Hydrological Processes最新文献

{"title":"Correction to “Variability of Stream Extents Controlled by Flow Regime and Network Hydraulic Scaling”","authors":"","doi":"10.1002/hyp.70117","DOIUrl":"https://doi.org/10.1002/hyp.70117","url":null,"abstract":"<p>Lapides, D. A., C. D. Leclerc, H. Moidu, D. N. Dralle, and W. J. Hahm. 2021. “Variability of Stream Extents Controlled by Flow Regime and Network Hydraulic Scaling.” <i>Hydrological Processes</i> 35, no. 3: e14079.</p><p>In Table 1, column “L (km/km²)” includes incorrect data, and the column label is not clear as to which correct data are included in the column. For comprehensiveness and clarity, we replace the incorrect column with three correct columns: “L persistent from NHD (km/km²),” “L total from NHD (km/km²),” and “L from aQ^b (km/km²).” To clarify the data in the three columns, we add a note in the table caption for Table 1: “L persistent from NHD includes marked perennial channels, and L total from NHD includes marked perennial and intermittent channels. L from aQ^b and Q are given as the mean value ± the standard deviation.” The contents of the corrected columns are as follows:\u0000 </p><p>In the “Open Research” section, the citation “Supporting code is available on GitHub (https://zenodo.org/record/4057320; Leclerc et al., 2020a).” is incorrect. The referenced code supplement does not contain the code produced for this study. This should read: “Supporting code is available on GitHub (https://zenodo.org/records/14939383; Lapides et al., 2020a).”</p><p>We apologise for these errors.</p>","PeriodicalId":13189,"journal":{"name":"Hydrological Processes","volume":"39 3","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hyp.70117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial and Temporal Variation in the Three Main Hydrological States of Temporary Streams in a Swiss Pre-Alpine Catchment","authors":"Rick S. Assendelft,&nbsp;Ilja van Meerveld","doi":"10.1002/hyp.70018","DOIUrl":"https://doi.org/10.1002/hyp.70018","url":null,"abstract":"<p>There are three main hydrological states for temporary (i.e., non-perennial) streams: dry streambed, standing water (pools), and flowing water. These states and the changes between them uniquely influence the physical, chemical and biological conditions and processes in temporary streams. Therefore, it is important to characterise temporary stream dynamics based on the three states. However, there is a lack of high spatiotemporal resolution data of the three states across stream networks. In this study, a network of 30 multi-sensor monitoring systems was used to acquire 5-min data of the three states across a small pre-Alpine headwater catchment during a 3-month monitoring period. The standing water state was most common in the upper part of the catchment, while the flowing water state occurred more frequently downstream. The dry streambed state was dominant in a fault zone between two types of Flysch bedrock. The spatial variation of the hydrological state permanence was correlated to topography, specifically the local Topographic Wetness Index, channel slope and upslope contributing area, except for monitoring locations in the fault zone. The wetting pattern during precipitation events was a bottom-up pattern outside the fault zone and a top-down pattern in the fault zone. The spatial variation in the amount of precipitation prior to a state change and the soil moisture storage at the time of a state change were related to topography as well. The temporal variation in these wetness thresholds for state changes was influenced by the antecedent soil moisture conditions and precipitation intensity. Our findings highlight the influence of topography, geology, channel morphology and event characteristics on the variation of the three main hydrological states of temporary streams. Moreover, this work highlights the value of monitoring all three states and high temporal resolution state data. Monitoring only wet and dry states or at a lower temporal resolution (e.g., weekly) would not have captured any state changes for many of the monitoring locations, and, therefore, would have severely underestimated the temporary stream dynamics.</p>","PeriodicalId":13189,"journal":{"name":"Hydrological Processes","volume":"39 3","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hyp.70018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping 2D Hydraulic Tomography: Comparison of Deep Learning Algorithm and Quasi-Linear Geostatistical Approach","authors":"Minh-Tan Vu,&nbsp;Abderrahim Jardani","doi":"10.1002/hyp.70118","DOIUrl":"https://doi.org/10.1002/hyp.70118","url":null,"abstract":"<div>\u0000 \u0000 <p>In this study, we conduct a comparative analysis of the Quasi-Linear Geostatistical Approach (QLGA) and deep learning algorithms for 2D hydraulic tomography underground, exploiting synthetic and real hydraulic head data from field settings. The hydraulic dataset is derived from multiple pumping tests at the Hydroscan observatory in Normandy, aiming to map the transmissivity heterogeneity of the gravel aquifer along the