Ecological Risks Arising in the Regional Water Resources in Inner Mongolia Due to a Large-Scale Afforestation Project

Abstract

In recent years, a large-scale afforestation campaign has been implemented in Inner Mongolia, China, to control desertification and soil erosion. However, the water consumption associated with large-scale afforestation significantly impacts the water resources in Inner Mongolia, resulting in a major ecological risk. This study aimed to evaluate the ecological risk of water resources caused by afforestation in the region. In this study, using land cover data, normalized difference vegetation index (NDVI) data, and meteorological data, we performed trend analysis and used the water balance equation and water security index (WSI) to analyze the ecological risks of water resources caused by afforestation in Inner Mongolia from 2000 to 2020. The results show that (1) the afforestation area in Inner Mongolia was 5.37 × 10⁴ km² in 2000–2020; (2) afforestation in arid and semi-arid areas led to a reduction in water resources; (3) afforestation reduced water resources in the study area by 62 million cubic meters (MCM) per year; and (4) ~76% of afforestation regions faced ecological risks related to water resources. This study provides scientific suggestions for the sustainable development of regional water resources and afforestation.

1. Introduction

To protect the fragile ecosystem and economy, combat desertification, and control dust storms, the Chinese central and local governments have implemented a series of ecological engineering projects in China [1], such as the Slope Land Conversion Project, China’s Natural Forest Protection Project, River Shelterbelt Project, and the Returning Farmland to Forest and Grassland [2]. Ecological engineering slowed desertification and its expansion in China, significantly contributing to the world’s “greening” trend [3]. Chen et al. [3] indicated that the global green leaf area has increased by 5% since the early 2000s. China and India contributed 25% and 6.8%, respectively, to the increase in greening on land.

However, this significant land cover change resulting from afforestation very strongly affected the ecological environment, especially the water resources [4,5,6,7]. It was confirmed that forests could increase regional evapotranspiration and reduce total runoff discharge compared to the absence of forests under the same precipitation conditions. Some studies pointed out that 10–40% of annual precipitation was lost by the canopy interception [8]. It was shown that the total canopy interception was 76.6 mm in Shaanxi Province, northwestern China, accounting for 18.6% of the gross precipitation. Moreover, in forests, shallow roots extract soil water provided by precipitation, while groundwater is consumed through deep roots during the dry period [9]. Karimov et al. [10] found that shallow groundwater contribution to plant transpiration exceeds 60% in the upstream area of the Syrdarya River, in Central Asia. In the E**a Basin, China, Populus euphratica obtains 53% of its water from groundwater [11]. In the dry season, the groundwater uptake accounts for 73.2% [12]. Due to the effects of canopy interception and transpiration from soil and aquifer layers, the effective precipitation and groundwater recharge in forests are much lower than in other areas [13]. Keese et al. [14] carried out an unsaturated flow modeling study, and through simulations, they found that forests significantly decreased groundwater recharge by factors of 2 to 30 relative to the recharge in non-vegetated areas [14]. Therefore, it was concluded that large-scale tree planting in China could lead to regional water resource shortages. ** Studies (GIMMS) covering the period from 2000 to 2020, and data were collected from the Ecological Forecasting Lab of NASA’s Ames Research Center (https://ecocast.arc.nasa.gov, accessed on 12 April 2023). The annual evapotranspiration (ET) data (MOD16 data product) from 2000 to 2020 were obtained from the Land Processes Distributed Active Archive Center (https://lpdaac.usgs.gov/, accessed on 10 February 2023). The MOD16 evapotranspiration dataset is based on the logic of the Penman–Monteith equation, which includes inputs of daily meteorological data and MODIS remote sensing data products such as vegetation property dynamics and land cover [25]. NDVI and ET data were resampled to 1000 × 1000 m resolution. We obtained the 2000–2020 afforestation data from China Forestry Statistical Yearbooks and Inner Mongolia Forest Resources Inventory data (Forestry and Grassland Bureau of Inner Mongolia Autonomous Region, https://lcj.nmg.gov.cn/, accessed on 21 August 2023). We obtained the 2000–2020 water resource data from the Inner Mongolia Water Resource Bulletin (Water Resources Department of Inner Mongolia Autonomous Region, http://slt.nmg.gov.cn/, accessed on 21 August 2023).

