Journal of Hydrology最新文献

{"title":"Improving terrestrial evapotranspiration estimation of the Tibetan Plateau by coupling SEBS with machine learning-derived aerodynamic resistance","authors":"Yixi Kan ,&nbsp;Huaiyong Shao ,&nbsp;Yunjun Yao ,&nbsp;Yufu Li ,&nbsp;Xiaotong Zhang ,&nbsp;Jia Xu ,&nbsp;Xueyi Zhang ,&nbsp;Zijing Xie ,&nbsp;Jing Ning ,&nbsp;Ruiyang Yu ,&nbsp;Lu Liu ,&nbsp;Jiahui Fan ,&nbsp;Luna Zhang","doi":"10.1016/j.jhydrol.2025.133711","DOIUrl":"10.1016/j.jhydrol.2025.133711","url":null,"abstract":"<div><div>Land evapotranspiration (ET) on the Qinghai–Tibet Plateau (TP) is crucial for regulating worldwide atmospheric circulation. This research explores the integration of machine learning (ML) with a physical framework to improve ET estimation. We developed a coupled model that combines the surface energy balance system (SEBS) model with ML algorithms (SEBS-ML) to estimate ET effectively. Specifically, we employed the random forest (RF) algorithm to estimate aerodynamic resistance (ra), which significantly influences the turbulent transport between the surface and air. We evaluated the instantaneous and daily ET estimates using data from 17 eddy covariance flux tower sites and compared these estimates with those derived from three other ML strategies: linear regression (LR), ridge regression (RR), and support vector machine regression (SVM). We also compared the hybrid model with the SEBS model and pure machine learning strategy (PML). The results indicate that RF provides the most accurate estimates of daily ET among the 4 hybrid models, with R² and Kling–Gupta efficiency (KGE) values equal to 0.70 and 0.82. SVM performed less effectively than RF, while LR and RR were the least effective. The ensemble learning approach in the RF model appears to reduce the bias of the ensemble results by compensating for individual tree biases. Under extreme conditions, the hybrid model demonstrates superior generalization capability, with a relatively low energy irrationality rate and better extrapolation performance compared to the PML model.. The enhanced strategy enhances the quality of ET estimates but also adheres to physical constraints, thereby preventing the generation of implausible results. This research introduces an innovative approach for estimating ET that enhances the physical mechanisms and performance of the SEBS model, thereby improving its precision and scalability. This model provides vital insights into the hydrological and climatic changes occurring on the TP.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"661 ","pages":"Article 133711"},"PeriodicalIF":5.9,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322899","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":"Spring-neap variations in tidal duration asymmetry in the Pearl River Estuary: Dominant tidal combinations and long-term evolution","authors":"Jianliang Lin ,&nbsp;Shuai Hu ,&nbsp;Ping Zhang ,&nbsp;Linxi Fu ,&nbsp;Xianzhao He ,&nbsp;Huayang Cai ,&nbsp;Lixia Niu ,&nbsp;Xiaohe Zhang ,&nbsp;Qingshu Yang","doi":"10.1016/j.jhydrol.2025.133684","DOIUrl":"10.1016/j.jhydrol.2025.133684","url":null,"abstract":"<div><div>Tidal duration asymmetry (TDA), defined as the inequality in ebb and flood tide durations, significantly impacts sediment transport, estuarine morphology, and water resource management. This study investigates spring-neap variations in TDA at 21 stations within the Pearl River Estuary, focusing on their spatiotemporal variability, dominant tidal constituent combinations, and long-term trends under anthropogenic influences. Using spectral analysis, Pearson correlation, and relative change rate methods to tidal skewness (<em>γ</em>_N) time series, we identify dominant tidal constituent combinations (K₁-O₁-M₂ and M₂-M₄) driving spring-neap TDA variations. Our findings demonstrate a pronounced semi-monthly periodicity, characterized by enhanced ebb dominance during neap tides, with <em>γ</em>_N values generally 0.1–0.2 higher than during spring tides. Spatially, TDA shifts from river-dominated upstream zones (ebb dominance) to tide-dominated downstream areas (flood dominance), with the transition regions strongly modulated by shallow-water constituents (M₂-M₄). Over the 1966–2016 period, spring-neap TDA variability experienced substantial long-term changes (up to 0.003/year), clearly associated with major human interventions, such as dam construction, sand mining, and reclamation, altering tidal constituent amplitudes and phases.. These interventions primarily amplified non-linear interactions during neap tides, with sluice construction and reclamation reducing spring-neap TDA differences while reservoir regulation and sand mining increasing this difference. These findings underscore the importance of fluvial-tidal interactions and human impacts in shaping tidal asymmetry, providing critical insights for sustainable estuarine management.