Journal of Hydrology最新文献

{"title":"Trends and causal structures of rain-on-snow flooding","authors":"Nishant Kumar, Kanak Kanti Kar, Shivendra Srivastava, Sinan Rasiya Koya, Sudan Pokharel, Molly Likins, Tirthankar Roy","doi":"10.1016/j.jhydrol.2025.133938","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.133938","url":null,"abstract":"Rain-on-Snow (ROS) events have been under increased scrutiny in recent years due to their devastating impacts. An ROS event is marked by rain falling on pre-existing snowpacks, which poses a considerable risk of flooding. In this study, we proposed a new approach to defining ROS events with potential flooding (ROS-PF) by establishing thresholds on rainfall, snow water equivalent, air temperature, and dew point temperature simultaneously, thereby overcoming the limitations of existing definitions. We also included a threshold at the 90th percentile over discharge to identify the ROS events that lead to actual floods (ROS-AF). Using this framework, we analyzed the frequency and trends of ROS-PF and ROS-AF events across thousands of basins in North America, Europe, Chile, Brazil, and Australia. Our findings indicate that the western US, central Chile, and central Europe are the most vulnerable regions with the highest frequency of ROS events, all of which showed a significant increasing trend. Additionally, we employed two causal discovery algorithms to uncover the causal structures leading to ROS flooding: Fast Causal Inference (FCI) and Fast Greedy Equivalence Search (FGES). Each algorithm offers a distinct path to infer causality from observational data. We combined the outputs of FCI and FGES to establish the final causal structure illustrating the causal mechanisms of ROS-based floods. This study also identified rainfall, soil moisture, snow water equivalent, maximum temperature, and DPT as critical drivers of ROS flooding, although the causal mechanisms resulting in ROS flooding differ across the four continents","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"12 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669991","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":"Probability analysis of wet and dry encounters in the Yangtze and Yellow River basins under changing environmental conditions","authors":"Yuli Ruan, Lijun Jin, Jianyun Zhang, Zhongrui Ning, Guoqing Wang, Cuishan Liu, Zhenxin Bao, Weiru Zhao, Mingming Song","doi":"10.1016/j.jhydrol.2025.133953","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.133953","url":null,"abstract":"Assessing the probability of dry-wet runoff encounters under changing environmental conditions provides critical scientific support for sustainable water resource management and watershed security. Therefore, this study enhances the Generalized Additive Model for Location, Scale, and Shape (GAMLSS) through variable reconstruction and error correction, thus proposing an innovative methodology for assessing high-low runoff encounter probabilities in the Yangtze and Yellow River Basins under changing environmental conditions. Atmospheric circulation pattern analysis is further integrated to elucidate mechanisms underlying concurrent low-flow events. Key findings reveal that: (1) The Log-Normal distribution exhibits superior goodness-of-fit for runoff frequency in the Yangtze River’s headwater (source) and downstream regions, while Gamma and Normal distributions emerge as optimal for the upper and middle reaches, respectively. The Inverse Gaussian and Reverse Gumbel distributions demonstrate enhanced performance in the Yellow River Basin. (2) The optimized GAMLSS achieves remarkable accuracy, with empirical–theoretical value deviations constrained between − 0.1 and 0.1, and Nash-Sutcliffe Efficiency (NSE) values ranging from 0.9756 to 0.9966 across basins. (3) Analysis of 1963–2022 data identifies the highest dry-dry encounter probability (23.48 %) in the upper reaches of both basins, followed by headwater (20.89 %) and middle reaches (18.35 %), with the lowest probability (15.37 %) observed in lower reaches. (4) While the Zhimenda-Tangnaihai, Yichang-Toudaoguai, and Datong-Huayuankou combinations show decreasing dry encounter probabilities, the Dajin-Lanzhou combination exhibits a statistically significant upward trend (p < 0.05) in low-flow synchronicity. (5) Concurrent low-flow events in the Yangtze and Yellow Rivers are predominantly linked to two atmospheric circulation patterns: (a) the Lake Baikal high-pressure ridge, and (b) anomalous strengthening of the western Pacific subtropical high. This study advances hydrological extreme event prediction by integrating statistical modeling innovation with climatic mechanism analysis, providing critical insights for adaptive watershed management under global change scenarios.