Pub Date : 2026-10-28DOI: 10.1016/j.envsoft.2026.107214
Dasuni Amanda Salpadoru, Matthew P. Adams, Kate Helmstedt, David J. Warne
Ecological regime shifts are potentially a common property of ecosystems, describing transitions between alternative stable states that can represent healthy or unhealthy conditions under the same environmental drivers. Once a tipping point, defined as a critical threshold separating alternative stable states, is crossed, the system may degrade, and recovery can be difficult, making early detection essential for effective ecosystem management. Predicting these tipping points requires models that exhibit bistability, representing systems that can exist in two alternative stable states under identical environmental conditions. A key question is whether standard ecological monitoring data can be used to identify bistability and accurately estimate tipping points. Using the Carpenter model of lake eutrophication, which exhibits bistability between clear and polluted water states, we generate synthetic data under known stability regimes. Profile likelihood analysis is then applied to assess parameter identifiability and detect system stability and tipping points. Our results show that standard monitoring data do not always provide sufficient information to distinguish bistable from stable regimes. Importantly, for bistable systems, observations collected close to the tipping point improve parameter and stability identifiability, although this improvement remains strongly dependent on the observation noise level and sampling frequency
{"title":"Parameter estimation and identifiability analysis of stability and tipping points in potentially bistable ecosystems","authors":"Dasuni Amanda Salpadoru, Matthew P. Adams, Kate Helmstedt, David J. Warne","doi":"10.1016/j.envsoft.2026.107214","DOIUrl":"https://doi.org/10.1016/j.envsoft.2026.107214","url":null,"abstract":"Ecological regime shifts are potentially a common property of ecosystems, describing transitions between alternative stable states that can represent healthy or unhealthy conditions under the same environmental drivers. Once a tipping point, defined as a critical threshold separating alternative stable states, is crossed, the system may degrade, and recovery can be difficult, making early detection essential for effective ecosystem management. Predicting these tipping points requires models that exhibit bistability, representing systems that can exist in two alternative stable states under identical environmental conditions. A key question is whether standard ecological monitoring data can be used to identify bistability and accurately estimate tipping points. Using the Carpenter model of lake eutrophication, which exhibits bistability between clear and polluted water states, we generate synthetic data under known stability regimes. Profile likelihood analysis is then applied to assess parameter identifiability and detect system stability and tipping points. Our results show that standard monitoring data do not always provide sufficient information to distinguish bistable from stable regimes. Importantly, for bistable systems, observations collected close to the tipping point improve parameter and stability identifiability, although this improvement remains strongly dependent on the observation noise level and sampling frequency","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"15 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149292015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-10-28DOI: 10.1016/j.envsoft.2026.107220
Yinchi Zhang, Yuandong Min, Wanling Xu, Xiangyong Lei, Jiewen You, Yi Wang, Xiaobo Hao, Jianhui Wei, Ying Chen, Lu Gao, Harald Kunstmann
As hydrological models become more complex, the effects of alternative physical process representations remain insufficiently examined. Following the precipitation–land surface–soil pathway of error propagation, this study uses the Weather Research and Forecasting model with Hydrological extension (WRF/WRF-Hydro) to evaluate precipitation forcing, physical process configurations, and hydraulic parameters in the Minjiang River Basin. Factorial decomposition of eight deterministic simulations shows that the contrast between default and adjusted scheme configurations accounts for 16.49%–21.59% of their variance across four stations. Process diagnostics identify surface exchange and runoff–groundwater adjustments as key to improving streamflow simulation through reduced evapotranspiration and enhanced runoff generation. The precipitation–physical scheme interaction further reveals that the streamflow response to these adjustments depends on forcing choice. These findings highlight the value of evaluating physical process representation alongside forcing selection and parameter calibration, providing a framework for diagnosing configuration effects and guiding hydrological model development across diverse catchments.
