Environmental Modelling & Software最新文献

{"title":"Towards good practice In engaging users In evaluation of computer model Software: Introducing the critical appraisal approach (CAA)","authors":"Caroline Rosello ,&nbsp;Joseph H.A. Guillaume ,&nbsp;Peter Taylor ,&nbsp;Susan M. Cuddy ,&nbsp;Carmel A. Pollino ,&nbsp;Anthony J. Jakeman","doi":"10.1016/j.envsoft.2025.106469","DOIUrl":"10.1016/j.envsoft.2025.106469","url":null,"abstract":"<div><div>Good practices in model software development are essential for boosting uptake. While user-centric approaches are much advocated, challenges remain in including users in development due to diverse definitions for ‘users’, their perceived credibility as an information source, and the influence of market-based innovation choices and the anticipation of (future) demands. While enhancing user feedback could help in addressing these challenges, there is a notable lack of guidance for best practices focused on users, as the emphasis has traditionally been on developers.</div><div>To tackle this gap, we propose a Critical Appraisal Approach (CAA) for model software evaluation, informed by the Basin Futures model software and detailed steps in its evaluation, thereby providing guidance to support associated best practices for such software. The paper shows the CAA can assist in 1) enhancing shared understanding between users and developers, 2) coordinating development and evaluation, and 3) aligning development with market dynamics.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"190 ","pages":"Article 106469"},"PeriodicalIF":4.8,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868652","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}

{"title":"A data fusion approach to enhancing runoff simulation in a semi-arid river basin","authors":"Afshin Jahanshahi ,&nbsp;Haniyeh Asadi ,&nbsp;Hoshin Gupta","doi":"10.1016/j.envsoft.2025.106468","DOIUrl":"10.1016/j.envsoft.2025.106468","url":null,"abstract":"<div><div>Accurate streamflow modeling is crucial for water resource management in dry and semi-arid regions. This study proposes a novel approach combining machine learning (ML) with conceptual and physically-based models to address of traditional model limitations in Iran's semi-arid Jazmourian River Basin. The HBV and SWAT hydrological models are used for conceptual and physically-based simulations, respectively, while Support vector regression (SVR) and multilayer perceptron (MLP) integrate hydrological model outputs with hydro-meteorological variables. Using hydroclimatic data from two periods-1963-1989 (dry phase) and 1993–2019 (wetter phase)-the study evaluates model performance under contrasting conditions. The proposed \"fusion SVR\" and \"hybrid SVR with whale optimization algorithm\" (SVR-WOA) models demonstrate improved accuracy in simulating runoff peaks. The SVR-WOA model achieves a 26.17 % performance improvement over SWAT for 1993–2019 and 25.36 % for 1963–1989, with RMSE values of 9.90 m³/s and 10.33 m³/s, respectively. This highlights hybrid modeling's potential for diverse hydrological challenges.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"190 ","pages":"Article 106468"},"PeriodicalIF":4.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143837980","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}

