Spatial downscaling methods are widely used for the production of bioclimatic variables(e.g. temperature and precipitation) in studies related to species ecological niche and drainage basin management and planning. Th...Spatial downscaling methods are widely used for the production of bioclimatic variables(e.g. temperature and precipitation) in studies related to species ecological niche and drainage basin management and planning. This study applied three different statistical methods, i.e. the moving window regression(MWR), nonparametric multiplicative regression(NPMR), and generalized linear model(GLM), to downscale the annual mean temperature(Bio1) and annual precipitation(Bio12) in central Iran from coarse scale(1 km × 1 km) to fine scale(250 m ×250 m). Elevation, aspect, distance from sea and normalized difference vegetation index(NDVI) were used as covariates to create downscaled bioclimatic variables. Model assessment was performed by comparing model outcomes with observational data from weather stations. Coefficients of determination(R2), bias, and root-mean-square error(RMSE) were used to evaluate models and covariates. The elevation could effectively justify the changes in bioclimatic factors related to temperature and precipitation. Allthree models could downscale the mean annual temperature data with similar R2, RMSE, and bias values. The MWR had the best performance and highest accuracy in downscaling annual precipitation(R2=0.70; RMSE=123.44). In general, the two nonparametric models, i.e. MWR and NPMR, can be reliably used for the downscaling of bioclimatic variables which have wide applications in species distribution modeling.展开更多
The observation of demographical,economical or environmental indicators over time through maps is crucial.It enables analysing territories and helps stakeholders to take decisions.However,the understanding of Territor...The observation of demographical,economical or environmental indicators over time through maps is crucial.It enables analysing territories and helps stakeholders to take decisions.However,the understanding of Territorial Statistical Information(TSI)is compromised unless comprehensive description of both the statistical methodology used and the spatial and temporal references are given.Thus,in this paper,we stress the importance of metadata descriptions and of their quality that helps assessing data reliability.Furthermore,time-series of such TSI are paramount.They enable analysing a territory over a long period of time and likewise judging the effectiveness of reforms.In light of these observations,we present Spatio-Temporal evolutive Data Infrastructure(STeDI)an innovative Spatial Data Infrastructure(SDI)that enriches the description of a Digital Earth,providing a virtual representation of territories and of their evolution through statistics and time.STeDI aims at managing a whole dataflow of multi-dimensional,multi-scale and multi-temporal TSI,from their acquisition to their dissemination to scientists and policy-makers.The content of this SDI evolves autonomously thanks to automated processes and to a Web platform that help improving the quality of datasets uploaded by experts.Then,STeDI allows visualizing up-to-date time-series reflecting the human activities on a given territory.It helps policy-makers in their decision-making process.展开更多
文摘Spatial downscaling methods are widely used for the production of bioclimatic variables(e.g. temperature and precipitation) in studies related to species ecological niche and drainage basin management and planning. This study applied three different statistical methods, i.e. the moving window regression(MWR), nonparametric multiplicative regression(NPMR), and generalized linear model(GLM), to downscale the annual mean temperature(Bio1) and annual precipitation(Bio12) in central Iran from coarse scale(1 km × 1 km) to fine scale(250 m ×250 m). Elevation, aspect, distance from sea and normalized difference vegetation index(NDVI) were used as covariates to create downscaled bioclimatic variables. Model assessment was performed by comparing model outcomes with observational data from weather stations. Coefficients of determination(R2), bias, and root-mean-square error(RMSE) were used to evaluate models and covariates. The elevation could effectively justify the changes in bioclimatic factors related to temperature and precipitation. Allthree models could downscale the mean annual temperature data with similar R2, RMSE, and bias values. The MWR had the best performance and highest accuracy in downscaling annual precipitation(R2=0.70; RMSE=123.44). In general, the two nonparametric models, i.e. MWR and NPMR, can be reliably used for the downscaling of bioclimatic variables which have wide applications in species distribution modeling.
基金This work was supported by the French region Rhône-Alpes[grant number REGION 2015-DRH-0367].
文摘The observation of demographical,economical or environmental indicators over time through maps is crucial.It enables analysing territories and helps stakeholders to take decisions.However,the understanding of Territorial Statistical Information(TSI)is compromised unless comprehensive description of both the statistical methodology used and the spatial and temporal references are given.Thus,in this paper,we stress the importance of metadata descriptions and of their quality that helps assessing data reliability.Furthermore,time-series of such TSI are paramount.They enable analysing a territory over a long period of time and likewise judging the effectiveness of reforms.In light of these observations,we present Spatio-Temporal evolutive Data Infrastructure(STeDI)an innovative Spatial Data Infrastructure(SDI)that enriches the description of a Digital Earth,providing a virtual representation of territories and of their evolution through statistics and time.STeDI aims at managing a whole dataflow of multi-dimensional,multi-scale and multi-temporal TSI,from their acquisition to their dissemination to scientists and policy-makers.The content of this SDI evolves autonomously thanks to automated processes and to a Web platform that help improving the quality of datasets uploaded by experts.Then,STeDI allows visualizing up-to-date time-series reflecting the human activities on a given territory.It helps policy-makers in their decision-making process.