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"661 ","pages":"Article 133684"},"PeriodicalIF":5.9,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298832","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":"A comparative study of physics-informed neural network strategies for modeling water and nitrogen transport in unsaturated soils","authors":"Hamza Kamil ,&nbsp;Azzeddine Soulaïmani ,&nbsp;Abdelaziz Beljadid","doi":"10.1016/j.jhydrol.2025.133624","DOIUrl":"10.1016/j.jhydrol.2025.133624","url":null,"abstract":"<div><div>A deep understanding of subsurface flow dynamics—including water infiltration and the transport of single or multiple solutes in unsaturated soils—is critical for a wide range of engineering applications. Traditionally, these complex processes have been modeled using standard numerical solvers, which remain conventional in many studies. However, a recent and promising methodology gaining traction is physics-informed neural networks (PINNs). This approach is based on training neural networks to solve partial differential equations by combining available data with the physical principles embedded in the equations. In this study, we analyze several PINN solvers to tackle the coupled model of water flow and single or multispecies solute transport in unsaturated soils. This model is governed by the highly nonlinear Richards equation and advection–dispersion equations. To improve the training of the solvers, we integrate several strategies aimed at capturing the system’s full complexity.</div><div>The numerical experiments cover one- and two-dimensional scenarios, tackling forward and inverse problems. The results obtained from PINN are compared with reference solutions and experimental data sourced from existing literature. Our analysis underscores the effectiveness of employing sequential training alongside an adaptive activation technique for modeling the coupled water–solute system. This methodology not only improves accuracy and training efficiency but also enables an accurate estimation of the unknown ammonium nitrification rate from sparse measurements.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"661 ","pages":"Article 133624"},"PeriodicalIF":5.9,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330790","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 perspective of future climate warming impacts on harmful algae blooms in eutrophic Lake Taihu, China","authors":"Chixiao Cui ,&nbsp;Ting Pan ,&nbsp;Justin Brookes ,&nbsp;Qingji Zhang ,&nbsp;Boqiang Qin ,&nbsp;Yunlin Zhang ,&nbsp;Guangwei Zhu","doi":"10.1016/j.jhydrol.2025.133716","DOIUrl":"10.1016/j.jhydrol.2025.133716","url":null,"abstract":"<div><div>Elevated water temperatures accelerate nutrient cycling and metabolism in aquatic ecosystems. Eutrophic waters, such as Lake Taihu, historically has been suffered from cyanobacterial (<em>Microcystis</em> spp.) blooms, exhibiting heightened sensitivity to climate change. Nevertheless, the quantitative responses of this highly productive lake to future warming remains poorly predicted. This study employes a three-dimensional hydrodynamic model coupling a lake thermal model and cyanobacterial biomass model to project future water temperatures and associated phytoplankton biomass in Lake Taihu under multiple Shared Socioeconomic Pathways (SSPs), comparing scenarios for 2100 against current conditions (2015–2022 multi-year average). Results indicate that annual mean Lake Water Column Temperature (LWCT) will rise by 1.12 °C, 2.15 °C, 4.18 °C, and 4.97 °C under respective SSP scenarios, which is much higher than the global average increase of inland waters. The most pronounced warming occurs during autumn, while winter thermal stratification stability is projected to decline (vertical temperature deviation approach zero). Algal bloom intensity (by chlorophyll-a concentration) is projected to increase by 12.0%, 22.2%, 46.2% and 62.5%, indicating that climate warming may trigger extensive and severe blooms in highly productive waters. Sensitivity analyses reveal that a 40% nutrient reduction effectively controls cyanobacterial concentrations during 2060–2090 in all but the warmest scenario. These findings demonstrate that targeted nutrient mitigation can counteract the amplifying effects of climate warming on cyanobacterial blooms in eutrophic waters.