Spatio-Temporal Assessment of Meteorological Droughts Effect on Vegetation Droughts in Khorasan Razavi Province, Iran

Pouyan Dehghan Rahimabadi ¹ ( University of Tehran )
Sahar Nasabpour Molaei ² ( University of Tehran )
Esmail Heydari Alamdarloo ³ ( Department of Arid and Mountainous Reclamation Region, Faculty of Natural Resources, University of Tehran, Tehran, Iran )
Setareh Bagheri ⁴ ( University of Tehran )
Hossein Azarnivand ⁵ ( University of Tehran )

Submited date : 2023-04-21 Accepted date : 2023-10-31

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Spatio-Temporal Assessment of the Effect of Meteorological Droughts on Vegetation Droughts in Khorasan Razavi Province, Iran

 

 

 

Abstract

Vegetation cover is one of the living components of terrestrial ecosystems and plays an important role in many ecosystem processes that are strongly influenced by climatic events. Thus, meteorological droughts can significantly affect the vegetation cover, especially in arid and semi-arid regions that where vegetation is more sensitive to environmental conditions. This study was conducted with the aim of The aim of the present study is to analyze analyzing the effects of meteorological droughts on the vegetation cover in different Land Use Land Cover (LULC) types in Khorasan Razavi province, Iran. For this purpose, the correlation and Linear Regression (LR) between Standardized Vegetation Index (SVI) and meteorological drought indices including Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Index (SPI), with 3, 6 and 12-month time scales, was were investigated for the period of 2001-2020. Multitemporal SPEI and SPI were used to determine the sequence of meteorological drought in different LULC types. Based on the results, it was found that SVI values were negative in the years 2001, 2006, 2008, 2011, 2014 and 2015, in all LULC types, while in 2010, moderate rangeland experienced the most severe drought. The decreasing trend of SVI (increasing vegetation drought) was mostly observed in the southern parts of the province. The correlation between SVI and 6-month SPEI occupied a wider area than the other time scales (23.07%). The highest correlation between SVI and 12-month SPI was distinguished in dense forest, sparse forest, and poor rangeland, and occupied a wider area across the province (24.08%). Moreover, the highest (1.13) and lowest (0.75) changes in the slope of changes of SVI with multitemporal SPEI and SPI belonged to moderate forest and agricultural land, respectively. Generally, based on the results of this study, SPEI and SPI showed completely different values in different LULC types in different time scales. Therefore, different types of indicators should be considered to study the terrestrial ecosystems in order to better identify areas affected by meteorological drought.

 

Key words: Climate, Drought, Indicator, Trend, Vegetation

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Introduction

Vegetation cover provides valuable ecosystem services to various terrestrial ecosystems, such as carbon sequestration, livestock grazing and regulation of water resources (Pakeman et al., 2019).  It is predicted that future climate warming will have a great impact on terrestrial ecosystems, including rangeland degradation and desertification (Miao et al., 2015), increasing tree mortality, stimulating fires in ecosystems and reducing carbon sequestration in vegetation cover (Allen et al., 2010). Therefore, drought can be an important factor in the quantity and quality of vegetation (Zandi et al., 2021). A shift towards more variable rainfall leads to an overall reduction in the forage biomass resources and an increase in the vegetation cover vulnerability (Williams and Albertson, 2006). Hence, quantifying the vegetation cover response to meteorological droughts is essential for ecological management in terrestrial ecosystems (Wei et al., 2022). However, it is difficult to predict and quantify meteorological drought because it is a complex phenomenon, appears gradually, does not have a specific beginning and is affected by numerous variables. That is why the characteristics of drought, its intensity and frequency are spatially and temporarily different (Iglesias et al., 2009). Different terrestrial and meteorological parameters are important for the drought parameterization. Because the global air temperature is continuously increasing affected by climate change and the spatio-temporal pattern of rainfall and land use is expected to change significantly in the coming decades (Liu et al., 2014). Therefore, it is necessary to develop the more accurate and effective drought monitoring tools. In this case, Remote Sensing (RS) plays an important role in drought studies, due to the spatial and temporal advantages it can provide (West et al., 2019). 