{"title":"A post-processing machine learning framework for bias-correcting National Water Model outputs by accounting for dominant streamflow drivers","authors":"Savalan Naser Neisary ,&nbsp;Ryan C. Johnson ,&nbsp;Md Shahabul Alam ,&nbsp;Steven J. Burian","doi":"10.1016/j.envsoft.2025.106459","DOIUrl":"10.1016/j.envsoft.2025.106459","url":null,"abstract":"<div><div>While the National Water Model (NWM) provides high-resolution, large-scale streamflow data across the United States, its effectiveness as a key water resources management tool in the drought-prone Western US needs further investigation. Previous studies revealed that the NWM has limitations in controlled basins, impacted by reservoir operations and diversions not explicitly included within the model framework. Responding to the observed reduction in model skill throughout the Western US, we developed a model agnostic post-processing machine learning (PP-ML) framework to account for the impacts of water resources management and regionally dominant hydrological processes on model performance. For our case application of the PP-ML framework, we use daily NWM v2.1 retrospective flow rates as the hydrological model and input upstream reservoir storage, SNOTEL snow water equivalent, and catchment characteristics. Applying the PP-ML framework in the contributing Great Salt Lake watersheds, a key watershed of interest due to its drought-prone nature, we observed a 65%, 335%, and 25% improvement in the median Kling-Gupta Efficiency, Percent Bias, and Root Mean Square Error, respectively, for 30 gauged locations compared to the NWM outputs. Comparing model skills across different flow regimes and station types revealed a substantial (225%) improvement in low-flow estimates at stations with extensive upstream water infrastructure, such as those impacted by reservoir operations, as well as in catchments within negligible water management activities. The research underscores how post-processing hydrological model outputs with ML can account for the effects of water management activities on streamflow estimates, most notably without explicitly incorporating infrastructure rulesets, and demonstrate its capability in bias-correcting streamflow forecasts in response to the regionally dominant streamflow drivers.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"190 ","pages":"Article 106459"},"PeriodicalIF":4.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143837979","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}

{"title":"Interpolation of missing ozone data using graph machine learning and parameter analysis through eXplainable artificial intelligence comparison","authors":"Seonje Jung ,&nbsp;Junsu Gil ,&nbsp;Meehye Lee ,&nbsp;Clara Betancourt ,&nbsp;Martin Schultz ,&nbsp;Yunsoo Choi ,&nbsp;Taekyu Joo ,&nbsp;Daigon Kim","doi":"10.1016/j.envsoft.2025.106466","DOIUrl":"10.1016/j.envsoft.2025.106466","url":null,"abstract":"<div><div>Ozone (O₃), a short-lived climate pollutant, continues to increase despite policies aimed at suppressing its precursors in South Korea. The government operates approximately 500 observatories to monitor O₃ and trace gases. Researchers use these data to address the ongoing issue of increasing O₃ levels. However, challenges in data retrieval from observatories may introduce biases in O₃ studies. In this study, we developed a graph-based machine learning model to simulate missing O₃ concentrations for mitigate bias. The model incorporates spatiotemporal distribution characteristics using a merged observation dataset from South Korea in 2021. Regardless of region or length of missing data, the model effectively simulates O₃ variations with R² of up to 0.9 and RMSE of 3.6. To determine the influence of input parameters on O₃ interpolation, we used eXplainable AI methods. The results indicated that NO₂ is the most important factor in cities, while photochemical indicators are more influential in provinces.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"190 ","pages":"Article 106466"},"PeriodicalIF":4.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851401","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}

{"title":"Multivariate functional data analysis and machine learning methods for anomaly detection in water quality sensor data","authors":"Xurxo Rigueira ,&nbsp;David Olivieri ,&nbsp;Maria Araujo ,&nbsp;Angeles Saavedra ,&nbsp;Maria Pazo","doi":"10.1016/j.envsoft.2025.106443","DOIUrl":"10.1016/j.envsoft.2025.106443","url":null,"abstract":"<div><div>Reliable anomaly detection is crucial for water resources management, but the complexity of environmental sensor data presents challenges, especially with limited labeled data in water quality analysis. Functional data has experienced significant growth in anomaly detection, but most applications focus on unlabeled datasets. This study assesses the performance of multivariate functional data analysis and compares it with current machine learning models for detecting water quality anomalies on 18 years of expert-annotated data from four monitoring stations along Spain’s Ebro River. We propose and validate a multivariate functional model incorporating a new amplitude metric and a nonparametric outlier detector (Multivariate Magnitude, Shape, and Amplitude–MMSA). Additionally, a Random Forest-based machine learning architecture was developed for the same purpose, employing sliding windows and data balancing techniques. The Random Forest model demonstrated the highest performance, achieving an average F1 score of 93%, while MMSA exhibited robustness in scenarios with limited anomalous data or labels.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"190 ","pages":"Article 106443"},"PeriodicalIF":4.8,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143837977","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}

