Environmental Modelling & Software最新文献_第8页

Towards democratized flood risk management: An advanced AI assistant enabled by GPT-4 for enhanced interpretability and public engagement 走向民主化的洪水风险管理：由GPT-4支持的高级人工智能助手，以增强可解释性和公众参与

IF 4.6 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-12-12 DOI: 10.1016/j.envsoft.2025.106821

Rafaela Martelo , Kimia Ahmadiyehyazdi , Ruo-Qian Wang

Traditional flood risk communication fails to bridge the gap between complex technical data and the needs of the public, hindering effective response. This research addresses this gap by developing and validating a novel AI-powered assistant that uses GPT-4 to democratize flood risk information. Our core methodology includes a Retrieval-Augmented Generation (RAG) framework that synthesizes real-time flood warnings, geospatial data, and social vulnerability indices into clear, conversational responses. To validate its effectiveness, we conducted a mixed-methods evaluation, including a comparison across different GPT models. Key quantitative findings reveal that the assistant achieved high performance scores in general flood knowledge (5/5) and handling flash flood alerts (4.3/5). Response times averaged a rapid 12 s for non-function-calling queries, though more complex data retrieval tasks averaged 36 s, highlighting areas for optimization. Our comparison identified GPT-4o as the optimal model for balancing accuracy with response time. The broader implications of this work demonstrate that large language models can serve as powerful tools to translate complex environmental data for non-experts, paving the way for more equitable, engaging, and effective public participation in disaster risk management.

传统的洪水风险沟通无法弥合复杂的技术数据与公众需求之间的差距，阻碍了有效的应对。本研究通过开发和验证一种新型人工智能助手来解决这一差距，该助手使用GPT-4来实现洪水风险信息的民主化。我们的核心方法包括检索-增强生成（RAG）框架，该框架将实时洪水预警、地理空间数据和社会脆弱性指数综合为清晰的对话响应。为了验证其有效性，我们进行了混合方法评估，包括不同GPT模型的比较。关键的定量研究结果显示，该助手在一般洪水知识（5/5）和处理山洪警报（4.3/5）方面取得了很高的成绩。非函数调用查询的平均响应时间为12秒，而更复杂的数据检索任务的平均响应时间为36秒，这突出了需要优化的领域。我们的比较确定gpt - 40是平衡精度和响应时间的最佳模型。这项工作的更广泛意义表明，大型语言模型可以作为强大的工具，为非专家翻译复杂的环境数据，为更公平、更有吸引力和更有效的公众参与灾害风险管理铺平道路。

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引用次数: 0

Integrating field surveys and visual interpretation to enhance CSLE model of soil erosion response to LUCC in Southwest China 结合野外调查和目视解译改进西南地区土地利用变化对土壤侵蚀响应的CSLE模型

IF 4.6 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-12-11 DOI: 10.1016/j.envsoft.2025.106831

Rui Tan , Geng Guo , Kaiwen Huang , Zicheng Liu , Chaorui Wang , Jie Lin , Yizhong Huang

Absence of high-resolution spatial data on Soil and water conservation measures (SWCM) hampers the accuracy of erosion modeling, particularly in regions with complex terrain and frequent land use/cover changes (LUCC). This study integrated multi-source remote sensing (RS), field surveys, and visual interpretation to map SWCM distribution and estimate soil erosion. It further quantified the response of erosion to LUCC. Soil erosion conditions have improved, with an average annual decrease in erosion modulus of 0.51 % and a total reduction of approximately 9.5 × 10⁵ t. LUCC was characterized by cropland reduction, expansion of garden, and increasing landscape fragmentation. Garden development enhances economic returns but may exacerbate erosion when vegetation cover is insufficient. Nonetheless, under similar conservation intensity, slope, and elevation, conversion of cropland or bare land to woodland or garden effectively reduces erosion. The findings provide a new perspective for evaluating soil erosion in fragmented mountainous landscapes with complex management measures.

