Water Resources Research最新文献

{"title":"Rapid and Automatic UAV Detection of River Embankment Piping","authors":"Quntao Duan, Baili Chen, Lihui Luo","doi":"10.1029/2024wr038931","DOIUrl":"https://doi.org/10.1029/2024wr038931","url":null,"abstract":"With flooding events expected to increase in both intensity and frequency in the future due to climate change, ensuring the safety of river embankments is vital to withstand flood disasters. Piping is one of the most harmful river embankment hazards in the flood season, and recent advances in unmanned aerial vehicles (UAVs) and deep learning-based object detection have enabled efficient and automated hazard detection. In this study, a novel approach that integrates a UAV with deep learning-based object detection and edge computing was proposed for rapid and automatic piping detection. First, a total of 104 field simulation experiments were conducted across 12 different sites in flood-prone areas to fill gaps in the high-quality data set, and the UAV thermal infrared and visible data sets of river embankment piping were produced, including various times (forenoon, afternoon, and night), weather conditions (clear-sky, cloudy, and rainy), locations (bare land, paddy, grassland, and pond) and flight altitudes (10, 20, and 30 m). Second, the deep learning-based object detection model was selected and trained on the thermal infrared and visible data sets. The well-trained infrared and visible models have detection precisions of 92.7% and 70.4%, respectively, with recalls of 84.9% and 69.7%. Furthermore, both models exhibited great resistance to interference from several types of aquatic vegetation and could effectively detect piping on rainy days. The integration of a UAV and edge computing enabled real-time detection of piping. The proposed method enhances hazard detection efficiency, contributing to intelligent emergency embankment management.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"60 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143071607","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 Comprehensive Framework for Evaluation of Skeletonization Impacts on Urban Drainage Network Model Simulations","authors":"Yiran Ji, Feifei Zheng, Yongfei Yang, Jia Shuai, Yuan Huang, Zoran Kapelan, Dragan Savic","doi":"10.1029/2024wr038394","DOIUrl":"https://doi.org/10.1029/2024wr038394","url":null,"abstract":"Urban drainage network models (UDNMs) have been widely used to facilitate flood management. Typically, a UDNM is developed using data from Geographic Information Systems (GIS), and hence it consists of many short pipes and connection nodes or manholes. To improve modeling efficiency, a GIS-based model is generally skeletonized by removing many elements. However, there has been surprisingly a lack of knowledge on to what extent such skeletonization can affect the model's simulation accuracy, resulting in uncertainty in flood risk estimation. This paper makes the first attempt to quantitatively evaluate multidimensional impacts of different skeletonization levels on hydraulic properties of UDNMs. This goal is achieved by a new evaluation framework comprising of eight existing and new metrics that make use of hydrographs, main pipe hydraulics and flood distribution properties. A real-life UDNM is used to illustrate the new framework under various rainfall conditions and different skeletonization levels. The new framework is also used to compare the performance of two compensation methods in mitigating impacts caused by model skeletonization. Results obtained show that: (a) model skeletonization can significantly affect the magnitude of peak flow at the outfall, with a maximum overestimation of up to 20%, (b) hydraulics in main pipes can also be affected by model skeletonization with the maximum flow increasing up to 35%, and (c) model skeletonization may significantly alter the flood distribution properties which has been largely ignored in past studies. These findings provide guidance for UDNM skeletonization, where their associated impacts should be aware in engineering practice.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"36 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056748","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":"Rayleigh Invariance Allows the Estimation of Effective CO2 Fluxes Due To Convective Dissolution Into Water-Filled Fractures","authors":"Leon Keim, Holger Class","doi":"10.1029/2024wr037778","DOIUrl":"https://doi.org/10.1029/2024wr037778","url":null,"abstract":"Convective dissolution of <span data-altimg=\"/cms/asset/c7176014-d4da-4c15-bb81-9e762544bdf7/wrcr27617-math-0001.