Water Resources Research最新文献

{"title":"Improved Water Level Retrieval in Complex Riverine Environments: Sentinel-3 and Sentinel-6 Altimetry Over China's Rivers","authors":"Chenqi Fang, Di Long, Qi Huang, Fanyu Zhao, Huaichuan Liu, Xingwu Duan, Aizhong Hou","doi":"10.1029/2024wr039705","DOIUrl":"https://doi.org/10.1029/2024wr039705","url":null,"abstract":"The decline in in situ water level measurements since the 1980s has impeded our ability to fully understand hydrological and hydrodynamic processes, particularly in ungauged river reaches, and how global and regional water cycles respond to climate change. Satellite altimetry offers a valuable means of supplementing these gaps in river water level data, both temporally and spatially. However, existing radar waveform retracking techniques often struggle to accommodate rivers with varying morphologies and surrounding environments. This study presents an Improved Multiple Subwaveform Analysis (IMSA) algorithm based on the 50% Threshold and Ice-1 Combined (TIC) algorithm, incorporating noise filtering into the subwave search module and refining the retracking strategy for multiple subwaves, independent of coarse digital elevation models (DEMs). We validated the IMSA algorithm using in situ data from 23 gauging stations and applied it to Sentinel-3 and Sentinel-6 altimetry across 57 virtual stations (VSs) in China, covering rivers with widths ranging from 20 to 1,500 m, generating 79 validation results (each representing an RMSE value comparing altimetry with in situ measurements). The IMSA algorithm demonstrated significant enhancements at over 48 VSs with more than 64 validation results compared to the original TIC, achieving the lowest median RMSE of 0.61 m (0.13–0.50 m lower than the OCOG, Threshold, MWaPP, and TIC algorithms), with strong resilience to environmental noise. Error analysis revealed that the altimetric accuracy is primarily influenced by the underlying surface characteristics of VSs, with built-up areas exerting significant interference. Additional disturbances stem from surrounding waters, large slopes, river channels running parallel to the satellite's ground track, and unique features such as sandbars, braided and ice-covered rivers, and hydroelectric stations. The synthetic aperture radar (SAR) mode was found to mitigate some of these land cover impacts, further improving water level retrieval accuracy. Finally, the results show that river width and topography (whether mountainous or flat) do not inherently affect altimetric accuracy, provided that the on-board tracking system is supported by accurate prior DEMs and minimal slope interference.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"75 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823135","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":"Statistical Learning and Topkriging Improve Spatio-Temporal Low-Flow Estimation","authors":"J. Laimighofer, G. Laaha","doi":"10.1029/2024wr038329","DOIUrl":"https://doi.org/10.1029/2024wr038329","url":null,"abstract":"This study evaluates the potential of a novel hierarchical space-time model for predicting monthly low-flow in ungauged basins. The model decomposes the monthly low-flows into a mean field and a residual field, where the mean field represents the seasonal low-flow regime plus a long-term trend component. We compare four statistical learning approaches for the mean field, and three geostatistical methods for the residual field. All model combinations are evaluated using a hydrologically diverse dataset of 260 stations in Austria and the predictive performance is validated using nested 10-fold cross-validation. The best model for monthly low-flow prediction is a combination of a model-based boosting approach for the mean field and topkriging for the residual field. This model reaches a median <span data-altimg=\"/cms/asset/bd07921f-b370-432a-af44-864f4985be52/wrcr70042-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"285\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/wrcr70042-math-0001.png\"><mjx-semantics><mjx-mrow><mjx-msup data-semantic-children=\"0,1\" data-semantic- data-semantic-role=\"latinletter\" data-semantic-speech=\"upper R squared\" data-semantic-type=\"superscript\"><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi><mjx-script style=\"vertical-align: 0.363em;\"><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\" size=\"s\"><mjx-c></mjx-c></mjx-mn></mjx-script></mjx-msup></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00431397:media:wrcr70042:wrcr70042-math-0001\" display=\"inline\" location=\"graphic/wrcr70042-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><msup data-semantic-=\"\" data-semantic-children=\"0,1\" data-semantic-role=\"latinletter\" data-semantic-speech=\"upper R squared\" data-semantic-type=\"superscript\"><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-parent=\"2\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\">R</mi><mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\">2</mn></msup></mrow>${R}^{2}$</annotation></semantics></math></mjx-assistive-mml></mjx-container> of 0.73 across all stations, outperforming an XGBoost model on the same data set. Model performance is generally higher for stations with a winter regime (median <span