Environmental Modelling & Software最新文献_第8页

Development of an interactive web-based tool for flood risk analysis and climate–resilient road drainage design: RiskDRAIN 开发用于洪水风险分析和气候适应性道路排水设计的交互式网络工具：RiskDRAIN

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

Environmental Modelling & Software

Pub Date : 2026-02-01 Epub Date: 2026-01-09 DOI: 10.1016/j.envsoft.2026.106867

Mohammad Fereshtehpour , Rashid Bashir , Neil F. Tandon

As climate change intensifies extreme rainfall, traditional design storm methods based on stationary assumptions are increasingly inadequate, often leading to misdesigned drainage infrastructure. To address this and manage projection uncertainties, we developed RiskDRAIN, a web-based application designed for the risk-based adjustment of projected design storms. RiskDRAIN, which stands for Risk-based Design for Resilient Adaptation to Infrastructure Needs, integrates risk analysis with Canadian downscaled CMIP6 projections (CanDCS-M6). The framework incorporates Intensity-Duration-Frequency (IDF) curves derived from the IDF-CC tool, considering two projection techniques (Clausius-Clapeyron scaling and Equidistance Quantile Matching) with GEV and Gumbel distributions, covering a range of emission pathways (SSP2-4.5, SSP5-8.5) and future horizons. Through an interactive interface, users refine design storms by evaluating provincial and site-specific risks derived from hazard exposure and multi-dimensional vulnerability (socio-economic, transportation, and environmental). Validated through a highway drainage case study, RiskDRAIN empowers practitioners with a data-driven platform for cost-effective, climate-resilient infrastructure planning.

随着气候变化加剧极端降雨，基于固定假设的传统设计风暴方法越来越不充分，往往导致排水基础设施设计不当。为了解决这个问题并管理预测的不确定性，我们开发了RiskDRAIN，这是一个基于网络的应用程序，旨在根据预测设计风暴的风险进行调整。RiskDRAIN是基于风险的基础设施需求弹性适应设计，将风险分析与加拿大缩小版CMIP6预测（CanDCS-M6）相结合。该框架结合了来自IDF- cc工具的强度-持续时间-频率（IDF）曲线，考虑了GEV和Gumbel分布的两种投影技术（Clausius-Clapeyron缩放和等距离分位数匹配），涵盖了一系列发射路径（SSP2-4.5, SSP5-8.5）和未来视野。通过交互界面，用户通过评估来自危害暴露和多维脆弱性（社会经济、交通和环境）的省级和场地特定风险来完善设计风暴。通过高速公路排水案例研究，RiskDRAIN为从业者提供了一个数据驱动的平台，以实现成本效益高、适应气候变化的基础设施规划。

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引用次数: 0

A LLM-based agent for the construction of typhoon knowledge graphs 基于llm的台风知识图谱构建代理

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

Environmental Modelling & Software

Pub Date : 2026-02-01 Epub Date: 2026-01-08 DOI: 10.1016/j.envsoft.2026.106856

Yi Huang , Yongqi Xia , Ran Tao , Donglai Jiao , Xiangqiang Min , Jieying Zheng , Yuting Jiang , Wenjun Wu , Peijun Du

Conventional knowledge graphs (KGs) struggle to integrate fragmented typhoon disaster data due to error accumulation and inadequate modeling of complex spatiotemporal relationships. To overcome this, we propose TyphoonKGent, an agent driven by large language models (LLMs), which employs hierarchical knowledge representation to structurally encode typhoon evolution and impacts. It decomposes KG construction into specialized tasks (role-playing, spatiotemporal completion, entity alignment, lifecycle determination, event identification) with domain-optimized Chain-of-Thought (CoT) generation to enhance LLM reasoning for geospatial tasks. Built via efficient LoRA-based fine-tuning of distilled LLaMA/Qwen models, TyphoonKGent improves accuracy by 30 % over non-finetuned baseline models and outperforms DeepSeek-R1 under 3-shot inference by 2 %–5 %. Furthermore, visualization applications confirm its effectiveness in trajectory analysis, impact mapping, and real-time decision support. The proposed TyphoonKGent enables end-to-end KG construction, cross-domain adaptability via customizable CoTs, task-specific fine-tuning, and scalable dynamic updates for disaster management.

