This work considers the design-for-control of water distribution networks (WDN) for the joint optimization of performance and cost-related objectives. In particular, we focus on the problem of optimizing the placement (design) and settings (control) of pressure reducing valves to minimize leakage at minimum cost. We present an integrative hybrid method combining the complementary advantages of deterministic and evolutionary algorithms (EA) to efficiently approximate the Pareto front of the resulting non-convex bi-objective mixed-integer non-linear program. Design decisions are fixed by an outer multi-objective EA, while a non-linear programming solver is called during the fitness evaluation stage to compute continuous control settings. The algorithm is applied to case study and operational networks and evaluated against alternative heuristic methods based on computational performance and quality of the solutions returned. Our results show that the proposed method converges faster and more consistently than existing approaches, producing better trade-offs between cost and leakage reduction. In particular, the Pareto front approximations computed using the proposed integrative hybrid method are characterized by a more marked knee (i.e., more efficient trade-offs), while the achieved computational improvements facilitate the integration of expert feedback into the design-for-control of WDNs during offline planning.
{"title":"Hybrid Evolutionary-Exact Optimization Method for the Bi-Objective Design-For-Control of Water Distribution Networks","authors":"Aly-Joy Ulusoy, Ivan Stoianov","doi":"10.1029/2025wr040688","DOIUrl":"https://doi.org/10.1029/2025wr040688","url":null,"abstract":"This work considers the design-for-control of water distribution networks (WDN) for the joint optimization of performance and cost-related objectives. In particular, we focus on the problem of optimizing the placement (design) and settings (control) of pressure reducing valves to minimize leakage at minimum cost. We present an integrative hybrid method combining the complementary advantages of deterministic and evolutionary algorithms (EA) to efficiently approximate the Pareto front of the resulting non-convex bi-objective mixed-integer non-linear program. Design decisions are fixed by an outer multi-objective EA, while a non-linear programming solver is called during the fitness evaluation stage to compute continuous control settings. The algorithm is applied to case study and operational networks and evaluated against alternative heuristic methods based on computational performance and quality of the solutions returned. Our results show that the proposed method converges faster and more consistently than existing approaches, producing better trade-offs between cost and leakage reduction. In particular, the Pareto front approximations computed using the proposed integrative hybrid method are characterized by a more marked knee (i.e., more efficient trade-offs), while the achieved computational improvements facilitate the integration of expert feedback into the design-for-control of WDNs during offline planning.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"326 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146205041","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}
Aggregated evaluation metrics and overlooked hydrological process variability in individual streamflow events hinder understanding of how well hydrological processes are encoded in models. This study introduces a novel event-type-based multi-dimensional diagnostic framework to enhance model performance assessment and to identify process limitations. It evaluates the performance variation (in terms of timing and relative magnitude errors) for streamflow events of different types (e.g., snow-related events, rainfall on dry or wet soils) and using explainable machine learning (XAI) analyzes the relative importance of three possible error drivers: event properties, model process limitations (i.e., model fluxes and states), and initial model errors. The effectiveness of the proposed framework is assessed through a case study of a conceptual hydrological model applied to 340 German catchments. In this case study, the rainfall events on dry soils have higher timing errors, while relative magnitude errors prevail for the snow-related events. Across all event types, initial model errors before the streamflow event are the primary driver of both timing and magnitude errors. We also find that the hydrograph-related event properties and the model fluxes representing land surface dynamics are also important for magnitude errors regardless of the event type. The proposed framework provides valuable insights into how and why model performance varies across different error dimensions and under different event conditions, making it a powerful tool for advancing hydrological research and practice.
