The Gaur (Bos gaurus), a globally vulnerable and protected priority species in Nepal, has experienced habitat loss and fragmentation, poaching, and zoonotic diseases. As a consequence, their population is isolated significantly in Parsa National Park and Chitwan National Park. However, their distribution even in these protected areas are limited with topographical features. This study focuses on habitat suitability modeling of the Gaur in Parsa National Park utilizing the ensemble modeling approach to identify key eco-geographical and climatic variables influencing gaur suitable habitat and estimate suitability in and around Parsa National Park, Nepal. Potential eco-geographical variables, after multicollinearity test were integrated with ground presence points for analysis. The model achieved an Area Under Curve (AUC) and True Skill Statistics (TSS) value of 0.981 and 0.867 respectively indicating its effectiveness in predicting a suitable habitat for Gaur. It revealed that isothermality, waterholes, mean diurnal range, mean temperature of wettest quarter, settlements, slope, and river, influenced highly in Gaur’s habitat suitability in and around Parsa National Park. Study identified only 35.84% (327.09 km2) area was categorized as a suitable area (low-medium: 102.92 km2 (11.28%), medium to high: 101.08 km2 (11.07%) and optimum: 123.09 km2 (13.49%)) for gaur distribution. Eastern part of park (newly extended area around Halkhoriya lake) and south-central section of park (around Bhedaha, Mahadev, Bhata Khola) show the suitable habitat for Gaur. However, wildlife-friendly infrastructure in the East-West Highway (that fragments the park) within park can facilitate Gaur’s movement among these crucial habitat patches. These findings highlight priority to restore water sources to maintain long-term protection of species considering existing geological condition and climate change scenario in the park.

Soil erosion poses a significant environmental concern and threatens natural resources, resulting in decreased productivity and quality of soil. In Nepal, soil erosion arises from both natural factors such as excessive rainfall, weak geology, earthquakes, and human activities including deforestation, overgrazing, intensive agriculture, and unplanned infrastructure construction. A research study titled ”Soil Erosion Assessment using the Revised Morgan, Morgan Finney (RMMF) Model in a GIS Framework” was conducted in the Manahari Khola Sub-watershed of the Makwanpur district. The primary objective of the study was to evaluate the extent of soil erosion under the current Land Use and Land Cover (LULC) conditions. To perform the model, essential databases such as LULC parameters, soil parameters, rainfall parameters, and Digital Elevation Model (DEM) were generated using Landsat Images, landform maps based on FAO guidelines, data from the Hydrology Department, and Google Earth. The software tools ArcGIS 10.8 and ILWIS 3.3 Academic were utilized. The results of the RMMF soil erosion modeling indicated varying levels of soil erosion risk, ranging from Very Low to Very High. It was observed that forest and bush areas experienced lower rates of soil erosion, while barren land showed higher erosion rates. Additionally, the erosion susceptibility map illustrated that forested regions had a very low risk of soil erosion, followed by low to moderate risk in agricultural areas. Barren areas exhibited moderate to very high susceptibility to soil erosion. The study emphasized the need for proper conservation of cutting and cliff areas as well as barren land within the watershed due to their high to very high potential for soil erosion risk. Recommendations for the future included afforestation in barren areas, implementation of conservation farming practices in agricultural regions, and adoption of appropriate road stabilization measures.

Aim: To model and estimate the total above ground biomass (AGB) of forest with the best model out of five different regression models Location: Shreenagar Hill Forest, Tansen Municipality, Nepal Time Period: During the month of July, 2023 Major Taxa Studies: Pinus roxburghii Methods: Sentinel-2 satellite imagery and field-measured AGB at plot level were used. Field data were collected from a total of 26 sample. Randomly chosen 18 sample plots (SPs) (70%) were used to generate the model and remaining 8 SPs (30%) for validation of developed model. Using various bands with 10m spatial resolution, eleven VIs were calculated & correlated with field measured AGB at plot level. Results & Main Conclusions: Evaluating the fit statistics, quadratic regression model using NDVI with correlation coefficient (R) 0.92, coefficient of determination (R^2) 0.86, AIC (161.13) & BIC (164.69) was found as the best model. Predicted value of AGB from best model and observed value of AGB from field were used for model validation. Root mean square error (RMSE), R & R^2 were found as 13.3594 t.ha-1per plot, 0.9597 and 0.9211 respectively during the model validation. Therefore, the quadratic regression model with NDVI as best fit model was used to estimate the total AGB and carbon stock (CS) of study area. The average value of AGB & CS (including no vegetation area) for total study area were found 192.403 & 90.429 t.ha-1 respectively. The value of AGB & CS range from 0 to 233.451 & 0 to 109.722 t.ha-1 per pixel respectively. The benefits, possibilities, and effectiveness of combining Sentinel-2 VIs with field data to forecast biomass are demonstrated by this work. To reduce the estimation error & make wider application of research, very large sample size can be chosen by future researchers.

A document by Divya Bhattarai. Click on the document to view its contents.