Environmental Modelling & Software最新文献

{"title":"Climate change effects at basin-scale: Weathering rates and CO2 consumption assessment by using the reaction path modelling","authors":"Carmine Apollaro ,&nbsp;Ilaria Fuoco ,&nbsp;Giovanni Vespasiano ,&nbsp;Rosanna De Rosa ,&nbsp;Mauro F. La Russa ,&nbsp;Daniele Cinti ,&nbsp;Michela Ricca ,&nbsp;Alessia Pantuso ,&nbsp;Andrea Bloise","doi":"10.1016/j.envsoft.2025.106398","DOIUrl":"10.1016/j.envsoft.2025.106398","url":null,"abstract":"<div><div><em>Reaction Path Modelling</em> was used to calculate the fluxes in terms of solutes and CO₂ consumption during the water-rock interaction process at the basin-scale, considering the current and future climate scenarios (temperature and atmospheric CO₂ concentration) and two types of solid reagent (Silicate Solid Reagent-SSR and Carbonate-Silicate Reagent C-SSR). Two modelling were performed considering solid reagents and simulating their weathering in the current climate scenario and two other simulations were developed to consider the future climate scenario (Representative Concentration Pathways – RCP 8.5). The study highlights that although the higher temperature promotes an increase of total dissolved ions (TDS) into riverine waters, the higher temperature also causes a decrease in precipitation and, thus, in the runoff. This condition will lead to a reduction in weathering rate and CO₂ consumption at the basin scale. The main indirect effect of a negative CO₂ consumption budget is a further increase in CO₂ atmospheric concentration.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"187 ","pages":"Article 106398"},"PeriodicalIF":4.8,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519080","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":"Scientometric analysis of development and opportunities for research in digital agriculture innovation management","authors":"Shuangjin Wang ,&nbsp;Puxuan Wang ,&nbsp;Richard Cebula ,&nbsp;Maggie Foley ,&nbsp;Chen Liang","doi":"10.1016/j.envsoft.2025.106392","DOIUrl":"10.1016/j.envsoft.2025.106392","url":null,"abstract":"<div><div>Digital agriculture has transformed the landscape of agricultural technology innovation and has led to increased attention towards managing innovation in this domain. This study seeks to provide a comprehensive understanding of digital agriculture innovation management by proposing a new retrieval strategy and constructing a dataset of 1878 research papers from the WoS-SSCI core collection spanning the years 2000 through 2023. The research employs scientific methods and tools to analyze the overall development, collaboration networks, frontier hotspots, and contribution paths in the Chinese context, as well as future opportunities for research in digital agriculture innovation management. The study reveals that digital agriculture innovation management research has experienced accelorated growth since 2020 and is expected to undergo further changes in the near future. The keywords extracted from the WoS-SSCI core collection and CNKI (China National Knowledge Infrastructure) core database exhibit the characteristics of Zipf's Law, indicating certain terms are more frequently used than others. The analysis identifies 44 frontier hotspots in digital agriculture innovation management research within the WoS-SSCI, with topics such as “precision agriculture”, “remote sensing”, and “food security” displaying notable prominence in different sub-disciplines due to their high centrality and density. This scientometric analysis not only provides strategic guidance and methodological inspiration for theoretical research and disciplinary development in digital agriculture innovation management but also offers practical recommendations for implementing digital agriculture strategies and promoting rural development. The findings of this study lay a solid foundation for future research in digital agriculture innovation management and emphasize the potential for further advancements in this field.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106392"},"PeriodicalIF":4.8,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528665","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":"Semantic-driven parametric 3D geographic scene modeling: Integrating knowledge graphs and large language models","authors":"Pei Dang ,&nbsp;Jun Zhu ,&nbsp;Chao Dang ,&nbsp;Heng Zhang","doi":"10.1016/j.envsoft.2025.106399","DOIUrl":"10.1016/j.envsoft.2025.106399","url":null,"abstract":"<div><div>Parametric geographic scene modeling serves as the primary method for achieving large-scale rapid spatial visualization. However, balancing modeling efficiency and specificity of geographic entities poses significant challenges due to the complexity and diversity of real-world geographic environments. This study proposes a novel 3D geographic scene modeling approach that integrates knowledge graphs and large language models (LLMs). The method leverages the extensive pre-trained knowledge and inference capabilities of LLMs to autonomously infer and enhance semantic information of unknown geographic entities. Through progressive knowledge graphs, it transforms the semantic information of geographic entities into modeling parameters, ultimately achieving more intelligent 3D geographic scene modeling. Our approach addresses current limitations in parametric modeling by offering a flexible and adaptive solution capable of efficiently handling diverse geographic entities. Through case studies and comparative analyses, we examine the inference results and modeling effects under various prompt ratios, validating the effectiveness and advantages of this method.