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"675 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670051","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":"Dynamic development of global contiguous flash droughts: from an event-based spatiotemporal perspective","authors":"Dingkui Wang ,&nbsp;Xuezhi Tan ,&nbsp;Xinxin Wu ,&nbsp;Zeqin Huang ,&nbsp;Simin Deng ,&nbsp;Yaxin Liu ,&nbsp;Jianyu Fu ,&nbsp;Xuejin Tan ,&nbsp;Xitian Cai ,&nbsp;Bingjun Liu ,&nbsp;Haiyun Shi ,&nbsp;Long Yang ,&nbsp;Xiaohong Chen","doi":"10.1016/j.jhydrol.2025.133934","DOIUrl":"10.1016/j.jhydrol.2025.133934","url":null,"abstract":"<div><div>Flash droughts can induce serious adverse effects on local ecology due to their rapid intensification. However, individual flash drought events have not been thoroughly analyzed to demonstrate their dynamic evolution and changes. Here we use a 3-D connectivity algorithm to identify large contiguous flash drought events globally from an event-based perspective, which allows for effective tracking of their full spatiotemporal development. Results show that during 1980–2020, 2322 large contiguous flash drought events occurred and mainly distributed globally in nine hotspots, with a strong seasonal preference for warm seasons. Flash drought events of longer lifetime and travel distance are more likely to occur at the high-latitudes. The intensity, duration, and frequency of these events increase statistically significantly, while their affected area and translation speed decrease. Although regional variations in propagation patterns exist, flash drought events tend to propagate more toward the northeast. Across much of the globe, the preceding meteorological conditions in over 50% of flash droughts are marked by the concurrence of elevated regional temperatures and precipitation deficits. The dominant role of individual drivers exhibits notable spatial heterogeneity, largely influenced by the latitude and regional weather systems. Precipitation deficits tend to be the primary driver in monsoon-affected regions, while elevated temperatures predominantly govern flash drought onset in the high-latitudes of the Eurasian continent. Precipitation deficits primarily (38.9%) determine the intensity of flash droughts, while high temperatures play a dominant role (42.2%) in the duration of flash droughts. Our results provide a new perspective for future projection of drought events.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 133934"},"PeriodicalIF":5.9,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669786","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":"FastGAS: a UAV-Enabled framework for fast and robust gravel auto-sieving in coastal and mountainous fluvial environments","authors":"Shizhao Gao, Haiying Mao, Ziqing Ji","doi":"10.1016/j.jhydrol.2025.133937","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.133937","url":null,"abstract":"The grain size distribution (GSD) of gravels plays a crucial role in understanding fluvial processes in coastal and mountainous areas. Conventional UAV-based sieving algorithms face two limitations: (1) Pixel calibration errors caused by elevation-dependent scaling in areas with significant slope variations; (2) Challenges in the efficiency and accuracy of batch processing of cyclical monitoring images. This study presents a fast gravel automated sieving (FastGAS) method incorporating calibration spheres to establish pixel-size correspondence, simultaneously reducing slope-induced calibration errors and serving as waypoint benchmarks. The proposed framework enables automatic batch processing through sphere detection, combined with optimized seed generation and four neighborhood search algorithms for efficient gravel segmentation and size distribution inversion. Validation conducted with 35 images from coastal and mountainous fluvial environments demonstrated strong agreement with manual measurements (NRMSE = 0.07–0.58), further confirmed by one-month continuous monitoring in Moon Bay, Yantai City, China. Comparative analysis showed FastGAS outperformed PebbleCountsAuto (0.24–0.98), pyDGS (0.57–2.69), and SediNet (0.73–9.13) in accuracy while maintaining competitive processing speed (28 s vs. SediNet’s 10 s). The method’s advantages in precision, stability, and computational efficiency suggest its strong potential for automated long-term monitoring of coastal and mountainous rivers.