Environmental Modelling & Software最新文献

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Parameter estimation and uncertainty quantification of rainfall-runoff models using data assimilation methods based on deep learning and local ensemble updates 基于深度学习和局部集合更新的数据同化方法的降雨径流模型参数估计和不确定性量化

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2025.106332

Lei Yao , Jiangjiang Zhang , Chenglong Cao , Feifei Zheng

Rainfall-runoff (RR) modeling is crucial for flood preparedness and water resource management. Accurate RR model predictions depend on effective parameter estimation and uncertainty quantification using observed data through data assimilation (DA). Traditional DA methods often struggle with challenges such as non-Gaussianity and equifinality. To address these challenges, this study introduces two ensemble smoother methods, i.e., ES_DL with a deep learning-based update, and ES_LU with a local ensemble update, aiming to enhance the calibration of RR models. To demonstrate the effectiveness of our proposed methods, we conduct a comprehensive analysis involving various DA techniques applied to parameter estimation of RR models. We compare these methods with traditional approaches, evaluating deep neural network architectures, iteration numbers, and measurement errors. The results unequivocally showcase the consistent reliability of ES_DL and ES_LU, especially the latter one, across diverse scenarios, establishing them as promising approaches for the effective calibration and uncertainty quantification of RR models.

降雨径流（RR）模型对防洪和水资源管理至关重要。准确的RR模型预测依赖于通过数据同化（DA）对观测数据进行有效的参数估计和不确定性量化。传统的数据分析方法经常面临非高斯性和等价性等问题。为了解决这些问题，本研究引入了两种集成平滑方法，即基于深度学习更新的ESDL和基于局部集成更新的ESLU，旨在增强RR模型的校准。为了证明我们提出的方法的有效性，我们进行了综合分析，涉及各种数据挖掘技术应用于RR模型的参数估计。我们将这些方法与传统方法进行比较，评估深度神经网络架构、迭代次数和测量误差。结果明确表明，ESDL和ESLU在不同情景下具有一致的可靠性，特别是后者，这表明它们是有效校准和不确定度量化RR模型的有希望的方法。

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引用次数: 0

Facilitating open data and open model integration with generic parameter input file generators in the CyberWater framework

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106266

Daniel Luna , Ranran Chen , Ahmed Sheba , Ryan Young , Yao Liang , Xu Liang

Effective data and model integration is crucial for exploring scientific questions in hydrology and other geosciences. The increasing heterogeneity and complexity of data and models pose integration challenges. CyberWater addresses these with an open-data and open-modeling framework. Featuring GUI-based workflows, it includes Data Agents for accessing diverse online data sources and a Generic Model Agent Toolkit for seamless, code-free model integration. This study introduces the Static Parameter Agent suite, a novel toolkit designed to streamline the creation and organization of parameter files required for various models. The toolkit enables users to efficiently and automatically generate files on demand, minimizing the time-consuming and error-prone manual preparation of complex parameter files. It further logs all changes to parameter values across each model simulation, ensuring a reproducible end-to-end process. It connects seamlessly with Geographic Information System (GIS) engines like GRASS GIS and has been tested on models including VIC, DHSVM, and CASA-CNP.

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引用次数: 0

A novel sample-enhancement framework for machine learning-based urban flood susceptibility assessment 基于机器学习的城市洪水易感性评估样本增强框架

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106314

Huabing Huang, Changpeng Wang, Zhiwen Tao, Jiayin Zhan

The commonly used random sampling method in machine learning-based flood susceptibility studies has two major issues: a default invalid assumption of spatial homogeneity and an inadequate number of non-flood samples. To address these issues, this study proposed a novel sample-enhancement framework to improve the quality of training samples on both flood and non-flood sides. Three one-way enhancements (two flood and one non-flood) and two joint enhancements were designed. The enhancements were evaluated against random sampling using four mainstream machine learning algorithms (ANN, RF, SVM, and XGBoost) across two heterogeneous urban regions in Guangzhou, China. The highest performances are achieved by the joint enhancements, which are followed by one-way enhancements and random sampling (no enhancement). Another important conclusion is that one-way enhancements exhibit divergent yet complementary effects. Flood enhancements primarily affect susceptibility distribution (mean value and standard deviation), while non-flood enhancements mainly influence binary classification performance (AUC).

