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Article

Impacts of Drought Severity and Frequency on Natural Vegetation Across Iran

by
Atefeh Torkaman Pary
1,*,
Pejvak Rastgoo
1,
Christian Opp
2,
Dirk Zeuss
1 and
Temesgen Alemayehu Abera
1
1
Department of Environmental Informatics, Faculty of Geography, Philipps-Universität Marburg, Deutschhausstraße 12, 35032 Marburg, Germany
2
Department of Soil Geography and Hydrogeography, Faculty of Geography, Philipps-Universität Marburg, Deutschhausstraße 10, 35032 Marburg, Germany
*
Author to whom correspondence should be addressed.
Water 2024, 16(22), 3334; https://doi.org/10.3390/w16223334
Submission received: 19 September 2024 / Revised: 7 November 2024 / Accepted: 14 November 2024 / Published: 20 November 2024
Browse Figures
Figure 1
<p>Study area (Iran). Map showing (<b>a</b>) location of the study area(background Image is from Google Earth 2024 and the boundary data is from the Global Administrative Areas (GADM), <a href="http://www.gadm.org" target="_blank">http://www.gadm.org</a>, accessed on 12 May 2024), (<b>b</b>) topographic elevation of the study area (30 m digital elevation model data from United States Geological Survey (USGS)) [<a href="#B40-water-16-03334" class="html-bibr">40</a>], (<b>c</b>) mean annual precipitation between 2001 and 2022 based on precipitation data from Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) [<a href="#B41-water-16-03334" class="html-bibr">41</a>], and (<b>d</b>) land cover map of the study area for the year 2021 adopted from the European Space Agency (ESA) [<a href="#B42-water-16-03334" class="html-bibr">42</a>].</p> "> Figure 2
<p>Workflow of methodology for evaluating drought characteristics and impact on natural vegetation. SPEI was used during the growing season (October to April).</p> "> Figure 3
<p>Spatial distribution of drought severity and frequency from 2001 to 2022 in Iran: (<b>a</b>) severity of drought events for all months; (<b>b</b>) severity of drought events in the growing season; (<b>c</b>) frequency of drought events for all months; and (<b>d</b>) frequency of drought events in the growing season.</p> "> Figure 4
<p>Trends in drought severity per year in Iran from 2001 to 2022 based on the 1-month SPEI during the growing season (October to April). Panel (<b>a</b>) shows the magnitude and direction of the drought severity trend per year and panel (<b>b</b>) shows drought severity classes based on the direction of change (i.e., increasing, decreasing, or stable). Statistically insignificant (<span class="html-italic">p</span> &gt; 0.05) changes (stable areas) are shaded in gray.</p> "> Figure 5
<p>Spatial distribution of drought vegetation relationships across Iran. Panel (<b>a</b>) shows the correlation coefficient between NDVI anomaly and SPEI for five time scales (1, 3, 6, 9, and 12 months) without time lag (red: negative correlation, green: positive correlation), panel (<b>b</b>) shows a 1-month lag, and panel (<b>c</b>) shows a 2-month lag. Significant correlations (<span class="html-italic">p</span> &lt; 0.05) during the growing season (October to April) across these time scales and lags are displayed, while non-significant correlations are masked. The dash vertical line shows the mean value.</p> ">
Versions Notes

Abstract

:
Drought recurrence is increasing in arid and semi-arid regions, and its effects are becoming more complicated due to climate change. Despite the increasing frequency of drought events, the sensitivity of natural vegetation to different levels of drought frequency and severity is not fully understood. Here, we aim to characterize the regional spatio-temporal patterns of drought frequency and severity and the response of vegetation across Iran at a high spatial resolution (5 km × 5 km). We examined the responses of three natural vegetation types (forest, grassland, and shrubland) to drought conditions across Iran using the Normalized Difference Vegetation Index (NDVI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at different time scales and temporal lags from 2001 to 2022. Our results showed that drought severity increased in 15%, decreased in 1%, and remained stable in 84% of the study area. The severity and frequency of drought showed spatial patterns across Iran (i.e., increased from northwest to southeast and central Iran). The correlation between the monthly NDVI anomaly and SPEI varied across vegetation types, SPEI accumulation period (SPEI-1-3-6-9-12), and temporal lags, revealing different sensitivities of vegetation to drought in Iran. All natural vegetation types showed the strongest responses two months after drought events. Forests, mostly located in northern Iran, showed lower sensitivity to drought onset and responded slower to drought severity than other vegetation classes (i.e., grasslands and shrublands). These findings highlight the importance of analyzing the sensitivity of natural vegetation at different levels of drought severity and frequency for land use planning and mitigation efforts.

