Environmental Modelling & Software最新文献

{"title":"Amadeus: Accessing and analyzing large scale environmental data in R","authors":"Mitchell Manware ,&nbsp;Insang Song ,&nbsp;Eva S. Marques ,&nbsp;Mariana Alifa Kassien ,&nbsp;Lara P. Clark ,&nbsp;Kyle P. Messier","doi":"10.1016/j.envsoft.2025.106352","DOIUrl":"10.1016/j.envsoft.2025.106352","url":null,"abstract":"<div><div>Environmental health research increasingly uses large scale spatial data to understand relationships between environmental factors and health outcomes. Data access and analysis tools which improve the timeliness and reproducibility of environmental health research are crucial for advancing the field. We present the <em>amadeus</em> package for R, a tool to improve access to and utility with large scale environmental data, primarily covering the United States. <em>amadeus</em> aims to reduce the learning curve for conducting spatial data analyses in R by providing functions which download, process, and calculate covariates from various publicly available environmental data sources. The functions promote interoperability with popular spatial data R packages and integration across other programming languages. Created and maintained with test-driven development, <em>amadeus</em> supports the reproducibility of environmental data acquisition and preparation. The <em>amadeus</em> package has diverse data access and integration applications, ranging from health-oriented studies in environmental epidemiology to ecology and climatology.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106352"},"PeriodicalIF":4.8,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377302","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}

{"title":"Adaptive Surrogate Model Assisted Swarm Intelligence for Parameter Inversion of complex hydrological models","authors":"Guhan Li ,&nbsp;Peng Shi ,&nbsp;Simin Qu ,&nbsp;Lingzhong Kong ,&nbsp;Xiaohua Xiang ,&nbsp;Qian Yang ,&nbsp;Yu Qiao ,&nbsp;Shiyu Lu","doi":"10.1016/j.envsoft.2025.106353","DOIUrl":"10.1016/j.envsoft.2025.106353","url":null,"abstract":"<div><div>Parameter inversion in hydrological models aims to estimate parameters from observed data, improving accuracy and understanding of the system. This process typically involves optimization algorithms to identify optimal parameter combinations, often resulting in significant computational costs due to the necessity for numerous model runs, particularly in complex hydrological models. To address this challenge, this study introduces the Adaptive Surrogate Model Assisted Swarm Intelligence (ASMA-SI) framework. ASMA-SI uses the iterative traces of swarm intelligence (SI) as a training sample set, fostering a tightly coupling between SI and the surrogate model while minimizing computational demands and enhancing search efficiency. The framework was applied to enhance three prominent SI algorithms: Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). Synthetic experiments and a case study were conducted to evaluate the inversion efficacy of ASMA-SI. In the synthetic experiments, ASMA-SI demonstrated faster convergence to the ‘true value’, while in the real-world case study, it outperformed in nearly all of the nine test groups, achieving better average performance metrics.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106353"},"PeriodicalIF":4.8,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388453","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":"MANG@COAST: A spatio-temporal modeling approach of muddy shoreline mobility based on mangrove monitoring","authors":"P.E. Augusseau ,&nbsp;C. Proisy ,&nbsp;A. Gardel ,&nbsp;G. Brunier ,&nbsp;L. Granjon ,&nbsp;T. Maury ,&nbsp;A. Mury ,&nbsp;A. Staquet ,&nbsp;V.F. Santos ,&nbsp;R. Walcker ,&nbsp;P. Degenne ,&nbsp;D. Lo Seen ,&nbsp;E.J. Anthony","doi":"10.1016/j.envsoft.2025.106345","DOIUrl":"10.1016/j.envsoft.2025.106345","url":null,"abstract":"<div><div>Highly dynamic wave-exposed muddy coasts harbouring mangrove ecosystems can be subject to both marked accretion and erosion depending on the complex interactions between mud and waves. We propose a multiscale modelling approach and empirical equations calibrated and integrated into a landscape dynamics model implemented on a mud-bank coast using the Ocelet language to simplify the complex processes driving sea-mangrove coastline dynamics and quantity them with 10 years of satellite observations of mangrove shoreline fluctuations.</div><div>We find that fluctuations in seafront mangroves can be simulated with acceptable accuracy along 200 km of coastline. In the absence of mud banks, seasonal wave forcing resulted in erosion rates reaching 1100 m/y. Our findings indicate that wave energy can be reduced by 90% at all locations when the width of mud banks exceeds 2000 m in front of the mangroves. Finally, we discuss the potential of this modeling approach for anticipating coastal changes.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106345"},"PeriodicalIF":4.8,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077716","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}