Meteorological droughts monitoring and the evaluation of its impact on vegetation cover is essential for proper management in terrestrial ecosystems. Traditionally, this goal has been achieved by the direct use of meteorological drought indices which is calculated from in situ observation data collected at a limited number of meteorological stations. While these indices are accurate, they are not capable of providing spatially accurate drought estimates due to geographic and economic constraints when establishing in situ stations (Ozelkan et al., 2016). However, todays, there are different indicators for drought diagnosing and monitoring in real time (Ghazaryan et al., 2020), announcing the beginning or end of the drought (Tarnavsky and Bonifacio, 2020), studying drought levels and drought response measures (Zargar et al., 2011), analyzing the quantitative effects of droughts on environmental variables at a various spatial and temporal scales (Tarnavsky and Bonifacio, 2020) and finally announcing drought conditions (Adedeji et al., 2020; Tsakiris and Vangelis, 2020). These indicators help drought warning, monitoring and contingency planning.

Several methods have been used to monitor and evaluate meteorological droughts and their effects on vegetation cover, through the development of various climatic and vegetation indices based on RS techniques. Ariapour et al. (2013) estimated the changes in vegetation cover and LULC using Landsat TM and ETM+ images in Sabzevar city. The results indicated the appropriateness of RS technology in order to accurately estimate the area of LULC and vegetation changes was confirmed by these researchers. Ezzine et al. (2014) investigated the consistency of three drought indices in different LULC classes during a 15-year period (1998-2012). Their results showed a stronger relationship between Standardized Precipitation Index (SPI) and Standardized Vegetation Index (SVI) than SPI and Standardized Water Index (SWI) in autumn and winter seasons. Su et al. (2017) proposed a framework of standard drought indicators to describe all aspects of drought, duration, onset, intensity, expansion and simultaneous examination of three types of drought such as agricultural, meteorological and hydrological droughts in order to harmonize the drought monitoring and reduce the contradictions caused by the lack of a global standard for measuring and describing drought. Safari Shad et al. (2017) investigated the drought in Isfahan province using SPI, TCI, VCI and NDVI. The results of the correlation among different drought indices showed that VCI and TCI have the highest and lowest correlations with meteorological droughts, respectively. Heydari Alamdarloo et al. (2020) investigated the effect of climate fluctuations on vegetation dynamics in Northwest of Iran. They reported that NDVI had the best correlation with temperature in 8-12 °C class with R2 value of 0.36 and precipitation in 213-300 mm classes with a R2 value of 0.38. Ebrahimi Khusfi and Zarei (2020) assessed the relationship between meteorological droughts and vegetation degradation using SPI and NDVI in the Meyghan plain in Markazi province. They reported that the decrease in precipitation increased the sensitivity of the low density vegetation cover (poor rangeland) to meteorological droughts compared to dense vegetation cover (agricultural land).

Considering the above and emphasizing the dangers of droughts, it is necessary to realize the relationship between vegetation cover and meteorological drought to understand the effects of water scarcity on vegetation production and initiate prevention. Since Khorasan-Razavi province, in northeast of Iran, has been introduced as one of the drought-prone regions in the country, which suffers a lot of damage due to this phenomenon every year (Erfanian and Alizadeh, 2009)Hence, this study aimed to investigate the meteorological drought in Khorasan Razavi province, Iranthis province, using  SPEI and SPI and their effect on vegetation cover in different LULC types. In this study both SPEI and SPI were used to assess the effect of each of them on vegetation cover and separate and compared their effects.

 

 

Materials and Methods

Study area

The case study is Khorasan Razavi province (33⁰ 52' - 37⁰ 52' N; 56⁰ 19' - 61⁰ 16' E), located in the northeast of Iran, with an area of 127222.67 Km2. About three fourth of the province is dominated by arid and semi-arid climates. The average annual rainfall is 250 mm, which changes from 116 mm/year in the north to 313 mm/year in the south and the annual mean temperature is about 14.5 °C (Ziyaee et al., 2018). Moreover, The the altitude varies between 231 and 3308 meters above sea level (Fig. 1).