data-altimg=\"/cms/asset/9899636a-7236-4a2c-900e-7b39fea3ce82/wrcr70042-math-0002.png\"></span><mjx-container ctx","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"5 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823109","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":"Has the Three Gorges Reservoir Impacted Regional Moisture Recycling?","authors":"Jia Wei, Weiguang Wang, Mingzhu Cao, Jianyun Zhang, Junliang Jin, Guoqing Wang, Hongbin Li, Xiaolong Pan, Zongchao Ye, Adriaan J. Teuling, Shuo Wang","doi":"10.1029/2024wr038208","DOIUrl":"https://doi.org/10.1029/2024wr038208","url":null,"abstract":"The Three Gorges Dam (TGD) and its impoundment significantly alter natural river properties and local land cover, drawing considerable concerns regarding its climatic and environmental effects. However, with the role of the Three Gorges Reservoir (TGR) in narrowing temperature ranges and changing precipitation patterns is well understood, its impact on moisture recycling is little known. Here, we tracked precipitation in the TGR basin back to evaporated moisture to explore the features of moisture recycling and quantify local evaporation ratios in the pre-dam (1980–2002) and post-dam (2003–2022) periods. The influences of the forcing data, simulation time steps and different tracking models on evaporation recycling are investigated. Relevant mechanisms are analyzed in terms of atmospheric motion, surface radiation, land cover changes and climate variability impacts. Results indicate that the precipitationshed shows a reduction in both summer and winter during the post-dam period. Local evaporation recycling ratios (ERRs) in TGR basin decrease by 0.46%, 1.07%, 0.59, 0.94% during the post-TGD period relative to the pre-TGD period in spring, summer, autumn and winter, respectively. Local evaporation contributions are limited in both the pre-dam and post-dam periods, especially in dry years. The reduced precipitation in TGR region is more dependent on upwind moisture, which results from the enhanced sinking motion and moisture divergence. Although different forcing data and simulation time steps show good agreement in spatial and temporal variations in the recycled moisture, the local ERRs are larger when calculated from the UTrack model than from the WAM-2layers model.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"108 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819922","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":"Methods for Predicting Bubble Size Distribution in Turbulent Flow","authors":"Pengcheng Li, David Z. Zhu, Hang Wang, Rongcai Tang","doi":"10.1029/2024wr038386","DOIUrl":"https://doi.org/10.1029/2024wr038386","url":null,"abstract":"Gas bubbles are commonly observed in both natural and human-made water systems, and their generation and distribution play pivotal roles in water quality and aquatic habitats. This study explores methods for predicting bubble size distribution within various types of turbulent flows. Models for bubble size distribution, both with and without bubble breakup, are developed and validated using experimental data from flows featuring return rollers at hydraulic jumps, skimming flows in stepped spillways, and bubbly flows in plunging jets. The experimental measurements reveal that turbulence kinetic energy dissipation rate, air void ratio, and Weber number influence bubble size distribution. These parameters are utilized to formulate the bubble size distribution model. When bubbles remain stable without breakup (i.e., when the Weber number is less than the critical Weber number), bubble size distribution at points and transects within fully developed turbulent flows can be accurately predicted. When the Weber number exceeds the critical value, the process of bubble breakup is considered to estimate the bubble size distribution. Additionally, numerical methods using the population balance model demonstrate that the initial bubble size fraction has minimal influence on the ultimate distribution in fully developed turbulent flows, while the air void ratio significantly impacts bubble size distribution. This study addresses the applicability and limitations of the bubble size distribution models and comprehensively discusses the advantages and disadvantages of each method, providing recommendations for their selection in both research and engineering applications.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"4 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819924","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 Large Multimodal Models for Urban Floodwater Depth Estimation","authors":"Heng Lyu, Shun'an Zhou, Ze Wang, Guangtao Fu, Chi Zhang","doi":"10.1029/2024wr039494","DOIUrl":"https://doi.org/10.1029/2024wr039494","url":null,"abstract":"Urban flood monitoring is crucial for understanding flood processes and implementing management strategies. However, current monitoring systems cannot comprehensively capture urban flooding dynamics. Here we explore the use of cutting-edge Large Multimodal Models (LMMs) to estimate floodwater depth from ground-level images, as alternative observational approaches. Evaluated on two urban flood image data sets, LMMs generate estimations exhibiting acceptable concordance to ground truth, with GPT-4 achieving the highest accuracy of 0.65 and a Spearman correlation coefficient of 0.88. Furthermore, a combined effect of image complexity and textual prompt is found to influence LMMs' performance. Our study systematically demonstrates, for the first time, that LMMs can be effective tools for imaging-based urban flood monitoring, enlarging the data for flood forecasting and model calibration.