Pub Date : 2025-02-16DOI: 10.1016/j.envsoft.2025.106379
Qian Zhang , Gary W. Shenk , Gopal Bhatt , Isabella Bertani
Reductions of nitrogen, phosphorus, and sediment loads have been the focus of watershed restoration in many regions for improving water quality, including the Chesapeake Bay. Watershed models and riverine monitoring data can provide important information on the progress of load reductions but do not always generate consistent interpretations. A new framework for integrated visualization and analysis of monitoring and modeling data, named “Monitored and Expected Total Reduction Indicator for the Chesapeake (METRIC),” was developed to provide spatially explicit trends for the subwatersheds of the Chesapeake Bay. METRIC contains up-to-date information on nitrogen, phosphorus, and sediment at 83, 66, and 66 stations, respectively, which can help watershed managers gauge expectations on the trajectory and pace of progress at localized scales. These results were further synthesized to better understand the spatial patterns of the response classes (i.e., agreement between the expected and monitored trends) across the Chesapeake Bay watershed.
{"title":"METRIC: An interactive framework for integrated visualization and analysis of monitored and expected load reductions for nitrogen, phosphorus, and sediment in the Chesapeake Bay watershed","authors":"Qian Zhang , Gary W. Shenk , Gopal Bhatt , Isabella Bertani","doi":"10.1016/j.envsoft.2025.106379","DOIUrl":"10.1016/j.envsoft.2025.106379","url":null,"abstract":"<div><div>Reductions of nitrogen, phosphorus, and sediment loads have been the focus of watershed restoration in many regions for improving water quality, including the Chesapeake Bay. Watershed models and riverine monitoring data can provide important information on the progress of load reductions but do not always generate consistent interpretations. A new framework for integrated visualization and analysis of monitoring and modeling data, named “Monitored and Expected Total Reduction Indicator for the Chesapeake (METRIC),” was developed to provide spatially explicit trends for the subwatersheds of the Chesapeake Bay. METRIC contains up-to-date information on nitrogen, phosphorus, and sediment at 83, 66, and 66 stations, respectively, which can help watershed managers gauge expectations on the trajectory and pace of progress at localized scales. These results were further synthesized to better understand the spatial patterns of the response classes (<em>i.e.</em>, agreement between the expected and monitored trends) across the Chesapeake Bay watershed.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106379"},"PeriodicalIF":4.8,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143532972","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}
Pub Date : 2025-02-16DOI: 10.1016/j.envsoft.2025.106378
I.C. Hanigan , W. Yu , C. Yuen , K. Gopi , L.D. Knibbs , C.T. Cowie , B. Jalaludin , M. Cope , M.L. Riley , J. Heyworth , L. Morawska , G.B. Marks , G.G. Morgan , Y. Guo
Ground-level ozone (O3) is a significant public health concern. We developed maps of monthly average 1-h maximum O3 concentrations in New South Wales, Australia (2005–2018), a region with sparse monitoring. For the first time Bayesian Maximum Entropy (BME) blending was used within a Deep Ensemble Machine Learning (DEML) framework for air pollution predictions. The DEML combined geographical predictors in random forest (RF), extreme gradient boosting (XGBoost), and gradient boosted machine (GBM) models with three meta-models. BME blending incorporated observed O3 data into posterior predictions. We generated 2.5 km × 2.5 km resolution gridded surfaces. The DEML estimates achieved an R2 of 0.89 and RMSE of 2.3 ppb in the held-out test dataset at monitors. DEML grid cell predictions (R2: 0.84, RMSE: 3.03 ppb) were improved by BME blending (R2: 0.89, RMSE: 2.49 ppb). Mean bias reduced from −0.7 ppb to −0.4 ppb. This demonstrates high accuracy and precision in a sparsely monitored region.
