Environmental Modelling & Software最新文献

{"title":"A stepwise back-correction function for precipitation representation in hydrologic models","authors":"Dany A. Hernandez ,&nbsp;Jorge A. Guzman ,&nbsp;Sandra R. Villamizar ,&nbsp;Maria L. Chu ,&nbsp;Camila Ribeiro ,&nbsp;Carlos R. de Mello","doi":"10.1016/j.envsoft.2026.106908","DOIUrl":"10.1016/j.envsoft.2026.106908","url":null,"abstract":"<div><div>This study addresses how spatial and temporal uncertainties in precipitation limit calibration of hydrological models. Adjusting model parameters alone cannot compensate for poorly represented precipitation at the model's lower resolution. A reanalysis framework that integrates traditional calibration with a stepwise precipitation back correction approach was introduced. Using a composite exponential error function, the method derives precipitation correction factors from mismatches between observed and simulated streamflow. The approach was tested with three hydrological models—SWAT, MIKE-SHE, and MHD—across watersheds in the United States and Brazil. The workflow involved an initial standard calibration, followed by iterative precipitation correction without altering model parameters, and a final recalibration incorporating the corrected precipitation. Results showed 10–18% improvements in KGE while maintaining PBIAS below 10% at most stations. The study highlights the value of constraining water balance to avoid unrealistic corrections and demonstrates how addressing precipitation uncertainties enhances model performance across diverse hydrological settings.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106908"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146152965","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":"The importance of system interactions in hydrodynamic models of parts of complex interconnected deltas","authors":"Niels M. Welsch,&nbsp;Jord J. Warmink,&nbsp;Suzanne J.M.H. Hulscher,&nbsp;Denie C.M. Augustijn","doi":"10.1016/j.envsoft.2025.106838","DOIUrl":"10.1016/j.envsoft.2025.106838","url":null,"abstract":"<div><div>Climate change affects river deltas worldwide. Hydrodynamic models are used to study these effects. However, choosing the spatial scale and boundary conditions for these models is complex due to interconnectivity within river deltas. We study how boundary conditions of a model covering only part of such systems are impacted by changing conditions outside of the domain. We couple different components of the Dutch river delta into a model covering the complete delta, and force it with a range of river discharges and sea levels. Results show that the impact depends on the distance to the boundaries, as well as the relative (upstream) discharge in the considered rivers. As these differences are found to propagate far upstream, these findings underline the importance of choosing appropriate downstream boundaries when modelling water levels in parts of interconnected systems influenced by changing conditions outside the modelled domain (e.g. sea level rise or changing hydrographs).</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106838"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956769","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":"Advancing water level prediction using clustering-based machine learning techniques in data-scarce regions","authors":"SangHyun Lee,&nbsp;Taeil Jang","doi":"10.1016/j.envsoft.2026.106899","DOIUrl":"10.1016/j.envsoft.2026.106899","url":null,"abstract":"<div><div>Accurate and scalable water level forecasting is essential for effective water resources management, particularly in regions with limited long-term records. We present a clustering-based framework for one- and three-day-ahead water level prediction in the Saemangeum Watershed, South Korea. Twenty-five monitoring stations were grouped into six hydrologically similar clusters using k-means clustering with wavelet-entropy features. Within each cluster, multilayer perceptron (MLP) models were trained using two strategies: (1) training only at the centroid station and (2) training at the station with the longest record in each cluster. The longest-record strategy showed strong agreement with observations, achieving mean Nash–Sutcliffe efficiency and root-mean-square error values of 0.97 and 0.06 for one-day-ahead forecasts, and 0.83 and 0.14 for three-day-ahead forecasts across all stations. By training one MLP per cluster and transferring it to all member stations, the framework reduces computational cost and provides a practical solution for large-scale water level forecasting in data-scarce environments.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106899"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071618","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":"SPAR-TC: A framework for accounting spatial representativeness in triple collocation","authors":"Diksha Gupta,&nbsp;C.T. Dhanya","doi":"10.1016/j.envsoft.2026.106874","DOIUrl":"10.1016/j.envsoft.2026.106874","url":null,"abstract":"<div><div>Triple collocation (TC) has been widely used to overcome the rarity of “ground truth” in geophysical measurements. While TC assumes all systems observe the same underlying geophysical variable, it does not inherently correct for spatial representativeness errors due to different spatial measurement systems. To address this, we propose the Spatially Representative Triple Collocation (SPAR-TC), which accounts for the spatial variability of the “ground truth” across different spatial scales. A synthetic soil moisture experiment assessed SPAR-TC sensitivity to spatial heterogeneity and sample size, followed by a real-world application with remotely sensed precipitation data. Results showed that SPAR-TC provides more reliable estimates of “true” error variance compared with traditional TC, especially in spatially heterogeneous regions. Both methods yield comparable dataset rankings; however, SPAR-TC provides error variance estimates more consistent with ground-based observations. Hence, SPAR-TC offers robust framework for addressing spatial representativeness errors and improves error quantification for datasets with differing spatial support.