Water Resources Research最新文献

Seasonal and Species‐Level Water‐Use Strategies and Groundwater Dependence in Dryland Riparian Woodlands During Extreme Drought 极端干旱期间旱地河岸林地的季节和物种水平用水策略及地下水依赖性

Water Resources Research Pub Date : 2024-04-01 DOI: 10.1029/2023wr035928

Jared Williams, John C. Stella, M. B. Singer, Adam M. Lambert, Steven L. Voelker, John E. Drake, Jonathan M. Friedman, L. Pelletier, Li Kui, Dar A. Roberts

{"title":"Seasonal and Species‐Level Water‐Use Strategies and Groundwater Dependence in Dryland Riparian Woodlands During Extreme Drought","authors":"Jared Williams, John C. Stella, M. B. Singer, Adam M. Lambert, Steven L. Voelker, John E. Drake, Jonathan M. Friedman, L. Pelletier, Li Kui, Dar A. Roberts","doi":"10.1029/2023wr035928","DOIUrl":"https://doi.org/10.1029/2023wr035928","url":null,"abstract":"Drought‐induced groundwater decline and warming associated with climate change are primary threats to dryland riparian woodlands. We used the extreme 2012–2019 drought in southern California as a natural experiment to assess how differences in water‐use strategies and groundwater dependence may influence the drought susceptibility of dryland riparian tree species with overlapping distributions. We analyzed tree‐ring stable carbon and oxygen isotopes collected from two cottonwood species (Populus trichocarpa and P. fremontii) along the semi‐arid Santa Clara River. We also modeled tree source water δ18O composition to compare with observed source water δ18O within the floodplain to infer patterns of groundwater reliance. Our results suggest that both species functioned as facultative phreatophytes that used shallow soil moisture when available but ultimately relied on groundwater to maintain physiological function during drought. We also observed apparent species differences in water‐use strategies and groundwater dependence related to their regional distributions. P. fremontii was constrained to more arid river segments and ostensibly used a greater proportion of groundwater to satisfy higher evaporative demand. P. fremontii maintained ∆13C at pre‐drought levels up until the peak of the drought, when trees experienced a precipitous decline in ∆13C. This response pattern suggests that trees prioritized maintaining photosynthetic processes over hydraulic safety, until a critical point. In contrast, P. trichocarpa showed a more gradual and sustained reduction in ∆13C, indicating that drought conditions induced stomatal closure and higher water use efficiency. This strategy may confer drought avoidance for P. trichocarpa while increasing its susceptibility to anticipated climate warming.","PeriodicalId":507642,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140785133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Anomalous Pressure Diffusion and Deformation in Two‐ and Three‐Dimensional Heterogeneous Fractured Media 二维和三维异质断裂介质中的异常压力扩散和变形

Water Resources Research Pub Date : 2024-04-01 DOI: 10.1029/2023wr036529

Sandro Andrés, M. Dentz, Luis Cueto‐Felgueroso

{"title":"Anomalous Pressure Diffusion and Deformation in Two‐ and Three‐Dimensional Heterogeneous Fractured Media","authors":"Sandro Andrés, M. Dentz, Luis Cueto‐Felgueroso","doi":"10.1029/2023wr036529","DOIUrl":"https://doi.org/10.1029/2023wr036529","url":null,"abstract":"In fractured and stress‐sensitive reservoirs and aquifers, hydromechanical coupling is important, in connection with their heat and solute transport properties, and because the fluid production or extraction leads to land subsidence and potentially to induced seismicity. Classical dual‐porosity poroelasticity (DPP) models cannot upscale pressure diffusion and deformation in fractured porous media, which are characterized by anomalous behaviors that manifest in strong tailing in the temporal evolution of flow rate and subsidence. We study these behaviors using detailed numerical simulations of fluid production in naturally fractured formations characterized by multi‐Gaussian distributions of the matrix permeability. We find that the tailing behaviors depend on the permeability contrast between fracture and matrix, on the permeability distribution in the matrix, and on the correlation length. We use a non–equilibrium, multi‐porosity model to quantify the coupled behaviors of anomalous pressure diffusion, fluid flow and deformation. The model is parameterized by medium and fluid properties, which set the characteristic pressure diffusion time scales. It allows to identify the emerging scaling regimes and scaling behaviors of flow rate and subsidence. We propose a model implementation that captures the full anomalous evolution of flow rates and displacements observed in the detailed numerical simulations in terms of the permeability distribution and matrix length scales. The presented results shed new light on the controls of medium heterogeneity and geometry on pressure diffusion, fluid production and subsidence in highly heterogeneous fractured media.","PeriodicalId":507642,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140758581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated Input Variable Selection for Analog Methods Using Genetic Algorithms 利用遗传算法自动选择模拟方法的输入变量