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"12 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669789","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":"Enhancing climate projections via machine learning: Multi-model ensemble of precipitation and temperature in the source region of the Yellow River","authors":"Qin Ju, Jinyu Wu, Tongqing Shen, Yueyang Wang, Huiyi Cai, Junliang Jin, Peng Jiang, Xuegao Chen, Yiheng Du","doi":"10.1016/j.jhydrol.2025.133945","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.133945","url":null,"abstract":"Accurately estimating future variations in precipitation and temperature in the Source Region of the Yellow River (SRYR) is critical, given its key role as the water conservation area of the Yellow River basin in China. To enhance the accuracy of regional climate projections, this study proposes a machine learning-enhanced ensemble framework comprising global climate model (GCM) selection, bias correction, and multi-model ensemble, effectively combining physical climate modeling with data-driven methods. Specifically, a rank score method was used to evaluate the performance of 22 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in reproducing precipitation and temperature over the SRYR. Based on this assessment, six top-performing GCMs were selected and subsequently bias-corrected. To determine the most effective ensemble strategy, four multi-model ensemble approaches—including weighted averaging (WA), random forest (RF), feedforward neural network (FNN), and long short-term memory (LSTM)—were employed to integrate the bias-corrected outputs from the selected models over the historical period. All ensemble approaches outperformed individual GCMs in reproducing historical climate variability, as evaluated by the Pearson correlation coefficient (<ce:italic>r</ce:italic>) and Nash–Sutcliffe efficiency (<ce:italic>NSE</ce:italic>). Among them, the LSTM method exhibited the highest overall accuracy and best capability in capturing temporal variability, and was thus selected to integrate future precipitation and temperature projections under the three Shared Socioeconomic Pathways (SSPs). Projections under the SSP1–2.6, SSP2–4.5, and SSP5–8.5 scenarios indicate that both precipitation and temperature will rise relative to the baseline period (1979–2014), with the strongest increases under SSP5–8.5. Winter precipitation exhibits the most pronounced seasonal increase, while seasonal differences in temperature rise are less distinct, with slightly stronger warming in winter. The machine learning–enhanced ensemble framework proposed in this study improves regional climate projections and provides a practical tool for guiding hydrological impact assessments and climate adaptation strategies in alpine basins.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"14 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669950","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":"Tracking dissolved organic carbon changes in agricultural runoff from rainstorms during maize (Zea mays L.) growth stages employing optical and molecular techniques","authors":"Longlong An ,&nbsp;Zicheng Zheng ,&nbsp;Shuqin He ,&nbsp;Tingxuan Li ,&nbsp;Xizhou Zhang ,&nbsp;Yongdong Wang ,&nbsp;Haiying Yu","doi":"10.1016/j.jhydrol.2025.133942","DOIUrl":"10.1016/j.jhydrol.2025.133942","url":null,"abstract":"<div><div>Agricultural runoff mobilizes a significant amount of dissolved organic carbon (DOC) from soils to aquatic systems, posing the dual threat of soil organic carbon loss and water pollution. The quantity, quality, and molecular composition of DOC in agricultural runoff during rainfall events may be influenced by tillage practices and crop growth stage. To test our hypotheses, we collected runoff samples from sloping croplands under two tillage practices (cross-ridge and downslope ridge) from 2020 to 2023. We analyzed the samples to determine the DOC concentrations and compositions of maize growth stages (seedling, elongation, tasseling, and maturity). This analysis was conducted using a combination of elemental analysis, excitation-emission matrix (EEM), and Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS). The results showed that DOC loss flux was highest at the seedling and elongation stages and lowest at the tasseling stage during rainstorms. The loss of tryptophan-like in runoff was the highest at the seedling stage, accounting for 55.13 %–59.82 % of the total DOC. The humification index (HIX) ranged from 0.63 to 0.77, indicating a low degree of DOC humification in runoff. The DOC was primarily of endogenous origin and exhibited a high degree of degradation. The proportion of CHONS compounds increased, and the export of lignin-like was the highest in the runoff at the elongation stage. High-molecular-mass (&gt; 450 Da) DOC had the largest proportion in the sloping cropland, occupying 44.63 %–48.49 % of the total DOC. High-molecular-mass DOC accumulates in the runoff during the growth stage. Runoff and DOC concentrations in the soil are the main factors driving DOC export in runoff. Cross-ridge tillage (CR) is an effective conservation tillage method that can significantly mitigate DOC loss in sloping croplands. Our study identified the differences in DOC quantity, quality, and molecular composition in runoff at different growth stages and revealed the mechanisms driving DOC export in agricultural runoff. These findings provide a theoretical foundation for effectively preventing DOC loss in sloping croplands and mitigating agricultural water pollution.