Journal of Hydrology最新文献

{"title":"Long-term carbon dioxide dynamics variability at submerged macrophyte habitat of a subtropical shallow lake","authors":"Lei Hong ,&nbsp;Cheng Hu ,&nbsp;Minliang Jiang ,&nbsp;Xuejing Shi ,&nbsp;Juhua Luo ,&nbsp;Qitao Xiao","doi":"10.1016/j.jhydrol.2025.133950","DOIUrl":"10.1016/j.jhydrol.2025.133950","url":null,"abstract":"<div><div>Submerged macrophytes are widespread and deemed fundamental components particularly in shallow lakes. They play a pivotal role by rendering essential ecosystem services, however, their roles in governing the carbon dioxide (CO₂) budget remain controversial and unclear, likely posing a significant challenge to the comprehensive understanding of CO₂ cycling within lake ecosystems. To fill the knowledge gaps, the dynamic variability of CO₂ within the submerged macrophytes habitats in a shallow subtropical lake located in eastern China was comprehensively investigated based on long-term (2005–2017) field measurements span different seasons. The findings revealed that the submerged macrophytes habitats were characterized by superior water quality, manifested as low nutrient loadings, reduced algal biomass, and heightened water clarity, when juxtaposed with open water regions of the lake devoid of macrophytes. The long-term measurements demonstrated that the submerged macrophytes habit functioned as relatively low CO₂ source, with an annual mean emissions flux of 13.55 ± 9.20 mmol m⁻² d⁻¹. The presence of macrophytes and good water quality (e.g. low nutrient loadings) likely contributed to low emissions within submerged macrophyte habitat via increasing CO₂ fixation and reducing CO₂ production, respectively. The temporal fluctuations in CO₂ emissions from submerged macrophyte habitats were closely associated with water clarity, which in turn highlighted the role of water quality in determining CO₂ variability within submerged macrophyte habitats. Furthermore, the long-term measurements uncovered significant inter-annual variations in the CO₂ emissions, highlighting the critical importance of long-term measurements to derive unbiased estimates of the CO₂ budgets within the submerged macrophyte habitats of inland lakes.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 133950"},"PeriodicalIF":5.9,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670049","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":"Spatio-temporal heterogeneities in hydrologic dynamics across the Asian Water Tower","authors":"Saugat Aryal,&nbsp;Yadu Pokhrel","doi":"10.1016/j.jhydrol.2025.133951","DOIUrl":"10.1016/j.jhydrol.2025.133951","url":null,"abstract":"<div><div>This study presents a multi-decadal (1979–2018) analysis of hydrologic changes across the entire Asian Water Tower (AWT) region, using high-resolution hydrological-hydrodynamic modeling. We find significant spatiotemporal heterogeneity in hydrological trends across the AWT basins, characterized by diverse changes in river discharge, water storage, flood regimes, and terrestrial water storage (TWS) dynamics. Western basins such as the Amu Darya and Tarim show increasing flood risks (up to ∼60% increase in flood occurrence) and significant snow water equivalent (SWE) contributions to TWS (up to ∼41%), while central basins are transitioning to regions of increasing water scarcity with strong subsurface storage contribution evident in the Ganges (up to ∼79%). The dominance of subsurface storage reaches its peak in the eastern basins, where the Yangtze and Yellow River exhibit the highest proportions (∼78% and ∼83% respectively), with the Yangtze further distinguished by a notable river storage contribution (∼21%). In contrast, southeastern basins including the Mekong, Irrawaddy, and Salween present complex, temporally varying patterns that defy simple categorization. These findings highlight the complex interplay of surface and subsurface processes in the AWT, underscoring the need for basin-specific approaches in water resource management and climate change adaptation strategies.