Environmental Modelling & Software最新文献_第10页

Unveiling uncertainties in soil organic carbon modeling: the critical role of climate response functions 揭示土壤有机碳模型中的不确定性：气候响应函数的关键作用

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-22 DOI: 10.1016/j.envsoft.2025.106537

Huiwen Li , Yue Cao , Jingfeng Xiao , Wenxin Zhang , Yiping Wu , Arshad Ali , Zuoqiang Yuan

{"title":"Unveiling uncertainties in soil organic carbon modeling: the critical role of climate response functions","authors":"Huiwen Li , Yue Cao , Jingfeng Xiao , Wenxin Zhang , Yiping Wu , Arshad Ali , Zuoqiang Yuan","doi":"10.1016/j.envsoft.2025.106537","DOIUrl":"10.1016/j.envsoft.2025.106537","url":null,"abstract":"<div><div>Accurately simulating soil organic carbon (SOC) dynamics is essential for carbon-related assessments. Process-oriented SOC models employ temperature (<em>f</em>(T)) and soil moisture (<em>f</em>(W)) response functions derived from specific conditions to simulate SOC responses to climate change, yet are widely applied in regional and global-scale studies. How these functions affect regional SOC simulations remains unclear. We evaluated the impacts of ten <em>f</em>(T) and nine <em>f</em>(W) functions using the Double Layer Carbon Model (DLCM) in the Qinling Mountains from 1982 to 2018. After calibration by Particle Swarm Optimization, DLCM estimated initial SOC with high spatial consistency (<em>R</em><sup>2</sup> > 0.9) and less than 1 % bias against machine learning based baseline maps over 85 % of the area. Different functions led to large SOC variations (up to 37 % in topsoil and 30 % in subsoil). Their combined impacts vary significantly under climate fluctuations, highlighting the need for accurate functions to improve SOC prediction in a changing climate.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106537"},"PeriodicalIF":4.8,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124922","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

Investigate the rainfall-runoff relationship and hydrological concepts inside LSTM 研究降雨径流关系和LSTM内部的水文概念

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-22 DOI: 10.1016/j.envsoft.2025.106527

Yuqian Hu , Heng Li , Chunxiao Zhang , Tianbao Wang , Wenhao Chu , Rongrong Li

{"title":"Investigate the rainfall-runoff relationship and hydrological concepts inside LSTM","authors":"Yuqian Hu , Heng Li , Chunxiao Zhang , Tianbao Wang , Wenhao Chu , Rongrong Li","doi":"10.1016/j.envsoft.2025.106527","DOIUrl":"10.1016/j.envsoft.2025.106527","url":null,"abstract":"<div><div>Recent studies have shown that LSTM performs well in runoff prediction in large sample regional modeling and can estimate hydrological concepts based on its internal information. However, compared to process-based models, it still produces erroneous predictions that violate the physical laws. To explore the reasons for the above phenomenon, this study analyzes the evolution of LSTM's performance in predicting runoff and estimating hydrological concepts when trained on a large basinscale dataset. Findings demonstrated that LSTM's representation of the rainfall-runoff relationship lags behind the formation of hydrological concepts. The representations of relations and concepts do not consistently increase with the number of training basins. There is a model that achieves the best representation of the rainfall-runoff relationship and hydrological concepts, ensuring physical consistency even under extreme conditions. These results suggest that LSTM, like process-based models, learns the rainfall-runoff relationship and hydrological concepts, but its confusion about these concepts may lead to inaccurate predictions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106527"},"PeriodicalIF":4.8,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138693","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

DigiAgriApp: a client-server application to monitor field activities DigiAgriApp：监控现场活动的客户机-服务器应用程序

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-19 DOI: 10.1016/j.envsoft.2025.106528

Marco Moretto , Luca Delucchi , Roberto Zorer , Damiano Moser , Franco Micheli , Andrea Paoli , Pietro Franceschi