 

 

Fig. 1 Location of Khorasan Razavi province in Iran

 

Land Use Land Cover

Land Use Land Cover (LULC) was used to represent the effect of meteorological drought on dynamics of different types of vegetation cover. In this study, LULC map was extracted from the LULC map prepared by Natural Resources and Watershed Management Organization of Iran (www.frw.ir). This map was divided LULC into 13 types including dense forest (3.78%), moderate forest (0.12%), sparse forest (1.44%), agricultural land (24.78%), good rangeland (2.49%), moderate rangeland (21.17%), poor rangeland (37.73%), bareland (2.51%), rock (0.48%), saltland (2.62%), sand dune (2.14%), urban (0.41%), wetland (0.33%) (Fig. 2).

 

 

Fig. 2 LULC map of Khorasan Razavi province

 

Methodology

In the present study, Standardized Vegetation Index (SVI) was adopted to represent the vegetation cover dynamics. SVI maps prepared using monthly Enhanced Vegetation Index (EVI) acquired from MOD13A2 product of MODIS for the period of 2001-2020. EVI maps for May were used, because natural vegetation cover is at its peak on this month in Khorasan Razavi province. Additionally, the monthly precipitation and temperature data for 10 meteorological stations existing in the study area within the period of 2000-2020 were used (Table 1). Two of the most widely used meteorological drought indices including Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Index (SPI) were calculated to characterize the intensity of meteorological drought. The 3, 6 and 12-month SPEI and SPI were considered as short, medium and long time scales, respectively. Afterwards, coefficient of determination (R2) and slope of the Linear Regression (LR) between SVI and multitemporal SPEI and SPI to assess the relationship between vegetation cover dynamics and meteorological droughts and vegetation cover dynamics. The maximum value of the R2 for each pixel was considered to evaluate the response of SVI to different SPEI and SPI time scales. This means that if a pixel of the SVI maps showed the best correlation with the 3-month SPEI, cumulative precipitation and reference evapotranspiration from February was computed.

 

Table 1 The properties of the meteorological stations

Row	Station Name	Longitude (E)	Latitude (N)	Altitude (m)	Type
1	Golmakan	36.48	59.28	1176.0	synoptic
2	Gonabad	34.35	58.68	1056.0	synoptic
3	Kashmar	35.27	58.47	1109.7	synoptic
4	Mashhad	36.24	59.63	999.2	synoptic
5	Neyshabur	36.27	58.8	1213.0	synoptic
6	Quchan	37.10	58.45	1287.0	synoptic
7	Sabzevar	36.21	57.65	962.0	synoptic
8	Sarakhs	36.54	61.15	977.6	synoptic
9	Torbat-E-Jam	35.29	60.56	950.4	synoptic
10	Torbat-E Heydariyeh	35.33	59.21	1451.0	synoptic

 

It should be noted that the response of vegetation cover to meteorological droughts were analyzed and compared in a 20-year period in seven LULC types including dense forest, moderate forest, sparse forest, good rangeland, moderate rangeland, poor rangeland and agricultural land. The bareland, rock, saltland, sand dune, urban, wetland classes were considered non-vegetated vegetation types and masked in the results.

 

Standardized Vegetation Index

The SVI is used forto monitoring the vegetation cover dynamics. In this study, EVI maps were used to generate SVI maps. EVI is a more robust proxy for biomass due to its improved resistance to soil and atmospheric contamination, compared to NDVI (Huete et al., 2002; Matsushita et al., 2007; Garroutte et al., 2016). The SVI is used for monitoring the vegetation cover dynamics. SVI This index describes the probability of variation of the normal EVI over the time, in a certain interval (Veneros and García, 2022).  In this study, This index SVI was computed from the EVI values for each pixel as Equation 1:

SVI =                         (Equation 1)

Where; SVI is Standardized Vegetation Index, EVIi is EVI for month i (May) and EVImean is mean value of EVI in May during 2001-2020 and STD is standard deviation of EVI in May during 2001-2020.

After calculating SVI for years 2001 to 2020, Mann-Kendall test was used to detect the vegetation cover trend.