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 133951"},"PeriodicalIF":5.9,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669990","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":"Convective dissolution in layered porous media with application to CO2 geological sequestration: Experimental and numerical insights into layering configuration and interface angle","authors":"Didi Li,&nbsp;Yizhen Chen,&nbsp;Suihong Chen","doi":"10.1016/j.jhydrol.2025.133952","DOIUrl":"10.1016/j.jhydrol.2025.133952","url":null,"abstract":"<div><div>Convective dissolution is crucial for the secure and permanent sequestration of CO₂ within deep saline aquifers. Despite the prevalence of inclined stratified formations at potential sequestration sites, there is a lack of systematic investigations about the combined effects of layering configurations and inclination angles on convective dissolution. This study endeavored to bridge this gap by conducting a series of laboratory experiments complemented by numerical simulations. The aim was to elucidate the impact of various inclination angles on convective mixing within diverse stratified structures. Our findings revealed that within stratified formations, phenomena such as finger accumulation and enhanced finger merging at decreasing-permeability interfaces co-occurred with typical fingering patterns in homogeneous media. Additionally, an amplified shielding effect was observed in increasing-permeability formations. Within our research scope, a more significant permeability contrast between layers was found to result in more pronounced variations in convective dissolution characteristics, without altering their inherent pattern. Inclined interfaces within stratified formations were found to further intensify the accumulation of leading fingers at decreasing-permeability interfaces and to facilitate penetration across increasing-permeability interfaces, with these effects being more pronounced at steeper angles. In our study, the average mass flux at interface was considerably higher in configurations with decreasing permeability, diminishing as the interface angle increased. These findings suggest that in practical carbon geological storage applications, employing layers with decreasing permeability and less steep interface angles, coupled with a higher permeability contrast between layers, could enhance the efficiency of dissolution sequestration within inclined stratified layers. This study provides critical insights into the optimization of carbon sequestration strategies within inclined, multi-layered saline aquifers, highlighting the importance of layer configuration and interface geometry in the convective dissolution process.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 133952"},"PeriodicalIF":5.9,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669959","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 ,&nbsp;Lijun Jin ,&nbsp;Jianyun Zhang ,&nbsp;Zhongrui Ning ,&nbsp;Guoqing Wang ,&nbsp;Cuishan Liu ,&nbsp;Zhenxin Bao ,&nbsp;Weiru Zhao ,&nbsp;Mingming Song","doi":"10.1016/j.jhydrol.2025.133953","DOIUrl":"10.1016/j.jhydrol.2025.133953","url":null,"abstract":"<div><div>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 &lt; 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.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 133953"},"PeriodicalIF":5.9,"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":"Trends and causal structures of rain-on-snow flooding","authors":"Nishant Kumar,&nbsp;Kanak Kanti Kar,&nbsp;Shivendra Srivastava,&nbsp;Sinan Rasiya Koya,&nbsp;Sudan Pokharel,&nbsp;Molly Likins,&nbsp;Tirthankar Roy","doi":"10.1016/j.jhydrol.2025.133938","DOIUrl":"10.1016/j.jhydrol.2025.133938","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 133938"},"PeriodicalIF":5.9,"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":"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 ,&nbsp;Haiying Mao ,&nbsp;Ziqing Ji","doi":"10.1016/j.jhydrol.2025.133937","DOIUrl":"10.1016/j.jhydrol.2025.133937","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 133937"},"PeriodicalIF":5.9,"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 ,&nbsp;Jinyu Wu ,&nbsp;Tongqing Shen ,&nbsp;Yueyang Wang ,&nbsp;Huiyi Cai ,&nbsp;Junliang Jin ,&nbsp;Peng Jiang ,&nbsp;Xuegao Chen ,&nbsp;Yiheng Du","doi":"10.1016/j.jhydrol.2025.133945","DOIUrl":"10.1016/j.jhydrol.2025.133945","url":null,"abstract":"<div><div>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 (<em>r</em>) and Nash–Sutcliffe efficiency (<em>NSE</em>). 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.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 133945"},"PeriodicalIF":5.9,"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,&nbsp;Archisha Bhar,&nbsp;Subhasish Das,&nbsp;Rajib Das,&nbsp;Gourab Banerjee,&nbsp;Asis Mazumdar","doi":"10.1016/j.jhydrol.2025.133947","DOIUrl":"10.1016/j.jhydrol.2025.133947","url":null,"abstract":"<div><div>Stage-discharge (<em>h</em>-<em>Q</em>) rating curve (<em>Q</em> = <em>ah^b</em>) defined by parameters <em>a</em> and <em>b</em> 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.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 133947"},"PeriodicalIF":5.9,"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}