Water Resources Research最新文献

{"title":"A Novel Dual-Clustering Approach for Identifying Hydrological Response Patterns From Catchment Characteristics and Environmental Changes","authors":"Yuhao Wang, Ke Zhang, Edward Park, Jie Liu, Yuning Luo, Shunzhang Li, Sheng Wang","doi":"10.1029/2025wr041613","DOIUrl":"https://doi.org/10.1029/2025wr041613","url":null,"abstract":"Understanding how catchments respond to environmental changes is critical for water resource management. However, few studies have systematically linked catchment characteristics, environmental changes, and hydrological responses. Therefore, this study proposes a novel dual-clustering approach for identifying hydrological response patterns. It constructs the catchment characteristic indicator system for the baseline period and introduces dynamic similarity indicators that reflect climate change and anthropogenic impacts to achieve dual clustering, thereby identifying hydrological response differences. Furthermore, it employs the eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) methods to identify key influencing factors of runoff change significance, providing interpretable insights into differences in hydrological responses. The approach is applied to the Haihe River Basin, indicating that 160 catchments are classified into nine static groups (A1–A9) based on catchment characteristics and five dynamic groups (B1–B5) based on environmental changes. The XGBoost model demonstrates good performance in identifying hydrological response patterns, SHAP analysis identifies the top four important factors as percentage of areas with substantial declines in the water table (positive), proportion of natural land use (positive), degree of humidity (negative), and mean elevation (positive). Catchments located in the northwestern mountainous areas are more susceptible to environmental changes, while those located in the southwestern mountainous areas and the southern plains show relatively stable response patterns. Additionally, environmental change patterns characterized by substantial water table decline are more likely to trigger significant runoff change. This approach provides new insights into the effects of interactions between static catchment characteristics and dynamic environmental changes on hydrological functioning.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"8 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383879","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":"Impact of Density Gradients and Momentum Ratios on Streamwise Circulation at River Confluences","authors":"Abdelrahman Hosny Abdou, Jason Duguay, Pascale Biron, Jay Lacey","doi":"10.1029/2025wr041585","DOIUrl":"https://doi.org/10.1029/2025wr041585","url":null,"abstract":"Mixing processes in confluences influence concentrations of water quality parameters such as suspended sediment loads and water chemistry in the post-confluent reach. Thus, improving our understanding of confluence flow structures and mixing is key to predicting the impact of the downstream propagation of pollutants and nutrients. Parameters such as density difference <span data-altimg=\"/cms/asset/01665dc1-ef2c-4cda-9454-08a6f4fa4f7d/wrcr70778-math-0001.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr70778:wrcr70778-math-0001\" display=\"inline\" location=\"graphic/wrcr70778-math-0001.png\">\u0000<semantics>\u0000<mrow>\u0000<mo stretchy=\"false\">(</mo>\u0000<mrow>\u0000<mi mathvariant=\"normal\">Δ</mi>\u0000<mi>ρ</mi>\u0000</mrow>\u0000<mo stretchy=\"false\">)</mo>\u0000</mrow>\u0000$({Delta }rho )$</annotation>\u0000</semantics></math> and momentum ratio <span data-altimg=\"/cms/asset/ec244274-2ca2-4633-ae2a-c380a7b0b4be/wrcr70778-math-0002.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr70778:wrcr70778-math-0002\" display=\"inline\" location=\"graphic/wrcr70778-math-0002.png\">\u0000<semantics>\u0000<mrow>\u0000<mo stretchy=\"false\">(</mo>\u0000<mrow>\u0000<mi>M</mi>\u0000<mi>r</mi>\u0000</mrow>\u0000<mo stretchy=\"false\">)</mo>\u0000</mrow>\u0000$(Mr)$</annotation>\u0000</semantics></math> significantly influence the flow field and mixing processes. This study uses eddy-resolved numerical simulations to investigate the impact of these parameters on the formation of density-driven streamwise-oriented vortices (density-driven SOVs) and mixing in a symmetric experimental confluence. Three values of <span data-altimg=\"/cms/asset/807f4c30-c683-4642-bf08-6b8618fb8175/wrcr70778-math-0003.