Spatial–temporal variation of ecosystem service values in Ebinur Lake Wetland National Natural Reserve from 1972 to 2016, Xinjiang, arid region of China

National nature reserves in China protect ecosystem and well-being in its region while benefiting humans by providing ecosystem services. Land-use/cover changes result in changes in the landscape pattern and affect the provision of ecosystem service value (ESV). The monetary evaluation of spatial–temporal changes in landscape patterns and ecosystem services in the arid region of China has been inadequately studied. Therefore, the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in the arid region of China was chosen as a study object to evaluate the spatial–temporal variation in ecosystem service values (ESVs) under changes in land use/cover. The research analysed land-use/cover change in ELWNNR using a Sankey diagram, the spatial–temporal variation in ELWNNR ESVs using ArcGIS. Meanwhile, it also analysed the landscape patterns and total ESVs of the core area, buffer area and experimental area of ELWNNR at a regional scale. Four Landsat data sets from 1972, 1998, 2007 and 2016 were chosen to estimate the changes in land-use/cover types and ESV in ELWNNR. Meanwhile, the sensitivity of ESV was also analysed to validate the estimation of ESV in ELWNNR. The results showed that the total ESV of ELWNNR was 3.12 × 109 CNY (USD$4.88 × 108) in 1972 and dropped to 2.71 × 109 CNY (USD$ 4.24 × 108) in 2016. The provision of total ESV in ELWNNR decreased by approximately 0.41 × 109 CNY (USD$ 6.42 × 107) from 1972 to 2016, although there were fluctuations in 1998 and 2007. The core area possessed the highest ESV among all the areas, showing an increase in the provision of ESV in ELWNNR. All the areas of ELWNNR became highly fragmented, and the landscape patterns became more complex during this time period. The results of the study provide sufficient information about the ecosystem services in the region in the absence of complete data sets and contribute to conservation planning of national nature reserves

Quantifying the spatial correlations between landscape pattern and ecosystem service value: A case study in Ebinur Lake Basin, Xinjiang, China

Human activities and environmental degradation have resulted in landscape structure changes and can eventually affect the ecosystem service value (ESV) of its region. Nevertheless, research on the spatial correlations between ESVs and landscape pattern changes is lacking. Thus, 13 landscape metrics and nine ESV types in Ebinur Lake Basin were chosen and used to analyse their spatial correlations using multiple linear regression models in this study. The results revealed that eight out of the 13 landscape metrics showed direct spatial correlations with ESV type, and there were landscape metrics that were positively and negatively correlated with the different ESV types. The interspersion and juxtaposition index (IJI), patch richness (PR), and patch richness density (PRD) had no effects on the provision of ESVs. The results also showed that the land-use/land-cover classification types play a linking role, as changes in land-use/land-cover affect the provision of ESVs and the fragmentation of landscape patterns. At the same time, the total ESV of Ebinur Lake Basin was 21.21 × 109 CNY in 2014. Wood and grassland contributed the highest ESV in Ebinur Lake Basin, i.e., 16.29 × 109 CNY, followed by water bodies and farmland, i.e., 1.785 × 109 CNY and 1.239 × 109 CNY, respectively. The regression models that were obtained quantitatively assessed how the changes in landscape patterns have affected the provision of ESVs. These models greatly contribute to the application of the ecosystem service approach in research as well as in practice and provide a better understanding of landscape planning in Ebinur Lake Basin

Hydrogen and oxygen isotope composition and water quality evaluation for different water bodies in the Ebinur Lake Watershed, Northwestern China