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr27617:wrcr27617-math-0001\" display=\"inline\" location=\"graphic/wrcr27617-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> is a well-known mechanism in geological storage of <span data-altimg=\"/cms/asset/38090b80-5d2f-4965-9290-82d974737d9e/wrcr27617-math-0002.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr27617:wrcr27617-math-0002\" display=\"inline\" location=\"graphic/wrcr27617-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>. It is triggered by gravitational instability which leads to the onset of free convection. The phenomenon is well studied in porous media, such as saline aquifers, and the literature provides substantial evidence that onset times and effective flux rates can be estimated based on a characterization of instabilities that uses the Darcy velocity. This work extends the study of convective dissolution to open water-filled fractures, where non-Darcy regimes govern the induced flow processes. Numerical simulations using a Navier-Stokes model with fluid density dependent on dissolved <span data-altimg=\"/cms/asset/1d0335bf-2d72-4fde-9056-8b7b0c5fd176/wrcr27617-math-0003.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr27617:wrcr27617-math-0003\" display=\"inline\" location=\"graphic/wrcr27617-math-0003.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> concentration were used to compute scenario-specific results for effective <span data-altimg=\"/cms/asset/8616990a-af88-4e8e-8afc-df5a3360a6c3/wrcr27617-math-0004.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr27617:wrcr27617-math-0004\" display=\"inline\" location=\"graphic/wrcr27617-math-0004.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> entry rates into an idealized fracture with varying aperture, temperature, and <span data-altimg=\"/cms/asset/7b7cb324-33c6-42d0-b082-bbfbb4f703c8/wrcr27617-math-0005.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr27617:wrcr27617-math-0005\" display=\"inline\" location=\"graphic/wrcr27617-math-0005.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> concentration at the gas-water interface. The results were analyzed in terms of dimensionless quantities. They revealed a Rayleigh invariance of the effective <span data-altimg=\"/cms/asset/1f946dde-1940-4185-b064-938bb6eb46cb/wrcr27617-math-0006.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr27617:wrcr27617-math-0006\" display=\"inline\" location=\"graphic/wrcr27617-math-0006.png\">\u0000<semantics>\u0000<mrow>\u0000<msub>\u0000<mt","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"1 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056641","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 Generalized Framework to Describe Unimodal and Bimodal Soil Hydraulic Properties Over Full Water Saturation Range","authors":"Yunquan Wang, Rui Ma, Harry Vereecken","doi":"10.1029/2024wr038450","DOIUrl":"https://doi.org/10.1029/2024wr038450","url":null,"abstract":"Soil hydraulic properties (SHPs) are impacted by various mechanisms such as soil structure, capillarity, and adsorption forces, often showing a bimodal shape. Developing soil hydraulic models (SHMs) that describe SHPs over the entire saturation range often involves balancing the representation of multiple processes while minimizing the number of free-fitted parameters. Existing SHMs rarely capture bimodal SHPs across the full moisture range or introduce a higher number of free-fitted parameters. In this study, we propose a novel framework to describe SHPs over the entire moisture range, accounting for the effects of soil structure, capillarity, adsorption forces, and vapor diffusion. In its four free-fitted parameters form, the proposed models can capture unimodal soil water retention curves and bimodal hydraulic conductivity curves (HCC). This model is well-suited for situations where small changes in water content near saturation are no longer detectable via measured SWRC, yet soil structure still causes a sharp decline in HCC near saturation. With one additional free-fitted parameter, the proposed models can capture both bimodal SWRC and HCC. Testing with 355 and 52 soil samples from two public datasets demonstrated that the proposed models performed exceptionally well in describing SHPs across the entire moisture range. The reported lowest root-mean-square error values were 0.005 and 0.009 cm³ cm⁻³ for fitting SWRCs, and 0.465 and 0.666 for predicting HCCs, respectively. Due to the minimal introduction of free-fitted parameters, the proposed framework showed significant application potential.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"36 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056648","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 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}