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr70778:wrcr70778-math-0003\" display=\"inline\" location=\"graphic/wrcr70778-math-0003.png\">\u0000<semantics>\u0000<mrow>\u0000<mi>M</mi>\u0000<mi>r</mi>\u0000</mrow>\u0000$Mr$</annotation>\u0000</semantics></math> with varying magnitudes of the densimetric Froude number <span data-altimg=\"/cms/asset/f412edb6-6913-40fd-9bd6-92c5dd52de02/wrcr70778-math-0004.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr70778:wrcr70778-math-0004\" display=\"inline\" location=\"graphic/wrcr70778-math-0004.png\">\u0000<semantics>\u0000<mrow>\u0000<mfenced close=\")\" open=\"(\" separators=\"\">\u0000<msub>\u0000<mi>F</mi>\u0000<mi>D</mi>\u0000</msub>\u0000</mfenced>\u0000</mrow>\u0000$left({F}_{D}right)$</annotation>\u0000</semantics></math>, representing <span data-altimg=\"/cms/asset/b4e47aa0-1234-4367-b75e-699364eb983d/wrcr70778-math-0005.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr70778:wrcr70778-math-0005\" display=\"inline\" location=\"graphic/wrcr70778-math-0005.png\">\u0000<semantics>\u0000<mrow>\u0000<mi mathvariant=\"normal\">Δ</mi>\u0000<mi>ρ</mi>\u0000</mrow>\u0000${Delta }rho $</annotation>\u0000</semantics></math> were considered in the numerical simulations. A clear pattern for the dominant streamwise circulation (circulation caused by the <span data-altimg=\"/cms/asset/ef31400c-0c92-4f80-b04f-2b30da84eb4f/wrcr70778-math-0006.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr70778:wrcr70778-math-0006\" display=\"inline\" location=\"graphic/wrcr","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"95 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147380845","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":"Satellite Solutions: Facing Chlorophyll-a Retrieval in Small Mountain Lakes in the Sierra Nevada, Spain","authors":"J. Llodrà-Llabrés, J. C. Pérez-Girón, T. Postma, C. Pérez-Martínez, D. Alcaraz-Segura, J. Martínez-López","doi":"10.1029/2026wr043523","DOIUrl":"https://doi.org/10.1029/2026wr043523","url":null,"abstract":"National and international regulations enforce monitoring programmes of water quality to guide management actions of inland water ecosystems. Our study evaluates the effect of spectral and spatial resolutions on the estimation of chlorophyll-<i>a</i> concentrations in mountain lakes, and derives implications for addressing the adjacency effect, which is critical and understudied in small water bodies. Five lakes in Sierra Nevada (Spain) were repeatedly sampled during 2020, 2021, and 2023, and a total of 100 chlorophyll-<i>a</i> samples with suitable coincident satellite imagery were analyzed. Laboratory-obtained chlorophyll-<i>a</i> concentrations were modeled comparing up to 86 spectral indices and bands as predictors from three satellites: Sentinel-2 (12 bands, 20 m/pixel), Planet (8 bands, 3 m/pixel) and WorldView-3 (11 bands, 1.24 m/pixel). Our results showed that multivariate models for estimating chlorophyll-<i>a</i> using spectral indices did not perform significantly better than using bands alone. The best models always had multiple predictors and included green and near-infrared bands. Models based on Sentinel-2 and Planet (<i>R</i>_adj² &gt; 0.45) outperformed those of WorldView-3 (<i>R</i>_adj² ∼ 0.37), confirming that the latter performed worst despite higher spatial resolution. Regarding distance to shoreline, the Planet model showed the most consistent performance, with stable <i>R</i>_adj² values and low RMSE even at 3 m from shore with a high level of accuracy (<i>R</i>_adj² ∼ 0.3; RMSE ∼ 1.15 μg L⁻¹). Data and models are released to facilitate near-real-time monitoring of these vulnerable ecosystems, where field sampling is extremely challenging.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"10 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383912","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":"Integrating Deep Learning and Distance-Based Clustering to Optimize the Field Scale In Situ Uranium Leaching System in Heterogeneous Reservoirs","authors":"Wenjie Qiu, Dianguang Liu, Yun Yang, Jian Song, Weimin Que, Zhengbang Liu, Haicheng Weng, Jianfeng Wu, Jichun Wu","doi":"10.1029/2025wr041741","DOIUrl":"https://doi.org/10.1029/2025wr041741","url":null,"abstract":"Utilization of the integrated simulation-optimization models for supporting decisions of the in situ leaching (ISL) design of uranium (U) mining is often hampered by the physicochemical heterogeneity within the sandstone reservoirs. Nevertheless, the conventional