1. Introduction

Climate change poses a significant challenge to global water availability and resource management, with rising temperatures and increased drought risk affecting many countries [1]. Droughts, characterized by prolonged periods of reduced rainfall, are particularly threatening to agriculture and result from complex interactions between climatic factors and surface conditions [2,3,4]. Long-term droughts, characterized by abnormally low rainfall and water scarcity, have significant environmental impacts [5,6,7,8], affecting ecosystems, agriculture, water resources, and human livelihoods [9].
Various indices that indicate drought severity and health have been developed to comprehensively assess the impact of drought on vegetation [10]. The Standard Precipitation Evapotranspiration Index (SPEI) is commonly used to characterize droughts [11]. By integrating satellite-based observations with ground-based meteorological data into the SPEI, researchers can accurately quantify the spatio-temporal dynamics of drought conditions [12]. At the same time, remote sensing techniques provide unprecedented opportunities to monitor vegetation dynamics in response to drought stress [13,14]. In particular, due to their high temporal frequency, high spatial resolution, and wider spatial coverage, remote sensing technologies allow researchers to monitor changes in vegetation dynamics over large spatial scales and extended time periods, providing critical insights into the long-term effects of drought on natural ecosystems [15,16]. Vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) are often used as proxies for monitoring vegetation health and vigor [17,18]. The NDVI is widely used in drought studies to capture vegetation greenness, reflecting plant health and photosynthetic activity [19]. It effectively detects vegetation stress, making it a key tool for monitoring drought impacts on ecosystems [20]. The NDVI’s sensitivity to drought-induced declines in vegetation, its ease of integration with indices like the SPEI, and its broad spatial–temporal coverage from satellites like the Moderate Resolution Imaging Spectroradiometer (MODIS) enable reliable, large-scale vegetation monitoring over time [21,22]. Time series analyses of these indices can detect changes in vegetation greenness, biomass, and productivity during drought events [10,23]. By examining temporal trends and spatial patterns in vegetation dynamics, researchers can elucidate the mechanisms driving vegetation responses to drought [24], including physiological adaptations [25] and species-specific tolerances [25,26]. Combining information on drought characteristics with vegetation responses is a prerequisite for a holistic assessment of ecosystem resilience to drought stress [22].
In arid and semi-arid climates, the interplay between drought severity, duration, and frequency profoundly affects the health and composition of natural vegetation [27]. Iran has experienced recurrent droughts in recent decades [28], the effects of which have been exacerbated by climate change, as demonstrated by numerous studies [29,30,31]. However, a critical knowledge gap exists in understanding the detailed characteristics (i.e., severity, duration, and frequency) of these drought events, how drought severity changes over time, and the response of natural vegetation (forest, shrubland, and grassland) to drought across Iran. Previous studies in Iran have mainly focused on the effects of drought on agricultural productivity and yield [32,33,34], neglecting the response of natural vegetation to different drought characteristics [35,36,37]. Given the importance of natural vegetation in ecosystems and its sensitivity to climate change, a deeper understanding of its response to drought is essential. This knowledge is crucial for adaptation planning, including water conservation, sustainable land management, drought-resistant species, land restoration, and early warning systems in semi-arid and arid regions where climate change will exacerbate drought conditions.
The main objective of this study is to evaluate the spatio-temporal response of natural vegetation to different levels of drought severity and frequency during the last two decades (2001–2022) in Iran. We used SPEI and NDVI time series data from 2001 to 2022 to characterize drought events and vegetation responses. The specific objectives are to (1) evaluate the spatio-temporal characteristics of drought severity and frequency across Iran and (2) assess the sensitivity of natural vegetation to different levels of drought severity.