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"17 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-Resolution National-Scale Water Modeling Is Enhanced by Multiscale Differentiable Physics-Informed Machine Learning","authors":"Yalan Song, Tadd Bindas, Chaopeng Shen, Haoyu Ji, Wouter J. M. Knoben, Leo Lonzarich, Martyn P. Clark, Jiangtao Liu, Katie van Werkhoven, Sam Lamont, Matthew Denno, Ming Pan, Yuan Yang, Jeremy Rapp, Mukesh Kumar, Farshid Rahmani, Cyril Thébault, Richard Adkins, James Halgren, Trupesh Patel, Arpita Patel, Kamlesh Arun Sawadekar, Kathryn Lawson","doi":"10.1029/2024wr038928","DOIUrl":"https://doi.org/10.1029/2024wr038928","url":null,"abstract":"The National Water Model (NWM) is a key tool for flood forecasting, planning, and water management. Key challenges facing the NWM include calibration and parameter regionalization when confronted with big data. We present two novel versions of high-resolution (∼37 km²) differentiable models (a type of hybrid model): one with implicit, unit-hydrograph-style routing and another with explicit Muskingum-Cunge routing in the river network. The former predicts streamflow at basin outlets whereas the latter presents a discretized product that seamlessly covers rivers in the conterminous United States (CONUS). Both versions use neural networks to provide a multiscale parameterization and process-based equations to provide a structural backbone, which were trained simultaneously (“end-to-end”) on 2,807 basins across the CONUS and evaluated on 4,997 basins. Both versions show great potential to elevate future NWM performance for extensively calibrated as well as ungauged sites: the median daily Nash-Sutcliffe efficiency of all 4,997 basins is improved to around 0.68 from 0.48 of NWM3.0. As they resolve spatial heterogeneity, both versions greatly improved simulations in the western CONUS and also in the Prairie Pothole Region, a long-standing modeling challenge. The Muskingum-Cunge version further improved performance for basins >10,000 km². Overall, our results show how neural-network-based parameterizations can improve NWM performance for providing operational flood predictions while maintaining interpretability and multivariate outputs. The modeling system supports the Basic Model Interface (BMI), which allows seamless integration with the next-generation NWM. We also provide a CONUS-scale hydrologic data set for further evaluation and use.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"25 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819903","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 Surface Cover Types and Coverage on Hydraulic Parameters of Overland Flow","authors":"Kai Zhang, Ning Li, Suhua Fu, Dike Feng","doi":"10.1029/2024wr039133","DOIUrl":"https://doi.org/10.1029/2024wr039133","url":null,"abstract":"Surface cover influences the hydraulic parameters of overland flow, subsequently affecting soil erosion. Therefore, exploring the flow dynamic mechanisms under different surface cover types is crucial. A series of flume experiments were conducted to investigate the impact of surface cover on the hydraulic parameters of overland flow. The specific experimental conditions were as follows: one slope gradient (15°) and one flow discharge (1.0 × 10⁻³ m³ s⁻¹), four cover types (corn residue, rock fragment, sweet potato, and corn stem), and seven coverage percentages ranging from 0% to 70%. The results indicated that the cover of non-submerged state was the most effective at reducing flow velocity, with cover of submerged state being the least effective. Under the four cover conditions, flow velocity, Froude number, flow depth, and shear stress exhibited significant power function relationships with coverage (<i>R</i>² &gt; 0.91). The relationships between Reynolds number and stream power with coverage were not significant under corn residue and rock fragment cover (<i>P</i> &gt; 0.05), but showed significant power function relationships under sweet potato cover (<i>R</i>² &gt; 0.88). The cover type alters the form of the cover and the flow submergence degree, leading to the change of hydraulic radius, thereby influencing the hydraulic parameters of overland flow. The findings provide scientific evidence for understanding the flow dynamic mechanisms under surface cover and improving the predictive accuracy of soil erosion process models.