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Article

Assessment of Vegetation Vulnerability in the Haihe River Basin Under Compound Heat and Drought Stress

by
Hui Yin
1,
Fuqing Bai
1,
Huiming Wu
2,
Meng Yan
2 and
Shuai Zhou
3,*
1
College of Civil Engineering and Architecture, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China
2
Guangzhou Zhukeyuan Engineering Survey and Design Co., Ltd., Guangzhou 510610, China
3
School of Water Conservancy and Hydroelectric Power, Hebei University of Engineering, No. 19 Taiji Road, Handan Economic and Technological, Development Zone, Handan 056038, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(23), 10489; https://doi.org/10.3390/su162310489
Submission received: 21 October 2024 / Revised: 25 November 2024 / Accepted: 26 November 2024 / Published: 29 November 2024
(This article belongs to the Special Issue Sustainability of the Water Cycle System and Water Utilization in a Changing Environment)
Browse Figures
Figure 1
<p>Geographical location, meteorological stations, and spatial distribution of water systems in the Hai River Basin.</p> "> Figure 2
<p>Research flow chart of the study.</p> "> Figure 3
<p>The trend test results for annual precipitation and annual average temperature in the watershed.</p> "> Figure 4
<p>The results of the variability test for the annual precipitation and annual average temperature in the watershed.</p> "> Figure 5
<p>Evolutionary patterns of the multi-scale (1–12 months) meteorological drought time history based on the SPI (blue represents a humid climate, while red indicates a dry climate. The same color appearing in consecutive periods signifies the duration of either drought or humidity).</p> "> Figure 6
<p>Results of the correlation analysis between the SPI at multiple scales (1–12 months) and STI at the 1-month scale.</p> "> Figure 7
<p>Identification results for the optimal marginal distribution functions for the 3-month SPI and 1-month STI.</p> "> Figure 8
<p>The optimal Copula functions for each station in the Haihe River Basin.</p> "> Figure 9
<p>Joint cumulative probability distributions of the 3-month-scale SPI and 1-month-scale STI.</p> "> Figure 10
<p>Joint return period of the 3-month-scale SPI and 1-month-scale STI.</p> "> Figure 11
<p>Distribution of correlation coefficients between the SPEI, STI, and sNDVI on a 3-month time scale from June to August in the Haihe River Basin (CC stands for correlation coefficient).</p> "> Figure 12
<p>Probability distribution of vegetation loss in response to drought, high temperatures, and compound dry and hot climates in the Haihe River Basin from June to August (PVL stands for the probability of vegetation loss).</p> ">
Versions Notes

Abstract

:
With the intensification of global warming, droughts and heatwaves occur frequently and widely, which have a serious impact on the healthy growth of vegetation. The challenge is to accurately characterize vegetation vulnerability under compound heat and drought stress using correlation-based methods. This article uses the Haihe River Basin, an ecologically sensitive area known for experiencing droughts nine out of ten years, as an example. Firstly, using daily precipitation and maximum temperature data from 38 meteorological stations in the basin from 1965 to 2019, methods such as univariate linear regression and the Mann–Kendall mutation test were employed to identify the temporal variation patterns of meteorological elements in the basin. Secondly, the Pearson correlation coefficient and other methods were applied to determine the most likely months for compound dry and hot events, and the joint distribution pattern and recurrence period of concurrent high temperature and intense drought events were explored. Finally, a vegetation vulnerability assessment model based on Vine Copula in compound dry and hot climates was constructed to quantify the relationship of the response of watershed vegetation to different extreme events (high temperature, drought, and compound dry and hot climates). The results indicated that the basin’s precipitation keeps decreasing, evaporation rises, and the supply–demand conflict grows more severe. The correlation between the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) is strongest at the 3-month scale from June to August. Meanwhile, in most areas of the basin, the Standardized Normalized Difference Vegetation Index (sNDVI) is positively correlated with the SPI and negatively correlated with the STI. Compared to a single drought or high-temperature event, compound dry and hot climates further exacerbate the vegetation vulnerability of the Haihe River Basin. In compound dry and hot climates, the probability of vegetation loss in June, July, and August is as high as 0.45, 0.32, and 0.38, respectively. Moreover, vegetation vulnerability in the southern and northwestern mountainous areas of the basin is higher, and the ecological risk is severe. The research results contribute to an understanding of the vegetation’s response to extreme climate events, aiming to address terrestrial ecosystem risk management in response to climate change.

