Water Resources Research最新文献

{"title":"Rapid Fault Leakage Modelling for CO2 Storage in Saline Aquifers","authors":"Hariharan Ramachandran, Iain de Jonge-Anderson, Ikhwanul Hafizi Musa, Uisdean Nicholson, Chee Phuat Tan, Sebastian Geiger, Florian Doster","doi":"10.1029/2025wr041074","DOIUrl":"https://doi.org/10.1029/2025wr041074","url":null,"abstract":"Simulating the fluid flow along fault zones at different scales is essential for predicting the CO₂ leakage and containment during injection and storage. However, this can be challenging, especially in the early stages of a storage project when knowledge of the reservoir and caprock is limited and the cost of obtaining the relevant data is high. This study develops a tool for fast screening of fault leakage at the site screening stage. The tool uses a vertically integrated reservoir model coupled with a newly developed upscaled fault leakage function based on source/sink relations. The fault is conceptualized as an increased vertical permeability through the caprock due to the presence of a fracture network in the damage zone and a reduced horizontal permeability in the reservoir due to fault throw and presence of a low-permeability fault core. The proposed tool is validated against numerical simulations demonstrating strong agreement in predicting leakage rates under varying reservoir conditions. The model's capabilities are further tested through simulation cases, including a field-scale application in the Malay Basin. These cases revealed key insights into the roles of fault permeability and fault capillary entry pressure in controlling leakage and highlighted the importance of accurately characterizing these properties to mitigate risks. The computationally efficient model presented in this study is a valuable tool for quantifying uncertainties in key fault parameters, and other constitutive relations that affect the behavior of the storage reservoir and potential fault leakage.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"31 ( Pt 1) 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147732341","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 Critical Role of Surge Autocorrelation in Data-Driven Nowcasting of Typhoon-Induced Storm Surge","authors":"Yujia Wang, Dingqi Yang, Ka-Veng Yuen, Huabin Shi","doi":"10.1029/2025wr040850","DOIUrl":"https://doi.org/10.1029/2025wr040850","url":null,"abstract":"Typhoon-induced storm surges have been the most destructive disasters in coastal regions. The nowcasting of storm surge, in which the prediction lead time is not greater than 3 hr, is critical to disaster emergency response and recovery, enabling governments to make decisions and take actions according to the latest situation and prediction. Machine learning (ML) methods have been increasingly applied to storm surge predictions, but few ML studies focused on short-term nowcasts. Moreover, conflicting conclusions often arise regarding the superiority of ML methods and model input variables for storm surge predictions. This study highlights the significance of surge autocorrelation, that is, the correlation of surge height with its local historical values, in the design of ML models for storm surge nowcasts. It is shown in the comparative analyses of model input variables that, independent of the adopted ML methods, models relying solely on historical surge heights as inputs are sufficient to achieve reasonably accurate nowcasts of surge height. Conversely, models without historical surge heights in their inputs yield comparable prediction errors. Involving surge autocorrelation, simple multilayer perceptron models perform even better than sequential-compatible recurrent neural network category of models. Moreover, it is illustrated that the surge autocorrelation inhibits the performance of Transformer and XGBoost models for storm surge nowcasts. This study shows the possibility to achieve efficient and accurate nowcasts (a lead time under 3 hr) of localized storm surge only based on limited observations by local tide gauge, supporting decision-making in disaster emergency response and recovery within small-scale coastal regions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"33 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147735570","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":"Hydrodynamics of Four Moments in the Life of a Floodplain Forest in Compound Channels","authors":"Luiz E. D. de Oliveira, Johannes G. Janzen, Frederik Folke, Florian Wittmann, Nils P. Huber, Mário J. Franca, Carlo Gualtieri","doi":"10.1029/2024wr038968","DOIUrl":"https://doi.org/10.1029/2024wr038968","url":null,"abstract":"Natural rivers often function as compound channels, comprising main channel and floodplain. Flow dynamics in these systems, particularly during floods, are influenced by factors such as vegetation, and flood intensity. Floodplain vegetation significantly modifies