Urban Climate最新文献_第8页

Comparing extreme rainfall exposure to climate-focused planning efforts: A mixed methods analysis in the northeastern United States 比较极端降雨暴露与以气候为重点的规划工作：美国东北部的混合方法分析

IF 6 2区工程技术

Urban Climate Pub Date : 2025-02-01 DOI: 10.1016/j.uclim.2024.102258

Naomi Miller , Donovan Finn , Kevin A. Reed

{"title":"Comparing extreme rainfall exposure to climate-focused planning efforts: A mixed methods analysis in the northeastern United States","authors":"Naomi Miller , Donovan Finn , Kevin A. Reed","doi":"10.1016/j.uclim.2024.102258","DOIUrl":"10.1016/j.uclim.2024.102258","url":null,"abstract":"<div><div>This paper analyzes the motivation for climate planning at the local level in three states in the northeastern United States. Cataloging climate-focused plans in 461 coastal and riverine municipalities in New York, New Jersey, and Connecticut we compare these efforts to the incidence of extreme precipitation from 2000 to 2021 based on climatologies of precipitation derived from a large-scale meteorological dataset. Localities' experience with extreme precipitation and their climate planning status is also compared with a selection of socio-demographic indicators. For the region analyzed, climate-focused planning is relatively rare, but coastal communities experienced more frequent extreme precipitation events and appear to have engaged in more climate-focused planning relative to riverine communities and also are more likely to cite scientific evidence of climate change as justification for planning. Notably, some coastal communities engaged in climate-related planning despite having a higher proportion of Republican voters, who are typically more conservative and skeptical of both climate change and public policy efforts to address it. Finally, some storms, even when not climatologically extreme, nonetheless leave a lasting impression because of their impacts. These have significant implications for local efforts to address climate challenges, showing how extreme climatological events may drive government decisions and highlighting additional factors that may also amplify or impede planning for climate change.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102258"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ambient temperature-related attributable risk for emergency asthma hospitalizations and length of stay in Hong Kong 环境温度与哮喘紧急住院的归因风险及在港逗留时间

IF 6 2区工程技术

Urban Climate Pub Date : 2025-02-01 DOI: 10.1016/j.uclim.2024.102240

Hong Qiu , Shengzhi Sun , Tze-Wai Wong , Xing Qiu , Kin-Fai Ho , Eliza Lai-Yi Wong

{"title":"Ambient temperature-related attributable risk for emergency asthma hospitalizations and length of stay in Hong Kong","authors":"Hong Qiu , Shengzhi Sun , Tze-Wai Wong , Xing Qiu , Kin-Fai Ho , Eliza Lai-Yi Wong","doi":"10.1016/j.uclim.2024.102240","DOIUrl":"10.1016/j.uclim.2024.102240","url":null,"abstract":"<div><div>We aimed to examine the association of ambient temperature with asthma exacerbations and assess the temperature-attributable disease burden changes. Daily count of asthma emergency hospitalizations and corresponding length of hospital stay, daily mean temperature, relative humidity, and air pollution concentrations from 2004 to 2019 in Hong Kong were collected. Time-series quasi-Poisson model integrated with the distributed-lag-nonlinear model was used to examine the relationships of temperature with asthma hospitalizations and length of stay. Measures of the risk attributable to nonoptimal temperature were calculated to summarize the disease burden and hospital utilization for periods of 2004–2011 and 2012–2019, respectively, and compared the temporal changes. Significantly higher risks at cold/cool temperatures for both admission counts and bed-days were found. Around 19.7 % (95 % CI: 14.1–24.3 %) of hospitalization counts and 22.6 % (95 % CI: 15.5–28.4 %) of bed-days were attributed to ambient temperature, which mainly occurred on cold and cool days. Compared with the early period of 2004–2011, the cold temperature-related attributable fraction in 2012–2019 decreased from 11.0 % to 8.9 % (<em>p</em> = 0.005) for admission counts but increased from 10.8 % to 12.6 % (<em>p</em> = 0.003) for bed-days. Hospital utilization and expenditure due to the longer hospital stays during cold days would play an adverse role in the healthcare system.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102240"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Disentangling the non-linear relationships and interaction effects of urban digital transformation on carbon emission intensity 城市数字化转型对碳排放强度的非线性关系及交互效应分析

