Water Resources Research最新文献

{"title":"A Fluid Flow-Based Deep Learning (FFDL) Architecture for Subsurface Flow Systems With Application to Geologic CO2 Storage","authors":"Zhen Qin, Yingxiang Liu, Fangning Zheng, Behnam Jafarpour","doi":"10.1029/2024wr037953","DOIUrl":"https://doi.org/10.1029/2024wr037953","url":null,"abstract":"Prediction of the spatial-temporal dynamics of the fluid flow in complex subsurface systems, such as geologic <span data-altimg=\"/cms/asset/e8d9a12e-44a1-40cc-8d23-35b4ae308bd5/wrcr27625-math-0001.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr27625:wrcr27625-math-0001\" display=\"inline\" location=\"graphic/wrcr27625-math-0001.png\">\u0000<semantics>\u0000<mrow>\u0000<msub>\u0000<mtext>CO</mtext>\u0000<mn>2</mn>\u0000</msub>\u0000</mrow>\u0000${text{CO}}_{2}$</annotation>\u0000</semantics></math> storage, is typically performed using advanced numerical simulation methods that solve the underlying governing physical equations. However, numerical simulation is computationally demanding and can limit the implementation of standard field management workflows, such as model calibration and optimization. Standard deep learning models, such as RUNET, have recently been proposed to alleviate the computational burden of physics-based simulation models. Despite their powerful learning capabilities and computational appeal, deep learning models have important limitations, including lack of interpretability, extensive data needs, weak extrapolation capacity, and physical inconsistency that can affect their adoption in practical applications. We develop a Fluid Flow-based Deep Learning (FFDL) architecture for spatial-temporal prediction of important state variables in subsurface flow systems. The new architecture consists of a physics-based encoder to construct physically meaningful latent variables, and a residual-based processor to predict the evolution of the state variables. It uses physical operators that serve as nonlinear activation functions and imposes the general structure of the fluid flow equations to facilitate its training with data pertaining to the specific subsurface flow application of interest. A comprehensive investigation of FFDL, based on a field-scale geologic <span data-altimg=\"/cms/asset/f88073dd-2502-48d9-89f5-8e7b6f48b9ee/wrcr27625-math-0002.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr27625:wrcr27625-math-0002\" display=\"inline\" location=\"graphic/wrcr27625-math-0002.png\">\u0000<semantics>\u0000<mrow>\u0000<msub>\u0000<mtext>CO</mtext>\u0000<mn>2</mn>\u0000</msub>\u0000</mrow>\u0000${text{CO}}_{2}$</annotation>\u0000</semantics></math> storage model, is used to demonstrate the superior performance of FFDL compared to RUNET as a standard deep learning model. The results show that FFDL outperforms RUNET in terms of prediction accuracy, extrapolation power, and training data needs.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"50 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050828","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":"Unraveling the Distinct Roles of Snowmelt and Glacier-Melt on Agricultural Water Availability: A Novel Indicator and Its Application in a Glacierized Basin of China’s Arid Region","authors":"Hang Zha, Fan Zhang, Hongbo Zhang, Shizhen Tang, Longhui Zhang, Lun Luo","doi":"10.1029/2023wr036898","DOIUrl":"https://doi.org/10.1029/2023wr036898","url":null,"abstract":"Mountain runoff is a vital water source for irrigation in global arid regions. Investigating the roles of major mountain runoff components such as snowmelt and glacier-melt, on downstream irrigation water availability is important for understanding water resource security. Snowmelt and glacier-melt have different timing and availability. However, their potentially distinct impacts on water supplies for downstream irrigation have been seldom investigated previously. This study proposes a novel indicator, Irrigation Dependence on runoff Component (<i>IDC</i>), to assess the individual impact of different runoff components on irrigation water availability, considering water supply, water demand and their relationship. Applying <i>IDC</i> to the Yarkant River basin (YRB) in China’s arid region, mainly fed by mountain runoff from the northern Tibetan Plateau, reveals that, despite glacier-melt runoff being the primary contributor to total runoff in the YRB, irrigation water availability is generally more reliant on snowmelt runoff due to seasonal variations in the water supply/demand relationship. Further sensitivity tests under 48 climate scenarios indicate that <i>IDC</i> of glacier-melt runoff significantly increases under drier climates, while that of snowmelt runoff decreases as the climate warms, implying potentially increased importance of glacier-melt in future, especially under drier conditions and during the transition season. Additionally, the crucial role of anthropogenic factors, including changes in planting area and irrigation water use efficiency, in influencing irrigation water demand is highlighted for improved estimation. This study provides important implications on how cryosphere changes impact water resources management and an efficient indicator for further studies in glacierized arid basins globally.