{"title":"Event-Type-Based Multi-Dimensional Diagnostics of Process Limitations in Hydrological Models","authors":"Zhenyu Wang, Larisa Tarasova, Ralf Merz","doi":"10.1029/2025wr040264","DOIUrl":"https://doi.org/10.1029/2025wr040264","url":null,"abstract":"Aggregated evaluation metrics and overlooked hydrological process variability in individual streamflow events hinder understanding of how well hydrological processes are encoded in models. This study introduces a novel event-type-based multi-dimensional diagnostic framework to enhance model performance assessment and to identify process limitations. It evaluates the performance variation (in terms of timing and relative magnitude errors) for streamflow events of different types (e.g., snow-related events, rainfall on dry or wet soils) and using explainable machine learning (XAI) analyzes the relative importance of three possible error drivers: event properties, model process limitations (i.e., model fluxes and states), and initial model errors. The effectiveness of the proposed framework is assessed through a case study of a conceptual hydrological model applied to 340 German catchments. In this case study, the rainfall events on dry soils have higher timing errors, while relative magnitude errors prevail for the snow-related events. Across all event types, initial model errors before the streamflow event are the primary driver of both timing and magnitude errors. We also find that the hydrograph-related event properties and the model fluxes representing land surface dynamics are also important for magnitude errors regardless of the event type. The proposed framework provides valuable insights into how and why model performance varies across different error dimensions and under different event conditions, making it a powerful tool for advancing hydrological research and practice.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"27 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146205040","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}
Evapotranspiration (ET) and groundwater recharge are fundamental components of the terrestrial water cycle, critical for understanding land–atmosphere interactions, ecosystem dynamics, and climate feedbacks. However, large-scale mapping of these fluxes is hindered by sparse in situ observations and uncertainties in land surface models. We present a scalable variational data assimilation (VDA) framework—the Land Integrated Data Assimilation system (LIDA-2)—for mapping ET and recharge by jointly assimilating hourly GOES land surface temperature (LST; 5 km) and SMAP surface soil moisture (SSM; 9 km, every 2–3 days) into a coupled water balance and dual-source energy model. LIDA-2 estimates key parameters—soil and canopy evaporative fractions (EFs, EFc), bulk heat transfer coefficients (CHN), and the Brooks–Corey parameter (B)—while quantifying uncertainty via second-order Hessian analysis. Implemented over the Oklahoma Panhandle and Southern Great Plains (2016–2018) at 5 km resolution, LIDA-2 produces high-resolution maps of ET and recharge. Model outputs were evaluated against in situ soil moisture, latent heat flux, ERA5-Land data, and groundwater well anomalies. Results show substantial improvement in ET and soil moisture estimates relative to open-loop runs, particularly during dry-down periods. Annual recharge patterns match hydrogeologic gradients and prior regional studies, while daily soil moisture and EF outputs reveal seasonally varying land–atmosphere coupling: strongest in spring, greater in sandy soils, and higher in grasslands than croplands. The framework preserves physical realism, supports ecohydrological analysis, and offers a robust, observation-informed approach—explicitly designed for GOES LST and SMAP SSM—for mapping water and energy fluxes at scale, with applications to drought monitoring, groundwater sustainability, and climate feedback research.