{"title":"Deep ensemble machine learning with Bayesian blending improved accuracy and precision of modelled ground-level ozone for region with sparse monitoring: Australia, 2005–2018","authors":"I.C. Hanigan , W. Yu , C. Yuen , K. Gopi , L.D. Knibbs , C.T. Cowie , B. Jalaludin , M. Cope , M.L. Riley , J. Heyworth , L. Morawska , G.B. Marks , G.G. Morgan , Y. Guo","doi":"10.1016/j.envsoft.2025.106378","DOIUrl":"10.1016/j.envsoft.2025.106378","url":null,"abstract":"<div><div>Ground-level ozone (O<sub>3</sub>) is a significant public health concern. We developed maps of monthly average 1-h maximum O<sub>3</sub> concentrations in New South Wales, Australia (2005–2018), a region with sparse monitoring. For the first time Bayesian Maximum Entropy (BME) blending was used within a Deep Ensemble Machine Learning (DEML) framework for air pollution predictions. The DEML combined geographical predictors in random forest (RF), extreme gradient boosting (XGBoost), and gradient boosted machine (GBM) models with three meta-models. BME blending incorporated observed O<sub>3</sub> data into posterior predictions. We generated 2.5 km × 2.5 km resolution gridded surfaces. The DEML estimates achieved an R<sup>2</sup> of 0.89 and RMSE of 2.3 ppb in the held-out test dataset at monitors. DEML grid cell predictions (R<sup>2</sup>: 0.84, RMSE: 3.03 ppb) were improved by BME blending (R<sup>2</sup>: 0.89, RMSE: 2.49 ppb). Mean bias reduced from −0.7 ppb to −0.4 ppb. This demonstrates high accuracy and precision in a sparsely monitored region.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"187 ","pages":"Article 106378"},"PeriodicalIF":4.8,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-15DOI: 10.1016/j.envsoft.2025.106375
Xin-qiang Zhou , Yong-ming Shen , Jun Tang
A coupled hydrodynamic-water quality model was employed to investigate water quality improvement in Waihai of Lake Dianchi under different water diversion scenarios, including different volumes, inflow/outflow locations, and seasonal allocations. The accuracy of coupled model was reasonably validated against observed data on water level and temperature, total phosphorus (TP), total nitrogen (TN), dissolved oxygen (DO) and chlorophyll-a (Chl-a) concentrations. Further analysis reveals water diversion significantly improves Waihai's water quality. In northern Waihai, the annual average TP and TN concentrations decrease by 27.2% and 26.1%. The average Chl-a concentration decreases by 36.8% during wet season. Increasing water diversion volume emerges as the most effective strategy for improving water quality. Designating the Panlong River as the inlet and the Jiezhi Gate as the primary outlet for diverted water proves more advantageous in reducing TP, TN, and Chl-a concentrations. Concentrating diverted water during wet season leads to more substantial reductions in Chl-a concentrations.
{"title":"Numerical modeling of water diversion impacts on water quality improvement in Lake Dianchi","authors":"Xin-qiang Zhou , Yong-ming Shen , Jun Tang","doi":"10.1016/j.envsoft.2025.106375","DOIUrl":"10.1016/j.envsoft.2025.106375","url":null,"abstract":"<div><div>A coupled hydrodynamic-water quality model was employed to investigate water quality improvement in Waihai of Lake Dianchi under different water diversion scenarios, including different volumes, inflow/outflow locations, and seasonal allocations. The accuracy of coupled model was reasonably validated against observed data on water level and temperature, total phosphorus (TP), total nitrogen (TN), dissolved oxygen (DO) and chlorophyll-a (Chl-a) concentrations. Further analysis reveals water diversion significantly improves Waihai's water quality. In northern Waihai, the annual average TP and TN concentrations decrease by 27.2% and 26.1%. The average Chl-a concentration decreases by 36.8% during wet season. Increasing water diversion volume emerges as the most effective strategy for improving water quality. Designating the Panlong River as the inlet and the Jiezhi Gate as the primary outlet for diverted water proves more advantageous in reducing TP, TN, and Chl-a concentrations. Concentrating diverted water during wet season leads to more substantial reductions in Chl-a concentrations.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"187 ","pages":"Article 106375"},"PeriodicalIF":4.8,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437682","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}
Pub Date : 2025-02-14DOI: 10.1016/j.envsoft.2025.106365
Ikram El Hazdour , Michel Le Page , Lahoucine Hanich , Adnane Chakir , Oliver Lopez , Lionel Jarlan
Accurate and synoptic estimation of Evapotranspiration (ET) is crucial for water management. A Google Earth Engine workflow is implemented to estimate daily ET at 30m. The algorithm uses Landsat and ERA5-Land datasets and includes the Two Source Energy Balance (TSEB) model, an Artificial Neural Network for Leaf Area Index, and a gap-filling approach based on crop coefficient. The outputs were evaluated against four local flux towers in a semi-arid site in Morocco (wheat, maize, watermelon, olive), and compared to another high-resolution ET (SSEBop product). The results demonstrated good performances (RMSE between 0.67 mm/day and 2 mm/day, low MBE), while SSEBop product generally underestimated ET. Better performance of the TSEB-GEE workflow was found when aggregating ET to weekly and monthly timescales. The workflow offers ease of model implementation to deliver reliable daily plot-scale ET estimates, offering the potential for broader-scale applications in semi-arid Mediterranean regions, encompassing various crops and facilitating historical analysis.
