Abstract

Forests play a key role in water conservation. Based on official statistics and field surveys, this paper uses the indirect market evaluation method (IMEM), and especially the water balance method (WBM), to assess the quantity and value of forest water conservation in 2016 in Zhalantun City in Inner Mongolia, China. The results indicate that the value of forest water conservation in Zhalantun is RMB 15.085 billion yuan (RMB is an abbreviated form of ‘Renminbi’, which stands for currency of People's Republic of China. It's primary unit is the yuan), which accounts for 80% of gross domestic product (GDP), and which is 3.74 times the added value of the primary industry, and 1.56 and 2.96 times the added value of the secondary and tertiary industries, respectively, in the same year. The paper also discusses some related issues and points out that the negative effects should also be considered and calculated when using the WBM to evaluate forest water conservation services, and forest water conservation services management and environmental statistics should be established in advance to better serve management planning and policy making.

INTRODUCTION

The study of the relationship between forests and water began at the beginning of the twentieth century (Hewlett & Hibbert 1967), and focused on the impact of forest changes (mainly deforestation rather than afforestation) on water production in the drainage basin. Ice & Stednick (2004) systematically summarized 95 basin experiments in the United States and concluded that the catchment yield increases as forest harvesting increases. Regarding the impact of forests on river flow, some scholars believe that forests have no significant effect on the annual output of the river basins (Bosch & Hewlett 1982). However, the forest cover of the river watershed has a significant impact on annual water production. For every 10% increase in the forest coverage, the river flow increased by about 19 mm·yr−1. Meanwhile, the British research results are the opposite: forest vegetation has been shown to reduce water production (Creed et al. 2011). Studies in Japan and Germany show that deforestation can directly increase runoff (Buttle et al. 2009), while many American scholars think that small-scale catchments and watersheds (below tens of square kilometers) can reduce the annual runoff due to the existence of forests. Eisenbies et al. (2007) found that deforestation can increase annual runoff to a certain extent (hundreds or thousands of square kilometers). In contrast, Andreassian (2004) found that the annual runoff increased with the increase of forest coverage. However, the study results of Zhou (1999) show that there is no certain regularity between the increase and decrease of the annual runoff and the size of the catchment areas of forests.

Different experts have explained the services of forest water conservation from different perspectives. Jin et al. (2005) pointed out that the forest ecosystem has the following functions: water storage, runoff regulation, flood prevention, water recharge, and water purification. Li & Wang (2012) found that different types of forests have different ways of conserving water because of their biological characteristics and stand structure. Zhang & Du (2010) found that forest ecosystems have good water conservation functions, which can be achieved through three functional layers (canopy layer, litter layer, and forest soil layer) that can affect the water balance of the forest ecosystem. According to Yu et al. (2004), the forest has the function of conservation. Meanwhile, Lee (1984) argues the function of water is mainly determined by the joint action of the forest canopy, litter layer, and soil layer.

The water cycle of the forest ecosystem mainly refers to the condensation of water vapor in the atmosphere and the precipitation of rainfall. Research on the physical quantity of water in forest conservation cannot be separated from the water cycle of the forest ecosystem (Wu 2007). Therefore, based on the forest ecosystem water cycle, scholars use various methods such as WBM, impoundment estimation, and the runoff coefficient method to calculate the physical quantity of water (Jiang 2003). Moreover, Hou (1995) and Li (1999) evaluated the theories and methods of determining forest ecological value both at home and abroad, so as to incorporate the value of water conservation into the national economic accounting system. In short, domestic and foreign scholars generally use alternative engineering methods when studying the water conservation value of forest resources. In addition, some studies use demand pricing, production function, cost pricing, and other water pricing theories and methods to calculate the value of water conservation (Lu & Yao 2004).

We know that since the UN Conference on Environment and Development held in 1992, all countries in the world have made sustainable development a common goal (Shi & Zhang 2014). At present, China's forestry development strategy is mainly based on the construction of ecological civilization. That is, it seeks to integrate long-term forestry development into the overall social and economic development of the country, and it is an inevitable choice for promoting a sustainable relationship between humans and nature. With the continuous increase in civilization's demand for water resources and the drastic deterioration of global water resources, the process of water conservation through forest management has attracted more and more attention (Andreassian 2004). At present, fields such as ecological economy, and resource and environmental economy focus more on the economic value of water conservation. Thus, the monetary value accounting of water conservation is an important part of the research on forest ecosystem services. Its research is closely related to the development of multidisciplinary sciences such as ecology, environmental economics, and forest hydrology, which can provide an important basis for sustainable forest management. Therefore, it has an important theoretical and practical significance for forestry's sustainable development and ecosystem service management.