{"title":"Attention scores and peak perception in long-term ozone prediction using deep learning","authors":"Zhihao Xu ,&nbsp;Danni Xu ,&nbsp;Wenguang Li ,&nbsp;Puyu Lian ,&nbsp;Yuheng Chen ,&nbsp;Fangyuan Yang ,&nbsp;Kaihui Zhao","doi":"10.1016/j.envsoft.2025.106467","DOIUrl":"10.1016/j.envsoft.2025.106467","url":null,"abstract":"<div><div>To address the limitations of traditional ozone (O₃) forecasting models, this study established a novel Transformer-based model integrating attention scores and the peak perception. Attention scores dynamically quantify nonlinear relationships between O₃ and influencing factors, while peak perception method penalizes peak O₃ errors, ensuring accurate predictions during O₃ exceedance events. Our results demonstrate that the proposed model significantly improves the prediction accuracy for both hourly and maximum daily 8-h average O₃ concentrations, with the R² value increasing from 0.56 to 0.83 and the mean squared error decreasing from 0.47 to 0.42. Wind direction, wind speed, and carbon monoxide emerged as dominant factors during pollution episodes. Additionally, the model exhibits a high potential for predictability over extended lead times, with a mean absolute error of 0.67 at 24 h, stabilizing at 0.75 at 72 h. This approach enhances simulation accuracy and provides policymakers extended response windows for O₃ control strategies.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"189 ","pages":"Article 106467"},"PeriodicalIF":4.8,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816404","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}

{"title":"Urban Flood Risk analysis using the SWAGU-coupled model and a cloud-enhanced fuzzy comprehensive evaluation method","authors":"Jinhui Hu ,&nbsp;Changtao Deng ,&nbsp;Xinyu Chang ,&nbsp;Aoxuan Pang","doi":"10.1016/j.envsoft.2025.106461","DOIUrl":"10.1016/j.envsoft.2025.106461","url":null,"abstract":"<div><div>This study introduces the SWAGU model, which overcomes limitations of existing approaches by combining SWMM's robust pipe network modeling capabilities with ANUGA's advanced unstructured mesh-based surface flow simulation, enabling more accurate prediction of flood dynamics in complex urban environments. The model's outputs are integrated into an enhanced cloud model framework. This framework improves upon traditional fuzzy evaluation methods by introducing cloud model theory to better handle uncertainty in both expert judgments and membership functions, while also incorporating a novel approach for processing extreme values. A comparative analysis of multi-indicator and single-indicator approaches reveals that the multi-indicator method, offers a more comprehensive and objective evaluation of flood risk. The findings demonstrate a reduction of 50 %–60 % in low-risk areas compared to the single-indicator approach. This study underscores the superiority of integrating advanced hydrodynamic modeling with cloud-enhanced multi-criteria evaluation in providing more precise and robust flood risk management frameworks.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"189 ","pages":"Article 106461"},"PeriodicalIF":4.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823714","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}