{"title":"A GEE TSEB workflow for daily high-resolution fully remote sensing evapotranspiration: Validation over four crops in semi-arid conditions and comparison with the SSEBop experimental product","authors":"Ikram El Hazdour , Michel Le Page , Lahoucine Hanich , Adnane Chakir , Oliver Lopez , Lionel Jarlan","doi":"10.1016/j.envsoft.2025.106365","DOIUrl":"10.1016/j.envsoft.2025.106365","url":null,"abstract":"<div><div>Accurate and synoptic estimation of Evapotranspiration (ET) is crucial for water management. A Google Earth Engine workflow is implemented to estimate daily ET at 30m. The algorithm uses Landsat and ERA5-Land datasets and includes the Two Source Energy Balance (TSEB) model, an Artificial Neural Network for Leaf Area Index, and a gap-filling approach based on crop coefficient. The outputs were evaluated against four local flux towers in a semi-arid site in Morocco (wheat, maize, watermelon, olive), and compared to another high-resolution ET (SSEBop product). The results demonstrated good performances (RMSE between 0.67 mm/day and 2 mm/day, low MBE), while SSEBop product generally underestimated ET. Better performance of the TSEB-GEE workflow was found when aggregating ET to weekly and monthly timescales. The workflow offers ease of model implementation to deliver reliable daily plot-scale ET estimates, offering the potential for broader-scale applications in semi-arid Mediterranean regions, encompassing various crops and facilitating historical analysis.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"187 ","pages":"Article 106365"},"PeriodicalIF":4.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-14DOI: 10.1016/j.envsoft.2025.106360
Sara Asadi , Patricia Jimeno-Sáez , Adrián López-Ballesteros , Javier Senent-Aparicio
Accurate streamflow prediction is crucial for effective water resources management and flood risk assessment. The Tagus Headwaters River Basin (THRB), a semi-arid watershed, serves over 10 million residents in Peninsular Spain and diverts water to the Segura River Basin. As the THRB nears its water allocation limits, precise streamflow simulations are essential for sustainable management. This study is especially important for arid and semi-arid watersheds, where previous research has shown that the performance of rainfall-runoff modeling using the LSTM AI-based technique declines in more arid catchments. This research enhances streamflow simulations in the THRB by combining the Soil and Water Assessment Tool (SWAT+) with a Long Short-Term Memory (LSTM) model. Five scenarios were evaluated, using different combinations of meteorological data and SWAT+ model outputs as LSTM input data. Results showed that coupled models generally provided more accurate daily streamflow estimates than standalone SWAT+ or LSTM models. Coupled LSTM and calibrated SWAT+ models significantly outperformed coupled LSTM and default SWAT+ models when using SWAT+ estimated streamflow as the sole input. Additionally, coupled models using different SWAT+ hydrological outputs and meteorological data as LSTM input data outperformed those using only SWAT+ estimated streamflow. This improvement was more notable in scenarios combining LSTM and default SWAT+ models, highlighting the SWAT+ default model’s effectiveness in capturing basin characteristics, reflected in hydrological metrics like lateral flow, percolation and soil water content. SHapley Additive exPlanations (SHAP) analysis revealed that SWAT+ outputs, especially lateral flow and percolation, were the most influential factors, with global importance ranging from 34% to 40% and 23% to 36% across all stations in the default scenario, respectively. These advancements enhance decision-making with more precise coupled model forecasts, particularly in arid and semi-arid watersheds like the THRB.