{"title":"Partitioning streamflow simulation uncertainty among precipitation forcing, physical process representation, and hydraulic parameters using WRF/WRF-Hydro model","authors":"Yinchi Zhang, Yuandong Min, Wanling Xu, Xiangyong Lei, Jiewen You, Yi Wang, Xiaobo Hao, Jianhui Wei, Ying Chen, Lu Gao, Harald Kunstmann","doi":"10.1016/j.envsoft.2026.107220","DOIUrl":"https://doi.org/10.1016/j.envsoft.2026.107220","url":null,"abstract":"As hydrological models become more complex, the effects of alternative physical process representations remain insufficiently examined. Following the precipitation–land surface–soil pathway of error propagation, this study uses the Weather Research and Forecasting model with Hydrological extension (WRF/WRF-Hydro) to evaluate precipitation forcing, physical process configurations, and hydraulic parameters in the Minjiang River Basin. Factorial decomposition of eight deterministic simulations shows that the contrast between default and adjusted scheme configurations accounts for 16.49%–21.59% of their variance across four stations. Process diagnostics identify surface exchange and runoff–groundwater adjustments as key to improving streamflow simulation through reduced evapotranspiration and enhanced runoff generation. The precipitation–physical scheme interaction further reveals that the streamflow response to these adjustments depends on forcing choice. These findings highlight the value of evaluating physical process representation alongside forcing selection and parameter calibration, providing a framework for diagnosing configuration effects and guiding hydrological model development across diverse catchments.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"15 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149292531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-10-23DOI: 10.1016/j.envsoft.2026.107216
Addis A. Alaminie, Jude Dzevela Kong, Mohammed Basheer
Distributed hydrological models are used in environmental and water resources management. However, their calibration is challenging due to uncertainty in behavioural parameters and the need to consider multiple performance metrics. A generalized metaheuristic tool can help overcome these challenges for hydrological models such as wflow_sbm and environmental simulators. We present Optienv, an open-source, simulator-agnostic, AI-assisted Python framework that couples external simulators with multi-objective evolutionary algorithms through a modular, configuration-driven architecture supporting parallel processing and post-calibration interpretation. The framework is demonstrated with wflow_sbm using four case studies from Nile Basin headwater systems: Gilgel Abay, Tana, Blue Nile, and Tekeze-Atbara. Results show that Optienv identified hydrologically interpretable Pareto solutions and reproduced dominant seasonal hydrograph structures with physically coherent spatial water-balance behaviour. Explainable AI analysis identified land-use-specific storage controls as dominant drivers of calibration response. These findings establish Optienv as a calibration tool for wflow_sbm and a transferable framework for other environmental simulators.
{"title":"AI-assisted multi-objective calibration tool for the wflow_sbm hydrological model: Applications in the Nile Basin","authors":"Addis A. Alaminie, Jude Dzevela Kong, Mohammed Basheer","doi":"10.1016/j.envsoft.2026.107216","DOIUrl":"https://doi.org/10.1016/j.envsoft.2026.107216","url":null,"abstract":"Distributed hydrological models are used in environmental and water resources management. However, their calibration is challenging due to uncertainty in behavioural parameters and the need to consider multiple performance metrics. A generalized metaheuristic tool can help overcome these challenges for hydrological models such as wflow_sbm and environmental simulators. We present Optienv, an open-source, simulator-agnostic, AI-assisted Python framework that couples external simulators with multi-objective evolutionary algorithms through a modular, configuration-driven architecture supporting parallel processing and post-calibration interpretation. The framework is demonstrated with wflow_sbm using four case studies from Nile Basin headwater systems: Gilgel Abay, Tana, Blue Nile, and Tekeze-Atbara. Results show that Optienv identified hydrologically interpretable Pareto solutions and reproduced dominant seasonal hydrograph structures with physically coherent spatial water-balance behaviour. Explainable AI analysis identified land-use-specific storage controls as dominant drivers of calibration response. These findings establish Optienv as a calibration tool for wflow_sbm and a transferable framework for other environmental simulators.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"28 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149292017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-27DOI: 10.1016/j.envsoft.2026.106897
Jorge Saavedra-Garrido , Jorge Arevalo , Luis De La Fuente , Aldo Tapia , Christopher Paredes-Arroyo , Ana Maria Cordova , Daira Velandia , Pablo Álvarez , Héctor Reyes-Serrano , Rodrigo Salas
Reliable short-term streamflow forecasting remains a key challenge due to data latency, uncertainty, and other real-world constraints. This study presents a regional Long Short-Term Memory (LSTM) model to predict daily mean and maximum streamflow across 340 points in Chile over a five-day horizon, explicitly accounting for operational limitations such as unavailable recent streamflow and delayed input data. Compared to locally trained models, the regional model demonstrates superior performance in temporal correlation and variance representation, with Kling–Gupta Efficiency (KGE) 0.6 at 156 points. Crucially, high-flow event prediction improves significantly: bias in the Fractional High-flow Volume (FHV) is reduced by 50% at the 90th percentile and 25% at the 99th percentile, demonstrating strong operational robustness with minimal degradation over the forecast horizon. These findings highlight the potential of regional deep learning models to offer scalable and resilient performance across diverse hydrological settings, supporting flood preparedness and water management.