2. Materials and Methods

2.1. Study Area

The study area is located in Iran, between 44 and 64° E and 25 and 40° N in Southwest Asia, and covers an area of approximately 1,648,195 km2 (Figure 1a). It is bordered to the north by the Caspian Sea and to the south by the Persian Gulf and the Oman Sea. The Zagros Mountains, reaching elevations of about 3500 m above sea level (asl), border the plateaus and basins of Central Iran to Southwest Iran, stretching northwest–southeast, while the Alborz Mountains, with peaks exceeding 5610 m asl, stretching west–east across North Iran, border the Caspian lowland (Figure 1b). Iran’s geographic diversity, shaped by these mountain ranges and seas, contributes to its diverse climate and ecosystems. The north experiences humid conditions, while the south faces hyper-arid conditions, according to the modified De Marton climate index [38]. The highest point is Damavand Peak at 5610 m asl in the Alborz Mountains and the lowest is on the southern side of the Caspian Sea, at 28 m below sea level (Figure 1b). Temperature extremes range from −30 °C in the northwestern region to 50 °C in the desert areas [39]. Precipitation in Iran occurs mainly between November and the end of April. Most of the precipitation occurs in the mountainous regions and along the coasts of the Caspian Sea. The average annual precipitation on the northern slopes of the Alborz range reaches more than 2000 mm. In contrast, the central areas receive less than 100 mm annually (Figure 1c). The average annual rainfall in Iran is 250 mm. Iran’s major land cover types include barren land, grassland, cropland, forest, and shrubland. Bare land and sparse vegetation classes cover > 60%, followed by grassland, cropland, and forest, mainly found in the northern part of the study area. Shrubland covers a smaller area in southwestern Iran (Figure 1d).

2.2. Data

2.2.1. SPEI Data

The 2001–2022 Standardized Precipitation Evaporation (SPEI) raster dataset at monthly time steps was obtained from the global high-resolution (5 km) drought datasets [43] and downloaded from the Natural Environment Research Council Centre for Environmental Data Analysis UK at https://catalogue.ceda.ac.uk/uuid/ac43da11867243a1bb414e1637802dec (accessed on 27 February 2024). SPEI values ranged from 1 to 48 months. They were derived from monthly precipitation data from CHIRPS (version 2) and Multi-Source Weighted-Ensemble Precipitation (MSWEP, version 2.8). Potential evapotranspiration (PET) data were sourced from the Global Land Evaporation Amsterdam Model (GLEAM, version 3.7a), including hourly potential evapotranspiration (hPET) from 1981 to 2022. The SPEI dataset has been reported to agree with observation-based estimates of SPEI and root zone soil moisture and vegetation health indices [43]. This study used the 1-, 3-, 6-, 9-, and 12-month SPEI to assess drought conditions at different time scales.

2.2.2. In Situ Rainfall Data

We used monthly rainfall data from synoptic stations across Iran from 2001 to 2022, obtained from the Iranian Meteorological Organization. A quality control process was implemented to ensure data accuracy, focusing on minimizing missing data over 22 years. Stations with more than 10% of their daily data missing were excluded from the analysis. The in situ precipitation data were used to validate the reliability of the high-spatial-resolution (5 km) SPEI data from Iran, which showed moderate performance compared to station data (for details, see Supplementary Figure S1).

2.2.3. NDVI Data

The MODIS NDVI was obtained from the MOD13Q1 product [44,45]. These 16-day composite NDVI data at a 250 m spatial resolution were accessed using Google Earth Engine (GEE) [46]. Monthly NDVI values were consolidated using the maximum value compositing method to ensure that the highest NDVI value within each month was used, minimizing the effects of cloud cover and other anomalies. This approach provides a more accurate representation of vegetation health and productivity over time, essential for analyzing drought’s effects on natural vegetation. To match the spatial resolution of the NDVI with SPEI data, it was resampled to a 5 km resolution.

2.2.4. Land Cover Data

Land cover data (ESA WorldCover 10 m 2021 v200) were obtained from the European Space Agency (ESA), which provides global land cover products [42]. The WorldCover product is based on a fully convolutional neural network. The ESA developed and validated it in near-real time using Sentinel-1 and Sentinel-2 data at a 10 m resolution for 2020 and 2021 [42]. This product consists of 11 land cover classes aligned with the United Nations Food and Agricultural Organization (UN-FAO) Land Cover Classification System (Figure 1d), and the data are also available on the GEE platform, from where they were downloaded for further analysis.

2.3. Methods

This study presents a framework for analyzing the effects of drought on natural vegetation (grassland, shrubland, and forest). The framework consists of two main steps: (1) identify drought severity, frequency, and trend by applying SPEI data at different accumulation periods (i.e., SPEI 1, 3, 6, 9, and 12) and (2) evaluate the response of natural vegetation to drought by analyzing changes in SPEI and NDVI anomalies at different time scales and time lags during drought events [47]. The methodological workflow (Figure 2) is detailed in the following sections. All analyses were conducted using R version 4.3.2, developed by the R Foundation for Statistical Computing, based in Vienna, Austria, and map composition was performed using QGIS version 3.32.3, developed by the international QGIS Development Team, with contributors worldwide.