水土保持措施（SWCM）高分辨率空间数据的缺乏影响了侵蚀模型的准确性，特别是在地形复杂、土地利用/覆盖变化频繁的地区。本研究将多源遥感、野外调查和目视解译相结合，绘制SWCM分布和估算土壤侵蚀。进一步量化了侵蚀对土地利用变化的响应。土壤侵蚀条件得到改善，侵蚀模数年均减少0.51%，总减少量约9.5 × 105 t。土地利用变化呈现耕地减少、园林扩大、景观破碎化加剧的特征。园林的发展提高了经济效益，但当植被覆盖不足时，可能会加剧侵蚀。然而，在相似的保护强度、坡度和高程下，将耕地或裸地转化为林地或花园可以有效地减少侵蚀。研究结果为复杂管理条件下破碎化山地景观土壤侵蚀评价提供了新的视角。

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引用次数: 0

FloodTransformer: Efficient real-time high-resolution flood forecasting 洪水变压器：高效的实时高分辨率洪水预报

IF 4.6 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-12-11 DOI: 10.1016/j.envsoft.2025.106832

Zhanzhong Gu , Jiachen Kang , Wenzheng Jin , Feifei Tong , Y. Jay Guo , Wenjing Jia

Flood forecasting is crucial for disaster planning and risk management, yet conventional hydrodynamic-based approaches are often slow in response and computationally intensive. We present a hybrid framework leveraging traditional hydrodynamic modelling with a novel AI model to enable accurate, real-time, and high-resolution flood prediction. To address the computational challenges of large-scale, dense flood prediction, we develop an efficient flood prediction model, FloodTransformer, which possesses three key novelties: variable-size cell embedding, tokenised time-sequence encoding, and physics-informed multi-task optimisation. These components effectively capture complex spatiotemporal dependencies, allowing accurate sequential predictions in a single run. Comprehensive evaluations on both simulated and historical flood events demonstrate FloodTransformer’s excellent accuracy and efficiency: NSE 0.9445, KGE 0.9759 for water-depth prediction, and IoU 0.8180, F1 0.8997 for inundation classification, outperforming all comparative models. With 3s inference enabling multiple horizons in one pass, FloodTransformer offers a robust and practical solution for operational flood risk management.

洪水预报对灾害规划和风险管理至关重要，然而传统的基于水动力学的方法往往反应缓慢，计算量大。我们提出了一个混合框架，利用传统的水动力学建模和一种新的人工智能模型，实现准确、实时和高分辨率的洪水预测。为了解决大规模、密集洪水预测的计算挑战，我们开发了一种高效的洪水预测模型，FloodTransformer，它具有三个关键的新颖之处：可变大小的单元嵌入、标记化时间序列编码和物理信息多任务优化。这些组件有效地捕获复杂的时空依赖关系，允许在一次运行中进行准确的顺序预测。对模拟和历史洪水事件的综合评价表明，FloodTransformer具有良好的精度和效率：深度预测的NSE为0.9445，KGE为0.9759，洪水分类的IoU为0.8180，F1为0.8997，优于所有比较模型。通过3秒推理，可以一次通过多个视界，FloodTransformer为操作洪水风险管理提供了强大而实用的解决方案。

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引用次数: 0

Development of a self-supervised deep learning framework for chlorophyll-a retrieval in data-scarce inland waters 数据稀缺内陆水域叶绿素a检索的自监督深度学习框架开发

IF 4.6 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-12-10 DOI: 10.1016/j.envsoft.2025.106817

Bongseok Jeong , Jihoon Shin , YoonKyung Cha

Deep learning and remote sensing-based chlorophyll-a (Chl-a) monitoring face challenges due to the optical complexity of inland waters and the scarcity of labeled data. To address these limitations, this study develops a self-supervised learning-based deep learning (SSL-DL) framework that leverages both labeled and unlabeled data. Three SSL-DL models are developed: a predictive SSL-DL model, which learns weak labels (incomplete labels); a generative SSL-DL model, which reconstructs input reflectance to capture underlying features; and an integrated SSL-DL model, which combines both. The models are applied to Sentinel-2 imagery of Daecheong and Paldang Lakes in South Korea. Results indicate that SSL-DL models outperform baseline models, with the integrated SSL-DL model achieving the highest test NSE (improvements of 0.1–0.36 over baselines in Daecheong Lake, improvements of 0.03–0.58 in Paldang Lake). The findings highlight the significance of SSL-DL in overcoming data limitations and enhancing scalability, demonstrating the potential for broader environmental remote sensing applications.