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"661 ","pages":"Article 133716"},"PeriodicalIF":5.9,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330793","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":"Parametrization and model development for simulating redox reactions related to degradation of natural organic matter in managed aquifer recharge","authors":"M. Jylhä-Ollila ,&nbsp;P. Kekäläinen ,&nbsp;H. Koivusalo","doi":"10.1016/j.jhydrol.2025.133715","DOIUrl":"10.1016/j.jhydrol.2025.133715","url":null,"abstract":"<div><div>Managed aquifer recharge (MAR) is commonly used as a treatment method to remove organic matter from surface water for a drinking water supply by filtering the water through soils in an aquifer. Groundwater, solute transport, and natural organic matter (NOM) degradation models are available for describing the processes, but their applications for practical assessment of MAR sites and their design have been limited. The objective of this study was to model the NOM processes, quantify the impacts of individual drivers on degradation, and identify the model parameters mainly controlling concentrations of organic matter, oxygen, Fe, and Mn. The methodological approach was to construct a 1-D reactive transport model for total organic carbon (TOC), dissolved oxygen (DO), Fe, Mn, and heat to address the key NOM processes in a lake-aquifer system, where lake water naturally infiltrates into an aquifer. The model was implemented with generalized likelihood uncertainty estimation (GLUE) and Sobol’s method to test the parameter sensitivity.The results showed that in addition to reactions in the aquifer, the lake bottom sediment model played a key role in bank infiltration through its control of DO concentrations. The model was able to represent the diminishing effect of strong temperature seasonality and smoothening of the temperature-related concentration variations in the aquifer. On the other hand, the model produced seasonal TOC dynamics close to the lake-aquifer interface, which was not detected in measurements. With GLUE, the TOC and DO concentrations in the model were most sensitive to the seepage velocity, the hydrodynamic dispersivity, and the reaction rate parameters controlling the oxygen-related degradation of TOC in the lake sediment and the aquifer. Sobol’s total order indexes also showed sensitivity to parameters that control the temperature dependency of reaction rates. In addition to these parameters, Mn and Fe concentrations were sensitive to reaction rate factors for the dissolution of Mn in the aquifer, but the result was less clear. The resulting identification of the key parameters provides a benchmark for the calibration of bank filtration models in the Nordic context.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"661 ","pages":"Article 133715"},"PeriodicalIF":5.9,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501894","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":"Estimating transient vertical hyporheic exchange fluxes from streambed temperatures using LSTM-autoencoder-enhanced physics-informed neural networks","authors":"Lei Ju ,&nbsp;Qiang Zheng ,&nbsp;Jiangjiang Zhang ,&nbsp;Shiwen Guo ,&nbsp;Fengrui Chen","doi":"10.1016/j.jhydrol.2025.133721","DOIUrl":"10.1016/j.jhydrol.2025.133721","url":null,"abstract":"<div><div>Accurately quantifying hyporheic exchange fluxes is crucial for understanding the transport and fate of contaminants and nutrients in the hyporheic zone. Over the past two decades, both physics-based analytical and numerical models, as well as data-driven models have been widely employed to infer these fluxes from streambed temperatures. However, each model type has notable limitations: physics-based models can suffer from structural errors that diminish inversion accuracy, while data-driven models often create input–output relationships without adequately considering the constraints imposed by established physical processes. To address these limitations, this study introduces a novel inversion framework termed LSTM-AE-PINN that integrates the physics-informed neural network (PINN) with the long short-term memory-based autoencoder (LSTM-AE) to estimate transient vertical hyporheic exchange fluxes (VHEFs). This framework leverages PINN to merge observational data with scientific principles and uses LSTM-AE to derive a low-dimensional representation of the VHEF time series, thereby streamlining parameter identification. The efficacy of LSTM-AE-PINN is evaluated through two synthetic case studies and one real-world application, demonstrating consistent superiority over PINN. It improves Kling-Gupta Efficiency (KGE) scores for VHEF estimations by 2.31 % to 88.62 %, with greater advantages in sparse or highly uncertain observational scenarios. This advancement not only refines VHEF estimation but also establishes a transferable template for inferring time-dependent parameters in broader hydrological contexts.