基于机器学习的洪水敏感性研究中常用的随机抽样方法存在两个主要问题：默认的空间均匀性假设无效和非洪水样本数量不足。为了解决这些问题，本研究提出了一种新的样本增强框架，以提高洪水侧和非洪水侧的训练样本质量。设计了三个单向增强（两个洪水增强和一个非洪水增强）和两个联合增强。使用四种主流机器学习算法（ANN， RF， SVM和XGBoost）在中国广州的两个异质城市区域对随机抽样进行了增强评估。通过联合增强实现了最高的性能，随后是单向增强和随机抽样（无增强）。另一个重要的结论是，单向增强表现出不同但互补的效果。洪水增强主要影响敏感性分布（均值和标准差），非洪水增强主要影响二元分类性能（AUC）。

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引用次数: 0

Environmental-Health Convergence: A deep learning-oriented decision support system for catalyzing sustainable healthy food systems 环境与健康融合：一个面向深度学习的决策支持系统，用于催化可持续健康食品系统

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106309

Prince Agyemang , Ebenezer M. Kwofie , Jamie I. Baum , Dongyi Wang , Emmanuel A. Kwofie

To generate evidence to address food system challenges, we developed an adaptable framework for multimodel assessment of the convergence effect of health and environmental drivers in food systems. We achieved this goal by developing a modeling framework that facilitates testing and applying four deep-learning algorithms using a case study of the United States's food system. Among the models tested, the bidirectional and single-layer long short-term memory models outperformed the others with α_E(2.75) and α_H(3.51) when predicting environmental drivers and health drivers, respectively. All the models tested performed better at predicting environmental than health drivers. The best-performing model for each dimension was deployed into the Food System Rapid Overview Assessment through Scenarios (FS-ROAS) tool. As we approach the endpoint of the transformative 2030 agenda, FS-ROAS can be a timely toolkit that enables stakeholders to explore diverse intervention scenarios in the context of short-medium and long-term goals for future food systems and generate evidence to guide future actions.

为了产生应对粮食系统挑战的证据，我们开发了一个适应性框架，用于多模型评估粮食系统中健康和环境驱动因素的趋同效应。我们通过开发一个建模框架来实现这一目标，该框架使用美国食品系统的案例研究来促进测试和应用四种深度学习算法。在预测环境驱动因素和健康驱动因素时，双向长短期记忆和单层长短期记忆模型分别以αE（2.75）和αH（3.51）优于其他模型。所有测试的模型在预测环境驱动因素方面都比健康驱动因素表现得更好。每个维度的最佳表现模型被部署到通过场景进行食品系统快速概述评估（FS-ROAS）工具中。随着我们接近2030年变革性议程的终点，FS-ROAS可以成为一个及时的工具包，使利益攸关方能够在未来粮食系统短期、中期和长期目标的背景下探索各种干预方案，并产生证据来指导未来的行动。

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引用次数: 0

Application of artificial intelligence in air pollution monitoring and forecasting: A systematic review 人工智能在大气污染监测与预报中的应用综述

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106312

Sreeni Chadalavada , Oliver Faust , Massimo Salvi , Silvia Seoni , Nawin Raj , U. Raghavendra , Anjan Gudigar , Prabal Datta Barua , Filippo Molinari , Rajendra Acharya

Air pollution poses a significant global health hazard. Effective monitoring and predicting air pollutant concentrations are crucial for managing associated health risks. Recent advancements in Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), offer the potential for more precise air pollution monitoring and forecasting models. This comprehensive review, conducted according to PRISMA guidelines, analyzed 65 high-quality Q1 journal articles to uncover current trends, challenges, and future AI applications in this field. The review revealed a significant increase in research papers utilizing ML and DL approaches from 2021 onwards. ML techniques currently dominate, with Random Forest being the most frequent method, achieving up to 98.2% accuracy. DL techniques show promise in capturing complex spatiotemporal relationships in air quality data. The study highlighted the importance of integrating diverse data sources to improve model accuracy. Future research should focus on addressing challenges in model interpretability and uncertainty quantification.

空气污染对全球健康构成重大危害。有效监测和预测空气污染物浓度对于管理相关的健康风险至关重要。人工智能（AI）的最新进展，特别是机器学习（ML）和深度学习（DL），为更精确的空气污染监测和预测模型提供了潜力。根据PRISMA指南进行的这项全面审查，分析了65篇高质量的Q1期刊文章，以揭示该领域当前的趋势、挑战和未来的人工智能应用。该审查显示，从2021年起，使用ML和DL方法的研究论文显着增加。机器学习技术目前占主导地位，随机森林是最常用的方法，准确率高达98.2%。深度学习技术在捕捉空气质量数据中复杂的时空关系方面显示出前景。该研究强调了整合不同数据源以提高模型准确性的重要性。未来的研究应着重解决模型可解释性和不确定性量化方面的挑战。