flow structures, affecting hydraulic conveyance and ecological functions. As floodplain forests develop through succession, their composition and hydraulic characteristics evolve, altering the flow in compound channels. This study examines the impact of forest succession, forest management and flood intensity on compound channel hydrodynamics. The forest composition was based on a forest in the Upper Rhine, and was heterogeneously distributed through the experimental flume. Our results revealed that forest aging reduces, due to the reduction in the leaf area index (LAI), velocity differences and shear, narrowing the mixing layer. Consequently, lateral mass exchanges decreased and a hydraulically smoother and more uniform flow was created in the floodplain, with implications for hydraulic modeling. Forest management practices, such as selective vegetation removal, can significantly alter flow hydrodynamics, particularly water depth. Flood intensity influences the peak mean transverse velocity, backwater effect, and lateral discharge distribution. Solute dispersion remains mainly advective in the main channel, with flood intensity exerting limited influence. In contrast, vegetation enhances tracer dispersion within the floodplain and at the channel–floodplain interface. The study highlights the limitations of traditional analytical models and emphasizes the need for approaches that incorporate natural vegetation distribution, such as the one developed here. These findings underscore the importance of integrating forest succession into river management to maintain flood protection and navigability.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"13 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708741","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":"Multi-Sensor Airborne Remote Sensing for Calibrating Hydrodynamic and Sediment Transport Models in Coastal Louisiana Wetlands (Atchafalaya–Terrebonne)","authors":"Ali Reza Payandeh, Marc Simard, Cathleen E. Jones, Alexandra Christensen, Daniel Jensen, Michael Denbina, Talib Oliver-Cabrera","doi":"10.1029/2025wr042357","DOIUrl":"https://doi.org/10.1029/2025wr042357","url":null,"abstract":"This study integrates high resolution remote sensing data from NASA's Delta-X mission with a process based hydrodynamic and sediment transport model to improve predictions of water levels and suspended sediment dynamics in the Mississippi River Delta, in coastal Louisiana, USA, focusing on Atchafalaya and Terrebonne basins. A two dimensional Delft3D Flexible Mesh model was implemented using spatially variable bottom friction maps derived from optical imagery (AVIRIS-NG and Sentinel-2) to represent vegetation heterogeneity. Hydrodynamic calibration leveraged airborne interferometric radar measurements (Airborne Surface Water and Ocean Topography (AirSWOT) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR)) of water surface elevations and water level changes, while validation used in situ tide gauge records from spring and fall 2021. Model comparison with AirSWOT measurements in channels showed that spatially explicit roughness parameterizations substantially improved performance. Results were notably better for the spring acquisitions (root mean square error = 0.09 m; <i>R</i>² = 0.82), likely due to higher wind speeds and steeper water surface slopes that increased surface roughness and improved radar retrieval performance. UAVSAR provided additional spatial constraints on transient water level changes across wetlands at ∼30-min intervals, further informing roughness calibration. Model deviation from UAVSAR were typically within <span data-altimg=\"/cms/asset/84fda304-fbfd-41fe-af2f-30f739efc083/wrcr70871-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"15\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/wrcr70871-math-0001.png\"><mjx-semantics><mjx-mrow><mjx-mo data-semantic- data-semantic-role=\"addition\" data-semantic-speech=\"plus or minus\" data-semantic-type=\"operator\"><mjx-c></mjx-c></mjx-mo></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00431397:media:wrcr70871:wrcr70871-math-0001\" display=\"inline\" location=\"graphic/wrcr70871-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><mo data-semantic-=\"\" data-semantic-role=\"addition\" data-semantic-speech=\"plus or minus\" data-semantic-type=\"operator\">±</mo></mrow>$pm $</annotation></semantics></math></mjx-assistive-mml></mjx-container>4 cm, although performance degraded in forested and densely vegetated areas due to reduced radar coherence. Validation with in situ tide gauges confirmed model performance for tidal and subtidal variability. Sediment transport calibration using AVIRIS-NG total suspended solids allowed refinement of settling velocity and critical shear stress. Overall, the integration of remote sensing data into model calibration and parameterization led to measurable improvements in