Environmental Modelling & Software最新文献

Backward erosion piping in numerical models: A literature review 反向侵蚀管道的数值模型：文献综述

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-09-11 DOI: 10.1016/j.envsoft.2025.106681

E.M. van der Linde , M. Wewer , B.A. Robbins , O. Colomés , S.N. Jonkman , J.P. Aguilar-López

引用次数: 0

A web-based platform for integrated real-time water Information: A prototype system for Missouri 基于网络的综合实时水信息平台：密苏里州原型系统

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-09-10 DOI: 10.1016/j.envsoft.2025.106687

Ayesha Siddiqua , Bong-Chul Seo , Steven Corns , Joel Burken , Robert R. Holmes Jr. , Chang-Soo Kim

{"title":"A web-based platform for integrated real-time water Information: A prototype system for Missouri","authors":"Ayesha Siddiqua , Bong-Chul Seo , Steven Corns , Joel Burken , Robert R. Holmes Jr. , Chang-Soo Kim","doi":"10.1016/j.envsoft.2025.106687","DOIUrl":"10.1016/j.envsoft.2025.106687","url":null,"abstract":"<div><div>The study demonstrates a framework to develop a web-based platform that presents real-time water data from observations, model estimations, and model predictions over a pilot domain in Missouri, United States. The Missouri Water Information System (MoWIS) is a prototype water information portal that provides current hydrologic (rainfall, drought, and stream) conditions and future stream predictions based on national radar and stream monitoring networks. The primary data sources are USGS and NOAA APIs for river stage/streamflow observations and forecasts. The platform visualizes these data within a geospatial context through a map-based environment, accessible upon user interaction, to facilitate intuitive interpretation and spatial analysis. With a modular architecture designed for system scalability, the platform supports fully automated operations and responsive performance by offloading intensive data processing and modeling tasks to external servers. Since the platform leverages nationally available web services, the framework is readily transferable to other geographic regions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106687"},"PeriodicalIF":4.6,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046610","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

One-dimensional simulation of land subsidence in vertically-heterogeneous highly compressible aquitards coupled with data assimilation via ensemble Kalman filter 垂直非均质高压缩含水层地面沉降的一维模拟与集成卡尔曼滤波同化

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-09-10 DOI: 10.1016/j.envsoft.2025.106690

Berenice Zapata-Norberto , Eric Morales-Casique , Graciela S. Herrera

{"title":"One-dimensional simulation of land subsidence in vertically-heterogeneous highly compressible aquitards coupled with data assimilation via ensemble Kalman filter","authors":"Berenice Zapata-Norberto , Eric Morales-Casique , Graciela S. Herrera","doi":"10.1016/j.envsoft.2025.106690","DOIUrl":"10.1016/j.envsoft.2025.106690","url":null,"abstract":"<div><div>This study presents a methodology for calibrating a nonlinear groundwater flow and consolidation model in highly compressible, heterogeneous aquitards, focusing on vertical heterogeneity. Inspired by conditions in the Mexico basin, where the nature of the aquitard sediments, along with pore pressure monitoring through piezometers, plays a significant role. The model combines a nonlinear one-dimensional groundwater flow algorithm with an Ensemble Kalman Filter (EnKF) for data assimilation, correcting hydraulic head (<em>h</em>) and vertical hydraulic conductivity (<em>K</em>) distributions. Four reference cases were tested, and three data assimilation strategies were explored: (a) only <em>h</em> measurements, (b) only <em>K</em> measurements, and (c) both. Results show that all strategies provide satisfactory parameter estimations and settlement predictions, with the combined approach yielding the highest accuracy. While the method successfully simulates subsidence, its effectiveness diminishes if data assimilation only occurs in the initial simulation phase. This methodology has strong potential for predicting subsidence in real-world heterogeneous aquitards.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106690"},"PeriodicalIF":4.6,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046612","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 hybrid convolutional neural network for multi-station water level prediction: Enhancing navigation safety through spatial-temporal modelling 基于混合卷积神经网络的多站点水位预测：通过时空建模提高航行安全性

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-09-06 DOI: 10.1016/j.envsoft.2025.106671