2.3.1. Drought Identification and Characterization

We used the SPEI to identify drought. Different drought accumulation periods (1, 3, 6, 9, and 12 months) were used during the growing season (October to April). Drought severity (DS) was the cumulative absolute value of the SPEI from the month when the drought began (included) to the month the drought ended (excluded) [48]. A drought event was initiated when the SPEI remained negative and fell to −1.0 or below. The drought event ended when the SPEI became positive. We categorized drought severity into three levels based on the World Meteorological Organization (WMO) SPEI classification [49]. These categories were moderate (SPEI, −1.5 to −1), severe (SPEI, −2 to −1.5), and extreme drought (SPEI, −2 and less) [49].
Drought duration (DD) was defined as the period during which an area or region experienced below-normal precipitation [50]. Once a drought event was identified, the start and end dates were used to determine DD. It could be equal to the number of consecutive times between the beginning and the end of the drought, such as a seasonal, monthly, or annual scale. DD in months was calculated by taking the difference between the end and start months and adding one to include the start month in the duration count (Equation (1)).
Drought frequency (DF) was the number of drought events during the study period (22 years, 2001–2022, Equation (3)).
D r o u g h t   D u r a t i o n = E n d   o f   d r o u g h t   t i m e − B e g i n n i n g   o f   d r o u g h t   t i m e
D r o u g h t   S e v e r i t y = S u m   o f   a b s o l u t e   v a l u e   o f   S P E I ( ≤ − 1 ) d u r a t i o n
D r o u g h t   F r e q u e n c y = N u m b e r   o f   d r o u g h t   e v e n t s T o t a l   n u m b e r   o f   m o n t h s
The trend in drought severity from 2001 to 2022 was analyzed for each pixel across Iran using the Mann–Kendall nonparametric test to assess the relationship between drought severity and time [51,52,53]. Statistically significant and non-significant pixel (p > 0.05) changes were identified and mapped separately.

2.3.2. NDVI Anomaly

The NDVI anomaly is a widely used method for detecting and mapping vegetation response to drought [54,55,56]. Vegetation changes can be monitored by analyzing NDVI anomalies over time and across regions. Positive anomalies indicate healthier than normal vegetation, while negative anomalies indicate stressed vegetation, making them important indicators for detecting the effects of drought on vegetation [57,58].
To derive the NDVI anomaly [59], we first calculated the mean NDVI ( N D V I m e a n ) during the growing season, which runs from October to April in our study area [60,61], using the NDVI time series from 2001 to 2022. The formula used for this calculation is represented by Equation (4):
N D V I m e a n = N D V I i , 1 + N D V I i , 2 + … … , N D V I i , n n
where N D V I m e a n is the mean NDVI of month i ; n is the year (2001–2022).
After the calculation of N D V I m e a n for the growing season, the standard deviation N D V I S D was calculated using the following expression (Equation (5)):
N D V I S D = ∑ i = 1 n N D V I i − N D V I m e a n 2 n
In this context, n represents the number of years (limited to 22 years in our analysis).
The NDVI anomaly was calculated using Equation (6) for each grid cell within the study area:
N D V I a n o m a l y = N D V I i − N D V I m e a n N D V I S D
where N D V I a n o m a l y is the monthly NDVI anomaly for each year in the growing season.

2.3.3. Correlation Between NDVI Anomaly and SPEI

To investigate the response of natural vegetation to drought events, we performed a spatial correlation analysis between monthly NDVI anomalies and SPEI time series in the “corLocal” function from the raster package in R, applying different time lags to the NDVI anomalies [62,63], which showed the normal density distribution (Figure S3). We utilized an ordinary correlation analysis to evaluate the relationship between NDVI anomalies and cumulative SPEI, which involved calculating the Pearson correlation coefficients by measuring the linear association between two variables (monthly NDVI anomaly and SPEI time series) to determine the effect of drought on natural vegetation [64]. To evaluate the cumulative effect of drought on NDVI, the SPEI time scale corresponding to the maximum correlation was obtained by the Pearson correlation that was tested at different time scales (1, 3, 6, 9, and 12 months) for each pixel from 2001 to 2022. In cases where time lags were applied, we shifted the NDVI anomaly time series accordingly before performing the correlation analysis. This allowed us to assess the influence of different lag periods on the relationship between NDVI anomaly and SPEI. Through this process, we identified the combination of time scales and lags that resulted in the highest level of statistical significance. This allowed us to determine the most influential temporal factor (time lag) in the relationship between vegetation class and drought conditions (Equation (7)).
r x , y = ∑ i = 1 n x i − x ¯ y i − y ¯ ∑ i = 1 n x i − x ¯ 2 ∑ i = 1 n y i − y ¯ 2
In the provided equation, r x , y represents the correlation coefficient, n illustrates the length of the time series, and i represents the year number of the study period (1–22). x i and y i correspond to the NDVI anomaly and the SPEI value in year i , respectively; x ¯ and y ¯ represent the mean NDVI and the mean SPEI data from 2001 to 2022. The correlation coefficient was interpreted as follows: (1) if r x , y was greater than zero, the relationship between the NDVI anomaly and the SPEI value was positive; (2) if r x , y was less than zero, the relationship between the NDVI anomaly and the SPEI value was negative; and (3) a value of zero r x , y indicated that there was no relationship between the two variables [65].