由于内陆水域的光学复杂性和标记数据的稀缺性，基于深度学习和遥感的叶绿素-a监测面临着挑战。为了解决这些限制，本研究开发了一个基于自我监督学习的深度学习（SSL-DL）框架，该框架利用了标记和未标记的数据。开发了三个SSL-DL模型：一个预测SSL-DL模型，它学习弱标签（不完整标签）；生成式SSL-DL模型，重建输入反射率以捕获底层特征；以及将两者结合起来的集成SSL-DL模型。该模型应用于韩国大清湖和八堂湖的Sentinel-2图像。结果表明，SSL-DL模型优于基线模型，其中综合SSL-DL模型的测试NSE最高（大清湖比基线提高0.1 ~ 0.36，八堂湖比基线提高0.03 ~ 0.58）。研究结果强调了SSL-DL在克服数据限制和增强可扩展性方面的重要性，展示了更广泛的环境遥感应用的潜力。

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引用次数: 0

Digital-twin tool for a drinking water distribution system using augmented reality and EPANET 使用增强现实和EPANET的饮用水分配系统的数字孪生工具

IF 4.6 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-12-10 DOI: 10.1016/j.envsoft.2025.106829

Ji-Ye Park , Kwang-Ju Kim , Minhyuk Jeung , In-Su Jang , Jung-Won Yu , Mi-Seon Kang , Hyun-Su Bae , Changyoon Jeong , Sang-Soo Baek

Urban water distribution networks are typically represented as 2D models using points and lines, which fail to capture spatial complexity and structural detail. To address these limitations, this study develops an augmented reality (AR) toolbox integrated with a digital twin (DT) framework. The motivation behind this research lies in the need for more intuitive, spatially aware visualization tools to support water infrastructure management and public understanding. AR enables the overlay of virtual content onto real environments, enhancing interpretation of pipe structures and simulation outcomes. A 3D water distribution system was generated from EPANET model data, and a mobile AR application was developed. The system visualizes pollutant dispersion and flow rates through spatially aligned 3D pipe objects. Simulation results are mapped to real-world coordinates, offering enhanced clarity and user engagement. The system is designed to be user-friendly and accessible to nontechnical stakeholders, enabling real-time, location-based interaction with complex water network data.

城市配水网络通常是用点和线表示的二维模型，无法捕捉空间复杂性和结构细节。为了解决这些限制，本研究开发了一个与数字孪生（DT）框架集成的增强现实（AR）工具箱。这项研究背后的动机在于需要更直观、空间感知的可视化工具来支持水基础设施管理和公众理解。AR可以将虚拟内容覆盖到真实环境中，增强对管道结构和模拟结果的解释。利用EPANET模型数据生成了三维配水系统，并开发了移动增强现实应用程序。该系统通过空间排列的3D管道物体可视化污染物扩散和流速。模拟结果映射到现实世界的坐标，提供增强的清晰度和用户参与。该系统的设计是用户友好的，非技术利益相关者也可以使用，可以与复杂的水网络数据进行实时的、基于位置的交互。

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引用次数: 0

Development of a web-based No coding machine learning platform for hydrology and environmental management - MoolML 开发基于web的水文和环境管理无编码机器学习平台- MoolML

IF 4.6 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-12-09 DOI: 10.1016/j.envsoft.2025.106830

Sangjoon Bak , Jeongho Han , Gwanjae Lee , Naehyeon Nam , Joo Hyun Bae , Yeonji Jeong , Hyungjin Shin , Kyoung Jae Lim , Seoro Lee