{"title":"Coupling SWAT+ with LSTM for enhanced and interpretable streamflow estimation in arid and semi-arid watersheds, a case study of the Tagus Headwaters River Basin, Spain","authors":"Sara Asadi , Patricia Jimeno-Sáez , Adrián López-Ballesteros , Javier Senent-Aparicio","doi":"10.1016/j.envsoft.2025.106360","DOIUrl":"10.1016/j.envsoft.2025.106360","url":null,"abstract":"<div><div>Accurate streamflow prediction is crucial for effective water resources management and flood risk assessment. The Tagus Headwaters River Basin (THRB), a semi-arid watershed, serves over 10 million residents in Peninsular Spain and diverts water to the Segura River Basin. As the THRB nears its water allocation limits, precise streamflow simulations are essential for sustainable management. This study is especially important for arid and semi-arid watersheds, where previous research has shown that the performance of rainfall-runoff modeling using the LSTM AI-based technique declines in more arid catchments. This research enhances streamflow simulations in the THRB by combining the Soil and Water Assessment Tool (SWAT+) with a Long Short-Term Memory (LSTM) model. Five scenarios were evaluated, using different combinations of meteorological data and SWAT+ model outputs as LSTM input data. Results showed that coupled models generally provided more accurate daily streamflow estimates than standalone SWAT+ or LSTM models. Coupled LSTM and calibrated SWAT+ models significantly outperformed coupled LSTM and default SWAT+ models when using SWAT+ estimated streamflow as the sole input. Additionally, coupled models using different SWAT+ hydrological outputs and meteorological data as LSTM input data outperformed those using only SWAT+ estimated streamflow. This improvement was more notable in scenarios combining LSTM and default SWAT+ models, highlighting the SWAT+ default model’s effectiveness in capturing basin characteristics, reflected in hydrological metrics like lateral flow, percolation and soil water content. SHapley Additive exPlanations (SHAP) analysis revealed that SWAT+ outputs, especially lateral flow and percolation, were the most influential factors, with global importance ranging from 34% to 40% and 23% to 36% across all stations in the default scenario, respectively. These advancements enhance decision-making with more precise coupled model forecasts, particularly in arid and semi-arid watersheds like the THRB.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106360"},"PeriodicalIF":4.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1016/j.envsoft.2025.106368
Michael D. Stevens , Saul G. Ramirez , Eva-Marie H. Martin , Norman L. Jones , Gustavious P. Williams , Kyra H. Adams , Daniel P. Ames , Sarva T. Pulla
We estimate long-term groundwater storage loss in California's Central Valley (CV) using a novel data imputation method that combines in situ data with Earth Observations to generate temporally and spatially interpolated groundwater elevations. We combine these data with storage coefficient maps to produce time series of groundwater volume changes which compare well with previously published groundwater storage change estimates for the valley. We also compare our results to groundwater storage changes we calculated using Gravity Recovery and Climate Experiment (GRACE) mission data and show that the two storage estimates are well correlated, but the GRACE volume estimates are lower due the well-known “leakage” effect. While other researchers have accounted for leakage by scaling the GRACE results using various factors and assumptions, our method demonstrates a direct method for calibrating GRACE estimated groundwater change, which can then be applied to future GRACE results in the CV with confidence.