3. Results

3.1. Spatial Distribution of Drought Severity and Frequency

The drought severity and frequency analysis throughout Iran between 2001 and 2022 indicated considerable variations across different regions (Figure 3). At the monthly time scale, drought severity ranged from 9 to 85 (i.e., the sum of the absolute value of the SPEI from the month when the drought began to the month the drought ended) during the growing period (Figure 3b), while, during the whole year, the values varied between 9 and 149 (Figure 3a). The northern regions near the Caspian Sea, characterized by dense tree cover, had drought severity values significantly lower than 50 during the study period. Coastal areas in southwestern Iran along the Oman Sea and the Persian Gulf also showed reduced drought severity. Conversely, the central and western parts of Iran, covered by sparse vegetation and bare land, were most affected by severe droughts and often experienced intense and prolonged droughts.
The drought frequency ranged from 5 to 142 months from 2001 to 2022 (Figure 3c). During the growing season, the maximum frequency recorded was 80 months (Figure 3d). In terms of drought frequency, the central and western regions of Iran were particularly vulnerable (Figure 3c). Conversely, the northern regions of Iran and its mountainous parts had a lower drought frequency.
Overall, the severity and frequency of droughts followed a similar pattern across Iran. Regions with lower rainfall and higher evaporation tended to experience the most severe and frequent droughts.

3.2. Trends in Drought Severity in Iran

To understand the temporal patterns of drought severity in Iran, a trend was calculated using the Mann–Kendall test of the annual average drought severity in the growing season between 2001 and 2022 (Figure 4). Drought severity increased in 15%, decreased in 1%, and remained stable in 84% of the study area. Thus, although most parts of Iran were stable, about one-third of the country experienced an increase in drought severity from 2001 to 2022.
In northern Iran, which is predominantly humid (Figure 4), drought severity showed a mostly stable trend, with some exceptions of a decreasing trend, while dry areas in central, southern, and western Iran predominantly showed an increase in drought severity.

3.3. Trends in Drought Severity in Natural Vegetation Classes

Trends in drought severity across vegetation classes are presented in Table 1. On average, 18% of grasslands experienced an increase in drought severity and 10% showed a decrease, with a net increase in drought severity of 8%. Around 17% of shrublands experienced an average increase in drought severity and a 9% decrease. Hence, when considering both the increase and decrease in drought severity, the net change for shrublands was an 8% increase.
Forests had an average increase in drought severity of 17%, similar to shrublands, but experienced a slightly lower average decrease of 8%, resulting in a net increase of 11%.

3.4. Drought Vegetation Relationships Across Iran

The spatial distribution of Spearman’s correlation between the NDVI anomaly and SPEI across Iran is provided in Figure 5. The strength of the correlations varied depending on the chosen time scales and lags, showing significant spatial heterogeneity. In particular, throughout Iran, the correlation between SPEI and NDVI was lowest when evaluated without a time lag, while it increased significantly after 1 and 2 months, as shown in Figure 5 and Table 2. Specifically, based on the 3-month SPEI, vegetation showed increased sensitivity to drought after a 2-month lag, as evidenced by mean correlation values of 0.73 and 0.74, respectively (Figure 5, Table 2).

3.5. Response of Each Vegetation Class to Drought Events

The responses of each natural vegetation class (tree cover, shrubland, and grassland) to drought over different time scales and lags were analyzed separately and are presented in Table 3.
The forest’s response to lagged drought events showed different patterns than those of other vegetation classes (Figure S2, Table 3). This means that in the forest, the correlation increased with the time lag (i.e., from 0.77 for a 1-month lag to 0.80 for a 2-month lag) and from a 1-month SPEI (0.80) to a 6-month SPEI (0.89). While shrublands and grasslands showed a higher correlation already with the 1-month lag and 3-month SPEI, forests responded to longer drought events.

