Shashank Kumar Anand, Matteo Bertagni, Felipe Aburto, Salvatore Calabrese
Enhanced weathering (EW), the addition of finely ground silicate rock powder (RP) to soil, has emerged as a promising carbon removal strategy. However, quantifying weathering rates in soils remains challenging, as most continuum-scale EW models do not adequately account for the fraction of RP surface area (SA) that is wet at a given soil moisture and thus actively weathering. Here, we study how soil pore structure, RP particle size distribution, and RP mixing degree within the soil control water-rock contact. Using a soil-physics-based framework, we derive a scaling factor that quantifies the wet fraction of RP SA as a function of soil moisture and mixing degree within soil pores. This scaling factor varies nonlinearly with soil moisture for typical soil pore structures and RP particle size distributions, countering previous zero-order (independent of soil moisture) or linear assumptions. The scaling factor evolves dynamically with hydrological fluctuations and, for a given pore structure and RP mixing degree, it can span nearly two orders of magnitude with changes in median particle size. To illustrate its application, we integrate the derived scaling factor into the Soil Model for Enhanced Weathering and examine the sensitivity of simulated weathering fluxes to mixing degree under otherwise identical conditions. Under low mixing, results show that average weathering rates are roughly two orders of magnitude lower than under perfect mixing over 1 year of application. Our work provides a mechanistic, computationally efficient framework for representing water-rock contact in soil, offering a pathway to improve continuum-scale EW models.
{"title":"Soil Structure and Mixing Controls on Water-Rock Contact: Implications for Enhanced Weathering","authors":"Shashank Kumar Anand, Matteo Bertagni, Felipe Aburto, Salvatore Calabrese","doi":"10.1029/2025wr041479","DOIUrl":"https://doi.org/10.1029/2025wr041479","url":null,"abstract":"Enhanced weathering (EW), the addition of finely ground silicate rock powder (RP) to soil, has emerged as a promising carbon removal strategy. However, quantifying weathering rates in soils remains challenging, as most continuum-scale EW models do not adequately account for the fraction of RP surface area (SA) that is wet at a given soil moisture and thus actively weathering. Here, we study how soil pore structure, RP particle size distribution, and RP mixing degree within the soil control water-rock contact. Using a soil-physics-based framework, we derive a scaling factor that quantifies the wet fraction of RP SA as a function of soil moisture and mixing degree within soil pores. This scaling factor varies nonlinearly with soil moisture for typical soil pore structures and RP particle size distributions, countering previous zero-order (independent of soil moisture) or linear assumptions. The scaling factor evolves dynamically with hydrological fluctuations and, for a given pore structure and RP mixing degree, it can span nearly two orders of magnitude with changes in median particle size. To illustrate its application, we integrate the derived scaling factor into the Soil Model for Enhanced Weathering and examine the sensitivity of simulated weathering fluxes to mixing degree under otherwise identical conditions. Under low mixing, results show that average weathering rates are roughly two orders of magnitude lower than under perfect mixing over 1 year of application. Our work provides a mechanistic, computationally efficient framework for representing water-rock contact in soil, offering a pathway to improve continuum-scale EW models.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"86 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110283","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}
Floods are the second-most deadly natural hazard in Australia, following heatwaves. Monitoring flood extent and depth in near real-time (NRT) is crucial to minimize loss of life and socio-economic impacts. This study leverages advanced computing, data management systems, and high-quality data, including river gauge data APIs and Australian Water Outlook, Digital Earth Australia, Google Earth Engine and Amazon Web Service, to develop a flood monitoring workflow in Australia. Our framework provides NRT 5-m spatial resolution flood extent and depth maps using airborne LiDAR observations through three approaches: (a) gauge data, (b) coupled hydrological and hydrodynamics model, and (c) satellite observations (i.e., Sentinel-1, Sentinel-2, Landsat-7/8/9). We evaluated this flood monitoring framework in seven river catchments across Australia, using both deterministic and ensemble modes. This study highlights the importance of low-latency gauge data for flood monitoring, as well as the necessity of high-resolution airborne LiDAR DEMs for accurate flood mapping. In ungauged areas, the ensemble modeling approach enhances the model's ability to capture flood inundation dynamics. In cases where this remains challenging, multi-source remote sensing can help mitigate the limitations of the modeling approach. We also demonstrated the potential for transferring this flood monitoring framework to other regions around the world. Overall, this study advances the operationalization of high-resolution flood analytics, offering a replicable blueprint to strengthen community resilience against escalating flood risks under climate change.
