Abstract
In order to understand the characteristics and dynamic changes of the eco-climate in the Yangtze River Delta (YRD) urban agglomeration, based on the daily observation data of air temperature, precipitation, relative humidity and wind speed from 111 meteorological stations in the region, as well as the MODIS normalized difference vegetation index (NDVI) and land surface temperature (LST) data, remote sensing, GIS and principal component analysis (PCA) methods were used, and the existing remote sensing ecological index (RSEI) was improved. A regional integrated eco-climatic index (IECI) model was constructed from the aspects of dryness/wetness, comfortableness, greenness, heat and air cleanliness, and the dynamic change characteristics of the eco-climate in YRD urban agglomeration were analyzed. The results showed that during the period from 2001 to 2018, the fractional vegetation cover (FVC) of the urban agglomeration increased significantly with a linear trend of 2.80% per decade, the mixed layer height (MLH) decreased significantly at a rate of 51.3m per decade, and the regional IECI decreased significantly with a linear trend of 0.10 per decade, showing a regional overall decreasing trend. Human activities and urbanization have significant effects on eco-climate change in the YRD urban agglomeration. It is necessary to strengthen the protection and improvement of eco-climate while promoting economic and social development, so as to achieve harmonious coexistence between humans and nature.
Keywords
0 Introduction
As the highest spatial organizational form of urban development, urban agglomerations have become an important engine for China's rapid economic growth and a core area for driving regional development [1]. However, with the continuous advancement of urbanization and rapid economic development, urban agglomerations are facing many problems, such as population expansion, resource shortages, and environmental pollution [1-4], which affect the quality of life of urban residents and the sustainable development of social economy. The ecological climate characteristics, ecological security, and living environment quality of urban agglomerations have become the research foreword and hot issues in climatology, ecology, and environmental science, and have attracted widespread attention from many scholars [5-8].
Some studies have been conducted to analyze the ecological and climate-related characteristics and changes of urban agglomerations [1, 4-6], but most of them are based on the Remote Sensing Ecological Index (RSEI) proposed by Xu Hanqiu [9], which couples vegetation index, humidity component, surface temperature and building index/soil index evaluation indicators to evaluate urban ecological conditions based on greenness, humidity, heat and dryness [10, 11]. This does not fully consider the fact that the main body of the city is residents and that urban ecological and climate changes are most significantly and directly affected by residents' activities [12, 13].
In recent years, some studies have made appropriate improvements to the RSEI index and achieved good application results. For example, Ma Pengfei [14] added PM2.5 concentration to the RSEI and constructed a new remote sensing ecological index including greenness, humidity, dryness, heat and PM2.5 concentration indicators; Wang Meiya and Xu Hanqiu [15] established a remote sensing index for ecological evaluation of megacities, which includes seven important influencing factors: air quality index, road network density, ecological connectivity, heat, greenness, dryness and humidity; Shi Youyu [8] selected ecological environment, human living environment climate comfort, air quality and disastrous weather indicators to evaluate the ecological and climate livability of 11 cities in Hebei Province.
The evaluation of the ecological climate environment of urban agglomerations is generally carried out by combining the Analytic Hierarchy Process (AHP) with the expert consultation method (Delphi method) , from the aspects of establishing the evaluation index system, calculating the index weight assignment, and building a comprehensive evaluation model. Compared with the AHP-Delphi method, the principal component analysis method (PCA) can determine the weight according to the contribution of each index to the principal component, thus overcoming the drawback of artificially determining the index weight. Therefore, the combination of PCA and RSEI index can reduce the subjectivity and difficulty of index extraction in practical applications, and ensure that the results are more objective and reasonable [9].
With the support of MODIS satellite remote sensing ecological environment data and meteorological station climate data, this paper established an ecological climate evaluation index system and model for the Yangtze River Delta urban agglomeration based on the improved RSEI index and PCA analysis method, and carried out a study on the changes in regional ecological climate during2001-2018 . The research results have very important practical significance and scientific reference value for regional ecological civilization construction, livable urban renewal, and climate change risk adaptation.
1 Materials and Methods
1.1 Data
The data used in this paper mainly include: daily and four observation times (02:00, 08:00, 14:00 and 20:00) or three observation times (08:00, 14:00 and 20:00) of temperature, humidity, wind speed, precipitation, cloud cover and other observation data of 111 stations in the Yangtze River Delta urban agglomeration from 2001 to 2018. The data comes from the National Meteorological Science Data Center (http://data.cma.cn) and has undergone preliminary quality control.
