Water Resources Research最新文献

{"title":"Hydrological Connectivity of Distributary-Confluence Geomorphic Unit: A Case Study of H-Shaped Features Within River Networks","authors":"Xifen Chen, Huan Liu, Leiping Ye, Ren Jie, Zhaohui Deng, Jiaxue Wu","doi":"10.1029/2024wr037408","DOIUrl":"https://doi.org/10.1029/2024wr037408","url":null,"abstract":"The H-shaped feature, characterized by a single connecting channel (CC) linking two inflows, is a common geomorphological unit in delta river networks. This structure plays a critical role in redistributing upstream flows, affecting the hydrological connectivity of the network. Despite previous studies on geometric structures and flow distribution, the mechanisms influencing hydrological connectivity remain poorly understood due to the complex structure of deltas and the interactions among various controlling factors. This study investigates the flow distribution and hydrological connectivity of H-shaped structures using numerical simulations and graph theory. Results indicate that, in H-shaped structures, the gravitational pressure caused by variations in Upstream Discharge Ratios (UDR) and CC topography generates uneven spatial velocity fields, resulting in different levels of diversion capacity in the CC. The hydrological connectivity indexes of the distributary subnetwork increase with the growing diversion capacity of the CC, whereas the confluence subnetwork exhibits the opposite trend. The CC's influence on downstream flow regulation and hydrological connectivity is influenced by the structure itself. Enhanced CC diversion capacity in H-shaped structures balances flow distribution and strengthens system resilience. The study emphasizes that simpler river network topologies concentrate flows in fewer channels while maintaining strong subnetwork exchanges, while complex networks distribute flows broadly but reduce inter-subnetwork connectivity. Therefore, we recommend that deltaic river network management consider their topological characteristics and implement strategies such as constructing additional CC or modifying existing channel topography to enhance flow exchange capacity. These findings offer valuable insights for global management and conservation of deltaic river networks.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"183 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143745698","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":"Can Typical Land Surface Model Parameterizations Support the Expected Soil Moisture Assimilation Efficiency?","authors":"Jianhong Zhou, Jianzhi Dong, Huihui Feng, Kun Yang, Wade T. Crow, Zhiyong Wu, Xin Tian, Jiaxin Tian, Xiaogang Ma, Yaozhi Jiang","doi":"10.1029/2024wr038702","DOIUrl":"https://doi.org/10.1029/2024wr038702","url":null,"abstract":"Remote sensing (RS) soil moisture retrievals are frequently assimilated into land surface models (LSMs) to enhance model estimates. However, soil moisture data assimilation (DA) efficiency is highly model-dependent, making it imperative to investigate whether current LSMs can achieve expected DA efficiencies and identify potential model limitations for DA. Here, we examine soil moisture DA efficiency based on a typical LSM by benchmarking it against a reference soil moisture merging scheme (i.e., assigning weights to combine multiple products into a single one). Both the merged and DA soil moisture estimates are comparable since they are based on identical error estimation theory and the same RS soil moisture data sets. In theory, the DA soil moisture estimates should be superior to the merged results—since DA can characterize the temporal variation of model error and propagate DA benefits into subsequent forecast steps. However, ground-based validation results indicate that DA soil moisture performs worse than simply merged results in regions where the LSM is less precise than RS retrievals. Further combing synthetic experiment, we confirm that the unexpected DA results are primarily attributable to land parameterization uncertainty, which leads to an unrealistic representation of soil moisture events (e.g., dry-downs) and significantly hampers the DA application. Given this, soil moisture DA is likely to remain suboptimal in achieving its desired goals. Therefore, this study emphasizes the urgency and necessity of reducing model parameterization uncertainty in land DA systems.