way suffers from a high conceptual uncertainty due to almost ubiquitous simplifying assumptions used in model parameterizations. Additionally, the increasing complexity of process-based reactive transport simulators results in substantial computational demands, limiting the feasibility of conducting numerous model evaluations. Addressing the optimization challenges posed by geological uncertainty typically involves Monte Carlo-based population search methods with evolutionary algorithms which are often computationally intensive and suffer from excessive model redundancy. This study presents a novel optimization framework for identifying the optimal well control strategies for a field-scale neutral ISL of U mining system in the Songliao Basin, China. The proposed approach integrates a deep learning-based proxy model with distance-based clustering components. Specifically, a ResNet-LSTM network is employed to predict dynamic U recovery concentration. A small subset of representative reservoir realizations is selected through clustering analysis, effectively capturing the uncertainty space without relying on the full ensemble. The subset is then embedded into a heuristic evolutionary algorithm with the objective of maximizing economic benefits. The results demonstrate that this integrated framework significantly enhances the decision-making process in a computationally efficient way. By integrating the proxy model with cluster-based realization selection, the proposed procedure achieves a 15.2% improvement in net present value compared to unoptimized scenarios. Overall, the framework provides a versatile and powerful tool for robust optimization in heterogeneous reservoirs.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"96 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147439839","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":"Latent Data Assimilation for Efficient and Accurate Groundwater Modeling","authors":"Yongda Liu, Xi Chen, Zitao Wang, Jianzhi Dong","doi":"10.1029/2025wr042424","DOIUrl":"https://doi.org/10.1029/2025wr042424","url":null,"abstract":"Groundwater model predictions are often inaccurate due to uncertainties in model structure, heterogeneous parameters, and initial conditions, leading to error accumulation during simulations. Traditional data assimilation (DA) faces severe computational challenges in high-dimensional systems due to the costly inversion of large covariance matrices. In addition, the inaccurate estimation of background and observation error statistics introduces further biases. To address these challenges, we develop and evaluate an integrated framework that couples a computationally efficient deep learning surrogate model for rapid prediction with Latent Data Assimilation (LDA) to accurately correct simulations. The framework employs dimensionality reduction, specifically Proper Orthogonal Decomposition (POD), to project the high-dimensional physical state into a low-dimensional latent space. Data assimilation is then performed in this reduced space using the Ensemble Kalman Filter (EnKF). Results demonstrate that POD provides a robust representation of simulated concentration fields and interpolated observations for dimensionality reduction. The EnKF operating in the latent space effectively reduces prediction errors. Key advantages of the LDA framework include: enabling sparse observations to effectively inform global state updates through the low-dimensional latent variables, achieving higher accuracy comparable to traditional physical-space DA while requiring significantly fewer observations, and inherently filtering high-frequency noise from the initial condition during the dimensionality reduction process. Collectively, these features establish LDA as a powerful and computationally efficient methodology for enhancing predictive accuracy and managing uncertainty in complex and high-dimensional groundwater systems.