{"title":"Mapping Evapotranspiration and Diffuse Recharge via Variational Assimilation of GOES LST and SMAP Soil Moisture","authors":"Asif Mahmood, Leila Farhadi","doi":"10.1029/2025wr040507","DOIUrl":"https://doi.org/10.1029/2025wr040507","url":null,"abstract":"Evapotranspiration (ET) and groundwater recharge are fundamental components of the terrestrial water cycle, critical for understanding land–atmosphere interactions, ecosystem dynamics, and climate feedbacks. However, large-scale mapping of these fluxes is hindered by sparse in situ observations and uncertainties in land surface models. We present a scalable variational data assimilation (VDA) framework—the Land Integrated Data Assimilation system (LIDA-2)—for mapping ET and recharge by jointly assimilating hourly GOES land surface temperature (LST; 5 km) and SMAP surface soil moisture (SSM; 9 km, every 2–3 days) into a coupled water balance and dual-source energy model. LIDA-2 estimates key parameters—soil and canopy evaporative fractions (EF<sub>s</sub>, EF<sub>c</sub>), bulk heat transfer coefficients (C<sub>HN</sub>), and the Brooks–Corey parameter (B)—while quantifying uncertainty via second-order Hessian analysis. Implemented over the Oklahoma Panhandle and Southern Great Plains (2016–2018) at 5 km resolution, LIDA-2 produces high-resolution maps of ET and recharge. Model outputs were evaluated against in situ soil moisture, latent heat flux, ERA5-Land data, and groundwater well anomalies. Results show substantial improvement in ET and soil moisture estimates relative to open-loop runs, particularly during dry-down periods. Annual recharge patterns match hydrogeologic gradients and prior regional studies, while daily soil moisture and EF outputs reveal seasonally varying land–atmosphere coupling: strongest in spring, greater in sandy soils, and higher in grasslands than croplands. The framework preserves physical realism, supports ecohydrological analysis, and offers a robust, observation-informed approach—explicitly designed for GOES LST and SMAP SSM—for mapping water and energy fluxes at scale, with applications to drought monitoring, groundwater sustainability, and climate feedback research.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"18 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146205042","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}
Mixing-limited reactions in unsaturated porous media are controlled by complex pore-scale processes arising from air and water phases coexistence. Decreasing water saturation increases flow heterogeneity, creating preferential flow paths and dead-end regions (DER) that alter solute distribution and reaction efficiency. Transmitting pores (TP) enhance mixing via interface deformation driven by stretching and shrinking. Conversely, DER act as low-velocity traps, contributing to mixing through diffusion and delayed reactant release. A unified understanding of their distinct roles in mixing interface evolution and upscaled reaction rates remains limited. Using high-resolution multiphase flow simulations, we investigate how water saturation influences mixing interface evolution across Péclet numbers. We develop a two-compartment model that separately accounts for interface deformation in TP and solute trapping in dead-end regions. We show that, even under unsaturated conditions, the mixing interface deformation within TP eventually plateaus once a balance between stretching and diffusion is reached. In contrast, interface segments in DER are governed by the dynamic interplay between the generation of new trapped segments and the decay of existing ones. This controls the late-time behavior of interface length, which continues to grow until it reaches saturation. Our framework reproduces the observed mixing dynamics and provides a simple expression linking reaction rate to the total mixing interface length. The results demonstrate that under low saturation, the prolonged elongation of the interface substantially enhances reaction rates, highlighting the critical role of saturation-driven heterogeneity in reactive transport.
{"title":"Role of Dead-End Regions and Transmitting Pores in Mixing and Reactivity in Unsaturated Porous Media","authors":"Saif Farhat, Guillem Sole-Mari, Diogo Bolster","doi":"10.1029/2025wr041699","DOIUrl":"https://doi.org/10.1029/2025wr041699","url":null,"abstract":"Mixing-limited reactions in unsaturated porous media are controlled by complex pore-scale processes arising from air and water phases coexistence. Decreasing water saturation increases flow heterogeneity, creating preferential flow paths and dead-end regions (DER) that alter solute distribution and reaction efficiency. Transmitting pores (TP) enhance mixing via interface deformation driven by stretching and shrinking. Conversely, DER act as low-velocity traps, contributing to mixing through diffusion and delayed reactant release. A unified understanding of their distinct roles in mixing interface evolution and upscaled reaction rates remains limited. Using high-resolution multiphase flow simulations, we investigate how water saturation influences mixing interface evolution across Péclet numbers. We develop a two-compartment model that separately accounts for interface deformation in TP and solute trapping in dead-end regions. We show that, even under unsaturated conditions, the mixing interface deformation within TP eventually plateaus once a balance between stretching and diffusion is reached. In contrast, interface segments in DER are governed by the dynamic interplay between the generation of new trapped segments and the decay of existing ones. This controls the late-time behavior of interface length, which continues to grow until it reaches saturation. Our framework reproduces the observed mixing dynamics and provides a simple expression linking reaction rate to the total mixing interface length. The results demonstrate that under low saturation, the prolonged elongation of the interface substantially enhances reaction rates, highlighting the critical role of saturation-driven heterogeneity in reactive transport.