Wetlands are sensitive indicators of climate change and have a profound impact on the supply of water resources in surrounding areas. In this study, the hydrochemical, isotopic characteristics (δ18O and δ2H) of groundwater and surface water (lake, reservoir, and river) in the Ebinur Lake Watershed, northwestern China, were investigated to reveal the relationships between various water bodies. The results suggest that the groundwater is alkaline and has pH and total dissolved solids (TDS) values less than those of surface water. Ca2+ and SO42- are the major ions in the groundwater and river water, whereas lake water and reservoir water are enriched in Na+ and SO42-. With the decrease in elevation, both groundwater and river water are affected by carbonate dissolution at high elevation and by evaporitic rock dissolution at low elevation; thus, the water surrounding Ebinur Lake is subjected to runoff affected by intense evaporation-dissolution of evaporitic rocks. The stable isotope compositions suggested that the upstream part of the river is recharged by glacial meltwater from high mountains, whereas the middle-downstream parts of the river are recharged by low-elevation precipitation. Shallow groundwater and reservoir water are mainly recharged by river water and are more enriched in the downstream part of river. Water samples were also classified according to different indices, such as chemical oxygen demand (COD), NH3-N, volatile phenol, sulfate, Zn, Co, Cu, total hardness, and Cr6+, and results showed that most groundwater is suitable for drinking and irrigation purposes. Except for Cr6+, the metal concentrations are within permissible limits. However, both groundwater and reservoir water are affected to some extent by nearby rivers from anthropogenic activity

Estimation of the Fe and Cu Contents of the Surface Water in the Ebinur Lake Basin Based on LIBS and a Machine Learning Algorithm

Traditional technology for detecting heavy metals in water is time consuming and difficult and thus is not suitable for quantitative detection of large samples. Laser-induced breakdown spectroscopy (LIBS) can identify multi-state (such as solid, liquid, and gas) substances simultaneously, rapidly and remotely. In this study, water samples were collected from the Ebinur Lake Basin. The water samples were subjected to LIBS to extract the characteristic peaks of iron (Fe) and copper (Cu). Most of the quantitative analysis of LIBS rarely models and estimates the heavy metal contents in natural environments and cannot quickly determine the heavy metals in field water samples. This study creatively uses the Fe and Cu contents in water samples and the characteristics of their spectral curves in LIBS for regression modelling analysis and estimates their contents in an unknown water body by using LIBS technology and a machine learning algorithm, thus improving the detection rate. The results are as follows: (1) The Cu content of the Ebinur Lake Basin is generally higher than the Fe content, the highest Fe and Cu contents found within the basin are in the Ebinur Lake watershed, and the lowest are in the Jing River. (2) A number of peaks from each sample were found of the LIBS curve. The characteristic analysis lines of Fe and Cu were finally determined according to the intensities of the Fe and Cu characteristic lines, transition probabilities and high signal-to-background ratio (S/B). Their wavelengths were 396.3 and 324.7 nm, respectively. (3) The relative percent deviation (RPD) of the Fe content back-propagation (BP) network estimation model is 0.23, and the prediction ability is poor, so it is impossible to accurately predict the Fe content of samples. In the estimation model of BP network of Cu, the coefficient of determination (R2) is 0.8, the root mean squared error (RMSE) is 0.1, and the RPD is 1.79. This result indicates that the BP estimation model of Cu content has good accuracy and strong predictive ability and can accurately predict the Cu content in a sample. In summary, estimation based on LIBS improved the accuracy and efficiency of Fe and Cu content detection in water and provided new ideas and methods for the accurate estimation of Fe and Cu contents in water

Using the vegetation-solar radiation (VSr) model to estimate the short-term gross primary production (GPP) of vegetation in Jinghe county, XinJiang, China

Gross Primary Production (GPP) is the sum of ecosystem photosynthetic production and is an important variable in the study of the carbon cycle. Remote sensing methods were used to calculate GPP and compare the GPP of different regions with various land cover types (LCTs). We used a remote sensing-based vegetation-solar radiation (VSr) GPP estimation model to simulate changes in short-term GPP where vegetation ecosystem measurements are lacking. The study was conducted in 2011 at a typical arid and semi-arid oasis site in Jinghe County, Xinjiang, North China. The VSr model was appraised using 254 global radiation and MODIS GPP data points, including 84 grassland vegetation points (GP), 86 forest vegetation points (FP), and 84 cultivated land vegetation points (CP). The model was developed using the enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and solar radiation was obtained by calculation. Our results indicated that the updated VSr model improved the GPP accuracy compared with the standard MODIS GPP product by decreasing the root mean square errors (RMSEs) by 5.083, 4.802 and 3.076 for the GP, FP and CP sites, respectively. A comparison of test samples of GPP MODIS products and the VSr GPP model calculation showed that the VSr model had higher accuracy and stability. The VSr model can be used to simulate changes in short-term GPP where vegetation ecosystem measurements are lacking