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"28 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383880","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":"Evaluating Evapotranspiration Simulation Performance in 30 Conceptual Hydrological Models: Insights Into ET Representation Across Diverse Climates","authors":"Shuyue Wu, Yuting Yang, Changming Li, Wenjing Yang, Jianshi Zhao","doi":"10.1029/2025wr040017","DOIUrl":"https://doi.org/10.1029/2025wr040017","url":null,"abstract":"Existing comparative studies on conceptual hydrological model structures have generally focused on streamflow, with less attention to evapotranspiration (ET). Yet, appropriate ET representation in conceptual models is crucial for obtaining reliable hydrological simulations, especially under climate change and in ungauged basins. To address this gap, we investigated 30 conceptual models for their ET representations and ability to reproduce two state-of-the-art ET products (PML-V2 and FLUXCOM-X-BASE) across 507 CAMELS-US catchments with diverse climates and landscapes. FLUXNET ET from nearby sites was used for additional validation. The conceptual model ensemble outperformed a benchmark model that relies on PET and long-term water balance in most catchments. Models with different ET representations showed distinct ET simulation performance, highlighting the importance of selecting appropriate ET representations. The appropriate ET representations vary across climates. Linear and nonlinear ET–soil–moisture equations with a parameter governing the long-term ET-to-PET ratio are sufficient for accurately reproducing ET products in humid, summer-rainfall regions, where model equifinality in ET simulation is high. In arid catchments, considering the contribution of lower soil storage to ET generation was necessary to reproduce ET products, especially during rainless periods. In humid winter-rainfall-dominated catchments, explicit representation of interception evaporation constrained by interception capacity or storage was critical. Models that performed well in reproducing product-based ET also performed well when evaluated against FLUXNET ET, supporting the robustness of our findings. This study provides guidance on appropriately representing the conversion of PET and precipitation into ET across diverse climates, thereby helping to constrain model structural uncertainty.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"31 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147380844","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":"Deriving Long-Term Operating Rules for Cascade Hydropower Plants Compensating for Short-Term Uncertainties in Wind and Solar Power Generation","authors":"Mengke Lin, Jianjian Shen, Xianren Ai, Yuqian Wang","doi":"10.1029/2025wr042181","DOIUrl":"https://doi.org/10.1029/2025wr042181","url":null,"abstract":"Hydropower offers both regulation and scale advantages that can respond to the substantial grid flexibility requirements arising from the integration of variable renewable energy (VRE). However, its short-term regulatory capability heavily depends on long-term operating rules, which largely overlook short-term grid flexibility needs. This study proposes a novel method for deriving operating rules of hydro-wind-solar complementary systems (HWPCS) that accounts for short-term flexible power allocation in different months. Typical historical hydropower output curves, along with peak capacity and peak duration, are initially used to quantify grid flexibility demands. Under grid demand constraints, a short-term simulation model is developed to generate feasible hydropower output intervals and peak-shaving electricity curves, which capture the responsiveness of long-term generation to short-term power fluctuations. Finally, a multi-objective optimization model is formulated to simultaneously maximize energy production and regulation capability, subject to hydropower output interval constraints, thereby deriving optimal long-term operating rules. A practical engineering cases demonstrate that: (a) The system's regulation capability is most effective when hydropower generation is maintained at around 50% of installed capacity, with a wind-dominant mix calling for even higher optimal output; (b) The overall regulation capability of the system improves by 30.2%, while the grid demand deficit drops by 12%. Wet-year conditions further amplify this advantage; and (c) Extending peak-shaving durations will reduce overall benefits, though the marginal impact decreases with peak duration. These findings highlight the potential of HWPCS to improve system flexibility and support large-scale VRE integration.