Seine riverbanks, which is critical for understanding and optimising hydrological processes. Two distinct inversion methodologies are addressed to decipher the piezometric data: a process-based approach—QLGA—widely recognised for its effectiveness in depicting aquifer hydraulic properties, and a data-driven approach based on Convolutional Neural Networks (CNNs). The QLGA method relies on iterative linearisation with calculations of the Jacobian matrix to minimise an objective function, while the CNN approach directly approximates operators through a novel circular architecture that allows for determining heterogeneity and evaluating its response within a single solver. Results from both methods demonstrate their efficacy in capturing subsurface heterogeneity where the resolution of local details is constrained by the limited number of piezometric measurements. While QLGA achieves a better fit between simulated and observed data, the CNN method effectively handles complex features while reducing smoothing in inversion solutions. When applied to real cases, both methods show strong agreement with observations from synthetic studies, emphasising their accuracy and comparability. The choice between QLGA and deep learning approaches thus depends on problem-specific requirements, data availability, and interpretability needs, providing valuable insights for advanced subsurface characterisation.</p>\u0000 </div>","PeriodicalId":13189,"journal":{"name":"Hydrological Processes","volume":"39 3","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evapotranspiration Partitioning Using Flux Tower Data in a Semi-Arid Ecosystem","authors":"Kanak Kanti Kar,&nbsp;Ryan Haggerty,&nbsp;Harmandeep Sharma,&nbsp;Dipankar Dwivedi,&nbsp;Tirthankar Roy","doi":"10.1002/hyp.70083","DOIUrl":"https://doi.org/10.1002/hyp.70083","url":null,"abstract":"<p>Information about evapotranspiration (ET) and its components, that is, evaporation and transpiration, is crucial for a wide range of water and ecosystem management applications. However, partitioning ET into its two components is often challenging because of their spatiotemporal variabilities and lack of process understanding. This study developed a machine learning (ML) framework to shed light on ET processes and assess the relative importance of different drivers by incorporating hydrometeorology and biomass productivity variables. The Shapley Additive Explanations (SHAP) approach was applied to enhance explainability and rank the importance of ET drivers and their components. A total of 62 variables covering hydrometeorological and biomass productivity dimensions were considered from the Reynolds Creek Critical Zone Observatory (CZO) station in Idaho. The variable importance assessment identified the leading drivers individually for evaporation, transpiration and ET (soil water content for evaporation, vapour pressure deficit for transpiration and soil water content for ET). The results further highlighted the value of combining hydrometeorological and biomass productivity variables to achieve better predictability of ET processes.</p>","PeriodicalId":13189,"journal":{"name":"Hydrological Processes","volume":"39 3","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hyp.70083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impacts of the Grain for Green Project on Soil Moisture in the Yellow River Basin, China","authors":"Zichun Zhao,&nbsp;Haijun Huang,&nbsp;Jie Wang,&nbsp;Guanbin Feng,&nbsp;Luyi Li,&nbsp;Tong Sun,&nbsp;Yanzhong Li,&nbsp;Jiangfeng Wei,&nbsp;Xitian Cai","doi":"10.1002/hyp.70112","DOIUrl":"https://doi.org/10.1002/hyp.70112","url":null,"abstract":"<div>\u0000 \u0000 <p>The Grain for Green Project is a significant environmental protection initiative in China designed to maintain ecological benefits through large-scale vegetation restoration. Such projects primarily affect vegetation cover, which in turn influences soil moisture dynamics. This study investigates the changes in surface soil moisture and total soil moisture in the Yellow River Basin before and after the implementation of the Grain for Green Project, thereby assessing its impact on soil moisture conditions. By calculating the trends of soil moisture and NDVI for the periods 1982–1998 and 1999–2014, the effects of the Grain for Green Project on soil moisture were evaluated. We employed partial correlation analysis to obtain the relationship between soil moisture and NDVI. Additionally, an Long Short-Term Memory (LSTM) network model and the SHapley Additive exPlanations (SHAP) values were used to identify the key factors influencing soil moisture. The results indicated that the areas with a significant increase in vegetation are mainly concentrated in the middle reaches of the Yellow River Basin. Moreover, the Grain for Green Project has resulted in a decreasing trend in surface soil moisture and total soil moisture across more than 60% of the Yellow River Basin, with an average reduction of 0.016 m³·m⁻³·decade⁻¹ in the trend of surface soil moisture and 0.021 m³·m⁻³·decade⁻¹ in the trend of total soil moisture. Furthermore, precipitation was found to have the greatest impact on surface soil moisture, while temperature had the most significant influence on total soil moisture. This study provides valuable insights into the effectiveness of the Grain for Green Project in promoting vegetation growth and soil moisture conservation and encourages sustainable management of land and water resources in the Yellow River Basin and beyond.