2.3. Methods

2.3.1. Identifying the Afforestation Regions

The identification of afforestation regions is an important prerequisite to evaluating the ecological risk of water resources caused by afforestation [26,27]. Compared with natural forestland, the NDVI of artificial forestland is more easily affected by human activities, and the change trend is more obvious [28]. In this study, the NDVI and land cover data were used to analyze the spatial characteristics of vegetation and forestland, afforestation areas were identified through the combination of the two, and finally, sample points were used to verify the results (Figure 3). The main steps are as follows:

(1)

Trend analysis was used to identify areas with significant increases in the NDVI in Inner Mongolia from 2000 to 2020. Trend analysis is a linear regression analysis of time-dependent variables [29,30]. The constantly changing properties of vegetation may be reflected in the trend of changes in the NDVI for each grid by applying linear trend analysis. In this study, the trend of NDVI change in Inner Mongolia from 2000 to 2020 was determined using a unitary linear regression model, and the slope of the trend was calculated using the least-square method as follows:

$$Slope=\frac{n{\sum }_{i=1}^n\left(i\times {NDVI}_i\right)-{\sum }_{i=1}^n\times {\sum }_{i=1}^n{NDVI}_i}{n{\sum }_{i=1}^n{i}^2-{\left({\sum }_{i=1}^{n}i\right)}^2}$$

(1)

where $Slope$ refers to the trend of vegetation change, $n$ is the number of years studied ($n$ = 21 in this study), $i$ is the ordinal number of a given year, and ${NDVI}_i$ denotes the NDVI value for year $i$; in the case of a $Slope$ > 0, this indicates that the NDVI tends to increase.

A significance test is often used to assess the accuracy of the trend change. In this study, we assessed the significance of trends using the F-test (p < 0.05). The calculation’s formula is as follows:

$$F=U\times \frac{n-2}{Q}$$

(2)

$$U={\sum }_{i=1}^n{\left(\widehat{y_i}-\overline{y}\right)}^2$$

(3)

$$Q={\sum }_{i=1}^n{\left(y_i-\widehat{y_i}\right)}^2$$

(4)

where $U$ refers to the sum of squares of errors, $Q$ is the regression square sum, $y_i$ is the NDVI value for year $i$, $\widehat{y_i}$ is the NDVI regression value for fear $i$, $\overline{y}$ is the average NDVI value in n year, and $i$ is the ordinal number of a given year.

(2)

Five periods of land cover data (2000, 2005, 2010, 2015, and 2020) were used to analyze the increase in forestland regions during 2000–2020. The histogram analysis method was used to calculate the NDVI values of the increased forestland regions, and the NDVI range of 20–80% was considered the NDVI threshold of the afforestation regions.

(3)

Afforestation areas were determined by overlap** the results obtained in the prior two steps. The NDVI of the increased region that fell within the NDVI threshold of the afforestation region was used to identify an afforestation area. The accuracy of afforestation identification was mainly verified with two kinds of data. First, the afforestation area determined during 2000–2020 in this study was verified with statistical data. From 2000 to 2020, the government in Inner Mongolia carried out four forest resource inventories, the forest area increased from 20.51 × 10⁴ km² to 26.15 × 10⁴ km², and the afforestation area was 5.64 × 10⁴ km² (Figure 4). The second series of data were sampling points, which were used to verify the spatial distribution of afforestation and mainly comprised 23 field survey samples and 147 samples collected from the Tsinghua University global land cover dataset (Figure 5). The overall identification accuracy was 78.24%.

To better demonstrate the ecological risks of water resources caused by afforestation, the extracted afforestation raster data (1000 × 1000 m) were converted into point data through areal averaging.

2.3.2. Water Balance

To understand the influence of afforestation on water resources in Inner Mongolia, the water balance equation was used to calculate the change in water resources in afforestation regions [31], which can be described as follows:

$$Q=P-{ET}_a-\Delta S$$

(5)

where $Q$ refers to the water resource (mm); $P$ is the precipitation (mm); ${ET}_a$ is the actual evapotranspiration (mm); and $\Delta S$ indicates basin water resource change (mm), which is generally assumed to be zero in the long term [15,16,21,22]. In the study area, deep groundwater is usually extracted for agricultural irrigation, and afforestation activities mainly affect shallow groundwater. Therefore, the influence of agricultural irrigation water on the water balance of afforestation is ignored in the calculation.

In order to detect the changing trend of water resources caused by afforestation during 2000–2020, the least-square linear regression model was used.

2.3.3. Ecological Risk

The water security index (WSI) was used to evaluate the ecological risk of water resources caused by afforestation, which quantified regional water security from the perspective of regional supply and demand balance [32]. The equation is as follows:

$$WSI=lg\left(\frac{P}{D}\right)=lg\left(\frac{Pe}{{ET}_a}\right)$$

(6)

where $P$ refers to the water resource supply (mm), assuming that the afforestation area receives its water supply only through precipitation; $Pe$ is the effective precipitation (mm) and is calculated using the soil conservation service method developed by the U.S. Department of Agriculture (USDA); $D$ is the water resource requirement (mm); and ${ET}_a$ was considered the water resource requirement of the afforestation area. Thus, $WSI$ < −0.5 indicates high risk; −0.5 ≤ $WSI$ < 0 indicates low risk; 0 ≤ $WSI$ < 0.5 indicates low security; and 0.5 ≤ $WSI$ < 1 indicates high security.