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"16 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147380843","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":"Model-Based Interpretation of Solute Exports and Carbon Partitioning During Shale Weathering in a Mountainous Hillslope","authors":"Lucien Stolze, Dipankar Dwivedi, Carl Steefel, Sergi Molins, Wenming Dong, Curtis Beutler, Alexander Newman, Kenneth Williams","doi":"10.1029/2025wr041597","DOIUrl":"https://doi.org/10.1029/2025wr041597","url":null,"abstract":"The weathering of sedimentary rocks in high-elevation catchments influences freshwater quality and the global carbon cycle. While individual biogeochemical mechanisms involved in this process are relatively well understood, quantifying their contributions to solute export and carbon fluxes under natural, transient conditions remains challenging. Here, we implement a numerical multidimensional and multiphase model to simulate coupled hydrological and biogeochemical processes in a shale-underlain, snow-dominated hillslope in the Rocky Mountains, Colorado. The model captures the dynamic interplay between soil respiration, mineral weathering, and climate-driven hydrological forcing, reproducing observed soil CO₂ dynamics, groundwater chemistry, and subsurface flow. Our results reveal that seasonal snowmelt enhances carbonate weathering by promoting the infiltration of CO₂-rich water to depth, while pyrite oxidation is primarily sensitive to low water saturation that facilitates O₂ diffusion through the regolith. Topography modulates the spatial distribution of shale weathering, as steeper slopes enhance lateral drainage, favoring the delivery of reactants to greater depths. While shale weathering at our site acts as a transient carbon sink, with silicates and carbonates buffering acidity and promoting atmospheric CO₂ consumption (1% of soil-derived CO₂), the exported dissolved inorganic carbon is predominantly geogenic (∼73%). Consequently, when accounting for long-term marine carbonate precipitation. The current weathering regime represents a net source of carbon to the atmosphere. The oxidation of pyrite and petrogenic organic carbon together release approximately 0.9 mol·m⁻²·yr⁻¹ of CO₂. Our findings highlight the role of topography, hydroclimate, and the coupling between acid-base reactions in shaping the carbon balance and the solute exports in mountainous critical zones.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"40 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147380875","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 Hybrid Data Assimilation Approach Integrating Kalman With Deep Learning-Based Updates for Nonlinear and Non-Gaussian Groundwater Systems","authors":"Lei Yao, Jiangjiang Zhang, Yuxin Ye, Chenglong Cao, Jianyun Zhang, Junliang Jin","doi":"10.1029/2024wr039665","DOIUrl":"https://doi.org/10.1029/2024wr039665","url":null,"abstract":"In hydrological research, data assimilation (DA) is a powerful tool for integrating observational data with numerical models, significantly enhancing predictive accuracy. However, nonlinear groundwater systems often exhibit high-dimensional and non-Gaussian characteristics in observations, parameters, and state variables, posing substantial challenges for traditional DA methods such as Markov chain Monte Carlo and ensemble smoother based on the Kalman update (&lt;span data-altimg=\"/cms/asset/5aa53644-a4ae-48c2-b0be-53d90ede5f84/wrcr70703-math-0001.png\"&gt;&lt;/span&gt;&lt;mjx-container ctxtmenu_counter=\"867\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"&gt;&lt;mjx-math aria-hidden=\"true\" location=\"graphic/wrcr70703-math-0001.png\"&gt;&lt;mjx-semantics&gt;&lt;mjx-mrow&gt;&lt;mjx-msub data-semantic-children=\"0,4\" data-semantic- data-semantic-role=\"unknown\" data-semantic-speech=\"ES Subscript left parenthesis normal upper K right parenthesis\" data-semantic-type=\"subscript\"&gt;&lt;mjx-mtext data-semantic-annotation=\"clearspeak:unit\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"5\" data-semantic-role=\"unknown\" data-semantic-type=\"text\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mtext&gt;&lt;mjx-script style=\"vertical-align: -0.177em;\"&gt;&lt;mjx-mrow data-semantic-children=\"2\" data-semantic-content=\"1,3\" data-semantic- data-semantic-parent=\"5\" data-semantic-role=\"leftright\" data-semantic-type=\"fenced\" size=\"s\"&gt;&lt;mjx-mo data-semantic- data-semantic-operator=\"fenced\" data-semantic-parent=\"4\" data-semantic-role=\"open\" data-semantic-type=\"fence\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"4\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;mjx-mo data-semantic- data-semantic-operator=\"fenced\" data-semantic-parent=\"4\" data-semantic-role=\"close\" data-semantic-type=\"fence\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;/mjx-mrow&gt;&lt;/mjx-script&gt;&lt;/mjx-msub&gt;&lt;/mjx-mrow&gt;&lt;/mjx-semantics&gt;&lt;/mjx-math&gt;&lt;mjx-assistive-mml