METHODS AND DATA

Methods

The evaluation method of water conservation services is similar to the common ecosystem service evaluation, and it has three major categories: the direct market evaluation method (DMEM), the indirect market evaluation method (IMEM), and the hypothetical market evaluation method (HMEM) (Rao et al. 2014). As part of the ecosystem's functions, the forest water conservation service is the forest ecosystem function that is most used by human beings; how much it is affected by its own characteristics is often evaluated using the water balance method (WBM), which is an indirect method.

The IMEM is suitable for those ecosystem services without actual market transactions but with a substitute market. It works by calculating the cost for applying certain technical means to obtain certain forest ecosystem services with the same result, and thus it indirectly evaluates the forest ecosystem's service value (John 1996).

In IMEM, a substitute method is often used in which some corresponding goods or services in the market reflect the values of substituted ones. Beyond that, the opportunity cost method, travelling cost method, shadow project approach, etc. are also often used (Westman 1977).

In this paper, forest water conservation services refer to the services of water regulation and water quality purification in the forest ecosystem; the methods of assessing physical and monetary quantities are as follows.

Forest water regulation

We know that runoff refers to the flow of water along the ground or underground. When the rainfall exceeds the soil infiltration rate, the flow of water along the surface is called surface flow (or overland flow). The water that infiltrates the soil and flows along underground is called underground flow, while the flow of water along a shallow layer of soil is called a subsurface flow or interflow. Forest water regulation (FWR) mainly refers to water flowing through the canopy, litter, root, and soil layers of the forest which is regulated through good permeability of woodland that intercepts and absorbs rainfall, so as to make surface runoff a rare occurrence in woodland areas (Zhou 1999).

Physical quantity. Through the interception and storage of rainfall, the forest changes the distribution of rainfall and alleviates the problem of surface runoff. Thus, the forest plays a regulating role in runoff.

FWR is closely related to the forest area, amount of precipitation, forest evaporation, and surface runoff. From the perspective of the balance of water quantity, the total amount of FWR is the difference between rainfall and evapotranspiration as well as other water consumption. The total annual amount of FWR can be calculated according to the water quantity balance of the forest region. The formula for calculating FWR is as follows: 
formula
(1)
in which, is the amount of FWR, m3·yr−1; P is rainfall quantity, mm·yr−1; E is forest evapotranspiration (FE) quantity, mm·yr−1; C is surface runoff quantity (SRQ), mm·yr−1; and A is forest area, ha.
Importantly, FE, or the quantity of forest transpiration and evaporation, is the product of the amount of evaporation of forest water conservation and the amount of water evaporation in forestland. FE can be calculated by the following formula: 
formula
(2)
in which, are calculated as above, while R is the evapotranspiration rate, and = transpiration and evaporation quantity/amount of rainfall in the same period of time; is a dimensionless unit.
Monetary quantity. The monetary quantity of FWR mainly depends on its price set standards. Reservoir water accumulation is essentially similar to FWR. Replacement engineering (RE) is usually used to evaluate the monetary quantity of FWR. It is always used to calculate the cost of water accumulation in a reservoir for an engineering approach to determining the value of FWR. The value formula for evaluating the annual water regulation of a forest ecosystem is: 
formula
(3)
in which, is the value of the forest's annual water regulation, RMB yuan·yr−1; is the investment in reservoir construction (land occupation resettlement compensation, project cost, maintenance cost, etc.), RMB yuan·m−3.

Forest water quality purification

Forest water quality purification (FWQP) mainly refers to the precipitation of organic pollutants, after the adsorption and intercept of the canopy and ground cover, and the filtration by the woodland's soil layer. According to the research, the forest has the beneficial effect of purifying water. After the precipitation is filtered through the forest ecosystem, organic contamination in rainfall can be reduced by more than 80% (Klemperer 2003).

Physical quantity. The forest has a role in regulating the amount of water in its area, which can also play a role in purifying water. Therefore, the FWR quantity is the same as the quantity of water purification each year; the calculation formula of FWQP is the same as the calculation in Formula (1) of FWR.

Monetary quantity. The theory for forest water purification is the same as tap water purification, thus the commodity price of water is influenced by water purification cost. Evaluating the value of water purification reveals the average water price for residents, and thereby the annual value of the water purification of the forest ecosystem is calculated. The formula for calculating monetary quantity is: 
formula
(4)
in which, is the value of annual forest water purification, RMB yuan·yr−1; and K is the cost of purifying water, RMB yuan· m−3.

Forest water conservation value

Forest water conservation value is the sum of FWR and FWQP, and the formula is: 
formula
(5)
in which, is the annual value of forest water conservation, RMB yuan·yr−1.