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"24 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734442","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 Role of Lithology on Concentration-Discharge Relationships and Carbon Export in Two Adjacent Headwater Catchments","authors":"L. Giggy, M. Zimmer","doi":"10.1029/2024wr037086","DOIUrl":"https://doi.org/10.1029/2024wr037086","url":null,"abstract":"Headwater catchments have strong impacts on downstream waterways, near-shore ecosystems, and the quality of water available for growing human populations. Thus, understanding how water and solutes are exported through these upland landscapes is critically important. A growing body of literature highlights the interaction of topography, climate, and the critical zone structure as a key control on streamflow and chemical export. However, more focused work is needed to pinpoint how variability in subsurface structure across lithologically complex regions impacts streamflow and chemical signals at catchment outlets. Here, we aim to better understand how lithology and subsurface critical zones modulate streamflow response and solute export patterns in two central coastal California headwater catchments that are similar in topography, vegetation, and climate but have different lithologies. We monitored streamflow and collected surface water samples at the catchment outlets for dissolved major ions and organic carbon (DOC) for two consecutive water years. The catchment with mélange bedrock displayed much flashier hydrologic behavior with 7.8 times higher peak flow values and 1.9 times higher mean event concentrations of DOC, suggesting shorter and shallower hydrologic flow paths that likely arise from regions of shallower bedrock. Despite distinct hydrologic behavior and DOC export, dissolved major ion concentrations were broadly similar and chemostatic, which may be driven by rapid chemical reactions in the critical zone of both catchments. Our work contributes to building an integrated understanding of how subtle differences in catchment structure can have profound impacts on how water and solutes are routed through headwater catchments.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"37 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734443","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":"Propagation From Meteorological to Hydrological Drought: Characteristics and Influencing Factors","authors":"Hao Xiong, Juntai Han, Yuting Yang","doi":"10.1029/2024wr037765","DOIUrl":"https://doi.org/10.1029/2024wr037765","url":null,"abstract":"Meteorological droughts can reverberate through the water cycle, impacting water resources, ecosystems, agriculture, and socio-economic sectors. Despite this, there remains a scarcity of studies delving into the transition from meteorological to hydrological droughts and the influencing factors operating at the event level. Using long-term observations spanning nominally from 1979 to 2017, we identified and matched meteorological and hydrological droughts based on standardized drought indices across 1089 catchments globally, and examined the characteristics of drought propagation and their determinants at both catchment and event scales. Our analysis reveals that, at the catchment scale, drought severity weakens in approximately 65% of catchments, while intensity experiences a decline in 96% of catchments during the propagation. Moreover, we find that the propagation ratio of severity (or intensity) is significantly and negatively correlated with the severity (or intensity) of meteorological droughts. This implies that major drought events tend to undergo more substantial attenuation in severity (or intensity) during propagation compared to minor droughts. This finding is also obtained at the event scale. Moreover, we find that the event-scale drought propagation ratio (for both severity and intensity) tends to be higher (indicating less attenuation) in catchments characterized by larger seasonal precipitation variation, higher snowfall fraction, steeper slopes, or larger soil water holding capacity. Conversely, it is lower (indicating more dampening) in catchments with dense vegetation cover. These findings offer crucial insights into drought propagation dynamics across catchments globally, thereby informing the development of more effective drought monitoring and management strategies.