hydrodynamic and sediment predictions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"1 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147695320","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":"Quantifying Impacts of CO2 Radiative and Vegetation Physiological Forcing on Land Potential Evapotranspiration and Dryness/Wetness Changes Under a High CO2 Emissions Scenario","authors":"Shanlei Sun, Jialing Xu, Qianrong Ma, Yifang Zhang, Qianxue Zu, Mengyuan Mu, Wenjian Hua, Yang Zhou, Botao Zhou","doi":"10.1029/2025wr042216","DOIUrl":"https://doi.org/10.1029/2025wr042216","url":null,"abstract":"Increased atmospheric CO₂ affects climate through radiative and physiological forcing, but their specific roles in the transient changes in future potential evapotranspiration (PET) and dryness/wetness remain unclear. Using simulations from seven Earth System Models of the Coupled Model Intercomparison Project Phase 6, we quantify the contributions of radiative forcing, physiological forcing, their interaction, and the direct CO₂ physiological effect on surface resistance to projected changes in annual PET and dryness/wetness relative to a historical baseline. Annual PET is projected to increase globally over land, affecting approximately 98 ± 6% of land. Global land shows an overall drying tendency, with wetting and drying projected over 41 ± 9% and 59 ± 9%, respectively. The increases in PET and the drying trend are primarily attributed to radiative forcing through surface warming. Spatially, radiative forcing dominates PET increases across 98 ± 6% of land by modulating temperature and net radiation. For dryness/wetness changes, radiative forcing, physiological forcing, their interaction, and the direct CO₂ physiological effect dominate over 73 ± 5%, 4 ± 3%, 3 ± 3%, and 20 ± 6% of land, respectively. Radiative forcing exerts its dominant influence mainly through its effects on precipitation (22 ± 4% of land) and temperature (51 ± 6%). In regions where physiological forcing or its interaction with radiative forcing prevails, dryness/wetness changes are primarily linked to associated precipitation variations. Overall, this study clarifies how radiative forcing, physiological forcing, their interaction, and the direct CO₂ physiological effect shape the transient changes in future PET and dryness/wetness.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"11 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147695400","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":"Transient Wave-Based Data Assimilation for Localizing Multiple Leaks in Water Pipe Networks","authors":"Qiuru Chen, Jiangjiang Zhang, Huan-Feng Duan, Tong-Chuan Che","doi":"10.1029/2025wr042176","DOIUrl":"https://doi.org/10.1029/2025wr042176","url":null,"abstract":"Due to aging without timely renewal, hidden leaks continually occur in urban water distribution systems. Time-domain full-waveform inversion is a robust and flexible method for localizing multiple leaks in water pipe networks. However, as a deterministic estimator, this method assumes precise knowledge of model parameters and only gives a single-point estimate of unknown leak parameters, which is insufficient for decision-making as it fails to quantify the localization confidence and uncertainty. Moreover, the equifinality of underlying physics violates the uniqueness of inverse problem and makes this method a high-dimensional non-convex optimization. To address these problems, this paper proposes a stochastic Bayesian estimator, namely iterative local updating ensemble smoother (ILUES), for localizing multiple leaks in water pipe networks. ILUES explores the possible multi-modal posterior distributions of leak parameters, which not only localize leaks but also quantify localization confidence. Numerical experiments indicate that ILUES maintains robust localization accuracy in environments with significant model parameter uncertainties and strong ambient noise, even at signal-to-noise ratio as low as &lt;span data-altimg=\"/cms/asset/17e00a3e-5282-42bf-8b1e-de811837f106/wrcr70852-math-0001.png\"&gt;&lt;/span&gt;&lt;mjx-container ctxtmenu_counter=\"317\" 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/wrcr70852-math-0001.png\"&gt;&lt;mjx-semantics&gt;&lt;mjx-mrow data-semantic-annotation=\"clearspeak:simple\" data-semantic-children=\"1\" data-semantic-content=\"0\" data-semantic- data-semantic-role=\"negative\" data-semantic-speech=\"negative 10\" data-semantic-type=\"prefixop\"&gt;&lt;mjx-mo data-semantic- data-semantic-operator=\"prefixop,−\" data-semantic-parent=\"2\" data-semantic-role=\"subtraction\" data-semantic-type=\"operator\" rspace=\"1\" style=\"margin-left: 0.056em;\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mn&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:wrcr70852:wrcr70852-math-0001\" display=\"inline\" location=\"graphic/wrcr70852-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"&gt;&lt;semantics&gt;&lt;mrow