Water Resources Research最新文献

{"title":"Evaluation of Multiple Groundwater Management Targets by Applying Frequency, Duration, and Magnitude Metrics to Water Table Depth Targets","authors":"Mingjun Wang, Bo Xu, Chi Zhang, Yong Peng, Yu Li, Bing Yu, Xinqiang Du","doi":"10.1029/2023wr036730","DOIUrl":"https://doi.org/10.1029/2023wr036730","url":null,"abstract":"Groundwater resource management faces significant challenges due to groundwater overdraft and waterlogging. Establishing thresholds of the water table depth (WTD) is crucial to ascertain whether WTDs align with ranges conducive to the health of social-ecological systems. However, existing studies often overlook multiple protection targets, dominant targets across different seasons, and spatial variations of thresholds. The long-term effects of WTDs exceeding threshold ranges of the WTD also need to be further explored. Here we propose a novel framework for calculating grid-scale thresholds across seasons, incorporating multiple targets. This framework calculates frequency, duration, and magnitude metrics, offering an evaluation of multiple groundwater management targets over decades. We apply this framework to the lower Tao'er River Basin in China, revealing threshold depths for shallow water tables ranges of 1.16–2.05 m and 1.16–4.05 m during non-growth and growth periods, while threshold depths for deep water tables ranges from 6.28–33.54 m and 1.96–30.72 m, respectively. Climate change scenarios demonstrate minimal frequency changes but significant deterioration in duration and magnitude compared to the historical scenario. Grids with duration of transgressions more than 12 months expand by 1–2 times, while grids exceeding thresholds of the WTD by 2 m increase by 37%–81% under climate change and intensified pumping scenarios. A 20% increase in groundwater pumping leads to an average rise of 151%, 224%, and 147% deterioration in frequency, duration, and magnitude. Furthermore, 1%–6% of grids face dual challenges of groundwater storage reduction and waterlogging. These findings can inform groundwater resource management under various potential futures.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"47 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878052","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":"Modeling Extreme Meteorological Droughts From Paleo-Climatic Reconstructions: A Metastatistical Framework","authors":"Maria Francesca Caruso, David Johnny Peres, Antonino Cancelliere, Marco Marani","doi":"10.1029/2024wr038640","DOIUrl":"https://doi.org/10.1029/2024wr038640","url":null,"abstract":"Droughts have pervasive societal impacts and remain difficult to characterize observationally, due to the limited number of droughts sampled in instrumental records. One approach to improving the statistical basis of drought occurrence probability estimation is to extend the observational record using proxy climatic archives, such as those based on tree-ring information. Additionally, since droughts are rare and characterized by multiannual durations and inter-arrival times, it is important to devise and apply statistical techniques that make full use of available information to improve our ability to quantify the rarest droughts. We extract data from a publicly available tree-ring based Palmer Drought Severity Index (PDSI) data set, the Old World Drought Atlas, for two sites in Italy where long rainfall and temperature observational time series are leveraged for a meaningful comparison. Drought events are defined in terms of drought deficit volumes below a threshold PDSI value, and are studied through the Metastatistical Extreme Value Distribution (MEVD) to quantify the occurrence probability of extreme drought events. The estimation uncertainty associated with a variety of possible assumptions in MEVD analysis is studied, in specific comparison with the performance obtained using the traditional Generalized Extreme Value distribution, through a cross-validation methodology. Results suggest that MEVD-based formulations are more robust and flexible with respect to traditional ones. The combination of paleoclimatic data and methodologies capable of using most of the existing information provides more reliable estimates of drought recurrence times, which may be used to design more effective drought risk management plans.