Data

The study's data are mainly from Zhalantun's forest inventory from 2016, and the State Economy and Social Development Statistics Report, 2016 (National Bureau of Statistics of China 2012; Statistics Bureau of Zhalantun City 2017). The study also examines the Governmental Work Report in Zhalantun, as well as the 12th and 13th five-year plans for Zhalantun. Furthermore, we looked at the Inner Mongolia Autonomous Region Principal Functional Area Plan, Forest Area Ecological Protection and Economy Transition document, and relevant data provided by the Zhalantun Forestry Bureau, the Statistics Bureau, as well as a survey on the sites in Zhalantun City (Zhang & Pan 2016).

RESULTS AND DISCUSSION

Results based on the above methods and data are discussed below. We calculated the physical quantity and value of forest water conservation services in Zhalantun City for 2016 and obtained the following results. In the calculation, to accurately reflect the value of forest water conservation, the amount of rainfall and the amount of evapotranspiration of different types of forest were estimated according to the average of different years and months from 2011 to 2016.

Physical quantity of forest water conservation

The physical quantity of water conservation is calculated with Formulas (1) and (2), and the main required coefficients are the forest area, the amount of rainfall, the amount of FE, and the amount of surface runoff.

Here, according to China's forest law, forest area refers to the area of trees and bamboo growth with a canopy density above 0.2 degree, the area of shrubby tree according to regulations of the government, the area of forest land inside farm land, and the area of trees planted by the side of villages, farm houses and along roads and rivers (National Bureau of Statistics of China 2012).

The amount of rainfall

The average monthly rainfall and annual rainfall for Zhalantun City are shown in Table 1.

Table 1

Monthly and yearly average rainfall quantity from 2011 to 2016

Month Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. Total 
Precipitation (mm) 12.4 20.3 90.4 102.9 103.2 110.4 126.1 125.2 30.2 5.9 10.5 17.7 755.2 
Month Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. Total 
Precipitation (mm) 12.4 20.3 90.4 102.9 103.2 110.4 126.1 125.2 30.2 5.9 10.5 17.7 755.2 

Forest evapotranspiration

FE is associated with rainfall quantity, as well as forest transpiration and evaporation quantity; FE varies widely among different types of forest, as has been realized from long-term observation of forest ecological stations in China. The average transpiration and evaporation rates of the major forest types in Zhalantun City from 2011 to 2016 are provided by the Water Resource Bureau of Zhalantun, and the specific data are given in Tables 2 and 3.

Table 2

Yearly average FE quantity in Zhalantun from 2011 to 2016

Year 2011 2012 2013 2014 2015 2016 
Evapotranspiration (mm) 949.7 896.3 811.6 883.7 804.1 793.4 
Year 2011 2012 2013 2014 2015 2016 
Evapotranspiration (mm) 949.7 896.3 811.6 883.7 804.1 793.4 
Table 3

The average evapotranspiration rate of forest types in Zhalantun from 2011 to 2016

Forest type Larch Pinus sylvestris var Oak tree White birch Black birch Aspen Poplar Willow Elm 
Evapotranspiration (mm) 0.639 0.501 0.750 0.615 0.646 0.694 0.610 0.707 0.573 
Forest type Larch Pinus sylvestris var Oak tree White birch Black birch Aspen Poplar Willow Elm 
Evapotranspiration (mm) 0.639 0.501 0.750 0.615 0.646 0.694 0.610 0.707 0.573 

Also, according to data provided by the Water Resource Bureau of Zhalantun, Table 4 gives the average evapotranspiration quantity of the nine dominant tree species from 2011 to 2016. In the calculation, in order to write fluently, we mainly use the English name for tree species, not the Latin name to reflect the water conservation quantity of various types of forests (the dominant tree species). These nine tree species are larch (Larix gmelinii (Rupr.) Kuzen.), Pinus sylvestris var (Pinus sylvestris var. mongolica Litv.), oak tree (Quercus mongolica Fisch. ex Ledeb.), white birch (Betula platyphylla Suk.), black birch (Betula davurica Pall.), aspen (Populus davidiana), poplar (Populus L.), willow (Salix babylonica), and elm (Ulmus pumila L.).

Table 4

The yearly and monthly average evapotranspiration quantity of various types of forests in Zhalantun from 2011 to 2016

