scholarly journals Analysis of Factors Influencing the Lake Area on the Tibetan Plateau Using an Eigenvector Spatial Filtering Based Spatially Varying Coefficient Model

2021 ◽  
Vol 13 (24) ◽  
pp. 5146
Author(s):  
Zhexin Xiong ◽  
Yumin Chen ◽  
Huangyuan Tan ◽  
Qishan Cheng ◽  
Annan Zhou
Keyword(s):  
Influencing Factors ◽  
Water Cycle ◽  
Spatial Filtering ◽  
Inhibitory Effect ◽  
Tibet Plateau ◽  
The Tibetan Plateau ◽  
Lake Area ◽  
Varying Coefficient ◽  
Eigenvector Spatial Filtering ◽  
Spatially Varying

Lakes on the Tibet Plateau (TP) have a significant impact on the water cycle and water balance, and it is important to monitor changes in lake area and identify the influencing factors. Existing research has failed to quantitatively identify the changes and influencing factors of lakes in different regions of the TP. Thus, an eigenvector spatial filtering based spatially varying coefficient (ESF-SVC) model was used to analyze the relationship between lake area and climatic and terrain factors in the inner watershed of the TP from 2000 to 2015. A comparison with ordinary regression and spatial models showed that the ESF-SVC model eliminates spatial autocorrelation and has the best model fit and complexity. The experiments demonstrated that precipitation, snow melt, and permafrost moisture release, as well as the area of vegetation and elevation difference in the watershed, can significantly promote the expansion of lakes, while evapotranspiration and days of mean daily temperature above zero have an inhibitory effect on lake area expansion. The degree of influence of each factor also differs significantly over time and across regions. Spatially quantitative modeling of lake area in the TP using the ESF-SVC method is a new attempt to provide novel ideas for lake research.

scholarly journals Using an Eigenvector Spatial Filtering-Based Spatially Varying Coefficient Model to Analyze the Spatial Heterogeneity of COVID-19 and Its Influencing Factors in Mainland China

2022 ◽  
Vol 11 (1) ◽  
pp. 67
Author(s):  
Meijie Chen ◽  
Yumin Chen ◽  
John P. Wilson ◽  
Huangyuan Tan ◽  
Tianyou Chu
Keyword(s):  
Risk Factors ◽  
Health Risk ◽  
Spatial Heterogeneity ◽  
Influencing Factors ◽  
Mainland China ◽  
Spatial Filtering ◽  
Varying Coefficient Model ◽  
Health Risk Factors ◽  
Eigenvector Spatial Filtering ◽  
Spatially Varying

The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of corresponding health risk factors in different places, but the problem of spatial heterogeneity has not been adequately addressed. The purpose of this paper was to explore how selected health risk factors are related to the pandemic infection rate within different study extents and to reveal the spatial varying characteristics of certain health risk factors. An eigenvector spatial filtering-based spatially varying coefficient model (ESF-SVC) was developed to find out how the influence of selected health risk factors varies across space and time. The ESF-SVC was able to take good control of over-fitting problems compared with ordinary least square (OLS), eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models, with a higher adjusted R2 and lower cross validation RMSE. The impact of health risk factors varied as the study extent changed: In Hubei province, only population density and wind speed showed significant spatially constant impact; while in mainland China, other factors including migration score, building density, temperature and altitude showed significant spatially varying impact. The influence of migration score was less contributive and less significant in cities around Wuhan than cities further away, while altitude showed a stronger contribution to the decrease of infection rates in high altitude cities. The temperature showed mixed correlation as time passed, with positive and negative coefficients at 2.42 °C and 8.17 °C, respectively. This study could provide a feasible path to improve the model fit by considering the problem of spatial autocorrelation and heterogeneity that exists in COVID-19 modeling. The yielding ESF-SVC coefficients could also provide an intuitive method for discovering the different impacts of influencing factors across space in large study areas. It is hoped that these findings improve public and governmental awareness of potential health risks and therefore influence epidemic control strategies.

scholarly journals Using an Eigenvector Spatial Filtering Based Spatially Varying Coefficient Model to Analyze the Spatial Heterogeneity of COVID-19 and Its Influencing Factors in Mainland China