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106399"},"PeriodicalIF":4.8,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547079","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":"Interpretable transformer model for national scale drought forecasting: Attention-driven insights across India","authors":"Ashish Pathania,&nbsp;Vivek Gupta","doi":"10.1016/j.envsoft.2025.106394","DOIUrl":"10.1016/j.envsoft.2025.106394","url":null,"abstract":"<div><div>The impacts of climate change are increasingly evident through the rise in severe droughts globally. These events result in intensified socio-economic and environmental effects. Proactive drought management requires effective forecasting and an improved understanding of the underlying hydro-climatic variables. The present study focuses on developing a national-scale drought forecasting model tailored to the diverse climatic zones of India. This model leverages the attention-based transformer framework to forecast SPEI-3 values at a lead time of 30, 60, and 90 days respectively while interpreting the complex spatiotemporal dependencies. The model predicted the SPEI-3 values with Root Means Square Error (RMSE) of 0.67 ± 0.08 and Nash-Sutcliffe Efficiency coefficient (NSE) of 0.51 ± 0.14 at a lead time of 30 days. Prediction uncertainty through quantile forecasting enhances the model's utility for effective decision-making and risk management. Model performance varies on the seasonal scale with higher accuracy in post-monsoon (Oct–Nov) and a relative decline in the pre-monsoon (March–May) season. Among large-scale climate drivers, the Indian Ocean Dipole (IOD) was found to have the highest attention representing its significant influence over Indian drought dynamics compared to other global circulation indices. While involving the static variables, the attention to spatial coordinates was found to be higher than elevation. However, in dynamic variables, precipitation, and past SPEI-3 values exhibited the most significant impact. Plots of temporal attention explain the seasonal variability present in the model's predictions. This research presents a comprehensive model, which advances our knowledge of the dynamics of drought forecasting in India.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"187 ","pages":"Article 106394"},"PeriodicalIF":4.8,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511635","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":"Letting ecosystems speak for themselves: An unsupervised methodology for mapping landscape acoustic heterogeneity","authors":"Nestor Rendon ,&nbsp;Maria J. Guerrero ,&nbsp;Camilo Sánchez-Giraldo ,&nbsp;Víctor M. Martinez-Arias ,&nbsp;Carolina Paniagua-Villada ,&nbsp;Thierry Bouwmans ,&nbsp;Juan M. Daza ,&nbsp;Claudia Isaza","doi":"10.1016/j.envsoft.2025.106373","DOIUrl":"10.1016/j.envsoft.2025.106373","url":null,"abstract":"<div><div>Passive Sonic Monitoring (PSM) refers to the analysis of patterns and structures shaped by sound, offering a complementary approach to traditional landscape analysis methods, such as satellite imagery. In particular, satellite-based methods alone may overlook specific dynamics of the organism at multiple taxonomic levels and local abiotic interactions. This paper introduces a novel unsupervised methodology for mapping similarities between soundscapes. Using Gaussian Mixture Models (GMM), this approach generates soundscape maps that reveal ecological processes throughout the day. We applied our methodology to data from 94 sites within a heterogeneous Colombian Orinoquia ecosystem. We found correlations between the cluster maps, satellite images, and biotic presences (bird and amphibian sonotypes). Our results align with established remote-sensing data and uncover previously unrecognized sonic patterns, offering new ecological insights that complement traditional landscape assessments. Our approach bridges the gap between image satellite-based assessments and ecological sonic processes, paving the way for comprehensive long-term biodiversity monitoring.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"187 ","pages":"Article 106373"},"PeriodicalIF":4.8,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465072","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":"Wind-generated flow modeling and future circulation prediction of lakes under complex wind field - A case study of Qinghai Lake","authors":"Junyang Li ,&nbsp;Yuanfu Zhang ,&nbsp;Yuxiu Li ,&nbsp;Kai Ma ,&nbsp;Zhikang Wang ,&nbsp;Xiaohan Zhang ,&nbsp;Yuchuan Yi ,&nbsp;Pengxiang Lu ,&nbsp;Zhiqian Gao ,&nbsp;Min Wang","doi":"10.1016/j.envsoft.2025.106371","DOIUrl":"10.1016/j.envsoft.2025.106371","url":null,"abstract":"<div><div>The response mechanism of lake flow under the influence of a complex wind field is a cross-disciplinary hotspot in the fields of hydrology and modern sedimentation