Water Resources Research Pub Date : 2024-04-01 DOI: 10.1029/2023wr035715

P. Horton, O. Martius, S. Grimm

{"title":"Automated Input Variable Selection for Analog Methods Using Genetic Algorithms","authors":"P. Horton, O. Martius, S. Grimm","doi":"10.1029/2023wr035715","DOIUrl":"https://doi.org/10.1029/2023wr035715","url":null,"abstract":"Analog methods (AMs) have long been used for precipitation prediction and climate studies. However, they rely on manual selections of parameters, such as predictor variables and analogy criteria. Previous work showed the potential of genetic algorithms (GAs) to optimize most of the AM parameters. This research goes one step further and investigates the potential of GAs for automating the selection of the input variables and the analogy criteria (distance metric between two data fields) in AMs. Our study focuses on the prediction of daily precipitation in central Europe, specifically Switzerland, as a representative case. Comparative analysis against established methods demonstrates the superiority of GA‐optimized AMs in terms of predictive accuracy. The selected input variables exhibit strong associations with key meteorological processes that influence the generation of precipitation. Further, we identify a new analogy criterion inspired by the Teweles‐Wobus criterion, which consistently performs better than other Euclidean distances and could be used in classic AMs. In contrast to conventional stepwise selection approaches, GA‐optimized AMs display a preference for a flatter structure characterized by a single level of analogy and an increased number of variables. Overall, our study demonstrates the successful application of GAs in automating input variable selection for AMs, with potential implications for application in diverse locations and data exploration to predict alternative predictands. In a broader context, GAs could be used to perform input variable selection in other data‐driven methods, opening perspectives for a broad range of applications.","PeriodicalId":507642,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140780611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Topography‐Based Particle Image Velocimetry of Braided Channel Initiation 基于地形的粒子图像测速仪测量编织水道的起始点

Water Resources Research Pub Date : 2024-04-01 DOI: 10.1029/2023wr035229

Youwei Wang, Ajay B. Limaye, A. Chadwick

{"title":"Topography‐Based Particle Image Velocimetry of Braided Channel Initiation","authors":"Youwei Wang, Ajay B. Limaye, A. Chadwick","doi":"10.1029/2023wr035229","DOIUrl":"https://doi.org/10.1029/2023wr035229","url":null,"abstract":"River channels shape landscapes through gradual migration and abrupt avulsion. Measuring the motion of braided rivers, which have multiple channel threads, is particularly challenging, limiting predictions for landscape evolution and fluvial architecture. To address this challenge, we extended the capabilities of image‐based particle image velocimetry (PIV)—a technique for tracking channel threads in images of the surface—by adapting it to analyze topographic change. We applied this method in a laboratory experiment where a straight channel set in non‐cohesive sediment evolved into a braided channel under constant water and sediment fluxes. Topography‐based PIV successfully tracked the motion of channel threads if displacements between observations were less than the channel‐thread width, consistent with earlier results from image‐based PIV. We filtered spurious migration vectors with magnitudes less than the elevation grid spacing, or with high uncertainties in magnitude and/or direction. During braided channel initiation, migration rates varied with the channel planform development, showing an increase as incipient meanders developed, a decrease during the transitional braiding phase, and consistently low values during the established braiding phase. In this experimental setup, migration rates varied quasi‐periodically along stream at the half scale of initial meander bends. Lateral migration with respect to the mean flow direction was much more pronounced than streamwise migration, accounting for approximately 80% of all detected motion. Results demonstrate that topography‐based PIV has the potential to advance predictions for bank erosion and landscape evolution in natural braided rivers as well as bar preservation and stratigraphic architecture in geological records.","PeriodicalId":507642,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140776418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved 30‐m Evapotranspiration Estimates Over 145 Eddy Covariance Sites in the Contiguous United States: The Role of ECOSTRESS, Harmonized Landsat Sentinel‐2 Imagery, Climate Reanalysis, and Deep Neural Network Postprocessing 美国毗连地区 145 个涡度协方差站点 30 米蒸散量的改进估算：ECOSTRESS、统一陆地卫星哨兵-2 图像、气候再分析和深度神经网络后处理的作用