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"15 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146210363","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}
Alessandro Michielotto, Alvise Finotello, Bruno Matticchio, Davide Tognin, Riccardo A. Mel, Alessandra Feola, Daniele P. Viero, Luca Carniello, Andrea D’Alpaos
Estuaries provide vital ecosystem services, but the communities and ecosystems they support are increasingly threatened by flooding driven by climate change and sea level rise. Hard-engineering solutions like levees, seawalls, river diversions, and storm-surge barriers help mitigate flooding risk, but their combined operation within the same estuary can result in complex interactions, leading to unintended and difficult-to-predict ecological and environmental consequences. Here, we investigated the northern Venice Lagoon, where a spillway in a river levee bordering the lagoon and a floodgate system at the lagoon inlets operate as flood defenses. Using numerical modeling informed by field data, we evaluated their combined impacts on lagoon hydrodynamics during November 2019—a month marked by extreme rainfall and storm surges that triggered multiple spillway activations and severe flooding in Venice City. We compared scenarios with and without floodgate activation and assessed the effects of projected sea level rise over a 40-year timespan. Our results show that floodgate closures reduce salinity by limiting tidal propagation and increasing hydraulic heads at the spillway, which enhances freshwater inflow by up to 40%. Future sea-level rise scenarios predict more frequent and longer floodgate closures (up to +174 hr monthly), boosting freshwater inflow through the spillway and increasing lagoonal water levels (up to +3.6 cm). This might necessitate earlier floodgate activations, further widening areas affected by salinity changes. Our findings highlight the need to carefully evaluate interactions between flood-defense measures to protect coastal cities while safeguarding estuarine ecosystem resilience under climate change and rising anthropogenic pressures.
{"title":"Salinity Variations in the Venice Lagoon (Italy) Induced by Safeguard Structures: A Challenging Trade-Off Between Urban and Ecosystem Protection in the Face of Climate Change","authors":"Alessandro Michielotto, Alvise Finotello, Bruno Matticchio, Davide Tognin, Riccardo A. Mel, Alessandra Feola, Daniele P. Viero, Luca Carniello, Andrea D’Alpaos","doi":"10.1029/2025wr041173","DOIUrl":"https://doi.org/10.1029/2025wr041173","url":null,"abstract":"Estuaries provide vital ecosystem services, but the communities and ecosystems they support are increasingly threatened by flooding driven by climate change and sea level rise. Hard-engineering solutions like levees, seawalls, river diversions, and storm-surge barriers help mitigate flooding risk, but their combined operation within the same estuary can result in complex interactions, leading to unintended and difficult-to-predict ecological and environmental consequences. Here, we investigated the northern Venice Lagoon, where a spillway in a river levee bordering the lagoon and a floodgate system at the lagoon inlets operate as flood defenses. Using numerical modeling informed by field data, we evaluated their combined impacts on lagoon hydrodynamics during November 2019—a month marked by extreme rainfall and storm surges that triggered multiple spillway activations and severe flooding in Venice City. We compared scenarios with and without floodgate activation and assessed the effects of projected sea level rise over a 40-year timespan. Our results show that floodgate closures reduce salinity by limiting tidal propagation and increasing hydraulic heads at the spillway, which enhances freshwater inflow by up to 40%. Future sea-level rise scenarios predict more frequent and longer floodgate closures (up to +174 hr monthly), boosting freshwater inflow through the spillway and increasing lagoonal water levels (up to +3.6 cm). This might necessitate earlier floodgate activations, further widening areas affected by salinity changes. Our findings highlight the need to carefully evaluate interactions between flood-defense measures to protect coastal cities while safeguarding estuarine ecosystem resilience under climate change and rising anthropogenic pressures.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"1 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146205045","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}
Byung Joon Lee, Trung Tin Huynh, Thi Thuy Trang Pham, Michael Fettweis, Jin Hur, Sang Deuk Lee, Jasper A. Vrugt, Qilong Bi, Xiaoteng Shen, Nathan Terseleer, Sungyun Lee, Yun Young Choi
