The flow of liquid water through snow is a complex and poorly understood problem in snow hydrology. This paper reviews current understanding of the lateral flow of water through snow. We determined that the main physical processes producing lateral flow are: (a) hydraulic barriers at layer interfaces, (b) soil saturation overland/through-snow flow, and (c) infiltration excess through-snow flow. These processes result in increased potential for lateral flow where snowpacks have more complex stratigraphy and the rate of snowmelt input is greater than the storage or infiltration capacity of the underlying soil. A global snow classification shows lateral flow through snow is important for consideration in 75% of the total global cryosphere and 50% of global seasonal snow coverage. Lateral flow is important for 70% of the cryosphere in North America and 46% of the Cryosphere in Europe and Asia. Knowledge gaps in current understanding outline future research needs which include: (a) improving hydrologic model structures to include lateral flow through snow, (b) expanded research in parameterizing the hydraulic properties of snow, and (c) further understanding of the spatial and temporal scale of lateral flow through snow processes.
{"title":"Review: The Importance of Lateral Flow Through Snow in Hydrological Processes Globally","authors":"R. W. Webb, N. Ohara, H. P. Marshall, J. McNamara","doi":"10.1029/2025wr040776","DOIUrl":"https://doi.org/10.1029/2025wr040776","url":null,"abstract":"The flow of liquid water through snow is a complex and poorly understood problem in snow hydrology. This paper reviews current understanding of the lateral flow of water through snow. We determined that the main physical processes producing lateral flow are: (a) hydraulic barriers at layer interfaces, (b) soil saturation overland/through-snow flow, and (c) infiltration excess through-snow flow. These processes result in increased potential for lateral flow where snowpacks have more complex stratigraphy and the rate of snowmelt input is greater than the storage or infiltration capacity of the underlying soil. A global snow classification shows lateral flow through snow is important for consideration in 75% of the total global cryosphere and 50% of global seasonal snow coverage. Lateral flow is important for 70% of the cryosphere in North America and 46% of the Cryosphere in Europe and Asia. Knowledge gaps in current understanding outline future research needs which include: (a) improving hydrologic model structures to include lateral flow through snow, (b) expanded research in parameterizing the hydraulic properties of snow, and (c) further understanding of the spatial and temporal scale of lateral flow through snow processes.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"1 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146021780","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 Covariability of Extreme Floods Over the Coterminous United States: Co-Dependency Measures and Their Statistical Significance","authors":"Kichul Bae, Jeongwoo Hwang, A. Sankarasubramanian","doi":"10.1029/2025wr041262","DOIUrl":"https://doi.org/10.1029/2025wr041262","url":null,"abstract":"Understanding the spatial structure of extreme floods is critical both for reliable design flood estimation and for coordinated development of regional response and flood mitigation strategies. Yet, analysis of rare, high-magnitude floods is challenged by the limited sample size. This study investigates the spatial covariability of extreme floods across the coterminous United States (CONUS) for large return periods (2–100 years) by proposing three distinct co-dependency measures: (a) annual co-occurrence probability (<span data-altimg=\"/cms/asset/7027a4c3-d219-4443-8ee2-318f8f214c3e/wrcr70683-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"188\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/wrcr70683-math-0001.png\"><mjx-semantics><mjx-mrow><mjx-msub data-semantic-children=\"0,1\" data-semantic- data-semantic-role=\"unknown\" data-semantic-speech=\"CP Subscript annual\" data-semantic-type=\"subscript\"><mjx-mtext data-semantic-annotation=\"clearspeak:unit\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"unknown\" data-semantic-type=\"text\"><mjx-c></mjx-c><mjx-c></mjx-c></mjx-mtext><mjx-script style=\"vertical-align: -0.15em;\"><mjx-mtext data-semantic-annotation=\"clearspeak:unit\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"unknown\" data-semantic-type=\"text\" size=\"s\"><mjx-c></mjx-c><mjx-c></mjx-c><mjx-c></mjx-c><mjx-c></mjx-c><mjx-c></mjx-c><mjx-c></mjx-c></mjx-mtext></mjx-script></mjx-msub></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00431397:media:wrcr70683:wrcr70683-math-0001\" display=\"inline\" location=\"graphic/wrcr70683-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><msub data-semantic-=\"\" data-semantic-children=\"0,1\" data-semantic-role=\"unknown\" data-semantic-speech=\"CP Subscript annual\" data-semantic-type=\"subscript\"><mtext data-semantic-=\"\" data-semantic-annotation=\"clearspeak:unit\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-role=\"unknown\" data-semantic-type=\"text\">CP</mtext><mtext data-semantic-=\"\" data-semantic-annotation=\"clearspeak:unit\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-role=\"unknown\" data-semantic-type=\"text\">annual</mtext></msub></mrow>${text{CP}}_{text{annual}}$</annotation></semantics></math></mjx-assistive-mml></mjx-container>), (b) 7-day co-occurrence probability (<span data-altimg=\"/cms/asset/29d52946-17f1-4017-ae55-0afeff5d9d68/wrcr70683-math-0002.png\"></span><mjx-container ctxtmenu_counter=\"189\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/wrcr70683-math-0002.png\"><mjx-semantics><mjx-mrow><mjx-msub data-semantic-children=\"0,1\" data-semantic- d","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"45 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146005671","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}
