Spatiotemporal variations of wetlands in the northern Xinjiang with relationship to climate change

Abstract

Wetlands in arid region especially in the northern Xinjiang Autonomous Region, China (NXC) are sensitive to climate change and thus have been the focus of global change studies in terms of their ecological importance. However, spatial changes in wetland area drivers are rarely considered in the analytical driver literature. This is surprising given the importance spatial heterogeneity in geomorphology and climatic conditions have in the region. This study is aimed at investigating the characteristics of wetland dynamic and determine whether this changes are relating to drought variation in NXC for a long time scale (from 1980 to 2015). Land transition matrix analysis was used to illustrate the Transition Characteristics between wetland and other land-use types, and the standard precipitation-evapotranspiration index (SPEI) was calculated to investigate the changes of Dry–wet regime in NXC and assess the effects on wetlands. The results showed that wetlands area increased from 10,627.57 km² to 11,966.84 km² mainly caused by the conversion from grasslands and desert to wetlands during 1980–2015 in NXC. Simultaneously, SPEI showed an increasing trend during 1960–2015, indicating an alleviating tendency of drought intensity. The area of wetlands increased with the increase of SPEI, furthermore, the correlation analysis also indicated that climatic change, especially a warmer and more humid tendency, was the main factor driving the spatiotemporal changes of wetlands in NXC. These findings can contribute to a better understanding wetland changes and implementing wetland resource management and restoration.

Appendix A

Methods

Trend analysis methods

Tests for the detection of significant trends in climatologic time series can be classified as parametric and non-parametric methods. Parametric trend tests require data to be independent and normally distributed, while non-parametric trend tests require only that the data be independent. In this study, two non-parametric methods (Mann–Kendall and Sen’s slope estimator) were used to detect the meteorological variables' trends (Nourani et al. 2018).

Mann–Kendall trend test

In this paper, the Mann–Kendall trend test, which is highly commended for general use by the World Meteorological Organization, were used to characterize the trends for several precipitation-related indices and to test their significance. The rank-based Mann–Kendall method is a nonparametric method, commonly used to assess the significance of monotonic trends in of the climate data (Zhang et al. 2009). The procedure of MK trend test adopted in this study is as follows:

Let the original time series be x₁, x₂,….. x_n, and r_i denote the cumulative number of the i-th sample x_i greater than x_j, the meaning statistic:

$$S_{k} = \sum\limits_{i = 1}^{k} {r_{i} } (k = 2,3,.....,n)$$

(1)

Under the assumption of random independence of the original time series,

$$UK_{k} = \frac{{\left( {S_{k} - E\left( {S_{k} } \right)} \right)}}{{\sqrt {Var\left( {S_{k} } \right)} }}(k = 1,2,,.....,n)$$

(2)

where, E(S_k) and Var(S_k) are the mean and variance of cumulative S_k. All UK_k will form a UF curve. The reliability test can be used to determine whether there is an obvious change trend. Apply the same method to the inverse sequence, to another curve UB. UF > 0, then the table sequence shows an upward trend, and UF < 0 shows a downward trend. If the two curves of UF and UB intersect at the critical point, the moment corresponding to the intersection point is the time when the abrupt transition begins (Fu and Wang 1992; Wang et al. 2010; Wei 2006).