The dynamics of suspended particulate matter (SPM) plays a crucial role in determining water quality, sediment transport, and biogeochemical cycles in inland, estuarine, and coastal water resources. Flocculation processes strongly influence the SPM dynamics via aggregation and breakage under various hydrodynamic and biogeochemical conditions. This study introduces a mechanistic and diagnostic framework that combines a two-class population balance equation (TCPBE) model with Bayesian calibration to simulate flocculation–transport behavior in both laboratory- (time-dependent batch) and field-scale (one-dimensional vertical) systems. Laboratory experiments with biopolymer–clay and microalgae–clay mixtures and field observations from an estuarine turbidity maximum zone are used to derive a comprehensive data set for model validation. Bayesian inference enables the estimation of uncertain model parameters while characterizing their statistical properties, thus supporting the mechanistic interpretation of flocculation dynamics. By quantifying how ionic strength and microbial physiology regulate flocculation kinetics and elucidating the turbulence-driven coupling between flocculation kinetics and sediment transport over tidal cycles, the framework demonstrates its suitability as a process-based diagnostic tool capable of effectively capturing SPM dynamics under various conditions. This framework has strong potential to advance the understanding of flocculation dynamics and support a range of applications in inland and estuarine sediment-laden water systems, including river, reservoir, esturine and coastal waters.
{"title":"Diagnosing the Flocculation–Transport Dynamics of Suspended Particulate Matter Using a Two-Class Population Balance Model and Bayesian Calibration","authors":"Byung Joon Lee, Trung Tin Huynh, Thi Thuy Trang Pham, Michael Fettweis, Jin Hur, Sang Deuk Lee, Jasper A. Vrugt, Qilong Bi, Xiaoteng Shen, Nathan Terseleer, Sungyun Lee, Yun Young Choi","doi":"10.1029/2025wr041729","DOIUrl":"https://doi.org/10.1029/2025wr041729","url":null,"abstract":"The dynamics of suspended particulate matter (SPM) plays a crucial role in determining water quality, sediment transport, and biogeochemical cycles in inland, estuarine, and coastal water resources. Flocculation processes strongly influence the SPM dynamics via aggregation and breakage under various hydrodynamic and biogeochemical conditions. This study introduces a mechanistic and diagnostic framework that combines a two-class population balance equation (TCPBE) model with Bayesian calibration to simulate flocculation–transport behavior in both laboratory- (<i>time-dependent batch</i>) and field-scale (one-dimensional vertical) systems. Laboratory experiments with biopolymer–clay and microalgae–clay mixtures and field observations from an estuarine turbidity maximum zone are used to derive a comprehensive data set for model validation. Bayesian inference enables the estimation of uncertain model parameters while characterizing their statistical properties, thus supporting the mechanistic interpretation of flocculation dynamics. By quantifying how ionic strength and microbial physiology regulate flocculation kinetics and elucidating the turbulence-driven coupling between flocculation kinetics and sediment transport over tidal cycles, the framework demonstrates its suitability as a process-based diagnostic tool capable of effectively capturing SPM dynamics under various conditions. This framework has strong potential to advance the understanding of flocculation dynamics and support a range of applications in inland and estuarine sediment-laden water systems, including river, reservoir, esturine and coastal waters.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"32 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146184430","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}
Kenneth C. Gourley, Richard A. Bennett, Christopher Harig
We use geodetic data to show that hydrologically distinct sub-regions in the Southwest United States act independently of one another. The limited number of Global Navigation Satellite System (GNSS) stations and resolution of Gravity Recovery and Climate Experiment (GRACE) make hydrological partitioning difficult to unravel, especially in the Colorado River Basin which comprises a diversity of climates due to its highly variable topography. Here, we compare GNSS station vertical displacement data, GRACE surface mass change data, and snow water equivalent (SWE) data using elastic surface displacement modeling and signal localization techniques. We focus on a region composed of Arizona, New Mexico, Colorado, and Utah, allowing for the examination of variations in the Colorado River Basin, the primary source of water for the region's municipalities, agriculture, and ecosystems. We demonstrate that the accumulation and melt of snow have a first-order control on the timing of vertical displacement in this region. There exists a region-dependent seasonal partitioning between when GNSS and GRACE sense changes in the distribution of terrestrial water storage. In the Wasatch Range of central Utah, GNSS stations sense loading due to changes in the snowpack one to 2 months in advance of GRACE; in the Southern Rocky Mountains of Colorado, GNSS stations sense loading due to changes in the snowpack one to 3 months in advance of GRACE; and in the lower Colorado River Basin of Arizona, GRACE senses loading due to changes in river runoff three or more months in advance of GNSS stations.