{"title":"Groundwater storage loss in the central valley analysis using a novel method based on in situ data compared to GRACE-derived data","authors":"Michael D. Stevens , Saul G. Ramirez , Eva-Marie H. Martin , Norman L. Jones , Gustavious P. Williams , Kyra H. Adams , Daniel P. Ames , Sarva T. Pulla","doi":"10.1016/j.envsoft.2025.106368","DOIUrl":"10.1016/j.envsoft.2025.106368","url":null,"abstract":"<div><div>We estimate long-term groundwater storage loss in California's Central Valley (CV) using a novel data imputation method that combines <em>in situ</em> data with Earth Observations to generate temporally and spatially interpolated groundwater elevations. We combine these data with storage coefficient maps to produce time series of groundwater volume changes which compare well with previously published groundwater storage change estimates for the valley. We also compare our results to groundwater storage changes we calculated using Gravity Recovery and Climate Experiment (GRACE) mission data and show that the two storage estimates are well correlated, but the GRACE volume estimates are lower due the well-known “leakage” effect. While other researchers have accounted for leakage by scaling the GRACE results using various factors and assumptions, our method demonstrates a direct method for calibrating GRACE estimated groundwater change, which can then be applied to future GRACE results in the CV with confidence.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106368"},"PeriodicalIF":4.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421572","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}
Pub Date : 2025-02-12DOI: 10.1016/j.envsoft.2025.106362
François Miville , Philippe Renard , Chloé Fandel , Marco Filipponi
Modeling groundwater flow using physically based models requires knowing the geometry of the karst conduit network. Often, this geometry is not accessible and unknown. It is therefore crucial to be able to model it. This paper presents pyKasso, an open-source Python package that generates those geometry based on a pseudo-genetic approach. The model accounts for multiple data sources: a 3D geologic model, the position of known inlets and outlets, the statistical distribution of fractures or inception features, and known base levels. This approach simplifies previously published work by considering a 3D anisotropic fast marching algorithm. The paper presents the structure of the code and explains in detail how it can be used from the most simple 2D situation to a complex 3D case.
{"title":"pyKasso: An open-source three-dimensional discrete karst network generator","authors":"François Miville , Philippe Renard , Chloé Fandel , Marco Filipponi","doi":"10.1016/j.envsoft.2025.106362","DOIUrl":"10.1016/j.envsoft.2025.106362","url":null,"abstract":"<div><div>Modeling groundwater flow using physically based models requires knowing the geometry of the karst conduit network. Often, this geometry is not accessible and unknown. It is therefore crucial to be able to model it. This paper presents pyKasso, an open-source Python package that generates those geometry based on a pseudo-genetic approach. The model accounts for multiple data sources: a 3D geologic model, the position of known inlets and outlets, the statistical distribution of fractures or inception features, and known base levels. This approach simplifies previously published work by considering a 3D anisotropic fast marching algorithm. The paper presents the structure of the code and explains in detail how it can be used from the most simple 2D situation to a complex 3D case.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106362"},"PeriodicalIF":4.8,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421557","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}
Pub Date : 2025-02-12DOI: 10.1016/j.envsoft.2025.106370
Yining Wang , Bingtian Li , Wei Zhou , Yunxiu Ge
With the development of the ocean economy, accurate forecasting using ocean models has become increasingly important. Existing parallel versions of the Princeton Ocean Model (POM) often feature complex code and limited portability. To address these issues and meet the computational demands of high-resolution ocean models while reducing program runtime, we developed an OpenACC-based parallel version of POM. Our approach migrates all computational components to the GPU using OpenACC, providing better maintainability and portability. We identified parallelizable sections and used Nsight Systems to analyze bottlenecks, reducing the transfer time efficiently between CPU and GPU. We tested the model's accuracy and performance under various simulation durations and resolutions. The results show a slight reduction in accuracy, while the speedup improved significantly, ranging from 11.75 to 45.04 with increased simulation duration and resolution. This work enhances the usability and efficiency of POM, making it more suitable for ocean forecasting and advanced research applications.
{"title":"Parallel Princeton Ocean Model based on OpenACC","authors":"Yining Wang , Bingtian Li , Wei Zhou , Yunxiu Ge","doi":"10.1016/j.envsoft.2025.106370","DOIUrl":"10.1016/j.envsoft.2025.106370","url":null,"abstract":"<div><div>With the development of the ocean economy, accurate forecasting using ocean models has become increasingly important. Existing parallel versions of the Princeton Ocean Model (POM) often feature complex code and limited portability. To address these issues and meet the computational demands of high-resolution ocean models while reducing program runtime, we developed an OpenACC-based parallel version of POM. Our approach migrates all computational components to the GPU using OpenACC, providing better maintainability and portability. We identified parallelizable sections and used Nsight Systems to analyze bottlenecks, reducing the transfer time efficiently between CPU and GPU. We tested the model's accuracy and performance under various simulation durations and resolutions. The results show a slight reduction in accuracy, while the speedup improved significantly, ranging from 11.75 to 45.04 with increased simulation duration and resolution. This work enhances the usability and efficiency of POM, making it more suitable for ocean forecasting and advanced research applications.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"187 ","pages":"Article 106370"},"PeriodicalIF":4.8,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428811","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}
Pub Date : 2025-02-11DOI: 10.1016/j.envsoft.2025.106364
Francisco Rodríguez-Gómez , José del Campo-Ávila , Luis Pérez-Urrestarazu , Domingo López-Rodríguez
Mitigating Urban Heat Island (UHI) effects has become a challenge to improve urban sustainability. The simulation tool URSUS_LST has been developed to allow urban planners to estimate how the addition of different green infrastructure elements would affect temperature. To achieve this, a new methodology was defined based on data mining, geospatial image processing and the knowledge of experts in the domain that predicts the Land Surface Temperature (LST) of any location within a city. It consists of a first data mining phase in which the real LST and the different urban elements of the nearby environment are considered: buildings, vegetation and water bodies. In a second phase, different regression models are induced to predict LST. Additionally, considering the most accurate models, the relevant attributes and their relationships are identified. A real application of the tool in the city of Malaga (Spain) has been used as an example of its usefulness.