</p>\u0000 </div>","PeriodicalId":13189,"journal":{"name":"Hydrological Processes","volume":"39 3","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hydrological Whiplash: Highlighting the Need for Better Understanding and Quantification of Sub-Seasonal Hydrological Extreme Transitions","authors":"John Hammond,&nbsp;Bailey Anderson,&nbsp;Caelan Simeone,&nbsp;Manuela Brunner,&nbsp;Eduardo Muñoz-Castro,&nbsp;Stacey Archfield,&nbsp;Eugene Magee,&nbsp;Rachael Armitage","doi":"10.1002/hyp.70113","DOIUrl":"https://doi.org/10.1002/hyp.70113","url":null,"abstract":"<div>\u0000 \u0000 <p>In this commentary, we aim to (1) describe ways that hydrological intensification and hydrological whiplash (sub-seasonal transitions between hydrological extremes) may impact water management decision-making, (2) introduce the complexities of identifying and quantifying hydrological extreme transitions, (3) discuss the processes controlling hydrological transitions and trends in hydrological extremes through time, (4) discuss considerations involved in modeling hydrological extreme transitions, and (5) motivate additional research by suggesting priority research questions that diverge from an assumption of independence between extreme events.</p>\u0000 </div>","PeriodicalId":13189,"journal":{"name":"Hydrological Processes","volume":"39 3","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling of Total Phosphorus and Nitrate Using a Travel Time Approach in the Duck River Catchment, Australia","authors":"Zahra Riazi,&nbsp;Andrew William Western","doi":"10.1002/hyp.70104","DOIUrl":"https://doi.org/10.1002/hyp.70104","url":null,"abstract":"<p>Total phosphorus (TP) and nitrate are important non-conservative contaminants of streams. They vary strongly in response to climatic, hydrologic, and other drivers and are affected by different flow paths. Water residence and travel time distributions carrying information about sources of streamflow can potentially provide a basis for modelling nitrate and TP dynamics. In this study, we use a travel time model coupled with age—concentration relationships to simulate nitrate and TP concentrations in the Duck River catchment, NW Tasmania, Australia. A modified version of the Tran-SAS model was used with time-varying beta storage selection functions, calibrated against high-frequency electrical conductivity (EC) observations. Concentrations of TP and nitrate were then modelled using the water TTDs coupled with age-concentration relationships for TP and nitrate. This approach separated biogeochemical effects from water travel time and ensured consistent TTDs underpinning the transport of different nutrients. Two years (2008 and 2009 water years) of high-frequency nutrient concentrations were used for model calibration and validation. It was initially hypothesised that the age-concentration relationships for nitrate and TP could be temporally fixed, with the seasonal variation in residence time distribution capturing any seasonality in nutrient behaviour. The models performed moderately under this hypothesis; however, residual analysis clearly demonstrated seasonal declines in the concentrations of TP and nitrate during events across the high flow season. Simulations of TP and nitrate were markedly improved by using different source concentrations: one for the early high flow season and the other for the remainder of the year. Both Nash-Sutcliffe Efficiency and the combined seasonal and event dynamics of nitrate and TP were markedly improved by using different source concentrations for these two different periods. This suggests that land management and biogeochemical processing are important influences on the temporal dynamics of nutrients in streams. The study informs future developments of TTD-based water quality modelling and demonstrates the need to include temporally dynamic nutrient source concentrations for young water.