Environmental Modelling & Software最新文献

Sensitivity and uncertainty in habitat suitability modeling: guidance and pitfalls for ecological applications 生境适宜性建模的敏感性和不确定性：生态学应用的指导和缺陷

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-04-04 DOI: 10.1016/j.envsoft.2026.106984

Kiara Cushway, Colton Shaw, S. Kyle McKay, Todd Swannack

引用次数: 0

Regional vs local LSTM models for short-term streamflow forecasting under operational constraints 区域与本地LSTM模型在操作约束下的短期流量预测

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 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

{"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}

引用次数: 0

MAformer: A multivariate prediction framework with adaptive multi-scale decomposition and phase correction for water quality in aquaculture environments MAformer：具有自适应多尺度分解和相位校正的水产养殖环境水质多元预测框架

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-02-12 DOI: 10.1016/j.envsoft.2026.106905

Haoran Xing, Ying Li, Dashe Li, Huanhai Yang

{"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}

引用次数: 0

A python framework for differentiable hydrological modeling and research workflow automation 可微分水文建模和研究工作流自动化的Python框架

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-01-24 DOI: 10.1016/j.envsoft.2026.106895

Wenyu Ouyang , Shuolong Xu , Yikai Chai , Laihong Zhuang , Zhihong Liu , Lei Ye , Xinzhuo Wu , Yong Peng , Chi Zhang

{"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}

引用次数: 0

Modeling hydrologic response to wildfires in the Pacific Northwest with a modified calibration technique 用改进的校准技术模拟太平洋西北地区野火的水文响应

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-01-24 DOI: 10.1016/j.envsoft.2026.106896

Hyunwoo Kang , Cameron E. Naficy , Kevin D. Bladon

{"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}

引用次数: 0

A stepwise back-correction function for precipitation representation in hydrologic models 水文模型降水表示的逐步反校正函数

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-02-10 DOI: 10.1016/j.envsoft.2026.106908

Dany A. Hernandez , Jorge A. Guzman , Sandra R. Villamizar , Maria L. Chu , Camila Ribeiro , Carlos R. de Mello

{"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}

引用次数: 0

The importance of system interactions in hydrodynamic models of parts of complex interconnected deltas 系统相互作用在复杂互联三角洲部分水动力模型中的重要性

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-01-06 DOI: 10.1016/j.envsoft.2025.106838

Niels M. Welsch, Jord J. Warmink, Suzanne J.M.H. Hulscher, Denie C.M. Augustijn

引用次数: 0

Advancing water level prediction using clustering-based machine learning techniques in data-scarce regions 在数据稀缺地区使用基于聚类的机器学习技术推进水位预测

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 10.1016/j.envsoft.2026.106899

SangHyun Lee, Taeil Jang

{"title":"Advancing water level prediction using clustering-based machine learning techniques in data-scarce regions","authors":"SangHyun Lee, Taeil Jang","doi":"10.1016/j.envsoft.2026.106899","DOIUrl":"10.1016/j.envsoft.2026.106899","url":null,"abstract":"<div><div>Accurate and scalable water level forecasting is essential for effective water resources management, particularly in regions with limited long-term records. We present a clustering-based framework for one- and three-day-ahead water level prediction in the Saemangeum Watershed, South Korea. Twenty-five monitoring stations were grouped into six hydrologically similar clusters using k-means clustering with wavelet-entropy features. Within each cluster, multilayer perceptron (MLP) models were trained using two strategies: (1) training only at the centroid station and (2) training at the station with the longest record in each cluster. The longest-record strategy showed strong agreement with observations, achieving mean Nash–Sutcliffe efficiency and root-mean-square error values of 0.97 and 0.06 for one-day-ahead forecasts, and 0.83 and 0.14 for three-day-ahead forecasts across all stations. By training one MLP per cluster and transferring it to all member stations, the framework reduces computational cost and provides a practical solution for large-scale water level forecasting in data-scarce environments.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106899"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071618","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}

引用次数: 0

SPAR-TC: A framework for accounting spatial representativeness in triple collocation SPAR-TC：三重搭配中空间代表性的核算框架

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-01-12 DOI: 10.1016/j.envsoft.2026.106874

Diksha Gupta, C.T. Dhanya

{"title":"SPAR-TC: A framework for accounting spatial representativeness in triple collocation","authors":"Diksha Gupta, C.T. Dhanya","doi":"10.1016/j.envsoft.2026.106874","DOIUrl":"10.1016/j.envsoft.2026.106874","url":null,"abstract":"<div><div>Triple collocation (TC) has been widely used to overcome the rarity of “ground truth” in geophysical measurements. While TC assumes all systems observe the same underlying geophysical variable, it does not inherently correct for spatial representativeness errors due to different spatial measurement systems. To address this, we propose the Spatially Representative Triple Collocation (SPAR-TC), which accounts for the spatial variability of the “ground truth” across different spatial scales. A synthetic soil moisture experiment assessed SPAR-TC sensitivity to spatial heterogeneity and sample size, followed by a real-world application with remotely sensed precipitation data. Results showed that SPAR-TC provides more reliable estimates of “true” error variance compared with traditional TC, especially in spatially heterogeneous regions. Both methods yield comparable dataset rankings; however, SPAR-TC provides error variance estimates more consistent with ground-based observations. Hence, SPAR-TC offers robust framework for addressing spatial representativeness errors and improves error quantification for datasets with differing spatial support.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106874"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956764","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}

引用次数: 0

Development of a web-based tool for rapid flood inundation modeling 开发基于网络的快速洪水淹没建模工具

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-03-01 Epub Date: 2026-01-13 DOI: 10.1016/j.envsoft.2026.106876

Dawei Xiao , Binjie Yuan , Zhengxu Guo , Wanhong Yang , Jingchao Jiang , Min Chen , Guonian Lv , Junzhi Liu

{"title":"Development of a web-based tool for rapid flood inundation modeling","authors":"Dawei Xiao , Binjie Yuan , Zhengxu Guo , Wanhong Yang , Jingchao Jiang , Min Chen , Guonian Lv , Junzhi Liu","doi":"10.1016/j.envsoft.2026.106876","DOIUrl":"10.1016/j.envsoft.2026.106876","url":null,"abstract":"<div><div>To address the growing risk of floods under global climate change, management agencies need flood inundation modeling to support decision-making and emergency response. However, traditional desktop-based modeling remains a complex and time-consuming process, making it difficult for users to perform rapid flood simulations. To overcome this limitation, this study developed a web-based rapid flood modeling tool based on the LISFLOOD-FP model. Each key step involved in the modeling process—such as data preparation, preprocessing, model run and calibration, and postprocessing— was encapsulated into an automated executable workflow. These workflows were deployed on servers, published as web services, and invoked from a web-based interface, significantly streamlining and simplifying the modeling process. Four flood events in the upper Missouri River Basin were successfully simulated to showcase the tool's capability. This user-friendly web-based tool enables users to conduct flood inundation modeling quickly, thereby lowering user barriers and facilitating timely flood risk mitigation.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106876"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961785","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}

引用次数: 0