{"title":"Quantifying Changes in Water Loading in the U.S. Southwest via Comparison of GNSS, GRACE, and SWE Data Sets","authors":"Kenneth C. Gourley, Richard A. Bennett, Christopher Harig","doi":"10.1029/2025wr040324","DOIUrl":"https://doi.org/10.1029/2025wr040324","url":null,"abstract":"We use geodetic data to show that hydrologically distinct sub-regions in the Southwest United States act independently of one another. The limited number of Global Navigation Satellite System (GNSS) stations and resolution of Gravity Recovery and Climate Experiment (GRACE) make hydrological partitioning difficult to unravel, especially in the Colorado River Basin which comprises a diversity of climates due to its highly variable topography. Here, we compare GNSS station vertical displacement data, GRACE surface mass change data, and snow water equivalent (SWE) data using elastic surface displacement modeling and signal localization techniques. We focus on a region composed of Arizona, New Mexico, Colorado, and Utah, allowing for the examination of variations in the Colorado River Basin, the primary source of water for the region's municipalities, agriculture, and ecosystems. We demonstrate that the accumulation and melt of snow have a first-order control on the timing of vertical displacement in this region. There exists a region-dependent seasonal partitioning between when GNSS and GRACE sense changes in the distribution of terrestrial water storage. In the Wasatch Range of central Utah, GNSS stations sense loading due to changes in the snowpack one to 2 months in advance of GRACE; in the Southern Rocky Mountains of Colorado, GNSS stations sense loading due to changes in the snowpack one to 3 months in advance of GRACE; and in the lower Colorado River Basin of Arizona, GRACE senses loading due to changes in river runoff three or more months in advance of GNSS stations.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"13 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160934","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}
Dynamic gravel bed rivers experience frequent changes in channel position and flow distribution between branches, which can alter the location and extent of flooding. Changes in flow routing can significantly impact livelihoods, habitats and infrastructure in adjacent floodplains. Here, we test whether variations in seasonal discharge patterns cause instability in channel position and flow distribution in a gravel bed river system around a major channel bifurcation. Satellite images of the Karnali River, Nepal, were assessed over a 30-year period to identify changes in channel position and flow partitioning downstream of the bifurcation. These observations were compared with daily discharge records to establish whether the sequencing of peak monsoonal flows coincided with geomorphic changes in the river. Changes to flow partitioning trends were consistently preceded by monsoon seasons with two large peak flows, suggesting a history-dependent threshold in the channels. To explain this observation, we use grain-size data from gravel bars that reveal variable grain clustering and bed armoring across the channel network. We propose that two high discharges are needed to transition between phases of bifurcation stability or instability, where the first event acts to break down the bed armor layer, allowing the second high flow to drive enlargement/closure of branches and reworking of the bed. Our findings suggest that flow sequencing is an important driver in flow distribution and stability at bifurcations in gravel bed rivers. Although the focus is on Himalayan rivers, the findings may be of relevance in other areas that experience changing seasonal flood regimes.