Water Resources Research Pub Date : 2024-04-01 DOI: 10.1029/2023wr036313

Taufiq Rashid, D. Tian

{"title":"Improved 30‐m Evapotranspiration Estimates Over 145 Eddy Covariance Sites in the Contiguous United States: The Role of ECOSTRESS, Harmonized Landsat Sentinel‐2 Imagery, Climate Reanalysis, and Deep Neural Network Postprocessing","authors":"Taufiq Rashid, D. Tian","doi":"10.1029/2023wr036313","DOIUrl":"https://doi.org/10.1029/2023wr036313","url":null,"abstract":"This study developed and evaluated 30‐m daily evapotranspiration (ET) estimates using the Priestley‐Taylor Jet Propulsion Laboratory (PT‐JPL) model with ECOSTRESS, Moderate MODIS, harmonized Landsat Sentinel‐2 (HLS) imagery, ERA5‐Land reanalysis, and eddy covariance measurements. The new daily 30‐m ET showed significantly improved performance (overall, r = 0.8, RMSE = 1.736, KGE = 0.466) at 145 EC sites over contiguous United States compared to the current 70‐m ECOSTRESS ET (overall, r = 0.485, RMSE = 4.696, KGE = −0.841). A deep neural network postprocessing model trained with ET measurements from EC sites further improved the performance on test sites that were not used for model training (overall, r = 0.842, RMSE = 0.88, KGE = 0.792). The 30‐m ET estimation biases were significantly related to the biases in the upwelling longwave (RUL) and downwelling shortwave radiation (RDS) inputs, with ET estimates driven by MODIS radiation showing higher biases compared to those driven by ERA5‐Land radiation. The error diagnosis using random forest indicates that ET biases tend to be larger under higher ET estimates, and RUL and RDS were the primary contributors to the high bias at the higher ET ranges, with partial dependence plots revealing that the estimation biases tend to be higher under more humid environment, denser vegetation covers, and high net radiation conditions. In conclusion, higher spatial resolution satellite imagery of vegetation characteristics and higher temporal resolution radiation data, combined with continent‐wide EC measurements and deep learning, provided substantial added value for improving ET estimations at the field scale (30‐m).","PeriodicalId":507642,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140794651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Remote Sensing of Multitemporal Functional Lake‐To‐Channel Connectivity and Implications for Water Movement Through the Mackenzie River Delta, Canada 加拿大麦肯齐河三角洲多时功能性湖泊-河道连通性遥感及其对水流运动的影响

Water Resources Research Pub Date : 2024-04-01 DOI: 10.1029/2023wr036614

W. Dolan, T. Pavelsky, A. Piliouras

{"title":"Remote Sensing of Multitemporal Functional Lake‐To‐Channel Connectivity and Implications for Water Movement Through the Mackenzie River Delta, Canada","authors":"W. Dolan, T. Pavelsky, A. Piliouras","doi":"10.1029/2023wr036614","DOIUrl":"https://doi.org/10.1029/2023wr036614","url":null,"abstract":"The Mackenzie River Delta in Canada is a mediator of hydrological transport between the expansive Mackenzie River watershed and the Beaufort Sea. Within the delta, lakes frequently act as water and sediment traps, limiting or delaying the movement of material to the coastal ocean. The degree to which this filtering takes place depends on the ease with which sediment‐laden water is transported from distributary channels into deltaic lakes, referred to as functional lake‐to‐channel connectivity, which varies both spatially and temporally. Tracking of connectivity has previously been limited to either small regions of the delta or has focused on a snapshot of connectivity at a single instance in time. Here we describe an algorithm that uses Landsat imagery to track summertime functional lake‐to‐channel connectivity of 10,362 lakes between 1984 and 2022 on an image‐by‐image basis. We calculate a total average connected lake area of 1400.7 km2 during the 2 weeks after peak discharge, 763.6 km2 higher than previous estimates, suggesting a larger influence of connected lakes on water movement through the delta than previously estimated. We also identify water level thresholds that lead to the initiation of high sediment river water movement into 5,989 lakes (908 lakes with uncertainty ≤±0.5 m), and identify an additional 2899 lakes whose connectivity does not vary at all. As the Arctic hydrological cycle responds to climate change, this work lays a foundation for tracking the movement of water, and the matter it carries, from the Mackenzie River watershed to the Beaufort Sea.","PeriodicalId":507642,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140782380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How to Choose Suitable Physics‐Based Models Without Tuning and System Identification for Model‐Predictive Control of Open Water Channels? 如何为明渠的模型预测控制选择合适的物理模型，而无需进行调整和系统识别？