Huihui Feng, Jianhong Zhou, Zhiyong Wu, Jianzhi Dong, Luca Brocca, Long Zhao, Hai He, Hui Fan
Hydrological models are typically calibrated using historical ground-based streamflow observations to constrain model uncertainty. However, such a calibration strategy can lead to unrealistic model parameters and is not applicable in data-sparse regions where streamflow observations are unavailable. Motivated by this limitation, a novel model calibration approach that leverages remote sensing (RS) soil moisture retrievals has been recently developed based on the assumption of perfect rank correlation. It calibrates model parameters by maximizing the rank correlation between RS pre-storm soil moisture and modeled storm-scale runoff coefficient (i.e., the ratio of runoff to precipitation). However, this calibration approach has so far been limited to basin-scale applications and evaluated only in terms of storm-scale runoff coefficients rather than actual streamflow simulations. Here, we extend the calibration approach to a grid-by-grid parameter calibration framework within the Variable Infiltration Capacity (VIC) model and incorporate a routing scheme to enable streamflow simulation. The model simulations are evaluated against independent ground-based streamflow observations and other hydrological variables, including ground-based soil moisture and RS-based terrestrial water storage (TWS) and evapotranspiration (ET). Results show that the RS-based calibration approach produces VIC streamflow simulations comparable to the conventional calibration using ground-based streamflow in semi-humid and humid basins—achieving a mean Nash-Sutcliffe coefficient above 0.68. In addition, the calibration method leads to improvements in both VIC TWS and ET estimates (with average correlation increments of 0.06 and 0.07, respectively). The study offers valuable insights for streamflow modeling in data-sparse regions.
{"title":"Optimizing Soil Moisture-Runoff Coupling Strength With Remotely Sensed Soil Moisture for Improved Hydrological Modeling","authors":"Huihui Feng, Jianhong Zhou, Zhiyong Wu, Jianzhi Dong, Luca Brocca, Long Zhao, Hai He, Hui Fan","doi":"10.1029/2024wr039571","DOIUrl":"https://doi.org/10.1029/2024wr039571","url":null,"abstract":"Hydrological models are typically calibrated using historical ground-based streamflow observations to constrain model uncertainty. However, such a calibration strategy can lead to unrealistic model parameters and is not applicable in data-sparse regions where streamflow observations are unavailable. Motivated by this limitation, a novel model calibration approach that leverages remote sensing (RS) soil moisture retrievals has been recently developed based on the assumption of perfect rank correlation. It calibrates model parameters by maximizing the rank correlation between RS pre-storm soil moisture and modeled storm-scale runoff coefficient (i.e., the ratio of runoff to precipitation). However, this calibration approach has so far been limited to basin-scale applications and evaluated only in terms of storm-scale runoff coefficients rather than actual streamflow simulations. Here, we extend the calibration approach to a grid-by-grid parameter calibration framework within the Variable Infiltration Capacity (VIC) model and incorporate a routing scheme to enable streamflow simulation. The model simulations are evaluated against independent ground-based streamflow observations and other hydrological variables, including ground-based soil moisture and RS-based terrestrial water storage (TWS) and evapotranspiration (ET). Results show that the RS-based calibration approach produces VIC streamflow simulations comparable to the conventional calibration using ground-based streamflow in semi-humid and humid basins—achieving a mean Nash-Sutcliffe coefficient above 0.68. In addition, the calibration method leads to improvements in both VIC TWS and ET estimates (with average correlation increments of 0.06 and 0.07, respectively). The study offers valuable insights for streamflow modeling in data-sparse regions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"63 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146005670","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}
A. Lackner, W. Lidberg, A. M. Ågren, I. F. Creed, K. Bishop