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"661 ","pages":"Article 133721"},"PeriodicalIF":5.9,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306740","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":"Future precipitation extremes and urban flood risk assessment using a non-stationary and convection-permitting climate-hydrodynamic modeling framework","authors":"Patrick Laux ,&nbsp;David Feldmann ,&nbsp;Francesco Marra ,&nbsp;Hendrik Feldmann ,&nbsp;Harald Kunstmann ,&nbsp;Katja Trachte ,&nbsp;Nadav Peleg","doi":"10.1016/j.jhydrol.2025.133607","DOIUrl":"10.1016/j.jhydrol.2025.133607","url":null,"abstract":"<div><div>Urban planners and engineers rely on historical climate data to plan and design flood protection infrastructure that should withstand extreme flooding events with 1% annual exceedance probability (the 100-year flood). Here, we examine how hourly precipitation extremes are expected to change as temperatures rise and how this will affect urban flooding. The changes to short-duration rainfall extremes, often insufficiently considered in practice, are addressed utilizing a new temperature conditional extreme precipitation scaling approach and a novel regional climate convection-permitting model ensemble for +2 °C and +3 °C global warming scenarios. Based on hydrodynamic modeling, we estimate how future precipitation extremes translate into flood risks in two pre-alpine communes in Germany. Ignoring the impacts of climate change may lead to severe underestimations of flood risks. The +3 °C global warming scenario translates into an increase of 60% of affected buildings by the highest flood risk category (water level of 1 m and above). The increase in flow intensities will be greater in the commune characterized by steeper terrain. The results suggest that recently planned or implemented infrastructure projects may not be adequately equipped to cope with the anticipated effects of climate change in the coming decades.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"661 ","pages":"Article 133607"},"PeriodicalIF":5.9,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144304891","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":"High-resolution snow water equivalent estimation derived from downscaled snow depth and non-constant snow density in Chinese Altai Mountains","authors":"Ziqiang Li ,&nbsp;Xuejiao Wu ,&nbsp;Wei Zhang ,&nbsp;Xinyue Zhong ,&nbsp;Muxin Yue ,&nbsp;Yaqin Li ,&nbsp;Yongping Shen ,&nbsp;Rensheng Chen","doi":"10.1016/j.jhydrol.2025.133708","DOIUrl":"10.1016/j.jhydrol.2025.133708","url":null,"abstract":"<div><div>Snow water equivalent (SWE) plays a critical role in managing freshwater resources in mountainous areas. Due to the spatial heterogeneity of snow, high-resolution SWE mapping is essential for accurate mountainous snow monitoring. However, the coarse spatial resolution of commonly used SWE datasets introduces uncertainties regarding their accuracy and applicability in these areas. This study combined multi-source remote sensing data fusion downscaling algorithm (MDFDA), Random Forest (RF) model and non-constant snow density (SDE) to reconstruct SWE at 1-km resolution in Chinese Altai mountains (CAM), demonstrating nearly 50 % and 17 % reductions in relative error compared to 25-km reference SWE data and SWE derived from the constant SDE. First, we applied MDFDA to downscale the 25-km passive microwave-derived snow depth (SD) data to 1-km. Next, we input the downscaled SD, along with spatiotemporal and climatological covariates, into RF model to obtain accuracy-optimized downscaled SD (RFSD). Finally, we converted the RFSD to SWE by utilizing two non-constant SDE conversion methods (Power law formula and SDE gridded data). The inclusion of covariates in RF model significantly improved the SD estimation accuracy, with the Pearson correlation coefficient (R) increasing from 0.61 to 0.96. The SWE derived from the Power law formula showed R of 0.85, whereas SDE gridded data yielded improved R of 0.9. Based on reconstructed SWE data, we found statistically significant differences in SWE (<em>p</em> &lt; 0.01) between CMA and non-mountainous areas (NMA) in February and April. Our results are helpful for enhancing high-resolution SD/SWE estimations and hydrological research in mountainous areas.