Forest type Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. Total 
Larch (mm) 7.9 13.0 57.8 65.8 65.9 70.5 80.6 80.0 19.3 3.8 6.7 11.3 482.6 
Pinus sylvestris var (mm) 6.2 10.2 45.3 51.6 51.7 55.3 63.2 62.7 15.1 3.0 5.3 8.9 378.4 
Oak tree (mm) 9.3 15.2 67.8 77.2 77.4 82.8 94.6 93.9 22.7 4.4 7.9 13.3 566.5 
White birch (mm) 7.6 12.5 55.6 63.3 63.5 67.9 77.6 77.0 18.6 3.6 6.5 10.9 464.6 
Black birch (mm) 8.0 13.1 58.4 66.5 66.7 71.3 81.5 80.9 19.5 3.8 6.8 11.4 487.9 
Aspen (mm) 8.6 14.1 62.7 71.4 71.6 76.6 87.5 86.9 21.0 4.1 7.3 12.3 524.1 
Poplar (mm) 7.6 12.4 55.1 62.8 63.0 67.3 76.9 76.4 18.4 3.6 6.4 10.8 460.7 
Willow (mm) 8.8 14.4 63.9 72.8 73.0 78.1 89.2 88.5 21.4 4.2 7.4 12.5 534.2 
Elm (mm) 7.1 11.6 51.8 59.0 59.1 63.3 72.3 71.7 17.3 3.4 6.0 10.1 432.7 
Forest type Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. Total 
Larch (mm) 7.9 13.0 57.8 65.8 65.9 70.5 80.6 80.0 19.3 3.8 6.7 11.3 482.6 
Pinus sylvestris var (mm) 6.2 10.2 45.3 51.6 51.7 55.3 63.2 62.7 15.1 3.0 5.3 8.9 378.4 
Oak tree (mm) 9.3 15.2 67.8 77.2 77.4 82.8 94.6 93.9 22.7 4.4 7.9 13.3 566.5 
White birch (mm) 7.6 12.5 55.6 63.3 63.5 67.9 77.6 77.0 18.6 3.6 6.5 10.9 464.6 
Black birch (mm) 8.0 13.1 58.4 66.5 66.7 71.3 81.5 80.9 19.5 3.8 6.8 11.4 487.9 
Aspen (mm) 8.6 14.1 62.7 71.4 71.6 76.6 87.5 86.9 21.0 4.1 7.3 12.3 524.1 
Poplar (mm) 7.6 12.4 55.1 62.8 63.0 67.3 76.9 76.4 18.4 3.6 6.4 10.8 460.7 
Willow (mm) 8.8 14.4 63.9 72.8 73.0 78.1 89.2 88.5 21.4 4.2 7.4 12.5 534.2 
Elm (mm) 7.1 11.6 51.8 59.0 59.1 63.3 72.3 71.7 17.3 3.4 6.0 10.1 432.7 

According to the relevant formula and the data from Tables 1 and 3, the yearly and monthly average evapotranspiration quantity of different types of forests can be calculated, as shown in Table 4.

Surface runoff quantity

There is a close relationship between surface runoff and forest types and rainfall (Klemperer 2003). Based on monthly rainfall quantity variation data and surface runoff data of different forest types provided by Zhalantun City, the surface runoff of the dominant tree species is estimated by using curve-fitting. We use the runoff of different tree species as dependent variable y, and the rainfall as the independent variable x for regression. In the estimation of SRQ, the two main tree species of regression curves for Larix gmelinii and Betula platyphylla Suk. are shown as Figures 1 and 2. The regression curves of other tree species are omitted.

Figure 1

Estimation of monthly surface runoff for Larix gmelinii.

Figure 1

Estimation of monthly surface runoff for Larix gmelinii.

Figure 2

Estimation of monthly surface runoff for Betula platyphylla Suk.

Figure 2

Estimation of monthly surface runoff for Betula platyphylla Suk.

According to regression results, the regression models of monthly average surface runoff for different forest types in Zhalantun City are estimated as shown in Table 5. All regression models have passed statistical tests, indicating that there is a certain relationship between monthly surface runoff y and monthly rainfall quantity x.

Table 5

Monthly surface runoff y (mm) and monthly rainfall quantity x (mm) models

Forest type Models R2 Sig. 
Larch y= 0.2824x+ 0.1948 0.8047 0.023 
Pinus sylvestris var y= 0.004x2 − 0.0135x + 0.1 0.7945 0.035 
Oak tree y= 0.2336exp(0.0121x0.7935 0.033 
White birch y= 0.2063x − 0.0826 0.9231 0.001 
Black birch y= 0.1462exp(0.0137x0.7632 0.028 
Aspen y= 0.0282x+ 0.118 0.9421 0.001 
Poplar y= 0.4342x+ 0.122 0.8737 0.025 
Willow y= 0.1532exp(0.02110x0.8926 0.030 
Elm y= 0.3224x+ 0.2230 0.8326 0.031 
Forest type Models R2 Sig. 
Larch y= 0.2824x+ 0.1948 0.8047 0.023 
Pinus sylvestris var y= 0.004x2 − 0.0135x + 0.1 0.7945 0.035 
Oak tree y= 0.2336exp(0.0121x0.7935 0.033 
White birch y= 0.2063x − 0.0826 0.9231 0.001 
Black birch y= 0.1462exp(0.0137x0.7632 0.028 
Aspen y= 0.0282x+ 0.118 0.9421 0.001 
Poplar y= 0.4342x+ 0.122 0.8737 0.025 
Willow y= 0.1532exp(0.02110x0.8926 0.030 
Elm y= 0.3224x+ 0.2230 0.8326 0.031 

Based on the above regression models in Table 5, the average surface runoff of different forest types in Zhalantun from 2011 to 2016 is estimated as shown in Table 6.

