Quantitative analysis of the impacts of terrestrial environmental factors on precipitation variation over the Beibu Gulf Economic Zone in Coastal Southwest China

Zhao, Yinjun; Deng, Qiyu; Qing, Lin; Cai, Chunting

doi:10.1038/srep44412

Cited by 17 publications

(10 citation statements)

References 33 publications

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“…It is widely used in the field of health to detect the correlation of distribution of disease incidence and their impact factors. However, Zhao et al [53] use this model to analysis the impacts of terrestrial environmental factors on precipitation variation over the Beibu Gulf Economic Zone in Coastal Southwest China. Foroogh et al [54] used this method to analyze the relationship between air temperature and land use, elevation, latitude et al Therefore, we use this detector model for quantitative analysis of the relationship between snow melt and related factors, like soil temperature, soil humidity, or topographic parameters.…”

Section: Spatial Analysis Methodsmentioning

confidence: 99%

Evaluation and Hydrological Simulation of CMADS and CFSR Reanalysis Datasets in the Qinghai-Tibet Plateau

Jun

Shanguan

Liu

et al. 2018

Water

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Multisource reanalysis datasets provide an effective way to help us understand hydrological processes in inland alpine regions with sparsely distributed weather stations. The accuracy and quality of two widely used datasets, the China Meteorological Assimilation Driving Datasets to force the SWAT model (CMADS), and the Climate Forecast System Reanalysis (CFSR) in the Qinghai-Tibet Plateau (TP), were evaluated in this paper. The accuracy of daily precipitation, max/min temperature, relative humidity and wind speed from CMADS and CFSR are firstly evaluated by comparing them with results obtained from 131 meteorological stations in the TP. Statistical results show that most elements of CMADS are superior to those of CFSR. The average correlation coefficient (R) between the maximum temperature and the minimum temperature of CMADS and CFSR ranged from 0.93 to 0.97. The root mean square error (RMSE) for CMADS and CFSR ranged from 3.16 to 3.18 °C, and ranged from 5.19 °C to 8.14 °C respectively. The average R of precipitation, relative humidity, and wind speed for CMADS are 0.46; 0.88 and 0.64 respectively, while they are 0.43, 0.52, and 0.37 for CFSR. Gridded observation data is obtained using the professional interpolation software, ANUSPLIN. Meteorological elements from three gridded data have a similar overall distribution but have a different partial distribution. The Soil and Water Assessment Tool (SWAT) is used to simulate hydrological processes in the Yellow River Source Basin of the TP. The Nash Sutcliffe coefficients (NSE) of CMADS+SWAT in calibration and validation period are 0.78 and 0.68 for the monthly scale respectively, which are better than those of CFSR+SWAT and OBS+SWAT in the Yellow River Source Basin. The relationship between snowmelt and other variables is measured by GeoDetector. Air temperature, soil moisture, and soil temperature at 1.038 m has a greater influence on snowmelt than others.

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Section: Spatial Analysis Methodsmentioning

confidence: 99%

Evaluation and Hydrological Simulation of CMADS and CFSR Reanalysis Datasets in the Qinghai-Tibet Plateau

Jun

Shanguan

Liu

et al. 2018

Water

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“…The choice of an appropriate discretization method is important because discrete results can directly affect the subsequent GDM results. Many studies recommend using the largest q value as the optimal discretization criterion [58,59]. Therefore, in our study, we used the maximum q value as a criterion function to find the optimal layering method, ensuring the optimal range.…”

