Drought monitoring in Iran using the perpendicular drought indices

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Abstract

This paper aims at finding, evaluating and refining an appropriate drought estimation method for semi-arid regions, e.g., Iran using remote sensing. Recently developed methods, the Perpendicular Drought Index (PDI) and Modified Perpendicular Drought Index (MPDI), are selected as satellite based drought indices in this study. Time series of the Moderate Resolution Imaging Spectroradiometer (MODIS) images are collected over the region spanning the time interval from February 2000 to December 2005, and the PDI and MPDI are calculated. Then, these indices are evaluated against meteorological drought indices including Z-score (Z), China-Z Index (CZI) and Modified China-Z Index (MCZI) over 180 meteorological observing stations in Iran. The results show that there is a statistically significant correlation between the PDI and MPDI and regional surface dryness and drought conditions. It is further confirmed that the PDI is performing well for bare soil applications or early stages of vegetation growth, while the MPDI is best for vegetated surfaces yet effective for bare soils. Since Iran is characterized by semi-arid or arid climatic conditions, the perpendicular drought indices could be used as simple remote sensing-based drought indices in Iran and in other developing countries with similar climatic conditions.

Highlights

▸ Identifying and refining a drought index for arid, semi-arid regions, e.g., Iran. ▸ Comparison of remote sensing based drought indices with meteorological drought indices based on precipitation. ▸ Further validation of perpendicular indices in detection and measuring of regional droughts.

Introduction

Drought is a complex phenomenon whose severity is related to a specific climatic region and local energy and water balance status. In general, drought can be defined as a period of abnormally dry weather, which further results in a change in vegetation cover condition (Heim, 2002, Tucker and Choudhury, 1987). Over the last three decades, the frequency and intensity of droughts have increased (Hulme and Kelly, 1993, Mccarthy et al., 2001), and there has been a large drying trend over many parts of the world which has been suffering elevated water crisis (Dai et al., 2004, Ghulam et al., 2008). The proportion of land surface in extreme drought is projected to increase in the future, particularly in continental interiors during summer months (IPCC, 2008). The consequences would be catastrophic if this trend continues as projected by climate change scenarios e.g. by decreasing of precipitation and/or increasing of potential evapotranspiration in combination with increasing water demand of growing population (Parry et al., 2007, Shindell et al., 2006). Therefore, detecting drought onsets and ends, assessing its impact on agriculture, environment and economy and finding the connection between climate change and spatio-temporal dynamics of droughts using satellite-derived information is of particular interest to scientists and policy makers to mitigate drought effects in future climate scenarios.

In recent years, a simple method for the estimation of surface dryness, namely the perpendicular drought index (PDI), has been developed (Ghulam et al., 2007a) and further demonstrated to be effective in large-scale applications over western China using Moderate Resolution Imaging Spectroradiometer (MODIS) data (Qin et al., 2008, Rungsipanich and Chansury, 2008). Regarding inherent constraints and limitations of the PDI, Ghulam et al. (2007b) modified the perpendicular drought index. The modified perpendicular drought index (MPDI) is based on the combination of two important indicators of drought soil moisture (SM) and fraction of green vegetation (fv). The method shows potential advantages for regional surface dryness estimation as reported by Ghulam et al. (2007b), Ebrahimi et al. (2010), yet extensive testing and validation are needed over different climate and hydrologic regimes to understand the efficacy of the indices. In another case study, Shahabfar and Eitzinger (2011) applied the indices to detect crop water stress in agricultural fields mainly planted with winter wheat. Based on observed data at 13 meteorological stations over ten different agro-ecological zones in Iran, their results demonstrated that there is a statistically significant correlation between the PDI and MPDI and the water balance parameters including climatic water balance, crop water balance, crop evapotranspiration and irrigation water demand, indicating the potential of the indices in detecting crop drought conditions. However, drought is a complicated issue subject to evapotranspiration, temperature, rainfall, available soil moisture content and ground water. Like many other methods developed to estimate surface biophysical variables, remote sensing of drought is at the crux of a common problem in algorithm development—the inability to generalize across studies over different geographic regions. Numerous case studies applied to varying agro-climatic test sites are essential to generalize such new developments in both space and time.