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"72 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143723595","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":"Thank You to Our 2024 Reviewers","authors":"Georgia Destouni, Marc Bierkens, Andrea Castelletti, Simone Fatichi, Shafiqul Islam, Madan Kumar Jha, Stefan Kollet, Di Long, Chiyuan Miao, Hamid Moradkhani, Audrey Sawyer, Kamini Singha, Kerstin Stahl, Peter Troch, Ellen Wohl, Yi Zheng","doi":"10.1029/2025wr040485","DOIUrl":"https://doi.org/10.1029/2025wr040485","url":null,"abstract":"On behalf of the editorial board of <i>Water Resources Research (WRR)</i> and the entire water science community, we want to express our most heartfelt gratitude to all who reviewed manuscripts for the journal in 2024. Your great efforts have ensured and improved the high quality and impact of the WRR papers and generally of research in our field. In 2024, the 2832 individuals listed below have contributed 4,510 peer reviews, with the 373 people indicated in italics reviewing three or more papers for the journal. The Editors, Associate Editors and Authors of WRR sincerely thank all our reviewers and look forward to the exciting advances that will be published in WRR in 2025, the 60th anniversary year of the journal, whose first issue was published in March 1965.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"64 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143723596","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":"Signatures of Varying Climate on Geomorphic and Topologic Characteristics of Channel Networks","authors":"Aysan H. Bavojdan, Sevil Ranjbar, Dingbao Wang, Arvind Singh","doi":"10.1029/2024wr038760","DOIUrl":"https://doi.org/10.1029/2024wr038760","url":null,"abstract":"Channel networks are important landscape features that transport water, sediment, and nutrients. Their emergence and evolution are controlled by the competition between hillslope and fluvial processes on landscapes. Investigating the geomorphic and topologic properties of these networks is crucial for quantifying the roles of processes in creating distinct patterns of channel networks and developing models to predict the network dynamics under changing environment. Here, we study the response of landscapes to changing climatic forcing via numerical-modeling and the topographic analysis of natural landscapes. We use a physically-based numerical landscape evolution model to investigate the channel network structure for varying hillslope and fluvial processes represented by different magnitudes of soil transport (&lt;span data-altimg=\"/cms/asset/995c63ec-1694-4581-963c-6d40c9288038/wrcr70083-math-0001.png\"&gt;&lt;/span&gt;&lt;mjx-container ctxtmenu_counter=\"251\" 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/wrcr70083-math-0001.png\"&gt;&lt;mjx-semantics&gt;&lt;mjx-mrow&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-role=\"latinletter\" data-semantic-speech=\"upper D\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&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:wrcr70083:wrcr70083-math-0001\" display=\"inline\" location=\"graphic/wrcr70083-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"&gt;&lt;semantics&gt;&lt;mrow&gt;&lt;mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-role=\"latinletter\" data-semantic-speech=\"upper D\" data-semantic-type=\"identifier\"&gt;D&lt;/mi&gt;&lt;/mrow&gt;$D$&lt;/annotation&gt;&lt;/semantics&gt;&lt;/math&gt;&lt;/mjx-assistive-mml&gt;&lt;/mjx-container&gt;) and fluvial incision (&lt;span data-altimg=\"/cms/asset/b95b9be8-7406-4354-872e-563fa492f394/wrcr70083-math-0002.png\"&gt;&lt;/span&gt;&lt;mjx-container ctxtmenu_counter=\"252\" 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/wrcr70083-math-0002.png\"&gt;&lt;mjx-semantics&gt;&lt;mjx-mrow&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-role=\"latinletter\" data-semantic-speech=\"upper K\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&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:wrcr70083:wrcr70083-math-0002\" display=\"inline\" location=\"graphic/wrcr70083-math-0002.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"&gt;&lt;semantics&gt;&lt;mrow&gt;&lt;mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-role=\"latinletter\" data-semantic-speech=\"upper","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"31 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734445","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":"Contribution of Moisture Recycling to Water Availability in China","authors":"Qiang An, Liu Liu, Lixin Wang, Kun Yang, Yongming Cheng, Jing Liu, Guanhua Huang","doi":"10.1029/2024wr038054","DOIUrl":"https://doi.org/10.1029/2024wr038054","url":null,"abstract":"Moisture recycling is an integral component of the hydrological cycle, enhancing regional water availability by returning evaporated moisture as precipitation either locally or downwind. In China, characterized by uneven water resource distribution and regional water scarcity, understanding moisture recycling dynamics is crucial for sustainable water management. Our study quantified the contribution of moisture recycling to precipitation and water