{"title":"climdex-kit: An open software for climate index calculation, sharing and analysis towards tailored climate services","authors":"Piero Campalani,&nbsp;Alice Crespi,&nbsp;Massimiliano Pittore,&nbsp;Marc Zebisch","doi":"10.1016/j.envsoft.2025.106442","DOIUrl":"10.1016/j.envsoft.2025.106442","url":null,"abstract":"<div><div>The paper presents the open-source software <em>climdex-kit</em> which includes modules to compute, analyze and visualize climate indices based on the input data, target domain and temporal extent defined by the user. It is intended to ease the retrieval and interpretation of meaningful information for climate change studies and support the development of climate services for sectoral applications with flexible options for tailoring the expected outputs. It currently includes the computation of 38 indices based on temperature and precipitation, describing both mean and extreme climate conditions, and it is designed to work with multi-model ensembles of climate projections. The tool is written in Python and integrates utilities from the well-established Climate Data Operators (CDO) and NetCDF Operators (NCO) libraries. The specific capabilities for filtering, aggregating and visualizing insightful information out of the computed indices ensembles make this software rather unique in the still rich landscape of available akin libraries, and are thought to help users to produce tailored results improving the understanding and communication of future climate change. The package <em>climdex-kit</em> can be used directly in interactive Python shells or be integrated as a building block in more complex data processing workflows. New indices can be easily configured and the software can be re-used on the spatial and temporal domains required by the specific application. Moreover, with its utilities to publish the climate indices in open catalogues, this software can be a one stop shop for a FAIR and simplified computation and management of such complex multi-scenario settings. To show the functionalities of the package as well as its potential use for conducting regional climate assessments, the application of <em>climdex-kit</em> to an ensemble of climate model projections for the Trentino – South Tyrol region (north-eastern Italy) is presented. Examples of derived information for a selection of indices are reported and different visualization options discussed.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"190 ","pages":"Article 106442"},"PeriodicalIF":4.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847574","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}

{"title":"More than modelling: Building trust for positive change in water resources management","authors":"Robert B. Sowby ,&nbsp;Andrew J. South ,&nbsp;Norman L. Jones ,&nbsp;Easton G. Hopkins ,&nbsp;Daniel P. Ames","doi":"10.1016/j.envsoft.2025.106465","DOIUrl":"10.1016/j.envsoft.2025.106465","url":null,"abstract":"<div><div>Hydrologic modelling plays a vital role in water resources management but often falls short of achieving the positive change modelers envision. In this position paper we argue that a key contributing factor is the lack of trust and shared understanding among modelers, decision-makers, and the public. Models need to be trusted first—a social challenge as well as a technical one. Through three case studies—involving groundwater development, a national-scale runoff model, and a declining lake ecosystem—we analyze the interactions between technical modelling, stakeholder engagement, and policy outcomes, drawing on principles from both hydrologic and social sciences. We recommend that hydrologic modelers foster transparency, balance model authority with flexibility, and tailor stakeholder engagement to overall project needs. Implementing these recommendations will enhance the legitimacy of hydrologic models, increasing the likelihood of achieving positive, sustainable change in water resource systems.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"189 ","pages":"Article 106465"},"PeriodicalIF":4.8,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816403","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}

{"title":"Advancing timely satellite precipitation for IMERG-ER using GOES-16 data and a U-net convolutional neural network modelling approach","authors":"Mateo Vélez-Hernández ,&nbsp;Paul Muñoz ,&nbsp;Esteban Samaniego ,&nbsp;María José Merizalde ,&nbsp;Rolando Célleri","doi":"10.1016/j.envsoft.2025.106457","DOIUrl":"10.1016/j.envsoft.2025.106457","url":null,"abstract":"<div><div>Timely precipitation information is essential for water resources management and hazard monitoring. In regions with limited ground-based measurements, satellite precipitation products (SPPs) provide a valuable alternative, though data latency often creates an information gap for real-time applications. This study addresses the latency gap of IMERG-ER using a U-Net-based Convolutional Neural Network (CNN) model, trained with near-instantaneous GOES-16 satellite data. The optimal combination of GOES-16 infrared bands (6.2, 6.9, 7.3, 8.4, and 11.2 μm) was determined to enhance IMERG-ER predictions. The CNN model's performance, evaluated with both quantitative and qualitative metrics, showed an RMSE of 0.46 mm/h, a Pearson's correlation coefficient of 0.60, and a Critical Success Index of 0.53. The model performed well in predicting low-intensity precipitation (&lt;3 mm/h), which occurs 97 % of the time, but faced challenges with high-intensity events due to data imbalance. These findings advance the use of SPPs and deep learning for operational hydrology.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"189 ","pages":"Article 106457"},"PeriodicalIF":4.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807739","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}