{"title":"DigiAgriApp: a client-server application to monitor field activities","authors":"Marco Moretto , Luca Delucchi , Roberto Zorer , Damiano Moser , Franco Micheli , Andrea Paoli , Pietro Franceschi","doi":"10.1016/j.envsoft.2025.106528","DOIUrl":"10.1016/j.envsoft.2025.106528","url":null,"abstract":"<div><div>Farming is increasingly data-driven, leveraging high-frequency and precision data from IoT devices, sensors, and remote tools. Effective data collection, organization, and management are essential to link datasets with agronomic details, forming the foundation for predictive models. These models, using AI and machine learning, optimize decision-making, forecast crop yields, predict pest outbreaks, and enhance resource use. High-quality, diverse data integration is key to building accurate tools that address agriculture's complexity, boosting productivity and resilience. We introduce DigiAgriApp, an open-source client-server application for centralized farming data management. It tracks crop details, sensor readings, irrigation, field operations, production statistics, and emissions for Life Cycle Assessment. Initially developed for the Fondazione Edmund Mach, DigiAgriApp has evolved into a versatile tool. Users can access a public server or deploy a private instance via Docker, making it ideal for institutions, farmers, and corporations alike.</div><div>DigiAgriApp is available at <span><span>https://digiagriapp.gitlab.io/digiagriapp-website/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106528"},"PeriodicalIF":4.8,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124921","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

Position paper: Common mistakes and solutions for a better use of correlation- and regression-based approaches in environmental sciences 立场文件：在环境科学中更好地利用基于相关和回归的方法的常见错误和解决办法

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-19 DOI: 10.1016/j.envsoft.2025.106526

Damien Tedoldi , Boram Kim , Santiago Sandoval , Nicolas Forquet , Bruno Tassin

{"title":"Position paper: Common mistakes and solutions for a better use of correlation- and regression-based approaches in environmental sciences","authors":"Damien Tedoldi , Boram Kim , Santiago Sandoval , Nicolas Forquet , Bruno Tassin","doi":"10.1016/j.envsoft.2025.106526","DOIUrl":"10.1016/j.envsoft.2025.106526","url":null,"abstract":"<div><div>While empirical modelling remains a popular practice in environmental sciences, an alarming number of misuses of correlation- and regression-based techniques are encountered in recent research, although these techniques are described in courses and textbooks. This position paper reviews the most common issues, and provides theoretical background for understanding the interests and limitations of these methods, based on their underlying assumptions. We call for a reconsideration of misleading practices, including: the application of linear regression to data points that do not display a linear pattern, the failure to pinpoint influential points, the inappropriate extrapolation of empirical relationships, the overrated search for “statistical significance”, the pooling of data belonging to different populations, and, most importantly, calculations without data visualization. We urge reviewers to be vigilant on these aspects. We also recall the existence of alternative approaches to overcome the highlighted shortcomings, and thus contribute to a more accurate interpretation of the results.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106526"},"PeriodicalIF":4.8,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144194031","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 localized weather predictions for precision agriculture: A Time-Series Mixer approach for hazardous event detection 改进精准农业的局部天气预报：用于危险事件检测的时间序列混合方法

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-15 DOI: 10.1016/j.envsoft.2025.106509

Marco Zanchi , Stefano Zapperi , Stefano Bocchi , Oxana Drofa , Silvio Davolio , Caterina A.M. La Porta

{"title":"Improving localized weather predictions for precision agriculture: A Time-Series Mixer approach for hazardous event detection","authors":"Marco Zanchi , Stefano Zapperi , Stefano Bocchi , Oxana Drofa , Silvio Davolio , Caterina A.M. La Porta","doi":"10.1016/j.envsoft.2025.106509","DOIUrl":"10.1016/j.envsoft.2025.106509","url":null,"abstract":"<div><div>Natural environmental systems and human activities are deeply interconnected, especially in agriculture. Despite advancements in agricultural techniques, weather remains a critical factor influencing crop yields and livestock health. Precision agriculture relies on weather predictions to mitigate environmental risks caused by weather. However, numerical weather predictions are generated by global or regional numerical models, lacking the resolution to capture site-specific conditions. Artificial intelligence can address this gap by integrating numerical weather predictions data with local station observations. This study employs the Time-Series Mixer (TSMixer) neural network to forecast temperature, wind speed, relative humidity, and precipitation over a 45-hour horizon. Trained with predictions from the MOLOCH model and data from ARPA stations near six agricultural sites in Northern Italy, TSMixer achieves greater accuracy than the MOLOCH model. Additionally, TSMixer excels in detecting hazardous events for precision agriculture, including frost damage, heat stress, and germination block, highlighting its value for environmental risk management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"191 ","pages":"Article 106509"},"PeriodicalIF":4.8,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144067547","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