Developing data-driven models for hydrology and environmental management is challenging for non-experts, such as field engineers and environmental practitioners, due to limited coding experience and the complexity of model training and validation. To address this, we developed MoolML, a free, web-based, no-coding machine learning platform for simplified regression and classification modeling. The name MoolML is derived from the Korean word “물” (mool), meaning “water,” combined with Machine Learning (ML). MoolML integrates key functions such as data preprocessing, model training and prediction, hyperparameter tuning, cross-validation, feature importance analysis, and weather data collection, along with visualization tools for intuitive result presentation. The platform enables users to manage the entire modeling process without coding expertise while supporting data sharing and collaboration. The applicability and efficiency of developing ML models through the platform were tested using hydrological and environmental datasets from South Korea, and it is expected to support comprehensive watershed management.

由于编码经验有限以及模型训练和验证的复杂性，开发水文和环境管理的数据驱动模型对于非专家（如现场工程师和环境从业人员）来说是具有挑战性的。为了解决这个问题，我们开发了MoolML，这是一个免费的、基于web的、无编码的机器学习平台，用于简化回归和分类建模。“MoolML”这个名字是韩语“水”的意思“月光”和机器学习（ML）结合而成的。MoolML集成了数据预处理、模型训练和预测、超参数调优、交叉验证、特征重要性分析和天气数据收集等关键功能，以及用于直观结果呈现的可视化工具。该平台使用户无需编码专业知识即可管理整个建模过程，同时支持数据共享和协作。利用韩国的水文和环境数据集测试了通过该平台开发ML模型的适用性和效率，预计将支持综合流域管理。

{"title":"Development of a web-based No coding machine learning platform for hydrology and environmental management - MoolML","authors":"Sangjoon Bak , Jeongho Han , Gwanjae Lee , Naehyeon Nam , Joo Hyun Bae , Yeonji Jeong , Hyungjin Shin , Kyoung Jae Lim , Seoro Lee","doi":"10.1016/j.envsoft.2025.106830","DOIUrl":"10.1016/j.envsoft.2025.106830","url":null,"abstract":"<div><div>Developing data-driven models for hydrology and environmental management is challenging for non-experts, such as field engineers and environmental practitioners, due to limited coding experience and the complexity of model training and validation. To address this, we developed <strong>MoolML</strong>, a free, web-based, no-coding machine learning platform for simplified regression and classification modeling. The name MoolML is derived from the Korean word “물” (mool), meaning “water,” combined with Machine Learning (ML). MoolML integrates key functions such as data preprocessing, model training and prediction, hyperparameter tuning, cross-validation, feature importance analysis, and weather data collection, along with visualization tools for intuitive result presentation. The platform enables users to manage the entire modeling process without coding expertise while supporting data sharing and collaboration. The applicability and efficiency of developing ML models through the platform were tested using hydrological and environmental datasets from South Korea, and it is expected to support comprehensive watershed management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106830"},"PeriodicalIF":4.6,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732502","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

Artificial intelligence enhanced litter pollution mapping: Integrating citizen science with geospatial and social data 人工智能增强了垃圾污染制图：将公民科学与地理空间和社会数据相结合

IF 4.6 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-12-09 DOI: 10.1016/j.envsoft.2025.106823

Hadiseh Rezaei , Keiron. P. Roberts , Farzad Arabikhan , Steve Fletcher , Antaya March , Fay Couceiro , David Bacon , David. J. Hutchinson , John. B. Williams

Citizen science provides extensive litter data, but inconsistent recording limits its use in environmental modelling and decision making. We present a scalable AI-assisted framework that harmonises two major UK datasets, Marine Debris Tracker and Litterati, into a unified, spatially detailed resource. Over 460,000 records (2015–2024) were standardised through a rules-to-embeddings-to-LLM cascade (schema-constrained Llama 3.1) for material classification. Items were clustered by material using K-means at a validated 200 m scale and linked to OpenStreetMap amenities within 500 m to identify accumulation hotspots and contextual features such as parks or transport hubs. Plastic dominated nationally, accounting for 71 percent of entries, while integration with UK Census 2021 data enabled demographic and health analyses where plastic remained highest (68.9 percent). This reproducible framework demonstrates how artificial intelligence can harmonise citizen-science data and enhance spatial modelling to inform targeted pollution prevention and sustainable waste-management strategies.