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引用次数: 0

Modelling wildfire spread and spotfire merger using conformal mapping and AAA-least squares methods 用保角映射和aaa最小二乘法模拟野火蔓延和火点合并

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106303

Samuel J. Harris, N.R. McDonald

A two-dimensional model of wildfire spread and merger is presented. Three features affect the wildfire propagation: (i) a constant basic rate of spread term accounting for radiative and convective heat transfer, (ii) the unidirectional, constant ambient wind, and (iii) a fire-induced pyrogenic wind. Two numerical methods are proposed to solve for the pyrogenic potential. The first utilises the conformal invariance of Laplace’s equation, reducing the wildfire system to a single Polubarinova–Galin type equation. The second method uses a AAA-least squares method to find a rational approximation of the pyrogenic potential. Various wildfire scenarios are presented and the effects of the pyrogenic wind and the radiative/convective basic rate of spread terms investigated. Firebreaks such as roads and lakes are also included and solutions are found to match well with existing numerical and experimental results. The methods proposed in this work are suitably fast and new to the field of wildfire modelling.

提出了野火蔓延与合并的二维模型。影响野火传播的三个特征：(i)考虑辐射和对流传热的恒定基本传播率项，（ii）单向恒定的环境风，以及（iii）火灾诱发的热原风。提出了两种求解热原势的数值方法。第一种方法利用拉普拉斯方程的保形不变性，将野火系统简化为一个单一的Polubarinova-Galin型方程。第二种方法采用aaa最小二乘法求热原势的有理近似。提出了不同的野火情景，并研究了热原风和辐射/对流基本传播率的影响。还包括道路和湖泊等防火屏障，并发现解决方案与现有的数值和实验结果相匹配。本文提出的方法对于野火建模领域来说是非常快速和新颖的。

引用次数: 0

Land-N2N: An effective and efficient model for simulating the demand-driven changes in multifunctional lands Land-N2N：一个模拟多功能土地需求驱动变化的有效模型

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2025.106318

Yifan Gao, Changqing Song, Zhifeng Liu, Sijing Ye, Peichao Gao

Land is multifunctional. Among all land change models, the only model capable of modeling multifunctional land changes is the CLUMondo model. However, the CLUMondo model is ineffective and inefficient. In the study, we addressed the problems by improving the CLUMondo model through four strategies, resulting in the improved version named “Land-N2N”. To evaluate the Land-N2N model, we designed six comparative experiments. In these experiments, we established the land systems using an upscaling approach based on Globeland30 data. Our finding shows that the effectiveness and efficiency of the Land-N2N model are better than the CLUMondo model. Specifically, the effectiveness of the Land-N2N model improved by 36% when measured with Kappa and by 377% when measured with Figure of Merit (FoM). Additionally, the efficiency of the Land-N2N model increased by 80%. The utility of the Land-N2N model lies in its ability to offer scientific solutions for land management by forecasting land changes.

土地是多功能的。在所有的土地变化模型中，唯一能够模拟多功能土地变化的模型是clondo模型。然而，克隆多模型是无效和低效的。在本研究中，我们通过四种策略对clondo模型进行了改进，从而得到了改进版本“Land-N2N”。为了评价Land-N2N模型，我们设计了6个比较实验。在这些实验中，我们使用基于Globeland30数据的升级方法建立了土地系统。结果表明，Land-N2N模型的有效性和效率均优于克隆多模型。具体来说，Land-N2N模型的有效性在用Kappa测量时提高了36%，在用优点图（FoM）测量时提高了377%。此外，Land-N2N模型的效率提高了80%。land - n2n模型的实用性在于它能够通过预测土地变化为土地管理提供科学的解决方案。

引用次数: 0

Ensemble data assimilation for operational streamflow predictions in the next generation (NextGen) framework 下一代（NextGen）框架中用于操作流预测的集成数据同化

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106306

Ehsan Foroumandi , Hamid Moradkhani , Witold F. Krajewski , Fred L. Ogden

The National Weather Service (NWS) operates the National Water Model (NWM) to provide continental-scale streamflow forecasting across the United States. Despite the broad scope of NWM, it faces limitations in delivering operational-level predictions. To overcome these limitations, the NWS embarked on development of the Next Generation Water Resources Modeling Framework (NextGen). However, a key shortcoming of the NextGen and NWM is the lack of robust data assimilation (DA) step. This study provides a DA module that incorporates the Ensemble Kalman Filter (EnKF), and the Particle Filter (PF) for use within the NextGen framework. The effectiveness of the developed module is evaluated by assimilating the in-situ observations to the Conceptual Functional Equivalent model, a simplified version of the current NWM, demonstrating the first advanced DA application on this model. The results show that both DA methods effectively enhance the performance of the model prediction, while the PF outperforms the EnKF.