This paper also uses the Normalized Difference Vegetation Index (NDVI) data (http://reverb.echo.nasa.gov) and Land Surface Temperature (LST) data (https://ladsweb.nascom.nasa.gov/search/) from 2001 to 2018 extracted by the MODIS satellite of NASA. The temporal and spatial resolutions of the NDVI data are16 days and 250 meters, respectively, and they come from the MOD13Q1 product; the temporal and spatial resolutions of the LST data are8 days and 1 km, respectively, and they come from the MOD11A2 product.
1.2 Research Methods
1.2.1 Selection of eco-climate indicators
This paper selects eco-climate indicators that are closely related to the urban living environment, including Standardized Precipitation Evapotranspiration Index (SPEI) [16], Human Comfort Days (HCD) [7], Fractional Vegetation Cover (FVC) , Land Surface Temperature (LST) [17], and Mixed Layer Height (MLH) [18]. This paper studies the spatiotemporal dynamic characteristics of the eco-climate in the Yangtze River Delta urban agglomeration from the aspects of regional dryness and humidity, comfort, greenness, heat, and air cleanliness.
1.2.2 Construction of comprehensive eco-climate index model
The Integrated Eco-climatic Index (IECI) is used to reflect the overall state of the regional eco-climate. Since the dimensions of the five eco-climatic indicators are different, in order to reduce the impact of the values of different indicators on the evaluation results, the following formula is first used to standardize them.
(1)
Where: NI is the normalized index value; I is the numerical value of the index; Imax and Imin are the maximum and minimum values of the index respectively.
SPEI, HCD, FVC, LST and MLH in the Yangtze River Delta urban agglomeration since2001. Since the cumulative variance contribution rate of the first three principal components reached 88.5%, and the eigenvalues of the first three principal components were greater than 1, it is believed that the first three principal components can reflect the overall eco-climate status of the Yangtze River Delta urban agglomeration.
Regional comprehensive eco-climate index model is constructed by the component score coefficient matrix and variance contribution rate of the first three main components, which is expressed as:
(2)
Where: EC is the comprehensive eco-climate index (IECI) , NILST, NIFVC, NIPEI, NIHCD and NIMLH are the standardized values of LST, FVC, SPEI, HCD and MLH respectively. The larger the IECI value, the better the eco-climate conditions.
1.2.3 Analysis of dynamic changes in ecological climate
Based on the annual data of single eco-climate indicators and comprehensive indexes of the Yangtze River Delta urban agglomeration from 2001 to 2018, the ordinary least squares (OLS) regression method [19] was used to generate the trend values of the site/grid scale. At the same time, based on the annual values of all sites/grids in the study area, the annual value series of the eco-climate indicators or indices of the entire Yangtze River Delta urban agglomeration were generated by averaging, and the interannual variation curve was plotted in Microsoft Excel. According to the interannual dynamics, linear variation trend value and determination coefficient value of the curve, the temporal fluctuation and change tendency of the eco-climate characteristics in the entire study area were analyzed.
2 Results Analysis
2.1 Spatial variation of SPEI and HCD
During the period from 2001 to 2018, the change of SPEI in the Yangtze River Delta urban agglomeration was between-0.70 and 0.35 in most areas of the Yangtze River Delta urban agglomeration, and the spatial range of SPEI reduction was much larger than the range of SPEI increase, that is, the Yangtze River Delta urban agglomeration showed a drying trend (Figure1a) . In the southern and northern regions of the Yangtze River Delta urban agglomeration, the spatial regularity of the difference in SPEI change trends was not obvious. In the past 18 years, there was no obvious spatial regularity in the change of HCD. In most areas, the HCD change was between-6 and 6d/10a (Figure1b) . In the northern and southern regions, the areas with reduced HCD were more than those with increased HCD, while in the central region, the areas with increased HCD were slightly more than those with decreased HCD.
Fig.1Spatial changes of SPEI (unit: /10a) and HCD (unit: d/10a) in the Yangtze River Delta urban agglomeration.
2.2 Spatial variations of FVC and LST
During the period from 2001 to 2018, FVC in the Yangtze River Delta urban agglomeration showed an increasing trend in most areas, but the increase was small, mostly between 0 and 10%/10a (Figure2a) . In some cities and their surrounding areas, FVC decreased at a rate of no more than 10%/10a, and the decreasing areas were mainly concentrated in southern Jiangsu, Shanghai, and northern Zhejiang. In the central urban area of Shanghai and some coastal areas of central Jiangsu, FVC showed a more obvious increase. In the past 18 years, LST has shown an increasing trend in most areas of the north and central and eastern regions, including most areas of southern Jiangsu, Shanghai, and northeastern Zhejiang, with an increase range of 0 to 1.2℃/10a, but in some areas of the south and west, LST decreased at a rate of 0 to 0.9℃/10a, and the decreasing areas were mainly concentrated in southeastern Anhui, western and southern Zhejiang (Figure2b) .