{"title":"Regional vs local LSTM models for short-term streamflow forecasting under operational constraints","authors":"Jorge Saavedra-Garrido , Jorge Arevalo , Luis De La Fuente , Aldo Tapia , Christopher Paredes-Arroyo , Ana Maria Cordova , Daira Velandia , Pablo Álvarez , Héctor Reyes-Serrano , Rodrigo Salas","doi":"10.1016/j.envsoft.2026.106897","DOIUrl":"10.1016/j.envsoft.2026.106897","url":null,"abstract":"<div><div>Reliable short-term streamflow forecasting remains a key challenge due to data latency, uncertainty, and other real-world constraints. This study presents a regional Long Short-Term Memory (LSTM) model to predict daily mean and maximum streamflow across 340 points in Chile over a five-day horizon, explicitly accounting for operational limitations such as unavailable recent streamflow and delayed input data. Compared to locally trained models, the regional model demonstrates superior performance in temporal correlation and variance representation, with Kling–Gupta Efficiency (KGE) <span><math><mo>≥</mo></math></span> 0.6 at 156 points. Crucially, high-flow event prediction improves significantly: bias in the Fractional High-flow Volume (FHV) is reduced by <span><math><mo>∼</mo></math></span>50% at the 90th percentile and <span><math><mo>∼</mo></math></span>25% at the 99th percentile, demonstrating strong operational robustness with minimal degradation over the forecast horizon. These findings highlight the potential of regional deep learning models to offer scalable and resilient performance across diverse hydrological settings, supporting flood preparedness and water management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106897"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-12DOI: 10.1016/j.envsoft.2026.106905
Haoran Xing, Ying Li, Dashe Li, Huanhai Yang
Accurate prediction of dissolved oxygen (DO) is crucial for intelligent decision-making in aquaculture. However, achieving this goal is challenging due to nonstationarity, multi-period aliasing, and local phase shifts inherent in DO series. We propose the Multi-scale Adaptive transformer (MAformer) for water quality prediction. First, the hierarchical adaptive smoothing decomposer stabilizes long-term patterns while preserving short-term details. Second, a multi-period phase-aligned attention module achieves cross-period synchronization. Third, the phase-shift correction attention module enhances robustness to short-term disturbances. Experiments on six marine ranching datasets from diverse geographical and climatic regions demonstrate that MAformer significantly outperforms seven state-of-the-art baseline models. For instance, it achieves an average reduction of 9.59% in MAE and 7.96% in RMSE, alongside improvements of 8.39% in and 6.25% in KGE. These results confirm MAformer’s superior capability as a reliable and generalizable tool for intelligent aquaculture management.
