Climate Change, Land Use, and Vegetation Evolution in the Upper Huai River Basin

Abstract

Land-use/land-cover change and climate change have changed the spatial–temporal distribution of water resources. The Huai River Basin shows the spatial and temporal changes of climate from 1960 to 2016 and land-use/land-cover changes from 1995 to 2014. Thus, this study aims to investigate climate change, land use, and vegetation evolution in the Upper Huai River Basin. The Mann–Kendall test (MK), Innovative Trend Analysis Method (ITAM), and Sen’s slope estimator test were used to detect climate change trends. The land-use/land-cover change was also examined using a transformation matrix and Normalized Difference Vegetation Index (NDVI). The results of this study revealed that precipitation has shown a slightly decreasing trend during the past 56 years. However, the air temperature has increased by 1.2 °C. The artificial and natural vegetation and wetland were decreased by 12,097 km², 3207 km², and 641 km², respectively. On the other hand, resident construction land and artificial water bodies increased by 2277 km² and 3691 km², respectively. This indicates that the land cover has significantly changed during the past 30 years. The findings of this study will have implications for predicting the water resources safety and eco-environment of The Huai River Basin. The spatial distribution showed an uneven change in the Huai River Basin. Together, we suggested that the variability of water resources availability in the Huai River Basin was mainly attributed to climate variability, while land use change plays a key role in the sub-basins, which experienced dramatic changes in land use.

1. Introduction

Climate change has a significant influence on vegetation and water resources in river basins across the globe [1,2,3]. The production of large amounts of greenhouse gases will exacerbate global temperature rise [4]. Land-use/land-cover (LULC) change is linked with climate change because of its diverse environmental impacts [5]. It is also an important driving force of environmental changes across all spatial and temporal scales. LULC change contributes significantly to earth atmosphere interactions, forest fragmentations, and loss of biodiversity [6]. It is also one of the factors contributing to climate change. However, the disorderly expansion of urban construction land and the massive loss of ecological land has restricted the sustainable development of the overall ecological environment [4].

In the past decades, water resource shortages have become increasingly severe in many parts of the world, associated with the impacts of climate variability and population expansion. To study the impacts of climate change, the Haui River Basin is a suitable area. Climate variables include precipitation, sunshine hours, air temperature, cloudiness, pressure, rainfall/snow, and wind velocity humidity. These metrological variables interrelate directly or indirectly and significantly influence the environment and living entities [6,7,8]. Land surface temperature is an imperative ecological factor, and its warming tendency affects the topsoil [9].

The rate of biochemical and plant growth is significantly influenced by soil temperature [6]. Temperature and precipitation influences other variables that have a direct impact on vegetation growth during the concurrent year [10]. Climate change can greatly affect land cover by altering vegetation [5,6,7,8,9]. Changes in vegetation cover alter the strength of the sink and may turn it into a source of green house gasses as a result of land processses such as deforestation [11]. The most used vegetation quantity index derived from remote-sensing resources is the normalized difference vegetation index (NDVI) [11]. The NDVI is a compelling index for monitoring vegetation, soil, and water [1,12].

Land-use/land-cover change (LUCC) is the major content of research on world climate and environmental change [13]. It has a huge impact on resources, environment, biodiversity, artificial water environment changes, and economic and social development [14]. Land-use change serves as the ideal starting point for coupled systems about human–environment research in land systems, and the core content of the LUCC dynamic change process has gradually become the focus of scholars. In the past, land-use investigation has merely recognized various land-use scales and structures, however, this method is unable to detect the evolution of the different vegetation types. Lousia employed the TM imagery to analyze land-use/land-cover changes between 1990 to 2004 in Manica province, Mozambique. Ma Xiaoxue et al. [9] explicitly analyzed the characteristics and driving mechanism of land use of the Qinhuai river basin; they proposed the main driving mechanisms of land use in this basin were climate change, economic development, population growth, and the improvement of people’s living standards [15]. Wang Xiulan and Zhang Jin et al. organized relevant methods to research the impacts of change of different land use/cover; they presented the meaning and significance of these models of land resources quantity change, land-use degree change, and land demand forecast in the study of land-use change [16,17,18].