Table 6

Average yearly and monthly SRQ of different forest types in Zhalantun from 2011 to 2016

Forest type Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. Total 
Larch (mm) 3.70 5.93 25.72 29.25 29.34 31.37 35.81 35.55 8.72 1.86 3.16 5.19 215.61 
Pinus sylvestris var (mm) 0.55 1.47 31.57 41.06 41.31 47.36 62.00 61.11 3.34 0.16 0.40 1.11 291.45 
Oak tree (mm) 0.27 0.30 0.70 0.81 0.81 0.89 1.07 1.06 0.34 0.25 0.27 0.29 7.06 
White birch (mm) 2.48 4.11 18.57 21.15 21.21 22.69 25.93 25.75 6.15 1.13 2.08 3.57 154.81 
Black birch (mm) 0.17 0.19 0.50 0.60 0.60 0.66 0.82 0.81 0.22 0.16 0.17 0.19 5.10 
Aspen (mm) 0.47 0.69 2.67 3.02 3.03 3.23 3.67 3.65 0.97 0.28 0.41 0.62 22.71 
Poplar (mm) 4.22 6.77 29.37 33.40 33.50 35.82 40.88 40.59 9.96 2.13 3.61 5.93 246.15 
Willow (mm) 0.20 0.24 1.03 1.34 1.35 1.57 2.19 2.15 0.29 0.17 0.19 0.22 10.96 
Elm (mm) 4.22 6.77 29.34 33.40 33.50 35.82 40.88 40.59 9.96 2.13 3.61 5.93 246.15 
Forest type Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. Total 
Larch (mm) 3.70 5.93 25.72 29.25 29.34 31.37 35.81 35.55 8.72 1.86 3.16 5.19 215.61 
Pinus sylvestris var (mm) 0.55 1.47 31.57 41.06 41.31 47.36 62.00 61.11 3.34 0.16 0.40 1.11 291.45 
Oak tree (mm) 0.27 0.30 0.70 0.81 0.81 0.89 1.07 1.06 0.34 0.25 0.27 0.29 7.06 
White birch (mm) 2.48 4.11 18.57 21.15 21.21 22.69 25.93 25.75 6.15 1.13 2.08 3.57 154.81 
Black birch (mm) 0.17 0.19 0.50 0.60 0.60 0.66 0.82 0.81 0.22 0.16 0.17 0.19 5.10 
Aspen (mm) 0.47 0.69 2.67 3.02 3.03 3.23 3.67 3.65 0.97 0.28 0.41 0.62 22.71 
Poplar (mm) 4.22 6.77 29.37 33.40 33.50 35.82 40.88 40.59 9.96 2.13 3.61 5.93 246.15 
Willow (mm) 0.20 0.24 1.03 1.34 1.35 1.57 2.19 2.15 0.29 0.17 0.19 0.22 10.96 
Elm (mm) 4.22 6.77 29.34 33.40 33.50 35.82 40.88 40.59 9.96 2.13 3.61 5.93 246.15 

The amount of forest water conservation

In the administrative area of Zhalantun City, there are three main forestry bureaus: Chaihe Forestry Bureau, Nanmu Forestry Bureau, and Zhalantun Forestry Bureau; there are also other, smaller forestry bureaus. (Here, other forestry bureaus refer to Chuoer Forestry Bureau, Aershan Forestry Bureau, and Wuchagou Forestry Bureau. Their total area is about 14.5% of the total area of Zhalantun City and this area does not belong to the Zhalantun City administrative management.) We have calculated the average yearly forest water conservation of various forest types in Zhalantun from 2011 to 2016 according to Formula (1) and the data in Tables 16: the average yearly water conservation quantity of the main forest types in Chaihe Forestry Bureau in 2011 to 2016 is 368.65 million m3, while figures for the Nanmu Forestry Bureau and the Zhalantun Forestry Bureau are 444.74 million m3 and 518.07 million m3, respectively.

In the calculation of forest water conservation quantity in the whole area of Zhalantun, the average annual surface runoff is calculated as 100 mm based on Table 6; rainfall quantity, transpiration and evaporation quantity are 755.2 mm and 793.4 mm, respectively, in Tables 1 and 2. According to Formula (1), forest water conservation in 2016 is calculated as shown in Table 7.

