Spatio-Temporal Distribution of Drought in the Belt and Road Area During 1998–2015 Based on TRMM Precipitation Data

Bai, Yongqing; Wang, Juanle; Wang, Yujie; Han, Xuehua; Tsydypov, Bair Z.; Ochir, Altansukh; Davaasuren, Davaadorj

doi:10.5814/j.issn.1674-764x.2017.06.002

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…Furthermore, the Mann-Kendall method is used to verify the significance of the trend derived from Theil-Sen Median. The Mann-Kendall method was originally proposed by Mann [11], and is only used to detect a change trend of the sequence, and Kendall and Sneyers [27] have further improved this method so that it can more effectively determine the starting position of various changing trends, and it has a wide detection range, a high degree of quantification and full of vitality [28]. The process of the Mann-Kendall trend test is as follows:…”

Section: ) Tendency Analysis Methodsmentioning

confidence: 99%

“…The rescaled range analysis (R/S), also known as Hurst exponent, is an effective method to quantitatively describe the longterm dependence of time series information. The Hurst exponent quantifies a sustainable and effective method for representing long-term sequence data, being able to pry into future trends based on past data to a certain extent, and has been used in the fields of hydrology [28] and meteorology [29]. The basic principle is:…”

Section: ) Future Trend Analysis Methodsmentioning

confidence: 99%

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Spatiotemporal Analysis of Vegetation Changes Along the Belt and Road Initiative Region From 1982 to 2015

et al. 2020

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As a key ecological zone of terrestrial ecosystem, the Belt and Road Initiative (BRI) region has experienced a significant change of vegetation coverage in recent years. Using the GIMMS NDVI3g, this study investigated the patterns of spatiotemporal variation of vegetation coverage in the BRI region during the period 1982-2015. The Theil-Sen Median trend analysis and Mann-Kendall method were used to analyze the data, followed by the calculation of Hurst index in order to analyze future trends of vegetation coverage. In addition, possible environmental factors affecting this variation were identified by using the partial correlation analysis and residual analysis methods. The results of the study showed that (1) the normalized difference vegetation index (NDVI) of the study area revealed a slow decrease during 1982-2015, with the linear tendency being-0.1%/10a. During this period, a stable increase was detected before 1997 (linear tendency 1.4%/10a), followed by a sharp decline after 1997 (linear tendency-1.8%/10a). (2) In spatial, the areas with increased vegetation NDVI are mainly distributed in Europe, India and China, whereas the regions with decreased vegetation NDVI are mainly distributed in northern Russia, Central Asia, Southeast Asia, the Malay Islands, and northeast China, of which, the magnitude of decrease in the north of Russia is particularly remarkable. This phenomenon indicates that vegetation activities in high latitude regions declined, such as coniferous forest of subfrigid zone and tundra vegetation. (3) The same characteristic of vegetation coverage change were stronger than the reverse characteristics. A total of 89.2% of the study area's vegetation will change in the same way as in the past, with a continuously increasing area accounting for 34.0% and a continuously decreasing area accounting for 21.6%. (4) Although climate change may play a key role in vegetation growth trends on a long-term scale, human activities are also an important factor driving vegetation change in the BRI area, especially for areas with increased vegetation coverage such as China, India, and Europe.

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Section: ) Tendency Analysis Methodsmentioning

confidence: 99%

Section: ) Future Trend Analysis Methodsmentioning

confidence: 99%

Spatiotemporal Analysis of Vegetation Changes Along the Belt and Road Initiative Region From 1982 to 2015

et al. 2020

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“…Wang et al [53] analyzed TRMM precipitation products with the observation data of meteorological stations in the Tianshan Mountains and its surrounding areas, and the results showed that the TRMM products had good applicability. A large number of research results showed that the accuracy of TRMM precipitation products was higher on monthly and annual scales [54,55], which could be used for analyzing the dynamic variability of a long-time precipitation sequence [56].…”

Section: Introductionmentioning

confidence: 99%

Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region

Wang

Xia

2019

Applied Sciences

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Focusing on water resources assessment in ungauged or sparse gauged areas, a comparative evaluation of areal precipitation was conducted by remote sensing data, limited gauged data, and a fusion of gauged data and remote sensing data based on machine learning. The artificial neural network (ANN) model was used to fuse the remote sensing precipitation and ground gauge precipitation. The correlation coefficient, root mean square deviation, relative deviation and consistency principle were used to evaluate the reliability of the remote sensing precipitation. The case study in the Qaidam Basin, northwest of China, shows that the precision of the original remote sensing precipitation product of Tropical Precipitation Measurement Satellite (TRMM)-3B42RT and TRMM-3B43 was 0.61, 72.25 mm, 36.51%, 27% and 0.70, 64.24 mm, 31.63%, 32%, respectively, comparing with gauged precipitation. The precision of corrected TRMM-3B42RT and TRMM-3B43 improved to 0.89, 37.51 mm, –0.08%, 41% and 0.91, 34.22 mm, 0.11%, 42%, respectively, which indicates that the data mining considering elevation, longitude and latitude as the main influencing factors of precipitation is efficient and effective. The evaluation of areal precipitation in the Qaidam Basin shows that the mean annual precipitation is 104.34 mm, 186.01 mm and 174.76 mm based on the gauge data, corrected TRMM-3B42RT and corrected TRMM-3B43. The results show many differences in the areal precipitation based on sparse gauge precipitation data and fusion remote sensing data.

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Spatio-temporal evolution and non-stationary characteristics of meteorological drought in inland arid areas

Chang

Wang

et al. 2021

Ecological Indicators

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Spatio-Temporal Distribution of Drought in the Belt and Road Area During 1998–2015 Based on TRMM Precipitation Data

Cited by 7 publications

References 9 publications

Spatiotemporal Analysis of Vegetation Changes Along the Belt and Road Initiative Region From 1982 to 2015

Spatiotemporal Analysis of Vegetation Changes Along the Belt and Road Initiative Region From 1982 to 2015

Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region

Spatio-temporal evolution and non-stationary characteristics of meteorological drought in inland arid areas

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