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"99 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813930","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":"Operational Interval Extraction Based on Long-Short Term Memory Networks for Building More Feasible Reservoir Operation Models","authors":"Yalian Zheng, Pan Liu, Qian Cheng, Huan Xu, Xinran Luo, Weibo Liu, Xiao Li, Hao Ye, Hongxuan Lei, Wei Zhang","doi":"10.1029/2024wr038147","DOIUrl":"https://doi.org/10.1029/2024wr038147","url":null,"abstract":"Advances in data analytics create an opportunity to enhance reservoir operation. A challenge arising is how to utilize operational data to form realistic constraints of the reservoir operation practice. To address this issue, a novel approach is proposed to extract operational intervals of reservoir outflow by a deep learning method, namely the physics-guided long-short term memory network. The knowledge-informed reservoir operation (KIRO) model was built by adding derived operational intervals of outflow as constraints for the traditional reservoir operation (TRO) model. KIRO couples (a) an optimization model to search for optimal operation schemes, and (b) operational intervals of reservoir operators' decisions based on realistic factors. China's Qingjiang cascade reservoir including Shuibuya, Geheyan, and Gaobazhou reservoirs is used as a case study. Results show that KIRO can yield more physically feasible operation schemes than TRO due to its additional constraints. Specifically, KIRO avoids excessive reservoir water level fluctuations and outflow variations compared with TRO. Moreover, the extracted operational interval can help uncover implicit demands of real-world operation, for example, the KIRO model accurately identified the cascade reservoir unit maintenance events from 31 January 2019, to 31 March 2019, and the operation schemes were aligned more closely with the power demands. This study provides a new method for building more feasible reservoir operation models based on deep learning.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"10 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143806188","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":"Data Enabled Predictive Control for Water Distribution Systems Optimization","authors":"Gal Perelman, Avi Ostfeld","doi":"10.1029/2024wr039059","DOIUrl":"https://doi.org/10.1029/2024wr039059","url":null,"abstract":"Recent developments in control theory, coupled with the growing availability of real-time data, have paved the way for improved data-driven control methodologies. This study explores the application of the Data-Enabled Predictive Control (DeePC) algorithm to optimize the operation of water distribution systems (WDS). WDS are characterized by inherent uncertainties and complex nonlinear dynamics. Hence, classic control strategies involving physical model-based or state-space methods are often difficult to implement and scale. The DeePC method suggests a paradigm shift by utilizing a data-driven approach. The technique employs a finite set of input-output samples (control settings and measured data) to learn an unknown system's behavior and derive optimal policies, effectively bypassing the need for an explicit mathematical model of the system. In this study, DeePC is applied to two WDS control applications of pressure management and chlorine disinfection scheduling, demonstrating superior performance compared to standard control strategies.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"25 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143814190","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":"Visual-Analytics Bridge Complexity and Accessibility for Robust Urban Water Planning","authors":"Marta Zaniolo, Meagan S. Mauter, Sarah M. Fletcher","doi":"10.1029/2024wr037633","DOIUrl":"https://doi.org/10.1029/2024wr037633","url":null,"abstract":"Urban water resources planning is complicated by unprecedented uncertainty in supply and demand. Real-world planning often simplifies the full range of uncertainty faced by a system into a limited set of deterministic scenarios to enhance accessibility for decision-makers and the public. However, overlooking uncertainty can expose the system to failures. On the other end of the spectrum, academically developed tools for scenario analysis rigorously quantify the combined effects of multiple sources of uncertainty, but the practical application of these models is limited by the challenges of information visualization and communication of results. In short, municipal water supply planners lack access to planning frameworks that effectively integrate a rigorous treatment of uncertainty with accessible, user-friendly visual and interactive tools to enhance user accessibility. In this work, we fill this gap by proposing Visual-Robust Decision Making, and demonstrate an application for the city of Santa Barbara (SB), CA. Santa Barbara faces multiple uncertainties from pending state and federal regulations to changing hydrology and water demand. The city seeks to increase its water portfolio robustness by expanding its seawater desalination plant, but must decide how much capacity to add. We introduce computational tools that assess uncertainty across nine uncertain drivers identified with the help of water planners in SB. To allow public participation in the desalination expansion decision, we develop interactive visual-analytics to aid decision-makers and stakeholders in navigating complex scenario analysis outcomes. Our results quantify the tradeoffs between increased capacity and system robustness and aim to enhance participation and uncertainty characterization of urban water planning efforts.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"60 1 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143814189","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}