Decadal trends in the concentration of dissolved organic carbon (DOC) in surface water have gained considerable attention due to their significance for aquatic ecology and drinking water quality. Spatial patterns in DOC dynamics hold clues to the causes of DOC variation. Recent developments in digital mapping provide high-resolution information on soil moisture and how the length of stream networks, including drainage ditches, changes with discharge. This study characterized riparian corridors across multiple flow conditions and spatial extents, showing that although soil moisture became wetter closer to the stream, between-catchment differences in soil moisture composition were similar across 10, 100 m, and whole-catchment extents. The study explored how catchment factors influencing spatial and temporal variation in DOC in 145 Swedish watercourses could be explained using high-resolution spatial data in corridors along stream networks that expand and contract with flow. Catchment-wide characteristics mapped at coarser scales, combined with meteorological factors and stream flow, explained 64%–77% of observed mean DOC and the influences of seasonality and discharge. Adding high-resolution soil moisture data and considering them in corridors of different widths did not improve explanation of DOC variation. However, variation in high-resolution soil moisture contained information important for explaining mean DOC and daily DOC variation. Ditch density and changes in mesic soil moisture class were important for explaining mean DOC, while stream density affected the influence of discharge. Although high-resolution soil moisture data did not add explanatory power beyond coarser-scale information, they deepened understanding of how soil moisture and topography influence DOC dynamics.
{"title":"Scales of Landscape Influence on Dissolved Organic Carbon Dynamics in Boreal Surface Water","authors":"A. Lackner, W. Lidberg, A. M. Ågren, I. F. Creed, K. Bishop","doi":"10.1029/2025wr041513","DOIUrl":"https://doi.org/10.1029/2025wr041513","url":null,"abstract":"Decadal trends in the concentration of dissolved organic carbon (DOC) in surface water have gained considerable attention due to their significance for aquatic ecology and drinking water quality. Spatial patterns in DOC dynamics hold clues to the causes of DOC variation. Recent developments in digital mapping provide high-resolution information on soil moisture and how the length of stream networks, including drainage ditches, changes with discharge. This study characterized riparian corridors across multiple flow conditions and spatial extents, showing that although soil moisture became wetter closer to the stream, between-catchment differences in soil moisture composition were similar across 10, 100 m, and whole-catchment extents. The study explored how catchment factors influencing spatial and temporal variation in DOC in 145 Swedish watercourses could be explained using high-resolution spatial data in corridors along stream networks that expand and contract with flow. Catchment-wide characteristics mapped at coarser scales, combined with meteorological factors and stream flow, explained 64%–77% of observed mean DOC and the influences of seasonality and discharge. Adding high-resolution soil moisture data and considering them in corridors of different widths did not improve explanation of DOC variation. However, variation in high-resolution soil moisture contained information important for explaining mean DOC and daily DOC variation. Ditch density and changes in mesic soil moisture class were important for explaining mean DOC, while stream density affected the influence of discharge. Although high-resolution soil moisture data did not add explanatory power beyond coarser-scale information, they deepened understanding of how soil moisture and topography influence DOC dynamics.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"43 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146021996","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}
J. Huang, M. Barandun, J. Richard-Cerda, M. Hoelzle, E. Pohl
The Pamir Mountains, a critical water source for Central Asia, require accurate quantification of runoff components for water resource management under climate change. Uncertainties in precipitation data are known to greatly affect hydrological model accuracy, leading to the widespread use of multi-data calibration methods to avoid internal error compensation effects between snow and glacier accumulation and melt processes. Traditional approaches incorporating runoff, snow cover fraction, and glacier mass balance are frequently employed in the region's hydrological model calibration; yet we find this calibration approach to still result in significant uncertainties in the quantification of baseflow, snowmelt, and glacier runoff. Here we show winter baseflow calibration to provide a previously overlooked yet powerful constraint on model parameters, not only constraining baseflow but also enhancing the estimation of snowmelt and glacier runoff through groundwater parameters' control on hydrograph characteristics. Even with low-quality forcing data, winter baseflow calibration guides parameters toward more realistic values of runoff estimates, improving model reliability. Using five different forcing data sets, we show that incorporating winter baseflow alongside traditional calibration variables (runoff, snow cover, and glacier mass balance) reduces uncertainty ranges from 34%–61% to 8%–21% for snowmelt, 5%–17% to 3%–11% for glacier runoff, and 33%–50% to 7%–21% for baseflow estimates. Though parameter equifinality remains a challenge, winter baseflow calibration consistently enhances model accuracy, emphasizing its vital role in refining hydrological predictions in alpine, data-scarce, and climate-sensitive regions.
