Asiyeh Tayebi, Mohammad Hossein Mokhtari, Kaveh Deilami
{"title":"Revisiting Iran's climate classification: A fresh perspective utilizing the köppen-geiger method","authors":"Asiyeh Tayebi, Mohammad Hossein Mokhtari, Kaveh Deilami","doi":"10.1007/s00704-024-05176-2","DOIUrl":null,"url":null,"abstract":"<p>Empirical climate classification is a process that makes environmental conditions understandable to humans by using climatic elements. Köppen-Geiger (KG) is a popular climate classification method that uses long-term precipitation and temperature data to classify climate into five primary groups. However, long-term continuous meteorological data is heavily exposed to data scarcity, particularly in a national scale. This research study addresses this challenge by leveraging satellite imageries, multilinear regression models and spatial interpolation within the context of entire country of Iran between 2016 and 2019. Accordingly, this study examined statistical relationship between 14 explanatory variables under four main categories of MODIS-LST, MODIS-NDVI, MODIS-TVDI, GPM-precipitation and SRTM-DEM against ground-based precipitation and temperature data (dependent variables). The spatial interpolation model (i.e. Krigging and Co-krigging) was directly developed from weather observation station datasets. A total of 332 synoptic stations were selected, 67% of which were used in modeling and the remaining 33% in testing. Accuracy assessment was performed with Kappa statistics. Overall, this research study developed three KG classification maps. These include a map per precipitation and temperature from regression model and spatial interpolation and a point-based maps from unused climate data in modelling. This study identified three KG main climate groups of arid, warm temperate and snow and eight KG sub-groups of hot desert, cold steppe, cold desert, hot steppe, warm temperate climate with dry hot summer, snow climate with dry hot summer, warm temperate climate with dry warm summer and snow climate with dry warm summer. A comparison between those maps (kappa = 0.75) showed the higher accuracy of regression-based KG maps against spatial interpolation maps. This study contributes to a more detailed monitor of climate change across countries and regions with sparse distribution of weather observation data.</p>","PeriodicalId":22945,"journal":{"name":"Theoretical and Applied Climatology","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Theoretical and Applied Climatology","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s00704-024-05176-2","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Empirical climate classification is a process that makes environmental conditions understandable to humans by using climatic elements. Köppen-Geiger (KG) is a popular climate classification method that uses long-term precipitation and temperature data to classify climate into five primary groups. However, long-term continuous meteorological data is heavily exposed to data scarcity, particularly in a national scale. This research study addresses this challenge by leveraging satellite imageries, multilinear regression models and spatial interpolation within the context of entire country of Iran between 2016 and 2019. Accordingly, this study examined statistical relationship between 14 explanatory variables under four main categories of MODIS-LST, MODIS-NDVI, MODIS-TVDI, GPM-precipitation and SRTM-DEM against ground-based precipitation and temperature data (dependent variables). The spatial interpolation model (i.e. Krigging and Co-krigging) was directly developed from weather observation station datasets. A total of 332 synoptic stations were selected, 67% of which were used in modeling and the remaining 33% in testing. Accuracy assessment was performed with Kappa statistics. Overall, this research study developed three KG classification maps. These include a map per precipitation and temperature from regression model and spatial interpolation and a point-based maps from unused climate data in modelling. This study identified three KG main climate groups of arid, warm temperate and snow and eight KG sub-groups of hot desert, cold steppe, cold desert, hot steppe, warm temperate climate with dry hot summer, snow climate with dry hot summer, warm temperate climate with dry warm summer and snow climate with dry warm summer. A comparison between those maps (kappa = 0.75) showed the higher accuracy of regression-based KG maps against spatial interpolation maps. This study contributes to a more detailed monitor of climate change across countries and regions with sparse distribution of weather observation data.
期刊介绍:
Theoretical and Applied Climatology covers the following topics:
- climate modeling, climatic changes and climate forecasting, micro- to mesoclimate, applied meteorology as in agro- and forestmeteorology, biometeorology, building meteorology and atmospheric radiation problems as they relate to the biosphere
- effects of anthropogenic and natural aerosols or gaseous trace constituents
- hardware and software elements of meteorological measurements, including techniques of remote sensing