1. Introduction

Vegetation is a crucial element of terrestrial ecosystems, and its state changes significantly impact climate change, the carbon balance, and water cycle processes [1,2]. Amid climate warming, frequent global droughts and high temperatures have severely impacted the healthy growth of terrestrial vegetation. Vegetation degradation can cause irreversible damage to the structure and function of terrestrial ecosystems [3,4]. Due to the strong correlation between drought and high temperatures, a combined drought and heat event forms, further exacerbating the impact on vegetation. For example, Hu et al. indicates that during different growth stages of summer maize, high temperatures, drought, and their combined stress reduce the activity of antioxidant enzymes and the content of soluble proteins in the leaves, accelerating plant aging [5]. Evaluating the response of vegetation to combined drought and heat events is crucial for understanding ecosystem mechanisms in climate change and developing effective mitigation and adaptation strategies. This has practical significance for ensuring regional food security, disaster prevention, and mitigation, among other things.
In recent years, researchers have conducted extensive research on the spatiotemporal evolution of characteristics of single factors such as meteorological drought and high temperature under the background of climate warming, and have achieved fruitful research results [6]. In terms of a meteorological drought assessment, Wang et al., based on a three-dimensional spatiotemporal clustering algorithm, identified meteorological drought and vegetation drought events in the Yellow River Basin (YRB) from 1982 to 2020, depicted the dynamic characteristics of typical drought events, and clarified the intrinsic connections and propagation characteristics between meteorological drought and vegetation drought events [7]. Zheng et al. analyzed the response relationship of vegetation in Argentina to meteorological drought and groundwater drought events using multi-source data and found that both meteorological drought and groundwater drought have significant impacts on various types of vegetation [8]. Previous studies have concentrated on examining meteorological and heatwave disasters from a univariate perspective, underestimating the synergistic effects of extreme heat and drought events, which become more pronounced with climate warming. Yoon et al. used the Northwestern, Great Plains, and Northeastern regions of the United States as examples to reveal the driving forces behind compound drought–heatwave events. They pointed out that limitations in water and energy (such as insufficient precipitation, high evaporation, and rapid soil moisture reduction) are important reasons for their occurrence [9]. Arain et al., using eddy covariance flux measurement data from 2003 to 2019, investigated the impact of compound heat and drought events on carbon sequestration in an eastern white pine forest in eastern Ontario, Canada. They found that air temperature was the dominant controlling factor for NEP across all three different-aged stands, while drought, a limiting factor for both gross ecosystem productivity and ecosystem respiration, had a smaller impact on NEP [10]. Popa et al. studied the Norwegian ecosystem to reveal the effects of drought stress on spruce forests at different altitudes. They pointed out that in low-altitude areas (<800 m), the drought resistance of Norway spruce is lowest, but resilience is higher, whereas at high altitudes (>1400 m), higher drought resistance is associated with lower resilience [11].
In the past, numerous studies have conducted long-term monitoring of local small-scale vegetation growth through field campaigns [12,13], eddy covariance measurements [14,15], and model simulations [16,17]. These studies have found that drought is one of the most devastating disasters affecting vegetation growth. However, conducting long-term continuous monitoring of vegetation dynamics on a large scale is challenging in terms of cost and technology for ground-based observations. Satellite-based remote sensing indices, such as the Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Normalized Difference Vegetation Index (NDVI), can accurately characterize vegetation dynamics over the long term [18]. Currently, there is no standardized definition for compound hot–dry events. Hao et al. [19] determined the thresholds for precipitation and temperature using the 25th and 75th percentiles, respectively. A compound hot–dry event is indicated when the temperature exceeds the 75th percentile and precipitation falls below the 25th percentile. Li et al. [20] defined extreme combined drought and heat as an SPEI less than −1.3 and an STI greater than 1.3. Some scholars have also used the Copula function to connect the SPEI and STI, constructing a combined drought and heat index to characterize the combined drought and heat condition [21]. Based on the above indicators, establishing a correlation between vegetation dynamics and the drought or high-temperature index can evaluate the impacts of drought and high temperature on large-scale vegetation dynamics. The correlation between vegetation and the drought index is a commonly used method for studying the response of vegetation to drought [22], but the overall correlation coefficient is difficult to accurately capture the tail dependence between extreme events and vegetation conditions. Even if the correlation between two variables is not significant, they may still have a tail dependence, which is a concern when studying extreme events [23]. Scholars have used probability models such as the Copula function and meta Gaussian to study the vulnerability of vegetation to extreme events such as high temperature and drought. However, accurately capturing the interdependence between multiple variables (three or more) with the commonly used Copula function is difficult, which affects the vulnerability assessment results [24,25]. Vine Copula can connect the edge distributions of multiple variables through two-dimensional Copula pairs to construct a high-dimensional joint distribution function, which can flexibly characterize the tail dependence between different variables [26,27]. However, there are few studies that use Vine Copula to quantify the response relationship of watershed vegetation to different extreme events (high temperature, drought, and compound dry and hot climates).
The Haihe River Basin is often referred to as experiencing “nine droughts in ten years.” With the intensification of global warming, high temperatures and droughts are especially frequent here, severely impacting vegetation growth and harming the ecological environment. It is also the region with the least precipitation and the most severe drought in the eastern coastal areas [20,21]. This study employed methods such as univariate linear regression and the M-K mutation test to identify the temporal variation patterns of meteorological elements in the watershed. It also explored the joint distribution patterns and recurrence periods of concurrent high temperature and drought intensity events. Simultaneously, a vegetation vulnerability assessment model based on Vine Copula in compound dry and hot climates was constructed to analyze the response of vegetation in the Haihe River Basin to combined drought and heat events. The research results contribute to a deeper understanding of the vegetation response to extreme climate events, aiding in the risk management of terrestrial ecosystems in response to climate change.

2. Study Area and Data

The Haihe River Basin is located between 112–120° E and 35–43° N, with a size of 320,000 km2. It is situated in a temperate semi-humid, semi-arid climate zone, where the ecological environment is fragile. The southeastern plains are a major grain-producing region in China, accounting for 9.40% of the country’s total grain output [28]. The western part of the basin is a plateau and the Taihang mountain area, the northern part is the Inner Mongolian Plateau, and the southeastern part is a plain area. The spatial and temporal variation patterns of meteorological elements in the basin are complex and changeable, with the annual average temperature rising significantly at a rate of 0.30 °C. The specific geographical location and climate characteristics result in frequent droughts and severe water and soil erosion in the watershed. Meanwhile, the distribution of rainfall in the basin is uneven, with 75–95% concentrated in the summer flood season. The difference in precipitation between wet years and dry years is as much as 2.0 times. The long-term average surface evaporation in the basin is between 1000 and 1400 mm, which is significantly higher than the annual precipitation, resulting in a substantial water deficit and a high risk of meteorological drought events. The natural vegetation in the basin is mainly located in the Yanshan Mountains and areas to their north, as well as the Taihang Mountains and areas to their west [29]. The geographical location of the watershed and the spatial distribution of meteorological stations are shown in Figure 1.
The meteorological data collected in this article are daily precipitation and maximum temperature data from 38 meteorological stations in the basin from 1965 to 2019, sourced from the Meteorological Data Sharing Service Network and the China Science Resources and Environmental Science and Data Center (https://www.resdc.cn/) (accessed on 10 January 2022). Using the NDVI provided by GIMMS to characterize vegetation conditions, data sourced from the National Center for Atmospheric Research in the United States (https://climatedataguide.ucar.edu/climate-data/ndvi-normalized-difference-vegetation-index-noaa-avhrr) (accessed on 15 June 2020), with a time span of 1982–2019 and a spatial resolution of 1/12° × 1/12°. Meanwhile, this study uses the average of two NDVI scenes per month to represent the vegetation growth for that month. This NDVI product, serving as a proxy for vegetation greenness and photosynthesis, has been widely used in ecological research. It is noteworthy that in order to quantify the response of watershed vegetation to different extreme events (such as high temperatures, drought, and compound hot–dry conditions), this study employs the inverse distance weighting method to interpolate precipitation and maximum temperature data from meteorological stations to sNDVI grids, effectively ensuring a consistent data resolution.