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"77 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878053","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":"Physically Based Dimensionless Features for Pluvial Flood Mapping With Machine Learning","authors":"Mark S. Bartlett, Jared Van Blitterswyk, Martha Farella, Jinshu Li, Curtis Smith, Anthony J. Parolari, Lalitha Krishnamoorthy, Assaad Mrad","doi":"10.1029/2024wr039086","DOIUrl":"https://doi.org/10.1029/2024wr039086","url":null,"abstract":"Rapid delineation of flash flood extents is critical to mobilize emergency resources and to manage evacuations, thereby saving lives and property. Machine learning (ML) provides a promising solution for this rapid delineation, offering a computationally efficient alternative to high-resolution 2D flood models. However, even when trained on diverse geographic regions, ML models typically require retraining to perform well in new locations, and therefore often fail to generalize to never-before-seen conditions. To improve ML generalization, we apply Buckingham <span data-altimg=\"/cms/asset/9bde1150-4fbb-408d-930e-ecd7898e8c64/wrcr70130-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"419\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/wrcr70130-math-0001.png\"><mjx-semantics><mjx-mrow><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-role=\"greekletter\" data-semantic-speech=\"normal upper Pi\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00431397:media:wrcr70130:wrcr70130-math-0001\" display=\"inline\" location=\"graphic/wrcr70130-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-role=\"greekletter\" data-semantic-speech=\"normal upper Pi\" data-semantic-type=\"identifier\" mathvariant=\"normal\">Π</mi></mrow>${Pi }$</annotation></semantics></math></mjx-assistive-mml></mjx-container> theorem to derive dimensionless terms across multiple spatial scales. These multiscale <span data-altimg=\"/cms/asset/a3860706-f6f9-4f77-9d6c-30863c8d787b/wrcr70130-math-0002.png\"></span><mjx-container ctxtmenu_counter=\"420\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/wrcr70130-math-0002.png\"><mjx-semantics><mjx-mrow><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-role=\"greekletter\" data-semantic-speech=\"normal upper Pi\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00431397:media:wrcr70130:wrcr70130-math-0002\" display=\"inline\" location=\"graphic/wrcr70130-math-0002.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-role=\"greekletter\" data-semantic-speech=\"normal upper Pi\" data-semantic-type=\"identifier\" mathvariant=\"normal\">Π</mi></mrow>${Pi }$</annotation></semantics></math></mjx-assistive-mml></mjx-c","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"27 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143872490","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":"Competitive Roles of DNRA and Denitrification on Organic Nitrogen Dynamics in Partially Saturated Soil-Water Systems","authors":"Lecheng Liu, Tianyuan Zheng, Yingying Qiu, Yujie Hao, Haoran Ma, Xilai Zheng, Alberto Guadagnini","doi":"10.1029/2024wr038705","DOIUrl":"https://doi.org/10.1029/2024wr038705","url":null,"abstract":"We focus on the competition between nitrate/nitrite ammonification (also termed dissimilatory nitrate reduction to ammonium (DNRA)) and denitrification processes taking place across partially saturated water-soil systems. The study is motivated by the observation that the joint presence of dissolved organic nitrogen (DON) and redox fluctuation in the vadose zone poses potential risks for generation of nitrates (NO₃⁻-N) that can then be reduced to ammonium (NH₄⁺-N) through DNRA. We examine nitrogen dynamics induced in natural soil samples subject to controlled drying-wetting cycles. Upon experimental evidences, we estimate the parameters driving the kinetics associated with nitrogen transformation. This enables us to document a competition between DNRA and denitrification during wetting periods. We find that the increasing the carbon-to-nitrogen (C/N) ratio in the system yields a significant increase of DNRA rates, with a corresponding increase of their contribution to nitrate reduction. The rate of DNRA is documented to be (<i>a</i>) significantly faster in loam than in sandy loam, due to dissolved carbon release from loam aggregates, and (<i>b</i>) more effective in the presence of amino acid than urea in the natural soil, due to the role of amino acid as carbon source. Our analysis further suggests the relevance of hydrogeochemical factors (e.g., moisture variation, soil texture, and C/N ratio) on DON transformation through the influence of functional microorganisms. These insights advance our understanding of nitrogen dynamics in agroecosystems, which has significant implications for environmental management practices aimed at controlling NO₃⁻-N pollution in partially saturated soils.