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"3 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143031248","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":"Machine Learning Prediction of Tritium-Helium Groundwater Ages in the Central Valley, California, USA","authors":"Abdullah Azhar, Indrasis Chakraborty, Ate Visser, Yang Liu, Jory Chapin Lerback, Erik Oerter","doi":"10.1029/2024wr038031","DOIUrl":"https://doi.org/10.1029/2024wr038031","url":null,"abstract":"Groundwater ages provides insight into recharge rates, flow velocities, and vulnerability to contaminants. The ability to predict groundwater ages based on more accessible parameters via Machine Learning (ML) would advance our ability to guide sustainable management of groundwater resources. In this study, ML models were trained and tested on a large data set of tritium concentrations &lt;span data-altimg=\"/cms/asset/2c84bf4c-65ef-408f-b284-a7eb7282f213/wrcr27674-math-0001.png\"&gt;&lt;/span&gt;&lt;mjx-container ctxtmenu_counter=\"50\" 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/wrcr27674-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 n equals 2410 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-children=\"1,3\" data-semantic-content=\"2\" data-semantic- data-semantic-parent=\"6\" data-semantic-role=\"equality\" data-semantic-type=\"relseq\"&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- data-semantic-operator=\"relseq,=\" data-semantic-parent=\"4\" data-semantic-role=\"equality\" data-semantic-type=\"relation\" rspace=\"5\" space=\"5\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&gt;&lt;mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"4\" data-semantic-role=\"integer\" data-semantic-type=\"number\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mn&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: 0.056em;\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mo&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:wrcr27674:wrcr27674-math-0001\" display=\"inline\" location=\"graphic/wrcr27674-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"&gt;&lt;semantics&gt;&lt;mrow data-semantic-=\"\" data-semantic-children=\"4\" data-semantic-content=\"0,5\" data-semantic-role=\"leftright\" data-semantic-speech=\"left parenthesis n equals 2410 right parenthesis\" data-semantic-type=\"fenced\"&gt;&lt;mo data-semantic-=\"\" data-semantic-operator=\"fenced\" data-semantic-parent=\"6\" data-semantic-role=\"open\" data-semantic-type=\"fence\" stretchy=\"false\"&gt;(&lt;/mo&gt;&lt;mrow data-semantic-=\"\" data-semantic-children=\"1,3\" data-semantic-content=\"2\" data-semantic-parent=\"6\" data-semantic-role=\"equality\" data-semantic-type=\"relseq\"&gt;&lt;mi da","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"8 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143031347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Control of Groundwater-Lake Interaction Zone Structure on Spatial Variability of Lacustrine Groundwater Discharge in Oxbow Lake","authors":"Xiaoliang Sun, Yao Du, Jiawen Xu, Hao Tian, Yamin Deng, Yiqun Gan, Yanxin Wang","doi":"10.1029/2024wr039334","DOIUrl":"https://doi.org/10.1029/2024wr039334","url":null,"abstract":"Lacustrine groundwater discharge (LGD) is an important water and nutrient source for lakes. Despite its importance, high-resolution quantifying the spatial variability of LGD remains challenging. Particularly, little studies have explored the impact of the interaction zone structure between lakes and aquifers on this variability. Present study presents a high-resolution quantitative estimation of LGD spatial patterns in an oxbow lake by combining thermal remote sensing with a ²²²Rn mass balance model. The vertical distribution characteristics of various parameters including lake water temperature, ²²²Rn concentration, electrical conductivity, and <i>δ</i>¹⁸O were examined to elucidate the influence of groundwater on the distribution pattern of lake surface temperature (LST). Regression equations were formulated to correlate LST with lake water ²²²Rn concentration across different water depth zones, enabling the inverse calculation of the ²²²Rn concentration in the water of the entire lake. Utilizing a ²²²Rn mass balance model across all grid points, the LGD rate was determined to vary from 0 to 330.96 mm/d, with an average of 55.02 ± 19.61 mm/d. In shallow water zones, the accumulation of lacustrine sediments has resulted in isolation from confined aquifers, causing LGD to primarily occur as springs in nearshore lake areas. Conversely, the direct connection between the deepwater zone of the lake and the water-rich confined aquifer has resulted in a higher LGD rate in the lake interior. Present study not only offers a novel approach for quantifying the spatial patterns of LGD but also provides valuable insights for LGD studies conducted in lakes globally.