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106874"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956764","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 a web-based tool for rapid flood inundation modeling","authors":"Dawei Xiao ,&nbsp;Binjie Yuan ,&nbsp;Zhengxu Guo ,&nbsp;Wanhong Yang ,&nbsp;Jingchao Jiang ,&nbsp;Min Chen ,&nbsp;Guonian Lv ,&nbsp;Junzhi Liu","doi":"10.1016/j.envsoft.2026.106876","DOIUrl":"10.1016/j.envsoft.2026.106876","url":null,"abstract":"<div><div>To address the growing risk of floods under global climate change, management agencies need flood inundation modeling to support decision-making and emergency response. However, traditional desktop-based modeling remains a complex and time-consuming process, making it difficult for users to perform rapid flood simulations. To overcome this limitation, this study developed a web-based rapid flood modeling tool based on the LISFLOOD-FP model. Each key step involved in the modeling process—such as data preparation, preprocessing, model run and calibration, and postprocessing— was encapsulated into an automated executable workflow. These workflows were deployed on servers, published as web services, and invoked from a web-based interface, significantly streamlining and simplifying the modeling process. Four flood events in the upper Missouri River Basin were successfully simulated to showcase the tool's capability. This user-friendly web-based tool enables users to conduct flood inundation modeling quickly, thereby lowering user barriers and facilitating timely flood risk mitigation.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106876"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961785","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":"Climate Risk STAC: A living metadata catalog of geospatial data for climate risk assessments","authors":"Lena Reimann ,&nbsp;Dirk Eilander ,&nbsp;Timothy Tiggeloven ,&nbsp;Milana Vuckovic ,&nbsp;Matti Kummu ,&nbsp;Andrea Vajda ,&nbsp;Jeremy S. Pal ,&nbsp;Maurizio Mazzoleni ,&nbsp;Fredrik Wetterhall ,&nbsp;Jeroen C.J.H. Aerts","doi":"10.1016/j.envsoft.2026.106906","DOIUrl":"10.1016/j.envsoft.2026.106906","url":null,"abstract":"<div><div>Climate risks are increasing globally due to climate change, driven by intensifying climate hazards and changes in socioeconomic conditions that drive exposure and vulnerability. Climate Risk Assessments (CRAs) constitute a tool to understand such risks based on the analysis of geospatial datasets. However, CRA data are often scattered across different platforms, thereby inhibiting their Findability, Accessibility, Interoperability, and Reusability (FAIR). To make CRA data FAIR, we develop Climate Risk STAC, a living metadata catalog of open-access geospatial datasets that is hosted in a collaborative environment for continuous development. Climate Risk STAC (version 1.0) currently includes 214 metadata entries from nine different hazards, five types of exposed elements, and seven vulnerability categories. All data entries can be explored in a user-friendly browser which eases data selection. We encourage contributions of new datasets to maintain a growing, community-led catalog that reflects state-of-the-art CRA concepts and data.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106906"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146152964","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":"ADAPT: A novel IoT-driven analytical data assimilation method based on phase-space tuning for long-sequence water quality forecasting","authors":"Mingzhuang Sun ,&nbsp;Zhili Li ,&nbsp;Guangtao Fu ,&nbsp;Haifeng Jia","doi":"10.1016/j.envsoft.2026.106882","DOIUrl":"10.1016/j.envsoft.2026.106882","url":null,"abstract":"<div><div>Water quality models often suffer from performance degradation due to parameter obsolescence caused by external environmental changes. To address this, this study proposes a novel framework named Analytical Data Assimilation via Phase-space Tuning (ADAPT). Unlike traditional data assimilation methods that directly update state variables, ADAPT dynamically calibrates model parameters by establishing a robust link between parameters and water quality dynamics using Aquaformer, a Transformer-based deep learning model driven by phase-space reconstruction. The method was validated through digital-twin and real-world experiments on the Diannong River, China. Results demonstrate that ADAPT significantly outperforms the Ensemble Kalman Filter, reducing prediction errors by 36.26 % at monitored sites and 54.66 % at unmonitored sites. ADAPT exhibits superior transferability and stable error control, effectively overcoming the limitations of traditional methods in spatial generalization. This study provides a reliable, physics-informed solution for high-frequency auto-calibration in smart water management systems.