 

Mann-Kendall test

The Mann-Kendall test was developed by Mann (1945) and then evolved by Kendall (1975). This test is calculated based on Equation 2:

                        (Equation 2)

Where; the values of xi and xj are consecutive data, n is the length of the time series, and the sign function can be calculated as Equation 3:

                        (Equation 3)

The mean of )E (S)) and the variance (Var (S)) are obtained as Equations 4 and 5:

                        (Equation 4)

                         (Equation 5)

Where; tp is the number of sequences for pth value and p is the number of sequence values. The second component in the above formula is an adjustment for the sequence or sensitive data. The standardized statistics of ZM test are obtained from the Equation 6.

                        (Equation 6)

The positive and negative values of the ZM trend reflect increasing and decreasing trend, respectively. The threshold of -1.96 > ZM > +1.96 shows a significant trend at 95% confidence level.

 

Precipitation Evapotranspiration Index

SPEI is an extended form of SPI. This index is developed to consider precipitation and potential evapotranspiration in determining meteorological drought. Unlike SPI, SPEI shows the main effect of increase in temperature on water demand. Thus, SPEI is an appropriate index to assess the intensity of drought in the increasing global warming (Vicente-Serrano et al., 2010). In this study, the monthly SPEI was calculated to determine the climate change trends to investigate the response of vegetation to climate change using precipitation and potential evapotranspiration data.

To estimate potential evapotranspiration, Thornthwaite (1948) equation was used as:

                         (Equation 7)

Where;  is the correction coefficient that is determined based on the month and the latitude of the studied area,  is the average temperature of the month in °C, I is thermal index calculated for the whole year and α is a coefficient computed based on I.

Next, the monthly water deficit or surplus (Equation 9) is computed and after that,  is calculated.

                        (Equation 8)

                        (Equation 9)

Where; i is the respective year, j is the respective month and k is time scale (k= 3, 6 and 12).

In order to standardize the series of water deficit or surplus observations to determine SPEI according to Vicente-Serrano et al. (2013), three-factor log-logistic distribution was used as the most appropriate adaptive distribution based on Equation 10:

                         (Equation 10)

Where; α, β, and γ are scale, shape, and boundary factors, respectively, for D values in the interval γ < D < ∞. Also, Equation 11 shows the probability distribution function of the observation series of water deficit or surplus according to the log-logistic distribution.

                        (Equation 11)

Eventually, based on F(x), the SPEI was computed as Equation 12:

                        (Equation 12)

Where; , , , ,  and .  if P=1-F(x) but if P > 0.5, P was replaced by 1-P, where; P represents precipitation.

SPEI has positive and negative values, the more negative value indicates the more drought intensiy and the positive values represent wet condition.

 

Standardized Precipitation Index

SPI was computed with monthly precipitation data for the period of 2000–2020 to assess the intensity of meteorological drought. To calculate the SPI, the precipitation data for each station should be fitted to the gamma probability distribution function. The probability density function of this distribution is given in Equation 13 (McKee et al., 1993):

(Equation 13)

Where; x is the amount of precipitation, α is the shape parameter, β is the scale parameter, and Γ(α) represents the gamma function. It should be noted that x, α and β values must be greater than zero. α and β are calculated using Equations 14, 15 and 16:

Where; n is the number of observed precipitation. In addition,  is the average cumulative precipitation for a month during the statistical period. The calculated parameters allow the precipitation distribution in the station to be effectively represented by a mathematical cumulative probability function according to Equation 17:

(Equation 14)
(Equation 15)
(Equation 16)

(Equation 17)

Since the gamma function is not defined for the values of x = 0 and the precipitation distribution may take a zero value, the cumulative probability is obtained using Equation 18:

(Equation 18)

Where; q is the probability of zero and H(x) is the cumulative probability function with x = 0 data. Then, it is transformed into a standard normal distribution to produce the cumulative SPI values of H(x) (McKee et al., 1993).

 

Calculation of coefficient of determination (R2) and LR

Using coefficient of determination, the correlation of SVI in relation to multitemporal SPEI and time was calculated according to Equation 19:

                         (Equation 19)

Where; R2 is coefficient of determination and RSS and TSS         are Sum of Squares of Residuals and Total Sum of Squares, respectively. The R2 values ranges from 0 to 1, which higher values represent better agreement between the compared data (Pogačar et al., 2022).