IF 6 2区工程技术

Urban Climate Pub Date : 2025-02-01 DOI: 10.1016/j.uclim.2024.102283

Wentao Wang , Shenghua Zhou , Dezhi Li , Yang Wang , Xuefan Liu

{"title":"Disentangling the non-linear relationships and interaction effects of urban digital transformation on carbon emission intensity","authors":"Wentao Wang , Shenghua Zhou , Dezhi Li , Yang Wang , Xuefan Liu","doi":"10.1016/j.uclim.2024.102283","DOIUrl":"10.1016/j.uclim.2024.102283","url":null,"abstract":"<div><div>The inexorable rise of urban digital transformation (UDT) underscores the imperative of comprehending its complex relationships with carbon emissions intensity (CEI). Existing studies primarily focus on the linear relationships between individual UDT variables and CEI, overlooking non-linear dynamics and interactive effects, which may result in incomplete estimations. To address these gaps, this study develops an interpretable machine learning (IML) model that integrates machine learning (ML) techniques and SHAP (SHapley Additive exPlanations), to uncover the non-linear relationships and interaction effects of UDT on CEI. The results reveal the following: (1) The proposed IML model achieves high accuracy in modeling the relationships between multiple UDT variables and CEI (R<sup>2</sup> = 0.932, RMSE = 0.899, MAE = 0.543, 2); (2) Non-linear relationships between all UDT variables and CEI are confirmed, and two types of threshold points are identified where variable impacts shift from negative to positive and vice versa; (3) Interactive effects among UDT variables are examined, with thresholds quantified and U-shaped and inverted U-shaped trends identified. These findings provide a foundation for policymakers and urban managers to implement strategies that simultaneously advance digital transformation and promote low-carbon development.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102283"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Impact of urban land use and anthropogenic heat on winter and summer outdoor thermal comfort in Beijing

IF 6 2区工程技术

Urban Climate Pub Date : 2025-02-01 DOI: 10.1016/j.uclim.2025.102306

Jiangkang Qian , Linlin Zhang , Uwe Schlink , Xinli Hu , Qingyan Meng , Jianfeng Gao

{"title":"Impact of urban land use and anthropogenic heat on winter and summer outdoor thermal comfort in Beijing","authors":"Jiangkang Qian , Linlin Zhang , Uwe Schlink , Xinli Hu , Qingyan Meng , Jianfeng Gao","doi":"10.1016/j.uclim.2025.102306","DOIUrl":"10.1016/j.uclim.2025.102306","url":null,"abstract":"<div><div>It is important to clarify the role of urbanization on outdoor thermal comfort (OTC) in regions threatened by both heat and cold. Quantitative studies on the impact of urbanization factors, including urban land use (LU) and anthropogenic heat (AH), on the winter thermal environment are lacking. This study conducted climate simulations using the Weather Research and Forecasting (WRF) model and optimized the relevant parameter inputs. Based on sensitivity experiments, it quantitatively analyzed the impact of LU and AH inputs on regional climate and OTC in Beijing during winter and summer. OTC was assessed using physiologically equivalent temperature (PET) and the universal thermal climate index (UTCI). The results indicate that urban LU significantly enhanced outdoor heat stress in summer, although the humidity-reducing effect of LU mitigated this impact partially. In contrast, LU caused notable temperature decreases and humidity increases in winter, which exacerbated the intensity of cold stress expressed by PET. It decreased by 0.32 °C during the daytime and 1.83 °C at night, despite lower wind speeds having an offsetting effect. The overall influence of AH was relatively subdued, consistently resulting in elevated temperatures and wind speeds, yet reduced humidity, with more pronounced effects observed during nighttime and winter. AH further intensified the heat stress induced by LU in the summer, whereas in winter it had a mitigating effect on the cold outdoor environment, but could not counteract the negative effects of LU at night.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102306"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143027306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effects of bus station density on urban air pollution: An empirical analysis based on propensity score matching