Changes in land cover may occur as a result of the shift in vegetation cover. The determination of land-use and land-cover change is a potent tool for comprehending and evaluating the crucial connection between socioeconomic activities and natural processes [19]. Specifically, it is essential to examine the changes in the different land use/land cover in naturally sensitive areas [20]. The arid and semi-arid regions of central Asia are the most sensitive areas to climate change [21,22,23]. The Huai River Basin could be the largest demonstrative of these regions [24,25]. Climate change has a significant impact on the Huai River, with water quality being the most sensitive parameter [26]. In recent decades, water resources shortages have become increasingly severe in many parts of the world, associated with the impacts of climate variability and population expansion. Additionally, changes in hydrological cycles may attribute to the change in climate and land uses [27].

The Huai River Basin is spatially all-embracing, with prominent environmental gradients principally determined by temperature and precipitation on wide-ranging scales. Hence, the Huai River Basin is an ideal place to observe the changing aspects of landscape structures and biological communities [21]. Changes in the hydroclimate can also alter lake conditions, land cover, and vegetation cover [22,23,24,25,26,27,28,29]. The main aim of the study is to investigate spatiotemporal trends of climate changes during 1960 to 2016 and examine the vegetation change as well as land-use/land-cover changes during 1985 to 2014 in the Huai River Basin. The main objectives of this are (i) to identify historical climatic trends with land use change, (ii) to determine the extent to which observed trends in land-use/cover change can be reproduced, and (iii) to determine the extent to which trends in vegetation cover change in the region under study.

2. Study Area

The study basin is found between 111°56′ E~109°15′ E and 30°57′ N~37°50′ N with area coverage of 207 × 105 km [24]. The mean annual precipitation and temperature of the basin is 883 mm and 11~16 °C, respectively. The average annual surface evaporation is between 600~1500 mm [23]. The basin is known to experience water shortages (Figure 1).

3. Data Source

3.1. Meteorological Data

Climate data were gathered for this study from six distinct representatives meteorological and water gauge stations located throughout the Huai River Basin (

Table 1. List of representative Meteorological stations’ information.

Stations	Altitude (m)	Latitude (N)	Longitude (E)	Annual Mean Precipitation (mm)	Annual Mean Temperature (°C)
Xiangcheng	149.1	32.383333	113.416667	1124.69	15.37
Zhumadian	82.7	33.533333	114.016667	953.42	15.12
Gushi	42.9	32.163333	115.616667″	1064.62	15.64
Fuyang	60.5	31.733333	116.516667	910.07	15.34
Xinyang	68.1	31.413333	116.316667″	839.09	15.52
Xichong	71.5	31.563333	114.116667	1089.32	15.77

). The duration of the study was from 1960 to 2016. The basic information of the representative stations are presented in (Table 1).

3.2. Data on Land Cover

For validation, the topographic map, the soil map, the ecological landscape potential map, the forest map, and the vegetation map were selected. From 1982 to 2014, Landsat Thematic Mapper (TM) and Landsat Enhanced Thematic Mapper (ETM+) maps with a 30 m resolution were used to classify land cover. The National Geomatics Center of China in 2014 described the Global Land covers dataset (GlobeLand30) product, from which the study area’s satellite data for land covers were derived. The name, code, and definition of land cover classification are presented in

Table 2. Gray scale value and vegetation coverage equivalent distribution table.

Rating	Gray Scale Value Division	The Percentage of Vegetation Cover	Land-Use/Landcover Type	Vegetation Coverage Evaluation
Level 1	191~255	>60%	Forest land, dense shrub land, and shrub land.	Excellent
Level 2	156~190	30%~60%	Potential degraded land, good farmland, high coverage grassland, and forest land.	Good
Level 3	139~155	15%~30%	Grassland, the middle plain has fixed sand, and beach.	Medium
Level 4	128~138	5%~15%	Forest land, desert grassland, and scattered vegetation.	Subalternation
Level 5	below 128	below 5%	Artificial water areas, desert, residential areas, etc.	Inferior

. Over the past 30 years, the land cover spatial pattern data were interpreted using all Landsat TM and ETM + images downloaded from USGS (http://landsat.usgs.gov/ accessed on 1 September 2019).

3.3. Vegetation Data

The MODIS NDVI product (MOD13Q1), which was obtained from NASA’s land processes distributed active archive center, served as the source for the normalized difference vegetation index (NDVI) data. The MODIS surface reflectance values from the red band (610–680 nm) and the near-infrared band (780–890 nm) was used to calculate the NDVI after they were adjusted for molecular scattering, ozone absorption, and aerosols [30]. The MOD13 Q1 products, which include 12 scientific data sets with a 16-day temporal sampling period and a spatial resolution of 250 m × 250 m, were derived from the most recent version.