Additionally, LR analysis can be used to simulate the changes trends. The slope coefficient of the SVI changes in relation to multitemporal SPEI and SPI was determined using Equation 20:

                (Equation 20)

Where; Xi and Yi are independent and dependent variables values in ith year, respectively, and n represents the number of years during the study period (n=20). Generally, slope < 0 reflects that the dependent variable shows has an increasing trend, while slope > 0 reflects that the dependent variable shows a decreasing trend.. Slope = 0 shows      that the dependent variable has no trend.???????????????????????

The SPEI and SPI calculations were carried out and coded in MATLAB R2021b for each station on 3, 6 and 12-month time scales as short, medium and long time scales, respectively. Then, they were interpolated across the study area using Inverse Distance Weighted (IDW) model in ArcMap 10.8.1 to obtain SPEI maps and SPI maps. The pixel size and position were considered as SVI maps. Moreover, the coefficient of determination and LR were conducted in TerrSet 2020 v.19.0.6. The flowchart of the procedure used in this study is shown in Fig. 3.

 

 

Fig. 3 Flowchart of the procedure employed in this study

 

Results

Trend of SVI

Fig. 4 shows the occurrence and cessation of vegetation drought in in different LULC types Razavi Khorasan province over the past 20 years. The lower SVI values indicate greater severity of agricultural drought. As it can be seen, in the years 2001, 2006, 2008, 2011, 2014 and 2015, the SVI values in all LULC types are negative, which indicates that the vegetation is not in a good condition and drought affects all LULC types. While in 2010, moderate rangeland has experienced the most severe drought. The years 2019 and 2020 recorded the highest SVI values in all LULC types, which indicates the cessation of drought conditions and better vegetation status, which is related to the rainfall in the previous years.

 

 

Fig. 4 SVI Changes in different LULC types over time

 

Fig. 5 and Table 2 show the trend of SVI and relative percentage of SVI trend in Different LULC types, respectively, over the period of 2001-2020. According to the analysis of the Mann Kendall trend test, large areas of Razavi Khorasan province are not under any drought trend (99.42%). The increasing trend of SVI (suitable condition of vegetation cover over time) is observed in a sporadically in the province. These pixels show relief from drought conditions. On the other hand, the decreasing trend of SVI (reduction of vegetation cover or occurrence of drought) with less number of pixels is observed in the southern part of the province.

 

 

Fig. 5 The trend of SVI changesTrend of SVI changes

 

According to the results presented in Table 2, the highest (23.67%) and the lowest (3.43%) relative percentages of the increasing trend of SVI belong to the moderate rangeland and moderate forest, respectively. Additionally, moderate forest has the highest relative percentage of the no-trend among other LULC types. The highest (1.08) and lowest (0.00) relative percentages of the decreasing trend are also observed in agricultural land and with the same value in moderate forest and sparse forest, respectively.

 

Table 2 Relative percentage of SVI trend classes in different LULC types (%)

LULC Type	Decreasing Trend	No Trend	Increasing Trend
Dense Forest	0.49	79.74	19.77
Moderate Forest	0.00	96.57	3.43
Sparse Forest	0.00	76.18	23.82
Good Rangeland	0.01	86.00	13.99
Moderate Rangeland	0.10	76.23	23.67
Poor Rangeland	0.59	82.35	17.06
Agricultural Land	1.08	85.40	13.52
Total Area	0.58	81.67	17.75

 

SVI-SPEI and SVI-SPI

Fig. 6 indicates the spatial distribution of the highest correlation between SVI and multitemporal SPEI and SPI. This map shows the response of agricultural drought to meteorological droughts. The correlation of SVI with 6-month SPEI has covered a wider area than other time scales (3 and 12-month). In other words, agricultural drought is associated with medium-term rainfall at the most regions. In fact, the distribution of agricultural drought severity has a strong relationship with this time scale of precipitation.