IF 6 2区工程技术

Urban Climate Pub Date : 2025-02-01 DOI: 10.1016/j.uclim.2025.102313

Xiatong Hao , Xiaojian Hu , Ke Zhang , Liang Wang , Chunwen Wang

{"title":"Effects of bus station density on urban air pollution: An empirical analysis based on propensity score matching","authors":"Xiatong Hao , Xiaojian Hu , Ke Zhang , Liang Wang , Chunwen Wang","doi":"10.1016/j.uclim.2025.102313","DOIUrl":"10.1016/j.uclim.2025.102313","url":null,"abstract":"<div><div>Previous studies on optimizing bus station layouts have primarily focused on objectives such as increasing accessibility and convenience, but overlooking the effects on urban air quality, especially the accumulation effect of multiple bus stations and the mutual influence between adjacent ones. This study contributes to both the literature and practical urban planning by investigating the effects of bus station density on traffic-related air pollutants in Tongzhou through an empirical analysis using the Propensity Score Matching (PSM) approach. The results indicate that adding bus stations in previously unserved urban areas can lower CO and NO<sub>2</sub> levels, but may increase PM10 and PM2.5 concentrations. To best reduce CO and NO<sub>2</sub> concentrations, the optimal distances between adjacent stations should be 1500 m and 1250 m, and the optimal densities are 5 stations within a 2000 m radius and 2 stations within a 1250 m radius, respectively. Besides, the highest increases in PM10 and PM2.5 concentrations occur where 3 stations are within a 2000 m radius, warranting attention. These results offer empirical support for urban planners to optimize bus station layouts while considering the improvement of urban air quality. Additionally, the methodological framework of this study has potential applications in exploring the environmental impacts of various transportation infrastructures.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102313"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial decision-making for urban flood vulnerability: A geomatics approach applied to Al-Ain City, UAE

IF 6 2区工程技术

Urban Climate Pub Date : 2025-02-01 DOI: 10.1016/j.uclim.2025.102297

Mona S. Ramadan , Ahmed Hassan Almurshidi , Siti Fatin Mohd Razali , Elnazir Ramadan , Aqil Tariq , Robert M. Bridi , Md Atiqur Rahman , Shamma Albedwawi , Meera Alshamsi , Mariam Alshamisi , Salma Alrashdi , Shamma Alnaqbi , Hind Alhammadi , Naeema Al Hosani

{"title":"Spatial decision-making for urban flood vulnerability: A geomatics approach applied to Al-Ain City, UAE","authors":"Mona S. Ramadan , Ahmed Hassan Almurshidi , Siti Fatin Mohd Razali , Elnazir Ramadan , Aqil Tariq , Robert M. Bridi , Md Atiqur Rahman , Shamma Albedwawi , Meera Alshamsi , Mariam Alshamisi , Salma Alrashdi , Shamma Alnaqbi , Hind Alhammadi , Naeema Al Hosani","doi":"10.1016/j.uclim.2025.102297","DOIUrl":"10.1016/j.uclim.2025.102297","url":null,"abstract":"<div><div>Urban flash floods present significant challenges, especially in arid regions like Al-Ain City, UAE, where rapid urbanization and climatic extremes exacerbate vulnerabilities. This study addresses the critical need for an accurate flood vulnerability assessment by integrating Geographic Information Systems (GIS), Remote Sensing (RS), the Analytical Hierarchy Process (AHP), and Multi-Criteria Decision Analysis (MCDA). By systematically evaluating key physical and social factors—such as population density, impervious surfaces, elevation, and rainfall intensity—the research identifies high-risk flood-prone areas, particularly in central districts like Al-Jimi and Al-Muwaiji. GIS spatial modeling, supported by remote sensing data, enabled the generation of a detailed vulnerability map, categorizing zones into low, medium, and high-risk categories. The findings reveal that dense urbanization, low elevation, and inadequate drainage infrastructure significantly increase vulnerability. Conversely, areas with higher elevations and natural vegetation, like Jebel Hafeet, exhibit resilience. The methodology's robustness lies in its integration of diverse data sources, weighted overlay analysis, and pairwise comparisons, ensuring precision in identifying and prioritizing mitigation strategies. This research not only provides actionable insights for urban planning and disaster risk management in Al-Ain but also underscores the potential of combining GIS, RS, AHP, and MCDA in environmental decision-making to foster climate resilience and sustainable urban development.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102297"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explainable artificial intelligence models for proposing mitigation strategies to combat urbanization impact on land surface temperature dynamics in Saudi Arabia 提出缓解战略以应对城市化对沙特阿拉伯地表温度动态影响的可解释人工智能模型