{"title":"Advancing Near-Real-Time Flood Inundation Mapping in Australia","authors":"Jiawei Hou, Wendy Sharples, Angelica Tarpanelli, Luigi Renzullo, Fitsum Woldemeskel, Elisabetta Carrara","doi":"10.1029/2025wr040640","DOIUrl":"https://doi.org/10.1029/2025wr040640","url":null,"abstract":"Floods are the second-most deadly natural hazard in Australia, following heatwaves. Monitoring flood extent and depth in near real-time (NRT) is crucial to minimize loss of life and socio-economic impacts. This study leverages advanced computing, data management systems, and high-quality data, including river gauge data APIs and Australian Water Outlook, Digital Earth Australia, Google Earth Engine and Amazon Web Service, to develop a flood monitoring workflow in Australia. Our framework provides NRT 5-m spatial resolution flood extent and depth maps using airborne LiDAR observations through three approaches: (a) gauge data, (b) coupled hydrological and hydrodynamics model, and (c) satellite observations (i.e., Sentinel-1, Sentinel-2, Landsat-7/8/9). We evaluated this flood monitoring framework in seven river catchments across Australia, using both deterministic and ensemble modes. This study highlights the importance of low-latency gauge data for flood monitoring, as well as the necessity of high-resolution airborne LiDAR DEMs for accurate flood mapping. In ungauged areas, the ensemble modeling approach enhances the model's ability to capture flood inundation dynamics. In cases where this remains challenging, multi-source remote sensing can help mitigate the limitations of the modeling approach. We also demonstrated the potential for transferring this flood monitoring framework to other regions around the world. Overall, this study advances the operationalization of high-resolution flood analytics, offering a replicable blueprint to strengthen community resilience against escalating flood risks under climate change.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"288 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129273","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}
Chen Ding, Kewei Chen, Yang Zhan, Chunmiao Zheng, Zhilin Guo
Evaporation is a major pathway of surface water loss in wetlands, yet its influence on subsurface feedbacks remains poorly understood. Using an integrated surface–subsurface hydrologic and solute transport model, we show that evaporation can induce hysteresis between evaporative demand and the upwelling of groundwater and solutes, with the strength of this feedback governed by sediment permeability and shaped by site-specific hydrologic and topographic conditions. Under low-permeability (<1 × 10−12 m2) conditions, evaporation leads to lagged and prolonged groundwater and tracer upwelling, whereas high-permeability sediments respond more directly to evaporative forcing. Ponded water depth, land surface slope, and evaporation rate regulate the magnitude of upwelling fluxes, while rainfall and fluctuating groundwater levels can reverse flow direction. These findings highlight evaporation as an indirect yet critical driver of wetland water and solute exchange, with important implications for biogeochemical cycling and the hydrologic resilience of wetland ecosystems under a changing climate.
{"title":"Evaporation-Induced Hysteresis in Surface Water-Groundwater Exchange in Wetlands","authors":"Chen Ding, Kewei Chen, Yang Zhan, Chunmiao Zheng, Zhilin Guo","doi":"10.1029/2025wr041445","DOIUrl":"https://doi.org/10.1029/2025wr041445","url":null,"abstract":"Evaporation is a major pathway of surface water loss in wetlands, yet its influence on subsurface feedbacks remains poorly understood. Using an integrated surface–subsurface hydrologic and solute transport model, we show that evaporation can induce hysteresis between evaporative demand and the upwelling of groundwater and solutes, with the strength of this feedback governed by sediment permeability and shaped by site-specific hydrologic and topographic conditions. Under low-permeability (<1 × 10<sup>−12</sup> m<sup>2</sup>) conditions, evaporation leads to lagged and prolonged groundwater and tracer upwelling, whereas high-permeability sediments respond more directly to evaporative forcing. Ponded water depth, land surface slope, and evaporation rate regulate the magnitude of upwelling fluxes, while rainfall and fluctuating groundwater levels can reverse flow direction. These findings highlight evaporation as an indirect yet critical driver of wetland water and solute exchange, with important implications for biogeochemical cycling and the hydrologic resilience of wetland ecosystems under a changing climate.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"17 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110856","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}
Jesus Guzmán Pérez, Javier Montoya Martinez, David Angulo-Garcia
Total Suspended Solids (TSS) significantly degrade water quality by reducing light penetration and oxygen availability, while facilitating the transport of toxic contaminants. Managing TSS in watersheds requires an understanding of both hydrological connectivity and pollutant dynamics; however, these efforts are significantly constrained by data scarcity, particularly in extensive or remote watersheds in developing countries. This study develops a network-based advection-reaction model to simulate TSS transport across the Canal del Dique watershed. The watershed is represented as a directed graph, where rivers and streams form the edges of the network, and confluence points serve as nodes. To address the challenge of data scarcity, machine learning techniques are employed to estimate missing TSS values at unmonitored locations, and an optimization framework is implemented to determine the most effective TSS mitigation strategies. Results highlight the role of hydrological connectivity in TSS transport, with the model revealing that at low mitigation levels, interventions should prioritize high-TSS nodes. As mitigation resources increase, interventions shift toward pollutant source nodes and less connected areas, preventing downstream pollutant accumulation. This study demonstrates that highly connected nodes, although crucial for flow, are less effective targets for pollution control. The proposed methodology offers a novel, data-driven approach for optimizing TSS mitigation strategies, providing a scientifically grounded tool for improving water quality management. By prioritizing resource allocation in critical areas, this work enhances the efficiency of watershed management and supports sustainable water resource policies, especially in data-limited regions.
