur the spatiotemporal evolution process of historical typical drought events. Both the correlation coefficient and drought propagation ratio showed that drought propagation from meteorological drought to agricultural was larger degree than from meteorological drought to hydrological in China. Moreover, drought had relatively stronger propagation degree from meteorological to agricultural in climate regions of the warm-temperate humid and sub-humid north China, the subtropical humid central and south China, and the tropic humid south China. The drought propagation ratio could complement the uncertainty in the propagation direction shortcomings of the correlation coefficient. This study would be helpful to understand the formation and evolution of drought and provides a reference for three-dimensional spatial prediction and monitoring of drought.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"57 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990567","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":"Soil moisture shapes the responses of Quercus wutaishansea forest stand transpiration to potential evapotranspiration","authors":"Bingbing Liu, Pengtao Yu, Xue Zhang, Yiheng Wu, Jiamei Li, Yanfang Wan, Yushi Bai, Xiaocha Wei, Lili Liu, Yanhui Wang, Yipeng Yu, Xiao Wang, Zebin Liu, Lihong Xu","doi":"10.1016/j.jhydrol.2025.132679","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.132679","url":null,"abstract":"Stand transpiration (T) is an important biophysical indicator of the forest hydrological cycle and ecosystem energy partitioning and can be used to determine the effects of drought on forest ecosystems. Therefore, clarifying how changes in soil moisture affect the mechanism of transpiration response to potential evapotranspiration (PET) is crucial for developing forest management strategies based on soil moisture conditions, especially in natural forests in drought-prone semi-arid regions. In the present study, we partitioned the effects of relative extractable soil water (REW) and PET on T in <ce:italic>Quercus wutaishansea</ce:italic> forest stands in the Liupan Mountains, northwest China. The results showed that the reduction in REW due to drought resulted in a significant decrease in T. When REW was higher, i.e., above 0.5, there was a linear relationship of T with PET but an exponential relationship when REW was lower than 0.5. Moreover, REW in the soil layer of 20–60 cm rather than that in the soil layer of 0–20 cm plays a decisive role in T during drought. More REW, such that at the mid- and downslope sites, would be helpful to mitigate the decline in T under drought to some extent compared with less REW that at the upslope sites. These remind us that the soil moisture in semi-arid regions should be paid more attention in forest management and vegetation restoration in future.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"102 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990570","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}