Spatiotemporal Extremes of Temperature and Precipitation During 1960–2015 in the Yangtze River Basin (China) and Impacts on Vegetation Dynamics

Recently, extreme climate variation has been studied in different parts of the world, and the present study aims to study the impacts of climate extremes on vegetation. In this study, we analyzed the spatiotemporal variations of temperature and precipitation extremes during 1960–2015 in the Yangtze River Basin (YRB) using the Mann-Kendall (MK) test with Sen’s slope estimator and kriging interpolation method based on daily precipitation (P), maximum temperature (Tmax), and minimum temperature (Tmin). We also analyzed the vegetation dynamics in the YRB during 1982–2015 using Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) datasets and investigated the relationship between temperature and precipitation extremes and NDVI using Pearson correlation coefficients. The results showed a pronounced increase in the annual mean maximum temperature (Tnav) and mean minimum temperature (Txav) at the rate of 0.23 °C/10 years and 0.15 °C/10 years, respectively, during 1960–2015. In addition, the occurrence of warm days and warm nights shows increasing trends at the rate of 1.36 days/10 years and 1.70 days/10 years, respectively, while cold days and cold nights decreased at the rate of 1.09 days/10 years and 2.69 days/10 years, respectively, during 1960–2015. The precipitation extremes, such as very wet days (R95, the 95th percentile of daily precipitation events), very wet day precipitation (R95p, the number of days with rainfall above R95), rainstorm (R50, the number of days with rainfall above 50 mm), and maximum 1-day precipitation (RX1day), all show pronounced increasing trends during 1960–2015. In general, annual mean NDVI over the whole YRB increased at the rate of 0.01/10 years during 1982–2015, with an increasing transition around 1994. Spatially, annual mean NDVI increased in the northern, eastern, and parts of southwestern YRB, while it decreased in the YRD and parts of southern YRB during 1982–2015. The correlation coefficients showed that annual mean NDVI was closely correlated with temperature extremes during 1982–2015 and 1995–2015, but no significant correlation with precipitation extremes was observed. However, the decrease in NDVI was correlated with increasing R95p and R95 during 1982–1994

Association analysis between spatiotemporal variation of vegetation greenness and precipitation/temperature in the Yangtze River Basin (China)

The variation in vegetation greenness provides good understanding of the sustainable management and monitoring of land surface ecosystems. The present paper discusses the spatial-temporal changes in vegetation and controlling factors in the Yangtze River Basin (YRB) using Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) for the period 2001-2013. Theil-Sen Median trend analysis, Pearson correlation coefficients, and residual analysis have been used, which shows decreasing trend of the annual mean NDVI over the whole YRB. Spatially, the regions with significant decreasing trends were mainly located in parts of central YRB, and pronounced increasing trends were observed in parts of the eastern and western YRB. The mean NDVI during spring and summer seasons increased, while it decreased during autumn and winter seasons. The seasonal mean NDVI shows spatial heterogeneity due to the vegetation types. The correlation analysis shows a positive relation between NDVI and temperature over most of the YRB, whereas NDVI and precipitation show a negative correlation. The residual analysis shows an increase in NDVI in parts of eastern and western YRB and the decrease in NDVI in the small part of Yangtze River Delta (YRD) and the mid-western YRB due to human activities. In general, climate factors were the principal drivers of NDVI variation in YRB in recent years

Evaluation of Spatial and Temporal Performances of ERA-Interim Precipitation and Temperature in Mainland China

ERA-Interim has been widely considered as a valid proxy for observations at global and regional scales. However, the verifications of ERA-Interim precipitation and temperature in mainland China have been rarely conducted, especially in the spatial and long-term performances. Therefore, in this study, we employed the interpolated ground station (STA) data to evaluate the spatial and temporal patterns and trends of ERA-Interim precipitation and temperature during 1980-2012. The results showed that relatively weaker performances were observed in ERA-Interim precipitation, with the skill score (S index) ranging from 0.41 to 0.50. Interannual ERA-Interim precipitation presented comparable trends with STA precipitation at the annual and seasonal scales. Spatial patterns of empirical orthogonal function (EOF) modes and corresponding principal components were evidently different between annual ERA-Interim and STA precipitation. For temperature, annual and seasonal patterns of ERA-Interim data were in good consistency with those of STA over China with the S index ranging from 0.59 to 0.70. Yet interannual STA temperature recorded stronger warming trends (from 0.37K decade(-1) of wintertime to 0.53 Kdecade(-1) of springtime) at the annual and seasonal scales compared to corresponding periods for ERA-Interim temperature (from 0.03Kdecade 21 of wintertime to 0.25Kdecade(-1) of summertime). Overall, ERA-Interim precipitation and temperature had good agreement with STA data in east China with lower elevation (< 1000m above sea level), but good agreements were not observed in west China with higher elevation. The findings suggest that caution should be paid when using ERA-Interim precipitation and temperature in areas with complex orography