{"title":"Optimizing Total Suspended Solids Mitigation in a Data-Limited Watershed: A Network-Based Advection–Reaction Model Applied to the Canal del Dique, Colombia","authors":"Jesus Guzmán Pérez, Javier Montoya Martinez, David Angulo-Garcia","doi":"10.1029/2025wr040945","DOIUrl":"https://doi.org/10.1029/2025wr040945","url":null,"abstract":"Total Suspended Solids (TSS) significantly degrade water quality by reducing light penetration and oxygen availability, while facilitating the transport of toxic contaminants. Managing TSS in watersheds requires an understanding of both hydrological connectivity and pollutant dynamics; however, these efforts are significantly constrained by data scarcity, particularly in extensive or remote watersheds in developing countries. This study develops a network-based advection-reaction model to simulate TSS transport across the Canal del Dique watershed. The watershed is represented as a directed graph, where rivers and streams form the edges of the network, and confluence points serve as nodes. To address the challenge of data scarcity, machine learning techniques are employed to estimate missing TSS values at unmonitored locations, and an optimization framework is implemented to determine the most effective TSS mitigation strategies. Results highlight the role of hydrological connectivity in TSS transport, with the model revealing that at low mitigation levels, interventions should prioritize high-TSS nodes. As mitigation resources increase, interventions shift toward pollutant source nodes and less connected areas, preventing downstream pollutant accumulation. This study demonstrates that highly connected nodes, although crucial for flow, are less effective targets for pollution control. The proposed methodology offers a novel, data-driven approach for optimizing TSS mitigation strategies, providing a scientifically grounded tool for improving water quality management. By prioritizing resource allocation in critical areas, this work enhances the efficiency of watershed management and supports sustainable water resource policies, especially in data-limited regions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"46 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135226","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}
Planting structure drive agricultural water use and is critical to groundwater depletion in the North China Plain (NCP). However, the effects of planting structure changes on groundwater depletion are rarely quantified, and severely depleted areas are often overlooked in previous planting structure optimization studies. This study developed a groundwater stress index (GWSI) to assess current groundwater drought and future risks and identify high groundwater stress zones (HGSZ). Groundwater depletion was estimated by integrating land surface model and AquaCrop outputs. A structural equation model was developed to assess the effects of planting structure to groundwater depletion, and a GWSI-based optimization model was proposed to alleviate groundwater depletion, particularly in HGSZ. Results identified an HGSZ near the Henan–Hebei border, where the groundwater decline rate (−21.90 mm/year) was more than twice the NCP average (−8.73 mm/year). Under present planting structures, groundwater use remained unsustainable, with annual consumption exceeding recharge by 46.53 mm/year across the NCP and 97.09 mm/year in the HGSZ. Depletion was primarily affected by the planting area and spatial dispersion of winter wheat. Planting area expansion mitigated the effect of spatial redistribution on groundwater depletion, and it varied by crop. The optimization model reduced net groundwater depletion by 30.61 mm/year in the NCP and 63.23 mm/year in the HGSZ. The results highlighted the need to adjust planting structures, and revealed the effects to groundwater depletion, and demonstrated that partially converting rotation areas to single-season cropping and shifting the rest southeastward effectively alleviated groundwater depletion. These findings provided an evidence base for designing region-specific groundwater-resource management strategies in the NCP.