3. Research Method

This study employed methods such as univariate linear regression and the M-K mutation test to identify the temporal variation patterns of meteorological elements in the watershed. Simultaneously, a vegetation vulnerability assessment model based on Vine Copula under compound dry and hot climates was constructed to analyze the response of vegetation to combined drought and heat events. The research flow chart of this paper is shown in Figure 2.

3.1. Mann–Kendall (M-K) Mutation Test Method

The Mann–Kendall nonparametric mutation test method accurately identifies whether a sequence has undergone mutation by analyzing the changes in the trends of UFk and UBk during a certain period [30,31]. Assume the annual runoff series is Xi (i = 1, 2, …, n), and construct the rank sequence Sk. Assuming the sequence being tested satisfies the assumption of independence and randomness, the mean and variance of Sk are E ( S k ) = k ( k − 1 ) / 4 and V a r ( S k ) = k ( k − 1 ) ( 2 k + 5 ) / 72 , respectively, and then the formula for calculating the statistic UFk is given as follows:
U F k = | S k − E ( S k ) | V a r ( S k )   ( k = 1 , 2 , … , n )
where UFk is a standard normal distribution, and UFk and UBk represent statistical measures. At confidence level a, if | U F k | > U a , it means that the sequence has significant trend changing characteristics, with values greater than/less than 0, representing that the detected sequence shows an upward/downward trend at the confidence level, respectively. Meanwhile, arranging the UFk sequence in reverse order yields the statistical UBk curve. If and only if, at the confidence level, the two intersect, then the point is considered the location of the mutation in the detected sequence.

3.2. Standardized Precipitation Index (SPI)

Due to the SPI requiring minimal input data while being powerful and simple to calculate, it is widely used in drought monitoring and assessing meteorological droughts in watersheds or local areas. It effectively reflects characteristics such as the intensity and duration of meteorological droughts [32,33]. This SPI solely uses precipitation data as the input and can quantify precipitation deficits over various time scales (1, 3, 9, and 24 months). The purpose of this article is to identify the months most susceptible to compound high-temperature and drought events, and so the SPI time scale is chosen as 1–12 months. The calculation process is detailed in the reference.
It is worth noting that this article uses the STI to characterize the intensity of high-temperature events. Its calculation process is consistent with the SPI, but it uses monthly average temperature data as the input. In this study, the opposite values of SPI (−SPI) were used to represent the drought intensity; hence, a higher value of −SPI represents a stronger drought. This makes it more convenient for the subsequent fitting of probability distributions.

3.3. Copula Function and Its Parameter Optimization Process

Sklar proposed the Sklar theorem in 1959, stating that the joint distribution function of multiple variables can be described by the marginal distribution of each variable and the Copula function, also known as the “connection function” [34,35]. The Sklar theorem is expressed as follows:
F ( x 1 , x 2 , … , x n ) = C ( F ( x 1 ) , F ( x 2 ) , … , F ( x n ) )
f ( x 1 , x 2 , … , x n ) = c ( f ( x 1 ) , f ( x 2 ) , … , f ( x n ) ) × Π n = 1 N f n ( x n )
In the equation, F(xn) and f(xn), respectively, represent the marginal distribution function and density function of each variable.
The relationship between the Copula distribution function C ( F ( x 1 ) , F ( x 2 ) , … , F ( x n ) ) and c ( f ( x 1 ) , f ( x 2 ) , … , f ( x n ) ) density function M is
c ( f ( x 1 ) , f ( x 1 ) , … , f ( x n ) ) = ∂ n C ( F ( x 1 ) , F ( x 2 ) , … , F ( x n ) ) ∂ F ( ( x 1 ) ∂ F ( x 2 ) … ∂ F ( x n ) )
Copula functions primarily consist of two types: the elliptic function family and the Archimedean function family. The former includes normal and t-functions, while the latter encompasses Gumbel, Clayton, and Frank functions, which utilize differences in tail features to describe various types of dependency relationships. The Copula function used in this article is shown in Table 1.
In this article, the Copula function is employed to construct a two-dimensional joint cumulative probability distribution of the average temperature and drought intensity (-SPI) at each station in the Haihe River Basin, aiming to calculate the potential occurrence probability of combined extreme high temperature and drought events. Meanwhile, due to the positive and negative values of the meteorological drought assessment index SPI and the high-temperature event characteristic STI, this article utilizes only three marginal distribution functions: a normal distribution (Norm), extreme value distribution (EV), and generalized extreme value distribution (GEV). The Akaike Information Criterion (AIC) [36] is used to perform goodness of fit tests on each marginal distribution to determine the optimal marginal distribution function for each meteorological station.

3.4. Calculation of the Joint Return Period of Combined High Temperature and Drought Events

The joint recurrence interval in this article represents the recurrence interval of meteorological drought and high temperature events that both exceed a certain threshold. It is calculated by the two-dimensional joint exceedance probability p, and its calculation formula is
R P = 1 / p
The formula for calculating the two-dimensional joint transcendental probability is
p = P ( X > x ∩ Y > y ) = 1 − u − v + C ( u , v )
In the formula, X and Y represent the STI and drought intensity (−SPI), respectively; x and y represent the threshold values of two-dimensional variables, and in this article, their values are 0.50; and FX and FY are the marginal distribution functions of STI and −SPI, respectively. C(u, v) is the two-dimensional joint cumulative probability when X < x and Y < y.