传统的知识图（knowledge graph, KGs）由于误差积累和对复杂时空关系建模不足，难以整合碎片化的台风灾害数据。为了克服这个问题，我们提出了一个由大型语言模型（llm）驱动的智能体TyphoonKGent，它采用分层知识表示对台风的演变和影响进行结构化编码。它将KG构建分解为专门的任务（角色扮演、时空完成、实体对齐、生命周期确定、事件识别），并生成领域优化的思维链（CoT），以增强LLM对地理空间任务的推理能力。通过高效的基于lora的精炼LLaMA/Qwen模型微调，TyphoonKGent比未经微调的基线模型提高了30%的精度，在3次推理下比DeepSeek-R1高出2% - 5%。此外，可视化应用证实了其在轨迹分析、影响映射和实时决策支持方面的有效性。拟议的台风kgent支持端到端KG构建，通过可定制的CoTs实现跨域适应性，特定于任务的微调和可扩展的灾害管理动态更新。

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引用次数: 0

RegreSSM: A novel software tool for downscaling the SMAP L3 soil moisture operational product utilizing the Ts/VI feature space and Sentinel-3 data 回归：一种利用Ts/VI特征空间和Sentinel-3数据对SMAP L3土壤湿度操作产品进行降尺度处理的新型软件工具

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

Environmental Modelling & Software

Pub Date : 2026-02-01 Epub Date: 2025-12-17 DOI: 10.1016/j.envsoft.2025.106836

George P. Petropoulos , Spyridon E. Detsikas , Vasileios Anagnostopoulos , Christina Lekka

Herein we present RegreSSM, a software tool that enables the downscaling of SMAP L3 Surface Soil Moisture (SSM) operational product from 36 km to 1 km by the fusion of optical and thermal data retrieved from Sentinel-3 platform. The downscaling method is based on the well-established properties of the Ts/VI feature space. Most of the existing soil moisture downscaling methods are computationally complex, require advanced expertise, and lack standalone tools suitable for operational or non-expert use. To address these limitations, this study proposes a simple and accessible framework for generating high-resolution SSM maps using only land surface temperature and vegetation cover as inputs. The tool has been developed in python programming language as a stand-alone application and can be executed in any operational system. The application offers automated and reproducible workflows for spatiotemporal matching and processing of SMAP L3 SSM products and Sentinel-3 dataset. The software tool's practical application is demonstrated over the Iberian Peninsula, where validation of the SMAP L3 product performed for all calendar year 2022 using in-situ observations from the REMEDHUS operational network stations. Results showed a satisfactory retrieval of SSM with a small average bias of 0.01 m³/m³, a MAD of 0.06 m³/m³, a RMSD of 0.07 m³/m³, and a satisfactory R² of 0.63, confirming the ability of the proposed downscaling framework and RegreSSM to retrieve SSM at the 1 km spatial resolution. Results obtained herein were also compared to the validation metrics reported for operational RS-based SSM products, with typically reported uncertainty of 0.04 m³/m³. The availability of RegreSSM to the SSM users' community consists an important step towards the standardization of downscaling procedures as well as bridging the spatial gap of existing operational SM products to the requirements of the fine-scale applications. It also contributes towards advancing the deployment of geo-processing tools utilizing the synergies between state-of-the-art methods and RS data available today from the most sophisticated satellites in orbit.