{"title":"The Effects of Planting Structure on Groundwater Depletion and Optimization Strategies in the North China Plain","authors":"Chengru Jia, Shikun Sun, Yongshan Liang, Ruihua Shen, Jinfeng Zhao, Yali Yin, Yubao Wang, Xining Zhao","doi":"10.1029/2025wr041114","DOIUrl":"https://doi.org/10.1029/2025wr041114","url":null,"abstract":"Planting structure drive agricultural water use and is critical to groundwater depletion in the North China Plain (NCP). However, the effects of planting structure changes on groundwater depletion are rarely quantified, and severely depleted areas are often overlooked in previous planting structure optimization studies. This study developed a groundwater stress index (GWSI) to assess current groundwater drought and future risks and identify high groundwater stress zones (HGSZ). Groundwater depletion was estimated by integrating land surface model and AquaCrop outputs. A structural equation model was developed to assess the effects of planting structure to groundwater depletion, and a GWSI-based optimization model was proposed to alleviate groundwater depletion, particularly in HGSZ. Results identified an HGSZ near the Henan–Hebei border, where the groundwater decline rate (−21.90 mm/year) was more than twice the NCP average (−8.73 mm/year). Under present planting structures, groundwater use remained unsustainable, with annual consumption exceeding recharge by 46.53 mm/year across the NCP and 97.09 mm/year in the HGSZ. Depletion was primarily affected by the planting area and spatial dispersion of winter wheat. Planting area expansion mitigated the effect of spatial redistribution on groundwater depletion, and it varied by crop. The optimization model reduced net groundwater depletion by 30.61 mm/year in the NCP and 63.23 mm/year in the HGSZ. The results highlighted the need to adjust planting structures, and revealed the effects to groundwater depletion, and demonstrated that partially converting rotation areas to single-season cropping and shifting the rest southeastward effectively alleviated groundwater depletion. These findings provided an evidence base for designing region-specific groundwater-resource management strategies in the NCP.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"289 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110282","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}
Marit G. A. Hendrickx, Jan Vanderborght, Pieter Janssens, Eric Laloy, Sander Bombeke, Evi Matthyssen, Anne Waverijn, Jan Diels
Affordable autonomous soil sensors and IoT technology enable real-time soil moisture monitoring, which offers opportunities for real-time model calibration and irrigation optimization. We introduce an irrigation decision support system SWIM2 (Sensor Wielded Inverse Modeling of a Soil Water Irrigation Model), a digital twin that integrates continuous sensor data and unbiased, periodic soil samples with an FAO-based soil water balance model using a Bayesian inverse modeling algorithm, DREAM(ZS) (DiffeRential Evolution Adaptive Metropolis). SWIM2 estimates 12 soil and crop parameters and their associated probability distributions and correlations, providing soil moisture predictions with uncertainty estimates. The SWIM2 framework is illustrated and validated in a real-time setup for 18 vegetable cropping cycles on agricultural fields in Flanders, Belgium, with in situ precipitation data. Although using minimal prior knowledge and despite sensor bias, SWIM2 achieves robust soil moisture predictions for a 7-day horizon, with accuracies comparable to sensor measurements. Predictions improve substantially in precision within the first 20 calibration days and maintain high predictive power throughout the growing season. The impact of in situ measurements and temporal covariance of the observational errors (“error covariance”) was assessed, indicating that good knowledge of the error covariance and independent soil moisture samples are essential to correct for sensor bias and ensure accurate model calibration, while continuous sensor data ensure accurate and precise estimates of the dynamics. This study demonstrates the use of soil moisture sensor data in a Bayesian inverse modeling framework, offering practical solutions for real-time soil moisture prediction and irrigation decision-making, enhancing water management across agricultural fields.
