Environmental Modelling & Software最新文献_第2页

Development of a component-based integrated modeling framework for urban flood simulation 基于组件的城市洪水模拟集成建模框架的开发

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2023-11-01 DOI: 10.1016/j.envsoft.2023.105839

Haocheng Wang , Songshan Yue , Zhuo Zhang , Fei Guo , Yongning Wen , Min Chen , Guonian Lü

{"title":"Development of a component-based integrated modeling framework for urban flood simulation","authors":"Haocheng Wang , Songshan Yue , Zhuo Zhang , Fei Guo , Yongning Wen , Min Chen , Guonian Lü","doi":"10.1016/j.envsoft.2023.105839","DOIUrl":"10.1016/j.envsoft.2023.105839","url":null,"abstract":"<div><p><span>Integrating urban flood models can offer more comprehensive solutions for urban waterlogging problems. The tightly coupled approaches adopted in existing integrated models are self-enclosed. When modelers try to integrate models, considerable effort is required to rewrite codes and build the new coupled model. To help modelers reuse urban flood models and configure different coupled solutions, we propose a component-based method. The model components were built to achieve the reuse of models based on the Basic Model Interface (BMI). The bidirectional coupled approach was designed to support the interchange of water. Moreover, a model interaction hub was developed to configure different coupling work. An integrated modeling framework for urban flood simulation was thus constructed. The Storm Water Management Model (SWMM), ANUGA, and LISFLOOD-FP were employed to demonstrate the capabilities of this framework through experiments involving two integrated solutions. The results show that the proposed integrated modeling framework can provide a reusable solution for urban </span>stormwater simulation.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"169 ","pages":"Article 105839"},"PeriodicalIF":4.9,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71507929","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

Analysis and comparison of coupled and uncoupled simulations with the COAWST model during the Gloria Storm (January 2020) in the northwestern Mediterranean Sea 地中海西北部格洛里亚风暴（2020年1月）期间耦合和非耦合模拟与COAWST模型的分析和比较

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2023-11-01 DOI: 10.1016/j.envsoft.2023.105830

Jordi Iglesias , Ildefonso Cuesta , Clara Salueña , Jordi Moré , Jordi Solé

{"title":"Analysis and comparison of coupled and uncoupled simulations with the COAWST model during the Gloria Storm (January 2020) in the northwestern Mediterranean Sea","authors":"Jordi Iglesias , Ildefonso Cuesta , Clara Salueña , Jordi Moré , Jordi Solé","doi":"10.1016/j.envsoft.2023.105830","DOIUrl":"10.1016/j.envsoft.2023.105830","url":null,"abstract":"<div><p>Numerical simulations of the ocean and atmosphere provide crucial information for climate policies and socio-economic decisions. A coupled atmosphere–ocean model can potentially improve the representation of processes and forecast fields compared to an uncoupled one. This work aims to assess under which conditions the coupled model performs better than the uncoupled one and evaluate those differences in a high-energy storm in the northwestern Mediterranean area, which was modeled to compare coupled and uncoupled simulations of storm Gloria (January 2020). The model was validated and verified with observations from weather stations, atmospheric soundings, buoys, and the operational models of METEOCAT. Although the coupling of the models does not substantially affect the atmospheric large-scale flows, a significant impact has been found for the small and mesoscale structures. On the ocean, differences between the coupled and uncoupled models arise over the entire spatial and temporal scales, which the coupling exhibits better performance.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"169 ","pages":"Article 105830"},"PeriodicalIF":4.9,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1364815223002165/pdfft?md5=9da4d68e7068e8d47f553a4200c666d4&pid=1-s2.0-S1364815223002165-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71507928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inverse aqueous transport modeling for emergency response 用于应急响应的逆向水传输建模

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2023-11-01 DOI: 10.1016/j.envsoft.2023.105836

V. Turner, D. Werth, R.L. Buckley

引用次数: 0

DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling DeepGR4J：一种用于概念降雨径流建模的深度学习混合方法

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2023-11-01 DOI: 10.1016/j.envsoft.2023.105831

Arpit Kapoor , Sahani Pathiraja , Lucy Marshall , Rohitash Chandra

{"title":"DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling","authors":"Arpit Kapoor , Sahani Pathiraja , Lucy Marshall , Rohitash Chandra","doi":"10.1016/j.envsoft.2023.105831","DOIUrl":"10.1016/j.envsoft.2023.105831","url":null,"abstract":"<div><p>Despite the considerable success of deep learning methods in modelling physical processes, they suffer from a variety of issues such as overfitting and lack of interpretability. In hydrology, conceptual rainfall-runoff models are simple yet fast and effective tools to represent the underlying physical processes through lumped storage components. Although conceptual rainfall-runoff models play a vital role in supporting decision-making in water resources management and urban planning, they have limited flexibility to take data into account for the development of robust region-wide models. The combination of deep learning and conceptual models has the potential to address some of the aforementioned limitations. This paper presents a sub-model hybridization of the GR4J rainfall-runoff model with deep learning architectures such as convolutional neural networks (CNN) and long short-term memory (LSTM) networks. The results show that the hybrid models outperform both the base conceptual model as well as the canonical deep neural network architectures in terms of the Nash–Sutcliffe Efficiency (NSE) score across 223 catchments in Australia. We show that our hybrid model provides a significant improvement in predictive performance, particularly in arid catchments, and generalizing better across catchments.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"169 ","pages":"Article 105831"},"PeriodicalIF":4.9,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1364815223002177/pdfft?md5=fed4e0da5d005d6b49d32c2518235ffa&pid=1-s2.0-S1364815223002177-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71518902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Numerical analysis of seepage faces and subaerial groundwater discharge near waterbodies and on uplands 水体附近和高地渗流面和陆上地下水排泄的数值分析

