The main objective of this study is to assess the prediction and success rate based on bivariate frequency ratio (FR), weight of evidence (WoE), Shannon entropy (SE), and information value (IV) models for landslide susceptibility in the sedimentary terrain of Nepal Himalaya, as the area is facing threat for sustainable development as well as sustainable resource management. This study also seeks to evaluate the causative factors for landslide susceptibility. Initially, a landslide inventory map was created, consisting of 1158 polygons. These polygons were randomly divided into two sets, with a ratio of 70% for training and 30% for testing data. The multicollinearity approach was evaluated to assess the relevance of selected conditioning variables and their inclusion in the model construction process. The area under the curve (AUC) and other arithmetic evaluation methods were employed to validate and compare the outcomes of the models. In comparison, the predictive accuracy of the FR model surpasses that of the IV and SE models. The success rates, ranked in descending order, are as follows: WoE (79.9%), FR (75.3%), IV (74.4%), and SE (73.2%). Similarly, the success rates of four distinct models, namely WoE, FR, IV, and SE, are 85%, 78.75%, 78.57%, and 77.2%, correspondingly. All models have an accuracy and prediction rate exceeding 70%, making them suitable for assessing landslide susceptibility in the Siwalik Hills of Nepal. Nevertheless, the weight of evidence model provides more precise outcomes than other models. This study is expected to provide important information for road and settlement sustainability in the study area.

This study identifies the impact of climate change and adaptation practices on agriculture in the Rautahat district of central Nepal by analyzing the atmospheric temperature, rainfall pattern, soil moisture, and direct field survey. The impact and status of crop production systems are emphasized. Primary data on crop production system were collected through household surveys and adaptation practices in crop production were collected through focus group discussions, key informants’ interviews, and direct observations. Time series data on key climatic variables and productivity of major crops were collected from the government sources. Mann–Kendall trend analysis and Sen’s Slope methods were used for the analysis and quantification of temperature and rainfall trends. Spearman’s rank correlation analysis was performed to find the relation of seasonal rainfall with the crop yields. The study showed that the annual average rainfall was decreasing at the rate of 10.21 mm/year and the annual mean temperature had increased at a rate of 0.02 °C/year over the last 30 years, but their variations were found to be statistically insignificant. Seasonal rainfall also increased, except for the post-monsoon rain. Estimation of Soil Moisture Index through remote sensing technique indicates that it has been reduced considerably over the past 15 years at the beginning of the monsoon. It was observed that farmers have been using different adaptation measures like the use of high-yielding varieties of crops, enhanced irrigation system, switching to hybrid seed, and increased access to pesticides. As a result, the yields of major crops including rice, maize, wheat, sugarcane, potatoes, and pulses all showed increasing trends during 1999 to 2014. However, the total costs of production of all crops have increased many fold as a consequence of the cost associated with the arrangements for such adaptation measures and shifts towards market-based commodities.

This study focuses on the land use change and climatic variability assessment around Kamala watershed, Sindhuli district, Nepal. The study area covers two municipalities and eight Village Development Committees (VDCs). In this paper, land use change and the climatic variability are examined. The study was focused on analyzing the changes in land use area within the period of 1995 to 2014 and how the climatic data have evolved in different meteorological stations around the watershed. The topographic maps, Google Earth images and ArcGIS 10.1 for four successive years, 1995, 2005, 2010, and 2014 were used to prepare the land use map. The trend analysis of temperature and precipitation data was conducted using Mann Kendall trend analysis and Sen’s slope method using R (3.1.2 version) software. It was found that from 1995 to 2014, the forest area, river terrace, pond, and landslide area decreased while the cropland, settlement, and orchard area increased. The temperature and precipitation trend analysis shows variability in annual, maximum, and seasonal rainfall at different stations. The maximum and minimum temperature increased in all the respective stations, but the changes are statistically insignificant. The Sen’s slope for annual rainfall at ten different stations varied between −38.9 to 4.8 mm per year. Land use change and climatic variability have been analyzed; however, further study is required to establish any relation between climatic variability and land use change.