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"12 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143862062","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":"Carbon Emissions From Low-Order Streams in a Tropical, High-Elevation, Peatland Ecosystem Are Mediated by Catchment Morphology","authors":"Keridwen M. Whitmore, Amanda G. DelVecchia, Elizabeth Farquhar, Gerard Rocher-Ros, Esteban Suárez, Diego A. Riveros-Iregui","doi":"10.1029/2024wr038036","DOIUrl":"https://doi.org/10.1029/2024wr038036","url":null,"abstract":"Inland waters emit large amounts of carbon and are key players in the global carbon budget. Particularly high rates of carbon emissions have been reported in streams draining mountains, tropical regions, and peatlands. However, few studies have examined the spatial variability of CO₂ concentrations and fluxes occurring within these systems, particularly as a function of catchment morphology. Here we evaluated spatial patterns of CO₂ in three tropical, headwater catchments in relation to the river network and stream geomorphology. We measured dissolved carbon dioxide (<i>p</i>CO₂), aquatic CO₂ emissions, discharge, and stream depth and width at high spatial resolutions along multiple stream reaches. Confirming previous studies, we found that tropical headwater streams are an important source of CO₂ to the atmosphere. More notably, we found marked, predictable spatial organization in aquatic carbon fluxes as a function of landscape position. For example, <i>p</i>CO₂ was consistently high (&gt;10,000 ppm) at locations close to groundwater sources and just downstream of hydrologically connected wetlands, but consistently low (&lt;1,000 ppm) in high gradient locations or river segments with larger drainage areas. Taken together, our findings suggest that catchment area and stream slope are important drivers of <i>p</i>CO₂ and gas transfer velocity (<i>k</i>) in mountainous streams, and as such they should be considered in catchment-scale assessments of CO₂ emissions. Furthermore, our work suggests that accurate estimation of CO₂ emissions requires understanding of dynamics across the entire stream network, from the smallest seeps to larger streams.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"52 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143858059","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":"Feasibility Demonstration of Using the Signal-to-Noise Ratio Observations From Geodetic GNSS Receivers to Retrieve Dry Snow Density","authors":"J. Zhang, W. Wan, H. Liang, W. Ma, B. Liu, C. Liang, Z. Guo, X. Wan","doi":"10.1029/2023wr036657","DOIUrl":"https://doi.org/10.1029/2023wr036657","url":null,"abstract":"The geodetic Global Navigation Satellite System (GNSS) receiver has been proven to retrieve snow depth using the phase change rate of the signal-to-noise ratio (SNR) observations. Snow density can be related to snow permittivity and is theoretically sensitive to the amplitude of the GNSS reflected signal. However, retrieving snow density using the SNR observations is challenging due to the difficulty in extracting the reflected amplitude since it hides in the interference waveform and changes with the satellite elevation angle. Overcoming this issue by taking an indirect path, this study proposes a novel GNSS Signal Amplitude Ratio Model (GSARM) that relates the corrected amplitude ratio (&lt;span data-altimg=\"/cms/asset/0055f5f9-ac75-4e92-a674-15eab6033bfe/wrcr70127-math-0264.png\"&gt;&lt;/span&gt;&lt;mjx-container ctxtmenu_counter=\"244\" 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/wrcr70127-math-0264.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=\"greekletter\" data-semantic-speech=\"alpha\" 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:wrcr70127:wrcr70127-math-0264\" display=\"inline\" location=\"graphic/wrcr70127-math-0264.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=\"greekletter\" data-semantic-speech=\"alpha\" data-semantic-type=\"identifier\"&gt;α&lt;/mi&gt;&lt;/mrow&gt;$alpha $&lt;/annotation&gt;&lt;/semantics&gt;&lt;/math&gt;&lt;/mjx-assistive-mml&gt;&lt;/mjx-container&gt;) to the snow permittivity and the resulting snow density. First, the model extracts the instantaneous amplitude from SNR observations to derive an initial amplitude ratio (&lt;span data-altimg=\"/cms/asset/4e8bb1d2-0ef2-45b8-8877-1718aa8832dc/wrcr70127-math-0001.png\"&gt;&lt;/span&gt;&lt;mjx-container ctxtmenu_counter=\"245\" 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/wrcr70127-math-0001.png\"&gt;&lt;mjx-semantics&gt;&lt;mjx-mrow&gt;&lt;mjx-msub