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"28 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143026445","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":"Physics-Guided Deep Learning Model for Daily Groundwater Table Maps Estimation Using Passive Surface-Wave Dispersion","authors":"José Cunha Teixeira, Ludovic Bodet, Agnès Rivière, Amélie Hallier, Alexandrine Gesret, Marine Dangeard, Amine Dhemaied, Joséphine Boisson Gaboriau","doi":"10.1029/2024wr037706","DOIUrl":"https://doi.org/10.1029/2024wr037706","url":null,"abstract":"Monitoring groundwater tables (GWTs) remains challenging due to limited spatial and temporal observations. This study introduces an innovative approach combining an artificial neural network, specifically a multilayer perceptron (MLP), with continuous passive Multichannel Analysis of Surface Waves (passive-MASW) to construct GWT depth maps. The geologically well-constrained study site includes two piezometers and a permanent 2D geophone array recording train-induced surface waves. At each point of the array, dispersion curves (DCs), displaying Rayleigh-wave phase velocities <span data-altimg=\"/cms/asset/f0d9b693-7331-4aa5-a654-78f5ac9fba29/wrcr27656-math-0001.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr27656:wrcr27656-math-0001\" display=\"inline\" location=\"graphic/wrcr27656-math-0001.png\">\u0000<semantics>\u0000<mrow>\u0000<mfenced close=\")\" open=\"(\" separators=\"\">\u0000<msub>\u0000<mi>V</mi>\u0000<mi>R</mi>\u0000</msub>\u0000</mfenced>\u0000</mrow>\u0000$left({V}_{R}right)$</annotation>\u0000</semantics></math> over a frequency range of 5–50 Hz, were measured daily from December 2022 to September 2023, and latter resampled over wavelengths from 4 to 15 m, to focus on the expected GWT depths (1–5 m). Nine months of daily <span data-altimg=\"/cms/asset/0f52fe16-966b-40c5-b835-263b40d7b16f/wrcr27656-math-0002.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr27656:wrcr27656-math-0002\" display=\"inline\" location=\"graphic/wrcr27656-math-0002.png\">\u0000<semantics>\u0000<mrow>\u0000<msub>\u0000<mi>V</mi>\u0000<mi>R</mi>\u0000</msub>\u0000</mrow>\u0000${V}_{R}$</annotation>\u0000</semantics></math> data near one piezometer, spanning both low and high water periods, were used to train the MLP model. GWT depths were then estimated across the geophone array, producing daily GWT maps. The model's performance was evaluated by comparing inferred GWT depths with observed measurements at the second piezometer. Results show a coefficient of determination (R²) of 80% at the training piezometer and of 68% at the test piezometer, and a remarkably low root-mean-square error (RMSE) of 0.03 m at both locations. These findings highlight the potential of deep learning to estimate GWT maps from seismic data with spatially limited piezometric information, offering a practical and efficient solution for monitoring groundwater dynamics across large spatial extents.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"18 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991576","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 Cluster-Based Data Assimilation Approach to Generate New Daily Gridded Time Series Precipitation Data in the Himalayan River Basins","authors":"Japjeet Singh, Vishal Singh, Chandra Shekhar Prasad Ojha","doi":"10.1029/2024wr037324","DOIUrl":"https://doi.org/10.1029/2024wr037324","url":null,"abstract":"Recent studies show variations in precipitation-gridded data set accuracy with changing geographical parameters. Ensemble precipitation products, combining diverse data sets, offer global-scale effectiveness, but applying them to regional studies, particularly in small to medium-sized sub-basins, presents challenges in addressing precipitation dependence on specific geographical conditions. Here, we present a newly developed Clusters Based-Minimum Error approach to assimilate different open-source gridded precipitation data sets for forming an accurate precipitation product over small to medium-sized hilly terrain basins, with limited precipitation gauges. This methodology generates the New Gridded Precipitation Data Set (NGPD) from 1991 to 2022 for the Upper Ganga Basin in the western Himalaya, covering approximately 22,292 km². The study utilizes nine open-source gridded precipitation data sets and 11 observed precipitation gauges, NGPD is evaluated through station-wise, grid-wise, and elevation-wise analyses using statistical parameters, quantile-quantile plots, daily coefficient of determination, Rainfall Anomaly Index, and seasonality/precipitation pattern analyses. Results demonstrate the superior performance of NGPD compared to other gridded precipitation sources across various evaluation metrics. Nash-Sutcliffe Efficiency (NSE), Coefficient of determination (<i>R</i>²), and Root mean squared error (RMSE) range from 0.67 to 0.90, 0.73–0.93, and 4.4–10.69 mm/day, respectively, w.r.t 11 observed precipitation gauges. NGPD outperforms the widely used IMD data set in India, exhibiting a monthly scale improvement of 18.47% and 17.7% in average NSE and <i>R</i>² values, respectively. Additionally, the methodology is also successfully applied to the Tamor Basin in Nepal, proving its reliability for various Himalayan regions. This approach reliably creates accurate gridded precipitation data sets for hilly sub-basins, especially in Himalayan regions with limited station data.