公民科学提供了大量的垃圾数据，但是不一致的记录限制了它在环境建模和决策中的应用。我们提出了一个可扩展的人工智能辅助框架，该框架将两个主要的英国数据集（海洋碎片跟踪器和Litterati）协调成一个统一的、空间详细的资源。通过规则-嵌入- llm级联（模式约束Llama 3.1）进行材料分类，超过460,000条记录（2015-2024）被标准化。在经过验证的200米尺度上，使用K-means将项目按材料聚类，并与500米范围内的OpenStreetMap设施相关联，以确定积累热点和环境特征，如公园或交通枢纽。塑料在全国占主导地位，占71%，而与英国2021年人口普查数据相结合，实现了人口和健康分析，其中塑料仍然最高（68.9%）。这个可复制的框架展示了人工智能如何协调公民科学数据并增强空间建模，从而为有针对性的污染预防和可持续废物管理战略提供信息。

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引用次数: 0

A generalized user-friendly method for fusing observational data and chemical transport model (Gen-Friberg V1.0: GF-1) 一种融合观测数据和化学输运模型的通用用户友好方法（Gen-Friberg V1.0: GF-1）

IF 4.6 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-12-06 DOI: 10.1016/j.envsoft.2025.106827

Zongrun Li , Abiola S. Lawal , Bingqing Zhang , Kamal J. Maji , Pengfei Liu , Yongtao Hu , Armistead G. Russell , M. Talat Odman

A generalized, user-friendly data fusion method (Gen-Friberg) to reduce differences between chemical transport models (CTMs) and observational data is implemented to be compatible with widely used CTMs such as CMAQ, GEOS-Chem, and WRF-Chem. Key source code improvements included encapsulating the data fusion algorithm within a single function and enabling parallel processing to minimize runtime for long simulations. We applied the data fusion method to CMAQ outputs and observations from 2010 to 2019 to evaluate the method's performance. After data fusion, pollutant concentration fields showed improved performance. Additionally, we assessed the generalizability of the data fusion method by demonstrating its effectiveness in reducing bias in the GEOS-Chem and WRF-Chem concentration fields using evaluations based on 2017 simulations. Comparisons across CMAQ, GEOS-Chem, and WRF-Chem with and without data fusion demonstrate that data fusion reduces inter-model discrepancies, yielding more consistent concentration fields for use in health and policy assessments.

实现了一种通用的、用户友好的数据融合方法（Gen-Friberg），以减少化学输运模型（CTMs）与观测数据之间的差异，从而与CMAQ、GEOS-Chem和WRF-Chem等广泛使用的CTMs兼容。关键的源代码改进包括将数据融合算法封装在单个函数中，并支持并行处理以最大限度地减少长时间模拟的运行时间。我们将数据融合方法应用于2010年至2019年的CMAQ输出和观测，以评估该方法的性能。数据融合后，污染物浓度场的性能得到改善。此外，我们还评估了数据融合方法的通用性，通过基于2017年模拟的评估来证明其在减少GEOS-Chem和WRF-Chem浓度场偏差方面的有效性。在CMAQ、GEOS-Chem和WRF-Chem之间进行数据融合和不进行数据融合的比较表明，数据融合减少了模型间的差异，产生了更一致的浓度场，可用于卫生和政策评估。

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引用次数: 0

Modelling near-surface ice content and midwinter melt events in mineral soils 模拟矿物土壤近地表冰含量和冬至融化事件

IF 4.6 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-12-05 DOI: 10.1016/j.envsoft.2025.106816