A 33-year NPP monitoring study in southwest China by the fusion of multi-source remote sensing and station data

Knowledge of regional net primary productivity (NPP) is important for the systematic understanding of the global carbon cycle. In this study, multi-source data were employed to conduct a 33-year regional NPP study in southwest China, at a 1-km scale. A multi-sensor fusion framework was applied to obtain a new normalized difference vegetation index (NDVI) time series from 1982 to 2014, combining the respective advantages of the different remote sensing datasets. As another key parameter for NPP modeling, the total solar radiation was calculated by the improved Yang hybrid model (YHM), using meteorological station data. The verification described in this paper proved the feasibility of all the applied data processes, and a greatly improved accuracy was obtained for the NPP calculated with the final processed NDVI. The spatio-temporal analysis results indicated that 68.07% of the study area showed an increasing NPP trend over the past three decades. Significant heterogeneity was found in the correlation between NPP and precipitation at a monthly scale, specifically, the negative correlation in the growing season and the positive correlation in the dry season. The lagged positive correlation in the growing season and no lag in the dry season indicated the important impact of precipitation on NPP.Comment: 20 pages, 11 figure

Aerosol Optical Properties Over Mount Song, a Rural Site in Central China

Seasonal variations of aerosol optical depth (AOD), Ångström exponent (α), single scattering albedo (SSA), water vapor content (WVC), aerosol size distribution and refractive index at Mount Song, a rural site in Central China are analyzed using ground-based sunphotometer data for the period April 2012 to May 2014 for the first time. The results show that the area is highly polluted even the major anthropogenic emission sources are far away. Seasonal mean AOD is high (0.72 ± 0.52) during summer (June–August) season and low (0.51 ± 0.38) during autumn (September–November) season. The monthly mean α is very low (0.81 ± 0.30) in the month of April and very high (1.32 ± 0.23) in the month of September with annual average value 1.1. Analysis of the frequency distributions of AOD and α in each season indicates presence of fine-mode particles. Strong seasonal variations in SSA is likely due to local biomass burning and regional transport of anthropogenic aerosol particles, Seasonal mean SSA at 440 nm wavelength are 0.89 ± 0.04, 0.91 ± 0.06, 0.89 ± 0.07 and 0.92 ± 0.05, respectively during spring, summer, autumn and winter seasons. It is also shown that the scattering capacity of the fine-mode particles is relatively higher during summer compared to other seasons. The aerosol volume size distributions show pronounced seasonal variations in volume concentration, peak radius and the fine-mode particles are evidently dominant during summer season due to the hygroscopic growth. A distinct seasonal variations in aerosol parameters confirm the transport of polluted air mass at Mount Song

Human-induced intensification of terrestrial water cycle in dry regions of the globe

Anthropogenic climate change (ACC) strengthens the global terrestrial water cycle (TWC) through increases in annual total precipitation (PRCPTOT) over global land. While the increase in the average global terrestrial PRCPTOT has been attributed to ACC, it is unclear whether this is equally true in dry and wet regions, given the difference in PRCPTOT changes between the two climatic regions. Here, we show the increase in PRCPTOT in dry regions is twice as fast as in wet regions of the globe during 1961–2018 in both observations and simulations. This faster increase is projected to grow with future warming, with an intensified human-induced TWC in the driest regions of the globe. We show this phenomenon can be explained by the faster warming and precipitation response rates as well as the stronger moisture transport in dry regions under ACC. Quantitative detection and attribution results show that the global increase in PRCPTOT can no longer be attributed to ACC if dry regions are excluded. From 1961–2018, the observed PRCPTOT increased by 5.63%~7.39% (2.44%~2.80%) over dry (wet) regions, and as much as 89% (as little as 5%) can be attributed to ACC. The faster ACC-induced TWC in dry regions is likely to have both beneficial and detrimental effects on dry regions of the globe, simultaneously alleviating water scarcity while increasing the risk of major flooding