data-semantic-children=\"0,1\" data-semantic- data-semantic-role=\"greekletter\" data-semantic-speech=\"alpha 0\" data-semantic-type=\"subscript\"&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"greekletter\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;mjx-script style=\"vertical-align: -0.15em;\"&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\" size=\"s\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mn&gt;&lt;/mjx-script&gt;&lt;","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"269 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143858016","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":"Spatial Hydrographs of River Flow and Their Analysis for Peak Event Detection in the Context of Satellite Sampling","authors":"Arnaud Cerbelaud, Cédric H. David, Sylvain Biancamaria, Jeffrey Wade, Manu Tom, Renato Prata de Moraes Frasson, George H. Allen, Hana Thurman, Denis Blumstein","doi":"10.1029/2024wr038444","DOIUrl":"https://doi.org/10.1029/2024wr038444","url":null,"abstract":"The study of river dynamics has long relied on the analysis of traditional in situ hydrographs. This graphical representation of temporal variability at a given location is so ubiquitous that the mere term “hydrograph” is widely recognized as a time series. While such a “temporal hydrograph” is well suited for in situ data analysis, it fails to represent hydrologic variability across space at a given time; a perspective that characterizes satellite-based hydrologic observations. Here we argue that the concept of “spatial hydrograph” should be the focus of its own dedicated scrutiny. We build “space series” of river discharge and present their analysis in the context of peak flow event detection. We propose the use of peak event spatial coverage, referred to as “length”, as an analog to event duration. Our analysis is performed in the Mississippi basin using a dense in situ network. We reveal that peak flow events range in length from around 75 to 1,800 km with a median (mean) value of 330 (520) km along the basin's largest rivers. Our analysis also suggests that spatial sampling needs to be a factor of 4 (2) finer in resolution than peak flow lengths to detect 81% ± 13% (70% ± 20%) of events and to estimate their length within 84% ± 3% (67% ± 12%) median accuracy. We evaluate the connection between temporal and spatial scales of peak flows and show that events with longer durations also affect larger extents. We finally discuss the implications for the design of satellite missions concerned with capturing floods across space.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"25 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143853188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Non-Sigmoidal-Curve-Dependent Dynamic Threshold Method Improves Precipitation Phase Partitioning in the Northern Hemisphere","authors":"Lina Liu, Liping Zhang, Qin Zhang, Gangsheng Wang, Zhiling Zhou, Xiao Li, Zhenyu Tang","doi":"10.1029/2024wr038636","DOIUrl":"https://doi.org/10.1029/2024wr038636","url":null,"abstract":"Given the significant impact of precipitation phase transitions on water and energy balances, accurate phase partitioning is essential for hydrological modeling. Many commonly used precipitation phase partitioning methods (PPMs) rely on sigmoidal curve assumptions to determine thresholds, leading to biased partitioning results. Here we developed a non-sigmoidal-curve-dependent dynamic threshold method (NSDT) to establish time-varying and spatially varying thresholds for classifying precipitation into rain, snow, and sleet in the Northern Hemisphere. The NSDT avoids curve-fitting errors by directly calculating thresholds from snowfall and rainfall frequency curves. In this method, relative humidity and elevation are the two most influential variables to precipitation phase, and single-threshold and dual-threshold strategies are employed separately across different relative humidity ranges. The results show that station thresholds derived from NSDT have marked spatial variability. Furthermore, the NSDT performs well and robustly, with accuracy exceeding 80% over the wet-bulb temperature range [−10°C, 10°C] at each elevation range, relative humidity subinterval, and sub-time period. The NSDT outperforms six commonly used PPMs, especially at high elevations. Regarding the wet-bulb temperature range of [−4°C, 4°C], NSDT exhibits accuracy improvements ranging from 1.0% to 11.8% (0.4%–14.5%) across all elevation (relative humidity) subintervals compared to other PPMs. Overall, the NSDT method developed herein improves precipitation phase partitioning, which is expected to enhance the simulation accuracy of land surface models and hydrological models and provide a theoretical basis for a more accurate understanding of hydrological processes.