{"title":"Field-Scale Soil Moisture Predictions in Real Time Using In Situ Sensor Measurements in an Inverse Modeling Framework: SWIM2","authors":"Marit G. A. Hendrickx, Jan Vanderborght, Pieter Janssens, Eric Laloy, Sander Bombeke, Evi Matthyssen, Anne Waverijn, Jan Diels","doi":"10.1029/2025wr041324","DOIUrl":"https://doi.org/10.1029/2025wr041324","url":null,"abstract":"Affordable autonomous soil sensors and IoT technology enable real-time soil moisture monitoring, which offers opportunities for real-time model calibration and irrigation optimization. We introduce an irrigation decision support system SWIM<sup>2</sup> (Sensor Wielded Inverse Modeling of a Soil Water Irrigation Model), a digital twin that integrates continuous sensor data and unbiased, periodic soil samples with an FAO-based soil water balance model using a Bayesian inverse modeling algorithm, DREAM<sub>(ZS)</sub> (DiffeRential Evolution Adaptive Metropolis). SWIM<sup>2</sup> estimates 12 soil and crop parameters and their associated probability distributions and correlations, providing soil moisture predictions with uncertainty estimates. The SWIM<sup>2</sup> framework is illustrated and validated in a real-time setup for 18 vegetable cropping cycles on agricultural fields in Flanders, Belgium, with in situ precipitation data. Although using minimal prior knowledge and despite sensor bias, SWIM<sup>2</sup> achieves robust soil moisture predictions for a 7-day horizon, with accuracies comparable to sensor measurements. Predictions improve substantially in precision within the first 20 calibration days and maintain high predictive power throughout the growing season. The impact of in situ measurements and temporal covariance of the observational errors (“error covariance”) was assessed, indicating that good knowledge of the error covariance and independent soil moisture samples are essential to correct for sensor bias and ensure accurate model calibration, while continuous sensor data ensure accurate and precise estimates of the dynamics. This study demonstrates the use of soil moisture sensor data in a Bayesian inverse modeling framework, offering practical solutions for real-time soil moisture prediction and irrigation decision-making, enhancing water management across agricultural fields.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"4 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089466","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}
Monitoring removal performance of permeable reactive barriers (PRBs) for groundwater nitrate remediation and distinguishing remediation mechanism contributions remains a key challenge. Based on flow-through column experiments, this study integrated spectral induced polarization (SIP) monitoring with reactive transport modeling to investigate the dynamics of <span data-altimg="/cms/asset/ffec4d71-fa2b-435b-8806-2539a3eae7b5/wrcr70705-math-0001.png"></span><math altimg="urn:x-wiley:00431397:media:wrcr70705:wrcr70705-math-0001" display="inline" location="graphic/wrcr70705-math-0001.png"> <semantics> <mrow> <mi mathvariant="normal">N</mi> <msubsup> <mi mathvariant="normal">O</mi> <mn>3</mn> <mo>−</mo> </msubsup> </mrow> $mathrm{N}{mathrm{O}}_{3}^{-}$</annotation> </semantics></math> removal by zero-valent iron (ZVI) and activated carbon (AC) mixtures. SIP parameters link material changes to <span data-altimg="/cms/asset/f191a1bb-f5f6-489a-962a-21a93fd3b84e/wrcr70705-math-0002.png"></span><math altimg="urn:x-wiley:00431397:media:wrcr70705:wrcr70705-math-0002" display="inline" location="graphic/wrcr70705-math-0002.png"> <semantics> <mrow> <mi mathvariant="normal">N</mi> <msubsup> <mi mathvariant="normal">O</mi> <mn>3</mn> <mo>−</mo> </msubsup> </mrow> $mathrm{N}{mathrm{O}}_{3}^{-}$</annotation> </semantics></math> removal performance. The strong correlation between normalized chargeability and cumulative removal capacity of <span data-altimg="/cms/asset/cdc5ec5d-93b3-4eca-bbe0-5e095bcf4c15/wrcr70705-math-0003.png"></span><math altimg="urn:x-wiley:00431397:media:wrcr70705:wrcr70705-math-0003" display="inline" location="graphic/wrcr70705-math-0003.png"> <semantics> <mrow> <mi mathvariant="normal">N</mi> <msubsup> <mi mathvariant="normal">O</mi> <mn>3</mn> <mo>−</mo> </msubsup> </mrow> $mathrm{N}{mathrm{O}}_{3}^{-}$</annotation> </semantics></math> constrained reactive transport model errors, with average relative errors of 12.5% and 21% for predicted <span data-altimg="/cms/asset/f7949311-5b0b-44d9-a2c5-22f388ae98c3/wrcr70705-math-0004.png"></span><math altimg="urn:x-wiley:00431397:media:wrcr70705:wrcr70705-math-0004" display="inline" location="graphic/wrcr70705-math-0004.png"> <semantics> <mrow> <mi mathvariant="normal">N</mi> <msubsup> <mi mathvariant="normal">O</mi> <mn>3</mn> <mo>−</mo> </msubsup> </mrow> $mathrm{N}{mathrm{O}}_{3}^{-}$</annotation> </semantics></math> breakthrough concentrations. The presence of Ca<sup>2+</sup> and <span data-altimg="/cms/asset/afa59b81-4537-4895-9a7c-d3faa07f5296/wrcr70705-math-0005.png"></span><math altimg="urn:x-wiley:00431397:media:wrcr70705:wrcr70705-math-0005" display="inline" location="graphic/wrcr70705-math-0005.png"> <semantics> <mrow> <msubsup> <mi mathvariant="normal">HCO</mi> <mn>3</mn> <mo>−</mo> </msubsup> </mrow> ${mathrm{HCO}}_{3}^{-}$</annotation> </semantics></math> in solution promoted the corrosion of ZVI and the total <span data-altimg="/cms/asset/17531011-b258-498e-bfa0-53b36e5ccb3a/wrcr70705-ma