本文提出了一种软件工具RegreSSM，通过融合Sentinel-3平台检索的光学和热数据，可以将SMAP L3表层土壤湿度（SSM）业务产品从36公里降尺度到1公里。该降尺度方法是基于已建立的Ts/VI特征空间的特性。大多数现有的土壤湿度降尺度方法计算复杂，需要高级专业知识，并且缺乏适合操作或非专业使用的独立工具。为了解决这些限制，本研究提出了一个简单易用的框架，用于仅使用地表温度和植被覆盖作为输入来生成高分辨率SSM地图。该工具是用python编程语言开发的独立应用程序，可以在任何操作系统中执行。该应用程序为SMAP L3 SSM产品和Sentinel-3数据集的时空匹配和处理提供了自动化和可重复的工作流程。该软件工具的实际应用在伊比利亚半岛进行了演示，在那里，通过REMEDHUS操作网络站的现场观测，对2022年全年的SMAP L3产品进行了验证。结果表明，反演结果令人满意，平均偏差为0.01 m3/m3， MAD为0.06 m3/m3， RMSD为0.07 m3/m3， R2为0.63，证实了所提出的降尺度框架和回归模型在1 km空间分辨率下反演SSM的能力。本文获得的结果也与运行RS-based SSM产品报告的验证指标进行了比较，通常报告的不确定性为0.04 m3/m3。回归模型对SSM用户社区的可用性是实现降尺度过程标准化的重要一步，同时也弥补了现有可操作SM产品与精细尺度应用需求之间的空间差距。它还有助于利用最先进的方法与目前最先进的在轨卫星提供的遥感数据之间的协同作用，推进地理处理工具的部署。

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引用次数: 0

Coupling a micro-genetic algorithm with RegCM5 for improving extreme precipitation simulations over Southeast Asia 微遗传算法与RegCM5耦合改进东南亚极端降水模拟

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

Environmental Modelling & Software

Pub Date : 2026-02-01 Epub Date: 2026-01-10 DOI: 10.1016/j.envsoft.2026.106871

Zixuan Zhou , Ji Won Yoon , Thanh Nguyen-Xuan , Jina Hur , Seon Ki Park , Eun-Soon Im

Regional climate models (RCMs) are essential for producing fine-scale climate information, but their effectiveness is highly sensitive to the combination of physical parameterizations and optimal settings of key parameters. To tackle this problem, this study develops a coupled modeling system that integrates a micro-genetic algorithm (μGA) with the Regional Climate Model version 5 (RegCM5), focusing on optimizing parameters in the Tiedtke convection scheme, crucial for precipitation simulations. Using the benchmarking version of RegCM5 for Southeast Asia, we aim to identify the optimal parameter set that enhances performance for three extreme precipitation events. The evaluation of this parameter set is then conducted by simulating six additional extreme events. Results show that simulations with optimized parameters improve both precipitation and temperature compared to the default model, significantly reducing biases, particularly over ocean regions. Our coupled RegCM5-μGA system will aid the broader RegCM5 community in enhancing model performance in their target regions.

区域气候模式是产生精细尺度气候信息的关键，但其有效性对物理参数化和关键参数的最佳设置的组合高度敏感。为了解决这一问题，本研究开发了一个将微遗传算法（μGA）与区域气候模式第5版（RegCM5）相结合的耦合建模系统，重点优化了降水模拟中关键的Tiedtke对流方案的参数。使用RegCM5东南亚基准版本，我们的目标是确定提高三种极端降水事件性能的最佳参数集。然后通过模拟另外六个极端事件对该参数集进行评估。结果表明，与默认模式相比，使用优化参数的模拟改善了降水和温度，显著减少了偏差，特别是在海洋区域。我们的耦合RegCM5-μGA系统将帮助更广泛的RegCM5社区提高模型在目标区域的性能。

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引用次数: 0

An efficient multi-objective optimization method for inter-basin water diversion based on the rotation vector method for exploring the convex objective space 基于旋转矢量法探索凸目标空间的高效跨流域调水多目标优化方法

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

Environmental Modelling & Software

Pub Date : 2026-02-01 Epub Date: 2026-01-08 DOI: 10.1016/j.envsoft.2026.106858

Zekun Li , Bin Xu , Ping-an Zhong , Jiaying Tan , Ran Mo , Xinrong Wang , Zichen Ren , Guoqing Wang , Jianyun Zhang , Jiangyuan Li , Scott E. Boyce

Inter-basin water diversion systems involve multiple sources, users, and objectives with complex interrelations and numerous decision variables. Classical

ε

-constraint method often produce infeasible or redundant solutions in multi-objective optimization. To address this, the limitations of the classical

ε

-constraint method are analyzed, and long-term water diversion models with convex objectives are reformulated as convex programming problems. Based on convex programming characteristics, a rotation vector method is proposed to explore the boundary range of the objective space and optimize

ε

-constraint threshold combinations, reducing infeasibility and redundancy. The method is applied to testing functions and the South-to-North Water Diversion Eastern Route Project in China, targeting minimization of water shortage, diversion volume, and ecological water supplementation. Results indicate that the main conflicts arise from competition between water supply and ecological demands. Compared with the classical

ε

-constraint method, the proposed algorithm increases the Pareto set hypervolume by 35 % and decreases spacing by 15 %, enhancing diversity and uniformity.