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"661 ","pages":"Article 133708"},"PeriodicalIF":5.9,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144304946","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":"Deciphering coastal plain groundwater dynamics: insights from satellite and hydrologic data in Pinghu City, China","authors":"Chengcheng Wu ,&nbsp;Chengpeng Lu ,&nbsp;Edward Park ,&nbsp;Yunfeng Wang ,&nbsp;Yangcun Xie ,&nbsp;Alain M. Plattner ,&nbsp;Zhibin Li ,&nbsp;Bo Liu ,&nbsp;Longcang Shu ,&nbsp;Yong Zhang","doi":"10.1016/j.jhydrol.2025.133706","DOIUrl":"10.1016/j.jhydrol.2025.133706","url":null,"abstract":"<div><div>Groundwater, the planet’s largest active freshwater resource, plays a critical role in sustaining ecosystems, economies, and societies. Over the past two decades, China’s groundwater regulation policies have significantly elevated groundwater levels (GWLs) in many regions. However, some areas have experienced unexpected or anomalous GWL declines unrelated to groundwater extraction. The purpose of this study is to investigate the causes of anomalous groundwater dynamics processes in a coastal multilayered aquifer in Pinghu city, China. By leveraging multi-source satellite data analyses, including GRACE, InSAR, and isotope techniques, this study identified that leakage within groundwater systems and changes in precipitation, evapotranspiration were the primary drivers of groundwater storage (GWS) reduction across all monitoring stations in Pinghu City, China. The Random Forest model yielded the highest accuracy (R² = 0.72, CC = 0.85, RMSE = 0.11) among the three downscaling methods. A strong correlation (around 0.75) is observed between GWS changes and meteorological variables. The Hongni (HN) station, which monitors multiple deep confined aquifers, was particularly sensitive to GWS fluctuations. To interpret these dynamics, a conceptual model was developed to characterize the interactions within the multi-layered aquifer system, consisting of both unconfined and confined aquifers separated by leakable aquitards. The layered aquifer-aquitard structure observed in Pinghu City is a characteristic feature of coastal and alluvial depositional systems globally. The developed conceptual model was also proven to be effective in the coastal multilayered aquifer in Texas, USA. This model offers a valuable framework for investigating groundwater dynamics in coastal plains with analogous geological settings. The analysis further revealed a temporal effect of key factors influencing the response of deep aquifers, providing critical insights into the mechanisms driving changes in deep groundwater systems.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"661 ","pages":"Article 133706"},"PeriodicalIF":5.9,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144304893","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":"Stochastic framework reveals the controls of forest treatment – peakflow causal relations in rain environment","authors":"Henry C. Pham ,&nbsp;Younes Alila ,&nbsp;Peter V. Caldwell","doi":"10.1016/j.jhydrol.2025.133704","DOIUrl":"10.1016/j.jhydrol.2025.133704","url":null,"abstract":"<div><div>Decades of literature on forest treatment – peakflow relations have generated considerable disagreements and turned the topic into one regarded as enigmatic. Factors affecting peakflows are multiple and chancy and, hence, can only be investigated via a probabilistic approach. We analyze peakflow data using the peakflow frequency distribution framework in two pairs of control-treatment watersheds in the rain environment of an experimental forest in North Carolina. We demonstrate how a range of forest treatments can change the magnitude and frequency of all peakflows on record and how such effects can increase with increasing event size as a consequence of changes to the peakflow frequency distribution. Changes to the distribution’s mean (−0.2 to +47.4 %) and variance (−30.9 to +162.1 %) resulted in a range of effects from no significant impacts on peakflows to making larger peakflows becoming even larger (up to 105 % increase) and more frequent (up to 18 times more frequent). Changes to peakflows are attributed to treatment-induced suppression of evapotranspiration and changes to non-vegetative factors, which can alter the soil storage capacity, moisture available for runoff, and the efficiency of runoff arriving to the outlet. The dominant topographical aspect of the watershed, seasonal differences in storm types, extent to which the storm events are in-phase or out-of-phase with high antecedent soil moisture, and lagged runoff responses with watershed memory emerged as key indicators of the sensitivity of peakflows to forest treatment. The application of the stochastic framework in forest hydrology can help fully understand forest treatment – peakflows relations.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"661 ","pages":"Article 133704"},"PeriodicalIF":5.9,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144304894","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}