research. Qinghai Lake, influenced by multiple wind fields, exhibits complex internal hydrodynamics with largely unknown controlling factors. Current studies predominantly focus on the impact of climate and prevailing northwesterly winds on lake flow in Qinghai Lake, while other factors receive less consideration. Building on previous research, this thesis analyzes the wind-generated flow response under the combined influence of prevailing wind and land-lake breeze. A simulation model of lake flow under water balance conditions was constructed using the shallow water equation, integrating climatic data, multiple wind field conditions, wind speed, blowing range, and water depth. The accuracy of the adopted model is validated by comparison with measured circulation, water level, and flow rate data.Results indicate that the prevailing northwesterly wind in Qinghai Lake predominantly controls the main circulation, while the land-lake breeze governs the secondary circulation. The north wind inhibits the formation of certain secondary circulations, such as the counterclockwise circulation in the Hada Bay area and the clockwise circulation in the Dongnan Bay.On an interannual scale, the wind speed of the land-lake breeze along the north shore of Qinghai Lake is positively correlated with the total water volume and negatively correlated with temperature.These findings facilitate the simulation of circulation under complex wind field conditions and allow inference of future lake current development patterns based on predictions of climate and water level changes.</div><div>Currently, the modern sedimentary phenomena along the shore of Qinghai Lake are complex: wind-driven waves and littoral currents form shore sandbars on the southern shore, while wind-driven waves and littoral currents create longshore barriers and lagoons at the mouth of the Hargai River's northern tributary. Therefore, this study can provide a reference for research on modern sandy sedimentation and lake eutrophication in Qinghai Lake. It also offers theoretical support for sedimentary oil and gas reservoirs in similar lake basins.By analyzing the driving mechanisms of complex wind fields on lake circulation, this study can provide new perspectives and data support for the fundamental theoretical research of lake dynamics. It also lays the foundation for predicting the hydrodynamic responses of lakes under future climate change. Additionally, it offers a theoretical framework and practical reference for interdisciplinary research in limnology, sedimentology, ecology, and related fields.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"187 ","pages":"Article 106371"},"PeriodicalIF":4.8,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473910","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":"Quantifying the impact of different precipitation data sources on hydrological modeling processes in arid basin using transfer entropy","authors":"Qingling Bao ,&nbsp;Jianli Ding ,&nbsp;Jinjie Wang","doi":"10.1016/j.envsoft.2025.106376","DOIUrl":"10.1016/j.envsoft.2025.106376","url":null,"abstract":"<div><div>Multi-source precipitation products (MSPs) are critical for hydrologic modeling, but their spatial and temporal heterogeneity and uncertainty present challenges to simulation accuracy that need to be addressed urgently. This study assessed the impact of different precipitation data sources on hydrologic modeling in an arid basin. There were seven precipitation products and meteorological station interpolated data that were used to drive the hydrological model, and we evaluated their performance by fusing the six precipitation products through the dynamic bayesian averaging algorithm. Ultimately, the runoff simulation uncertainty was quantified based on the DREAM algorithm, and the information transfer entropy was used to quantify the differences in hydrologic simulation processes driven by different precipitation data. The results showed that CMFD and ERA5 weights were higher, and the DBMA fused precipitation annual mean value was about 309.83 mm with good simulation accuracy (RMSE of 1.46 and R² of 0.75). The simulation was satisfactory (NSE &gt;0.80) after parameter calibration and data assimilation for all driving data, with CHIRPS and TRMM performed better in the common mode, and HRLT and CMFD performed excellently in the glacier mode. The DREAM algorithm indicated less uncertainty for DBMA, CHIRPS and HRLT data. The entropy of information transfer revealed that precipitation occupied a significant position in information transfer, especially affecting evapotranspiration and surface soil moisture. CMFD and TPS CMADS were highest in snow water equivalent information entropy, and CHIRPS and TPS CMADS were highest in evapotranspiration information entropy. CDR, CHIRPS, ERA5-Land and IDW STATION had the highest snow water equivalent information entropy, DBMA and CMORPH had the highest runoff information entropy, CHIRPS and TRMM had the highest soil moisture information entropy, whereas ERA5, HRLT, and TPS CMADS had the highest evapotranspiration information entropy in glacial mode. This study reveals significant differences between different precipitation data sources in hydrological modeling of arid basin, which is an important reference for future water resources management and climate change adaptation strategies.