{"title":"Flow History Effects on River Bifurcation Dynamics in a Himalayan River","authors":"C. Cload, M. J. Creed, E. H. Dingle, L. Quick","doi":"10.1029/2024wr039351","DOIUrl":"https://doi.org/10.1029/2024wr039351","url":null,"abstract":"Dynamic gravel bed rivers experience frequent changes in channel position and flow distribution between branches, which can alter the location and extent of flooding. Changes in flow routing can significantly impact livelihoods, habitats and infrastructure in adjacent floodplains. Here, we test whether variations in seasonal discharge patterns cause instability in channel position and flow distribution in a gravel bed river system around a major channel bifurcation. Satellite images of the Karnali River, Nepal, were assessed over a 30-year period to identify changes in channel position and flow partitioning downstream of the bifurcation. These observations were compared with daily discharge records to establish whether the sequencing of peak monsoonal flows coincided with geomorphic changes in the river. Changes to flow partitioning trends were consistently preceded by monsoon seasons with two large peak flows, suggesting a history-dependent threshold in the channels. To explain this observation, we use grain-size data from gravel bars that reveal variable grain clustering and bed armoring across the channel network. We propose that two high discharges are needed to transition between phases of bifurcation stability or instability, where the first event acts to break down the bed armor layer, allowing the second high flow to drive enlargement/closure of branches and reworking of the bed. Our findings suggest that flow sequencing is an important driver in flow distribution and stability at bifurcations in gravel bed rivers. Although the focus is on Himalayan rivers, the findings may be of relevance in other areas that experience changing seasonal flood regimes.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"10 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146205044","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}
Guangli Su, Chunbao Xiong, Wei Zhan, Nannan Guo, Renjie Wei
Monitoring three-dimensional (3D) surface deformation in groundwater pumping areas remains a significant challenge, as conventional InSAR techniques are constrained by their limited sensitivity to north-south displacement. This study develops an inversion approach—the Vertical Gradient-Constrained Strain Model (VG-SM3D)—that combines multi-track (ascending and descending) InSAR line-of-sight data with a strain model and a physical prior relating horizontal deformation to the vertical deformation gradient. The method is validated using simulated data and applied to the Tianjin region, which experiences substantial subsidence due to groundwater extraction. VG-SM3D successfully reconstructs the 3D deformation field, revealing pronounced subsidence (peaking at ∼150 mm/yr) and horizontal motion (up to ∼20 mm/yr) that converges toward the centers of subsidence funnels. Comparisons with leveling and GNSS measurements demonstrate good agreement, with estimated uncertainties below 4 and 5 mm/yr for the horizontal and vertical components, respectively. The derived horizontal strain field shows compression within funnel centers and extension along their edges. Furthermore, comparisons with groundwater level data indicate that subsidence is spatially correlated with groundwater drawdown cones, while horizontal deformation aligns well with horizontal hydraulic gradients. This study provides an effective framework for retrieving 3D deformation in pumping zones and offers important insights into the interactions between groundwater extraction, aquifer stress, and surface displacement.