4. Methods

4.1. Climate Variable Analysis

4.1.1. Innovative Trend Analysis Method (ITAM)

ITAM divides dataseries into two equivalent sub-series and categorizes them in increasing order [6,15,31,32,33,34]. The X-axis (x_i:i = 1, 2, 3, …, n/2) and the Y-axis (x_j:j = n/2 +1, n/2 + 2, …, n) were then used to arrange the two sections on a coordinate system. There is no trend if time-series data are collected on a scattered plot along a 1:1 (45°) straight line. When data points accumulate above the 1:1 straight line, the trend is upward, and when data points accumulate below it, the trend is downward. The difference in mean values between x I and x j might reflect how far a data series is trending (Figure 2).

4.1.2. Mann–Kendall (M–K) Test Method

The Mann–Kendall (M–K) test is used to detect the trends of time series data [2,35]. The test statistics “S” is equated as follows:

$$S=\sum _{i=1}^{n-1} \sum _{j=i+1}^{n}sgn \left(x_j-x_i\right)$$

(1)

$$sgn\left(x_j-x_i\right)=\left\{\begin{array}{c}+1 if \left(x_j-x_i\right)>0 \\ 0 if \left(x_j-x_i\right)=0\\ -1 if \left(x_j-x_i\right)<0\end{array}\right.$$

(2)

where $x_j$ and $x_i$ represents the data points in periods $j$ and $i$. While the amount of data series is larger than or equivalent to 10 $\left(n\ge 10\right),$ the M–K test is then categorized by a standard distribution with the mean $E\left(S\right)=0$ and variance $Var\left(S\right),$ which is given as follows [35]:

$$E\left(S\right)=0$$

(3)

$$Var\left(S\right)=\frac{n\left(n-1\right)\left(2n+5\right)-{\sum }_{k=1}^m t_k\left(t_k-1\right)\left(2t_k+5\right)}{18}$$

(4)

where m is the number of the tied groups in the time series, and $t_k$ is the number of ties in the $k\mathrm{th}$ tied group. From this the test, the $Z$ statistics is obtained using approximation as follows:

$$Z=\left\{\begin{array}{cc}\hfill \frac{s-1}{\delta }\hfill & \mathrm{if} S>0 \hfill \\ \hfill 0,\hfill & \mathrm{if} S=0\hfill \\ \hfill \frac{s+1}{\delta }\hfill & \mathrm{if} S<0\hfill \end{array}\right.$$

(5)

4.1.3. Sen’s Slope Estimator Test

The slope Q_i between two data points is given by the equation [2,35].

$$Q_i=\frac{x_j-x_k}{j-k}, for i=1,2,\dots N$$

(6)

where $x_j$ and $x_k$ are data points at time $j$ and (j > k), respectively.

When there is only single datum in each time, then $N=\frac{n\left(n-1\right)}{2}$; n are a number of time periods. However, if there are many data in each year, then $N<\frac{n\left(n-1\right)}{2}$; n total number of observations [36]. The $N$ values of slope estimator are arranged from smallest to biggest. Then, the median of slope ($\mathsf{\beta }$) is computed as follows:

$$\beta =\left\{\begin{array}{c}Q\left[\left(N+1\right)/2\right] when N is odd \\ Q\left[\left(N/2\right)+Q\left(N+2\right)/\left(2\right)/\left(2\right)\right] when N is even\end{array}\right.$$

(7)

4.2. Analysis of Land Use and Land Cover

The overlaying operation was used to perform spatial analysis, which revealed how land use and land cover changed over time and established a connection between the two. A land cover transformation map was obtained and utilized for transformation matrix analysis by intersecting the two land cover/land use maps (1985 and 2014). The magnitudes of land cover shifts were calculated as [12].

$$CA=TA\left(t_2\right)-TA\left(t_1\right),$$

(8)

$$CE=\left[CA/TA\left(t_1\right) \right]\ast 100,$$

(9)

where: t_1 and t_2 represent the beginning and end, while TA, CA, and CE represent the total area, changed area, and extent of change, respectively. The Kappa coefficient (Kappa) was also calculated. The agreement between user-assigned ratings and the predefined producer ratings is measured by kappa. The difference between the amount of agreement that is present (the “observed” agreement) and the amount of agreement that would be expected to be present by chance alone (the “expected” agreement) is the basis of the calculation [37].

