Correlation analysis between the Aral Sea shrinkage and the Amu Darya River

The shrinkage of the Aral Sea,which is closely related to the Amu Darya River,strongly affects the sustainability of the local natural ecosystem,agricultural production,and human well-being.In this study,we used the Bayesian Estimator of Abrupt change,Seasonal change,and Trend(BEAST)model to detect the historical change points in the variation of the Aral Sea and the Amu Darya River and analyse the causes of the Aral Sea shrinkage during the 1950–2016 period.Further,we applied multifractal detrend cross-correlation analysis(MF-DCCA)and quantitative analysis to investigate the responses of the Aral Sea to the runoff in the Amu Darya River,which is the main source of recharge to the Aral Sea.Our results showed that two significant trend change points in the water volume change of the Aral Sea occurred,in 1961 and 1974.Before 1961,the water volume in the Aral Sea was stable,after which it began to shrink,with a shrinkage rate fluctuating around 15.21 km3/a.After 1974,the water volume of the Aral Sea decreased substantially at a rate of up to 48.97 km3/a,which was the highest value recorded in this study.In addition,although the response of the Aral Sea's water volume to its recharge runoff demonstrated a complex non-linear relationship,the replenishment of the Aral Sea by the runoff in the lower reaches of the Amu Darya River was identified as the dominant factor affecting the Aral Sea shrinkage.Based on the scenario analyses,we concluded that it is possible to slow down the retreat of the Aral Sea and restore its ecosystem by increasing the efficiency of agricultural water use,decreasing agricultural water use in the middle and lower reaches,reducing ineffective evaporation from reservoirs and wetlands,and increasing the water coming from the lower reaches of the Amu Darya River to the 1961–1973 level.These measures would maintain and stabilise the water area and water volume of the Aral Sea in a state of ecological restoration.Therefore,this study focuses on how human consumption of recharge runoff affects the Aral Sea and provides scientific perspective on its ecological conservation and sustainable development.

两种方法在地下水位估值中的应用

对于许多区域水资源问题,用数值方法进行潜水水流模拟时,需要给出每个节点上地下水位值。本文首先简单介绍了趋势面方法,然后着重阐述了泛克里格方法的基本原理及它们在地下水位估值中的应用,通过比较两种方法的计算结果可以得出泛克里格方法是进行地下水位估值的空间最优估计方法。

The spatiotemporal scale effect on vegetation interannual trend estimates based on satellite products over Qinghai-Tibet Plateau

The trend estimate of vegetation change is essential to understand the change rule of the ecosystem.Previous studies were mainly focused on quantifying trends or analyzing their spatial distribution characteristics.Nevertheless,the uncertainties of trend estimates caused by spatiotemporal scale effects have rarely been studied.In response to this challenge,this study aims to investigate spatiotemporal scale effects on trend estimates using Moderate-Resolution Imaging Spectroradiometer(MODIS)Normalized Difference Vegetation Index(NDVI)and Gross Primary Productivity(GPP)products from 2001 to 2019 in the Qinghai-Tibet Plateau(QTP).Moreover,the possible influencing factors on spatiotemporal scale effect,including spatial heterogeneity,topography,and vegetation types,were explored.The results indicate that the spatial scale effect depends more on the dataset with a coarser spatial resolution,and temporal scale effects depend on the time span of datasets.Unexpectedly,the trend estimates on the 8-day and yearly scale are much closer than that on the monthly scale.In addition,in areas with low spatial heterogeneity,low topography variability,and sparse vegetation,the spatiotemporal scale effect can be ignored,and vice versa.The results in this study help deepen the consciousness and understanding of spatiotemporal scale effects on trend detection.

Regional long-term trend of ground solar radiation in China over the past 50 years

Based on global solar radiation and related meteorological data from 57 stations in China between 1961 and 2009, we analyze the variation of surface global solar radiation (G) and its relationship to meteorological elements using linear-trend estimation, wavelet analysis, and the Mann-Kendall test. The results show that of the 33 stations with time series longer than 45 years, G is significant at the 95% confidence level. G has a decreasing trend at many stations, but results vary across different areas. The decadal departure percentage of G increased from the 1960s to 1970s, declined gradually after the 1970s, and decreased significantly in the 1980s. In the 1990s, the trend at a few sites slightly increased. The trend of cumulative variance is of four types, i.e. rise-fall, rise-fall-slight rise, rise-fall-rise, and not obvious. For changes within a year, the most obvious decline was in winter, and the rest of the year had a slight decrease. The major cycles of annual G were 6-9, 10-13, and 29-33 a. The inflection points were mostly in the 1970s. The reasons for greater changes were complex. Relevant meteorological elements were selected and analyzed by statistical methods. Trends in climatic parameters, such as annual average percentage of sunshine, annual average wind speed, and annual average of low cloud cover, were closely related to G. Thus, this indicated the potential causes of the observed trends in G. The long-term trend for annual G in some regions was also influenced by anthro- pogenic activities. Annual average percentage of sunshine and annual average wind speed were positively correlated with annual G, respectively.

Multi-Sliding Time Windows Based Changing Trend of Mean Temperature and Its Association with the Global-Warming Hiatus

Based on three global annual mean surface temperature time series and three Chinese annual mean surface air temperature time series,climate change trends on multiple timescales are analyzed by using the trend estimation method of multi-sliding time windows.The results are used to discuss the so-called global-warming hiatus during 1998-2012.It is demonstrated that different beginning and end times have an obvious effect on the results of the trend estimation,and the implications are particularly large when using a short window.The global-warming hiatus during 1998-2012 is the result of viewing temperature series on short timescales;and the events similar to it,or the events with even cold tendencies,have actually occurred many times in history.Therefore,the global-warming hiatus is likely to be a periodical feature of the long-term temperature change.It mainly reflects the decadal variability of temperature,and such a phenomenon in the short term does not alter the overall warming trend in the long term.