 

 

Fig. 6 The highest correlation between SVI and multitemporal SPEI and SPI

 

Table 3 shows area of effect of multitemporal SPEI and SPI on vegetation cover in different LULC types. The highest correlation value between SVI and 3, 6 and 12-month SPEI include 15.11%, 23.07% and 7.14%, respectively, while the highest correlation value between SVI and 3, 6 and 12-month SPI include 16.30%, 14.30% and 24.08% of the province, respectively.

The highest correlation between vegetation cover and 12-month SPI has been reported in dense forest, sparse forest, and poor rangeland. In other words, the 12-month time scale is more effective on the vegetation cover in poor rangeland than other time scales, and the vegetation is affected by long-term rainfall in this LULC type. On the other hand, the lowest correlation is observed in moderate forest and good rangeland in relation to 3-month SPEI. In other words, temperature and short-term rainfall affect the vegetation cover in these LULC types. Moderate rangeland and agricultural land are also affected by 6-month SPEI or medium-term temperature and precipitation.

 

Table 3 The relative effect of multitemporal SPEI and SPI in different LULC types (%)

LULC Type	SPEI-03	SPEI-06	SPEI-12	SPI-03	SPI-06	SPI-12
Dense Forest	10.50	20.97	10.75	16.46	11.00	30.32
Moderate Forest	30.99	21.76	0.22	14.21	24.48	8.34
Sparse Forest	12.00	4.54	6.11	30.52	10.16	36.67
Good Rangeland	24.31	21.70	1.97	18.42	16.73	16.87
Moderate Rangeland	17.41	22.18	4.63	24.03	10.70	21.05
Poor Rangeland	10.08	21.89	7.86	10.49	16.79	32.89
Agricultural Land	20.61	27.15	8.28	17.40	14.07	12.49
Total Area	15.11	23.07	7.14	16.30	14.30	24.08

 

Correlation of SVI-SPEI and SVI-SPI 

In Fig. 7, the correlation rate of SVI and multitemporal SPEI and SPI is shown in different colors. In darker pixels (dark blue), the correlation is higher. Most of the pixels with these values are concentrated in the northeast and southwest. While the correlation of SVI and multitemporal SPEI and SPI is lower in brighter pixels (pale yellow). Pixels with these values are scattered throughout the region, especially in the southern parts. However, the sensitivity of vegetation cover to meteorological drought is more in the eastern sides and some areas in north, west and south sides than central, northwestern, southwestern and southeastern parts.

 

 

Fig. 7 The correlation (R2) between SVI and multitemporal SPEI and SPI

 

Slope of LR of SVI-SPEI and SVI-SPI

Fig. 8 shows the slope of LR between SVI and multitemporal SPEI and SPI. The slope of SVI changes in relation to multitemporal SPEI and SPI is higher in darker pixels than in brighter pixels. In fact, the vegetation cover in darker pixels is more sensitive to meteorological drought or changing climate factors. So that the vegetation cover in these areas is more susceptible to the negative effects of meteorological drought. However, the spatial distribution of pixels with higher values does not follow a particular pattern, but it is less in west and southeast sides.

 

 

Fig. 8 The absolute value of the slope of LR between SVI and multitemporal SPEI and SPI

 

Table 4 represents R2 and slope of LR values between SVI and multitemporal SPEI and SPI in different LULC types. The highest (0.60) and lowest (0.37) correlations of SVI with multitemporal SPEI and SPI are higher in moderate forest and agricultural land, respectively, than other LULC types. The highest (1.13) and lowest (0.75) slopes of LR of SVI changes in relation to SPEI and SPI also belong to moderate forest and agricultural land, respectively. Based on the results of this part, moderate forest has a higher correlation and sensitivity to meteorological drought and climate change. Reducing the sensitivity of agricultural land to climate fluctuations is also related to human activities such as land management and irrigation.