$$K=P\left(A\right)-P\left(E\right)/1-P\left(E\right)$$

(10)

$$P\left(A\right)=\frac{\left(A+D\right)}{N},$$

(11)

$$P\left(E\right)=\left(\frac{A1}{N}\right)\ast \left(\frac{B1}{N}\right)+\left(\frac{A2}{N}\right)\ast \left(\frac{B2}{N}\right),$$

(12)

where $K$ is the Kappa coefficient, $P\left(A\right)$ is the number of times the $K$ raters agree, and is the number of times the $K$ rates are expected to agree only by chance, $A$ and $D$ are unchanged categories, $A1$ and $B1$ are subject’s categories, and $N$ is the change of results. ArcGIS 10.2 and the land-use transfer matrix were used to investigate a comprehensive land-use dynamics degree [38] of the Huai River Basin.

$$LC=\frac{{\sum }_{i=1}^n VL{U}_{i-j}}{{\sum }_{i=1}^n L{U}_i T} \times 100\%$$

(13)

where LC is the total land-use dynamic degree, Lui is the type of area, Lui−j is the total value of an area in land-use type I to J, and T is the amount of time that has passed since the last monitoring. LC is the land-use dynamic change rate when T is set for years.

We can generate five groups of transfer matrixes for various land-use types using five years of land-use data—1985–1990, 1990–2000, 2000–2005, 2005–2014, and 1985–2014—to comprehend the basin’s transfer situation.

4.3. Vegetation Analysis

One of the most widely used vegetation indices of plant biomass and activity is the normalized difference vegetation index (NDVI) which is used to denote a shift in an area’s vegetative greenness [1,39].

$$NDVI=\frac{\left(NIR-RED\right)}{ \left(NIR+RED\right) },$$

(14)

where the electromagnetic spectrum’s near-infrared (NIR) and red (RED) channels correspond to bands 2 and 1 of the MODIS (MOD13Q1) product, respectively. We used a linear regression model based on MODIS data to spread GIMMS data until 2014 due to the different spatial resolutions of GIMMS and MODIS. In addition, we utilized the same time data to check outspread data. Afterward, the NDVI average correlation coefficient reached greater than 90% in the Huai River Basin. The land-use type and the NDVI gray-scale value were additionally examined (Table 1) to determine vegetation coverage evolution tendency.

5. Results and Discussions

5.1. Trends of Observed Climate Changes

In the study region, positive and negative tendencies are present by the M–K test estimator, and annual mean precipitation, and display temporal variations (Figure 3). This is in good agreement with studies and annotations from various parts of China [8,24]. Since precipitation tends to decrease across the study basin, the anticipated decrease in precipitation determination will probably result in a decrease in water accessibility in the years to come [8].

The hydrological series and the supply of water resources for ecological units and society can be disrupted by a decrease in precipitation during the wet season [40]. The summer is the most precipitous time of year in the study area, contributing nearly 49.3% of the total precipitation, indicating a high intensity of precipitation and snowfall. Additionally, stream flow is at its best during this time of year. The squat rainy season, which begins in December and lasts through February (winter), accounts for approximately 7.3% of the total precipitation. Winter is more susceptible to the occurrence of persistent drought events, as revealed by the consequences of light precipitation concentration [30]. In China, a variety of trend exploration studies have been piloted at various spatiotemporal measures, yielding a wide range of results with various trend test parameters. Although they discovered a statistically significant upward trend in temperature, the situation regarding precipitation varied.

The rise in temperature is amongst the indices of global climate transformation. Globally, mean air temperature has increased by 0.85 °C from 1880 onwards, which is expected to move along in the near future [4,5,7,40]. The global large inland water bodies temperature has been promptly heating ever since 1980, at the rate of 0.05 ± 0.012 °C/year and by the maximum rate of 0.1 ± 0.011 °C/year [41]. An abrupt upward trend of average annual air temperature was detected in the upper reaches of the Huai River Basin via 1.2 °C or 0.021 °C/year during the deliberated chronological period from 1960 to 2016 (Figure 4b). It is nearly twice as fast as the global average heating rate (0.012 °C/year) [42]. The basin’s average annual temperature was found to be 15.5 °C. From 1990 onwards, a significant rise in temperature was observed.

The cumulative upward trend of air temperature could be the outcome of global warming, which is due to the “built-up heat island”, greenhouse influence, and long-standing climate inconsistency [6,7,8,9]. In the present study, the temperature during the summer season was higher. Similarly, Gu et al. [43]. found a higher magnitude of air temperature with an increasing trend in the summer seasons, which was superior to the other periods (Figure 5).