2021 ◽  
Author(s):  
Meijie Chen ◽  
Yumin Chen ◽  
John P Wilson ◽  
Huangyuan Tan ◽  
Tianyou Chu
Keyword(s):  
Risk Factors ◽  
Health Risk ◽  
Spatial Heterogeneity ◽  
Mainland China ◽  
Spatial Filtering ◽  
Hubei Province ◽  
Varying Coefficient Model ◽  
Varying Coefficient ◽  
Eigenvector Spatial Filtering ◽  
Spatially Varying

Abstract Background: The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of health risk factors on COVID-19 rates in different places, but the problem of spatial heterogeneity has not been adequately addressed.Methods: In this paper, we developed an Eigenvector Spatial Filtering based spatially varying coefficient model (ESF-SVC) to reveal the spatially varying impact of certain health risk factors on the COVID-19 spread. The experiment was conducted during 7 weeks within two study extents (Hubei province and mainland China). Spatial varying coefficient maps were produced for spatial pattern discovery.Results: Results showed that the ESF-SVC model could take good control of over-fitting problems, with average adjusted R2 16.31% (in Hubei province) and 10.25% (in mainland China) higher than that of GWR. The cross validation RMSE of ESF-SVC model was also the lowest. In Hubei province, Population density and wind speed had a significant impact on COVID-19 infection rates and that their effect was constant across cities. While in mainland China, migration score, building density, temperature and altitude showed significant impact and their effect varies across space. The influence of migration score was less contributive and less significant in cities around Wuhan than cities farther away, while the altitude showed stronger contributions in high altitude cities.Conclusions: Our study hopes to provide not only a feasible path to solve the problem of spatial autocorrelation and spatial heterogeneity in COVID-19 characterization but also an intuitive way to discover spatial patterns in large study areas, which could help people and government awareness of the potential health risks and shed some light in COVID-19 control strategies.

Mechanism of Lake Area Variations and Water Level Changes in Nam Co Lake

2013 ◽  
Vol 838-841 ◽  
pp. 1685-1692 ◽  
Author(s):  
Yan Du ◽  
Mo Wen Xie ◽  
Man Hu
Keyword(s):  
Water Level ◽  
Linear Regression Analysis ◽  
Geographical Location ◽  
Seepage Flow ◽  
Tibet Plateau ◽  
The Tibetan Plateau ◽  
Lake Area ◽  
Nam Co ◽  
Long Time ◽  
Nam Co Lake

The Tibetan Plateau is one of the best areas for the study because of its geographical location as well as human disturbance. AS one of the largest lakes in the Qinghai-Tibet Plateau, Nam Co Lake seepage underestimated for a long time. By linear regression analysis of hydrological data from 1970-2005, we qualitatively understands the water level operation mechanism. The result shows that the model deviates from 2000, compared with the actual water level. Correlation analysis indicates the Nam Co Lake seepage flow reduces after 2000. The Three Gorges project resulted in the uplift of the downstream water level, which exacerbates the rise of water level of Nam Co Lake. Owing to the non timeliness of underground seepage recharge, water level of downstream lake is difficult to simulate. According to the result and recent research, underground seepage may be a cycle, affecting the water level of all the lakes.

The evolution trend of geological disasters over spatial and temporal in the context of global warming —— taking the Qinghai-Tibet Plateau as an example

2020 ◽  
Author(s):  
Yiru Jia
Keyword(s):  
Climate Change ◽  
Tibetan Plateau ◽  
Influencing Factors ◽  
Rainy Season ◽  
Global Climate ◽  
Tibet Plateau ◽  
The Tibetan Plateau ◽  
Geological Disasters ◽  
The World ◽  
Disaster Data

<p>The Tibetan plateau (QTP) has the highest average elevation in the world. As the third pole in the world, it has the largest cryosphere system at low and mid latitudes. It is a sensitive area of climate change, and the climate change is more significant. Global climate change has led to higher temperatures and increased rainfall on the Tibetan Plateau. This will lead to changes in the frequency and pattern of geological disasters. This spatiotemporal change and its influencing factors are not clear, so we collected a total of 898 geological disasters in the QTP from 1905 to 2015. Then we process the data to obtain various meteorological indicators of the QTP and combine them with the changes in the distribution of disaster points. Furthermore, the distribution pattern of the disaster points with the spatiotemporal changes of slope, altitude, precipitation and temperature is obtained. Statistics on the disaster data corresponding to each meteorological index are then made. Through the analysis of the distribution map and the statistical results of the data, the correlation between the occurrence of geological disasters and each element is obtained. The disaster points are superimposed with multiple influencing factors, and the influence of multiple factors on the distribution of geological hazards is discussed. The results showed that geological disasters have gradually expanded from the traditional high-incidence area of southern and eastern edges to the interior. The frequency of disasters in high altitude areas is increasing, and gradually extended from the rainy season to the non-rainy season.</p>

scholarly journals Contrasting Evolution Patterns of Endorheic and Exorheic Lakes on the Central Tibetan Plateau and Climate Cause Analysis during 1988–2017