IF 6 2区工程技术

Urban Climate Pub Date : 2025-02-01 DOI: 10.1016/j.uclim.2024.102259

Javed Mallick , Saeed Alqadhi

{"title":"Explainable artificial intelligence models for proposing mitigation strategies to combat urbanization impact on land surface temperature dynamics in Saudi Arabia","authors":"Javed Mallick , Saeed Alqadhi","doi":"10.1016/j.uclim.2024.102259","DOIUrl":"10.1016/j.uclim.2024.102259","url":null,"abstract":"<div><div>Urbanization in Saudi Arabia has significantly altered land use and land cover (LULC), driving notable changes in land surface temperature (LST) dynamics. This study aims to analyze spatio-temporal LULC changes from 2002 to 2022 and their impact on LST, employing optimized machine learning models like Random Forest, Gradient Boosting, XGBoost, and LightGBM, enhanced by explainable artificial intelligence (XAI). Results show a complete loss of water bodies (from 99.25 km<sup>2</sup> to 0 km<sup>2</sup>), urban expansion (from 1344.38 km<sup>2</sup> to 1377.12 km<sup>2</sup>), and a decline in sparse vegetation (from 231,430.12 km<sup>2</sup> to 230,454.50 km<sup>2</sup>). Concurrently, LST increased, with temperatures rising from 25.08 °C–54.42 °C in 2018 to 26.08 °C–56.31 °C in 2022. The LightGBM model demonstrated the highest predictive accuracy with the lowest mean absolute error (MAE). SHAP analysis revealed that higher aerosol concentrations, air temperatures, and pollutants (CO, NO<sub>2</sub>, SO<sub>2</sub>) increase LST, while vegetation (NDVI) and water presence (NDWI) mitigate it. The study emphasizes the significant environmental impact of urbanization on LST and highlights the importance of integrated environmental management strategies, such as enhancing vegetation cover, optimizing water management, and minimizing pollution, to mitigate urban heat island effects and foster sustainable development in Saudi Arabia.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102259"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Forecasting the air pollution concentration with neural networks 用神经网络预测空气污染浓度

IF 6 2区工程技术

Urban Climate Pub Date : 2025-02-01 DOI: 10.1016/j.uclim.2024.102262

Jarosław Bernacki

引用次数: 0

Determining the key meteorological factors affecting atmospheric CO2 and CH4 using machine learning algorithms at a suburban site in China

IF 6 2区工程技术

Urban Climate Pub Date : 2025-02-01 DOI: 10.1016/j.uclim.2025.102312

Wanyu Liu , Zhenchuan Niu , Xue Feng , Weijian Zhou , Dan Liang , Guowei Wang , Lin Liu