4. Discussion

The findings of this study provide new insights into the spatial and temporal patterns of drought severity and frequency across Iran and how these patterns affect different types of natural vegetation. The results confirm the increasing vulnerability of Iran’s ecosystems to drought, especially in the central and southeastern regions, where drought severity and frequency have intensified between 2001 and 2022. This increase in drought stress has important implications for the country’s biodiversity, ecosystem services, and land management.

4.1. Spatial Distribution of Drought Characteristics

Our results revealed that drought severity and frequency are highly variable across Iran, with the most severe and frequent droughts occurring in the central and southeastern parts of the country. This pattern is consistent with Iran’s topographical and climatic conditions, where low precipitation and high evaporation rates create conditions conducive to prolonged droughts. Previous studies have also highlighted this region as being prone to more intense droughts due to erratic rainfall and limited water availability [66,67].
However, compared to earlier research, our study shows a more pronounced increase in drought frequency in these regions, suggesting that the impacts of climate change are accelerating and exacerbating these drought conditions. The northern regions, particularly near the Caspian Sea, experienced less severe droughts due to the humid climate and higher precipitation levels. This finding aligns with studies showing the mitigating effects of coastal and mountainous regions on drought severity [68,69,70,71].

4.2. Vegetation Response to Drought

One of the key contributions of this study is the assessment of how different types of natural vegetation respond to varying levels of drought severity and frequency. Grasslands and shrublands were highly sensitive to drought, with significant decreases in vegetation health observed shortly after drought onset. This heightened sensitivity is likely due to the shallow root systems of these vegetation types, which are more dependent on surface water availability [72,73,74]. As a result, they experience faster and more pronounced declines in NDVI under drought conditions. This result is consistent with those from other studies conducted in semi-arid environments, which have shown that grasslands and shrublands are particularly vulnerable to short-term fluctuations in water availability [75,76].
Forests, in contrast, exhibited a delayed response to drought, with the strongest correlation between NDVI and SPEI occurring with a two-month lag. This suggests that forest ecosystems have greater resilience to short-term droughts, likely due to their deeper root systems and greater capacity to access subsurface water reserves. However, our results also show that forests are not immune to drought, particularly prolonged drought conditions. The increased sensitivity of forests to longer-term drought (as shown by the stronger correlation with the 6-month SPEI) highlights the risk that even these relatively resilient ecosystems could face severe stress in the future as droughts become more frequent and prolonged [66,77].

4.3. Implications for Ecosystem Management and Drought Mitigation

The differential response of vegetation types to drought has important land use and ecosystem management implications. The heightened vulnerability of grasslands and shrublands suggests that these areas should be prioritized for drought mitigation efforts, such as promoting drought-resistant species, implementing improved water conservation practices, and establishing early warning systems for farmers and land managers. In contrast, forests, which are currently more resilient, may require longer-term monitoring to detect early signs of drought-induced stress, particularly during prolonged drought events.
Moreover, the spatial heterogeneity of drought severity and frequency across the country underscores the need for regionally tailored management strategies. More aggressive interventions may be needed in areas such as the central and southeastern regions, where drought severity is increasing, including afforestation, reforestation, and soil conservation programs to reduce land degradation and improve water retention.

4.4. Limitations and Future Directions

While this study comprehensively analyzes drought impacts on natural vegetation in Iran, several limitations should be acknowledged. First, the use of the NDVI as a proxy for vegetation health, while widely accepted, may not fully capture the complexity of vegetation responses, particularly in areas with sparse vegetation cover. Additionally, the reliance on remotely sensed data and the SPEI at a 5 km resolution means that small-scale, localized vegetation responses to droughts may be missed. In addition, the vegetation feedback on the drought was not included in our study. The future availability of dense networks of in situ observations and improvement in higher-resolution satellite data would help refine these findings in future studies.
Another limitation is the focus on natural vegetation types, excluding the potential impact of land management practices, such as agriculture and irrigation, which could also influence drought patterns and vegetation responses. Incorporating human-induced factors into the analysis could provide a more holistic understanding of the interaction between climate change, drought, and land use. Lastly, future research could explore the physiological mechanisms underlying vegetation resilience to drought, such as species-specific drought tolerance and water-use efficiency, to inform conservation and land management strategies.