美国国家气象局（NWS）运行国家水模型（NWM），提供全美国大陆尺度的河流流量预报。尽管NWM的范围很广，但它在提供操作级预测方面面临限制。为了克服这些限制，NWS开始开发下一代水资源建模框架（NextGen）。然而，下一代和NWM的一个主要缺点是缺乏稳健的数据同化（DA）步骤。本研究提供了一个集成了集成卡尔曼滤波器（EnKF）和粒子滤波器（PF）的数据处理模块，用于NextGen框架。开发的模块的有效性通过将现场观测同化到概念功能等效模型（当前NWM的简化版本）来评估，展示了该模型上的第一个高级数据分析应用。结果表明，两种数据分析方法都能有效地提高模型的预测性能，而PF方法优于EnKF方法。

引用次数: 0

Simple analysis of biodiversity response functions and multipliers for biodiversity offsetting and other applications 生物多样性响应函数和乘数在生物多样性补偿和其他应用中的简单分析

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2025.106322

Atte Moilanen , Pauli Lehtinen

Biodiversity offsets mean compensation for ecological losses caused by construction, development, land use or other human activities. They are commonly implemented via protection, restoration, or maintenance of habitats. The goal of offsetting is usually no net loss (NNL), which means that all net losses to biodiversity are fully compensated by commensurate net gains achieved via said offset actions. Here we collate and develop simple calculations for the determination of offset size (area) in the context of so-called multiplier approaches to offsets. We focus on the analysis of the response of habitat condition to action, which is a critical component of multiplier calculations, because the effectiveness and speed of different conservation actions and interventions can vary significantly. An excel application and R-code are included that implement calculations on offset response functions. The proposed methods are also relevant for other applications, including the generation of biodiversity credits for biodiversity credit markets.

生物多样性补偿是指补偿因建设、开发、土地利用或其他人类活动造成的生态损失。它们通常通过保护、恢复或维护栖息地来实现。抵消的目标通常是无净损失（NNL），这意味着生物多样性的所有净损失都可以通过上述抵消行动获得相应的净收益来充分补偿。在这里，我们整理和开发简单的计算，以确定所谓的乘数方法的偏移量大小（面积）的背景下。我们重点分析生境条件对行动的响应，这是乘数计算的关键组成部分，因为不同的保护行动和干预措施的有效性和速度可能会有很大差异。包括一个excel应用程序和r -代码，实现对偏移响应函数的计算。所提出的方法也适用于其他应用，包括为生物多样性信用市场生成生物多样性信用。

引用次数: 0

Integrated models of nutrient dynamics in lake and reservoir watersheds: A systematic review and integrated modelling decision pathway 湖泊与水库流域营养动态综合模型：系统综述与综合建模决策途径

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2025.106321

Floran Clopin , Ilaria Micella , Jorrit P. Mesman , Ma Cristina Paule-Mercado , Marina Amadori , Shuqi Lin , Lisette N. de Senerpont Domis , Jeroen J.M. de Klein

Eutrophication of inland water bodies is a serious environmental threat. This review explores current integrated models for lake and reservoir ecosystems that focus on nutrient dynamics at a catchment scale. Many studies applied either watershed or lake/reservoir models, however, 49 studies were finally selected that combined both. We derived a list of 21 watershed models, 23 lake/reservoir models, and 6 hybrid models in different sets of combinations, with a range of objectives (e.g. understanding the natural processes, predicting, and analysing climate change and land-use scenarios, or evaluating the different management options). Some integrated models had multiple applications whereas others were only applied once, with an uneven global geographical distribution.

To aid model selection by future users, we present a support tool discriminating the models by their features and application fields. This study encourages the development of open-source tools aiding interdisciplinary collaborations and further research in the field of integrated modelling.

内陆水体的富营养化是一个严重的环境威胁。本文综述了目前湖泊和水库生态系统的综合模型，这些模型侧重于流域尺度上的营养动态。许多研究要么采用流域模型，要么采用湖泊/水库模型，但最终选择了49项将两者结合起来的研究。我们得出了21个流域模型、23个湖泊/水库模型和6个不同组合的混合模型，这些模型具有一系列目标（如理解自然过程、预测和分析气候变化和土地利用情景，或评估不同的管理方案）。一些综合模型有多个应用，而另一些模型只应用一次，全球地理分布不均衡。

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引用次数: 0

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