Élise G. Devoie , Renato Pardo Lara , Aaron Berg , William L. Quinton , James R. Craig

Over winter freeze–thaw events are notoriously difficult to represent in hydrologic models and have serious implications for the hydrologic function of intermittently freezing regions. Changing climate is leading to more frequent mid-winter thaw events. Midwinter thaw events are often the cause of flooding due to the combined impacts of snowmelt, precipitation, and limited soil infiltrability. A numerically efficient, semi-analytical coupled thermal and mass transport model is presented that represents the ice content of near-surface soil, and reports the depth of freezing/thawing. The model tracks pore ice formation and mean soil temperature in terms of enthalpy. It is tested against data collected in Southern Saskatchewan and is shown to capably reproduce field observations of frozen, thawed or transitioning soils. This numerically efficient model can be incorporated into regional hydrologic models where it is expected to improve predictions of soil ice content, leading to improved estimates of over-winter streamflow and flood potential.

众所周知，冬季的冻融事件在水文模型中难以表示，并且对间歇性冻结地区的水文功能具有严重影响。不断变化的气候导致冬季中期解冻事件更加频繁。由于融雪、降水和有限的土壤渗透性的综合影响，冬至解冻事件往往是洪水的原因。提出了一种数值高效的半解析耦合热质输运模型，该模型反映了近地表土壤的冰含量，并报告了冻结/融化的深度。该模型用焓来跟踪孔隙冰的形成和平均土壤温度。根据在萨斯喀彻温省南部收集的数据对它进行了测试，结果表明它能够再现冰冻、解冻或过渡土壤的实地观测结果。这种数值上有效的模式可以被纳入区域水文模式，从而有望改善对土壤冰含量的预测，从而改善对越冬河流流量和洪水潜力的估计。

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引用次数: 0

Freshwater modeling in Aotearoa New Zealand: Current practice and future directions 新西兰奥特罗阿淡水建模：当前实践和未来方向

IF 4.6 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-12-05 DOI: 10.1016/j.envsoft.2025.106820

Katharina Dost , Kohji Muraoka , Anne-Gaelle Ausseil , Rubianca Benavidez , Brendon Blue , Nic Conland , Chris Daughney , Annette Semadeni-Davies , Linh Hoang , Anna Hooper , Theodore Alfred Kpodonu , Tapuwa Marapara , Richard McDowell , Trung Nguyen , Dang Anh Nguyet , Ned Norton , Deniz Özkundakci , Lisa Pearson , James Rolinson , Ra Smith , Jörg Wicker

Freshwater modeling is vital for addressing environmental and societal challenges. In two workshops preceding this article, we revealed issues in current modeling practices in New Zealand, with a focus on catchment-level water quality modelling. Predominant were low trust in models, lack of transparency, and models unfit for purpose. This article uses a root-cause analysis to explore these issues, identify causes, and propose solutions. We find that current best practices and research are a good foundation but insufficient to fulfill our freshwater research and management needs. We advocate for long-term national strategies with centralized funding, standardized documentation, data, models, evaluation techniques, and communication methods, along with a centralized open-access platform for collaboration. Our vision is to streamline modeling projects, enhance the accessibility and reliability of models, and foster more effective decision-making processes for the sustainable management of freshwater ecosystems.

淡水建模对于解决环境和社会挑战至关重要。在本文之前的两次研讨会中，我们揭示了新西兰当前建模实践中的问题，重点是集水区水质建模。主要是对模型的信任度低、缺乏透明度和模型不适合目的。本文使用根本原因分析来探索这些问题，确定原因，并提出解决方案。我们发现，目前的最佳实践和研究是一个良好的基础，但不足以满足我们的淡水研究和管理需求。我们提倡长期的国家战略，包括集中资金、标准化文件、数据、模型、评估技术和沟通方法，以及一个集中的开放获取合作平台。我们的愿景是简化建模项目，提高模型的可及性和可靠性，并为淡水生态系统的可持续管理促进更有效的决策过程。

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引用次数: 0