3.5. Assessment of the Vegetation Vulnerability in Compound Dry and Hot Climates

Firstly, through a correlation analysis, we examine the response relationship of the sNDVI to the SPI and STI, and preliminarily determine the impact of drought and high-temperature events on vegetation dynamics. However, since correlation coefficients cannot capture the tail dependence between extreme events and vegetation conditions, the probability of vegetation loss due to drought, high temperature, and combined drought and heat events is calculated based on the Copula function:
{ { F sNDVI | SPI ( sNDVI ⩽ s n d v i | SPI ⩽ s p i ) = F ( sNDVI ⩽ s n d v i , SPI ⩽ s p i ) F ( SPI ⩽ s p i ) = C ( s n d v i , s p i ) F ( s p i ) F sNDVI | STI ( sNDVI ⩽ s n d ν i | STI > s t i ) = F ( sNDVI ⩽ s n d ν i , STI > s t i ) F ( STI > s t i ) = F ( s n d ν i ) − C ( s t i , s n d ν i ) 1 − F ( s t i ) F sNDVI | SPI , STI ( sNDVI ⩽ s n d v i | SPI ⩽ s p i , STI > s t i ) = F ( sNDVI ⩽ s n d v i , SPI ⩽ s p i , STI > s t i ) F ( SPI ⩽ s p i , STI > s t i ) = C ( s p i , s n d v i ) − ( s p i , s t i , s n d v i ) F ( s p i ) − C ( s p i , s t i )
In the formula, F(·) represents the cumulative probability; C(·) represents the Copula joint probability; and FsNDVI|SPI, FsNDVI|STI, and FsNDVI|SPI,STI represent the probabilities of vegetation loss under drought, high temperature, and compound dry and hot climate conditions, respectively.
In dry and hot climates, the higher the probability of loss, the more susceptible vegetation growth is to the impact, increasing its vulnerability. When the number of variables exceeds two, the commonly used Copula function struggles to accurately describe the dependencies between variables. Vine Copula can decompose a high-dimensional joint probability density into a series of binary joint or conditional probability density combinations, better capturing the dependency relationships between variables and making the model results easier to interpret and understand. Using Vine Copula, the probability density function can be decomposed into
f ( x 1 , ⋯ , x n ) = ∏ k = 1 n f k ( x k ) × ∏ i = 1 n − 1 ∏ j = 1 n − i c i , i + j | 1 : ( i − 1 ) ( F ( x i | x 1 , ⋯ , x i − 1 ) , F ( x i + j | x 1 , ⋯ , x i − 1 ) )
Therefore, the three-dimensional joint probability density of sNDVI, SPI, and STI is expressed as follows:
c ( s n d v i , s p i , s t i ) = f s N D V I ( s n d v i ) ⋅ f S P I ( s p i ) ⋅ f S T I ( s t i ) ⋅ c S T I , s N D V I ( u S T I , u s N D V I ) ⋅ c S T I , S P I ( u S T I , u S P I ) ⋅ c s N D V I , S P I | S T I ( u s N D V I | S T I , u S P I , S T I )
In the formula, F(·) denotes the probability density, C(·) signifies the joint probability density, and u indicates the cumulative probability.
In this article, the SPI and STI are used to identify the probability of vegetation wilting under combined high-temperature and drought events:
P ( sNDVI < 0 | SPI ≤ − 1.3 , STI > 1.3 ) = P ( sNDVI < 0 , SPI ≤ − 1.3 ) − P ( sNDVI < 0 , SPI ≤ − 1.3 , STI ≤ 1.3 ) P ( SPI ≤ − 1.3 ) − P ( SPI ≤ − 1.3 , STI ≤ 1.3 )
The assessment of vegetation vulnerability includes two main steps: marginal distribution fitting and Vine Copula parameter estimation. The SPI and STI are fitted using a normal distribution to calculate the marginal probabilities. Five commonly used marginal distribution functions (normal, t, Gumbel, Clayton, and Frank) are selected to fit the sNDVI. The optimal distribution functions are determined using the AIC, Bayesian Information Criterion (BIC), and the Kolmogorov–Smirnov (K-S) test, resulting in the marginal probability of sNDVI. Subsequently, a Vine Copula model is employed to combine the marginal probabilities of the SPI, STI, and sNDVI, enabling the calculation of the conditional probabilities of sNDVI concerning drought, high temperature, and compound heat–dryness for the vulnerability assessment.

4. Results and Discussion

4.1. Trends and Mutation Test Results for Meteorological Elements in the Watershed

4.1.1. Trend Analysis of the Annual Precipitation and Average Temperature

Figure 3 shows the trend time history of annual precipitation and annual average temperature in the watershed detected by the univariate linear regression method. It is worth noting that this subsection uses 38 meteorological stations as response units, and calculates the area weight corresponding to each response unit based on the ArcGIS platform. Then, using a daily time step, the precipitation/temperature data from each meteorological station are multiplied by the respective weights to obtain the daily basin-wide precipitation and daily maximum temperature data, which in turn yield the annual precipitation and annual average maximum temperature series.
The annual precipitation and average temperature in the basin decrease at a rate of 4.50 mm/10a and increase at a rate of 0.35 °C/10a, respectively. This indicates that precipitation in the basin decreases, while the transpiration of vegetation, soil, etc., caused by warming increases, leading to a trend of aridification. Consequently, the contradiction between the supply and demand of water resources in the basin becomes more prominent. From a temporal perspective, before 1980, the average annual precipitation in the basin was about 535.80 mm/a, while afterward, it was about 500 mm/a. From 1996 to 2008, the annual precipitation in the basin was only 400 mm. Meanwhile, after 2005, the annual average temperature increased significantly, indicating a notable warming trend in the watershed.