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"122 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990590","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":"Effects of Rock Fragment Cover on the Sediment Transport Capacity of Overland Flow","authors":"Lixia Dong, Suhua Fu, Baoyuan Liu","doi":"10.1029/2024wr038621","DOIUrl":"https://doi.org/10.1029/2024wr038621","url":null,"abstract":"The reliable prediction of sediment transport capacity (<i>Tc</i>) is essential for soil erosion models. Although rock fragments are a common surface cover type, quantitative studies on their relationship with <i>Tc</i> are limited. <i>Tc</i> typically follows a power function with slope gradient (<i>S</i>) and flow discharge (<i>q</i>) under bare flumes, but varying exponents complicate practical application. This study aims to investigate the effect of rock fragment cover on <i>Tc</i>, explore the interactive effects of <i>S</i>, <i>q</i>, and cover on <i>Tc</i>, and ultimately develop a universal <i>Tc</i> prediction equation and assess its feasibility for different scenarios. Flume experiments on <i>Tc</i> with rock fragment cover have been conducted, and many existing <i>Tc</i> prediction equations have been reviewed. The results revealed that the effects of <i>S</i> and <i>q</i> on the relationship between rock fragment cover and <i>Tc</i> were minor and that the impact of rock fragment cover on the relationships of <i>S</i> and <i>q</i> with <i>Tc</i> was also not significant. Consequently, a new universal equation for <i>Tc</i> incorporating cover was developed. This equation featured fixed exponents of 1.66 for <i>S</i> and 1.22 for <i>q</i> and was applicable across various slope gradient, flow discharge, coverage and cover type conditions. Moreover, the impact of rock fragment cover on <i>Tc</i> reduction was significantly less than those of litter cover and stem basal cover (<i>P</i> &lt; 0.05). Therefore, the role of rock fragments should be considered separately in soil erosion models. These findings could significantly advance the practical application of the <i>Tc</i> prediction equation.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"15 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991580","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":"Physics-Informed Estimation of Tidal and Subtidal Flow Fields From ADCP Repeat Transect Data","authors":"H. Jongbloed, B. Vermeulen, A. J. F. Hoitink","doi":"10.1029/2023wr036038","DOIUrl":"https://doi.org/10.1029/2023wr036038","url":null,"abstract":"Acoustic Doppler current profilers (ADCPs) are a global standard in observing flow fields in rivers, estuaries and the coastal ocean. To date, it remains a labor intensive challenge to isolate mean flow fields governed by river discharge, tides and atmospheric forcing on the one hand, from small-scale turbulence, positioning imprecision, Doppler noise and erroneous backscatter, on the other hand. Here, we introduce a generic, new method of combining raw shipborne ADCP transect data with continuity and smoothness constraints to obtain better estimates of turbulence-averaged three-dimensional flow velocities in any type of open water body. The physical constraints are enforced with variable relative importance via generalized Tikhonov regularization. We demonstrate that in complex estuarine flow, this procedure allows for more reliable estimates of tidal amplitudes, phases and their gradients than what is possible with a purely data-based approach, by testing the method's generalization capabilities and robustness to turbulence and measurement noise on a data set retrieved at a tidal channel junction. The increased adherence to mass conservation and robustness to noise of various kinds allows for more reliable and verifiable estimates of Reynolds-averaged flow components, and subsequently, of terms in the Navier-Stokes equations.