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1962
Author(s):  
Zhilong Zhao ◽  
Yue Zhang ◽  
Zengzeng Hu ◽  
Xuanhua Nie
Keyword(s):  
Climate Change ◽  
Tibetan Plateau ◽  
Climatic Factors ◽  
Global Climate ◽  
Rapid Expansion ◽  
Annual Precipitation ◽  
The Tibetan Plateau ◽  
Lake Area ◽  
Climate Change Research ◽  
Mean Temperature

The alpine lakes on the Tibetan Plateau (TP) are indicators of climate change. The assessment of lake dynamics on the TP is an important component of global climate change research. With a focus on lakes in the 33° N zone of the central TP, this study investigates the temporal evolution patterns of the lake areas of different types of lakes, i.e., non-glacier-fed endorheic lakes and non-glacier-fed exorheic lakes, during 1988–2017, and examines their relationship with changes in climatic factors. From 1988 to 2017, two endorheic lakes (Lake Yagenco and Lake Zhamcomaqiong) in the study area expanded significantly, i.e., by more than 50%. Over the same period, two exorheic lakes within the study area also exhibited spatio-temporal variability: Lake Gaeencuonama increased by 5.48%, and the change in Lake Zhamuco was not significant. The 2000s was a period of rapid expansion of both the closed lakes (endorheic lakes) and open lakes (exorheic lakes) in the study area. However, the endorheic lakes maintained the increase in lake area after the period of rapid expansion, while the exorheic lakes decreased after significant expansion. During 1988–2017, the annual mean temperature significantly increased at a rate of 0.04 °C/a, while the annual precipitation slightly increased at a rate of 2.23 mm/a. Furthermore, the annual precipitation significantly increased at a rate of 14.28 mm/a during 1995–2008. The results of this study demonstrate that the change in precipitation was responsible for the observed changes in the lake areas of the two exorheic lakes within the study area, while the changes in the lake areas of the two endorheic lakes were more sensitive to the annual mean temperature between 1988 and 2017. Given the importance of lakes to the TP, these are not trivial issues, and we now need accelerated research based on long-term and continuous remote sensing data.

scholarly journals Extension of the Upper Yellow River into the Tibet Plateau: Review and New Data

Quaternary ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 14
Author(s):  
Zhengchen Li ◽  
Xianyan Wang ◽  
Jef Vandenberghe ◽  
Huayu Lu
Keyword(s):  
Tibetan Plateau ◽  
Yellow River ◽  
Google Earth ◽  
Tibet Plateau ◽  
The Yellow River ◽  
Published Data ◽  
The Tibetan Plateau ◽  
Large Lakes ◽  
River Terraces ◽  
The Tibet Plateau

The Wufo Basin at the margin of the northeastern Tibet Plateau connects the upstream reaches of the Yellow River with the lowland catchment downstream, and the fluvial terrace sequence in this basin provides crucial clues to understand the evolution history of the Yellow River drainage system in relation to the uplift and outgrowth of the Tibetan Plateau. Using field survey and analysis of Digital Elevation Model/Google Earth imagery, we found at least eight Yellow River terraces in this area. The overlying loess of the highest terrace was dated at 1.2 Ma based on paleomagnetic stratigraphy (two normal and two reversal polarities) and the loess-paleosol sequence (12 loess-paleosol cycles). This terrace shows the connections of drainage parts in and outside the Tibetan Plateau through its NE margin. In addition, we review the previously published data on the Yellow River terraces and ancient large lakes in the basins. Based on our new data and previous researches, we conclude that the modern Yellow River, with headwaters in the Tibet Plateau and debouching in the Bohai Sea, should date from at least 1.2 Ma. Ancient large lakes (such as the Hetao and Sanmen Lakes) developed as exorheic systems and flowed through the modern Yellow River at that time.

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