{"title":"Integrating Spectral Induced Polarization With Reactive Transport Modeling to Quantify Nitrate Remediation Capacity of ZVI-AC Permeable Reactive Barriers","authors":"Deqiang Mao, Xinmin Ma, Chen Chao, Nimrod Schwartz, Shiliang Liu, Jing Li, Alex Furman, Jiaming Zhang, Jian Meng","doi":"10.1029/2025wr042370","DOIUrl":"https://doi.org/10.1029/2025wr042370","url":null,"abstract":"Monitoring removal performance of permeable reactive barriers (PRBs) for groundwater nitrate remediation and distinguishing remediation mechanism contributions remains a key challenge. Based on flow-through column experiments, this study integrated spectral induced polarization (SIP) monitoring with reactive transport modeling to investigate the dynamics of <span data-altimg=\"/cms/asset/ffec4d71-fa2b-435b-8806-2539a3eae7b5/wrcr70705-math-0001.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr70705:wrcr70705-math-0001\" display=\"inline\" location=\"graphic/wrcr70705-math-0001.png\">\u0000<semantics>\u0000<mrow>\u0000<mi mathvariant=\"normal\">N</mi>\u0000<msubsup>\u0000<mi mathvariant=\"normal\">O</mi>\u0000<mn>3</mn>\u0000<mo>−</mo>\u0000</msubsup>\u0000</mrow>\u0000$mathrm{N}{mathrm{O}}_{3}^{-}$</annotation>\u0000</semantics></math> removal by zero-valent iron (ZVI) and activated carbon (AC) mixtures. SIP parameters link material changes to <span data-altimg=\"/cms/asset/f191a1bb-f5f6-489a-962a-21a93fd3b84e/wrcr70705-math-0002.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr70705:wrcr70705-math-0002\" display=\"inline\" location=\"graphic/wrcr70705-math-0002.png\">\u0000<semantics>\u0000<mrow>\u0000<mi mathvariant=\"normal\">N</mi>\u0000<msubsup>\u0000<mi mathvariant=\"normal\">O</mi>\u0000<mn>3</mn>\u0000<mo>−</mo>\u0000</msubsup>\u0000</mrow>\u0000$mathrm{N}{mathrm{O}}_{3}^{-}$</annotation>\u0000</semantics></math> removal performance. The strong correlation between normalized chargeability and cumulative removal capacity of <span data-altimg=\"/cms/asset/cdc5ec5d-93b3-4eca-bbe0-5e095bcf4c15/wrcr70705-math-0003.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr70705:wrcr70705-math-0003\" display=\"inline\" location=\"graphic/wrcr70705-math-0003.png\">\u0000<semantics>\u0000<mrow>\u0000<mi mathvariant=\"normal\">N</mi>\u0000<msubsup>\u0000<mi mathvariant=\"normal\">O</mi>\u0000<mn>3</mn>\u0000<mo>−</mo>\u0000</msubsup>\u0000</mrow>\u0000$mathrm{N}{mathrm{O}}_{3}^{-}$</annotation>\u0000</semantics></math> constrained reactive transport model errors, with average relative errors of 12.5% and 21% for predicted <span data-altimg=\"/cms/asset/f7949311-5b0b-44d9-a2c5-22f388ae98c3/wrcr70705-math-0004.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr70705:wrcr70705-math-0004\" display=\"inline\" location=\"graphic/wrcr70705-math-0004.png\">\u0000<semantics>\u0000<mrow>\u0000<mi mathvariant=\"normal\">N</mi>\u0000<msubsup>\u0000<mi mathvariant=\"normal\">O</mi>\u0000<mn>3</mn>\u0000<mo>−</mo>\u0000</msubsup>\u0000</mrow>\u0000$mathrm{N}{mathrm{O}}_{3}^{-}$</annotation>\u0000</semantics></math> breakthrough concentrations. The presence of Ca<sup>2+</sup> and <span data-altimg=\"/cms/asset/afa59b81-4537-4895-9a7c-d3faa07f5296/wrcr70705-math-0005.png\"></span><math altimg=\"urn:x-wiley:00431397:media:wrcr70705:wrcr70705-math-0005\" display=\"inline\" location=\"graphic/wrcr70705-math-0005.png\">\u0000<semantics>\u0000<mrow>\u0000<msubsup>\u0000<mi mathvariant=\"normal\">HCO</mi>\u0000<mn>3</mn>\u0000<mo>−</mo>\u0000</msubsup>\u0000</mrow>\u0000${mathrm{HCO}}_{3}^{-}$</annotation>\u0000</semantics></math> in solution promoted the corrosion of ZVI and the total <span data-altimg=\"/cms/asset/17531011-b258-498e-bfa0-53b36e5ccb3a/wrcr70705-ma","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"117 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110286","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}