{"title":"Winter Baseflow Calibration's Critical Role in Hydrological Modeling for the Pamir Region","authors":"J. Huang, M. Barandun, J. Richard-Cerda, M. Hoelzle, E. Pohl","doi":"10.1029/2025wr040043","DOIUrl":"https://doi.org/10.1029/2025wr040043","url":null,"abstract":"The Pamir Mountains, a critical water source for Central Asia, require accurate quantification of runoff components for water resource management under climate change. Uncertainties in precipitation data are known to greatly affect hydrological model accuracy, leading to the widespread use of multi-data calibration methods to avoid internal error compensation effects between snow and glacier accumulation and melt processes. Traditional approaches incorporating runoff, snow cover fraction, and glacier mass balance are frequently employed in the region's hydrological model calibration; yet we find this calibration approach to still result in significant uncertainties in the quantification of baseflow, snowmelt, and glacier runoff. Here we show winter baseflow calibration to provide a previously overlooked yet powerful constraint on model parameters, not only constraining baseflow but also enhancing the estimation of snowmelt and glacier runoff through groundwater parameters' control on hydrograph characteristics. Even with low-quality forcing data, winter baseflow calibration guides parameters toward more realistic values of runoff estimates, improving model reliability. Using five different forcing data sets, we show that incorporating winter baseflow alongside traditional calibration variables (runoff, snow cover, and glacier mass balance) reduces uncertainty ranges from 34%–61% to 8%–21% for snowmelt, 5%–17% to 3%–11% for glacier runoff, and 33%–50% to 7%–21% for baseflow estimates. Though parameter equifinality remains a challenge, winter baseflow calibration consistently enhances model accuracy, emphasizing its vital role in refining hydrological predictions in alpine, data-scarce, and climate-sensitive regions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"28 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042630","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}
Hannah Besso, Ross Mower, Justin M. Pflug, Jessica D. Lundquist
Real-time estimates of peak snow water equivalent (SWE) are critical to spring runoff forecasts in snow-dominated basins, but large uncertainties remain due to the high spatial and temporal variability of interannual peak SWE. Here we introduce new methods for calculating real-time distributed 1 April SWE in the Western US using patterns in annual SWE anomalies, which are consistent over large regions. Our methods capitalize on the high accuracy of SWE reanalysis products by combining historical (1990–2021) 1 April SWE from a reanalysis product with real-time point measurements from in situ snow stations to estimate current-year 1 April SWE. First, we used a clustering algorithm to determine which regions of the Western US historically have similar SWE anomalies. Then we tested several ways to estimate 1 April SWE in the Upper Colorado River Basin (UCRB). We tested historical SWE distributions using (a) parametric and (b) nonparametric distribution assumptions, combined with current-year observations from: (a) the geographically closest station to each grid cell, (b) the collection of stations within the same cluster as each grid cell, and (c) all stations in the UCRB. The most accurate method used a parametric distribution and the collection of stations from the same cluster. This produced distributed 1 April SWE with a median R2 of 0.64, percent bias of 0.49%, and a root mean squared error of 0.13 m compared to the SWE reanalysis data in withheld years. The methods demonstrated here could be used wherever historical gridded data and real-time point measurements exist.
