Revisiting Iran's climate classification: A fresh perspective utilizing the köppen-geiger method

IF 2.8 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES Theoretical and Applied Climatology Pub Date : 2024-09-11 DOI:10.1007/s00704-024-05176-2
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.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
重新审视伊朗的气候分类:利用柯本-盖革法的全新视角
经验气候分类是一个利用气候要素使人类理解环境条件的过程。柯本-盖革(Köppen-Geiger,KG)是一种流行的气候分类方法,它利用长期降水和气温数据将气候分为五大类。然而,长期连续的气象数据,尤其是全国范围内的气象数据严重缺乏。本研究利用卫星图像、多线性回归模型和空间插值,在 2016 年至 2019 年伊朗全国范围内解决了这一难题。因此,本研究考察了 MODIS-LST、MODIS-NDVI、MODIS-TVDI、GPM-降水和 SRTM-DEM 四大类 14 个解释变量与地面降水和温度数据(因变量)之间的统计关系。空间插值模型(即 Krigging 和 Co-krigging)是根据气象观测站数据集直接开发的。共选择了 332 个同步站,其中 67% 用于建模,其余 33% 用于测试。精度评估采用 Kappa 统计法。总之,这项研究绘制了三幅 KG 分类图。其中包括根据回归模型和空间插值法绘制的降水量和温度图,以及根据建模中未使用的气候数据绘制的点基图。这项研究确定了干旱、暖温带和雪域三个 KG 主气候群,以及炎热沙漠、寒冷草原、寒冷沙漠、炎热草原、夏季干热的暖温带气候、夏季干热的雪域气候、夏季干热的暖温带气候和夏季干热的雪域气候八个 KG 亚群。这些地图之间的比较(kappa = 0.75)表明,与空间插值地图相比,基于回归的 KG 地图具有更高的准确性。这项研究有助于更详细地监测气象观测数据分布稀少的国家和地区的气候变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Theoretical and Applied Climatology
Theoretical and Applied Climatology 地学-气象与大气科学
CiteScore
6.00
自引率
11.80%
发文量
376
审稿时长
4.3 months
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
期刊最新文献
Climatological analysis of rainfall over Hinatuan City, Surigao del Sur in eastern Mindanao—the wettest location in the Philippines Evaluating NEX-GDDP-CMIP6 performance in complex terrain for forecasting key freezing rain factors High-resolution projections of future FWI conditions for Portugal according to CMIP6 future climate scenarios On the variability of convective available potential energy in the Mediterranean Region for the 83-year period 1940–2022; signals of climate emergency Wavelet local multiple correlation analysis of long-term AOD, LST, and NDVI time-series over different climatic zones of India
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1