Quantification of basin-scale multiple ecosystem services in ecologically fragile areas
Introduction
The watershed ecosystem, which integrates the river and terrestrial ecosystems, is a complex system with strong integrity and marked spatial heterogeneity (Trimble, 2004, Ghermandi et al., 2009, Theodoropoulos et al., 2010, Cheng et al., 2014). In 1998, the World Resources Research Institute first evaluated the value and vulnerability of the ecosystem services from the perspective of watersheds (Revenga et al., 1999). Since then, several research achievements have considerably promoted the public’s understanding of ecosystem services, and its concepts and methods have been gradually applied to the formulation of policies for ecosystem management (de Groot et al., 2010, Bateman et al., 2013, Aschonitis et al., 2016, Cui et al., 2019, Chen et al., 2020). To quantify the watershed ecosystem services, several scholars (Locatelli et al., 2011, Zhongyuana and Huaa, 2011, Trabucchi et al., 2012, Costanza et al., 2014, Wang et al., 2014) adopted the global ecosystem service value determined by Costanza et al. (Costanza et al., 1997, Costanza et al., 2014) and the Chinese ecosystem service value obtained by Xie et al. (2015). However, the results of these two methods are the average values of the global and national scales, respectively. Such average values are, to some extent, different from that of the basin-scale, and this difference is magnified in ecologically fragile areas, owing to the notable spatial heterogeneity. Therefore, to accurately evaluate the watershed scale ecosystem services, it is necessary to modify the quantification of the ecosystem services by using the downscaling method.
An ecosystem service refers to both the direct and indirect products provided by ecosystems, including all the necessary conditions and processes (Costanza et al., 1997, Fisher et al., 2009, Syrbe et al., 2012). Several services are often socially valuable but may not have a comparable market value, for instance, food; energy; water; carbon storage; habitats for biodiversity; space for recreation, amenity, and living; and cultural services (Benton et al., 2018). An ecosystem that can provide the survival and development conditions for human beings is not a simple natural ecosystem, but a multiple socio-ecological system. Therefore, the ecosystem services related to human beings can also be called social-ecosystem services. Furthermore, scholars have demonstrated that from the perspectives of ecology, economy, and sociology (Boyd and Banzhaf, 2007), a human-affected ecosystem is a social-ecosystem. Accordingly, for a compound system, one should consider not only the benefits from the natural ecosystem but also the risk control (geotechnical and ecological engineering) approaches alongside the aesthetic value and policy intervention functions.
Several researches attempted to establish a model that could simulate and predict the value of the ecosystem services; such attempts included the basin hydrological model, integrated valuation of ecosystem services and tradeoff model (InVEST), artificial intelligence for ecosystem services model (ARIES), and social values for ecosystem services model (SVESM) (Goldstein et al., 2012, Bagstad et al., 2013). In particular, the basin hydrological model applied to wetland loss and restoration scenarios could provide local estimates with the ecosystem service (ES) provision related to flood control and nutrient removal (Pattison-Williams et al., 2018). Furthermore, several researchers focused on the function and service method, which could simulate the ES function in a small area by establishing the production equation between a single service function and the local ecological environment variable (Kareiva et al., 2003, Robertson et al., 2005, Costanza et al., 2014). However, this approach involves several input parameters (water supply, electrical supply, production, tourism, atmosphere regulating, water reserve and flood prevention, pollution reduction, and biological habitat function), which complicates the calculation process. Moreover, it is difficult to unify the parameter standards of each service value, and thus, this method is not preferable. Although the abovementioned models make the quantification of the ecosystem services at the basin-scale feasible, these models often reflect only the main services, and because each method has a different emphasis, the limitations of the parameter adjustment may lead to inaccurate results.