{"title":"Determining the key meteorological factors affecting atmospheric CO2 and CH4 using machine learning algorithms at a suburban site in China","authors":"Wanyu Liu , Zhenchuan Niu , Xue Feng , Weijian Zhou , Dan Liang , Guowei Wang , Lin Liu","doi":"10.1016/j.uclim.2025.102312","DOIUrl":"10.1016/j.uclim.2025.102312","url":null,"abstract":"<div><div>Atmospheric CO<sub>2</sub> and CH<sub>4</sub> were measured from March 2023 to February 2024 at a suburban site near the northern foot of the Qinling Mountains, China, aiming to determine the key meteorological factors that influencing the seasonal CO<sub>2</sub> and CH<sub>4</sub> dynamics using machine learning (ML) algorithms. Yearly average atmospheric CO<sub>2</sub> and CH<sub>4</sub> were 446.1 ± 10.0 ppm and 2118.9 ± 50.5 ppb, respectively. Anthropogenic emissions dominated atmospheric CO<sub>2</sub> and CH<sub>4</sub> changes in winter, and the excess CO<sub>2</sub> (ΔCO<sub>2</sub>) and CH<sub>4</sub> (ΔCH<sub>4</sub>) above background levels during the heating period were mainly from combustion emissions. We selected seven ML algorithms to determine the variable importance of meteorological factors, among which eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) demonstrating the best performance and ranking consistency of the factors. Humidity and temperature of the atmosphere and soil had the higher effect (XGBoost: 80.4 %; RF: 78.1 %) on CO<sub>2</sub> in spring, while humidity was crucial in winter, with variable importance of 69.3 % for XGBoost and RF. In summer and autumn, photosynthetic photon flux density and wind speed (WS) (totaling 35.2 % ∼ 50.7 %) dominated CO<sub>2</sub> dynamics. For CH<sub>4</sub>, atmospheric and soil humidity (totaling around 40.0 %) were key factors in spring, whereas atmospheric humidity was important in winter. WS had the largest effect in summer (XGBoost: 26.3 %; RF: 33.3 %) and autumn (XGBoost: 19.8 %; RF: 28.8 %). Meteorological processes like cold front passage significantly reduced CO<sub>2</sub> and CH<sub>4</sub> concentrations during haze events. XGBoost and RF have emerged as powerful tools for determining the key meteorological factor that favour seasonal GHGs evolution.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102312"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessment of hydrological behavioural changes of Noyyal watershed in Coimbatore district, India by using SWAT model 利用SWAT模型评价印度哥印拜陀Noyyal流域水文行为变化

IF 6 2区工程技术

Urban Climate Pub Date : 2025-02-01 DOI: 10.1016/j.uclim.2025.102285

Thangavelu Arumugam , Sapna Kinattinkara , Sampathkumar Velusamy , Manoj Shanmugamoorthy , Senthilkumar Veerasamy

{"title":"Assessment of hydrological behavioural changes of Noyyal watershed in Coimbatore district, India by using SWAT model","authors":"Thangavelu Arumugam , Sapna Kinattinkara , Sampathkumar Velusamy , Manoj Shanmugamoorthy , Senthilkumar Veerasamy","doi":"10.1016/j.uclim.2025.102285","DOIUrl":"10.1016/j.uclim.2025.102285","url":null,"abstract":"<div><div>The study aims to assess the hydrological behavioural changes of the Noyyal watershed, using the SWAT model. The SWAT model was used to stimulate a total of 15 years' of information on factors including rainfall, temperature, relative humidity, wind speed, and solar radiation. It was chose to study the Uncertainty Fitting procedure (SUFI-2) as the model for sensitivity analysis, calibration, and validation. The hydrological activity models were used with DEM, LULC data, soil, and climatological data for both types of sensitivity analyses, such as one-at-a-time and global sensitivity analysis. In this study, stream flow and sediment yield were calibrated and validated on a monthly basis. Calibration began over a 12-year period from 2003 to 2014, while validation actually occurred over a four-year period from 2011 to 2014. PBIAS, NSE, PSR, and R<sup>2</sup> statistical indices show the model performs “excellently” at simulating hydrology. In comparison to the various automatic calibration techniques, SUFI-2 was observed to be very acceptable and simple to use. The hydrological behaviour of the Noyyal watershed has changed dramatically over the last two decades.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102285"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142988096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0