5. Conclusions

This study evaluated the spatio-temporal effects of drought on three natural vegetation (forest, grassland, and shrubland) types across Iran from 2001 to 2022, using SPEI and NDVI data to capture drought patterns and vegetation responses. Our findings revealed significant spatial differences, with drought severity and frequency increasing from north to south and west to east. With its humid climate and dense forests, northern Iran experienced less severe droughts, while the arid central region with sparse vegetation was hit hardest. Grasslands were the most sensitive to drought, while forests exhibited greater resilience. These results highlight the critical need for comprehensive drought monitoring and adaptive management strategies to protect Iran’s vulnerable ecosystems. The varying responses of vegetation to drought severity and frequency emphasize the importance of developing region-specific management plans that account for the country’s diverse climatic and ecological conditions. As climate change continues to alter drought patterns, proactive measures are essential to mitigate the negative impacts on biodiversity, ecosystem services, and human livelihoods.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/w16223334/s1: Figure S1. Validation of satellite SPEI data against SPI calculated from the in situ weather station at different time scales (3, 6, 9, and 12 months) in Iran from 1990 to 2022. Figure S2. Probability density distribution graph showing the correlation between SPEI (1, 3, 6, 9, and 12 months) and NDVI anomaly at different time lags (i.e., no lag, 1-month lag, and 2-month lag) for (a) forest, (b) shrubland, and (c) grassland. Figure S3. Density distribution graph of NDVI anomaly during the study period (2001–2022) in Iran. Figure S4. Partial autocorrelation and autocorrelation graphs of NDVI anomaly time series across different land cover classes: (a) forest, (b) shrubland, and (c) grassland.

Author Contributions

Conceptualization, A.T.P., D.Z. and T.A.A.; methodology, A.T.P., T.A.A. and D.Z.; software, A.T.P.; validation, A.T.P.; formal analysis, A.T.P.; writing—original draft preparation, A.T.P.; writing—review and editing, D.Z., T.A.A., C.O. and P.R.; visualization, A.T.P.; supervision, D.Z., C.O. and T.A.A. All authors have read and agreed to the published version of the manuscript.

Funding

Open Access funding provided by the Open Access Publishing Fund of Philipps-Universität Marburg.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors upon request.