Table 7

Yearly forest water conservation quantity in the whole area of Zhalantun in 2016

Forestry bureau Forest area (ha) Annual water conservation (104 m3·yr−1
Chaihe Forestry Bureau 327,767 −45,297.40 
Nanmu Forestry Bureau 173,559 −23,985.85 
Zhalantun Forestry Bureau 480,816 −66,448.77 
Other forestry bureaus 204,938 −28,322.43 
Total 1,187,080 −164,054.46 
Forestry bureau Forest area (ha) Annual water conservation (104 m3·yr−1
Chaihe Forestry Bureau 327,767 −45,297.40 
Nanmu Forestry Bureau 173,559 −23,985.85 
Zhalantun Forestry Bureau 480,816 −66,448.77 
Other forestry bureaus 204,938 −28,322.43 
Total 1,187,080 −164,054.46 

Note: The results show annual water conservation is negative, which indicates that the annual FE in 2016 is greater than the annual forest precipitation.

Table 7 shows that the physical quantity of yearly forest water conservation in the entire area of Zhalantun in 2016 is −1.6405446 billion m3. Specifically, the physical quantity of yearly forest water conservation in Chaihe Forestry Bureau is −452.974 million m3, while the Nanmu Forestry Bureau and Zhalantun Forestry Bureau are −239.8585 million m3 and −664.4877 million m3, respectively. Other forestry bureaus are −283.2243 million m3 for 2016. This means that the annual FE is greater than the annual forest precipitation in Zhalantun. We know that many factors must be considered when estimating FE, such as air temperature, wind speed, and relative humidity (UNSD 2014). In the long term, natural water flows are affected by precipitation, evapotranspiration of crops, as well as abstractions and returns of water through different economic activities and households in a climatic area, such as countries or territories. In these areas, inflows and outflows of water are balanced. If we look at long-term trends, we find that when the quantity of outflows, including evapotranspiration and abstractions, is greater than the quantity of the inflows, like precipitation and the surface water from countries upstream, the external water supply of the territory needs to be balanced. Therefore, when calculating forest water conservation in a climatic area over the long term, where evapotranspiration of crops is greater than the precipitation, the negative benefits should be calculated because they can be supplemented from the outside area (UNSD 2014). Here, we assume Zhalantun City is a climatic area in which inflows and outflows of water are generally balanced. According to the Guidelines for the Compilation of Water Accounts and Statistics of the UNSD, the total annual forest water conservation in Zhalantun City in 2016 is −1.6405446 billion m3, and the quantity from the Zhalantun Forestry Bureau is the biggest, while the quantity from the Nanmu Forestry Bureau is the smallest.

Forest water conservation values

Forest water conservation values include the FWR value and FWQP value.

FWR value

The capacity investment per unit for the total investment in the reservoirs built in Zhalantun City in the last ten years is shown in Table 8.

Table 8

Capacity and investment of new built reservoirs in Zhalantun

Name of reservoir Reservoir capacity (million m3Total investment (RMB million yuan) Annual operating costs (RMB million yuan) Service life (year) The investment for unit capacity (RMB yuan·m−3
Kaoshan Reservoir 326.9 1,700 29 50 7.245 
Xiufeng Reservoir 164.4 986.5 17 50 6.208 
Hongxing Reservoir 149.2 1,100 17 50 4.381 
Total 640.5 3,786.5 63 – 5.945 
Name of reservoir Reservoir capacity (million m3Total investment (RMB million yuan) Annual operating costs (RMB million yuan) Service life (year) The investment for unit capacity (RMB yuan·m−3
Kaoshan Reservoir 326.9 1,700 29 50 7.245 
Xiufeng Reservoir 164.4 986.5 17 50 6.208 
Hongxing Reservoir 149.2 1,100 17 50 4.381 
Total 640.5 3,786.5 63 – 5.945 

From the above calculation results, we found that the average value of capacity investment in the reservoirs built in the last ten years in Zhalantun is RMB 5.945 yuan·m−3, which is used as the price for forest water conservation values in subsequent calculations.

Water purification cost

When evaluating the cost of forest water purification, we use the local average water price in Zhalantun in 2016 as the price of water purification. Municipal annual water supply and water supply prices in Zhalantun in 2016 are shown in Tables 9 and 10.

Table 9

Annual water supply in Zhalantun in 2016

Forestry bureau Residents living (m3Administrative cause (m3Industry (m3Business services (m3Special industries (m3Total 
Chaihe 1,436.5 451.0 325.2 302.3 28.8 2,543.8 
Nanmu 274.4 57.1 888.1 37.3 2.4 1,259.3 
Zhalantun 129.3 16.8 19.2 39.2 35.0 239.5 
Total 1,840.2 524.9 1,232.5 378.8 66.2 4,042.6 
Forestry bureau Residents living (m3Administrative cause (m3Industry (m3Business services (m3Special industries (m3Total 
Chaihe 1,436.5 451.0 325.2 302.3 28.8 2,543.8 
Nanmu 274.4 57.1 888.1 37.3 2.4 1,259.3 
Zhalantun 129.3 16.8 19.2 39.2 35.0 239.5 
Total 1,840.2 524.9 1,232.5 378.8 66.2 4,042.6 
Table 10