Water Resources Research Pub Date : 2024-04-01 DOI: 10.1029/2023wr035687

K. Horváth, B. V. van Esch, I. Pothof

{"title":"How to Choose Suitable Physics‐Based Models Without Tuning and System Identification for Model‐Predictive Control of Open Water Channels?","authors":"K. Horváth, B. V. van Esch, I. Pothof","doi":"10.1029/2023wr035687","DOIUrl":"https://doi.org/10.1029/2023wr035687","url":null,"abstract":"Model predictive control (MPC) is used to manage water systems, and its performance depends on the (internal or control‐oriented) model it is based on. Several models for the hydraulics of open water systems are presented in literature and used in applications, but their performance has not yet been investigated systematically, and no guideline exists on which model to select for a certain channel. The aim of this research is to present a guideline for model choice based on the geometry of the channel and the flow conditions. The guideline is developed by first categorizing the channels into four types, followed by performing time‐domain, frequency domain, and closed‐loop tests for all models and channel types. The evaluation of the tests shows that for short and wave‐dominated channels, the Muskingum, Integrator Delay, and Integrator Delay Zero models perform the best, while for longer channels the linear inertial model is the most suitable. Finally, a decision‐tree is presented how to choose the model. Lastly, a decision‐tree is introduced to aid in the selection of the most appropriate model.","PeriodicalId":507642,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140775081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed Hydrological Modeling With Physics‐Encoded Deep Learning: A General Framework and Its Application in the Amazon 利用物理编码深度学习的分布式水文建模：通用框架及其在亚马逊河流域的应用

Water Resources Research Pub Date : 2024-04-01 DOI: 10.1029/2023wr036170

Chao Wang, Shijie Jiang, Yi Zheng, Feng Han, Rohini Kumar, O. Rakovec, Siqi Li

{"title":"Distributed Hydrological Modeling With Physics‐Encoded Deep Learning: A General Framework and Its Application in the Amazon","authors":"Chao Wang, Shijie Jiang, Yi Zheng, Feng Han, Rohini Kumar, O. Rakovec, Siqi Li","doi":"10.1029/2023wr036170","DOIUrl":"https://doi.org/10.1029/2023wr036170","url":null,"abstract":"While deep learning (DL) models exhibit superior simulation accuracy over traditional distributed hydrological models (DHMs), their main limitations lie in opacity and the absence of underlying physical mechanisms. The pursuit of synergies between DL and DHMs is an engaging research domain, yet a definitive roadmap remains elusive. In this study, a novel framework that seamlessly integrates a process‐based hydrological model encoded as a neural network (NN), an additional NN for mapping spatially distributed and physically meaningful parameters from watershed attributes, and NN‐based replacement models representing inadequately understood processes is developed. Multi‐source observations are used as training data, and the framework is fully differentiable, enabling fast parameter tuning by backpropagation. A hybrid DL model of the Amazon Basin (∼6 × 106 km2) was established based on the framework, and HydroPy, a global‐scale DHM, was encoded as its physical backbone. Trained simultaneously with streamflow observations and Gravity Recovery and Climate Experiment satellite data, the hybrid model yielded median Nash‐Sutcliffe efficiencies of 0.83 and 0.77 for dynamic and distributed simulations of streamflow and total water storage, respectively, 41% and 35% higher than those of the original HydroPy model. Replacing the original Penman‒Monteith formulation in HydroPy with a replacement NN produces more plausible potential evapotranspiration (PET) estimates, and unravels the spatial pattern of PET in this giant basin. The NN used for parameterization was interpreted to identify the factors controlling the spatial variability in key parameters. Overall, this study lays out a feasible technical roadmap for distributed hydrological modeling in the big data era.","PeriodicalId":507642,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140783292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transformer Versus LSTM: A Comparison of Deep Learning Models for Karst Spring Discharge Forecasting 变压器与 LSTM：用于岩溶泉水排放预测的深度学习模型比较