Forecasting flood inundations in the dam-regulated Mahanadi River delta using integrated hydrologic-hydrodynamic-deep learning model 基于水文-水动力-深度学习综合模型的坝控马哈纳迪河三角洲洪涝预报

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-15 DOI: 10.1016/j.envsoft.2025.106523

Amina Khatun , Prachi Pratyasha Jena , Bhabagrahi Sahoo , Chandranath Chatterjee

{"title":"Forecasting flood inundations in the dam-regulated Mahanadi River delta using integrated hydrologic-hydrodynamic-deep learning model","authors":"Amina Khatun , Prachi Pratyasha Jena , Bhabagrahi Sahoo , Chandranath Chatterjee","doi":"10.1016/j.envsoft.2025.106523","DOIUrl":"10.1016/j.envsoft.2025.106523","url":null,"abstract":"<div><div>The efficacy of a deep learning error-updating model in predicting the hydrological model-simulated errors influenced by reservoir regulation is assessed. Two daily discharge forecasting model frameworks without (Case I) and with (Case II) error-updating of the discharge forecasts at a downstream location are developed. The best discharge forecasts are forced as inputs to a hydrodynamic model to simulate the forecasted flood inundations in the downstream region. The findings reveals that the discharge forecasts with the forecasted releases from the reservoir as upstream inflow boundary, post-error updating at the delta head (Case II) outperforms Case I with an <span><math><mrow><mi>N</mi><mi>S</mi><mi>E</mi></mrow></math></span> value of 0.83–0.94 at 1–5 days lead times. Moreover, this model (Case II) captures the flood peaks with the least error and narrowest uncertainty bands. Further, with a 49–52 % coincidence of observed and simulated flood inundation extent, the hydrodynamic model simulates the inundation forecasts with reasonable accuracy up to 5-days lead times.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"191 ","pages":"Article 106523"},"PeriodicalIF":4.8,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089072","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

Modeling the impact of smoke from prescribed fire on road visibility 模拟规定火灾产生的烟雾对道路能见度的影响

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-15 DOI: 10.1016/j.envsoft.2025.106510

Sara Brambilla, Diego Rojas, David J. Robinson, Alexander J. Josephson, Matthew A. Nelson, Rodman R. Linn

引用次数: 0

Stochastic generator for rainfall with a Hawkes process marked by an extended generalized Pareto and a vine copula 具有扩展广义Pareto和藤蔓联结的Hawkes过程的降雨随机发生器

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-15 DOI: 10.1016/j.envsoft.2025.106490

Antoine Chapon , Taha B.M.J. Ouarda , Nathalie Bertrand

{"title":"Stochastic generator for rainfall with a Hawkes process marked by an extended generalized Pareto and a vine copula","authors":"Antoine Chapon , Taha B.M.J. Ouarda , Nathalie Bertrand","doi":"10.1016/j.envsoft.2025.106490","DOIUrl":"10.1016/j.envsoft.2025.106490","url":null,"abstract":"<div><div>A stochastic generator for rainfall is built from a Hawkes process, which is modeling the occurrence and serial correlation of non-zero rainfall values. Hawkes processes are suited to model intermittent signals, which is the case of rainfall at a fine enough observation frequency. This Hawkes process has a two-scale intensity function accounting for two orders of clustering in rainfall time series. The rainfall amount of each non-zero value is modeled by an extended generalized Pareto (EGP) distribution with the whole range of rainfall as support, from low to extreme values. New parametric EGP forms adapted to high frequency rainfall time series are defined. The Hawkes process only models the serial correlation of occurrences but not that of the amounts. A conditional version of the EGP is hence developed by adding a copula, modeling the temporal dependence of rainfall amounts. A subsettable canonical vine copula models this dependency for multiple time lags, while accounting for the intermittency of non-zero rainfall values. An application to a 40 yr time series of hourly rainfall in France is presented. Simulations from the model reproduce adequately the marginal distribution of rainfall, the temporal clustering of events, and the autocorrelation. The simulations are also able to reproduce the intensity-duration-frequency relation of the IDF extreme value model, showing that this stochastic generator is suitable for risk assessment of duration-dependent extremes.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"191 ","pages":"Article 106490"},"PeriodicalIF":4.8,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084517","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