4.1.2. Test Results for Abrupt Changes in the Annual Precipitation and Average Temperature

Figure 4 shows the identification results for the years with variations in annual precipitation and annual average temperature in the watershed obtained using the Mann–Kendall nonparametric mutation test method. It is worth noting that UFk follows a standard normal distribution. By arranging the UFk sequence in reverse order, the UBk statistic curve is obtained. We found that the UFk and UBk curves derived from the annual precipitation and annual average temperature series from 1965 to 2019 intersect at a 95% confidence level. The former has multiple intersections, while the latter has only one. Specifically, the annual precipitation in the basin experienced significant changes in 1979, 1985, and 1989, while the annual average temperature underwent a significant change in 1991.
Hao et al. [37] found that the annual precipitation in the Haihe River Basin has shown a significant decreasing trend in the past 50 years, with a sudden change point in 1979, which is consistent with the results of this paper. Overall, the abrupt changes in annual precipitation and average temperature in the Haihe River Basin occurred in 1979 and 1991, respectively.

4.2. Identification and Diagnosis of Periods Prone to Compound High-Temperature and Drought Events

4.2.1. Analysis of the Time History Evolution of Multiscale (1–12 Months) Meteorological Drought Based on the SPI

Previous studies have concentrated on examining summer combined high-temperature and drought events in river basins or regions. Due to the spatial heterogeneity of meteorological and underlying surface factors, there are certain differences in the study periods of combined high-temperature and drought events across different watersheds. Therefore, this article utilizes the multi-scale SPI and 1-month-scale STI, employing the Spearman method to identify the month most likely to experience combined high-temperature and drought events.
Figure 5 shows the results of correlation tests between the multi-scale SPI (1–12 months) and the STI at a one-month scale. It is worth noting that, in this study, spring is defined as March to May, summer as June to September, autumn as October to November, and winter as December to February of the following year. Additionally, due to the multi-temporal scale characteristics of the SPI, such as SPI-3 representing a seasonal scale and SPI-12 representing an annual scale, this section calculates multi-scale SPI series (i.e., SPI-1, SPI-2 to SPI-12, etc.) using monthly accumulated precipitation data. The Spearman method was employed to determine the correlation between these and the one-month-scale STI at a 95% confidence level, as shown in Figure 5. We can see that from SPI-1 to SPI-12, accumulated precipitation increases, and the drought degree exhibits a clear temporal variation pattern. For instance, between 1996 and 2005, there were more red renderings (indicating severe aridification), while after 2005, the red renderings diminished, indicating a decrease in aridification. In summary, at multiple scales, the degree of aridification in a watershed can be roughly divided into three stages: before 1996, there was a clear pattern of alternating dry and wet changes; between 1996 and 2005, the aridification was severe; and after 2005, the aridification was relatively weak.

4.2.2. Identification Results for Months Prone to Combined High-Temperature and Drought Events

Figure 6 shows the results of correlation tests between the multi-scale SPI (1–12 months) and the STI at a one-month scale. Additionally, due to the multi-temporal scale characteristics of the SPI, such as SPI-3 representing a seasonal scale and SPI-12 representing an annual scale, this section calculates multi-scale SPI series (i.e., SPI-1, SPI-2 to SPI-12, etc.) using monthly accumulated precipitation data. The Spearman method was employed to determine the correlation between these and the one-month-scale STI at a 95% confidence level, as shown in Figure 6. Analysis of Figure 6 reveals that (1) the correlation between the SPI and STI at different scales varies monthly in spring, autumn, and winter. For instance, in June, as the calculated cumulative precipitation increases, the R between SPI and STI rises, while in September, it decreases. However, the correlation coefficient increases in most months. (2) In spring and winter, the correlation coefficients between SPI and STI are both below zero, indicating a negative correlation between the two. (3) At the SPI-3 scale, the correlation coefficient between SPI and STI is highest in July, reaching up to 0.28, suggesting that July is the most likely month for combined high-temperature and drought events in the Haihe River Basin at the seasonal scale. This is also a key reason why Xu [38] and Hao et al. [37] selected SPI-3 and summer for their research. Therefore, this paper proceeds to construct their joint distribution function using the 3-month-scale SPI and STI.

4.3. Fitting the Optimal Marginal Distribution Function and Copula Function for Combined High-Temperature and Drought Events

Based on the identification of the months most prone to combined high-temperature and drought events in the previous section and the results of the SPI calculation scale, this section uses the AIC (Akaike Information Criterion) to perform a goodness of fit test on their marginal distributions. The results are shown in Figure 7, indicating that in the SPI-3 and STI sequences, the optimal marginal distribution functions of each meteorological station in the basin are dominated by Norm and GEV, with no extreme value distribution. Meanwhile, using different detection factors, the proportion of optimal marginal distribution functions varies. For example, for SPI-3, 68% of meteorological stations in the basin have an optimal marginal distribution function of NORM, while for STI, this value rises to 86%. Moreover, the GEV distribution is mainly found in the central part of the basin near Jining and Zhangjiakou.
In this section, the five Copula functions from Table 1 were used to conduct a two-dimensional joint analysis of the high temperature intensity (STI) and drought intensity (-SPI). The AIC was employed to select the Copula function type with the best fitting effect for each site. The results are displayed in Figure 8. An analysis of Figure 8 reveals that the optimal Copula function for 33 sites is the Gumbel Copula function, while the remaining 5 sites are best described by the Clayton Copula function, with no other types of Copula functions.