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 133942"},"PeriodicalIF":5.9,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665740","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":"Novel mathematical expression for dynamic stage-discharge relationship of Rivers under flow unsteadiness explored through machine learning models via symbolic regression in PySR","authors":"Rijurekha Dasgupta, Archisha Bhar, Subhasish Das, Rajib Das, Gourab Banerjee, Asis Mazumdar","doi":"10.1016/j.jhydrol.2025.133947","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.133947","url":null,"abstract":"Stage-discharge (<ce:italic>h</ce:italic>-<ce:italic>Q</ce:italic>) rating curve (<ce:italic>Q</ce:italic> = <ce:italic>ah<ce:sup loc=\"post\">b</ce:sup></ce:italic>) defined by parameters <ce:italic>a</ce:italic> and <ce:italic>b</ce:italic> is a crucial tool for measuring riverflow. However, this relationship struggles with flow fluctuations, backwater effects, tides, and changes in cross-sectional geometry, impacting its accuracy. Machine learning (ML) algorithms model the discharge with sequential stages as input, capturing intricate non-linear relationships, despite their “black box” nature that lacks explicit mathematical expressions. This study aims, therefore, to explore ML models trained on stage-discharge data using symbolic regression to produce actual mathematical expressions for stage-discharge relationships. By using sequential stage data as independent variables, the effect of unsteadiness is captured on stage-discharge relationships for River Churni (in India), Brays Bayou, Little Fishing Creek, and Obion (in USA). Symbolic regression using PySR tool is newly implemented to derive the mathematical expressions, followed by ML-based rating curve models. For each river, five generic mathematical expressions have been derived through symbolic regression where discharge is evaluated as polynomials of powered terms of sequential stages, rather than relying on conventional relationships. These expressions are assessed based on their modeling performance, prediction accuracy, and physical relevance. The results indicate that the coefficient of determination has significantly increased by 9–56 % with ML models compared to conventional relationships. The evaluation results show the best performance of new stage-discharge equation, recognizing the unique mathematical framework of stage-discharge relationship at river cross-sections and establishing a methodology for deriving it. This study executes a novel application of symbolic regression to evaluate explicit mathematical expressions relating to the sequential stage and discharge.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"118 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669995","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":"Effect of karst conduit topological structure on the estimation accuracy of water storage variation","authors":"Yuxi Li, Longcang Shu, Chengpeng Lu, Bo Liu, Xiaonong Hu","doi":"10.1016/j.jhydrol.2025.133941","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.133941","url":null,"abstract":"In this study, we systematically investigated how changes in the topological structure of karst conduits affect the accuracy of karst water storage variation (KWSV) estimation. As primary groundwater pathways, karst conduits significantly control groundwater flow dynamics and influence water resource assessment accuracy, especially in heterogeneous aquifer systems. We developed a coupled equivalent porous medium–conduit groundwater flow numerical model to simulate conduit topology variations typical of late-stage karst aquifer evolution. Two critical topological parameters—vertex degree and branch conduit angle—were selected to represent structural variations. Spring flow recession data were analysed using exponential fitting methods to characterize the recession behaviours under varying conduit structures. By comparing simulated spring recession curves with fitted results, we clarified the mechanisms by which conduit topology regulates groundwater dynamics and influences KWSV estimation accuracy. Our findings provide essential insights for improving the reliability of karst groundwater resource assessments and contribute to theoretical advancements in karst hydrology research.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"26 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669996","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":"Propagation patterns of different degree meteorological droughts across the Yangtze River Basin: a three-dimensional drought feature identification approach with Copula modeling","authors":"Lisong Xing, Ruxin Zhao, Hongquan Sun, Zhuoyan Tan, Qingqing Fang, Ming Li, Krishnagopal Halder, Amit Kumar Srivastava","doi":"10.1016/j.jhydrol.2025.133857","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.133857","url":null,"abstract":"Extreme droughts have frequently affected the Yangtze River Basin (YRB), significantly impacting agriculture, ecosystems, and socio-economic conditions. However, it is