Water Resources Research最新文献

{"title":"Groundwater Storage Changes Using GRACE and ESA CCI Soil Moisture Products in Southern Victoria, Australia","authors":"Taejun Park, Ki‐Weon Seo, Dongryeol Ryu, Jae‐Seung Kim, Daeha Lee, Jianli Chen, Clark R. Wilson","doi":"10.1029/2024wr039346","DOIUrl":"https://doi.org/10.1029/2024wr039346","url":null,"abstract":"Groundwater depletion, driven by climate change and increasing extraction for irrigation, has increased the need for accurate monitoring. Traditional methods, such as in situ water table observations and pumping tests, are valuable for assessing groundwater availability and aquifer characteristics but are limited in capturing basin‐scale variations. The Gravity Recovery and Climate Experiment (GRACE) enables estimation of basin‐scale groundwater changes, though its observations also include surface water and soil moisture (SM) in the vadose zone. Therefore, additional data on non‐groundwater components are needed to isolate groundwater variations. In this study, we use the profile SM content for the top 0–120 cm of soil as an estimate of vadose zone SM, derived using an exponential filtering technique applied to European Space Agency's Climate Change Initiative for Soil Moisture (ESA CCI SM) and in situ data. This approach addresses limitations of conventional models, such as their inability to represent non‐natural or lateral water redistribution. Groundwater storage (GWS) changes in southern Victoria, Australia were estimated by subtracting the filtered SM from GRACE data and validated against in situ groundwater level observations for both unconfined and confined aquifers. The ESA CCI SM‐based estimates showed clear improvements in capturing seasonal and interannual variability of in situ GWS compared to conventional model‐based estimates. The proposed approach is potentially applicable to GWS estimation at continental scales.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"1 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145295151","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":"Practical Guidance for Improving Pump-Test Based Inference of Hydraulic Parameters in Heterogeneous Unconfined Aquifers","authors":"Mostafa Naderi, Xavier Sanchez-Vila, Hoshin Vijai Gupta","doi":"10.1029/2025wr041946","DOIUrl":"https://doi.org/10.1029/2025wr041946","url":null,"abstract":"Hydrogeological modeling relies on scale estimates for relevant hydraulic parameters. To obtain these, pump test data are typically interpreted using methods derived for idealized conditions (such as a spatially homogeneous medium). For unconfined aquifers, interpretation is made challenging by the time-evolving phreatic surface that imposes strong non-linearities in the equations. This study uses numerical simulation to examine the nature of parameter estimates obtained for heterogeneous unconfined aquifers using Neuman-based solutions. Results show that the representative horizontal extent of the cone of depression stabilizes when its radius approaches/exceeds <span data-altimg=\"/cms/asset/7e2c051c-0fe3-46ea-a1ed-d9ff28cef31b/wrcr70458-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"211\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/wrcr70458-math-0001.png\"><mjx-semantics><mjx-mrow data-semantic-children=\"2,1\" data-semantic-content=\"0\" data-semantic- data-semantic-role=\"equality\" data-semantic-speech=\"italic tilde 15\" data-semantic-type=\"relseq\"><mjx-mrow data-semantic- data-semantic-parent=\"3\" data-semantic-role=\"unknown\" data-semantic-type=\"empty\"></mjx-mrow><mjx-mo data-semantic-font=\"italic\" data-semantic- data-semantic-operator=\"relseq,∼\" data-semantic-parent=\"3\" data-semantic-role=\"equality\" data-semantic-type=\"relation\" rspace=\"5\" space=\"5\"><mjx-c></mjx-c></mjx-mo><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"3\" data-semantic-role=\"integer\" data-semantic-type=\"number\"><mjx-c></mjx-c><mjx-c></mjx-c></mjx-mn></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00431397:media:wrcr70458:wrcr70458-math-0001\" display=\"inline\" location=\"graphic/wrcr70458-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow data-semantic-=\"\" data-semantic-children=\"2,1\" data-semantic-content=\"0\" data-semantic-role=\"equality\" data-semantic-speech=\"italic tilde 15\" data-semantic-type=\"relseq\"><mrow data-semantic-=\"\" data-semantic-parent=\"3\" data-semantic-role=\"unknown\" data-semantic-type=\"empty\"></mrow><mo data-semantic-=\"\" data-semantic-font=\"italic\" data-semantic-operator=\"relseq,∼\" data-semantic-parent=\"3\" data-semantic-role=\"equality\" data-semantic-type=\"relation\" mathvariant=\"italic\">∼</mo><mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"3\" data-semantic-role=\"integer\" data-semantic-type=\"number\">15</mn></mrow>$mathit{sim }15$</annotation></semantics></math></mjx-assistive-mml></mjx-container> times the correlation length scale of aquifer heterogeneity. In practice, long pumping durations are needed to achieve this when length scales are large, presenting difficulties due to limited horizontal cone expa","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"1 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145289058","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":"Improved Hydrological Forecasting of Subseasonal Streamflow for the Irrawaddy and Mekong Rivers in Southeast Asia","authors":"Eunjee