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"187 ","pages":"Article 106376"},"PeriodicalIF":4.8,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471679","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 review on modelling forest biogeochemistry and the coupled forest – soil interactions in a changing world","authors":"Felix Sauke ,&nbsp;Rico Fischer ,&nbsp;Michael Rode","doi":"10.1016/j.envsoft.2025.106381","DOIUrl":"10.1016/j.envsoft.2025.106381","url":null,"abstract":"<div><div>Forests have many functions, including habitat provision, timber production, and carbon sequestration, but they are under increasing pressure. Coupled forest - soil models are therefore of great importance for a better understanding of forest development, and biogeochemical cycles in a changing world. This study provides a decision support framework for researchers to select appropriate models to study the forest - soil system. We analyzed 36 models, including forest models, soil models, and coupled forest - soil models. These models vary across spatio-temporal scales, from hourly to multi-year time steps, and from individual trees to catchments. We provide an overview about the general approaches and exact process implementations of those models to facilitate model choices for specific research questions. While an all-encompassing 'Swiss Army knife' forest - soil model which captures all features of both forest and soil complexity does not exist, tailor-made models are possible to suit specific research interests.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"187 ","pages":"Article 106381"},"PeriodicalIF":4.8,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518977","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":"Extrapolability improvement of machine learning-based evapotranspiration models via domain-adversarial neural networks","authors":"Haiyang Shi,&nbsp;Ximing Cai","doi":"10.1016/j.envsoft.2025.106383","DOIUrl":"10.1016/j.envsoft.2025.106383","url":null,"abstract":"<div><div>Machine learning-based evapotranspiration (ET) models capture complex nonlinear relationships but struggle with global extrapolation due to unbalanced data distribution, limiting accurate ET assessments crucial for understanding water and energy cycles. This study used Domain-Adversarial Neural Networks (DANN) to improve the geographical adaptability of ET models by mitigating site-level distributional discrepancies. DANN significantly enhanced ET prediction accuracy, with a median Kling-Gupta Efficiency (KGE) increase of 0.27 (p &lt; 0.001) and with a range from 0.06 to 0.58 for the middle 90% values compared to the traditional Leave-One-Out (LOO) method. DANN proves particularly effective for isolated sites and biome transition zones, reducing errors and avoiding low-accuracy predictions. By leveraging data from resource-rich areas, DANN strengthens the reliability of global-scale ET products, especially in ungauged regions. Future evaluations and improvements are necessary, such as using additional accuracy metrics beyond KGE and focusing on sites located at the intersection of several climate types and sites with unique soil-vegetation-atmosphere processes. This study demonstrates the potential of domain adaptation techniques to enhance the generalization and extrapolation capabilities of machine learning in hydrological predictions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"187 ","pages":"Article 106383"},"PeriodicalIF":4.8,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453054","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":"Development of dam inflow prediction technique based on explainable artificial intelligence (XAI) and combined optimizer for efficient use of water resources","authors":"Yong Min Ryu ,&nbsp;Eui Hoon Lee","doi":"10.1016/j.envsoft.2025.106380","DOIUrl":"10.1016/j.envsoft.2025.106380","url":null,"abstract":"<div><div>Accurate inflow forecasts are crucial for managing water resources, particularly in regions experiencing both floods and droughts. This study proposes a combined optimizer (CO) that combines adaptive moment and vision correction algorithms to improve the shortcomings of deep learning optimizers, thereby enhancing deep learning accuracy. CO improves the shortcomings of deep learning optimizers, such as storage space and local optimal solution convergence potential. Additionally, explainable artificial intelligence (XAI) was applied to CO, creating a model termed Dual-AI, which enhances interpretability and accuracy. As a result of application to Daecheong Dam in Korea, Dual-AI showed a maximum reduction of root mean squared error (RMSE) by approximately 3.68 (R2 increased by about 0.0628) in verification and approximately 678.4922 (R2 increased by about 0.0664) in prediction compared to the existing optimizer. Dual-AI shows potential for various hydrological applications, providing accurate forecasts to support effective water management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"187 ","pages":"Article 106380"},"PeriodicalIF":4.8,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508305","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}