Slower-decaying tropical cyclones produce heavier precipitation over China

The post-landfall decay of tropical cyclones (TC) is often closely linked to the magnitude of damage to the environment, properties, and the loss of human lives. Despite growing interest in how climate change affects TC decay, data uncertainties still prevent a consensus on changes in TC decay rates and related precipitation. Here, after strict data-quality control, we show that the rate of decay of TCs after making landfall in China has significantly slowed down by 45% from 1967 to 2018. We find that, except the warmer sea surface temperature, the eastward shift of TC landfall locations also contributes to the slowdown of TC decay over China. That is TCs making landfall in eastern mainland China (EC) decay slower than that in southern mainland China (SC), and the eastward shift of TCs landfall locations causes more TCs landfalling in EC with slower decay rate. TCs making landfall in EC last longer at sea, carry more moisture upon landfall, and have more favorable dynamic and thermodynamic conditions sustaining them after landfall. Observational evidence shows that the decay of TC-induced precipitation amount and intensity within 48 h of landfall is positively related to the decay rate of landfalling TCs. The significant increase in TC-induced precipitation over the long term, due to the slower decay of landfalling TCs, increases flood risks in China’s coastal areas. Our results highlight evidence of a slowdown in TC decay rates at the regional scale. These findings provide scientific support for the need for better flood management and adaptation strategies in coastal areas under the threat of greater TC-induced precipitation

Characteristics of Fine Particulate Matter (PM2.5) over urban, suburban and rural areas of Hong Kong

In urban areas, Fine Particulate Matter (PM2.5) associated with local vehicle emissions can cause respiratory and cardiorespiratory disease and increased mortality rates, but less in rural areas. However, Hong Kong may be a special case since the whole territory often suffers from regional haze from nearby mainland China, as well as local sources. Therefore, to understand which areas of Hong Kong may be affected by damaging levels of fine particulates, PM2.5 data were obtained from March 2005 to February 2009 for urban, suburban and rural air quality monitoring stations; namely Central (city area, commercial area, and urban populated area), Tsuen Wan (city area, commercial area, urban populated, and residential area), Tung Chung (suburban and residential area), Yuen Long (urban and residential area), and Tap Mun (remote rural area). To evaluate the relative contributions of regional and local pollution sources, the study aims to test the influence of weather conditions on PM2.5 concentrations. Thus meteorological parameters including temperature, relative humidity, wind speed, and wind directions were obtained from the Hong Kong Observatory.. The results showed that Hong Kong’s air quality is mainly affected by regional aerosol emissions, either transported from the land or ocean, as similar patterns of variations in PM2.5 concentrations were observed over urban, suburban, and rural areas of Hong Kong. Only slightly higher PM2.5 concentrations were observed over urban sites, such as Central, compared to suburban and rural sites, which could be attributed to local automobile emissions. Results showed that meteorological parameters have potential to explain 80% of the variability in daily mean PM2.5 concentrations at Yuen Long, 77% at Tung Chung, 72% at Central, 71% at Tsuen Wan, and 67% at Tap Mun during the spring to summer part of the year. The results provide not only a better understanding of the impact of regional long-distance transport of air pollutants on Hong Kong’s air quality but also a reference for future regional-scale collaboration on air quality management

Comparison of Different GPP Models in China Using MODIS Image and ChinaFLUX Data

Accurate quantification of gross primary production (GPP) at regional and global scales is essential for carbon budgets and climate change studies. Five models, the vegetation photosynthesis model (VPM), the temperature and greenness model (TG), the alpine vegetation model (AVM), the greenness and radiation model (GR), and the MOD17 algorithm, were tested and calibrated at eight sites in China during 2003–2005. Results indicate that the first four models provide more reliable GPP estimation than MOD17 products/algorithm, although MODIS GPP products show better performance in grasslands, croplands, and mixed forest (MF). VPM and AVM produce better estimates in forest sites (R2 = 0.68 and 0.67, respectively); AVM and TG models show satisfactory GPP estimates for grasslands (R2 = 0.91 and 0.9, respectively). In general, the VPM model is the most suitable model for GPP estimation for all kinds of land cover types in China, with R2 higher than 0.34 and root mean square error (RMSE) lower than 48.79%. The relationships between eddy CO2 flux and model parameters (Enhanced Vegetation Index (EVI), photosynthetically active radiation (PAR), land surface temperature (LST), air temperature, and Land Surface Water Index (LSWI)) are further analyzed to investigate the model’s application to various land cover types, which will be of great importance for studying the effects of climatic factors on ecosystem performances