An adaptive rainfall-runoff model for daily runoff prediction under the changing environment: Stream-LSTM 变化环境下日径流预报的自适应降雨径流模型：Stream-LSTM

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-15 DOI: 10.1016/j.envsoft.2025.106524

Feichi Hu , Qinli Yang , Junran Yang , Junming Shao , Guoqing Wang

{"title":"An adaptive rainfall-runoff model for daily runoff prediction under the changing environment: Stream-LSTM","authors":"Feichi Hu , Qinli Yang , Junran Yang , Junming Shao , Guoqing Wang","doi":"10.1016/j.envsoft.2025.106524","DOIUrl":"10.1016/j.envsoft.2025.106524","url":null,"abstract":"<div><div>The rainfall-runoff relationship frequently undergoes changes and exhibits a non-stationary state due to the impacts of climate and human activities. This non-stationarity often results in performance degradation of most existing runoff prediction models, which were designed and applied under the assumption of a stationary rainfall-runoff relationship. This study proposes an adaptive rainfall-runoff model (Stream-LSTM (Stream-Long Short-Term Memory)) based on data stream mining and deep learning for 1-day-ahead daily runoff prediction. The model consists of two main components: (1) a dynamic threshold adjustment strategy that automatically detects changes in the rainfall-runoff relationship, and (2) a network fine-tuning approach that preserves long-term memory while adapting to new patterns. Results in the Source Region of the Yellow River basin demonstrate that the proposed model achieves Nash-Sutcliffe Efficiency (NSE) of 0.91 with a decay period of 10 in a changing environment, outperforming the widely used data-driven models such as Long Short-Term Memory (LSTM) (0.71), Random Forest (RF) (0.66), eXtreme Gradient Boosting (XGBoost) (0.69) and Support Vector Regression (SVR) (0.61). The Stream-LSTM model performed well on peak runoff prediction with NSE values generally exceeding 0.70. Additionally, preliminary inter-basin testing on two selected basins in the CAMELS dataset indicates the potential applicability of the method. This study provides a promising method for dynamic daily runoff prediction in a non-stationary environment, which is of great significance for flood mitigation and regional water resource management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"191 ","pages":"Article 106524"},"PeriodicalIF":4.8,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089316","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 consistency of hydrologic event identification 提高水文事件识别的一致性

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-13 DOI: 10.1016/j.envsoft.2025.106521

Mohammad Masoud Mohammadpour Khoie , Danlu Guo , Conrad Wasko

{"title":"Improving the consistency of hydrologic event identification","authors":"Mohammad Masoud Mohammadpour Khoie , Danlu Guo , Conrad Wasko","doi":"10.1016/j.envsoft.2025.106521","DOIUrl":"10.1016/j.envsoft.2025.106521","url":null,"abstract":"<div><div>Identifying rainfall-runoff events is routinely performed in many hydrologic applications. Absence of a ground-based truth makes rainfall-runoff event identification largely subjective. As a result, current algorithms often disagree on the start and end of events, leading to events within a given set of rainfall and runoff time-series with inconsistent properties – referred to hereafter as ‘uncertainty in rainfall-runoff event identification’. In this study, the uncertainty associated with identifying rainfall-runoff events is assessed across Australia. A considerable uncertainty exists in the characteristics of identified rainfall-runoff events, including in their Runoff Coefficients (RCs). We propose a new objective metric to narrow the plausible set of parameters for identifying rainfall-runoff events. The metric demonstrates a substantial reduction in the uncertainty in rainfall-runoff event identification while improving the plausibility of the rainfall-runoff events chosen (up to a 25 % reduction in RCs >1) making the metric applicable for large-sample analyses of rainfall-runoff events.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"191 ","pages":"Article 106521"},"PeriodicalIF":4.8,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144067559","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