Influence of tree cover and urban area on streamflow in the United States using multiple statistical attribution techniques

2021 ◽  
Author(s):  
Bailey Anderson ◽  
Louise Slater ◽  
Simon Dadson ◽  
Annalise Blum
Keyword(s):  
Land Cover ◽  
Urban Area ◽  
Regression Models ◽  
The United States ◽  
Additive Models ◽  
Tree Cover ◽  
Panel Regression ◽  
Distributional Regression ◽  
Panel Regression Models ◽  
Modelling Approaches

<p>There is still limited quantitative understanding of the effects of tree cover and urbanisation on streamflow at large scales, making it difficult to generalize these relationships. We use the globally consistent European Space Agency (ESA) Climate Change Initiative (CCI) Global Land Cover dataset to estimate the relationships between streamflow, calculated as high (Q0.99), median (Q0.50), and low (Q0.01) flow quantiles, and urbanization or tree cover changes in 2865 catchments between the years 1992 through 2018. We apply three statistical modelling approaches and examine the consistencies and inconsistencies between them. First, we use distributional regression models -- generalized additive models for location, scale, and shape (GAMLSS) -- at each site and assess goodness-of-fit. Model fits suggested a strong association between land cover, especially urban area, and low and median flows at sites with statistically significant trends in streamflow. We then examine the sign of the distributional regression model coefficients to determine whether the inclusion of a land cover variable in the regression models results in a relative increase or decrease in flow, regardless of the overall direction of trends in streamflow. Finally, we use fixed effects panel regression models to estimate the average effect across all sites. Panel regression results suggested that a 1% increase in urban area corresponds to between a < 1% and 2.1% increase in streamflow for all quantiles. Results for the tree cover panel regression models were not significant. We highlight the value of statistical approaches for large-sample attribution of hydrological change, while cautioning that considerable variability exists across catchments and modelling approaches.</p>

scholarly journals Relationships between Renewable Energy and the Prevalence of Morbidity in the Countries of the European Union: A Panel Regression Approach

2021 ◽  
Vol 18 (12) ◽  
pp. 6548
Author(s):  
Robert Stefko ◽  
Beata Gavurova ◽  
Miroslav Kelemen ◽  
Martin Rigelsky ◽  
Viera Ivankova
Keyword(s):  
European Union ◽  
Renewable Energy ◽  
Statistical Methods ◽  
Regression Models ◽  
Kidney Diseases ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Panel Regression ◽  
The European Union ◽  
Panel Regression Models

The main objective of the presented study was to examine the associations between the use of renewable energy sources in selected sectors (transport, electricity, heating, and cooling) and the prevalence of selected groups of diseases in the European Union, with an emphasis on the application of statistical methods considering the structure of data. The analyses included data on 27 countries of the European Union from 2010 to 2019 published in the Eurostat database and the Global Burden of Disease Study. Panel regression models (pooling model, fixed (within) effects model, random effects model) were primarily used in analytical procedures, in which a panel variable was represented by countries. In most cases, positive and significant associations between the use of renewable energy sources and the prevalence of diseases were confirmed. The results of panel regression models could be generally interpreted as meaning that renewable energy sources are associated with the prevalence of diseases such as cardiovascular diseases, diabetes and kidney diseases, digestive diseases, musculoskeletal disorders, neoplasms, sense organ diseases, and skin and subcutaneous diseases at a significance level (α) of 0.05 and lower. These findings could be explained by the awareness of the health problem and the response in the form of preference for renewable energy sources. Regarding statistical methods used for country data or for data with a specific structure, it is recommended to use the methods that take this structure into account. The absence of these methods could lead to misleading conclusions.

scholarly journals Classification of Urban Area Using Multispectral Indices for Urban Planning

2020 ◽  
Vol 12 (15) ◽  
pp. 2503 ◽  
Author(s):  
Philip Lynch ◽  
Leonhard Blesius ◽  
Ellen Hines
Keyword(s):  
Land Cover ◽  
Urban Area ◽  
Urban Areas ◽  
Unsupervised Classification ◽  
Condition Index ◽  
The United States ◽  
United States Geological Survey ◽  
Classification Schemes ◽  
Florida Department