{"title":"Mapping 1 April SWE in the Western US Using Standardized Anomalies and Quantiles From SWE Reanalysis and In Situ Stations","authors":"Hannah Besso, Ross Mower, Justin M. Pflug, Jessica D. Lundquist","doi":"10.1029/2025wr040902","DOIUrl":"https://doi.org/10.1029/2025wr040902","url":null,"abstract":"Real-time estimates of peak snow water equivalent (SWE) are critical to spring runoff forecasts in snow-dominated basins, but large uncertainties remain due to the high spatial and temporal variability of interannual peak SWE. Here we introduce new methods for calculating real-time distributed 1 April SWE in the Western US using patterns in annual SWE anomalies, which are consistent over large regions. Our methods capitalize on the high accuracy of SWE reanalysis products by combining historical (1990–2021) 1 April SWE from a reanalysis product with real-time point measurements from in situ snow stations to estimate current-year 1 April SWE. First, we used a clustering algorithm to determine which regions of the Western US historically have similar SWE anomalies. Then we tested several ways to estimate 1 April SWE in the Upper Colorado River Basin (UCRB). We tested historical SWE distributions using (a) parametric and (b) nonparametric distribution assumptions, combined with current-year observations from: (a) the geographically closest station to each grid cell, (b) the collection of stations within the same cluster as each grid cell, and (c) all stations in the UCRB. The most accurate method used a parametric distribution and the collection of stations from the same cluster. This produced distributed 1 April SWE with a median <i>R</i><sup>2</sup> of 0.64, percent bias of 0.49%, and a root mean squared error of 0.13 m compared to the SWE reanalysis data in withheld years. The methods demonstrated here could be used wherever historical gridded data and real-time point measurements exist.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"51 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001530","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}
Bioaugmented microbially induced carbonate precipitation (MICP) is a potentially useful tool for permeability modification of the subsurface. There is, however, uncertainty surrounding how the transport and mineralization capability of augmenting organisms such as Sporosarcina pasteurii may vary with reservoir properties. Resolving these uncertainties requires further experimental work on natural rock samples; this necessitates, in turn, creative approaches to improving the reproducibility and generalizability of such experimental work. In this study, natural sandstones with different clay contents are processed to narrow grain size ranges and packed into columns, allowing the effect of clay content to be studied independently of pore size. Clay content is shown to have a significant effect on S. pasteurii attachment to rock surfaces, possibly due to the high specific surface area of clay minerals, while the effect of pore size is minor in the absence of straining. Furthermore, differences in S. pasteurii affinity for solid surfaces produce clear differences in the quantity and distribution of precipitate accumulation. When viable S. pasteurii cells are mostly surface-attached, precipitate accumulation begins almost immediately and precipitates appear to form primarily on grain surfaces. When only a small fraction of S. pasteurii is surface-attached, precipitate accumulation begins later but becomes significant with time. In this case, however, precipitates appear to form primarily in suspension, which may produce different precipitation efficiencies and precipitate morphologies based on mass transport conditions.
{"title":"Clay Content Mediates the Contribution of Suspended Sporosarcina Pasteurii to Microbial Mineralization in Sandstones","authors":"E. M. Albalghiti, B. R. Ellis","doi":"10.1029/2025wr040790","DOIUrl":"https://doi.org/10.1029/2025wr040790","url":null,"abstract":"Bioaugmented microbially induced carbonate precipitation (MICP) is a potentially useful tool for permeability modification of the subsurface. There is, however, uncertainty surrounding how the transport and mineralization capability of augmenting organisms such as <i>Sporosarcina pasteurii</i> may vary with reservoir properties. Resolving these uncertainties requires further experimental work on natural rock samples; this necessitates, in turn, creative approaches to improving the reproducibility and generalizability of such experimental work. In this study, natural sandstones with different clay contents are processed to narrow grain size ranges and packed into columns, allowing the effect of clay content to be studied independently of pore size. Clay content is shown to have a significant effect on <i>S. pasteurii</i> attachment to rock surfaces, possibly due to the high specific surface area of clay minerals, while the effect of pore size is minor in the absence of straining. Furthermore, differences in <i>S. pasteurii</i> affinity for solid surfaces produce clear differences in the quantity and distribution of precipitate accumulation. When viable <i>S. pasteurii</i> cells are mostly surface-attached, precipitate accumulation begins almost immediately and precipitates appear to form primarily on grain surfaces. When only a small fraction of <i>S. pasteurii</i> is surface-attached, precipitate accumulation begins later but becomes significant with time. In this case, however, precipitates appear to form primarily in suspension, which may produce different precipitation efficiencies and precipitate morphologies based on mass transport conditions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"27 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146000896","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}
Distributed acoustic sensing (DAS) with preexisting telecommunication optical fibers (dark fibers) has shown its ability to record rain-induced seismic noise with unprecedented high spatiotemporal resolution. This rain-induced noise exhibits strong correlations with rainfall intensity and rainwater discharge in pipeline sewers, highlighting its potential to infer rainwater flow characteristics. While raindrop impact models exist, a physical model linking stormwater discharge processes to DAS-recorded signals is still lacking. In this study, we introduce a data-driven method, deep embedded clustering (DEC), to automatically detect and classify rain-induced noise from massive DAS data, predicting the presence of moderate to heavy rain and the duration of stormwater discharge. We analyze continuous DAS recordings from 2019 to 2021 from a 4.2 km-long underground fiber-optic array in State College, PA. During training, the DEC model employs an autoencoder to learn the latent features from preprocessed spectrograms and then clusters these latent features into four clusters. Distinct features from spectrograms within each cluster reveal that four clusters correspond to background noise, rain-induced noise of varying rain intensities and stormwater discharge in sewers. Tests on unseen data sets in 2019 and 2021 demonstrate DEC's ability to not only predict rainfall rate levels but also indicate post-rain discharge durations. Furthermore, the model-derived post-rain discharge durations align with synthetic hydrograph estimates, yielding a drainage system time of concentration as 21 min in this region. Finally, we apply this workflow to two more locations to show the potential of spatial monitoring. Our results show that the combination of machine learning and fiber-optic sensing offers a scalable solution for improving stormwater management in urban environments.