{"title":"MAformer: A multivariate prediction framework with adaptive multi-scale decomposition and phase correction for water quality in aquaculture environments","authors":"Haoran Xing, Ying Li, Dashe Li, Huanhai Yang","doi":"10.1016/j.envsoft.2026.106905","DOIUrl":"10.1016/j.envsoft.2026.106905","url":null,"abstract":"<div><div>Accurate prediction of dissolved oxygen (DO) is crucial for intelligent decision-making in aquaculture. However, achieving this goal is challenging due to nonstationarity, multi-period aliasing, and local phase shifts inherent in DO series. We propose the Multi-scale Adaptive transformer (MAformer) for water quality prediction. First, the hierarchical adaptive smoothing decomposer stabilizes long-term patterns while preserving short-term details. Second, a multi-period phase-aligned attention module achieves cross-period synchronization. Third, the phase-shift correction attention module enhances robustness to short-term disturbances. Experiments on six marine ranching datasets from diverse geographical and climatic regions demonstrate that MAformer significantly outperforms seven state-of-the-art baseline models. For instance, it achieves an average reduction of 9.59% in MAE and 7.96% in RMSE, alongside improvements of 8.39% in <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> and 6.25% in KGE. These results confirm MAformer’s superior capability as a reliable and generalizable tool for intelligent aquaculture management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106905"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146209274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-24DOI: 10.1016/j.envsoft.2026.106895
Wenyu Ouyang , Shuolong Xu , Yikai Chai , Laihong Zhuang , Zhihong Liu , Lei Ye , Xinzhuo Wu , Yong Peng , Chi Zhang
This study introduces a Python-based framework for constructing differentiable hydrological models with a modular design to streamline research workflows. The framework integrates five key modules: hydrodataset and hydrodatasource for data preprocessing, hydromodel and torchhydro for traditional and differentiable modeling, and HydroDHM for orchestrating integrated workflows. The data modules automate preparation of diverse datasets, including open-access and proprietary resources. Hydromodel supports process-based model calibration and evaluation, while torchhydro enables neural network integration for differentiable models. HydroDHM coordinates these components through a unified interface for configuring and executing end-to-end modeling pipelines. Case studies in CAMELS basins demonstrate that differentiable models achieve comparable streamflow simulation performance to traditional approaches. By decoupling data handling from model development and providing uv-installable (and pip-compatible) modules, the framework ensures reproducibility, scalability, and adaptability across diverse hydrological contexts.
{"title":"A python framework for differentiable hydrological modeling and research workflow automation","authors":"Wenyu Ouyang , Shuolong Xu , Yikai Chai , Laihong Zhuang , Zhihong Liu , Lei Ye , Xinzhuo Wu , Yong Peng , Chi Zhang","doi":"10.1016/j.envsoft.2026.106895","DOIUrl":"10.1016/j.envsoft.2026.106895","url":null,"abstract":"<div><div>This study introduces a Python-based framework for constructing differentiable hydrological models with a modular design to streamline research workflows. The framework integrates five key modules: hydrodataset and hydrodatasource for data preprocessing, hydromodel and torchhydro for traditional and differentiable modeling, and HydroDHM for orchestrating integrated workflows. The data modules automate preparation of diverse datasets, including open-access and proprietary resources. Hydromodel supports process-based model calibration and evaluation, while torchhydro enables neural network integration for differentiable models. HydroDHM coordinates these components through a unified interface for configuring and executing end-to-end modeling pipelines. Case studies in CAMELS basins demonstrate that differentiable models achieve comparable streamflow simulation performance to traditional approaches. By decoupling data handling from model development and providing uv-installable (and pip-compatible) modules, the framework ensures reproducibility, scalability, and adaptability across diverse hydrological contexts.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106895"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-24DOI: 10.1016/j.envsoft.2026.106896
Hyunwoo Kang , Cameron E. Naficy , Kevin D. Bladon
The 2020 Labor Day fires in the Western Cascades of Oregon, USA, burned extensive forested areas, which altered hydrologic processes, water quality, aquatic ecosystems, and drinking water resources. Understanding wildfire severity effects on hydrologic processes is crucial for improved water resource management. Our study assessed wildfire severity impacts on hydrology using a modified calibration method for the Soil and Water Assessment Tool (SWAT) model. Calibration incorporated evapotranspiration and leaf area index to represent vegetation loss and hydrologic impacts. We also integrated a wildfire module to simulate fire effects on soil and vegetation parameters. This improved modeling approach effectively captured post-fire hydrologic behavior, especially increased high streamflows and reduced evapotranspiration, with greater changes linked to higher burn severity. These findings emphasize the importance of considering fire severity in hydrologic modeling, aiding proactive management and mitigation strategies to protect water supply and enhance ecosystem resilience in wildfire-prone regions.