An accelerating trend of global urbanization accompanying population growth makes frequently updated land use and land cover (LULC) maps critical. LULC maps have been widely created through the classification of remotely sensed imagery. Maps of urban areas have been both dichotomous (urban or non-urban) and entailing of discrete urban types. This study incorporated multispectral built-up indices, designed to enhance satellite imagery, for introducing new urban classification schemes. The indices examined are the new built-up index (NBI), the built-up area extraction index (BAEI), and the normalized difference concrete condition index (NDCCI). Landsat Level-2 data covering the city of Miami, FL, USA was leveraged with geographic data from the Florida Geospatial Data Library and Florida Department of Environmental Protection to develop and validate new methods of supervised and unsupervised classification of urban area. NBI was used to extract discrete urban features through object-oriented image analysis. BAEI was found to possess properties for visualizing and tracking urban development as a low-high gradient. NDCCI was composited with NBI and BAEI as the basis for a robust urban intensity classification scheme superior to that of the United States Geological Survey National Land Cover Database 2016. BAEI, implemented as a shadow index, was incorporated in a novel infill geosimulation of high-rise construction. The findings suggest that the proposed classification schemes are advantageous to the process of creating more detailed cartography in response to the increasing global demand.

scholarly journals Does ESG Disclosure Create Value to Firms? The Malaysian Case

2018 ◽  
pp. 515-521
Keyword(s):  
Social Norms ◽  
Regression Models ◽  
Financial Data ◽  
Panel Regression ◽  
Tobin’S Q ◽  
Publicly Traded ◽  
Tobin's Q ◽  
Publicly Traded Firms ◽  
Governance Disclosure ◽  
Panel Regression Models

We examine whether ESG (Environmental, Social and Governance) disclosure creates value to Malaysian firms. Based on the dataset of 37 Malaysian publicly traded firms, our results obtained from various panel regression models show that the overall ESG disclosure score and its environmental and governance pillars are positively associated with Tobin’s Q. This implies that Malaysian firms which act in accordance to social norms will be rewarded by the market. The outcomes of this research highlight the importance of non-financial data disclosure in Malaysian market.

Projecting future streamflow under changing climate and urban land cover across the UK

2021 ◽  
Author(s):  
Shasha Han ◽  
Louise Slater
Keyword(s):  
Land Cover ◽  
River Flow ◽  
Urban Land ◽  
Additive Models ◽  
Resources Management ◽  
Urban Land Cover ◽  
Streamflow Response ◽  
Land Use Development ◽  
The Uk ◽  
Distributional Regression

<p>Changes in precipitation and land cover are important drivers of change in catchment streamflow, yet quantifying their influence remains a major challenge. This work aims to understand how streamflow may evolve under different scenarios of future precipitation and urbanization across the UK. A collection of catchments from the National River Flow Archive (NRFA) that have experienced significant changes in flows and urbanization were selected. Both historical observations and future projections of precipitation and urban land cover were extracted within each study catchment, for different emissions and socio-economic scenarios including Representative Concentration Pathways (RCPs) and Shared Socio-Economic Pathways (SSPs). Distributional regression models – Generalised Additive Models for Location Scale and Shape (GAMLSS) – were developed using historical precipitation, land cover, and streamflow, and employed to project future streamflow using bias-corrected projections of precipitation and land cover. The results improve our understanding of streamflow response to climate and land cover changes and provide further insights for water resources management and land use development.</p>

League Ranking Mobility Affects Attendance: Evidence From European Soccer Leagues

2020 ◽  
Vol 21 (8) ◽  
pp. 808-828
Author(s):  
András Gyimesi
Keyword(s):  
Regression Models ◽  
Panel Regression ◽  
Competitive Balance ◽  
Uncertainty Of Outcome ◽  
Panel Regression Models

Ranking mobility belongs to the indicators of dynamic long-term competitive balance, as it is based on season to season changes in league rankings. If the uncertainty of outcome hypothesis holds at the league level, ranking mobility might increase demand for league games. This assumption is tested by using panel regression models on data of 19 European domestic soccer leagues. Ranking mobility is found to significantly affect average stadium attendance per game, particularly if only the top 5 ranking positions are considered. Results suggest that the closeness of competition across seasons is more important for the fans than within a season.

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