The M–K trend detection test result discovered that the annual mean temperatures have been considerably increasing throughout the period. The general increment in mean annual temperature in the study region then is principally indorsed to a rise in the minimum temperature. Increased temperatures in winter and spring will affect the precipitation phase and, as a consequence, the snow/precipitation ratio and the volume of water stored in snow cover will be changed. Therefore, the hydrology of rivers in the Northern Hemisphere is sensitive to climate change [44].

5.2. Change Detection and Classification of Land Cover

We may be able to comprehend the synoptic temporal change in the study area’s land cover types through the use of satellite data, which can make concurrent, synoptic, and repetitive annotations [45].

A maximum-likelihood classifier and the classification scheme in Table 2, have made five classification maps of the Huai River Basin. With substantial precision, the study region landscape was classified into 65 land-use classes (Figure 6).

In the present study, the artificial and natural vegetation, as well as the wetland, showed a decreasing trend of 12,097 km², 3207 km², and 641 km², respectively, during the period 1985–2014. Meanwhile, the area of resident construction land and artificial water showed an increasing trend by 2277 km² and 3691 km², respectively. However, the other areas only displayed a small change (Figure 7).

It should be noted that the artificial water bodies experienced the majority of the area’s greatest changes and urban land during the past 30 years. The dynamic degree was 9.2% and 6.4%, respectively. The area for wetland artificial vegetation and natural vegetation was diminished, and the shifting ratio was inferior. From the comprehensive land-use dynamic degree of the whole study years, the degree of intervention to land use by human activities was prevalent from 2005–2014. The dynamic degree is approximately 0.5% from 1985–2014, which implies that about 4.5% of the land use type has been transformed throughout this period every year (

Table 3. Land-use/land-cover dynamics of the Huai River Basin from 1985 to 2014 (%).

Land-Use/Cover Type	1985–1990	1990–2000	2000–2005	2005–2014	1985–2014
Artificial Vegetation (AV)	−0.56	0.16	0.21	−1.04	−1.32
Natural Vegetation (NV)	0.16	0.05	−1.09	−1.28	−2.38
Artificial water area (AW)	3.03	2.94	4.54	2.78	9.15
Wetland (W)	−0.71	0.31	−1.15	0.23	−1.44
Resident construction land (RCL)	3.45	0.76	−2.13	7.04	6.41
Others (O)	2.18	−1.57	−7.47	11.55	0.21
(LC) Comprehensive land-use dynamic degree	0.44	0.14	0.33	0.57	0.46

). In the course of the early 21st century, the population of the Huai River Basin had a high rate of growth; throughout this phase, the per capita possession of land resources was small, which caused a shift in land use to some extent (Figure 7).

RCL and AW bodies are formed from more than one third of artificial vegetation. More than 30% of Nevada is converted into additional land uses, such as empty land and sand. The leading causes for the alterations are hasty population growth, urbanization, ecological deterioration, and high intensity of socio-economic activities in the study area [46].

Natural factors are yet another significant cause of land-cover shift. The components of the land cover, for instance, may be impacted directly or indirectly by the effects of climate change. The overall pattern of regional vegetation was characterized by the shifting of various forest types, whereas the distribution of forest boundaries may not have changed as clearly [25]. When the climate changes rapidly, fast-growing pioneer communities expand rapidly and require a significant amount of migration distance. As a result, it is essential to define the size of changes in land cover (

Table 4. The Huai River Basin’s land-use and land-cover transformation matrix from 1985 to 2014 (in percent).

Land Use Type	AV	NV	AW	W	RCL	Others
Natural Vegetation	7.44	91.82	0.208	0.264	0.202	0.065
Artificial Vegetation	93.541	2.269	0.121	0.447	3.588	0.034
Artificial water area	31.086	6.071	42.135	15.643	5.03	0.035
Wetland	8.222	1.434	2.195	87.362	0.775	0.012
Resident construction land	45.984	1.682	0.299	0.305	51.668	0.062
Others	11.843	36.978	0.055	0.079	4.709	46.337

).

5.3. NDVI

MODIS vegetation indices produced on 16-day intervals and at multiple spatial resolutions provide consistent spatial and temporal comparisons of vegetation canopy greenness, a composite property of leaf area, chlorophyll, and canopy structure. Two vegetation indices were derived from atmospherically corrected reflectance in the red, near-infrared, and blue wave bands—the normalized difference vegetation index (NDVI). The strength of global NDVI data is their high temporal information content [1,47]. The common compositing time of 8–14 days provides at least 25–30 global NDVI datasets per year [2]. This ensures consistency with the historical and climate applications of NOAA’s AVHRR NDVI time series record, reducing variations in the canopy and soil and increasing sensitivity in dense vegetation conditions. The two products better describe the global range of states and processes of vegetation [47]. As a result, the NDVI was used to determine the vegetation cover (Figure 8).