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2023-11-01 DOI: 10.1016/j.envsoft.2023.105828

Prayas Rath , Etienne Bresciani , Jianting Zhu , Kevin M. Befus

引用次数: 0

DayCent-CUTE: A global sensitivity, auto-calibration, and uncertainty analysis tool for DayCent DayCent CUTE：DayCent的全球灵敏度、自动校准和不确定性分析工具

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2023-11-01 DOI: 10.1016/j.envsoft.2023.105832

Xiuying Wang , Jaehak Jeong , Seonggyu Park , Xuesong Zhang , Jungang Gao , Nélida E.Q. Silvero

{"title":"DayCent-CUTE: A global sensitivity, auto-calibration, and uncertainty analysis tool for DayCent","authors":"Xiuying Wang , Jaehak Jeong , Seonggyu Park , Xuesong Zhang , Jungang Gao , Nélida E.Q. Silvero","doi":"10.1016/j.envsoft.2023.105832","DOIUrl":"10.1016/j.envsoft.2023.105832","url":null,"abstract":"<div><p><span>Soil organic carbon<span> (SOC) is a crucial metric for mitigating greenhouse gas emissions and developing climate-smart agriculture. DayCent is widely used to simulate </span></span>SOC dynamics and soil trace gas fluxes in various ecosystems. In this study, we developed DayCent-CUTE (auto-Calibration, sensitivity, and Uncertainty analysis ToolSet) for conducting global sensitivity analysis (GSA), auto-calibration, and uncertainty analysis for the model. The tool encompassed a pair of GSA methods and two distinct parameter optimization methods.</p><p><span>A collection of 30 field experiments, encompassing 212 combinations of management treatments and 581 SOC measurements, was divided into 18 sites for calibration and 12 sites for independent model evaluation. The posterior parameter distribution obtained from the auto-calibration process reduces the model bias and RMSE values, while the Nash-Sutcliffe efficiency and R</span><sup>2</sup> values showed improvements. The DayCent-CUTE proves to be an efficient and flexible tool that enhances the applications of the DayCent model.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"169 ","pages":"Article 105832"},"PeriodicalIF":4.9,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71514378","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

Leveraging machine learning to automate regression model evaluations for large multi-site water-quality trend studies 利用机器学习自动化大型多站点水质趋势研究的回归模型评估

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2023-10-28 DOI: 10.1016/j.envsoft.2023.105864

Jennifer Murphy , Jeffrey Chanat

{"title":"Leveraging machine learning to automate regression model evaluations for large multi-site water-quality trend studies","authors":"Jennifer Murphy , Jeffrey Chanat","doi":"10.1016/j.envsoft.2023.105864","DOIUrl":"10.1016/j.envsoft.2023.105864","url":null,"abstract":"<div><p>Large multi-site trend studies provide an opportunity to evaluate progress of waterbodies towards water-quality goals across broad geographic areas. Such studies often aggregate the results of site-specific models and thus contend with evaluating each model for appropriate fit and statistical assumptions. We explored the use of four traditional machine learning models (logistic regression, linear and quadratic discriminant analysis, and k-nearest neighbors) to perform these checks and estimate probabilities that an analyst would publish or reject a site-specific trend model from a multi-site study. We trained these “model-checking models” (MCMs) using a national study of over 6000 trend models and tested the MCMs using a smaller set of novel trend models. Although the MCMs did not perform well enough to bypass analyst review entirely, we found incorporating an MCM into a larger evaluation workflow can reduce the number of trend models needing an analyst review by more than half.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"170 ","pages":"Article 105864"},"PeriodicalIF":4.9,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1364815223002505/pdfft?md5=16bffa72529db441d4848da0b2d0d618&pid=1-s2.0-S1364815223002505-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71514384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rapid flood modelling using HAND-FFA-SRC coupled approach and social media-based geodata in a coastal Chinese watershed 使用HAND-FA-SRC耦合方法和基于社交媒体的地理数据在中国沿海流域进行快速洪水建模

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2023-10-27 DOI: 10.1016/j.envsoft.2023.105862