Annual water supply price in Zhalantun in 2016

Forestry bureau Residents living (RMB yuan·m−3Administrative cause (RMB yuan·m−3Industry (RMB yuan·m−3Business services (RMB yuan·m−3Special industries (RMB yuan·m−3Total weighted average (RMB yuan·m−3
Chaihe 2.40 4.00 4.90 4.00 7.00 3.25 
Nanmu 2.40 4.00 4.90 4.00 7.00 4.29 
Zhalantun 2.40 4.00 4.90 4.00 7.00 3.65 
Total weighted average 2.40 4.00 4.90 4.00 7.00 3.25 
Forestry bureau Residents living (RMB yuan·m−3Administrative cause (RMB yuan·m−3Industry (RMB yuan·m−3Business services (RMB yuan·m−3Special industries (RMB yuan·m−3Total weighted average (RMB yuan·m−3
Chaihe 2.40 4.00 4.90 4.00 7.00 3.25 
Nanmu 2.40 4.00 4.90 4.00 7.00 4.29 
Zhalantun 2.40 4.00 4.90 4.00 7.00 3.65 
Total weighted average 2.40 4.00 4.90 4.00 7.00 3.25 

According to the annual water supply price in different forestry bureaus, as shown in Table 10, the final price of forest water purification is RMB 3.25 yuan·m−3. That is the average price of water supply in Zhalantun City in 2016 according to the substitution method of Guidelines for the Compilation of Water Accounts and Statistics of UNSD (UNSD 2014).

Water conservation value

Based on the above calculation, the forest water conservation value in Zhalantun in 2016 is calculated as shown in Table 11.

Table 11

Forest water conservation value in Zhalantun in 2016

Forestry bureau Forest area (ha) Water regulation value (hundred million RMB yuan) Water purification value (hundred million RMB yuan) The total value of water conservation (hundred million RMB yuan) Water conservation value per hectare (ten thousand RMB yuan·ha−1·yr−1
Chaihe Forestry Bureau 327,767 26.93 14.72 41.65 1.27 
Nanmu Forestry Bureau 173,559 14.26 7.80 22.06 1.27 
Zhalantun Forestry Bureau 480,816 39.50 21.60 61.10 1.27 
Other forestry bureaus 204,938 16.84 9.20 26.04 1.27 
Total 1,187,080 97.53 53.32 150.85 1.27 
Forestry bureau Forest area (ha) Water regulation value (hundred million RMB yuan) Water purification value (hundred million RMB yuan) The total value of water conservation (hundred million RMB yuan) Water conservation value per hectare (ten thousand RMB yuan·ha−1·yr−1
Chaihe Forestry Bureau 327,767 26.93 14.72 41.65 1.27 
Nanmu Forestry Bureau 173,559 14.26 7.80 22.06 1.27 
Zhalantun Forestry Bureau 480,816 39.50 21.60 61.10 1.27 
Other forestry bureaus 204,938 16.84 9.20 26.04 1.27 
Total 1,187,080 97.53 53.32 150.85 1.27 

Table 11 shows that the total value of forest water conservation in Zhalantun in 2016 is RMB 15.085 billion yuan, and forest water conservation value per hectare is RMB 12,700 yuan·ha−1·yr−1. In addition, the annual FWR value is RMB 9.753 billion yuan, and the annual forest water purification value is RMB 5.332 billion yuan. According to statistics, in 2016, the gross domestic product (GDP) of Zhalantun is RMB 18.79 billion yuan. This sum includes the added value of the primary industry, which was RMB 4.03 billion yuan, an increase of 4.3% over the previous year. The added value of the secondary industry reached RMB 9.66 billion yuan, an increase of 6.9% over the previous year. The added value of the tertiary industry was RMB 5.10 billion yuan, an increase of 11.7% over the previous year. The industrial structure was adjusted to 21.4:51.4:27.2. That means the primary industry accounts for 21.4% of GDP in 2016, the secondary industry for 51.4%, and the tertiary industry for 27.2%. The proportion of the secondary industry in the GDP is significantly greater than those of the primary and tertiary industries. GDP per capita was RMB 456,661 yuan, an increase of 8.7% over the previous year (Statistics Bureau of Zhalantun City 2017). Obviously, forest water conservation value is about 80% of GDP in 2016, and 3.74 times the added value of the primary industry, and 1.56 times and 2.96 times the added value of the secondary and tertiary industries, respectively. It also shows that the forest plays an important role in water conservation in Zhalantun, and it especially plays an important role in the primary industry development. Therefore, there is currently an urgent need to better protect and utilize the forests. In Zhalantun City, the primary industry mainly refers to agriculture, forestry, animal husbandry, and fishery; this includes forest management, which accounts for about 30% of the total value of the primary industry. Therefore, increasing GDP and the economic output of Zhalantun can be achieved by increasing and improving forest management. Many studies have shown that forest management is closely related to economic output in particular locations (Grunewald & Bastian 2015). It can increase local economic output by improving forest quality and water conservation capacity.