{"title":"Unsupervised Characterization of Rain-Induced Seismic Noise in Urban Fiber-Optic Networks Using Deep Embedded Clustering","authors":"Junzhu Shen, Tieyuan Zhu","doi":"10.1029/2025wr041137","DOIUrl":"https://doi.org/10.1029/2025wr041137","url":null,"abstract":"Distributed acoustic sensing (DAS) with preexisting telecommunication optical fibers (dark fibers) has shown its ability to record rain-induced seismic noise with unprecedented high spatiotemporal resolution. This rain-induced noise exhibits strong correlations with rainfall intensity and rainwater discharge in pipeline sewers, highlighting its potential to infer rainwater flow characteristics. While raindrop impact models exist, a physical model linking stormwater discharge processes to DAS-recorded signals is still lacking. In this study, we introduce a data-driven method, deep embedded clustering (DEC), to automatically detect and classify rain-induced noise from massive DAS data, predicting the presence of moderate to heavy rain and the duration of stormwater discharge. We analyze continuous DAS recordings from 2019 to 2021 from a 4.2 km-long underground fiber-optic array in State College, PA. During training, the DEC model employs an autoencoder to learn the latent features from preprocessed spectrograms and then clusters these latent features into four clusters. Distinct features from spectrograms within each cluster reveal that four clusters correspond to background noise, rain-induced noise of varying rain intensities and stormwater discharge in sewers. Tests on unseen data sets in 2019 and 2021 demonstrate DEC's ability to not only predict rainfall rate levels but also indicate post-rain discharge durations. Furthermore, the model-derived post-rain discharge durations align with synthetic hydrograph estimates, yielding a drainage system time of concentration as 21 min in this region. Finally, we apply this workflow to two more locations to show the potential of spatial monitoring. Our results show that the combination of machine learning and fiber-optic sensing offers a scalable solution for improving stormwater management in urban environments.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"30 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001487","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}
Water distribution systems (WDSs) utilize battery-powered sensors to monitor essential parameters like flow rate and pressure. Limited battery life requires reducing data upload frequencies to conserve energy, potentially compromising real-time monitoring vital for system reliability and performance. This challenge is addressed by leveraging temporal redundancies from daily cycles and spatial redundancies from sensor data correlations, enabling data extrapolation instead of continuous transmission. This study proposes an edge computing-based sensor scheduling method that optimizes data transmission frequency while maintaining high data accuracy, thereby extending sensor longevity without sacrificing monitoring capabilities. The proposed approach uses predictive models to forecast future sensor values over multiple time steps based on existing data redundancies. If the deviation between predicted and actual measurements is within a predefined threshold, data transmission is skipped, reducing sensor power consumption; otherwise, data is transmitted to ensure accuracy. Applied to a realistic WDS sensor network, the method achieved up to a 75% reduction in sensor energy consumption with 48 estimation steps and a 0.5 m error threshold, while maintaining a relative data error of only 0.7%. These results demonstrate the method's effectiveness in balancing energy savings with data reliability, suggesting a viable solution for enhancing WDS sustainability and efficiency.
