Spatiotemporal variation of correlation between vegetation cover and precipitation in an arid mountain-oasis river basin in northwest China

Mo, Kangle; Chen, Qiuwen; Chen, Cheng; Zhang, Jianyun; Wang, Li; Bao, Zhenxin

doi:10.1016/j.jhydrol.2019.04.044

Cited by 90 publications

(45 citation statements)

References 48 publications

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“…For instance, Zhao et al [62] studied the relationship between interannual vegetation change and climate factors, and found that increasing precipitation induced an increase in NDVI in general. Mo et al [63] concluded that most of the area in the arid basin shows a strong positive correlation between precipitation and NDVI at different temporal and spatial scales.…”

Section: Driving Mechanism Of Ecological Environmentmentioning

confidence: 99%

Ecological Environment Assessment in World Natural Heritage Site Based on Remote-Sensing Data. A Case Study from the Bayinbuluke

et al. 2019

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Ecological environment assessment would be helpful for a rapid and systematic understanding of ecological status and would contribute to formulate appropriate strategies for the sustainability of heritage sites. A procedure based on spatial principle component analysis was employed to measure the ecological status in Bayinbuluke; exploratory spatial data analysis and geo-detector model were introduced to assess the spatio-temporal distribution characteristics and detect the driving factors of the ecological environment. Five results are presented: (1) During 2007–2018, the average values of moisture, greenness, and heat increased by 51.72%, 23.10%, and 4.99% respectively, and the average values of dryness decreased by 56.70%. However, the fluctuation of each indicator increased. (2) The ecological environment of Bayinbuluke was improved from 2007 to 2018, and presented a distribution pattern that the heritage site was better than the buffer zone, and the southeast area was better than the northwest area. (3) The ecological environment presented a significant spatial clustering characteristic, and four types of spatial associations were proposed for assessing spatial dependence among the samples. (4) Elevation, protection partition, temperature, river, road, tourism, precipitation, community resident, and slope were statistically significant with respect to the changes in ecological status, and the interaction of any two factors was higher than the effect of one factor alone. (5) The remote-sensing ecological index (RSEI) could reflect the vegetation growth to a certain extent, but has limited ability to respond to species structure. Overall, the framework presented in this paper realized a visual and measurable approach for a detailed monitoring of the ecological environment and provided valuable information for the protection and management of heritage sites.

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Section: Driving Mechanism Of Ecological Environmentmentioning

confidence: 99%

Ecological Environment Assessment in World Natural Heritage Site Based on Remote-Sensing Data. A Case Study from the Bayinbuluke

et al. 2019

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“…Based on the downscaling precipitation model, precipitation in the TRB was mainly distribute in the mountains and was scarce in the plains. Precipitation was mostly concentrated in the South Tianshan Mountains and the Kunlun Mountains of this river basin, where there is relatively more coverage of vegetation [2,8,60,62]. The central plains had scarce precipitation.…”

Section: Discussionmentioning

confidence: 99%

“…The presentence of outliers in summer indicates the maximum in precipitation during the month, which was mainly due to the ununiform temporal variation of precipitation with high precipitation in summer and low precipitation in spring and winter [1,60]. Summer precipitation took over 50% of the total precipitation in the TRB, and the maximum precipitation in summer affect the overall annual precipitation in the TRB [2,8,61,62]. The downscaling model captured the precipitation extremes well, which means the very high and very low values were captured well in the downscaling results.…”

Section: The Accuracy Test For Downscaling Precipitationmentioning

confidence: 99%

Downscaling Precipitation in the Data-Scarce Inland River Basin of Northwest China Based on Earth System Data Products

Zuo

Chen

et al. 2019

Atmosphere

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Precipitation is a key climatic variable that connects the processes of atmosphere and land surface, and it plays a leading role in the water cycle. However, the vast area of Northwest China, its complex geographical environment, and its scarce observation data make it difficult to deeply understand the temporal and spatial variation of precipitation. This paper establishes a statistical downscaling model to downscale the monthly precipitation in the inland river basin of Northwest China with the Tarim River Basin (TRB) as a typical representation. This method combines polynomial regression and machine learning, and it uses the batch gradient descent (BGD) algorithm to train the regression model. We downscale the monthly precipitation and obtain a dataset from January 2001 to December 2017 with a spatial resolution of 1 km × 1 km. The results show that the downscaling model presents a good performance in precipitation simulation with a high resolution, and it is more effective than ordinary polynomial regression. We also investigate the temporal and spatial variations of precipitation in the TRB based on the downscaling dataset. Analyses illustrate that the annual precipitation in the southern foothills of the Tianshan Mountains and the North Kunlun Mountains showed a significant upward trend during the study periods, while the annual precipitation in the central plains presented a significant downward trend.

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“…Water resources are scarce and indispensable in arid and semi-arid areas. The eco-environmental quality in arid areas depends on the availability of water resources (Mo et al, 2019). In addition, surface water is an important part of the land water cycle, which affects the material exchange with and energy balance of the surface.…”

Section: Humidity Indexmentioning

confidence: 99%

Development of a large-scale remote sensing ecological index in arid areas and its application in the Aral Sea Basin

Wang

et al. 2021

J. Arid Land

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The Aral Sea Basin in Central Asia is an important geographical environment unit in the center of Eurasia. It is of great significance to the ecological protection and sustainable development of Central Asia to carry out dynamic monitoring and effective evaluation of the eco-environmental quality of the Aral Sea Basin. In this study, the arid remote sensing ecological index (ARSEI) for large-scale arid areas was developed, which coupled the information of the greenness index, the salinity index, the humidity index, the heat index, and the land degradation index of arid areas. The ARSEI was used to monitor and evaluate the eco-environmental quality of the Aral Sea Basin from 2000 to 2019. The results show that the greenness index, the humidity index and the land degradation index had a positive impact on the quality of the ecological environment in the Aral Sea Basin, while the salinity index and the heat index exerted a negative impact on the quality of the ecological environment. The eco-environmental quality of the Aral Sea Basin demonstrated a trend of initial improvement, followed by deterioration, and finally further improvement. The spatial variation of these changes was significant. From 2000 to 2019, grassland and wasteland (saline alkali land and sandy land) in the central and western parts of the basin had the worst ecological environment quality. The areas with poor ecological environment quality are mainly distributed in rivers, wetlands, and cultivated land around lakes. During the period from 2000 to 2019, except for the surrounding areas of the Aral Sea, the ecological environment quality in other areas of the Aral Sea Basin has been improved in general. The correlation coefficients between the change in the eco-environmental quality and the heat index and between the change in the eco-environmental quality and the humidity index were-0.593 and 0.524, respectively. Climate conditions and human activities have led to different combinations of heat and humidity changes in the eco-environmental quality of the Aral Sea Basin. However, human activities had a greater impact. The ARSEI can quantitatively and intuitively reflect the scale and causes of large-scale and long-time period changes of the eco-environmental quality in arid areas; it is very suitable for the study of the eco-environmental quality in arid areas.

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Spatiotemporal variation of correlation between vegetation cover and precipitation in an arid mountain-oasis river basin in northwest China

Cited by 90 publications

References 48 publications

Ecological Environment Assessment in World Natural Heritage Site Based on Remote-Sensing Data. A Case Study from the Bayinbuluke

Ecological Environment Assessment in World Natural Heritage Site Based on Remote-Sensing Data. A Case Study from the Bayinbuluke

Downscaling Precipitation in the Data-Scarce Inland River Basin of Northwest China Based on Earth System Data Products

Development of a large-scale remote sensing ecological index in arid areas and its application in the Aral Sea Basin

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