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"122 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991583","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":"Predicting Transient Anomalous Transport in Two-Dimensional Discrete Fracture Networks With Dead-End Fractures","authors":"HongGuang Sun, Dawei Lei, Yong Zhang, Jiazhong Qian, Xiangnan Yu","doi":"10.1029/2024wr038731","DOIUrl":"https://doi.org/10.1029/2024wr038731","url":null,"abstract":"Pollutant transport in discrete fracture networks (DFNs) exhibits complex dynamics that challenge reliable model predictions, even with detailed fracture data. To address this issue, this study derives an upscaled integral-differential equation to predict transient anomalous diffusion in two-dimensional (2D) DFNs. The model includes both transmissive and dead-end fractures (DEFs), where stagnant water zones in DEFs cause non-uniform flow and transient sub-diffusive transport, as shown by both literature and DFN flow and transport simulations using COMSOL. The upscaled model's main parameters are quantitatively linked to fracture properties, especially the probability density function of DEF lengths. Numerical experiments show the model's accuracy in predicting the full-term evolution of conservative tracers in 2D DFNs with power-law distributed fracture lengths and two orientation sets. Field applications indicate that while model parameters for transient sub-diffusion can be predicted from observed DFN distributions, predicting parameters controlling solute displacement in transmissive fractures requires additional field work, such as tracer tests. Parameter sensitivity analysis further correlates late-time solute transport dynamics with fracture properties, such as fracture density and average length. Potential extensions of the upscaled model are also discussed. This study, therefore, proves that transient anomalous transport in 2D DFNs with DEFs can be at least partially predicted, offering an initial step toward improving model predictions for pollutant transport in real-world fractured aquifer systems.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"5 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989104","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":"Changes in Snow Drought and the Impacts on Streamflow Across Northern Catchments","authors":"Juntai Han, Yuting Yang, Yuhan Guo, Changming Li, Ziwei Liu, Zhuoyi Tu, Haiyang Xi","doi":"10.1029/2024wr037492","DOIUrl":"https://doi.org/10.1029/2024wr037492","url":null,"abstract":"Snow drought, characterized by an anomalous reduction in snowpack, exerts profound hydrological and socioeconomic impacts in cold regions. Despite its significance, the influence of diverse snow drought types, including warm, dry, and warm-and-dry variants, on streamflow remains inadequately understood. Here we present the first hemispheric-scale, observation-based assessment of snow drought patterns and the impacts on seasonal and annual streamflow (&lt;i&gt;Q&lt;/i&gt;) across 3049 northern catchments over 1950–2020. Our findings reveal that catchments with a lower mean annual snowfall fraction (&lt;span data-altimg=\"/cms/asset/dd346584-775c-4480-a2ef-ad8e149b6fe0/wrcr27662-math-0001.png\"&gt;&lt;/span&gt;&lt;mjx-container ctxtmenu_counter=\"98\" 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/wrcr27662-math-0001.png\"&gt;&lt;mjx-semantics&gt;&lt;mjx-mrow&gt;&lt;mjx-mover data-semantic-children=\"2,3\" data-semantic- data-semantic-role=\"latinletter\" data-semantic-speech=\"f Subscript normal s Baseline overbar\" data-semantic-type=\"overscore\"&gt;&lt;mjx-over style=\"padding-bottom: 0.105em; margin-bottom: -0.544em;\"&gt;&lt;mjx-mo data-semantic- data-semantic-parent=\"4\" data-semantic-role=\"overaccent\" data-semantic-type=\"punctuation\"&gt;&lt;mjx-stretchy-h style=\"width: 0.852em;\"&gt;&lt;mjx-ext&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-ext&gt;&lt;/mjx-stretchy-h&gt;&lt;/mjx-mo&gt;&lt;/mjx-over&gt;&lt;mjx-base&gt;&lt;mjx-msub data-semantic-children=\"0,1\" data-semantic- data-semantic-parent=\"4\" data-semantic-role=\"latinletter\" 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=\"latinletter\" 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; margin-left: -0.06em;\"&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\" size=\"s\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;/mjx-script&gt;&lt;/mjx-msub&gt;&lt;/mjx-base&gt;&lt;/mjx-mover&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:wrcr27662:wrcr27662-math-0001\" display=\"inline\" location=\"graphic/wrcr27662-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"&gt;&lt;semantics&gt;&lt;mrow&gt;&lt;mover accent=\"true\" data-semantic-=\"\" data-semantic-children=\"2,3\" data-semantic-role=\"latinletter\" data-semantic-speech=\"f Subscript normal s Baseline overbar\" data-semantic-type=\"overscore\"&gt;&lt;msub data-semantic-=\"\" data-semantic-children=\"0,1\" data-semantic-parent=\"4\" data-semantic-role=\"latinletter\" data-semantic-type=\"subscript\"&gt;&lt;mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-parent=\"2\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;f&lt;/mi&gt;&lt;mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" d","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"11 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989105","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}