</p>","PeriodicalId":13189,"journal":{"name":"Hydrological Processes","volume":"39 3","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hyp.70104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal Evolution of Forest Road Rutting and Flow Pathways Examined Using Unoccupied Aerial Vehicles (UAVs)","authors":"Amanda D. Alvis,&nbsp;Charles H. Luce,&nbsp;Erkan Istanbulluoglu,&nbsp;Friedrich Knuth,&nbsp;Lauren Wittkopf,&nbsp;David Shean,&nbsp;Gregory Stewart","doi":"10.1002/hyp.70105","DOIUrl":"https://doi.org/10.1002/hyp.70105","url":null,"abstract":"<p>Flow pathways on unpaved forest roads are critical determinants of surface runoff and sediment transport. These flow pathways can be largely altered through road deformation caused by heavy traffic, with one of the most common types of deformation being ruts. Historically, rut development has been studied using cross-sectional analyses. More recently, remote sensing techniques, such as structure-from-motion (SfM) or terrestrial LiDAR scanning (TLS), have demonstrated their utility in mapping ruts on forest roads. However, applications of these data are limited, especially with respect to flow pathways on the road surface. Here we used SfM, with validation from TLS, to examine the spatially comprehensive development of ruts and their effects on forest road flow pathways and relative sediment transport potential. We carried out a small-scale experiment at two field sites in western Washington using unoccupied aerial vehicles (UAVs) to obtain digital elevation models (DEMs) of mainline logging road surfaces over 3 seasons. These UAV-derived DEMs were used in an elevation change analysis and a simple flow routing model to examine the evolution of ruts and the impacts thereof. We found that: (1) the relationship between measures of rut incision and time since grading was nonlinear at both sites for all seasons with sufficient data; (2) as ruts developed, the flow pathways on the road surface were altered; (3) the relative transport potential of the road surfaces increased overall as ruts developed; and (4) drainage system metrics reveal a threshold rut incision depth for increased transport potential and flow network change. Our results demonstrate that a great deal of useful information can be extracted by using SfM DEMs for the analysis of rut evolution. Additionally, our results allow us to examine how rutting may affect the utilisation of erosion control treatments in roadside ditch lines and the sediment yield of the road surface.</p>","PeriodicalId":13189,"journal":{"name":"Hydrological Processes","volume":"39 3","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hyp.70105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143639268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Changes in Vegetation Phenology and Water Use Efficiency Driven by Warming and Wetting in Northwest China","authors":"Wenqing Zhang,&nbsp;Yanling Bai,&nbsp;Liu Liu,&nbsp;Yudong Chen,&nbsp;Jiayi Zhang,&nbsp;Yurui Lun,&nbsp;Xiuping Li","doi":"10.1002/hyp.70110","DOIUrl":"https://doi.org/10.1002/hyp.70110","url":null,"abstract":"<div>\u0000 \u0000 <p>Vegetation phenology is a key indicator of climate change and plays a vital role in ecosystem water use efficiency (WUE), which balances carbon sequestration and water loss. As global climate change accelerates, understanding its effects on phenology and WUE is essential for comprehending ecosystem dynamics and carbon–water cycles. Northwest China (NWC), one of the driest regions at similar latitudes, is experiencing a rapid shift from a warm-dry to a warm-wet climate, posing significant challenges to its fragile ecosystem. In this study, we used reanalysis and satellite remote sensing datasets to analyse the changes in the start of the growing season (SOS), the end of the growing season (EOS) and the length of the growing season (LOS) for various vegetation types in the NWC from 1982 to 2015. The focus was on how temperature and precipitation variations influenced phenological dynamics and their subsequent impacts on Gross Primary Productivity (GPP), evapotranspiration (ET) and WUE. Our results show that NWC has experienced a significant warming and wetting trend, with the SOS advancing by 0.04 days per year and the EOS delaying by 0.04 days per year, leading to a notable extension of the LOS by 0.08 days annually. Temperature primarily drives the SOS advance, while precipitation changes in croplands and grasslands and temperature shifts in forests and shrublands dictate the EOS delays. WUE increased at a rate of 0.005 gC m⁻² mm⁻¹ year⁻¹, with temperature and precipitation influencing GPP and ET both directly and indirectly through phenological changes. The findings underscore the cascading effects of warming and wetting on vegetation phenology and WUE in the fragile NWC ecosystem. Changes in the vegetation growing season have had significant impacts on carbon and water fluxes, with varying effects across different vegetation types. This study provides valuable insights into the response mechanisms of vegetation to rapid climate change in arid and semi-arid regions and offers critical information for the sustainable management of water resources and agriculture in the NWC.</p>\u0000 </div>","PeriodicalId":13189,"journal":{"name":"Hydrological Processes","volume":"39 3","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Working Ever Faster—Or the Curious Case of Horton (1940)","authors":"Keith Beven","doi":"10.1002/hyp.70108","DOIUrl":"https://doi.org/10.1002/hyp.70108","url":null,"abstract":"<p>\u0000 \u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure>\u0000 </p>","PeriodicalId":13189,"journal":{"name":"Hydrological Processes","volume":"39 3","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hyp.70108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}