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Damage from climate change is widespread throughout the world. This change has brought about calamities, the most prevalent of which is the emergence of numerous droughts which are increasingly threatening human lives. In this paper, we studied the spatial and temporal variations of drought under the effect of climate change in the Huaibei Plain, which is a very important agricultural zone in China. Drought has attracted increasing attention in research due to its heavy impact on agriculture, the environment, livelihood, and food security. The SPEI (Standardized Precipitation Evapotranspiration Index) has been used in this study to express and identify drought events in the Huaibei Plain due to climate change. A general circulation model (GCM), HadGEM2-AO, which was the most appropriate for the study area's precipitation simulation, and three Representative Concentration Pathways (RCP), RCP 2.6, RCP 4.5, and RCP 8.5, were used to analyze and compare the drought effect for the baseline (1985–2017) and the future climate scenarios (2025–2090). At 3 and 6 months, the SPEI successfully detects agricultural drought in temporal and spatial variation. However, according to the analysis, more severe agricultural drought events are foreseen in the future than in the baseline because of climate change. SPEI performed better than SPI in detecting drought in the baseline and simulated data due to increased evapotranspiration. Between the SPEI-3 and SPEI-6, the Pearson coefficient correlation reveals a positive association. The Mann-Kendall test was used to cover the two studied periods in order to establish the drought trend. Both decreasing and increasing trends, in different timescales, were detected by Sen's Slope in the baseline and future periods with all RCPs. [ABSTRACT FROM AUTHOR]

In recent years, the frequent occurrence of extreme climate disasters has seriously affected agriculture in Northeast China. Based on precipitation data derived from 83 geographical stations in the study area for 60 years (1960–2019), we chose the reliable statistical methods of the Mann–Kendall test, Sen's slope, and the Standardized Precipitation Index, and regarded drought and flood as a whole in this paper, to identify the temporal and spatial variation characteristics of precipitation and extreme climate events in Northeast China. The results show that an increasing trend of disasters was detected at the 95% confidence level (Z = 2.3). According to the timescale analysis, abrupt climate changes started in 2006. Temporal and spatial distribution of extreme climate disasters, mainly drought and flood disasters, showed a significant upward trend from 2006 to 2019. According to the spatial analysis, the precipitation in Northeast China decreased from south to north and fluctuated less from east to west. Moreover, stations with extreme climate trends (trend of climatic anomaly with confidence level > 90%) followed the same spatial pattern as those with a high frequency of extreme climate disasters (more than 17.87 times/decade). The severity and frequency of extreme climate have increasingly threatened Northeast China in the past decade. In particular, the Northeast Plain experienced the most severe and extreme climate events that seriously threatened the study area in 2007, 2009, 2010, and 2019. Our results highlight the urgent need for the development of monitoring and early warning of droughts and flood disasters to reduce economic losses. [ABSTRACT FROM AUTHOR]

Over the past few decades, concentrations of carbon dioxide (CO2), a key greenhouse gas, have risen at a global rate of approximately 2 ppm/a. China is the largest CO2 emitter and is the principle contributor to the increase in global CO2 levels. Based on a satellite-retrieved atmospheric carbon dioxide column average dry air mixing ratio (XCO2) dataset, derived from the greenhouse gas observation satellite (GOSAT), this paper evaluates the spatial and temporal variations of XCO2 characteristics in China during 2009–2016. Moreover, the factors influencing changes in XCO2 were investigated. Results showed XCO2 concentrations in China increased at an average rate of 2.28 ppm/a, with significant annual seasonal variations of 6.78 ppm. The rate of change of XCO2 was greater in south China compared to other regions across China, with clear differences in seasonality. Seasonal variations in XCO2 concentrations across China were generally controlled by vegetation dynamics, characterized by the Normalized Difference Vegetation Index (NDVI). However, driving factors exhibited spatial variations. In particular, a distinct belt (northeast–southwest) with a significant negative correlation (r < −0.75) between XCO2 and NDVI was observed. Furthermore, in north China, human emissions were identified as the dominant influencing factor of total XCO2 variations (r > 0.65), with forest fires taking first place in southwest China (r > 0.47). Our results in this study can provide us with a potential way to better understand the spatiotemporal changes of CO2 concentration in China with NDVI, human activity and biomass burning, and could have an enlightening effect on slowing the growth of CO2 concentration in China. [ABSTRACT FROM AUTHOR]

The influence of urbanization on regional precipitation is one of the most important issues in hydrology. This paper selected the North Haihe Basin as the study area and explored the connection between summer precipitation and urbanization index (URBI) based on satellite precipitation and nighttime lights datasets. A moving spatial percentage anomaly method (MSP) was used to signify the local induced enhancement of precipitation (LIEP). Local indicators of spatial association (LISA) clustering for LIEP and URBI based on Bivariate Moran's I coupled with digital elevation model (DEM) segmentation was used to separate the impacts caused by urbanization and terrain uplifting, and thus, the regions of interest (ROI) where the urban induced enhancement of precipitation (UIEP) plays a dominant role were located. A geographically weighted regression (GWR) model was used to reveal the spatial variation of the sensitivity of UIEP to URBI within the ROI. Pearson correlation and cross-wavelet analyses (XWT) were adopted to investigate the teleconnection between UIEP and climate anomalies, using Niño3.4 and SOI as indices. The results indicated that: (1) Urbanization effects on precipitation spatial variation in the upstream mountainous area would be hidden away by topographic factors. (2) From the perspective of the basin, the highest urbanization level areas have the Lowest LIEP, while the suburban areas have the highest LIEP, and the rural areas are in the middle. (3) The UIEP and the URBI are generally negatively correlated within ROI. (4) UIEP versus Niño3.4 and SOI both show a significant common high-power period at a time scale of 2 years. This work can help comprehensively understand the hydrological response to urbanization. [ABSTRACT FROM AUTHOR]