data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-children=\"1\" data-semantic-content=\"0\" data-semantic-role=\"negative\" data-semantic-speech=\"negative 10\" data-semantic-type=\"prefixop\"&gt;&lt;mo data-semantic-=\"\" data-semantic-operator=\"prefixop,−\" data-semantic-parent=\"2\" data-semantic-role=\"subtraction\" data-semantic-type=\"operator\"&gt;−&lt;/mo&gt;&lt;mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"5 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147695322","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":"Evident Dependence of Dynamics of Baseflow on Groundwater Across the Contiguous United States","authors":"Ali R. Al-Aizari, Lichun Wang, Di Gao, Hylke Beck","doi":"10.1029/2025wr042225","DOIUrl":"https://doi.org/10.1029/2025wr042225","url":null,"abstract":"Understanding baseflow dynamics is essential for sustainable ecosystem and water resource management. While most studies have documented climate-baseflow relationships at regional scales, observational evidence of how groundwater influences baseflow dynamics across different spatiotemporal scales remains limited, substantially constraining mechanistic understanding. To address this gap, we conducted a systematic continental-scale analysis of groundwater-baseflow linkages across 18 major US watersheds from 1980 to 2020. We analyzed daily discharge from 7,016 stations and estimated baseflow using a recursive digital filter. We also identified 470 paired shallow groundwater well-gauge sites within 5-km proximity to evaluate hydraulic connectivity across multiple time scales. Key findings were as follows: (a) Annual baseflow trends revealed pronounced east-west spatial clustering, with predominantly increasing trends in eastern watersheds and decreasing trends in western watersheds. (b) Seasonal analysis indicated strong synchronicity between groundwater level fluctuations and baseflow dynamics, with matched trend directions from 68.75% (fall) to 92.86% (summer) of paired sites. Spearman correlations further demonstrated robust positive seasonal relationships (<i>ρ</i> = 0.686–0.791, all <i>p</i> &lt; 0.005). (c) At the monthly scale, linear regression and Spearman correlation analyses showed weak associations between baseflow and climate variables (<i>R</i>² = 0.02–0.31), but substantially stronger associations with upstream groundwater levels (<i>R</i>² = 0.61–0.95). (d) At the daily scale, strong associations between baseflow dynamics and groundwater levels (<i>R</i>² = 0.63–0.95) further support direct hydraulic connectivity between groundwater and river systems. Collectively, these findings indicate that groundwater is the dominant control on baseflow temporal variability across multiple timescales, whereas climate influences may emerge at annual or longer time scales due to groundwater travel times and long system memory. Our results underscore the importance of groundwater monitoring for improving baseflow prediction and water resources management.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"83 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147681692","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":"Depth-Aware Global Calibration of SM2RAIN-NWF Using Growing Neural Gas-Derived Hydroclimatic Clusters Across Heterogeneous Soils","authors":"Mohammad Saeedi, Subin Kim, Euiyoung Choi, Hyunglok Kim, Venkataraman Lakshmi","doi":"10.1029/2025wr041529","DOIUrl":"https://doi.org/10.1029/2025wr041529","url":null,"abstract":"Accurate rainfall information underpins land-surface water budgets, extreme-weather analyses, and climate-model evaluation. Yet in many regions, rain gauge networks are sparse, making conventional calibration of bottom up rainfall products difficult. To address this, we propose a self calibration framework that removes the need for a dedicated calibration phase. Our proposed approach systematically identifies bottom-up model parameters without relying on region-specific tuning by exploiting the <i>K</i>-means, Gaussian Mixture Model|Gaussian Mixture Models (GMM), Agglomerative Clustering (AC) and Growing Neural Gas (GNG) algorithms. To demonstrate its effectiveness, we apply this framework to soil moisture (SM) to RAIN by using Net Water Flux (SM2RAIN-NWF), a bottom-up rainfall estimation model that leverages SM variations to infer rainfall. This self-calibration strategy is particularly relevant, as it reduces dependence on traditional rain gauge data, making it well-suited for large or data-limited regions. In this study, we test this framework by comparing four clustering algorithms (K-means, GNG, GMM, and AC) against International Soil Moisture Network observations using hold-out and Leave-One-Out Cross-Validation approaches. This validation confirms the framework's robustness and the superiority of <i>K</i>-means and GNG. The <i>K</i>-means method provides high stability, with key performance metrics (Correlation Coefficient (<i>R</i>) and Probability of Detection) showing minimal change from the baseline. The GNG method demonstrates that cluster parameters can significantly outperform site-specific calibration, with correlation (<i>R</i>) gains exceeding +17% in key soil depths (5 &lt; SM_Depth ≤ 10 cm). With no need for reference rainfall, the method is ideal for data sparse or ungauged regions and supports scalable climate monitoring, reanalysis, and prediction.