Acknowledgments

We acknowledge the publication fund from the University of Marburg.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Study area (Iran). Map showing (a) location of the study area(background Image is from Google Earth 2024 and the boundary data is from the Global Administrative Areas (GADM), http://www.gadm.org, accessed on 12 May 2024), (b) topographic elevation of the study area (30 m digital elevation model data from United States Geological Survey (USGS)) [40], (c) mean annual precipitation between 2001 and 2022 based on precipitation data from Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) [41], and (d) land cover map of the study area for the year 2021 adopted from the European Space Agency (ESA) [42].
Figure 1. Study area (Iran). Map showing (a) location of the study area(background Image is from Google Earth 2024 and the boundary data is from the Global Administrative Areas (GADM), http://www.gadm.org, accessed on 12 May 2024), (b) topographic elevation of the study area (30 m digital elevation model data from United States Geological Survey (USGS)) [40], (c) mean annual precipitation between 2001 and 2022 based on precipitation data from Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) [41], and (d) land cover map of the study area for the year 2021 adopted from the European Space Agency (ESA) [42].
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Figure 2. Workflow of methodology for evaluating drought characteristics and impact on natural vegetation. SPEI was used during the growing season (October to April).
Figure 2. Workflow of methodology for evaluating drought characteristics and impact on natural vegetation. SPEI was used during the growing season (October to April).
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Figure 3. Spatial distribution of drought severity and frequency from 2001 to 2022 in Iran: (a) severity of drought events for all months; (b) severity of drought events in the growing season; (c) frequency of drought events for all months; and (d) frequency of drought events in the growing season.
Figure 3. Spatial distribution of drought severity and frequency from 2001 to 2022 in Iran: (a) severity of drought events for all months; (b) severity of drought events in the growing season; (c) frequency of drought events for all months; and (d) frequency of drought events in the growing season.
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Figure 4. Trends in drought severity per year in Iran from 2001 to 2022 based on the 1-month SPEI during the growing season (October to April). Panel (a) shows the magnitude and direction of the drought severity trend per year and panel (b) shows drought severity classes based on the direction of change (i.e., increasing, decreasing, or stable). Statistically insignificant (p > 0.05) changes (stable areas) are shaded in gray.
Figure 4. Trends in drought severity per year in Iran from 2001 to 2022 based on the 1-month SPEI during the growing season (October to April). Panel (a) shows the magnitude and direction of the drought severity trend per year and panel (b) shows drought severity classes based on the direction of change (i.e., increasing, decreasing, or stable). Statistically insignificant (p > 0.05) changes (stable areas) are shaded in gray.
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Figure 5. Spatial distribution of drought vegetation relationships across Iran. Panel (a) shows the correlation coefficient between NDVI anomaly and SPEI for five time scales (1, 3, 6, 9, and 12 months) without time lag (red: negative correlation, green: positive correlation), panel (b) shows a 1-month lag, and panel (c) shows a 2-month lag. Significant correlations (p < 0.05) during the growing season (October to April) across these time scales and lags are displayed, while non-significant correlations are masked. The dash vertical line shows the mean value.
Figure 5. Spatial distribution of drought vegetation relationships across Iran. Panel (a) shows the correlation coefficient between NDVI anomaly and SPEI for five time scales (1, 3, 6, 9, and 12 months) without time lag (red: negative correlation, green: positive correlation), panel (b) shows a 1-month lag, and panel (c) shows a 2-month lag. Significant correlations (p < 0.05) during the growing season (October to April) across these time scales and lags are displayed, while non-significant correlations are masked. The dash vertical line shows the mean value.
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Table 1. Area (in %) of vegetation classes (grasslands, shrublands, and forests) affected by drought severity in Iran from 2001 to 2022.
Table 1. Area (in %) of vegetation classes (grasslands, shrublands, and forests) affected by drought severity in Iran from 2001 to 2022.
Vegetation Classes% Affected by Increasing Trend% Affected by Decreasing TrendNet Change
Grasslands18%10%8%
Shrublands17%9%8%
Tree cover (Forest)17%8%11%
Table 2. The mean values of the Pearson correlations between NDVI anomaly and SPEI in Iran from 2001 to 2022 for different time scales.
Table 2. The mean values of the Pearson correlations between NDVI anomaly and SPEI in Iran from 2001 to 2022 for different time scales.
Time Scales 1 Month 3 Months 6 Months 9 Months 12 Months
No lag0.560.580.560.570.58
1-month lag0.720.740.710.690.68
2-month lag0.680.730.710.690.68
Table 3. Mean Pearson correlation between NDVI anomaly and SPEI in Iran for each vegetation class at multiple SPEI time scales and NDVI lags. The partial autocorrelation and autocorrelation NDVI anomaly are provided in Supplementary Figure S4.
Table 3. Mean Pearson correlation between NDVI anomaly and SPEI in Iran for each vegetation class at multiple SPEI time scales and NDVI lags. The partial autocorrelation and autocorrelation NDVI anomaly are provided in Supplementary Figure S4.
Time Scale 1 Month3 Months6 Months9 Months12 Months
GrasslandNo lag0.520.540.530.520.54
1-month lag0.770.830.810.780.78
2-month lag0.710.830.820.760.78
ShrublandNo lag0.530.50.50.510.52
1-month lag0.740.790.770.750.79
2-month lag0.70.790.790.750.79
Tree coverNo lag0.510.510.520.520.51
1-month lag0.770.870.840.840.83
2-month lag0.80.870.890.830.82
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Torkaman Pary, A.; Rastgoo, P.; Opp, C.; Zeuss, D.; Abera, T.A. Impacts of Drought Severity and Frequency on Natural Vegetation Across Iran. Water 2024, 16, 3334. https://doi.org/10.3390/w16223334

AMA Style

Torkaman Pary A, Rastgoo P, Opp C, Zeuss D, Abera TA. Impacts of Drought Severity and Frequency on Natural Vegetation Across Iran. Water. 2024; 16(22):3334. https://doi.org/10.3390/w16223334

Chicago/Turabian Style

Torkaman Pary, Atefeh, Pejvak Rastgoo, Christian Opp, Dirk Zeuss, and Temesgen Alemayehu Abera. 2024. "Impacts of Drought Severity and Frequency on Natural Vegetation Across Iran" Water 16, no. 22: 3334. https://doi.org/10.3390/w16223334

APA Style

Torkaman Pary, A., Rastgoo, P., Opp, C., Zeuss, D., & Abera, T. A. (2024). Impacts of Drought Severity and Frequency on Natural Vegetation Across Iran. Water, 16(22), 3334. https://doi.org/10.3390/w16223334

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