Iran has been one of the very few regions across the globe suffering from worst droughts over the century. Present climatic and precipitation data suggest that the occurrences of several drought years in Iran are a leading to profound ecological and socioeconomic effects in this region (The OFDA/CRED International Disaster Database, 2011). Global climate change studies predict increased frequency and duration of droughts for this region (Dai, 2011). However, there is currently no systematic way to determine the onset of drought over such a large area in a timely manner. Several reports of drought monitoring based on meteorological drought indices have been presented (Bajgiran et al., 2008, McKee et al., 1993). However, these indices are derived from climatic data such as precipitation that are frequently scattered or insufficient and may not be available in time for drought monitoring and decision-making. Therefore, remote sensing techniques may provide an efficient means for drought monitoring at regional and global scales with low cost in timely manner (Unganai and Kogan, 1998).

The main objective of this contribution is comprehensive evaluation of the performance of the PDI and the MPDI in regional surface dryness monitoring for arid and semi-arid regions, to aid drought monitoring, mitigation and prediction efforts in Iran. The results should provide guidance for appropriate implementation of these indices in surface drought estimation over different climatic zones in the considered region by an in-depth analysis of their advantages and constraints.

Section snippets

Study area

With an area of more than 1.6 million square km, Iran is the sixteenth largest country in the world. It is situated in the eastern part of the northern hemisphere in southwest Asia. The elevation ranges from below sea level to more than 5000 m above sea level. The temperature varies from −30 °C to +50 °C. The annual precipitation varies from approximately 25 mm in the Central Plateau to over 2000 mm in the Caspian Coastal Plain with a national annual average of 250 mm. Central Iran is a steppe-like

Meteorological drought indices

In one of the accompanying research (Shahabfar and Eitzinger, 2009), it was showed that meteorological drought indices including Z-score (Z), China-Z Index (CZI) and Modified China-Z Index (MCZI) might be used as field based drought monitoring indices with sufficient statistical significance. In this work, we calculated these indices over 180 meteorological stations located in different climatic zones in Iran (Fig. 1), and used to validate satellite derived drought indices. Details of these

Perpendicular drought index (PDI)

The mathematical formula for PDI can be written as (Ghulam et al., 2007a)PDI=1M2+1(RRed+MRNIR)where RNir and RRed are the atmospherically corrected surface reflectances of Red and Near Infra Red (NIR) bands of remotely sensed data, respectively, and M represents the slope of the soil line in the NIR–Red spectral feature space. The PDI is a line segment parallel to the soil line and perpendicular to the normal of the soil line that passes through the coordinate origin. PDI values vary between

Precipitation data

Monthly total precipitation, recorded at 180 meteorological stations located in different climatic regions in Iran, was analyzed (Fig. 1) for this study. The collected data cover the period of February 2000 to December 2005 were quality controlled by the Iran Meteorological Organization (IRIMO). All selected 180 weather stations are standard synoptic weather station according to the Word Meteorological Organization's standards. By using these data, three drought indices including Z-score (Z),

Relationship between remote sensing indices and precipitation

The coefficient of correlation between the monthly precipitation and PDI, MPDI, VCI and EVI for the studied time frame (2000–2005) over six climatic regions of Iran is shown in Table 3.

The results from linear regression analysis indicated that all these remote sensing indices have a statistically significant relationship with precipitation data in general. However, the degree of correlation varied among different climatic regions. EVI and VCI demonstrated the weakest relationship especially in

Conclusions

Multiple case studies and applications in different eco-systems are needed to test new developments to reach more generalized conclusion. This work is an extension of a sister paper which focused on the applications of the perpendicular drought indices in limited agricultural environment (Shahabfar and Eitzinger, 2011). Aiming at finding and refining a drought index that may be suitable for arid, semi-arid regions like Iran, the paper investigated a number of meteorological and remote sensing

Acknowledgements

This work was supported by the Austrian Agency for International Cooperation in Education and Research (OeAD). The authors would like to thank Prof. Werner Schneider for his constructive comments. They would also like to thank Institute of Meteorology (BOKU-Met) and Institute of Surveying, Remote Sensing and Land Information (IVFL),which provided required software and facilities for this study. Thanks to Iran Meteorological Organization (IRIMO), which provided the meteorological data required,

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