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"433 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147681691","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":"Phenological Shifts in Autumn Drive Changes in Ice-on Timing and Under-Ice Water Temperature in Dimictic Finnish Lakes","authors":"F. R. Ferrato, J. A. Culpepper, M. Pulkkanen, R. Kortet, S. Sharma","doi":"10.1029/2025wr042047","DOIUrl":"https://doi.org/10.1029/2025wr042047","url":null,"abstract":"Autumn is a pivotal but understudied season in limnology. As climate change alters the timing and duration of autumnal cooling, the implications for winter thermal structure and long-term ecosystem function in dimictic lakes remain uncertain. To address this gap, we synthesized 37–50 years of data from 47 Finnish lake sites, providing the first comprehensive long-term assessment of how autumn conditions indirectly govern winter thermal regimes. Furthermore, we examined how autumn surface warming, winter under-ice temperatures, and ice phenology affect maximum summer surface water temperature. We found that autumn surface waters warmed by 0.37°C per decade (<i>n</i> = 8), delaying ice-on by 4.1 days per decade (<i>n</i> = 47) over the last 50 years. Interestingly, later ice formation correlated with colder under-ice bottom water temperatures (<i>r</i> = −0.54), with a marked regime shift in 2002 toward later freezing and cooler bottom water conditions. Additionally, our structural equation model linked stronger autumn winds, higher autumn shortwave radiation, and lakes with larger areas to colder under-ice water temperatures, likely due to their effect on lake water cooling and ice formation. Furthermore, later ice-on and earlier ice-off led to higher maximum summer surface water temperature; however, under-ice water temperature had no direct effect. As climate warming prolongs open water periods and delays ice-on, extended water column mixing and atmospheric exposure can cool under-ice water temperatures. These shifts have potential consequences for oxygen and nutrient dynamics, winter habitat quality, and overall ecosystem resilience, emphasizing the need to integrate autumn dynamics into future limnological research and lake monitoring efforts.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"21 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147664077","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":"Control of Vegetation and Temperature on Topsoil Water Losses","authors":"Martin J. Baur, Lucas R. Vargas Zeppetello, Andrew D. Friend, Dara Entekhabi","doi":"10.1029/2025wr040632","DOIUrl":"https://doi.org/10.1029/2025wr040632","url":null,"abstract":"Due to its location at the interface between land surface and atmosphere, soil moisture (SM) plays an important role in modulating energy, water and carbon fluxes. During periods of decreasing SM, SM loss is dependent on evapotranspiration (ET), drainage and changes in plant water storage. Investigating SM loss can give important insights into these processes. Here we use 25 years of global remote sensing data to investigate how SM loss is controlled by vegetation and temperature. We find that positive vegetation anomalies lead to slower SM loss in most areas, except for cold boreal forests. We hypothesize that these effects arise from competing effects of soil shading, transpiration and root water uptake by the vegetation. The effect whereby positive vegetation anomalies increase SM loss is limited to high SM conditions and disappears at lower SM, likely due to water stress limiting transpiration. By analyzing temperature and vegetation anomalies jointly we find that the relationship between SM loss and temperature varies between regions, but vegetation cover effects persist across the full range of temperature anomalies. Using a simple energy and moisture budget model, we can reproduce observed vegetation and temperature effects, supporting the interpretation that vegetation controls topsoil SM loss through shading and transpiration. We also find widespread positive SM loss trends which indicates accelerated topsoil water cycling, likely due to higher atmospheric water demand driven by increasing temperatures.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"18 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147649299","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}