Mapping Climate Zones of Iran Using Hybrid Interpolation Methods

Abstract

Climate plays a key role in ecosystem services. Understanding microclimate change can be a significant help in making the right decision for ecosystems and buffering the effects of global warming. Given the large distances between meteorological stations and the changes in the climate variables within short distances, such variations cannot be detected just by using observed meteorological data. This study aimed at determining the spatial structure of the mean annual temperature, the annual average precipitation, and the climate zoning of Iran using data from 3825 stations from 2002 to 2016.The multivariate regression demonstrated the dependence of these variables on longitude, latitude, and elevation. Regression-kriging indicated a decline in temperature from east to west and northwest in high-altitude areas, while most precipitation values were observed over the Caspian Sea coastline and the Zagros Mountains. Climatic zoning showed that using auxiliary variables was very effective in detecting 24 climatic classes and understating the climate diversity in Iran. Hot to very hot and arid to very arid climate classes occupy the largest part of Iran, including the southeastern and southern desert regions. According to the generated climatic map, the large climatic diversity of Iran needs accurate policymaking regarding cultivation patterns and biodiversity. Visual comparisons of climatic zones with four remotely sensed agricultural-related variables showed that using such carefully produced climatic maps would be beneficial in classifying, assessing, and interpreting the remote sensed agricultural-related variables.

Climate zones in Iran

“…Our analysis is consistent with the results ofAlizadeh-Choobari and Najafi (2018) who identified six distinct climate zones in Iran based on the analysis of precipitation data over 50 synoptic stations across Iran. The identification of the semi-arid, arid and hyperarid climates in many parts of Iran is also consistent with the results of AsadiOskouei et al (2022) who concluded that large parts of Iran are characterized by an arid climate. Based on the Köppen-Geiger climate classification, Raziei (2022) also concluded that 33.5% of Iran is covered by dry and 44.6% of it by semi-arid climate.The highest monthly and seasonal precipitation over Iran occur in March (48.6 mm) and winter (134.1 mm), respectively, while the highest monthly and seasonal temperature occur in July (29.1 C) and summer (28 C), respectively.…”

“…For example, Alizadeh‐Choobari and Najafi (2018) identified six distinct climate zones in Iran based on the analysis of precipitation data over 50 synoptic stations across Iran. More recently, Asadi Oskouei et al (2022) have analysed ground‐based measurements over many stations across Iran and concluded that large parts of Iran are characterized by a hot and arid climate. Raziei (2022) has identified many distinct types of climates in Iran and argued that the diverse climate of Iran is caused by topographic diversity, a large latitudinal range and the land‐sea contrast.…”

“…The meteorological data of a much larger number of stations across Iran have been used in some recent studies. For example, the climate classification of Iran was conducted by Nouri and Homaee (2020) based on the analysis of precipitation to evapotranspiration ratio and by Asadi Oskouei et al (2022) by analysing temperature and precipitation. However, these studies have analysed annual mean values, while the climate of different regions may largely vary from month to month.…”

Spatially Non-Stationary Relationships between Changing Environment and Water Yield Services in Watersheds of China’s Climate Transition Zones

“…Uncertainties in the InVEST model predictions are due to a range of simplified presumptions about intricate socioecological processes (based on the Budyko hydrological framework) and the climate input errors (especially annual precipitation) [72]. The climate data utilized in this investigation were obtained by interpolating actual observations, and although data quality controls were performed in the data processing, the spatial heterogeneity of meteorological variables prevented the interpolated data from presenting local microclimates [73]. In addition, our study visualizes differences in the relationship between different sub-basins and WYs, with the selected drivers varying dramatically in explanatory power across sub-basins (Figure 7), for example, not explaining well the drivers of WYs in the Beiluo Basin.…”

An inovative regional frequency analysis approach for robust extreme precipitation assessment in data-rich and climatically diverse regions

“…In the realm of hydrological research, the application of RFA stands as an indispensable tool for assessing and quantifying the occurrence of extreme precipitation events across varied geographical regions (Yamada and others 2019). In Iran, a country characterized by diverse climatic zones and topographical features, the necessity for conducting RFA of precipitation data emerges as a paramount academic pursuit (Asadi Oskouei et al 2022). This imperative stems from the intrinsic complexities inherent in Iran's hydrological regime, compounded by the sparse distribution of meteorological stations, and the heightened vulnerability of its population to the impacts of extreme weather phenomena (Maghsoudi 2020).…”