 

Table 4 R2 and slope of LR values between SVI and multitemporal SPEI and SPI in different LULC types

LULC Type	R2		Slope
	MEAN	Standard Deviation	MEAN	Standard Deviation
Dense Forest	0.40	0.18	0.81	0.39
Moderate Forest	0.60	0.10	1.13	0.44
Sparse Forest	0.51	0.17	1.03	0.42
Good Rangeland	0.55	0.15	1.01	0.38
Moderate Rangeland	0.48	0.17	0.92	0.40
Poor Rangeland	0.44	0.16	0.85	0.37
Agricultural Land	0.37	0.19	0.75	0.37
Total Area	0.44	0.16	0.85	0.37

 

Discussion and Conclusion

In this study, multitemporal SPEI and SPI were used to investigate the recent meteorological drought in Razavi Khorasan province, because both precipitation and temperature, as two most important meteorological factors, are used as an index to achieve a better understanding in the droughts evolution (McKee et al., 1993). The multitemporal feature of these indices made it possible to analyze different types of droughts in different time scales. Additionally, the dominant meteorological drought time scales, in which drought affects vegetation cover, the extent and spatio-temporal characteristics of SPEI and SPI were investigated.

Based on the results, in 2001, 2006, 2008, 2011, 2014 and 2015, SVI values are negative in all LULC types. While in 2010 moderate rangeland has faced the worst agricultural drought conditions in the region during the past 20 years. Therefore, it can be concluded that these LULC types experienced severe agricultural and meteorological droughts in 2010.

It was also found that vegetation cover in large areas of Khorasan Razavi province is not affected by any drought increasing process. While the decreasing trend of SVI is mainly observed in the southern parts of the province. Therefore, it is essential to prioritize this part of the province to take any activities to deal with the drought.

In moderate forest and sparse forest, the highest relative percentage of decreasing trend is observed, which indicates that vegetation cover in these LULC types are more vulnerable to the meteorological droughts and,  water shortages. So that vegetation cover rapidly responds to variation in water availability (Schwinning and Sala, 2004). and Thus, more attention should be paid to LULC types in drought studies and planning policies.

The eastern half of the province had a higher correlation (R2) between meteorological fluctuations and vegetation cover. This is supported  by Behrang Manesh et al. (2019) who found that vegetation cover is more vulnerable to climate fluctuations in this part of Khorasan Razavi province.

 

In the study area, 6-month SPEI showed a higher correlation with SVI than other times scales. It should be noted that the SPEI is a common drought index that has the ability to combine precipitation and evaporation and transpiration and is one of the essential factors for estimating crop development and water surplus or deficit evaluation (Ndayiragije and Li, 2022). Agricultural drought was observed, in 3 and 6-month time scales, while according to Potop et al., (2014), 6 and 12-month time scales are related to the hydrological drought, which is useful for monitoring water resources. Moreover, Shi et al. (2021) reported that correlation between vegetation cover and meteorological factors are at high level in short or medium time scales in arid and semi-arid regions.

12-month SPI showed the highest correlation in dense forest, sparse forest, and poor rangeland. In fact, the impact of meteorological drought on the vegetation cover in these LULC types has occurred in longer time scales. It is important to mention that the water deficit of the previous months accumulated during SPEI calculation with longer time scales is an indicator to evaluate the moisture deficit, which has caused meteorological and agricultural droughts. The consequence of this also leads to the aggravation of hydrological drought and the reduction of water resources. In contrast, 3-month SPEI had the lowest correlation in moderate forest and good rangeland. Generally, it is inferred that different vegetation communities show different responses to meteorological drought with different time scales. The importance of this issue is highlighted in the study of Entezari et al. (2020). They introduced the knowledge of quantitative and qualitative characteristics of land use changes as valuable for environmental planning, land use and sustainable development. 

It was found that vegetation cover in the northeastern and southwestern parts of Khorasan Razavi province are more affected by meteorological drought. Also, the slope of SVI changes compared to multitemporal SPEI and SPI had different sensitivity in different regions. Since the most sensitive areas are more susceptible to the negative effects of meteorological drought on vegetation, it is necessary that areas with high sensitivity or affected by drought should be prioritized in planning to deal with or reduce the effects of drought and preserve water resources.