Lee, Randal D. Koster, Mauricio E. Arias, Yuna Lim, Yujin Zeng, Sophea Rom Phy, Jana Kolassa, Qing Liu, Thanh Duc Dang, Miguel Laverde-Barajas, Susantha Jayasinghe","doi":"10.1029/2025wr040561","DOIUrl":"https://doi.org/10.1029/2025wr040561","url":null,"abstract":"To provide a better subseasonal-to-seasonal (S2S) hydrological forecast, it is essential to investigate the factors that control streamflow prediction at time scales beyond that of traditional weather forecasts. Using a hydrological forecast framework built around NASA's Catchment-CN land model and GEOS S2S forecast meteorology, this study examines the predictive skill of subseasonal (∼30 days) streamflow in Southeast Asia and shows how that skill may be improved in combination with satellite-based rainfall information in areas for which the rain-gauge measurements are particularly poor. Initialized at four different times of a year, the prediction skill along the Irrawaddy River in Myanmar was significantly improved, going from no skill up to a correlation coefficient <i>R</i> of 0.65 during the wet season and up to 0.55 during the following transitional period by introducing Integrated Multi-satellitE Retrievals for GPM (IMERG) satellite-based precipitation into our land initialization methodology. The streamflow forecast skill along the Mekong River was reasonably high (<i>R</i> of 0.6–0.7) during the dry season before and after the utilization of IMERG data, and the wet-season forecast skill modestly increased up to <i>R</i> of 0.8. The accurate land initialization is found to contribute dominantly to the predictive skill of subseasonal streamflow; however, low rainfall forecast skill occasionally offsets the positive contribution from the land initialization. Our findings suggest an alternative way to enhance S2S hydrological forecasting in other large river basins where rain gauge information is limited and illustrate the need for a careful application of forecast rainfall to hydrological prediction during the transitional seasons.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"3 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145289061","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":"Experimental and Numerical Investigation on the Impact of Emergent Vegetation on the Hyporheic Exchange","authors":"S. H. Huang, R. Nuli, P. K. Kang, L. Shen, J. Q. Yang","doi":"10.1029/2025wr040217","DOIUrl":"https://doi.org/10.1029/2025wr040217","url":null,"abstract":"Hyporheic exchange leads to the transfer of gases, solutes, and fine particles across the sediment-water interface, playing a critical role in biogeochemical cycles and pollutant transport in aquatic environments. While in-channel vegetation has been recognized to enhance hyporheic exchange, the mechanisms remain poorly understood. Here, we investigated how an emergent vegetation canopy impacts hyporheic exchange using refractive index-matched flume experiments and coupled numerical simulations. Our results show that at the same mean surface flow velocity, vegetation increases the hyporheic exchange velocity by four times compared to the non-vegetated channel. However, the hyporheic exchange velocity does not increase further with increasing vegetation density. In addition, our results show that the hyporheic exchange velocity scales with the square root of sediment permeability. Our findings provide a predictive framework for hyporheic exchange in vegetated channels with varying vegetation densities and sediment permeabilities and could guide the future design of environmental management and restoration projects using vegetation.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"20 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145289059","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 Vadose Zone Storage Deficits in Modulating Groundwater Recharge and Streamflow in Seasonally Dry Watersheds","authors":"N. K. Benitez-Nelson, D. N. Dralle, W. J. Hahm, D. M. Rempe","doi":"10.1029/2024wr038788","DOIUrl":"https://doi.org/10.1029/2024wr038788","url":null,"abstract":"In forested, seasonally dry watersheds, winter rains commonly replenish water storage deficits in the vadose zone before recharging underlying hillslope groundwater systems that sustain streamflow. However, the relative inaccessibility of the subsurface limits our understanding of how groundwater recharge is moderated by vadose zone storage deficits generated by plant-water uptake. Here, we compare groundwater recharge inferred from the storage-discharge relationship with independent, distributed estimates of deficits across 12 undisturbed California watersheds. We find accrued dry season deficits primarily driven by evapotranspiration insufficiently explain inter-annual variability in the amount of precipitation required to generate groundwater recharge due to continued deficit accumulation between wet season storms. Tracking the deficit at the storm event-scale, however, reveals a characteristic response in groundwater to increasing rainfall not captured in the seasonal analysis that may improve estimates of the rainfall required to generate recharge and streamflow on a per-storm basis. Our findings demonstrate the potential for existing public data sets to better capture water partitioning within the subsurface and thus improve the prediction of rainfall-runoff behavior and summer water availability in rainfall-dominated, seasonally dry basins using a combined deficit-recharge approach.