{"title":"Derivation of characteristic physioclimatic regions through density-based spatial clustering of high-dimensional data","authors":"Sebastian Lehner ,&nbsp;Katharina Enigl ,&nbsp;Matthias Schlögl","doi":"10.1016/j.envsoft.2025.106324","DOIUrl":"10.1016/j.envsoft.2025.106324","url":null,"abstract":"<div><div>Physioclimatic regions are homogeneous geospatial entities that exhibit similar characteristics in both climatic conditions and the physiographic environment. They provide a foundation for a broad range of analyses in earth system sciences that are conditional on the prevailing climatological properties shaping geographical areas. However, delineating such regions is challenging due to high-dimensional input data and nonlinear processes in nature. We introduce a nonparametric clustering methodology to derive geospatial clusters with similar physioclimatic attributes, using a comprehensive dataset of climatological and geomorphometric indices from Austria. Our analysis workflow includes (1) Principal Component Analysis (PCA) for linear dimension reduction, (2) Uniform Manifold Approximation and Projection (UMAP) for nonlinear dimension reduction, (3) Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) for clustering and (4) random forest for feature importance assessment. Results show both agreement and differences compared to reference classification, thereby highlighting the need for quantitative performance evaluation and synoptic plausibility assessment. Findings include the identification of two characteristic clusters for inneralpine valleys in Western Austria and interfluves in the Styrian basin. This workflow offers a blueprint for delineating consistent geospatial regions for various applications. Clusters obtained with this approach may assist in unearthing new perspectives on regionalisation, provide new insights in the underlying characteristics determining these regions, and thus aid in the understanding of complex environmental patterns.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106324"},"PeriodicalIF":4.8,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077717","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}

{"title":"","authors":"Anjela Karunia Amalia ,&nbsp;Anggun Rosa Ajie Safira ,&nbsp;Anak Agung Eka Andiani ,&nbsp;Fuji Sintia Armi ,&nbsp;Eka Widya Utami","doi":"10.1016/j.envsoft.2025.106320","DOIUrl":"10.1016/j.envsoft.2025.106320","url":null,"abstract":"","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106320"},"PeriodicalIF":4.8,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143124386","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 real-time and modular weather station software architecture based on microservices","authors":"J. Bonilla ,&nbsp;J.A. Carballo ,&nbsp;V. Abad-Alcaraz ,&nbsp;M. Castilla ,&nbsp;J.D. Álvarez ,&nbsp;J. Fernández-Reche","doi":"10.1016/j.envsoft.2025.106337","DOIUrl":"10.1016/j.envsoft.2025.106337","url":null,"abstract":"<div><div>The increasing demand for accurate and real-time weather data has highlighted the limitations of traditional weather stations, which often lack the flexibility and scalability required for modern applications. This paper introduces a real-time and modular weather station software architecture based on microservices, designed to address these challenges. The proposed system leverages microservices to offer a scalable, flexible, and easily maintainable solution for weather data collection and dissemination. Its modular architecture allows seamless integration of various sensors and data sources, facilitating customization and the addition of new functionalities without disrupting existing operations. Real-time data processing is a critical feature, ensuring timely and accurate weather information. The use of microservices guarantees data accessibility from any location, supporting both local and remote applications. A case study at the Plataforma Solar de Almería (PSA) demonstrates the benefits of the proposed architecture, highlighting its effectiveness in integrating multiple weather stations and providing real-time weather data.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106337"},"PeriodicalIF":4.8,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077719","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}

{"title":"Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application","authors":"Xingtian Chen ,&nbsp;Yuhang Zhang ,&nbsp;Aizhong Ye ,&nbsp;Jinyang Li ,&nbsp;Kuolin Hsu ,&nbsp;Soroosh Sorooshian","doi":"10.1016/j.envsoft.2025.106350","DOIUrl":"10.1016/j.envsoft.2025.106350","url":null,"abstract":"<div><div>Pre-trained models like FourCastNet, Pangu and GraphCast have gained popularity in the meteorological field. In hydrology, data-driven rainfall-runoff models based on long short-term memory (LSTM) networks have been successfully applied for various purposes. As large-sample hydrological datasets (e.g., Caravan) continue to grow, it is foreseeable that pre-trained models tailored for hydrology will emerge. These pre-trained models have the potential to bypass the need for training data-driven models from scratch, enabling us to focus more swiftly on customized applications. Additionally, they offer opportunities explore model performance in changing environment, which is also a key consideration when using data-driven models in unseen scenarios. However, the hydrological field has seen limited attempts to employ, transfer, and fine-tune pre-trained models. This study aims to explore the possibility of using fine-tuning techniques to achieve a smooth transition of LSTM-based rainfall-runoff models from pre-training to post-application scenarios. By utilizing ERA5-Land reanalysis precipitation data within the Caravan dataset, we calibrated a pre-trained LSTM model for runoff simulation. Subsequently, we transitioned the model to use near-real-time satellite precipitation estimates as the input, targeting satellite-driven predictions. Our results show that fine-tuning parameters lead to improvements in various metrics, including the Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), and hydrological signature metrics such as high and low flows, compared to outcomes without parameter fine-tuning. Specifically, fine-tuning using locally calibrated models enhanced performance in 73.5% of the basins. In contrast, the results of fine-tuning regional models were mixed; while it benefited 55.1% of the basins, it also led to a deterioration in model performance in 44.9% of cases. This study is a pioneering exploration of the adaptability of LSTM models from pre-training to post-application. It also lays the groundwork for future investigations aimed at enhancing the adaptability of data-driven models to the impacts of changing environment.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106350"},"PeriodicalIF":4.8,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077718","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}