4.4. Joint Cumulative Probability and Recurrence Period Characteristics of Combined High-Temperature and Drought Events

The optimal Copula function for each site can be used to calculate the two-dimensional cumulative probability distribution of drought intensity (-SPI) and high temperature intensity (STI) at any site, which represents the probability of combined high-temperature and drought events occurring. This approach helps to reveal the impact of climate and underlying surface spatial heterogeneity on the joint cumulative probability of combined high temperature and drought events in the watershed. Thus, this section uses the sites in the southern (Yangcheng), central (Baoding), and northern (Qinhuangdao) parts of the groundwater funnel area as examples to identify the months most prone to compound high-temperature and drought events using methods such as the Pearson correlation coefficient. It also explores the joint distribution patterns and recurrence periods of concurrent high-temperature and intense drought events. Figure 9 illustrates the joint cumulative probability distribution of the 3-month-scale SPI and 1-month-scale STI for the Yangcheng, Qinhuangdao, and Baoding stations. The graph shows that the joint cumulative probabilities of high temperature and intense drought at these stations are 0.48, 0.47, and 0.47, respectively. As the drought intensity increases, the trend of joint cumulative probability at all three stations is similar. When the STI < −20, the joint cumulative probability between the two approaches zero as the drought intensity rises. However, during droughts, the joint cumulative probability value increases significantly with a higher temperature intensity.
Figure 10 illustrates the joint recurrence interval distribution of the 3-month-scale SPI and 1-month-scale STI for the Yangcheng, Qinhuangdao, and Baoding stations. An analysis of Figure 10 reveals that (1) due to the spatial heterogeneity of the watershed climate and underlying surface, the joint recurrence periods for the three stations differ in the time scale. In most years, the joint recurrence periods of high-temperature and drought events in the watershed range from once every 2 to once every 5 years. (2) At the Baoding station in the central part of the basin, the occurrence of extreme events combining high temperature and drought is the highest, followed by the Qinhuangdao station in the north, with the Yangcheng station in the south being the lowest. (3) The distribution map shows that in recent years, the frequency of extreme events combining high temperature and drought has become more pronounced (mostly highlighted in yellow), but the joint recurrence interval is around once every 2 years. (4) As the levels of high temperature and drought increase, the joint recurrence period of composite events significantly increases, but the frequency of occurrence decreases. (5) At higher temperatures, as the intensity of drought increases, the increase in the joint recurrence period of composite events becomes more pronounced.

4.5. Correlations Between the sNDVI and SPI, STI, and Vulnerability Assessment of Vegetation to Combined Drought and Heat Events

4.5.1. Distribution Characteristics of the Correlations Between the sNDVI, SPI, and STI

We selected a 3-month time scale for the SPI and STI to analyze the vegetation’s response to drought and high temperatures. Figure 11 shows the distribution of the Pearson correlation coefficients between the sNDVI, SPI, and STI in the Haihe River Basin from June to August. We found that in most areas of the Haihe River Basin, the sNDVI is positively correlated with the SPI and negatively correlated with the STI. From June to August, the areas where the sNDVI is positively correlated with the SPI account for 90.85% (70.85% significant), 67.25% (42.52% significant), and 80.64% (50.58% significant) of the total area. The regions with a negative correlation between the sNDVI and STI account for 47.63% (33.25% significant), 32.36% (27.24% significant), and 95.19% (76.32% significant) of the total area, respectively.
At the same time, under drought stress, the high values of positive correlation between the sNDVI and SPI in June were mainly concentrated in the southern part of the basin, with a correlation coefficient (CC) reaching approximately 0.34 (at a 95% confidence level). In July, the high values were still distributed in the southern basin, but the CC value decreased to about 0.18. Under high-temperature stress from June to July, high values were mainly concentrated in the northwest region of the basin, an area with dense forests and good ecological conditions. This area is located in a semi-arid zone with water scarcity, where vegetation growth is primarily controlled by water availability, making it highly sensitive and vulnerable to drought. Meanwhile, low-value areas were mainly distributed in the underground funnel areas in the southern basin.

4.5.2. Vulnerability Assessment Results for Vegetation to Combined Drought and Heat Events

It is worth noting that under the same composite dry–hot conditions, a higher loss probability indicates that vegetation growth is more easily affected, implying higher vulnerability. When the number of variables exceeds two, commonly used Copula functions (such as the Archimedean family) struggle to accurately describe the dependency between variables. In contrast, the Vine Copula function used in this study can decompose a high-dimensional joint probability density into a series of bivariate joint or conditional probability densities, better capturing the dependencies among variables and thereby making the model evaluation results easier to interpret and understand.
Based on the Vine Copula model, the probability of the sNDVI being less than 0 (sNDVI < 0) was calculated for three scenarios—extreme drought (SPI < −1.30), extreme high temperature (STI > 1.30), and extreme combined drought and heat events (SPI < −1.30, STI > 1.30)—to assess vegetation vulnerability under different extreme event stresses (see Figure 12). It is worth noting that under the same compound dry–hot conditions, a higher probability of loss indicates that vegetation growth is more easily affected, and the vulnerability is higher. When the number of variables exceeds two, commonly used Copula functions (such as the Archimedean family) struggle to accurately describe the dependency between variables. However, the Vine Copula function used in this paper can decompose high-dimensional joint probability density into a series of bivariate joint or conditional probability densities. This approach better captures the dependencies between variables, making the model evaluation results easier to interpret and understand.
From Figure 12, it is evident that from June to August, the average probabilities of vegetation loss due to drought stress in the Haihe River Basin were 0.33, 0.20, and 0.21, respectively. The probabilities of vegetation loss from high temperature stress were 0.33, 0.19, and 0.22, respectively. However, the probabilities of vegetation loss under combined dry and hot conditions were as high as 0.45, 0.32, and 0.38, respectively, significantly exceeding those under individual drought or high temperature stress. The areas with higher probabilities of loss were concentrated in the southern and northwestern mountainous regions of the basin, indicating greater vegetation vulnerability and severe ecological risks.
Meanwhile, we also found that under different climatic conditions (drought, high-temperature, or combined drought and heat stress), the probability of vegetation loss is highest in June, followed by August, and lowest in July. The vegetation in the southern region of the groundwater funnel area has the highest vulnerability to combined drought and heat stress and is significantly higher than the vulnerability to extreme high temperature or drought. This is mainly attributed to the fact that the growth of vegetation in the southern region of the basin is mainly controlled by water factors (severe groundwater overexploitation and a poor soil water storage capacity), and it is more sensitive and vulnerable to drought.