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106882"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014634","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":"Provincial policy simulation for steel decarbonization in China: a modular CGE-3E3S coupling framework","authors":"Yibo Li,&nbsp;Juan Li,&nbsp;Simin Guo,&nbsp;Mei Sun","doi":"10.1016/j.envsoft.2026.106868","DOIUrl":"10.1016/j.envsoft.2026.106868","url":null,"abstract":"<div><div>This article has developed a modular framework that integrates a provincial-level computable general equilibrium (CGE) model with a 3E3S (Energy, Economy, Environment, Sustainability, Strategy, Socio-Economic Stability) assessment to quantify the policy-driven low-carbon transition in Chinas iron and steel industry. The architecture of the framework separates data, scenarios, and solvers, incorporates mechanisms such as learning-by-doing, carbon tax, and tax-rebate instruments, and supports reproducible scenario management. When applied to policy shocks, the framework generates a composite, multi-dimensional transition impact at the provincial level. The results reveal significant spatial heterogeneity, with Hebei exhibiting the largest aggregate impact across all dimensions. In a carbon-tax and global-slowdown scenario, the transition impact reaches 0.852. This study presents detailed descriptions of the model components, I/O schemas, and workflow to facilitate reuse and adaptation for other regions or sectors. This approach demonstrates how integrated economy-wide modeling and indicator analytics can guide region-specific decarbonization strategies.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106868"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146000912","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":"Multi-scale feature fusion and uncertainty quantification in streamflow prediction: A temporal convolutional network approach with hybrid denoising","authors":"Yiping He ,&nbsp;Zhaocai Wang ,&nbsp;Heqin Cheng ,&nbsp;Weijie Ding","doi":"10.1016/j.envsoft.2026.106879","DOIUrl":"10.1016/j.envsoft.2026.106879","url":null,"abstract":"<div><div>Streamflow prediction, as a critical component of flood prevention and water resource management, plays a vital role in safeguarding lives, property, and socio-economic stability. However, the streamflow process is influenced by complex interactions among meteorological variability, topographic conditions, and human activities, exhibiting pronounced nonlinearity, non-stationarity, and spatiotemporal heterogeneity, which pose significant challenges to accurate prediction. This study proposes a novel hybrid streamflow prediction model, NRBO-VMD-Wavelet-TCN (NVWT), which integrates multi-source data with deep learning. The model adaptively optimizes Variational Mode Decomposition (VMD) using the Newton-Raphson-based optimizer (NRBO); it is also combined with Wavelet Thresholding Denoising (WTD), effectively suppressing noise while preserving critical hydrological signatures. A multidimensional feature system incorporating lagged, periodic, and cumulative features is constructed, after which Maximum Information Coefficient (MIC)-based feature selection is applied to enhance input efficiency. The Temporal Convolutional Network (TCN), which leverages dilated convolutions and residual connections, effectively captures long- and short-term dependencies. Experimental results across nine hydrological stations in the Hanjiang River Basin demonstrate the NVWT model's superiority in short-term prediction (1–5 days), with Nash-Sutcliffe Efficiency (NSE) exceeding 0.82 at all stations and peaking at 0.982 for the downstream Xiantao Station, significantly outperforming benchmarks (e.g., Gated Recurrent Unit (GRU), Transformer). Ablation studies confirm the efficacy of the hybrid denoising module and multidimensional features, especially during flood peaks and low-flow periods. Interval prediction metrics further validate the model's ability to quantify uncertainty. The Shapley Additive Explanation (SHAP) method reveals differential contributions of upstream lagged streamflow and meteorological factors, enhancing the model's interpretability. This study provides a methodological reference for streamflow prediction in complex watersheds, which has significant practical implications for enhancing the scientific basis of flood control decision-making under extreme climatic conditions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106879"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146000915","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":"Towards an improved language for river data analysis: Demonstration for the highly-regulated Ohio River basin","authors":"Gaurav Atreya ,&nbsp;Todd Steissberg ,&nbsp;Drew McAvoy ,&nbsp;Xi Chen ,&nbsp;Patrick Ray","doi":"10.1016/j.envsoft.2026.106866","DOIUrl":"10.1016/j.envsoft.2026.106866","url":null,"abstract":"<div><div>We present the Network Analysis and Data Integration (NADI) System for extracting, organizing, analyzing, and visualizing river data with upstream/downstream relationships. The NADI System consists of a Geographical Information System (GIS) tool that uses spatial methods to generate the network, and a Domain Specific Language (DSL) that provides a concise and intuitive syntax for data analysis and is extensible through a plugin system. We demonstrate the capabilities of NADI using a case study of the Ohio River basin, showing it to be well-suited for large-scale metadata analysis based on river connections. The result of the case study shows that approximately half of the USGS streamflow gages in the Ohio Basin were constructed after dam(s) upstream, and only 35% of the gages remain without any dams upstream. These unaffected gages only account for 1.2% of the measured streamflow, showing the scarcity of natural streamflow data.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"198 ","pages":"Article 106866"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961781","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}