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"2 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145289062","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":"Framework for Identification of Groundwater Contamination Source Based on Conditional Generative Adversarial Networks and Optimization Methods","authors":"Yaning Xu, Wenxi Lu, Zidong Pan, Zibo Wang","doi":"10.1029/2024wr039467","DOIUrl":"https://doi.org/10.1029/2024wr039467","url":null,"abstract":"Accurate groundwater contamination source identification (GCSI) is critical for ensuring water resource security and management. However, solving the GCSI problem often faces challenges of insufficient identification accuracy and difficulty in quantifying uncertainty. To address these issues, we propose a framework based on a bidirectional mapping strategy, optimization methods, and an adaptive enhancement iterative process to simultaneously identify contamination source characteristics and model parameters. Specifically, the framework directly models the probability distribution of the variables to be identified based on a conditional generative adversarial network (CGAN), generates diverse samples, and thus achieves the quantification of uncertainty. Additionally, the innovative integration of CGAN and optimization methods enables the optimization algorithm to utilize its strong search capability to further refine the identification results. Application results on a hypothetical case show that the framework outperforms the use of optimization methods alone in terms of accuracy, efficiency, and reliability. The proposed framework enhances identification accuracy and explicitly quantifies uncertainty, providing a new solution for GCSI challenges.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"1 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145289312","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":"Growing Season Precipitation Percolates to Groundwater Past Older Water in Storage Across a Temperate Agricultural Catchment","authors":"Joshua W. Snarski, Sylvain Kuppel, Conner Caridad, James Knighton","doi":"10.1029/2024wr038869","DOIUrl":"https://doi.org/10.1029/2024wr038869","url":null,"abstract":"How water is stored within- and released from-the vadose zone controls groundwater recharge, plant water uptake, and the movement of dissolved solutes (nutrients, carbon, pollutants). The goal of this study was to determine the age of water recharging groundwater during the growing season in a temperate agricultural catchment. We measured soil moisture and bulk soil water isotopic compositions (<i>δ</i>¹⁸O) twice per month at three locations across a hillslope as well as groundwater and surface water <i>δ</i>¹⁸O near the catchment outlet from March through October. We then calibrated ecohydrological models to these data with two competing representations of vadose zone flow: two-pore domain flow (TPD) and well-mixed flow (WM). Measurements of moisture <i>δ</i>¹⁸O across the upper 40 cm of the soil profile and in surface and groundwater all supported selection of TPD over WM as the more likely representation of vertical water movement through the vadose zone. Calibration of the TPD model resulted in substantially different soil parameter estimates from that of the WM model. The TPD model indicated that growing season percolate to groundwater was composed of water 1–2 weeks old, whereas evapotranspiration (ET) was sourced from prior seasons. In contrast, the WM model suggested that both percolate and ET originated as precipitation from prior months. These results carry significant implications for conceptual and numerical modeling of the fate and transport of nutrients that are surface applied to agricultural fields. Our findings highlight a critical need for improved process representations of soil water transport in hydrological and ecohydrological models.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"20 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145289060","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":"Explainable AI for Interpreting Spatiotemporal Groundwater Predictions","authors":"Stephanie R. Clark, Guobin Fu, Sreekanth Janardhanan","doi":"10.1029/2025wr041303","DOIUrl":"https://doi.org/10.1029/2025wr041303","url":null,"abstract":"As machine learning models become more widely relied on for groundwater predictions, the ability to interpret and explain these predictions is increasingly important. Explainable AI (XAI) tools are addressing this challenge by enhancing model transparency. Importantly, XAI also offers an early indication of its potential in broadening the role of machine learning in groundwater research — shifting it from a predictive tool to one that deepens understanding of system dynamics. This study explores the capacity of XAI to provide comprehensive insights into groundwater system behavior over large geographic scales. Spatiotemporal variations in groundwater levels and trends across Australia's Murray-Darling Basin (MDB) are investigated. Predominant drivers of groundwater changes are identified, revealing differences across subregions and extended timeframes, including during periods of drought. Insights are revealed on a geographic scale that would be difficult to obtain using physics-based or conceptual models, though the approach is equally applicable to surrogates and emulators of these models. This framework advances the interpretability of spatiotemporal environmental predictions through the incorporation of machine learning with explainability and visualisations—demonstrating the potential for machine learning to add value in hydrological research beyond the production of accurate predictions. Although the application of explainability in hydrological machine learning models is still relatively new, it is poised to become a standard component of future analyses. Through the considered adaptation of XAI methods to hydrological settings, researchers will enhance the acceptance and applicability of machine learning models for sustainable water resource management.