Search

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]

As a main grain production area in China, Northeast China (NEC) is highly influenced by the higher warming trend than elsewhere in China or even the globe. As the warming trend goes on, scientific understanding of the changes of thermal conditions in NEC will be essential for taking agricultural adaptation measures. In this paper, the high-resolution (25 km) corrected outputs of PRECIS (Providing Regional Climates for Impacts Studies) under the Representative Concentration Pathway 4.5 (RCP4.5) scenario were used to analyze the changes of the agro-climatic thermal resources in NEC. Results showed that accumulated temperature ≥10 °C (AT10) could increase by 300–500 °C·day, 500–900 °C·day, and 750–1000 °C·day in 2011–2040 (2020s), 2041–2070 (2050s), and 2071–2100 (2080s), respectively, relative to the baseline period of 1961–1990. The potential growing season (PGS) would then be prolonged by 11–19 days, 23–35 days, and 25–37 days in 2020s, 2050s and 2080s, respectively, compared to baseline. The spatial features for the changes of thermal indices would show a large increase of AT10 and an extension of PGS and Frost-free period (FFP) in mountainous areas compared to plains; the increment of AT10 would be greater in southern NEC than that in the north, with over 1000 °C·day in the southern parts and 700–800 °C·day in the northern parts in the 2080s. Additionally, the three thermal indices would increase rapidly from 2020s to 2050s compared to the period from 2050s to the 2080s, coincides with the greenhouse gas concentrations peak around the mid-period of the 21st century, and the decline towards the end of the 21st century under RCP4.5 forcing. The results of this paper could act as a guide to taking advantage of increasing thermal resources in NEC, and would be helpful for local practitioners in decision-making. [ABSTRACT FROM AUTHOR]

Simple Summary: The phenomenon of global climate change can impact the geographic range and abundance of species, thereby heightening the vulnerability of rare species to extinction. The reed parrotbill (Paradoxornis heudei David, 1872) is endemic to central and eastern China and is considered a near-threatened species according to the International Union for Conservation of Nature (IUCN). This study shows that temperature annual range, annual precipitation, and isothermality were the principal climatic factors to limit the habitat suitability of P. heudei. Presently, the suitable habitat for P. heudei is predominantly found in the central–eastern and northeast plains of China, especially the eastern coastal area. P. heudei is sensitive to climate change. In the future, northeastern China may serve as a potential suitable habitat for P. heudei. The phenomenon of global climate change can impact the geographic range and biodiversity, thereby heightening the vulnerability of rare species to extinction. The reed parrotbill (Paradoxornis heudei David, 1872) is endemic to central and eastern China, it is mainly distributed in the middle and lower reaches of the Yangtze River Plain and the Northeast Plain. In this study, eight of ten algorithms of the species distribution model (SDM) were used to evaluate the impact of climate change on the potential distribution of P. heudei under current and future climate scenarios and to analyze the possible related climate factors. After checking the collected data, 97 occurrence records of P. heudei were used. The relative contribution rate shows that among the selected climatic variables, temperature annual range (bio7), annual precipitation (bio12), and isothermality (bio3) were the principal climatic factors to limit the habitat suitability of P. heudei. The suitable habitat for P. heudei is primarily concentrated in the central–eastern and northeast plains of China, particularly in the eastern coastal region, spanning a mere area of 57,841 km2. The habitat suitability of P. heudei under different representative concentration pathway (RCP) scenarios was predicted to be different under future climatic conditions, but all of them had a larger range than the current one. The species distribution range could expand by more than 100% on average compared with the current range under the four scenarios in 2050, while it could contract by approximately 30% on average relative to the 2050 range in 2070 under different climate change scenarios. In the future, northeastern China may serve as a potential suitable habitat for P. heudei. The changes in the spatial and temporal distributions of P. heudei's range are of utmost importance in identifying high-priority conservation regions and devising effective management strategies for its preservation. [ABSTRACT FROM AUTHOR]

Air temperature (Tair) near the ground surface is a fundamental descriptor of terrestrial environment conditions and one of the most widely used climatic variables in global change studies. The main objective of this study was to explore the possibility of retrieving high-resolution Tair from the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products, covering complex terrain in Northeast China. The All Subsets Regression (ASR) method was adopted to select the predictors and build optimal multiple linear regression models for estimating maximum (Tmax), minimum (Tmin), and mean (Tmean) air temperatures. The relative importance of predictors in these models was evaluated via the Standardized Regression Coefficients (SRCs) method. The results indicated that the optimal models could estimate the Tmax, Tmin, and Tmean with relatively high accuracies (Model Efficiency ≥ 0.90). Both LST and day length (DL) predictors were important in estimating Tmax (SRCs: daytime LST = 0.53, DL = 0.35), Tmin (SRCs: nighttime LST = 0.74, DL = 0.23), and Tmean (SRCs: nighttime LST = 0.72, DL = 0.28). Models predicting Tmin and Tmean had better performance than the one predicting Tmax. Nighttime LST was better at predicting Tmin and Tmean than daytime LST data at predicting Tmax. Land covers had noticeable influences on estimating Tair, and even seasonal vegetation greening could result in temporal variations of model performance. Air temperature could be accurately estimated using remote sensing, but the model performance was varied across different spatial and temporal scales. More predictors should be incorporated for the purpose of improving the estimation of near surface Tair from the MODIS LST production. [ABSTRACT FROM AUTHOR]