The pixel’s NDVI values ranged from −1 to 1. The highest NDVI values were greater than 0.6, indicating the healthiest or richest vegetation. The normalized difference vegetation index had a significant difference in the study region. This is due to the prevalence of sufficient precipitation during the period from 1985 onwards. Contrarily, during 2000, the vegetation coverage had significantly declined, and the percentage of preeminent coverage area accounted for only about 8.7% of the study region. This was principally because the total mean annual precipitation significantly declined [44]. For the period from 2005–2014, the area of preeminent vegetation coverage declined by 30%; on the other hand, the area of lower vegetation coverage NDVI < 0.4 increased by about 75%. The major reason for the vegetation boost was the prevalence of adequate precipitation throughout the summer season [44], and to some extent, harsh climatic circumstances were reduced during this period that generated advantageous situations for vegetation growth. On the contrary, urbanization leads to a scattered configuration of vegetation coverage in the study region.

Temperature and precipitation may influence the dynamics of vegetation in the Huai River Basin. Changes in the global carbon and hydrology cycle, as well as feedback on climate change, could be caused by vegetation dynamics [25]. One of the factors that alter the ecosystem, water system circulation, and surface water supply balance is the cover of vegetation. There may be a number of factors connected to the change in the Huai River’s vegetation cover. This indicates a significant degree of climate change throughout the basin due to the coincidence of overlaps with climatic variations of the current weather. It is obvious that cultivated land areas are growing bigger and areas of vegetation are becoming smaller. This suggests that human activity may have altered the river basin’s land cover.

5.4. Implication of Climate Change over Land Cover/Use of Huai River Basin

In the Huai Basin, precipitation has decreased significantly over the past 56 years. The basin’s temperature has varied by approximately 1.2 °C over the same period (Figure 9). The most changed parts of the land covers in the Huai River Basin are the artificial vegetation, natural vegetation, and wetland, which showed a decrease of 12,098, 3028, and 640 km², respectively. This could be due to the increasing temperature and an overall decline pattern of precipitation in the study basin. On the other hand, the rest of the other land transformation type is related to human activities.

In this region, climate change may alter the land cover [48]. Large mountains and river basins have different levels of vegetation cover. This demonstrates that the mountain and river valley vegetation cover changed between 1985 and 2014 [49]. Additionally, the 2007 average annual temperature overlap was the highest (Figure 10). This is a result of shifting land cover as a result of intense human activity and global warming. Along the river valley, there is also a large amount of agricultural land. In addition, extensive human activity in the region has altered the vegetation cover of the Huai River Basin (Figure 10). The vegetation cover in the river basin will change if there are many people working there [49]. Artificial surfaces and cultivated land have increased as a result of intense human activity. Thus, this region’s vegetation and land cover may be significantly affected by climate change and human activity.

6. Conclusions

We assessed the Huai River Basin’s climate changes between 1960 and 2016, land use and land cover, and vegetation between 1985 and 2014 using remote sensing, classification of land use and land cover, and detection of vegetation.

The study area’s precipitation slightly changed between 1960 and 2016. On the other hand, the temperature often went up by 1.2 °C, leading to a significant increase over the past 40 years in the semi-arid central Asian region.

Artificial vegetation, natural vegetation, and wetland changed the most in the Huai River Basin with a decreasing trend. Construction land for homes significantly increased. The Huai River Basin’s land cover and land use have changed slightly over time, both in quantity and quality.

There was a clear shift in the vegetation cover in the river basin and mountainous regions. Between 1985 and 2014, the vegetation cover and land cover changed significantly. In the study area, these changes coincide with the process of climate change and human activity. Furthermore, the increase in run-off may also have a wider implication for increasing soil erosion and sedimentation. Thus, curving the trends of LULC towards increasing vegetation cover is very important to reduce wet season flow and surface run-off. Therefore, the concerned entities should take appropriates measures for the sustainable management of land as well as water resources.

Scientific research on the causes of land-cover change and its potential effects on the hydrological and ecological systems of the Huai River Basin is essential in the near future. Due to the limitation of data, this study only considers six stations for this analysis. Therefore, detail study should be conducted in the future by considering more sample stations.