跨流域调水系统涉及多个水源、用户和目标，具有复杂的相互关系和众多的决策变量。经典的ε-约束方法在多目标优化问题中往往产生不可行解或冗余解。针对这一问题，分析了经典ε约束方法的局限性，将具有凸目标的长期调水模型重新表述为凸规划问题。基于凸规划的特点，提出了一种旋转矢量法来探索目标空间的边界范围，优化ε约束阈值组合，减少了不可行性和冗余性。将该方法应用于中国的南水北调东线工程和功能测试中，以实现水资源短缺最小化、引水量最小化、生态补水最小化为目标。结果表明，水资源供给与生态需求之间的竞争是主要矛盾。与经典的ε-约束方法相比，该算法使Pareto集超体积增大35%，间隔减小15%，增强了多样性和均匀性。

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引用次数: 0

AI-driven knowledge synthesis for food web parameterisation 人工智能驱动的食物网参数化知识综合

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

Environmental Modelling & Software

Pub Date : 2026-02-01 Epub Date: 2026-01-07 DOI: 10.1016/j.envsoft.2026.106865

Scott Spillias , Elizabeth A. Fulton , Fabio Boschetti , Cathy Bulman , Joanna Strzelecki , Rowan Trebilco

We introduce a proof-of-concept framework, Synthesizing Parameters for Ecosystem modelling with LLMs (SPELL), that automates species grouping and diet matrix generation to accelerate food web construction for ecosystem models. SPELL retrieves species lists, classifies them into functional groups, and synthesizes trophic interactions by integrating global biodiversity databases (e.g., FishBase, GLOBI), species interaction repositories, and optionally curated local knowledge using Large Language Models (LLMs). We validate the approach through a marine case study across four Australian regions, achieving high reproducibility in species grouping (

<

99.7%) and moderate consistency in trophic interactions (51%–59%). Comparison with an expert-derived food web for the Great Australian Bight indicates strong but incomplete ecological accuracy: 92.6% of group assignments were at least partially correct and 82% of trophic links were identified. Specialized groups such as benthic organisms, parasites, and taxa with variable feeding strategies remain challenging. These findings highlight the importance of expert review for fine-scale accuracy and suggest SPELL is a generalizable tool for rapid prototyping of trophic structures in marine and potentially non-marine ecosystems.

我们引入了一个概念验证框架，使用LLMs （SPELL）进行生态系统建模的综合参数，该框架可自动进行物种分组和饮食矩阵生成，以加速生态系统模型的食物网构建。通过整合全球生物多样性数据库（如FishBase、GLOBI）、物种相互作用库和使用大型语言模型（llm）的可选管理的本地知识，SPELL检索物种列表，将它们分类到功能组，并综合营养相互作用。我们通过澳大利亚四个地区的海洋案例研究验证了该方法，在物种分组中实现了高重复性（99.7%），在营养相互作用中实现了中等一致性（51%-59%）。与大澳大利亚湾的专家衍生食物网的比较表明，生态准确性很强，但不完整：92.6%的群体分配至少部分正确，82%的营养联系被确定。专门的群体，如底栖生物、寄生虫和具有可变摄食策略的分类群仍然具有挑战性。这些发现强调了专家审查对精细尺度准确性的重要性，并表明SPELL是海洋和潜在的非海洋生态系统中营养结构快速原型的通用工具。

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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 : 2026-02-01 Epub 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

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 : 2026-02-01 Epub 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

GeoClimate intelligence platform: A web-based framework for environmental data analysis 地理气候情报平台：一个基于网络的环境数据分析框架