As it was determined through the correlation values, the highest (R2=0.60) and lowest (R2=0.37) correlations of SVI with multitemporal SPEI and SPI were observed in moderate forest and agricultural. However, these meteorological droughts affect vegetation cover in different ways such as water scarcity, and crop destruction (Poornima et al., 2023). Despite the fact that agricultural lands rely on various meteorological factors such as precipitation, temperature, humidity, etc. for the successful growth of crops, however, the sensitivity of agricultural land compared to other LULC types to meteorological drought changes related to human activities including land management and irrigation. So that in agricultural areas, the possibility of vegetation activity is provided throughout the year.

In this study, a comprehensive method was adopted to analyze the effects of meteorological drought on vegetation cover such that 1) using long time series (including 20 years of RS data, 2) analyzing the entire province and 3) using the two multitemporal drought indices (SPEI, SPI).

        

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ارزیابی مکانی – زمانی اثر خشکسالی خشکسالی‌های هواشناسی بر خشکسالی‌های پوشش گیاهی استان خراسان رضوی، ایران

 

 

 

چکیده

پوشش گیاهی یکی از اجزای زنده اکوسیستم‌های خشکی است و نقش مهمی در بسیاری از فرآیندهای اکوسیستمی ایفا می‌کند که به شدت تحت تأثیر رویدادهای اقلیمی می‌باشد. بنابراین، خشکسالی اقلیمی می‌تواند به طور قابل توجهی بر پوشش گیاهی تأثیر بگذارد، به ویژه در مناطق خشک و نیمه‌خشک که پوشش گیاهی به شرایط محیطی حساس‌تر است. این هدف از پژوهش با هدف حاضر، تحلیل اثرات خشکسالی هواشناسی بر پوشش گیاهی در انواع مختلف پوشش کاربری اراضی در استان خراسان رضوی استانجام شد. برای این منظور، همبستگی و رگرسیون خطی بین شاخص استاندارد شده پوشش گیاهی ی (SVI) و شاخص‌های خشکسالی هواشناسی شامل بارش - تبخیر و تعرق استاندارد شده (SPEI) و شاخص بارش استاندارد شده (SPI)، با مقیاس‌های زمانی 3، 6 و 12 ماهه، برای دوره زمانی 2001-2020 مورد بررسی قرار گرفت. برای تعیین توالی خشکسالی هواشناسی در انواع مختلف کاربری اراضی از SPEI و SPI چند زمانی استفاده شد. بر اساس نتایج به دست آمده، مشخص شد که در سال‌های 2001، 2006، 2008، 2011، 2014 و 2015 مقادیر SVI در تیپ پوشش گیاهی کاربری‌های مختلف اراضی منفی بوده است، در حالی که در سال 2010، کاربری مرتع با پوشش متوسط شدیدترین خشکسالی را تجربه کرده است. روند کاهشی  SVI (افزایش خشکسالی پوشش گیاهی) بیشتر در نواحی جنوبی استان مشاهده شد. همبستگی بینSVI  و SPEI با مقیاس زمانی 6 ماهه نسبت به سایر مقیاس‌های زمانی، مساحت وسیع‌تری را شامل شد (07/23%). همچنین بیشترین همبستگی بین SVI و SPI 12 ماهه در کاربری‌های جنگل انبوه، جنگل تنک و مرتع فقیر مشخص شد و منطقه وسیع‌تری را در سراسر استان به خود اختصاص داده است(08/24%). همچنین بیشترین (13/1) و کمترین (75/0) تغییر شیب SVI با SPEI و SPI چند زمانی به ترتیب مربوط به کاربری‌های جنگل‌های متوسط و اراضی کشاورزی بود. به طور کلی، بر اساس نتایج این مطالعه، مشخص شد که  SPEI و و SPI  در انواع مختلف کاربری اراضی در مقیاس‌های زمانی مختلف مقادیر کاملاً متفاوتی را نشان می‌دهند. بنابراین برای بررسی اکوسیستم‌های خشکی باید انواع مختلفی از شاخص‌ها در نظر گرفته شود تا مناطق تحت تأثیر خشکسالی هواشناسی بهتر شناسایی شوند.

 

کلمات کلیدی: اقلیم، خشکسالی، شاخص، روند، پوشش گیاهی

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