Water Resources Research Pub Date : 2024-04-01 DOI: 10.1029/2022wr032602

Anna Pölz, A. Blaschke, J. Komma, A. Farnleitner, J. Derx

{"title":"Transformer Versus LSTM: A Comparison of Deep Learning Models for Karst Spring Discharge Forecasting","authors":"Anna Pölz, A. Blaschke, J. Komma, A. Farnleitner, J. Derx","doi":"10.1029/2022wr032602","DOIUrl":"https://doi.org/10.1029/2022wr032602","url":null,"abstract":"Karst springs are essential drinking water resources, however, modeling them poses challenges due to complex subsurface flow processes. Deep learning models can capture complex relationships due to their ability to learn non‐linear patterns. This study evaluates the performance of the Transformer in forecasting spring discharges for up to 4 days. We compare it to the Long Short‐Term Memory (LSTM) Neural Network and a common baseline model on a well‐studied Austrian karst spring (LKAS2) with an extensive hourly database. We evaluated the models for two further karst springs with diverse discharge characteristics for comparing the performances based on four metrics. In the discharge‐based scenario, the Transformer performed significantly better than the LSTM for the spring with the longest response times (9% mean difference across metrics), while it performed poorer for the spring with the shortest response time (4% difference). Moreover, the Transformer better predicted the shape of the discharge during snowmelt. Both models performed well across all lead times and springs with 0.64–0.92 for the Nash–Sutcliffe efficiency and 10.8%–28.7% for the symmetric mean absolute percentage error for the LKAS2 spring. The temporal information, rainfall and electrical conductivity were the controlling input variables for the non‐discharge based scenario. The uncertainty analysis revealed that the prediction intervals are smallest in winter and autumn and highest during snowmelt. Our results thus suggest that the Transformer is a promising model to support the drinking water abstraction management, and can have advantages due to its attention mechanism particularly for longer response times.","PeriodicalId":507642,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140766934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Road Salt Legacies: Quantifying Fluxes of Chloride to Groundwater and Surface Water Across the Chicago Metropolitan Statistical Area 路盐遗产：量化芝加哥大都会统计区地下水和地表水中氯化物的通量

Water Resources Research Pub Date : 2024-02-01 DOI: 10.1029/2023wr035103

K. V. Van Meter, E. Ceisel

{"title":"Road Salt Legacies: Quantifying Fluxes of Chloride to Groundwater and Surface Water Across the Chicago Metropolitan Statistical Area","authors":"K. V. Van Meter, E. Ceisel","doi":"10.1029/2023wr035103","DOIUrl":"https://doi.org/10.1029/2023wr035103","url":null,"abstract":"Freshwater chloride concentrations have been increasing in North American surface waters for decades, largely driven by increases in the use of road salt, which is commonly applied as a deicer. In Chicago, thousands of tons of road salt are applied to roadways each winter, and increases in surface water chloride concentrations have been noted across the region since the mid‐1960s. While much of the applied salt runs directly off to nearby waterways during snowmelt events, some percolates to groundwater, affecting public supply wells and increasing the amount of chloride released to streams as baseflow during the non‐salting season. In the present study we have developed a spatially distributed chloride mass balance across the Chicago Metropolitan Statistical Area (CMSA) for a 30‐year period (1990–2020) to better our understanding of long‐term chloride fluxes and storage. Our results show that inputs of road salt to the region increased by 33% between 1990 and 2020. During this same period, riverine chloride loads across the region increased by 60%. Despite these increases in riverine chloride export, we find that chloride is accumulating in CMSA groundwater at a rate of ∼480 ktons year−1. We show that shallow aquifers, <30 m, exhibit only seasonal chloride storage, without long‐term accumulation. In contrast, at depths below 30 m, we find chloride concentrations to be increasing over time, indicating that legacy chloride is accumulating at deeper depths in CMSA groundwater. The present results highlight the importance of legacy chloride to long‐term water quality dynamics in North American cities.","PeriodicalId":507642,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140468820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0