Lei Fang , Zhenyu Zhang , Jinliang Huang

{"title":"Rapid flood modelling using HAND-FFA-SRC coupled approach and social media-based geodata in a coastal Chinese watershed","authors":"Lei Fang , Zhenyu Zhang , Jinliang Huang","doi":"10.1016/j.envsoft.2023.105862","DOIUrl":"10.1016/j.envsoft.2023.105862","url":null,"abstract":"<div><p>Flooding has catastrophic eﬀects worldwide. Rapid flood models, such as the height above nearest drainage (HAND) model, have lower complexity and data requirements than traditional models. However, input stage height data are often lacking because most gauged sites only provide estimates of discharge. In addition, performance is difficult to evaluate because flood extent data during extreme periods are often unavailable. Here, we developed a HAND-flood frequency analysis (FFA)-synthetic rating curve (SRC) approach and applied it to a coastal watershed in China. The HAND-FFA-SRC approach demonstrated effective and efficient flood forecasting, and the C values were 1.19, 1.20, and 1.16, respectively for the floods under moderate rainfall, heavy rainfall, and storm. Meanwhile, the accuracy of the model was highly impacted by the topographic characteristics of the watersheds. The C values were improved as the slope increased from 3° to 20° during the floods under different scenarios. Additionally, the effects of floods were evaluated under different return periods which indicated that the cropland is the most affected land use type but the risk for impervious surfaces is increasing. The proposed approach is viable for forecasting flood susceptibility and can improve resilience planning and flood management.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"170 ","pages":"Article 105862"},"PeriodicalIF":4.9,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71507435","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

HMS-REST v1.0: A plugin for the HEC-HMS model to provide RESTful services HMS-REST v1.0：HEC-HMS模型的插件，用于提供RESTful服务

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2023-10-23 DOI: 10.1016/j.envsoft.2023.105860

Qiaoying Lin , Bingqing Lin , Dejian Zhang , Jiefeng Wu , Xingwei Chen

{"title":"HMS-REST v1.0: A plugin for the HEC-HMS model to provide RESTful services","authors":"Qiaoying Lin , Bingqing Lin , Dejian Zhang , Jiefeng Wu , Xingwei Chen","doi":"10.1016/j.envsoft.2023.105860","DOIUrl":"10.1016/j.envsoft.2023.105860","url":null,"abstract":"<div><p><span>Web technologies facilitating location-independent participation and broad </span>knowledge sharing<span> are critically important for open science and participative management and decision-making. Therefore, it is crucial to enable hydrological models to run in a web environment. In this study, a representational state transfer<span><span><span> (REST) interface is proposed for the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model. The </span>web service interface (named HMS-REST) was built based on the Java </span>API<span> and Jython script system of HEC-HMS. HMS-REST allows users to retrieve and write model inputs and outputs and perform model execution. These functionalities were evaluated via a case study of pseudoensemble flood forecasting using a data set of six historical flood events from the Chuanchang watershed, southeastern China. Satisfactory performance was achieved in pseudoensemble flood forecasting in terms of the peak flow, total flood volume, peak flow timing and overall hydrograph fitting. This case study indicates that HMS-REST and HEC-HMS can be effectively integrated and demonstrates that the use of HEC-HMS with a web-based interface can facilitate pragmatic applications of the HEC-HMS model in the fields of model calibration, flood simulation and forecasting, and participatory decision-making.</span></span></span></p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"170 ","pages":"Article 105860"},"PeriodicalIF":4.9,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71519153","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

FlowDyn: A daily streamflow prediction pipeline for dynamical deep neural network applications FlowDyn：用于动态深度神经网络应用的每日流量预测管道

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2023-10-18 DOI: 10.1016/j.envsoft.2023.105854

S. Sadeghi Tabas , N. Humaira , S. Samadi , N.C. Hubig

{"title":"FlowDyn: A daily streamflow prediction pipeline for dynamical deep neural network applications","authors":"S. Sadeghi Tabas , N. Humaira , S. Samadi , N.C. Hubig","doi":"10.1016/j.envsoft.2023.105854","DOIUrl":"10.1016/j.envsoft.2023.105854","url":null,"abstract":"<div><p><span><span>This paper presents a dynamical neural network framework to understand how catchment systems respond to daily rainfall-runoff processes over time. We developed an interactive Python-based </span>deep neural network (DNN) package called FlowDyn (presented through a JS-based web platform) to simulate and forecast daily </span>streamflow<span><span> data for >180 gauging stations across the globe. Several DNN models, including long short-term memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid network of </span>convolutional neural network<span> and LSTM (ConvLSTM), as well as an auto encoder (AE) model were developed and integrated into the FlowDyn pipeline to analyze and forecast sequential daily streamflow values that are embedded within a web-based application for demonstration and visualization. Inputs were gathered from different web services, including the catchment attributes and meteorology for large-sample studies (CAMELS), the national climatic data center (NCDC), and the global runoff data center (GRDC). DNN configurations were trained and tested with an average accuracy rating of 0.83 across 183 river basins globally. FlowDyn simulation and performance demonstrated that different DNN models were able to learn both regionally consistent and location-specific hydrological behaviors. Through the findings of this paper, we advocate the merit of applying FlowDyn package in the field of daily rainfall-runoff prediction at both local and global scales.</span></span></p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"170 ","pages":"Article 105854"},"PeriodicalIF":4.9,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71507936","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