{"title":"URSUS_LST: URban SUStainability intelligent system for predicting the impact of urban green infrastructure on land surface temperatures","authors":"Francisco Rodríguez-Gómez , José del Campo-Ávila , Luis Pérez-Urrestarazu , Domingo López-Rodríguez","doi":"10.1016/j.envsoft.2025.106364","DOIUrl":"10.1016/j.envsoft.2025.106364","url":null,"abstract":"<div><div>Mitigating Urban Heat Island (UHI) effects has become a challenge to improve urban sustainability. The simulation tool URSUS_LST has been developed to allow urban planners to estimate how the addition of different green infrastructure elements would affect temperature. To achieve this, a new methodology was defined based on data mining, geospatial image processing and the knowledge of experts in the domain that predicts the Land Surface Temperature (LST) of any location within a city. It consists of a first data mining phase in which the real LST and the different urban elements of the nearby environment are considered: buildings, vegetation and water bodies. In a second phase, different regression models are induced to predict LST. Additionally, considering the most accurate models, the relevant attributes and their relationships are identified. A real application of the tool in the city of Malaga (Spain) has been used as an example of its usefulness.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106364"},"PeriodicalIF":4.8,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395431","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}
The spatial extent required for geographical model inputs depends on the model and input data characteristics, often differing from the user-defined area of interest (AOI). For example, a DEM input for stream network extraction should cover the upstream catchment area of the AOI. Determining proper spatial extents is crucial for both modeling accuracy and efficiency but is often complex and tedious , especially for workflows which may raise chain effect on varying spatial extents among diverse inputs. Few methods currently address this issue. This paper proposes an intelligent approach to automate spatial extent determination during geographical model workflow building, adapting to the user-defined AOI. The approach combines knowledge rules and heuristic modeling with advanced geoprocessing. Implemented in a prototype system, a case study on digital soil mapping for arbitrary-shaped AOI was conducted to validate the effectiveness of the approach, showing that it provides users with easy-to-use and accurate geographical modeling across broad applications.
{"title":"Intelligent determination of proper spatial extents for input data during geographical model workflow building","authors":"Zi-Yue Chen , Cheng-Zhi Qin , Liang-Jun Zhu , Cheng-Long Wu , Ying-Chao Ren , A-Xing Zhu","doi":"10.1016/j.envsoft.2025.106369","DOIUrl":"10.1016/j.envsoft.2025.106369","url":null,"abstract":"<div><div>The spatial extent required for geographical model inputs depends on the model and input data characteristics, often differing from the user-defined area of interest (AOI). For example, a DEM input for stream network extraction should cover the upstream catchment area of the AOI. Determining proper spatial extents is crucial for both modeling accuracy and efficiency but is often complex and tedious , especially for workflows which may raise chain effect on varying spatial extents among diverse inputs. Few methods currently address this issue. This paper proposes an intelligent approach to automate spatial extent determination during geographical model workflow building, adapting to the user-defined AOI. The approach combines knowledge rules and heuristic modeling with advanced geoprocessing. Implemented in a prototype system, a case study on digital soil mapping for arbitrary-shaped AOI was conducted to validate the effectiveness of the approach, showing that it provides users with easy-to-use and accurate geographical modeling across broad applications.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"187 ","pages":"Article 106369"},"PeriodicalIF":4.8,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444705","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号