Drought monitoring, which is a crucial component of drought management, aims to provide information that enables and supports people and organizations to take actions to reduce potential drought-related impact and damage. We usually tend to focus on drought when it is occurring and to react when crises strike. However, recent experiences and developments on drought knowledge are encouraging societies to shift from the traditional crisis-based management to a risk management approach.
While some developed countries enjoy drought monitoring and early warning systems, the lack of updated and reliable meteorological data is still the major limitation in establishing such useful tools in many developing countries like Iran. On the other hand, while the existence of reliable dataset in a given country is essential for drought monitoring, the organizations responsible for collecting meteorological data in Iran delay publishing the updated data. So, the lack of updated data is one of the most important obstacles for drought monitoring in Iran.
In this research, the possibility of using NCEP/NCAR grided precipitation dataset for drought monitoring in Iran was assessed. To this end, the NCEP/NCAR monthly precipitation rate corresponding to 52 grid points distributed all over Iran in a spatial resolution of 1.9 × 1.9 degree geographical longitude by latitude was utilized for the period 1951-2005. Moreover, monthly precipitation associated with 32 leading synoptic stations for the same period was also used for comparison purposes. The SPI time series for the 6 and 12 month time scales were calculated for all 52 grid points and 32 observations distributed across Iran. This index which is based only on precipitation is widely used in drought monitoring centers and it is in fact a useful tool for capturing the climate variability associated with water shortage or surplus in different areas. Furthermore, in analyzing the spatial and temporal variability of drought across Iran, we applied the Principal Component Analysis (PCA) coupled with Varimax rotation to the SPI field of SPI-6 and SPI-12 for both NCEP/NCAR and observational datasets. Therefore, the S-mode PCA was separately applied on the SPI time series for both time scales associated with each dataset in order to identify the modes of time and spatial variability of the SPI over the country. Finally, the extreme drought of 2008 was given more attention in this analysis due to its importance considering the severity and areal extent.
Applying PCA coupled with Varimax rotation to the SPI fields corresponding to both datasets have regionalized Iran into four distinctive sub-regions considering the time variability of SPI in both time scales used here. In order to verify the results, the Varimax rotated PC score time series obtained from NCEP/NCAR dataset was compared with their counterparts obtained from observational dataset. The results show that the spatial patterns of PC loadings obtained from both datasets are noticeably in agreement and their corresponding rotated PC score time series and are significantly correlated with each other; especially from 1970 onward. Furthermore, comparison of the anomaly maps of precipitation of the cold season of 2008, obtained from both NCEP/NCAR and observational dataset showed a considerable spatial co-variability among them. As a conclusion, it can be said that the NCEP/NCAR dataset detects well the spatial and temporal pattern of precipitation deficit and surplus across Iran and can be used to complement the information provided by observations for drought monitoring in Iran.