This paper aims at the spatiotemporal distribution of rainfall in Ethiopia and developing stochastic daily rainfall model. Particularly, in this study, we used a Markov Chain Analogue Year (MCAY) model that is, Markov Chain with Analogue year (AY) component is used to model the occurrence process of daily rainfall and the intensity or amount of rainfall on wet days is described using Weibull, Log normal, mixed exponential and Gamma distributions. The MCAY model best describes the occurrence process of daily rainfall, this is due to the AY component included in the MC to model the frequency of daily rainfall. Then, by combining the occurrence process model and amount process model, we developed Markov Chain Analogue Year Weibull model (MCAYWBM), Markov Chain Analogue Year Log normal model (MCAYLNM), Markov Chain Analogue Year mixed exponential model (MCAYMEM) and Markov Chain Analogue Year gamma model (MCAYGM). The performance of the models is assessed by taking daily rainfall data from 21 weather stations (ranging from 1 January 1984-31 December 2018). The data is obtained from Ethiopia National Meteorology Agency (ENMA). The result shows that MCAYWBM, MCAYMEM and MCAYGM performs very well in the simulation of daily rainfall process in Ethiopia and their performances are nearly the same with a slight difference between them compared to MCAYLNM. The mean absolute percentage error (MAPE) in the four models: MCAYGM, MCAYWBM, MAYMEM and MCAYLNM are 2.16%, 2.27%, 2.25% and 11.41% respectively. Hence, MCAYGM, MCAYWBM, MAYMEM models have shown an excellent performance compared to MCAYLNM. In general, the light tailed distributions: Weibull, gamma and mixed exponential distributions are appropriate probability distributions to model the intensity of daily rainfall in Ethiopia especially, when these distributions are combined with MCAYM.

The Huang-Huai-Hai Plain is one of the typical agri-ecosystems in China, which suffers from cold damage frequently resulting in substantial economic losses. In order to monitor the changes in the occurrence of cold damage in an effective and large-scale manner, and to determine their meteorological causes, this paper collected low temperature data from the agricultural meteorological stations and remote sensing data of MODIS from 2005 to 2015, and constructed a monitoring model of cold damage to winter wheat in Huang-Huai-Hai Plain based on the Logistic regression model. This model was used to analyze the spatio-temporal changes of cold damage of winter wheat in Huang-Huai-Hai Plain from 2011 to 2020, and correlation analysis was performed with the spatio-temporal changes of meteorological factors to ascertain how they affect cold damage. The results show that the harm from cold damage in winter wheat has been gradually decreasing from 2011 to 2020, and the cold damage areas with high probability and high frequency are moving from north to south. The meteorological elements with the greatest impacts on the degree of cold damage from stronger to weaker are heat, precipitation and sunshine duration, whose influence has spatial variability.

The response of long-term vegetation changes and climate change has been a hot topic in recent research. Previously, a Landsat-based fusion model was developed and used to produce a dataset of normalized vegetation index (NDVI) for the Three-River Headwater region on the Qinghai-Tibet Plateau with a spatial resolution of 30 m and the time spanning the nearly 30 years from 1990 to 2018. In this study, the NDVI was applied to an analysis of the spatial and temporal changes in the alpine grassland and the impacts from climate change using the Theil-Sen Median method and linear regression. The results showed that: (1) The regional mean NDVI was 0.39 and showed a spatial pattern of decreasing from the southeast to the northwest in the recent three decades. Among the three parks, the Lancang River Park had the highest NDVI (0.43), followed by the Yellow River Park (0.38) and Yangtze River Park (0.23). (2) An upward trending was found in the NDVI time series at a rate of 0.0031 yr^-1 (R² =0.62, P < 0.01) over the whole period of 1990-2018. The increasing rate (0.00649 yr^-1, R² =0.71, P < 0.01) in the latter period of 2005-2018 was nearly 2.3 times of that (0.00284 yr^-1, R² =0.31, P < 0.01) in the previous period of 1990-2005. In the latest periods, the three parks experienced rates that were 2.3 to 63 times the corresponding values in the early period. (3) The NDVI is correlated more positively with temperature than precipitation. The impacts of climate change decreased along with the coverage fraction from the higher, median and then lower levels. The climate change can explain 34% of the variability in the NDVI time series of the areas with a higher fraction of grassland coverage, while it was 31% for the median fraction and 20% for the lower fraction. This study is the first to use the 30 m NDVI dataset spanning nearly 30 years to analyze the spatial and temporal variability and climate impacts in the alpine grasslands of the Three-River Headwater region of the Qinghai-Tibet Plateau. The results provide a basis for assessments on the ecological management effects and ecological quality based on long-term baseline data with a higher spatial resolution.

Gandaki River Basin (GRB) is an important part of the central Himalayan region, which provides habitat for numerous wild species. However, climatic changes are making the habitat in this basin more vulnerable. This paper aims to assess the potential impacts of climate change on the spatial distributions of habitat changes for two vulnerable species, Himalayan black bear (Ursus thibetanus laniger) and common leopard (Panthera pardus fusca), using the maximum entropy (MaxEnt) species distribution model. Species occurrence locations were used along with several bioclimatic and topographic variables (elevation, slope and aspect) to fit the model and predict the potential distributions (current and future) of the species. The results show that the highly suitable area of Himalayan black bear within the GRB currently encompasses around 1642 km² (5.01% area of the basin), which is predicted to increase by 51 km² in the future (2050). Similarly, the habitat of common leopard is estimated as 3999 km² (12.19% of the GRB area), which is likely to increase to 4806 km² in 2050. Spatially, the habitat of Himalayan black bear is predicted to increase in the eastern part (Baseri, Tatopani and north from Bhainse) and to decrease in the eastern (Somdang, Chhekampar), western (Burtibang and Bobang) and northern (Sangboche, Manang, Chhekampar) parts of the study area. Similarly, the habitat of common leopard is projected to decrease particularly in the eastern, western and southern parts of the basin, although it is estimated to be extended in the southeastern (Bhainse), western (Harichaur and northern Sandhikhark) and north-western (Sangboche) parts of the basin. To determine the habitat impact, the environmental variables such as elevation, Bio 15 (precipitation seasonality) and Bio 16 (precipitation of wettest quarter) highly contribute to habitat change of Himalayan black bear; while Bio 13 (precipitation of wettest month) and Bio 15 are the main contributors for common leopard. Overall, this study predicted that the suitable habitat areas of both species are likely to be impacted by climate change at different altitudes in the future, and these are the areas that need more attention in order to protect these species.