{"title":"Modeling hydrologic response to wildfires in the Pacific Northwest with a modified calibration technique","authors":"Hyunwoo Kang , Cameron E. Naficy , Kevin D. Bladon","doi":"10.1016/j.envsoft.2026.106896","DOIUrl":"10.1016/j.envsoft.2026.106896","url":null,"abstract":"<div><div>The 2020 Labor Day fires in the Western Cascades of Oregon, USA, burned extensive forested areas, which altered hydrologic processes, water quality, aquatic ecosystems, and drinking water resources. Understanding wildfire severity effects on hydrologic processes is crucial for improved water resource management. Our study assessed wildfire severity impacts on hydrology using a modified calibration method for the Soil and Water Assessment Tool (SWAT) model. Calibration incorporated evapotranspiration and leaf area index to represent vegetation loss and hydrologic impacts. We also integrated a wildfire module to simulate fire effects on soil and vegetation parameters. This improved modeling approach effectively captured post-fire hydrologic behavior, especially increased high streamflows and reduced evapotranspiration, with greater changes linked to higher burn severity. These findings emphasize the importance of considering fire severity in hydrologic modeling, aiding proactive management and mitigation strategies to protect water supply and enhance ecosystem resilience in wildfire-prone regions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106896"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-10DOI: 10.1016/j.envsoft.2026.106908
Dany A. Hernandez , Jorge A. Guzman , Sandra R. Villamizar , Maria L. Chu , Camila Ribeiro , Carlos R. de Mello
This study addresses how spatial and temporal uncertainties in precipitation limit calibration of hydrological models. Adjusting model parameters alone cannot compensate for poorly represented precipitation at the model's lower resolution. A reanalysis framework that integrates traditional calibration with a stepwise precipitation back correction approach was introduced. Using a composite exponential error function, the method derives precipitation correction factors from mismatches between observed and simulated streamflow. The approach was tested with three hydrological models—SWAT, MIKE-SHE, and MHD—across watersheds in the United States and Brazil. The workflow involved an initial standard calibration, followed by iterative precipitation correction without altering model parameters, and a final recalibration incorporating the corrected precipitation. Results showed 10–18% improvements in KGE while maintaining PBIAS below 10% at most stations. The study highlights the value of constraining water balance to avoid unrealistic corrections and demonstrates how addressing precipitation uncertainties enhances model performance across diverse hydrological settings.
{"title":"A stepwise back-correction function for precipitation representation in hydrologic models","authors":"Dany A. Hernandez , Jorge A. Guzman , Sandra R. Villamizar , Maria L. Chu , Camila Ribeiro , Carlos R. de Mello","doi":"10.1016/j.envsoft.2026.106908","DOIUrl":"10.1016/j.envsoft.2026.106908","url":null,"abstract":"<div><div>This study addresses how spatial and temporal uncertainties in precipitation limit calibration of hydrological models. Adjusting model parameters alone cannot compensate for poorly represented precipitation at the model's lower resolution. A reanalysis framework that integrates traditional calibration with a stepwise precipitation back correction approach was introduced. Using a composite exponential error function, the method derives precipitation correction factors from mismatches between observed and simulated streamflow. The approach was tested with three hydrological models—SWAT, MIKE-SHE, and MHD—across watersheds in the United States and Brazil. The workflow involved an initial standard calibration, followed by iterative precipitation correction without altering model parameters, and a final recalibration incorporating the corrected precipitation. Results showed 10–18% improvements in KGE while maintaining PBIAS below 10% at most stations. The study highlights the value of constraining water balance to avoid unrealistic corrections and demonstrates how addressing precipitation uncertainties enhances model performance across diverse hydrological settings.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106908"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146152965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：