Fig.2Spatial variation of FVC (unit: %/ 10 a) and LST (unit:℃/10 a) in the Yangtze River Delta urban agglomeration.
2.3 Spatial variations of MLH and IECI
During the period from 2001 to 2018, the spatial variation of MLH in the Yangtze River Delta urban agglomeration was not obvious. Relatively speaking, it showed a downward trend in most areas, but the reduction range was mostly 0-160 m/10a (Figure3) . In the northern part of the Yangtze River Delta urban agglomeration, at the junction of southern Jiangsu and Anhui, MLH increased at a rate of 0-160 m/10a. In the past 18 years, IECI has basically shown a downward trend in the entire Yangtze River Delta urban agglomeration, with the downward trend mostly ranging from 0-0.14/10a, and the downward trend is most obvious in the northern part of the Yangtze River Delta, especially in southern Jiangsu, indicating that the large-scale urbanization and industrial development in the Yangtze River Delta region since this century have had a significant impact on the local ecological climate environment (Figure4) . In recent years, the Yangtze River Delta region, with 4% of the country's land area, has gathered about 17% of the country's population and created 24.4% of the country's total economic output (GDP) . It is the region with the highest economic contribution intensity in China, and the ecological environment has been in a state of serious overload for a long time. Therefore, the ecological climate of the Yangtze River Delta urban agglomeration shows an overall regional downward trend.
Fig.3Spatial variation of MLH in the Yangtze River Delta urban agglomeration (unit: m/10a) .
Fig.4Spatial variation of IECI in the Yangtze River Delta urban agglomeration.
2.4 Interannual distribution and changes of eco-climate indicators
During the period from 2001 to 2018, the average SPEI and HCD in the Yangtze River Delta urban agglomeration decreased with a linear trend of 0.23/10a and 0.53d/10a, respectively, but neither was statistically significant (Figure5 a and 5b) . The SPEI was the highest in 2001 (0.67) and the lowest in 2010 (−0.43) . The HCD was the highest in 2014 (216d) and the lowest in 2010 (173d) . The average FVC in the urban agglomeration increased significantly with a linear trend of 2.80%/10a (Figure5c) . The FVC was the highest in 2015 (55.93%) and the lowest in 2001 (50.86%) . The regional average LST increased with a linear trend of 0.21℃/10a during the period 2001−2018, but it was also not statistically significant. The LST was the highest in 2017 (16.1℃) and the lowest in 2012 (14.5℃) (Figure5d) . The regional average MLH decreased significantly with a linear trend of 51.3 m/10a. The MLH was the highest in 2001 (898.4 m) and the lowest in 2018 (786.2 m) (Figure5e) . Over the past 18 years, the regional average IECI decreased significantly with a linear trend of 0.10/10a (Figure5f) , indicating that the regional eco-climate is developing in a worsening direction as a whole. The IECI was the lowest in 2010 (0.031) and the highest in 2005 (0.425) .
Fig.5Interannual changes in individual and comprehensive eco-climate indicators in the Yangtze River Delta urban agglomeration from 2001 to 2018.
3 Conclusion
Based on the climate data of ground meteorological stations and MODIS remote sensing monitoring data, this paper improves the existing RSEI, constructs the IECI evaluation index system and model of the Yangtze River Delta urban agglomeration from the aspects of dryness and humidity, comfort, greenness, heat, and air cleanliness, and analyzes the dynamic change characteristics of the regional eco-climate. The research has important indicative significance for the coordinated development of ecological civilization construction and urbanization in the Yangtze River Delta urban agglomeration, and also has certain reference and reference value for regional resource and environmental carrying capacity evaluation and climate change risk adaptation.
During the period from 2001 to 2018, the IECI of the Yangtze River Delta urban agglomeration decreased significantly with a linear trend of 0.10/10a, and showed the characteristics of regional overall decrease, especially in the southern Jiangsu region, where the decrease trend was most obvious. During this period, FVC increased significantly with a linear trend of 2.80%/10a, MLH decreased significantly with a linear trend of 51.3m/10a, SPEI and HCD decreased slightly with linear trends of 0.23/10a and 0.53d/10a, respectively, and LST increased slightly with a linear trend of 0.21℃/10a.
IECI in the Yangtze River Delta urban agglomeration is the comprehensive result of global climate change, regional urbanization and human activities. Since the beginning of this century, large-scale urbanization, industrialization and human activities in the region have had a significant impact on the local ecological climate, but this paper does not conduct a quantitative analysis of it. In future studies, it is necessary to deeply analyze the regional ecological climate effects of urbanization and human activities, clarify their impact mechanisms, processes and contributions, and promote the protection and improvement of ecological climate while the local economy and society are developing rapidly, so as to achieve harmonious coexistence between man and nature.