Moreover, the value of forest water conservation services in Zhalantun Forestry Bureau is highest, at RMB 6.11 billion yuan, accounting for 41% of the total value. Chaihe Forestry Bureau is the second highest with respect to forest water conservation services; its value is RMB 4.165 billion yuan, accounting for 28% of the total value. The Nanmu Forestry Bureau is lowest; the value of its forest water conservation services is RMB 2.206 billion yuan, accounting for 15% of the total. The forest area of Zhalantun Forestry Bureau is largest, and forest water conservation value is highest, while those of Nanmu Forestry Bureau are smallest (Figure 3). Therefore, the value of forest water conservation services is closely associated with the forest area.

Figure 3

Forest water conservation value of Zhalantun City in 2016.

Figure 3

Forest water conservation value of Zhalantun City in 2016.

DISCUSSION

According to the above research, there are some issues that need to be discussed, especially with regard to forest water conservation services management:

  • (1)

    Does the negative benefit of forest water conservation services need to be calculated? Especially in some arid and semi-arid areas, rainfall is often less than evapotranspiration, and the value of forest water conservation calculated is often negative according to the WBM. In our study, the average rainfall from 2011 to 2016 was 755.2 mm, and the yearly forest transpiration and evaporation was 793.4 mm; the net difference using the WBM is obviously negative, so its calculated benefit is also negative. We think that this ‘negative benefit’ should also be calculated. In theory, the difference between precipitation and the amount of evapotranspiration and other water consumption in a local area is the amount of forest water conservation, and though sometimes it is negative, it should be calculated (United Nations 2012). There are also some deficiencies in the WBM. First, the evaporation of the forest is difficult to measure accurately. Second, spatial differences in the study area are easily ignored. Therefore, when this method is used for research, the study area should not be too large.

  • (2)

    The main purpose of evaluating forest water conservation services is to provide systematic information and to better serve the relevant management decision-making. Only by integrating information on the economy, water conservation, other natural resources and social information can such services be designed in an informed and integrated manner. This information also serves policy-makers when making decisions on water demand and supply. Furthermore, policy-makers need to be aware of the long-term consequences of their policies on water resources and the environment in general. Therefore, the main purpose of the evaluation of forest water conservation services is to better serve the long-term decision-making and relevant management.

  • (3)

    Environmental statistics, such as socioeconomic surveys of the many variables, are necessary in the evaluation and management of forest water conservation services. The forest tenure systems, access to markets, transport for produce, rainwater, evaporation, etc., can all contribute to the statistical design and analysis of the evaluation and management of forest water conservation services (Pereira 1989). The variables most difficult to assess are long-term observation data, such as runoff, evapotranspiration, etc., in the study area. Therefore, carrying out environmental statistics is required. In addition, the value of accurately assessing forest water conservation services depends on forest resource inventory data and long-term ecological observation data, such as forest area, runoff and evapotranspiration. These are also related to the government's financial investment and management level, especially the level of economic development in different regions (Grunewald et al. 2014). Therefore, developing the economy and improving forestry management levels are key ways to increase the value of forest water conservation services.

CONCLUSIONS

Forests play an important role in water conservation. Misuse of forests is increasing associated with accelerating population growth and the accompanying poverty. Destruction of forests causes soil erosion and sediment transport. Sedimentation destroys reservoir storage capacity and inhibits investment in power generation and irrigation, which then affects agricultural production and social stability. Therefore, protecting forests is to protect our water resources, which is, in turn, is to protect ourselves.

This article evaluated the service value of the forest conservation water resources in Zhalantun City. In 2016, forest water conservation is worth about 80% of GDP, and its value is about 3.74 times the added value of the primary industry, and 1.56 times and 2.96 times the added value of the secondary and tertiary industries, respectively. From the perspective of industrial management, the value of forest conservation water services is also closely related to the output value of the region's primary industry. From the perspective of investment, the improvement of regional economic development and forest management is beneficial to the improvement of forest water conservation capacity.

The research also suggests that forest water conservation services management and environmental statistics are necessary. Moreover, when using the WBM to evaluate the value of forest water conservation services, the negative effects also should be calculated; this is reasonable in theory. Similarly, in the view of management and the evaluation of forest water conservation services, the main purpose is to better serve relevant management decision-making, and relevant environmental statistics should be established in advance, and so on.

In short, the forest has the function of water conservation, and it plays an important role in social and economic development.

ACKNOWLEDGEMENTS

The study is supported by the National Key Research and Development Program (2016YFC0500905), and the major research projects (2017LD03) provided by the National Bureau of Statistics of China.

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