Zhi Yuan , Wenhai Li , Qian Zhang , Xiaoxiao Liu , Yi Liu , Jingxian Liu

{"title":"A hybrid convolutional neural network for multi-station water level prediction: Enhancing navigation safety through spatial-temporal modelling","authors":"Zhi Yuan , Wenhai Li , Qian Zhang , Xiaoxiao Liu , Yi Liu , Jingxian Liu","doi":"10.1016/j.envsoft.2025.106671","DOIUrl":"10.1016/j.envsoft.2025.106671","url":null,"abstract":"<div><div>Accurate prediction of multi-station water levels is crucial for mitigating flood risks and optimizing navigation management in complex riverine environments. Existing approaches often fail to capture the dynamic spatiotemporal interdependencies between monitoring stations, limiting their predictive performance and utility in operational decision-making. To address this challenge, we propose MSWLCN (Multi-Station Water Level Convolutional Network) model, a novel deep spatio-temporal convolution framework tailored for simultaneous and accurate prediction of daily water levels over consecutive days at multiple stations. This architecture integrates multi-layer Convolutional Long Short-Term Memory (ConvLSTM) and three-dimensional convolutional (Conv3D) networks with strong adaptations. The model explicitly extracts temporal dependencies and spatial correlations across stations through the spatio-temporal modelling architecture, enabling simultaneous prediction of multi-station water levels with complex changing characteristics. And we validate the framework using a comprehensive dataset spanning 1826 consecutive days from 19 hydrological stations along the Yangtze River, a globally significant navigational corridor. Experimental results demonstrate that the proposed MSWLCN outperforms conventional modelling methods in terms of prediction accuracy and computational efficiency. This research advances environmental modelling practices by offering a scalable solution for multi-station water level forecasting, with direct applications in water resource management and navigation safety assurance.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106671"},"PeriodicalIF":4.6,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044852","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

High-resolution precipitation downscaling in mainland Southeast Asia: A novel integration of BMA and U-Net CNN 东南亚大陆高分辨率降水降尺度：BMA和U-Net CNN的新整合

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-09-04 DOI: 10.1016/j.envsoft.2025.106682

Teerachai Amnuaylojaroen

{"title":"High-resolution precipitation downscaling in mainland Southeast Asia: A novel integration of BMA and U-Net CNN","authors":"Teerachai Amnuaylojaroen","doi":"10.1016/j.envsoft.2025.106682","DOIUrl":"10.1016/j.envsoft.2025.106682","url":null,"abstract":"<div><div>This study introduces a hybrid approach that combines Bayesian Model Averaging (BMA) with a U-Net Convolutional Neural Network (CNN) to improve precipitation estimates in mainland Southeast Asia. The method addresses key limitations of Global Climate Models in capturing fine-scale variability, particularly in topographically complex. An ensemble of five GCMs, supplemented by ERA5 reanalysis data, was used to produce high-resolution downscaled precipitation estimates. Compared to the original BMA, the hybrid model significantly improved performance, increasing the Symmetric Concordance Correlation Coefficient from 0.68 to 0.82 and reducing the Root Mean Squared Error from 1.63 to 1.27 (validated against ERA5). Validation using TRMM and IMERG data revealed similar enhancements. Additionally, Wasserstein distance analysis confirmed improved distributional similarity between model outputs and observed data. The most notable improvements occurred in mountainous areas, especially in northern Myanmar. This approach enhances the utility of climate data for water resource management and adaptation planning in Southeast Asia.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106682"},"PeriodicalIF":4.6,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027471","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

NextGen In A Box (NGIAB): Open-Source containerization of the NextGen framework to enable community-driven hydrology modeling NextGen In A Box (NGIAB): NextGen框架的开源容器化，可实现社区驱动的水文建模

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-09-01 DOI: 10.1016/j.envsoft.2025.106666

Arpita Patel , James Halgren , Zach Wills , Nels Frazier , Benjamin Lee , Joshua Cunningham , Jordan Laser , Mohammadsepehr Karimiziarani , Trupesh Patel , Giovanni Romero , Matthew Denno , Sam Lamont , Iman Maghami , Hari Teja Jajula , M. Shahabul Alam , Sifan A. Koriche , Manjila Singh , Nia Minor , Bhavya Duvvuri , Savalan Naser Neisary , Daniel P. Ames