availability across China and its sub-regions during 2000–2022 using an atmospheric moisture tracking model. Results revealed that 59% of China's evapotranspiration (ET) was recycled within the region, contributing 299 mm/year to precipitation and 98 mm/year to water availability. Distinct spatial patterns emerged, with the highest ET contribution to precipitation and water availability in subtropical Central and South China (477 mm/year precipitation; 210 mm/year water availability) and the Qinghai-Tibet Plateau (355 mm/year precipitation; 121 mm/year water availability). Water availability exchanges through moisture transport between sub-regions were quantified, with Northeast China and subtropical Central and South China being net water availability recipients of 31 and 67 billion m³/year, respectively. The remaining sub-regions were net exporters, with tropical South China and Northwest China experiencing the largest net exports, amounting to −40 and −30 billion m³/year, respectively. The study not only advances our understanding of the hydrological dynamics in China but also offers critical insights for sustainable water management practices aimed at mitigating challenges posed by water scarcity and climate change. It highlights the significance of integrating moisture recycling considerations into water resource management strategies to enhance resilience against hydroclimatic variabilities.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"22 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143723594","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":"Simulation of Spring Discharge Using Deep Learning, Considering the Spatiotemporal Variability of Precipitation","authors":"Chunmei Ma, Haoyu Jiao, Yonghong Hao, Tian-Chyi Jim Yeh, Junfeng Zhu, Huiqing Hao, Jiahui Lu, Jiankang Dong","doi":"10.1029/2024wr037449","DOIUrl":"https://doi.org/10.1029/2024wr037449","url":null,"abstract":"Sparse precipitation data in karst catchments challenge hydrologic models to accurately capture the spatial and temporal relationships between precipitation and karst spring discharge, hindering robust predictions. This study addresses this issue by employing a coupled deep learning model that integrates a variation autoencoder (VAE) for augmenting precipitation and a long short-term memory (LSTM) network for karst spring discharge prediction. The VAE contributes by generating synthetic precipitation data through an encoding-decoding process. This process generalizes the observed precipitation data by deriving joint latent distributions with improved preservation of temporal and spatial correlations of the data. The combined VAE-generated precipitation and observation data are used to train and test the LSTM to predict spring discharge. Applied to the Niangziguan spring catchment in northern China, the average performance of NSE, root mean square error, mean absolute error, mean absolute percentage error, and log NSE of our coupled VAE/LSTM model reached 0.93, 0.26, 0.15, 1.8, and 0.92, respectively, yielding 145%, 52%, 63%, 70% and 149% higher than an LSTM model using only observations. We also explored temporal and spatial correlations in the observed data and the impact of different ratios of VAE-generated precipitation data to actual data on model performances. This study also evaluated the effectiveness of VAE-augmented data on various deep-learning models and compared VAE with other data augmentation techniques. We demonstrate that the VAE offers a novel approach to address data scarcity and uncertainty, improving learning generalization and predictive capability of various hydrological models. However, we recognize that innovations to address hydrologic problems at different scales remain to be explored.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"183 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143713805","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":"Assessing Impacts of Hydropower Development on Downstream Inundation Using a Hybrid Modeling Framework Integrating Satellite Data-Driven and Process-Based Models","authors":"Son K. Do, Tien L. T. Du, Hyongki Lee, Chi-Hung Chang, Duong D. Bui, Ngoc T. Nguyen, Kel N. Markert, Johan Strömqvist, Peeranan Towashiraporn, Stephen E. Darby, Linh K. Bui","doi":"10.1029/2024wr037528","DOIUrl":"https://doi.org/10.1029/2024wr037528","url":null,"abstract":"Despite its energy benefits, hydropower dam development often causes ecological damages and social disruption, including downstream livelihood impacts, and biodiversity loss. Current methods for analyzing changes in downstream inundation extent due to dam operation typically rely