{"title":"Retrieving Three-Dimensional Deformation in Groundwater Pumping Areas Based on InSAR Data","authors":"Guangli Su, Chunbao Xiong, Wei Zhan, Nannan Guo, Renjie Wei","doi":"10.1029/2025wr040188","DOIUrl":"https://doi.org/10.1029/2025wr040188","url":null,"abstract":"Monitoring three-dimensional (3D) surface deformation in groundwater pumping areas remains a significant challenge, as conventional InSAR techniques are constrained by their limited sensitivity to north-south displacement. This study develops an inversion approach—the Vertical Gradient-Constrained Strain Model (VG-SM3D)—that combines multi-track (ascending and descending) InSAR line-of-sight data with a strain model and a physical prior relating horizontal deformation to the vertical deformation gradient. The method is validated using simulated data and applied to the Tianjin region, which experiences substantial subsidence due to groundwater extraction. VG-SM3D successfully reconstructs the 3D deformation field, revealing pronounced subsidence (peaking at ∼150 mm/yr) and horizontal motion (up to ∼20 mm/yr) that converges toward the centers of subsidence funnels. Comparisons with leveling and GNSS measurements demonstrate good agreement, with estimated uncertainties below 4 and 5 mm/yr for the horizontal and vertical components, respectively. The derived horizontal strain field shows compression within funnel centers and extension along their edges. Furthermore, comparisons with groundwater level data indicate that subsidence is spatially correlated with groundwater drawdown cones, while horizontal deformation aligns well with horizontal hydraulic gradients. This study provides an effective framework for retrieving 3D deformation in pumping zones and offers important insights into the interactions between groundwater extraction, aquifer stress, and surface displacement.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"224 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160939","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}
Paul C. Astagneau, Jonas Peters, Sandra Pool, Eduardo Muñoz-Castro, Manuela I. Brunner
Hydrological models are calibrated on specific periods based on how well simulations match streamflow observations by selecting the parameter vector(s) that provide the highest accuracy. This accuracy can decrease significantly during extrapolation to periods not seen during calibration, especially when they are characterized by different climate conditions. Here, we develop a novel calibration objective that relies on the joint calibration of model accuracy and time-invariance of residuals, hypothesizing that such invariance can improve the temporal generalizability of hydrological models. We test this approach using a hydrological model and 208 catchments in Western Germany. We evaluate it based on (a) the accuracy loss between calibration and evaluation, (b) the compromise between accuracy and residual invariance, (c) the dependence of residuals on variations in forcings, and (d) the instability of parameters between two calibration periods. We find that our approach reduces the loss in accuracy between calibration and extrapolation as compared to calibration approaches solely based on accuracy. Furthermore, our method combined with the Boxcox streamflow transformation weakens the dependence of residuals on variations in the forcing. However, we find that invariant residuals do not necessarily imply improved parameter stability between different calibration periods. Furthermore, the gain in temporal generalizability comes at the cost of a decrease in accuracy, which should be considered based on the application. Our results highlight that the new RESIdual STability (RESIST) calibration objective has the potential to improve the temporal generalizability of hydrological models for climate-impact studies.
{"title":"RESIdual STability (RESIST) Calibration for Improved Hydrological Model Time Generalizability","authors":"Paul C. Astagneau, Jonas Peters, Sandra Pool, Eduardo Muñoz-Castro, Manuela I. Brunner","doi":"10.1029/2025wr041435","DOIUrl":"https://doi.org/10.1029/2025wr041435","url":null,"abstract":"Hydrological models are calibrated on specific periods based on how well simulations match streamflow observations by selecting the parameter vector(s) that provide the highest accuracy. This accuracy can decrease significantly during extrapolation to periods not seen during calibration, especially when they are characterized by different climate conditions. Here, we develop a novel calibration objective that relies on the joint calibration of model accuracy and time-invariance of residuals, hypothesizing that such invariance can improve the temporal generalizability of hydrological models. We test this approach using a hydrological model and 208 catchments in Western Germany. We evaluate it based on (a) the accuracy loss between calibration and evaluation, (b) the compromise between accuracy and residual invariance, (c) the dependence of residuals on variations in forcings, and (d) the instability of parameters between two calibration periods. We find that our approach reduces the loss in accuracy between calibration and extrapolation as compared to calibration approaches solely based on accuracy. Furthermore, our method combined with the Boxcox streamflow transformation weakens the dependence of residuals on variations in the forcing. However, we find that invariant residuals do not necessarily imply improved parameter stability between different calibration periods. Furthermore, the gain in temporal generalizability comes at the cost of a decrease in accuracy, which should be considered based on the application. Our results highlight that the new RESIdual STability (RESIST) calibration objective has the potential to improve the temporal generalizability of hydrological models for climate-impact studies.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"1 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146205039","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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