{"title":"Edge Computing for Energy-Efficient Sensor Scheduling in Water Distribution Systems","authors":"Shaosong Wei, Tingchao Yu, Avi Ostfeld, Chengyin Liu, Shipeng Chu, Hao Shen","doi":"10.1029/2025wr040149","DOIUrl":"https://doi.org/10.1029/2025wr040149","url":null,"abstract":"Water distribution systems (WDSs) utilize battery-powered sensors to monitor essential parameters like flow rate and pressure. Limited battery life requires reducing data upload frequencies to conserve energy, potentially compromising real-time monitoring vital for system reliability and performance. This challenge is addressed by leveraging temporal redundancies from daily cycles and spatial redundancies from sensor data correlations, enabling data extrapolation instead of continuous transmission. This study proposes an edge computing-based sensor scheduling method that optimizes data transmission frequency while maintaining high data accuracy, thereby extending sensor longevity without sacrificing monitoring capabilities. The proposed approach uses predictive models to forecast future sensor values over multiple time steps based on existing data redundancies. If the deviation between predicted and actual measurements is within a predefined threshold, data transmission is skipped, reducing sensor power consumption; otherwise, data is transmitted to ensure accuracy. Applied to a realistic WDS sensor network, the method achieved up to a 75% reduction in sensor energy consumption with 48 estimation steps and a 0.5 m error threshold, while maintaining a relative data error of only 0.7%. These results demonstrate the method's effectiveness in balancing energy savings with data reliability, suggesting a viable solution for enhancing WDS sustainability and efficiency.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"142 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001554","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}
Martín Marañón, Alfredo Durán, Rigel Rocha, Monika Winder, Carmen Ledo, Virgilio Martínez, Alfredo Mendoza, Fernando Jaramillo
Water resource assessments are critical for ensuring water security (WS), particularly in rapidly urbanizing regions with increasing water demand and limited water monitoring capabilities. Earth observations and indirect indicators of surface and groundwater changes are valuable tools for developing such assessments. This study examines WS by combining trends in pumping energy consumption and water-induced ground deformation over time and space in the sprawling metropolitan region of Cochabamba, Bolivia. We integrate Interferometric Synthetic Aperture Radar data with pumping energy consumption records from an extensive well network in the period 2012 to 2022. Statistical analysis identifies four trends in energy consumption (increasing, decreasing, stable, and no consumption) and three in ground deformation (uplift, subsidence, and no change). Based on these trends, we define four WS scenarios: WS, Threatened Water Security, water insecurity (WI), and Reversible Water Insecurity. Results reveal predominant domestic groundwater use and an increasing trend in energy consumption by pumping. In more than 1000 of these wells, both unsustainable water use and subsidence occur, implying WI. This study demonstrates the potential of combining InSAR-derived ground deformation and pumping energy consumption as a cost-effective and scalable groundwater monitoring tool for WS assessments.
{"title":"InSAR Ground Deformation and Pumping Energy Consumption Reveal Urban Water Security","authors":"Martín Marañón, Alfredo Durán, Rigel Rocha, Monika Winder, Carmen Ledo, Virgilio Martínez, Alfredo Mendoza, Fernando Jaramillo","doi":"10.1029/2025wr040704","DOIUrl":"https://doi.org/10.1029/2025wr040704","url":null,"abstract":"Water resource assessments are critical for ensuring water security (WS), particularly in rapidly urbanizing regions with increasing water demand and limited water monitoring capabilities. Earth observations and indirect indicators of surface and groundwater changes are valuable tools for developing such assessments. This study examines WS by combining trends in pumping energy consumption and water-induced ground deformation over time and space in the sprawling metropolitan region of Cochabamba, Bolivia. We integrate Interferometric Synthetic Aperture Radar data with pumping energy consumption records from an extensive well network in the period 2012 to 2022. Statistical analysis identifies four trends in energy consumption (increasing, decreasing, stable, and no consumption) and three in ground deformation (uplift, subsidence, and no change). Based on these trends, we define four WS scenarios: WS, Threatened Water Security, water insecurity (WI), and Reversible Water Insecurity. Results reveal predominant domestic groundwater use and an increasing trend in energy consumption by pumping. In more than 1000 of these wells, both unsustainable water use and subsidence occur, implying WI. This study demonstrates the potential of combining InSAR-derived ground deformation and pumping energy consumption as a cost-effective and scalable groundwater monitoring tool for WS assessments.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"178 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993414","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}
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