{"title":"ASTERIX: Module for modeling the water flow on vegetated hillslopes","authors":"Stelian Ion ,&nbsp;Dorin Marinescu ,&nbsp;Stefan Gicu Cruceanu","doi":"10.1016/j.envsoft.2025.106336","DOIUrl":"10.1016/j.envsoft.2025.106336","url":null,"abstract":"<div><div>The paper presents an open source software for numerical integration of an extended Saint-Venant model used as a mathematical tool to simulate the water flow from laboratory up to large-scale spatial domains applying physically-based principles of fluid mechanics. Many in-situ observations have shown that vegetation plays a key role in controlling the hydrological flux at catchment scale. In case of heavy rains, the infiltration and interception processes cease quickly, the remaining rainfall gives rise to the Hortonian overland flow and the flash flood is thus initiated. In this context, we also address the following problem: how do the gradient of soil surface and the vegetation influence the water dynamics in the Hortonian flow? The mathematical model and ASTERIX were kept as simple as possible in order to be accessible to a wide range of stakeholders interested in understanding the complex processes behind the water flow on hillslopes covered by plants.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106336"},"PeriodicalIF":4.8,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077720","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}

{"title":"Doing hydrology when no in-situ data exists: Surrogate River discharge Model (SRM)","authors":"Hae Na Yoon ,&nbsp;Lucy Marshall ,&nbsp;Ashish Sharma ,&nbsp;Seokhyeon Kim","doi":"10.1016/j.envsoft.2025.106334","DOIUrl":"10.1016/j.envsoft.2025.106334","url":null,"abstract":"<div><div>The surrogate river discharge model (SRM) uses remote sensing surrogates of river discharge (SR) to estimate streamflow in ungauged basins. Integrating SR derived from L-band microwave data with climate inputs of rainfall and potential evapotranspiration, the model operates within a hydrological framework. While SR is strongly correlated with streamflow, it is unitless and requires calibration for physical coherence. Calibration translates SR into an actual discharge value using the average or mean discharge (QM) derived from the Budyko framework. A novel likelihood approach employing SR and QM eliminates reliance on direct discharge observations. Validation across three Australian catchments demonstrates satisfactory performance, with NSE &gt;0.6 and KGE &gt;0.6, highlighting its applicability in data-scarce regions. The SRM software includes tools for L-band microwave data acquisition, SR generation, and hydrological model calibration, enabling global application in river discharge estimation.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106334"},"PeriodicalIF":4.8,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143055237","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":"Enhancing hydrological modeling of ungauged watersheds through machine learning and physical similarity-based regionalization of calibration parameters","authors":"Arun Bawa ,&nbsp;Katie Mendoza ,&nbsp;Raghavan Srinivasan ,&nbsp;Fearghal O'Donchha ,&nbsp;Deron Smith ,&nbsp;Kurt Wolfe ,&nbsp;Rajbir Parmar ,&nbsp;John M. Johnston ,&nbsp;Joel Corona","doi":"10.1016/j.envsoft.2025.106335","DOIUrl":"10.1016/j.envsoft.2025.106335","url":null,"abstract":"<div><div>This study enhances hydrological modeling in ungauged watersheds by employing physical similarity and machine learning-based clustering for regionalizing the Soil and Water Assessment Tool (SWAT) model parameters at the HUC12 (hydrological unit code) watershed scale within a HUC02 basin. Eleven features, including environmental, topographical, soil, and hydrological properties, were utilized to identify physical similarities for watershed clustering. Machine learning techniques, including random forest and hierarchical clustering, were employed to transfer calibrated parameters from gauged to ungauged watersheds. Validation of parameter transfer over gauged SWAT model projects showed that 88% of the projects achieved calibrated status (KGE ≥0.5; PBIAS ≤25%). Additional validation using MODIS satellite evapotranspiration measurements confirmed the robustness of the approach. Results indicated that the proposed approach successfully captures physical similarities, and effectively captures flow patterns. Overall, the study highlights the potential of physical similarity-based clustering and machine learning techniques for improving hydrological modeling in ungauged watersheds.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106335"},"PeriodicalIF":4.8,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077721","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}