4.6. Discussion

With the intensification of global warming, droughts and heatwaves occur frequently and widely, which have a serious impact on the healthy growth of vegetation. When drought and heat occur together, the lack of soil moisture leads to an insufficient water supply for vegetation, affecting its growth. Additionally, rising temperatures increase the water vapor pressure difference, causing vegetation to close stomata to reduce water loss, inhibiting photosynthesis and leading to vegetation wilting or even death. Meteorological drought precedes agricultural drought, and a prolonged meteorological drought can deplete soil moisture. This article uses the meteorological drought index SPI to represent drought conditions. Studies indicate that the 3-month SPI effectively characterizes soil moisture drought and significantly impacts vegetation growth. By analyzing the correlation between the SPI and sNDVI at different time scales, it was found that vegetation dynamics in the Haihe River Basin are more sensitive to the 3-month SPI response.
Meanwhile, the southeastern plain area of the Haihe River Basin is a major grain-producing region in China, accounting for 9.40% of the country’s total grain output. However, due to its location in a semi-humid, semi-arid monsoon zone, the ecological environment is fragile, leading to frequent occurrences of disasters such as high temperatures, droughts, and floods [5,38]. This article diagnoses that the month most prone to combined high-temperature and drought events is July, and with the increase in the high temperature intensity, the joint cumulative probability significantly increases. Taking the Haihe River Basin as an example, Xiang et al. [39] used the SPEI and the Standardized Soil Moisture Index (SSMI) index to reveal the spatiotemporal variation of characteristics of meteorological and agricultural drought in the basin, and pointed out that the high incidence area of climate drought is in the central region of the basin. This conclusion is consistent with the highest occurrence frequency of combined high-temperature and extreme drought events in the central region of the basin obtained in this paper. Taking the Haihe Plain as an example, Han et al. [40] used the SPI to reveal that the drought characteristics in the basin have significantly increased since the 1990s, and the occurrence range of high-temperature and drought events during the maturity period of summer corn has significantly increased. This conclusion is consistent with the joint cumulative probability value obtained in this paper, which increases significantly with the increase in the high temperature intensity. Gao et al., by constructing a habitat suitability model, revealed the impacts of drought stress on organisms and habitats. They pointed out that an early decline in water levels caused by drought negatively affects the quality of waterbird habitats, reducing their habitat suitability [41].
In addition, based on the analysis of the correlation between the SPI, STI, and sNDVI, this article constructs a vegetation vulnerability assessment model using the Vine Copula function and statistically analyzes the vulnerability of vegetation to combined drought and heat stress. The research results contribute to a deeper understanding of the response of vegetation to extreme climate events and support the risk management of terrestrial ecosystems to address climate change. However, this study still has certain limitations. The calculations of correlations and vulnerability are based on the statistical relationship between drought and high-temperature indices and vegetation indices. Therefore, in future research, run length theory can be used to identify combined drought and heat events and then analyze the correlations between characteristic variables (such as the duration, intensity, etc.) and vegetation dynamics. This is of great significance for understanding the response mechanism of vegetation to combined drought and heat events. In addition, it is well known that vegetation vulnerability is influenced by both climate change and intense human activities. Since the northwestern part of the study area is mountainous and the southeastern part is an agricultural irrigation zone, it is less affected by human activities. Therefore, this paper only considers the assessment of vegetation vulnerability under changing climate conditions. The extent to which vegetation vulnerability may reach under different scales, different vegetation (plant) types, and different growth states, and how vegetation’s resistance and resilience to drought and heat stress can be addressed in the future are worthy of further discussion.

5. Conclusions

This article examines the Haihe River Basin, which often experiences “nine droughts in ten years”, as the research subject. The Mann–Kendall test, Vine Copula, and other methods are used sequentially to identify the temporal variation patterns of meteorological elements in the basin and to explore the joint distributions and recurrence periods of concurrent high temperature and intense drought events. Meanwhile, a vegetation vulnerability assessment model based on Vine Copula in compound dry and hot climates was constructed to analyze the probability of the response of vegetation in the Haihe River Basin to combined drought and heat events. The main conclusions obtained are as follows:
(1)
The annual precipitation and average temperature in the basin decrease at rates of 4.50 mm/10a and increase at 0.35 °C/10a, respectively, with variations noted in 1979 and 1991.
(2)
The watershed’s overall pattern shows a history of “obvious alternation of dry and wet—a high degree of aridification—low degree of aridification”, with July being the month with the highest occurrence of combined high-temperature and drought events on a seasonal scale.
(3)
The joint cumulative probabilities of high temperature and drought intensity at the Yangcheng, Qinhuangdao, and Baoding stations are 0.48, 0.47, and 0.47, respectively. The frequency of extreme combined high-temperature and drought events is highest in the central basin, followed by the northern part, and lowest in the southern part. The joint recurrence period of these events in the basin ranges from 2 to 5 years.
(4)
In most areas of the river basin, the sNDVI is positively correlated with the SPI and negatively with the STI. Compared to a single drought or high-temperature event, combined drought and heat events further exacerbate vegetation vulnerability in the Haihe River Basin. The probability of vegetation loss in June, July, and August in compound dry and hot climates is 0.45, 0.32, and 0.38, respectively. Vegetation vulnerability is relatively high in the southern and northwestern mountainous areas of the basin, posing severe ecological risks.

Author Contributions

All authors contributed to the study’s conception and design. H.Y.: writing—original draft preparation, conceptualization, methodology, and software. H.W.: conceptualization, methodology, reviewing, and editing. M.Y.: methodology, software, and editing. S.Z.: visualization and investigation. F.B.: reviewing and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the College Research Project of Zhejiang University of Water Resources and Electric Power (X20230002), the Zhejiang Provincial Natural Science Foundation of China under Grant LZJWZ22C030001, and the Research on Flood Evolution and Adaptive Regulation of Typical Watersheds in High Flood Areas under Uncertainty Conditions (HK2024000049).

