The study of spatial distribution of precipitation is an important aspect of water-related research like, hydrological modeling, disaster prediction and watershed management. But, spatial interpolations of annual average rainfall in Ethiopia using Simple and Universal Kriging type through different models has not yet investigated.  This study was focused on to select the best fit semivarogram model for annual rainfall interpolation and to map the spatial distribution of rainfall of Ethiopia. In this study, long term mean annual rainfall which was collected from 931 meteorological station were interpolated using different mathematical variogram models of Kriging to select the best fit variogram model useful to predict continuous surface values of annual average rainfall in Ethiopia. Spherical model was found to be the best fitted variogram model and most accurate model for interpolation using both Simple and Universal Kriging with the value of ME (0.005), RMSE (1.21) and MSDR (121.32) and ME (0.001), RMSE (0.88) and MSDR (144.11), respectively. Since the kriging results of spherical model indicate good model efficacy for this study, it may be useful to map spatial pattern of annual rainfall in Basin level in Ethiopia.

The concerns over land use and land cover (LULC) change have emerged on the global stage due to realization that changes occurring on the land surface also influence climate, ecosystem and its services. This study aimed to map the temporal dynamic of LULC patterns and LST in the Jarmet wetland in Ethiopia. The dynamics and pattern of changes for a period of 21 years (2000-2021) were analyzed using geospatial techniques. Multi-temporal satellite images from Landsat ETM⁺ and Landsat-8 OLI sensor data were used to extract land-cover maps. The Land Surface Temperature (LST) trend of the study areas was computed using MODIS satellite imagery (2000-2021). Supervised classification using a Maximum Likelihood Classifier (MLC) was applied to prepare LULC maps of the watershed. The accuracy of the classified map was assessed using high-resolution data, and ground realities have been verified and ascertained through field observations. The results revealed a decreased trend in wetland, forest, shrubland and grassland in the period of 21 years (2000-2021) by -1148.71ha, -1073.26 ha, -1480.1 ha, and -87.73 ha, respectively. On the other hand, farmland and plantation areas followed an increasing trend. LST revealed decreasing trend in terms of mean and minimum with a fraction change of -0.018 and -0.073, whereas the maximum LST value shows an increasing trend with 0.021. The overall accuracy was 84.41%, with Kappa index of 76.13%. The analysis and findings of the study highlight important policy implications for sustainable LULC management in the study area. The study suggests the design and implementation of a guided natural resource policy, stopping the illegal expansion of farmland and educating society about the value of the sustainable management of habitat reserves.

Poverty eradication and employment creation have been central objectives of planners and policymakers in developing countries like India. As a backdrop, a significant number of schemes were created to generate employment and foster long-term economic growth. This article examines the influence of employment initiatives on employment generation and economic upliftment in rural area of Kashmir. Both primary and secondary data sources are employed in this study. The data was analyzed by using a log-linear regression model and correlation analysis. Knowledge Index was used to determine respondents’ awareness of employment programs. The findings indicate a linkage between the amount of money spent and the creation of employment opportunities. The findings also demonstrate that changes in people’s income have the major impact on family expenditure, hospital and medical costs, children’s education, and household savings. Finally, the study concluded that increasing employment days and rural credit will result in more revenue in people’s hands and improved economic empowerment.

It’s no exaggeration to say that floods are among the most common and catastrophic natural disasters, with widespread impacts on ecosystems and human lives. The World Bank has concluded that India is one of the most flood-prone countries in the world. This research has been focused on identification of real-time flood hazard area in the Malda district (India) using SAR data, Sentinel-1A (C-band). ArcGIS, Erdas-Imagine, and SNAP were used for this analysis. These inundation layers were intersected with the Malda district village’s database to determine the full extent of the devastation caused by the floods and finally determined the real-time flood-impacted area (380.19 km² on 4^th September and 338.87 km² on 10^th September, 2019). It was noted that on 4^th September, 766 and 10^th September 765 villages were seriously devastated owing to water. Moreover, among the 15 blocks of this district, Harischandrapur-II (75.67%), Manikchak (68.60%) and Kaliachak-III (68.00%) have experienced the most flooding. Finally, in order to estimate the overall population, households and affected by the floods, stratified random sampling was carried out in the flood-impacted villages of the Malda district. The safest blocks are Chanchal-I, Gazole, Harischandrapur-I and Kaliachak-I and the highly vulnerable blocks are Harischandrapur-II, Kaliachak-III and Manikchak. According to the flood influences on children, the most vulnerable blocks are Malda Old, English Bazar, Kaliyachak-III, and Harishchandrapur-II.