With the development of ecosystem service research, the downscaling method with a correction factor was adopted to modify the value of the ecosystem services in China and worldwide. For example, by examining the association between ecosystem services intensity (ESI) and land use and land cover change (LULCC) from a spatial perspective at the county level (Chen et al., 2019), and adjusting the “unit area of ecosystem services value of China’s terrestrial ecosystem” by vegetation net primary productivity and the ability and willingness to pay into the county level (Aschonitis et al., 2016). Additionally, the values of small-scale ecosystem services such as at the province-scale (Chen et al., 2020) and county-scale (Guo et al., 2001, Chen et al., 2019, Cui et al., 2019) were obtained. However, neither of these values can accurately reflect the regional heterogeneity and the joint influence of the natural and anthropogenic factors (Mengist et al., 2020). In particular, in the case of ecologically fragile areas, in which mountainous hazards occur frequently, there exists spatial and temporal heterogeneity and differences in the payment for the ecosystem services in different regions and scales (Zhang et al., 2010, Chen et al., 2019, Mengist et al., 2020). Consequently, the valuation of watershed ecosystem services encounters the challenges of reflecting the imbalance of the spatial distribution, modifying the value equivalent factor in a unit area taking into account the regional vulnerability indicators, and reflecting the synergistic impact of natural and human factors (Trabucchi et al., 2012).
In this study, we focus on the ecosystem services of a debris flow area considering the basin scale. To the best of our knowledge, a debris flow area is an ecologically fragile area that is affected by a frequent debris flow, and risk identification and zoning are necessary. Thus, in this study, we selected the risk assessment indicators that can reflect the human and natural influence. Subsequently, according to the risk zoning result, it was discovered that the heterogeneity in the ecologically fragile watershed is closely related to the distribution of the hazard risk. Therefore, it is assumed that the differences in ecosystem services are caused by the different distribution of hazards, and the greater the hazard risk is, the less ecosystem services are. In this regard, the regional difference coefficient (RDC) was introduced and considered as the basin-scale correction index (BSCI) for the watershed ESs. In particular, this coefficient was used to modify the method proposed by Costanza et al. (1997) to value the ES from the global-scale (national scale) to the basin-scale, and to value the ES of the debris flow alluvial fan in Awang basin, Yunnan, China from 2012 to 2018. The primary objectives of this research were (1) to develop an ES quantification method considering the basin-scale by integrating the human factors and natural factors and considering the spatial heterogeneity, and (2) to assess the ESs in an ecologically fragile area (debris flow area) to clearly understand the problems and opportunities of debris flow alluvial fan use.
Section snippets
Study area
The Awang watershed is located at the south end of the Dongchuan District, Yunnan Province (103° 10′–103° 15′ E, 25° 52′–25° 55′ N), with the highest and lowest altitudes being 3240 and 1350 m, respectively. The corresponding village is in the Xiaojiang fault zone, which is characterized by complex geological conditions, frequent regional tectonic activities, and broken rock layers. The exposed soil is mainly laterite, formed by intense and prolonged weathering of basalt. This area is
Risk zoning
Fig. 2 shows that the different factors considerably influence risk zoning in the Dongchuan District, which leads to significant differences in the risk distribution. The zoning of the geological and geomorphic factors indicates that the risk distribution mainly covers the northwest and the eastern regions, and the southwest area is relatively safe (Fig. 2A). In contrast, the zoning of the meteorological and hydrologic factors shows that the risk is mainly concentrated in the west and the
Understanding proposed basin-scale ES method
Fu et al. (2015) indicated that the biggest barrier to the assessment of ESs is a lack of data; for instance, many projects on the ES assessment are based only on coarse datasets that lack the corrected resolutions (Naidoo et al., 2008, Eigenbrod et al., 2010). Thus, understanding how ESs accurately evaluate and quantify in complex and changing environments has become one main hurdle to uptake the concept into policy-making (Renard et al., 2015, Saidi et al., 2018, Holting et al., 2019).
Conclusion
In this study, the authors modified the ecosystem service formula based on the basin-scale and quantified the ecosystem service value of the Awang basin from 2012 to 2018. The results showed that the service value of cultivated land and water gradually decreased, and the service value of water changed considerably. The negative value of the construction land service value increases considerably, which indicates that the expansion of the construction land leads to a decrease in the complete
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This work was supported by the National Science Fund for Distinguished Young Scholars of China (Grant No. 41925030), the National Natural Science Foundation of China (Grant No. 41790434), and IMHE Youth S&T Fund (Grant No. SDS-QN-2108). We would also like to thank Editage [www.editage.cn] for English language editing.
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