{"title":"NextGen In A Box (NGIAB): Open-Source containerization of the NextGen framework to enable community-driven hydrology modeling","authors":"Arpita Patel , James Halgren , Zach Wills , Nels Frazier , Benjamin Lee , Joshua Cunningham , Jordan Laser , Mohammadsepehr Karimiziarani , Trupesh Patel , Giovanni Romero , Matthew Denno , Sam Lamont , Iman Maghami , Hari Teja Jajula , M. Shahabul Alam , Sifan A. Koriche , Manjila Singh , Nia Minor , Bhavya Duvvuri , Savalan Naser Neisary , Daniel P. Ames","doi":"10.1016/j.envsoft.2025.106666","DOIUrl":"10.1016/j.envsoft.2025.106666","url":null,"abstract":"<div><div>The NextGen Water Resources Modeling Framework (NextGen) greatly increases the flexibility and interoperability of hydrologic modeling workflows. However, deploying NextGen models remains challenging due to a complex installation process. To address this, we developed NextGen In A Box (NGIAB), an all-in-one distribution of the NextGen framework that simplifies deployment across environments using Docker and Singularity containers. NGIAB comes pre-configured with most components expected in version 4.0 of the National Water Model, which will use the NextGen framework. Additionally, NGIAB utilizes a cloud-based continuous integration/continuous deployment pipeline to automate releases. Through its ease of access and robust suite of included utilities, NGIAB promotes community engagement in hydrologic modeling and facilitates research-to-operation (R2O) pathways. In this work, we present the designs and technologies that enable these outcomes, along with studies and performance benchmarks that demonstrate NGIAB's applicability.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106666"},"PeriodicalIF":4.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923034","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

Friends don't let friends use Nash-Sutcliffe Efficiency (NSE) or KGE for hydrologic model accuracy evaluation: A rant with data and suggestions for better practice 朋友不让朋友使用纳什-萨特克利夫效率（NSE）或KGE来评估水文模型的准确性：这是一篇关于更好实践的数据和建议的评论

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-08-30 DOI: 10.1016/j.envsoft.2025.106665

Gustavious Paul Williams

{"title":"Friends don't let friends use Nash-Sutcliffe Efficiency (NSE) or KGE for hydrologic model accuracy evaluation: A rant with data and suggestions for better practice","authors":"Gustavious Paul Williams","doi":"10.1016/j.envsoft.2025.106665","DOIUrl":"10.1016/j.envsoft.2025.106665","url":null,"abstract":"<div><div>I evaluate the use of Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) for hydrologic model accuracy assessment. Using synthetic data with identical error distributions, (σ = 2), NSE and KGE values vary widely—from −190 to 0.999—due to flow characteristics, not model accuracy. Applying identical noise, (σ = 10), to 6595 U.S. gages, models with identical RMSE (∼10) results in NSE values from −325,272 to 1.0 which corresponds with flow variability rather than model fit. If noise is scaled to 25 % of mean flow, spatial patterns in NSE and KGE persist that reflect flow characteristics rather than accuracy and misrepresent accuracy. NSE and KGE are skill scores and useful for within-site model calibration, not cross-site accuracy comparisons. Metrics such as RMSE, normalized RMSE, or percent bias offer more interpretable, transferable accuracy evaluations. I advocate abandoning NSE and KGE for comparisons of model performance and urge hydrologists to adopt fit-for-purpose metrics. I present this study as a position paper, rather than a research paper, the limitations of NSE and KGE—particularly their dependence on flow variability and unsuitability for cross-site comparisons—are well known and have been addressed extensively in the literature. However, my experience and review of the literature indicate an over-reliance and misuse of these metrics.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106665"},"PeriodicalIF":4.6,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933614","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

Multi-objective optimization of nature-based solutions in urban stormwater management: A scoping review 基于自然的城市雨水管理解决方案的多目标优化：范围综述

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-08-29 DOI: 10.1016/j.envsoft.2025.106659

A. Bista , K.A.H. Paus , I. Seifert-Dähnn

{"title":"Multi-objective optimization of nature-based solutions in urban stormwater management: A scoping review","authors":"A. Bista , K.A.H. Paus , I. Seifert-Dähnn","doi":"10.1016/j.envsoft.2025.106659","DOIUrl":"10.1016/j.envsoft.2025.106659","url":null,"abstract":"<div><div>Multi-objective optimization (MOO) methods are increasingly used to optimize the multifunctional benefits and costs of Nature-based solutions (NbS) in urban water management. However, comprehensive reviews of current research in this area remain limited. This scoping review analyzed 110 optimization studies focused on NbS in urban water management. It examined key components of the simulation-optimization (S/O) framework, including simulation models, optimization algorithms, objectives, spatial and temporal configurations.</div><div>Twenty-three algorithms were assessed for their applications, strengths and limitations. NSGA-II was the most widely used, while advanced algorithms were applied in a few studies for complex, high-dimensional problems.</div><div>Water quantity (87 %) and costs (93 %) were the most studied objectives, whereas only 11 % of studies addressed other socio-environmental objectives. Various challenges in the S/O framework, such as integrating socio-environmental benefits, model complexity, uncertainties and optimal solution selection, are discussed. Future research should prioritize proper objectives selection, socio-environmental objectives integration and advanced dynamic NbS planning.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106659"},"PeriodicalIF":4.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989797","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