display=\"inline\" unselectable=\"on\"&gt;&lt;math altimg=\"urn:x-wiley:00431397:media:wrcr70703:wrcr70703-math-0001\" display=\"inline\" location=\"graphic/wrcr70703-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"&gt;&lt;semantics&gt;&lt;mrow&gt;&lt;msub data-semantic-=\"\" data-semantic-children=\"0,4\" data-semantic-role=\"unknown\" data-semantic-speech=\"ES Subscript left parenthesis normal upper K right parenthesis\" data-semantic-type=\"subscript\"&gt;&lt;mtext data-semantic-=\"\" data-semantic-annotation=\"clearspeak:unit\" data-semantic-font=\"normal\" data-semantic-parent=\"5\" data-semantic-role=\"unknown\" data-semantic-type=\"text\"&gt;ES&lt;/mtext&gt;&lt;mrow data-semantic-=\"\" data-semantic-children=\"2\" data-semantic-content=\"1,3\" data-semantic-parent=\"5\" data-semantic-role=\"leftright\" data-semantic-type=\"fenced\"&gt;&lt;mo data-semantic-=\"\" data-semantic-operator=\"fenced\" data-semantic-parent=\"4\" data-semantic-role=\"open\" data-semantic-type=\"fence\"&gt;(&lt;/mo&gt;&lt;mi da","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"69 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371332","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":"Longitudinal Mean Velocity and Turbulent Kinetic Energy Within an Emergent Canopy in Nonuniform Flows","authors":"Yonggang Zhang, Ping Wang, Ke Xiang, Zheng Gong, Zi Wu","doi":"10.1029/2025wr040864","DOIUrl":"https://doi.org/10.1029/2025wr040864","url":null,"abstract":"Vegetation-induced drag generates nonuniform water surface profiles through flow disruption, creating complex hydrodynamic conditions characterized by enhanced turbulence and energy dissipation. This study investigates the longitudinal velocity and turbulent kinetic energy (TKE) dynamics in emergent canopies under streamwise varying flow conditions. Laboratory flume experiments systematically examined four vegetation densities and two flow discharge scenarios. The results indicate that the time-mean longitudinal velocity and the TKE both enhance significantly downstream along the emergent canopy. Based on TKE budget, an analytical model to predict the longitudinal TKE evolution within the emergent canopy was developed. Compared to uniform flows in a vegetated channel, the flow nonuniformity gives rise to distinct terms including the streamwise-position dependent mean velocity &lt;span data-altimg=\"/cms/asset/7ea5b557-8d66-4e3c-8f15-b0f778ddc564/wrcr70719-math-0001.png\"&gt;&lt;/span&gt;&lt;mjx-container ctxtmenu_counter=\"473\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"&gt;&lt;mjx-math aria-hidden=\"true\" location=\"graphic/wrcr70719-math-0001.png\"&gt;&lt;mjx-semantics&gt;&lt;mjx-mrow data-semantic-annotation=\"clearspeak:simple\" data-semantic-children=\"0,4\" data-semantic-content=\"5,0\" data-semantic- data-semantic-role=\"simple function\" data-semantic-speech=\"upper U left parenthesis x right parenthesis\" data-semantic-type=\"appl\"&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-operator=\"appl\" data-semantic-parent=\"6\" data-semantic-role=\"simple function\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;mjx-mo data-semantic-added=\"true\" data-semantic- data-semantic-operator=\"appl\" data-semantic-parent=\"6\" data-semantic-role=\"application\" data-semantic-type=\"punctuation\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;mjx-mrow data-semantic-children=\"2\" data-semantic-content=\"1,3\" data-semantic- data-semantic-parent=\"6\" data-semantic-role=\"leftright\" data-semantic-type=\"fenced\"&gt;&lt;mjx-mo data-semantic- data-semantic-operator=\"fenced\" data-semantic-parent=\"4\" data-semantic-role=\"open\" data-semantic-type=\"fence\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"4\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;mjx-mo data-semantic- data-semantic-operator=\"fenced\" data-semantic-parent=\"4\" data-semantic-role=\"close\" data-semantic-type=\"fence\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;/mjx-mrow&gt;&lt;/mjx-mrow&gt;&lt;/mjx-semantics&gt;&lt;/mjx-math&gt;&lt;mjx-assistive-mml display=\"inline\" unselectable=\"on\"&gt;&lt;math altimg=\"urn:x-wiley:00431397:media:wrcr70719:wrcr70719-math-0001\" display=\"inline\" location=\"graphic/wrcr","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"59 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371331","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}