As a critical ecological transition zone between aquatic and terrestrial ecosystems, the water-level fluctuation zone significantly influences flow structure through vegetation morphology. Conventional analytical velocity models inadequately address the variation in vegetation with water depth. In this study, we developed a hydrodynamic coupled model with vertically varying leaf vegetation widths and derived its analytical solutions. We have updated the dynamic invasion width formula in the context of studying vegetation-flow interactions within water-level fluctuation zones. This work quantitatively investigates flow interactions at the main channel-floodplain interface, establishes a dynamic relationship between the resistance coefficient and vegetation geometric parameters, and proposes a modified Kármán coefficient expression incorporating free water layer corrections under submerged conditions. Experimental and numerical validation revealed the shear layer evolution mechanisms and turbulent kinetic energy redistribution patterns (vertical-lateral) under semi-vegetated conditions. This study overcomes the traditional assumption of vegetation homogeneity. The findings will provide a fundamental basis for research on dissolved oxygen variations and pollutant diffusion processes in the littoral zone under vegetation-flow interactions. It also analyzes the vertical variations in vegetation morphology within water-level fluctuation zones, and offering a high-precision analytical tool for eco-hydrological simulations under vertically graded vegetation configurations and associated hydrodynamic impacts in these zones.
{"title":"Analytical Model of Velocity Distribution and Penetration Characteristics in Water-Level Fluctuation Zone With Vegetation","authors":"An-Qi Li, Xiao-Bo Liu, Wei-Jie Wang, Zhuo-Wei Wang, Feng-Jiao Li, Ming-Yang Xu, Wei Huang","doi":"10.1029/2025wr041130","DOIUrl":"https://doi.org/10.1029/2025wr041130","url":null,"abstract":"As a critical ecological transition zone between aquatic and terrestrial ecosystems, the water-level fluctuation zone significantly influences flow structure through vegetation morphology. Conventional analytical velocity models inadequately address the variation in vegetation with water depth. In this study, we developed a hydrodynamic coupled model with vertically varying leaf vegetation widths and derived its analytical solutions. We have updated the dynamic invasion width formula in the context of studying vegetation-flow interactions within water-level fluctuation zones. This work quantitatively investigates flow interactions at the main channel-floodplain interface, establishes a dynamic relationship between the resistance coefficient and vegetation geometric parameters, and proposes a modified Kármán coefficient expression incorporating free water layer corrections under submerged conditions. Experimental and numerical validation revealed the shear layer evolution mechanisms and turbulent kinetic energy redistribution patterns (vertical-lateral) under semi-vegetated conditions. This study overcomes the traditional assumption of vegetation homogeneity. The findings will provide a fundamental basis for research on dissolved oxygen variations and pollutant diffusion processes in the littoral zone under vegetation-flow interactions. It also analyzes the vertical variations in vegetation morphology within water-level fluctuation zones, and offering a high-precision analytical tool for eco-hydrological simulations under vertically graded vegetation configurations and associated hydrodynamic impacts in these zones.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"30 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089465","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}
T. Chen, J. Tian, J. Sun, Z. Zhang, H. Chai, B. Lin, X. Fu
Flooding causes significant loss of life and economic damage and affects healthy development of society. Deep learning (DL) models demonstrate significant advantages in improving computational efficiency while maintaining accuracy. Existing research of predicting dynamic flood evolution still remains some gaps for predicting flooding maps from the initial time step, weak transferability for flood scenarios from unseen breaches, and potential enhancement of common neural network frameworks. This paper proposes a DL model called FloodUnet based on an improved U-Net architecture to achieve rapid and accurate prediction of flood evolution. FloodUnet can predict a series of flooding depth maps and maintain high-precision prediction. It achieves an average root mean square error of 0.2 m and an average Nash-Sutcliffe Efficiency coefficient of 0.9 on testing sets of unseen breaches and inflows through a 4-fold cross validation. It is three orders of magnitude faster than the hydrodynamic model with a 24-hr lead time. It has obvious advantage in prediction accuracy compared to ordinary convolutional neural network and U-Net. Residual module and channel attention mechanism can enhance feature representation for complex flood dynamics and ensures stability during multi-step rolling prediction.