Enhanced tensor factorization for spatiotemporal imputation of high-frequency water quality monitoring data 基于增强张量分解的高频水质监测数据时空插值

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-08-28 DOI: 10.1016/j.envsoft.2025.106667

Xuke Wu , Kun Shan , Friedrich Recknagel , Lan Wang , Mingsheng Shang

{"title":"Enhanced tensor factorization for spatiotemporal imputation of high-frequency water quality monitoring data","authors":"Xuke Wu , Kun Shan , Friedrich Recknagel , Lan Wang , Mingsheng Shang","doi":"10.1016/j.envsoft.2025.106667","DOIUrl":"10.1016/j.envsoft.2025.106667","url":null,"abstract":"<div><div>Automatic high-frequency monitoring (AHFM) of water quality is increasingly being deployed to enhance the management and scientific understanding of eutrophic lakes. However, the prevalence of missing data in such systems poses significant challenges, potentially compromising decision-making processes and distorting analytical or modelling outcomes. This study proposes an enhanced tensor factorization model, termed the Diverse Biases-integrated Adaptive Latent-factorization-of-tensors (DBAL), which decomposes high-dimensional data into low-rank components while incorporating diverse linear biases to capture temporal fluctuations and employing a differential evolution algorithm for adaptive hyperparameter optimization. Extensive empirical validation using real-world AHFM datasets from a large eutrophic lake in China demonstrates that our proposed DBAL consistently outperforms the state-of-the-art models, achieving 5.6 %–50.3 % and 5.7 %–43.7 % reductions in RMSE and MAE, alongside a 0.3 %–6.1 % improvement in R<sup>2</sup>. Notably, DBAL demonstrates variable-specific performance, with particularly accurate imputation for stable physical parameters while revealing greater challenges for biologically-active variables that exhibit stronger temporal dynamics.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106667"},"PeriodicalIF":4.6,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144916857","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

Improving the fidelity and performance of a conceptual flood inundation mapping approach using a machine learning-based surrogate model 使用基于机器学习的代理模型提高概念性洪水淹没映射方法的保真度和性能

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-08-27 DOI: 10.1016/j.envsoft.2025.106664

Berina Mina Kilicarslan , Qianqiu Longyang , Victor Obi , Sagy Cohen , Ehab Meselhe , Marouane Temimi

{"title":"Improving the fidelity and performance of a conceptual flood inundation mapping approach using a machine learning-based surrogate model","authors":"Berina Mina Kilicarslan , Qianqiu Longyang , Victor Obi , Sagy Cohen , Ehab Meselhe , Marouane Temimi","doi":"10.1016/j.envsoft.2025.106664","DOIUrl":"10.1016/j.envsoft.2025.106664","url":null,"abstract":"<div><div>This study focuses on enhancing the accuracy of Flood Inundation Mapping (FIM) by utilizing a surrogate modeling approach. The Height Above the Nearest Drainage (HAND) method is used as our baseline FIM approach. The terrain-based HAND-FIM framework was developed to allow large-scale applications at low computational costs. A Surrogate Model (SM) is constructed using machine learning-based methodologies to emulate the high-fidelity Hydrologic Engineering Center-River Analysis System (HEC-RAS) model. HAND-FIM, generated using streamflow data from the National Water Model, serves as the input to the SM, while the flood extent predicted by HEC-RAS for the same event serves as the target. Results demonstrate that SM reduces false alarms in HAND-FIM by 18 % while improving the Critical Success Index score by 26 %. Integrating the SM offers a promising approach for enhancing flood prediction accuracy, mitigating HAND-FIM limitations, and providing fast, cost-effective solutions for operational FIM applications, especially in data- and resource-limited regions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106664"},"PeriodicalIF":4.6,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933615","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