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

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

Evaluation of Various Tree-Based Ensemble Models for Estimating Solar Energy Resource Potential in Different Climatic Zones of China

Solar photovoltaic (PV) electricity generation is growing rapidly in China. Accurate estimation of solar energy resource potential (Rs) is crucial for siting, designing, evaluating and optimizing PV systems. Seven types of tree-based ensemble models, including classification and regression trees (CART), extremely randomized trees (ET), random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), gradient boosting with categorical features support (CatBoost) and light gradient boosting method (LightGBM), as well as the multi-layer perceotron (MLP) and support vector machine (SVM), were applied to estimate Rs using a k-fold cross-validation method. The three newly developed models (CatBoost, LighGBM, XGBoost) and GBDT model generally outperformed the other five models with satisfactory accuracy (R2 ranging from 0.893–0.916, RMSE ranging from 1.943–2.195 MJm−2d−1, and MAE ranging from 1.457–1.646 MJm−2d−1 on average) and provided acceptable model stability (increasing the percentage in testing RMSE over training RMSE from 8.3% to 31.9%) under seven input combinations. In addition, the CatBoost (12.3 s), LightGBM (13.9 s), XGBoost (20.5 s) and GBDT (16.8 s) exhibited satisfactory computational efficiency compared with the MLP (132.1 s) and SVM (256.8 s). Comprehensively considering the model accuracy, stability and computational time, the newly developed tree-based models (CatBoost, LighGBM, XGBoost) and commonly used GBDT model were recommended for modeling Rs in contrasting climates of China and possibly similar climatic zones elsewhere around the world. This study evaluated three newly developed tree-based ensemble models of estimating Rs in various climates of China, from model accuracy, model stability and computational efficiency, which provides a new look at indicators of evaluating machine learning methods

Formation Mechanism for Upland Low-Relief Surface Landscapes in the Three Gorges Region, China

Extensive areas with low-relief surfaces that are almost flat surfaces high in the mountain ranges constitute the dominant geomorphic feature of the Three Gorges area. However, their origin remains a matter of debate, and has been interpreted previously as the result of fluvial erosion after peneplain uplift. Here, a new formation mechanism for these low-relief surface landscapes has been proposed, based on the analyses of low-relief surface distribution, swath profiles, χ mapping, river capture landform characteristics, and a numerical analytical model. The results showed that the low-relief surfaces in the Three Gorges area could be divided into higher elevation and lower elevation surfaces, distributed mainly in the highlands between the Yangtze River and Qingjiang River. The analyses also showed that the rivers on both sides of the drainage divide have not yet reached equilibrium, with actively migrating drainage divides and river basins in the process of reorganizing. It was concluded that the low-relief surfaces in the Three Gorges area did not share a common uplift history, and neither were they peneplain relicts, but rather that the effect of “area-loss feedback” caused by river capture has promoted the formation of upland low-relief surface landscapes. A future work aims to present the contribution of accurate dating of low-relief surface landscapes