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"40 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Resolution Deep-Learning Surrogate Framework for Global Hydrological Models","authors":"B. Droppers, M. F. P. Bierkens, N. Wanders","doi":"10.1029/2024wr037736","DOIUrl":"https://doi.org/10.1029/2024wr037736","url":null,"abstract":"Global hydrological models are important decision support tools for policy making in today's water-scarce world as their process-based nature allows for worldwide water resources assessments under various climate-change and socio-economic scenarios. Although efforts are continuously being made to improve water resource assessments, global hydrological model computational demands have dramatically increased and calibrating them has proven difficult. To address these issues, deep-learning approaches have gained prominence in the hydrological community, in particular the development of deep-learning surrogates. Nevertheless, the development of deep-learning global hydrological model surrogates remains limited, as most surrogate frameworks only focus on natural water states and fluxes at a single spatial resolution. Therefore, we introduce a global hydrological model surrogate framework that integrates spatially distributed runoff routing, including lake outflow and reservoir operation, includes human activities, such as water abstractions, and can scale across spatial resolutions. To test our framework, we develop a deep-learning surrogate for the PCRaster Global Water Balance (PCR-GLOBWB) global hydrological model. Our surrogate performed well when compared to the model outputs, with a median Kling-Gupta Efficiency of 0.50, while predictions were at least an order of magnitude faster. Moreover, the multi-resolution surrogate performed similarly to several single-resolution surrogates, indicating limited trade-offs between the surrogate's broad spatial applicability and its performance. Model surrogates are a promising tool for the global hydrological modeling community, given their potential benefits in reducing computational demands and enhancing calibration. Accordingly, our framework provides an excellent foundation for the community to create their own multi-scale deep-learning global hydrological model surrogates.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"108 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841894","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":"Tidal Response of Groundwater in an Anisotropic Leaky Aquifer","authors":"Guoliang Wang, Chi-Yuen Wang, Yan Zhang, Jiangwei Zhang, Xiuyu Liang","doi":"10.1029/2024wr038851","DOIUrl":"https://doi.org/10.1029/2024wr038851","url":null,"abstract":"Groundwater tidal response analysis is a valuable tool for monitoring leakage in groundwater systems, yet the interpretation of this response has often been incomplete. Notably, the impact of anisotropic aquifer permeability on tidal response has not been addressed in existing models. This study presents an analytical model to examine the effect of anisotropy on the tidal response of an aquifer overlain by a semi-confined aquitard with finite storage. After verifying our model against previous models and numerical simulations, we fund: (a) At high vertical aquifer conductivity and aquitard leakage, the amplitude ratio of the tidal response is small, and the phase shift is positive, making our solution closely align with the existing leaky aquifer model. (b) As the vertical aquifer conductivity decreases, the amplitude ratio increases and the phase shift decreases and becomes negative at relatively low leakage, similar to that of a confined aquifer. (c) When the vertical aquifer conductivity is smaller relative to the horizontal one, the existing leaky aquifer model tends to underestimate the amplitude and overestimate the phase shift. (d) The aquitard storage has a significant effect on the tidal response of the aquifer when the aquitard leakage is large, but a negligible impact when the vertical aquifer conductivity is small. Applying our model to field data from four monitoring wells in the North China Plain, we find that when the shale content in the aquifer reaches 40.09%, our anisotropic model more effectively fits the observed phase shift compared to the existing leaky aquifer model.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"122 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841895","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}