Institutional Review Board Statement

The submitted work is original and has not been published elsewhere in any form or language.

Informed Consent Statement

All authors contributed to the study conception and design. All authors agree to submit this study to Sustainability.

Data Availability Statement

No data or codes are available.

Acknowledgments

It is our honor to publish our research in Sustainability, and of course, all the authors agree to publish this research in Sustainability.

Conflicts of Interest

Author Huiming Wu and Meng Yan were employed by the company Guangzhou Zhukeyuan Engineering Survey and Design Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Geographical location, meteorological stations, and spatial distribution of water systems in the Hai River Basin.
Figure 1. Geographical location, meteorological stations, and spatial distribution of water systems in the Hai River Basin.
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Figure 2. Research flow chart of the study.
Figure 2. Research flow chart of the study.
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Figure 3. The trend test results for annual precipitation and annual average temperature in the watershed.
Figure 3. The trend test results for annual precipitation and annual average temperature in the watershed.
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Figure 4. The results of the variability test for the annual precipitation and annual average temperature in the watershed.
Figure 4. The results of the variability test for the annual precipitation and annual average temperature in the watershed.
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Figure 5. Evolutionary patterns of the multi-scale (1–12 months) meteorological drought time history based on the SPI (blue represents a humid climate, while red indicates a dry climate. The same color appearing in consecutive periods signifies the duration of either drought or humidity).
Figure 5. Evolutionary patterns of the multi-scale (1–12 months) meteorological drought time history based on the SPI (blue represents a humid climate, while red indicates a dry climate. The same color appearing in consecutive periods signifies the duration of either drought or humidity).
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Figure 6. Results of the correlation analysis between the SPI at multiple scales (1–12 months) and STI at the 1-month scale.
Figure 6. Results of the correlation analysis between the SPI at multiple scales (1–12 months) and STI at the 1-month scale.
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Figure 7. Identification results for the optimal marginal distribution functions for the 3-month SPI and 1-month STI.
Figure 7. Identification results for the optimal marginal distribution functions for the 3-month SPI and 1-month STI.
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Figure 8. The optimal Copula functions for each station in the Haihe River Basin.
Figure 8. The optimal Copula functions for each station in the Haihe River Basin.
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Figure 9. Joint cumulative probability distributions of the 3-month-scale SPI and 1-month-scale STI.
Figure 9. Joint cumulative probability distributions of the 3-month-scale SPI and 1-month-scale STI.
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Figure 10. Joint return period of the 3-month-scale SPI and 1-month-scale STI.
Figure 10. Joint return period of the 3-month-scale SPI and 1-month-scale STI.
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Figure 11. Distribution of correlation coefficients between the SPEI, STI, and sNDVI on a 3-month time scale from June to August in the Haihe River Basin (CC stands for correlation coefficient).
Figure 11. Distribution of correlation coefficients between the SPEI, STI, and sNDVI on a 3-month time scale from June to August in the Haihe River Basin (CC stands for correlation coefficient).
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Figure 12. Probability distribution of vegetation loss in response to drought, high temperatures, and compound dry and hot climates in the Haihe River Basin from June to August (PVL stands for the probability of vegetation loss).
Figure 12. Probability distribution of vegetation loss in response to drought, high temperatures, and compound dry and hot climates in the Haihe River Basin from June to August (PVL stands for the probability of vegetation loss).
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Table 1. Five types of Copula function structural expressions.
Table 1. Five types of Copula function structural expressions.
Copula FunctionDensity Function ExpressionTail Characteristics
Normal C N ( u , v | ρ N ) = ∫ − ∞ Φ − 1 ( u ) ∫ − ∞ Φ − 1 ( v ) 1 2 π 1 − ρ N 2 ⋅ exp ( − s 2 − 2 ρ N s r + r 2 2 ( 1 − ρ N 2 ) ) d s d r Symmetrical tail features, gradually independent tail
t C t ( u , k v | ρ t , k ) = ∫ − ∞ Φ − 1 ( u ) ∫ − ∞ Φ − 1 ( v ) 1 2 π 1 − ρ t 2 ⋅ [ 1 + s 2 − 2 ρ t s r + r 2 k ( 1 − ρ N 2 ) ] − ( k + 2 ) / 2 d s d r Symmetrical tail features
Gumbel C G ( u , v | α ) = exp { − [ ( − ln u ) α + ( − ln v ) α ] 1 α } Asymmetric tail features, sensitive to the upper tail
Clayton C C ( u , v | α ) = max [ ( u − α + v − α − 1 ) − 1 α , 0 ] Asymmetric tail features, sensitive to the lower tail
Frank C F ( u , v | α ) = − 1 α ln ( 1 + ( e − u − 1 ) ( e − v − 1 ) e − α − 1 ) Symmetrical tail features, gradually independent tail
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MDPI and ACS Style

Yin, H.; Bai, F.; Wu, H.; Yan, M.; Zhou, S. Assessment of Vegetation Vulnerability in the Haihe River Basin Under Compound Heat and Drought Stress. Sustainability 2024, 16, 10489. https://doi.org/10.3390/su162310489

AMA Style

Yin H, Bai F, Wu H, Yan M, Zhou S. Assessment of Vegetation Vulnerability in the Haihe River Basin Under Compound Heat and Drought Stress. Sustainability. 2024; 16(23):10489. https://doi.org/10.3390/su162310489

Chicago/Turabian Style

Yin, Hui, Fuqing Bai, Huiming Wu, Meng Yan, and Shuai Zhou. 2024. "Assessment of Vegetation Vulnerability in the Haihe River Basin Under Compound Heat and Drought Stress" Sustainability 16, no. 23: 10489. https://doi.org/10.3390/su162310489

APA Style

Yin, H., Bai, F., Wu, H., Yan, M., & Zhou, S. (2024). Assessment of Vegetation Vulnerability in the Haihe River Basin Under Compound Heat and Drought Stress. Sustainability, 16(23), 10489. https://doi.org/10.3390/su162310489

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