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"26 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145288686","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":"Toward the Human Right to Water for Vulnerable Communities: The Effectiveness of Stakeholder Processes to Control Regional Shallow Groundwater Contamination by Nitrates","authors":"Iris T. Stewart, John Dialesandro, Samantha Lei, Lilah Foster","doi":"10.1029/2025wr040896","DOIUrl":"https://doi.org/10.1029/2025wr040896","url":null,"abstract":"Nitrate contamination in shallow drinking water wells is an urgent and persistent concern for agricultural regions and disadvantaged communities worldwide. As viable options for the large‐scale removal of nitrates from groundwater remain elusive, greater emphasis has been placed on stakeholder‐based integrative approaches, yet few have been developed, and fewer evaluated for their effectiveness. The Central Valley in California is one of the most important intensive agricultural regions globally, where such a stakeholder‐based process (CV‐SALTS) has been initiated, and which is poised to serve as a model for controlling nitrate contamination elsewhere. Based on the Groundwater Ambient Monitoring and Assessment Program data for the 2000–2023 period, we develop a new data sufficiency metric, quantify the uncertainties associated with establishing nitrate concentrations and their changes in space and time, the impact of Confined Animal Feeding Operations (CAFOs), seasonal variability and drought on nitrate levels, and how they are addressed through CV‐SALTS policies. Our findings suggest that there remain substantial uncertainties associated with where nitrate concentrations are above safe levels, but that they predominantly intersect with environmental justice communities. Severe drought conditions and the proximity of CAFOS significantly elevated nitrate concentrations, but had previously not been sufficiently monitored or considered. A new data sufficiency metric based on nitrate variability, maximum contaminant level exceedance, and observation density can support stakeholder processes in prioritizing areas for additional monitoring and risk reduction. Our findings form the basis for recommended policy changes that are transferable to other regions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"102 7 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145260945","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":"BURGER: A Bottom‐Up Regionalization Approach for Global Sub‐Daily Intensity‐Duration‐Frequency Data","authors":"J. M. Hoch, I. Probyn, F. Marra, C. Lucas, J. Bates, A. Cooper, H. J. Fowler, S. Hatchard, E. Lewis, J. Savage, N. Addor, C. Sampson","doi":"10.1029/2024wr039773","DOIUrl":"https://doi.org/10.1029/2024wr039773","url":null,"abstract":"Intensity‐Duration‐Frequency (IDF) curves require accurate observations which are not available everywhere. To provide globally consistent IDF maps, we harness the accuracy of Global Sub‐Daily Rainfall (GSDR) gauge observations and combine this with the power of a random forest regression model to regionalize the parameters of the SMEV (Simplified Metastatistical Extreme Value) distribution. After regionalization, it is possible to compute intensities for any combination of return period and duration up to 24 hr. These regionalized intensities are named BURGER, the “Bottom Up Regionalized Global Extreme Rainfall” data set. Comparing intensities from BURGER against those obtained at GSDR stations shows overall good agreement as supported by a median percentage bias around 0% and an interquartile range between −5% and 5%. Errors increase with less frequent events, indicating a too light tail of regionalized intensities, and show marked regional variations. Intensities from simulations excluding station data in Japan and Germany deviate up to 15% from those obtained with the station data included. A benchmark with a remote sensing‐based IDF data set did not reveal structurally lower agreement in ungauged regions compared to gauged regions, suggesting a reliable transfer to ungauged areas. Comparing results with other IDF data sets shows that differences between the underlying methods and data hamper a robust benchmark. For instance, while at some GSDR stations NOAA data agrees with BURGER data, NOAA data hardly agrees with empirically derived intensities at other stations. This first bottom‐up approach to global IDF data yields promising results and insights warranting future improvements.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"75 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261592","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}