{"title":"FloodUnet: A Rapid Spatio-Temporal Prediction Model for Flood Evolution Based on an Enhanced U-Net","authors":"T. Chen, J. Tian, J. Sun, Z. Zhang, H. Chai, B. Lin, X. Fu","doi":"10.1029/2025wr041427","DOIUrl":"https://doi.org/10.1029/2025wr041427","url":null,"abstract":"Flooding causes significant loss of life and economic damage and affects healthy development of society. Deep learning (DL) models demonstrate significant advantages in improving computational efficiency while maintaining accuracy. Existing research of predicting dynamic flood evolution still remains some gaps for predicting flooding maps from the initial time step, weak transferability for flood scenarios from unseen breaches, and potential enhancement of common neural network frameworks. This paper proposes a DL model called FloodUnet based on an improved U-Net architecture to achieve rapid and accurate prediction of flood evolution. FloodUnet can predict a series of flooding depth maps and maintain high-precision prediction. It achieves an average root mean square error of 0.2 m and an average Nash-Sutcliffe Efficiency coefficient of 0.9 on testing sets of unseen breaches and inflows through a 4-fold cross validation. It is three orders of magnitude faster than the hydrodynamic model with a 24-hr lead time. It has obvious advantage in prediction accuracy compared to ordinary convolutional neural network and U-Net. Residual module and channel attention mechanism can enhance feature representation for complex flood dynamics and ensures stability during multi-step rolling prediction.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"59 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110287","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}
Heng Dai, Yijie Yang, Fangqiang Zhang, Alberto Guadagnini, Jing Yang, Xiaochuang Bu, Lunche Wang, Songhu Yuan, Ming Ye
We rely on a global sensitivity analysis (GSA) approach to identify the dominant physical and biogeochemical controls on dissolved oxygen (DO) dynamics in riparian aquifers. The study is motivated by the observation that availability of DO is key to regulating redox conditions and associated processes in the subsurface. Yet, the complexity of coupled flow and transport models, combined with model input uncertainty challenges our ability to fully characterize system behavior. To address this issue, we integrate Bayesian network-based and variance-based methods into a comprehensive GSA framework, enabling a robust evaluation of parameter and process sensitivities. To overcome the high computational demand of GSA for complex numerical models, we develop surrogate models using deep learning approaches (i.e., multi-layer perceptrons and convolutional neural networks). Application of this framework to a high-resolution model of riparian DO transport reveals that river stage dynamics (i.e., period and amplitude of water level fluctuations) are primary drivers of DO supply to the aquifer system. Hydraulic conductivity, riverine DO concentration, and the maximum DO reaction rate exhibit important but localized effects, influencing different transport pathways including river water infiltration, entrapped air dissolution, and diffusion through the unsaturated zone. In contrast, parameters such as porosity, longitudinal dispersion, and van Genuchten soil parameters exhibit negligible influence. These findings underscore the value of combining deep learning and GSA to efficiently evaluate complex environmental systems and to guide model simplification and diagnosis.
{"title":"Identification of Key Factors Driving Dissolved Oxygen in Riparian Aquifers Through Deep Learning-Assisted Global Sensitivity Analysis","authors":"Heng Dai, Yijie Yang, Fangqiang Zhang, Alberto Guadagnini, Jing Yang, Xiaochuang Bu, Lunche Wang, Songhu Yuan, Ming Ye","doi":"10.1029/2025wr041884","DOIUrl":"https://doi.org/10.1029/2025wr041884","url":null,"abstract":"We rely on a global sensitivity analysis (GSA) approach to identify the dominant physical and biogeochemical controls on dissolved oxygen (DO) dynamics in riparian aquifers. The study is motivated by the observation that availability of DO is key to regulating redox conditions and associated processes in the subsurface. Yet, the complexity of coupled flow and transport models, combined with model input uncertainty challenges our ability to fully characterize system behavior. To address this issue, we integrate Bayesian network-based and variance-based methods into a comprehensive GSA framework, enabling a robust evaluation of parameter and process sensitivities. To overcome the high computational demand of GSA for complex numerical models, we develop surrogate models using deep learning approaches (i.e., multi-layer perceptrons and convolutional neural networks). Application of this framework to a high-resolution model of riparian DO transport reveals that river stage dynamics (i.e., period and amplitude of water level fluctuations) are primary drivers of DO supply to the aquifer system. Hydraulic conductivity, riverine DO concentration, and the maximum DO reaction rate exhibit important but localized effects, influencing different transport pathways including river water infiltration, entrapped air dissolution, and diffusion through the unsaturated zone. In contrast, parameters such as porosity, longitudinal dispersion, and van Genuchten soil parameters exhibit negligible influence. These findings underscore the value of combining deep learning and GSA to efficiently evaluate complex environmental systems and to guide model simplification and diagnosis.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"104 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089467","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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