Section: Optimal Range Of Vegetation Growthmentioning

confidence: 99%

Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982–2015

et al. 2020

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As a result of the unique geographical characteristics, pastoral lifestyle, and economic conditions in Mongolia, its fragile natural ecosystems are highly sensitive to climate change and human activities. The normalized difference vegetation index (NDVI) was employed in this study as an indicator of the growth status of vegetation. The Sen's slope, Mann-Kendall test, and geographical detector modelling methods were used to assess the spatial and temporal changes of the NDVI in response to variations in natural conditions and human activities in Mongolia from 1982 to 2015. The corresponding individual and interactive driving forces, and the optimal range for the maximum NDVI value of vegetation distribution were also quantified. The area in which vegetation was degraded was roughly equal to the area of increase, but different vegetation types behaved differently. The desert steppe and the Gobi Desert both in arid regions have degraded significantly, whereas the meadow steppe and alpine steppe showed a significant upward trend. Precipitation can satisfactorily account for vegetation distribution. Changes of livestock quantity was the dominant factor influencing the changes of most vegetation types. The interactions of topographic factors and climate factors have significant effects on vegetation growth. In the region of annual precipitation between 331 mm and 596 mm, forest vegetation type and pine sandy soil type were found to be most suitable for the growth of vegetation in Mongolia. The findings of this study can help us to understand the appropriate range or type of environmental factors affecting vegetation growth in Mongolia, based on which we can apply appropriate interventions to effectively mitigate the impact of environmental changes on vegetation.With the continuous development of earth observation system technology and the continuous enrichment of statistical models, the research content and methods of the relationships between vegetation, climate change, and human activities have become increasingly diverse. The normalized density vegetation index (NDVI), derived from infrared channel and near-infrared channel remote sensing data, is a good indicator of vegetation growth status and spatial distribution density of vegetation, and is linearly related to vegetation distribution density [6,7]. A large number of ecological studies have been carried out related to vegetation change and its influencing factors using NDVI and statistical models. Temperature and precipitation, as important factors in the natural environment, are often used as research priorities to assess their impact on vegetation. However, the dominant factors contributing to NDVI in different regions vary considerably. The variety in vegetation and its connection to climate change have been investigated by Du et al. [5] and Zheng et al. [8] in China. These studies identified precipitation as the key climatic factor governing variation in NDVI. However, Guo et al. [9] found that temperature was the dominant factor on vegetation (NDVI) growth ...

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“…Compared with traditional classification or partitioning algorithms such as K-means and SOM, the GDs have obvious advantages in spatial differentiation [25]. GDs have been used in land use [26], regional economy [27], public health [28], etc. In this study, we used the differential and factor detector (Equation ( 10)) to detect the explanatory power of the factors affecting DSAVs.…”

Section: Using Geographic Detectors To Identify the Significant Factors Of Dsavsmentioning

confidence: 99%

Exploring the Spatio-Temporal Dynamics of Development of Specialized Agricultural Villages in the Underdeveloped Region of China

Niu

2021

Land

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The development of specialized agricultural villages (DSAVs) is essential for rural revitalization. However, most current studies focus on the formation of specialized agricultural villages (SAVs), while the interpretation of DSAVs from the perspective of the geographical factors is still missing. In this study, we firstly employed the kernel density estimation to analyze the spatial pattern of DSAVs and then utilized the Geographic Detectors to explore which geographical factor(s) affected the SAVs of Henan, China in the formation (in 2010), steady (2011–2014), and rapid development (2015–2019) period of SAVs. The DSAVs were measured by gross product (GP), the employment rate (ER), and farmers’ income (FI) of SAVs. Eleven indicators described the geographic factors in five categories: terrains, resources, locations, markets, and economy. The results showed that the spatial pattern of DSAVs was from relatively uneven in the early formation to significantly clustering in the development period of SAVs. Specialized shiitake and Chinese herbal villages clustered in the mountain–plain transition zone. The aggregation of specialized coarse cereals villages was in the hill–plain transition zone. Specialized fruit and livestock villages gathered in the plain region. Further analyses were in these regions; compared with SAVs’ formation’s critical factors, the importance of terrain and location factors to DSAVs was decreasing, while market and economic factors were increasing in the development period of SAVs. The strongest changing was the development of specialized shiitake villages in the mountain–plain transition zone. These findings could provide guidance for the direction of DSAVs in underdeveloped areas.

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Quantitative analysis of the impacts of terrestrial environmental factors on precipitation variation over the Beibu Gulf Economic Zone in Coastal Southwest China

Cited by 17 publications

References 33 publications

Evaluation and Hydrological Simulation of CMADS and CFSR Reanalysis Datasets in the Qinghai-Tibet Plateau

Evaluation and Hydrological Simulation of CMADS and CFSR Reanalysis Datasets in the Qinghai-Tibet Plateau

Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982–2015

Exploring the Spatio-Temporal Dynamics of Development of Specialized Agricultural Villages in the Underdeveloped Region of China

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