on historical ground or satellite observations, or on coupled hydrological-hydrodynamic modeling. However, while the former fails to isolate hydropower impacts from climate variations, the latter suffers from extensive input data requirements and high computational burden. This study proposes a novel hybrid framework integrating satellite data-driven Forecasting Inundation Extents using REOF (Rotated Empirical Orthogonal Function) analysis (FIER), and the process-based Hydrological Predictions for the Environment (HYPE) model incorporating the Integrated Reservoir Operation Scheme (IROS). The framework enables the isolated assessment of long-term hydropower impacts on downstream inundation dynamics with computational efficiency and reduced ground data requirements, making it suitable for poorly gauged regions. Applying FIER-HYPE-IROS to the Lower Mekong River basin (LMB), a region significantly affected by dam proliferation impacting fisheries and agriculture, we found that dam operations decreased decadal-average wet season water levels by up to 5% and increased dry season levels by up to 11%. Wet season inundation occurrence decreased by 11 days and the inundated area by 6%, while dry season inundation occurrence extended by 6 days and the surface water area increased by 40%. Although the current framework does not explicitly assess the downstream hydrological modifications, it offers a cost-effective alternative for evaluating upstream alterations on inundation dynamics, such as dam operations, particularly in poorly gauged regions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"7 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143695311","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":"JAX-CanVeg: A Differentiable Land Surface Model","authors":"Peishi Jiang, Patrick Kidger, Toshiyuki Bandai, Dennis Baldocchi, Heping Liu, Yi Xiao, Qianyu Zhang, Carlos Tianxin Wang, Carl Steefel, Xingyuan Chen","doi":"10.1029/2024wr038116","DOIUrl":"https://doi.org/10.1029/2024wr038116","url":null,"abstract":"Land surface models consider the exchange of water, energy, and carbon along the soil-canopy-atmosphere continuum, which is challenging to model due to their complex interdependency and associated challenges in representing and parameterizing them. Differentiable modeling provides a new opportunity to capture these complex interactions by seamlessly hybridizing process-based models with deep neural networks (DNNs), benefiting both worlds, that is, the physical interpretation of process-based models and the learning power of DNNs. Here, we developed a differentiable land model, JAX-CanVeg. The new model builds on the legacy CanVeg by incorporating advanced functionalities through JAX in the graphic processing unit support, automatic differentiation, and integration with DNNs. We demonstrated JAX-CanVeg's hybrid modeling capability by applying the model at four flux tower sites with varying aridity. To this end, we developed a hybrid version of the Ball-Berry equation that emulates the water stress impact on stomatal closure to explore the capability of the hybrid model in (a) improving the simulations of latent heat fluxes &lt;span data-altimg=\"/cms/asset/b065d7e3-11c9-41c4-8b77-30f5669a4bc5/wrcr70035-math-0001.png\"&gt;&lt;/span&gt;&lt;mjx-container ctxtmenu_counter=\"1107\" 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/wrcr70035-math-0001.png\"&gt;&lt;mjx-semantics&gt;&lt;mjx-mrow data-semantic-children=\"4\" data-semantic-content=\"0,5\" data-semantic- data-semantic-role=\"leftright\" data-semantic-speech=\"left parenthesis upper L upper E right parenthesis\" data-semantic-type=\"fenced\"&gt;&lt;mjx-mo data-semantic- data-semantic-operator=\"fenced\" data-semantic-parent=\"6\" 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-mrow data-semantic-annotation=\"clearspeak:simple;clearspeak:unit\" data-semantic-children=\"1,2\" data-semantic-content=\"3\" data-semantic- data-semantic-parent=\"6\" data-semantic-role=\"implicit\" data-semantic-type=\"infixop\"&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-added=\"true\" data-semantic- data-semantic-operator=\"infixop,⁢\" data-semantic-parent=\"4\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\" 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-mrow&gt;&lt;mjx-mo data-semantic- data-semantic-operator=\"fenced\" data-semantic-parent=\"6\" data-semantic-role=\"close\" data-semantic-type=\"fence\" style=\"margin-left: 0.056em; margin-right","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"20 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143695312","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}