challenging to quantify these drought degrees due to their spatial and temporal complexities, such as varying drought durations, severities, and affected areas. In this study, we proposed a framework to quantify different degrees of drought events combined with different drought characteristics. It consists of a three-dimensional drought feature identification method (longitude-latitude-time continuum identification) and the Copula function. Then, we analyzed the spatial and temporal evolution patterns of drought events of different degrees in the YRB during 1951–2022, and reviewed the occurrence and progression of three notable droughts (in 2006, 2019, and 2022) documented in the YRB. Temporally, the YRB experienced a higher frequency of severe droughts from 1951 to 1980, and an increase in extreme drought events primarily after 2000. Spatially, severe and extreme droughts were concentrated in the middle reaches, whereas moderate and light droughts were more common in the Jinsha River Basin. The east–west migration was the main propagation characteristics of drought patterns at different levels in the YRB. Notably, extreme droughts mostly resulted from the convergence and overlapping of several smaller extreme drought events, creating a significantly larger drought-affected area and higher severity.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"3 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670126","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":"Seasonal and diurnal variations of pCO2 from reservoirs and lakes in Northeast China","authors":"Shiwei Liu, Zhidan Wen, Ge Liu, Yingxin Shang, Junbin Hou, Hui Tao, Chong Fang, Sijia Li, Xiangfei Yu, Jiarui Han, Jiping Liu, Kaishan Song","doi":"10.1016/j.jhydrol.2025.133917","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.133917","url":null,"abstract":"Inland water bodies serve as critical components of the global carbon cycle, yet the temporal variability of partial pressure of carbon dioxide (<ce:italic>p</ce:italic>CO<ce:inf loc=\"post\">2</ce:inf>) introduces significant uncertainty in CO<ce:inf loc=\"post\">2</ce:inf> flux estimations. This study presents an analysis based on 38 discrete sampling events conducted between 2018 and 2024 during the ice-free period across six lakes and reservoirs in Northeast China. Measurements of <ce:italic>p</ce:italic>CO<ce:inf loc=\"post\">2</ce:inf> and associated water quality parameters in surface waters were used to evaluate the temporal fluctuations of <ce:italic>p</ce:italic>CO<ce:inf loc=\"post\">2</ce:inf> and its driving factors. The findings highlight pronounced seasonal shifts in canyon reservoirs within the study region. During spring and summer, mean <ce:italic>p</ce:italic>CO<ce:inf loc=\"post\">2</ce:inf> levels were recorded at 211 μatm, indicative of a net carbon sink, with an average areal CO<ce:inf loc=\"post\">2</ce:inf> flux (fCO<ce:inf loc=\"post\">2</ce:inf>) of −5.9 mmol/m<ce:sup loc=\"post\">2</ce:sup>/d. However, in autumn, <ce:italic>p</ce:italic>CO<ce:inf loc=\"post\">2</ce:inf> levels markedly increased to 1316 μatm, signifying a substantial carbon source, with an average fCO<ce:inf loc=\"post\">2</ce:inf> of 42.8 mmol/m<ce:sup loc=\"post\">2</ce:sup>/d. In contrast, shallow water lakes exhibited relatively stable seasonal dynamics, consistently functioning as atmospheric carbon sinks throughout the ice-free period, with a mean fCO<ce:inf loc=\"post\">2</ce:inf> of −8.76 (±3.74) mmol/m<ce:sup loc=\"post\">2</ce:sup>/d across all seasons. Fixed-site experiments further revealed distinct diurnal fluctuations in <ce:italic>p</ce:italic>CO<ce:inf loc=\"post\">2</ce:inf> across different seasons, with the most pronounced daily variation (1350 μatm) occurring in September within reservoirs. Correlation analyses between water quality parameters and <ce:italic>p</ce:italic>CO<ce:inf loc=\"post\">2</ce:inf> suggest that the seasonal differences in canyon reservoirs are primarily driven by the formation and breakdown of thermal stratification. Conversely, the higher nutrient availability in shallow water lakes appears to enhance CO<ce:inf loc=\"post\">2</ce:inf> uptake from the atmosphere. Additionally, diurnal <ce:italic>p</ce:italic>CO<ce:inf loc=\"post\">2</ce:inf> fluctuations are largely governed by variations in surface water photosynthetic activity, modulated by solar radiation. These findings underscore the importance of incorporating both stratified and mixed periods into sampling protocols, with an emphasis measurements between 10:00 and 12:00 pm, to improve the accuracy of CO<ce:inf loc=\"post\">2</ce:inf> flux assessments. This study advances the understanding of carbon cycling in temperate lakes and reservoirs and contributes to reducing uncertainties in future carbon budget estimations.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"96 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670162","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}