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

Environmental Modelling & Software

Pub Date : 2026-02-01 Epub Date: 2025-12-13 DOI: 10.1016/j.envsoft.2025.106826

Saurav Bhattarai , Nawa Raj Pradhan , Rocky Talchabhadel

Environmental science education faces a critical barrier: programming requirements prevent students, novice researchers, and domain experts from accessing planetary-scale datasets. This study presents the GeoClimate Intelligence Platform, a web-based framework powered by Google Earth Engine (GEE) that eliminates programming barriers while maintaining research-grade analytical capabilities. The platform comprises five integrated modules: GeoData Explorer for climate dataset access, Climate Analytics implementing 20+ ETCCDI-compliant climate indices, Hydrology Analyzer for precipitation analysis and return periods, Product Selector for dataset validation, and Data Visualizer for interactive analysis. This modular design supports integrated workflows while maintaining analytical independence across specialized functions. Development was motivated by workshops where students found programming barriers insurmountable despite strong motivation. Educational validation through university coursework demonstrated effectiveness. Performance evaluation shows robust scalability from educational to research-scale applications. The platform requires only a GEE account and operates through web browsers, eliminating software installation. This accessibility transformation enables broader participation in data-driven environmental problem-solving with scientific rigor, democratizing sophisticated environmental analysis for educational and research communities.

环境科学教育面临着一个关键的障碍：编程要求阻碍了学生、研究新手和领域专家访问行星尺度的数据集。本研究提出了地球气候情报平台，这是一个基于网络的框架，由谷歌地球引擎（GEE）提供支持，在保持研究级分析能力的同时消除了编程障碍。该平台包括五个集成模块：用于气候数据集访问的GeoData Explorer，实现20多个符合etccdi的气候指数的climate Analytics，用于降水分析和回归期的Hydrology Analyzer，用于数据集验证的Product Selector，以及用于交互式分析的Data Visualizer。这种模块化设计支持集成工作流，同时保持跨专门功能的分析独立性。开发是由研讨会推动的，学生们发现编程障碍难以克服，尽管有很强的动机。通过大学课程证明教育有效性。性能评估显示了从教育到研究规模应用的强大可扩展性。该平台只需要一个GEE账户，并通过网络浏览器操作，无需安装软件。这种可访问性转变使更广泛的参与到数据驱动的环境问题解决中，并具有科学的严谨性，使教育和研究社区的复杂环境分析民主化。

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引用次数: 0

PyDRGHT: A comprehensive python package for drought analysis pydright：用于干旱分析的全面Python包

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

Environmental Modelling & Software

Pub Date : 2026-02-01 Epub Date: 2025-12-24 DOI: 10.1016/j.envsoft.2025.106847

Tolga Barış Terzi

Drought is an escalating environmental hazard with profound societal and ecological impacts, intensified by climate change. Effective monitoring and probabilistic assessment require integrated tools capable of capturing both univariate and multivariate characteristics, including the interdependent behavior of multiple hydroclimatic variables. This study introduces PyDRGHT, an open-source Python package for comprehensive drought analysis. PyDRGHT provides a unified framework for computing standardized univariate and multivariate drought indices, identifying drought characteristics, and conducting univariate and copula-based bivariate frequency analyses to enable transparent and reproducible probabilistic assessments. PyDRGHT's utility is demonstrated using long-term precipitation and streamflow records from the Seyhan River Basin, Türkiye (1965–2011), illustrating robust drought detection and characterization. By offering a flexible and robust platform within the Python ecosystem, PyDRGHT advances drought monitoring, risk assessment, and hydroclimatic research.

干旱是一种不断升级的环境危害，具有深远的社会和生态影响，并因气候变化而加剧。有效的监测和概率评估需要能够捕捉单变量和多变量特征的综合工具，包括多个水文气候变量的相互依赖行为。本研究介绍了pydright，一个用于全面干旱分析的开源Python包。pydright提供了一个统一的框架，用于计算标准化的单变量和多变量干旱指数，识别干旱特征，并进行单变量和基于copula的双变量频率分析，以实现透明和可重复的概率评估。pydright的实用性通过使用 rkiye（1965-2011）的塞汉河流域的长期降水和流量记录来证明，说明了强大的干旱检测和表征。通过在Python生态系统中提供一个灵活而强大的平台，pydright推进了干旱监测、风险评估和水文气候研究。

引用次数: 0