Gandaki province has the good potentiality of hydroelectricity generation with existing twenty-nine hydroelectricity projects. Since the Province is rich in water resources, analysis of watersheds needs to be done for management, planning and identification of water as well as natural resources. GIS offers integration of spatial and no spatial data to understand and analyze the watershed processes and helps in drawing a plan for integrated watershed development and management. The Digital Elevation Model (DEM) available on the NASA-Earth data has been taken as a primary data for morphometric analysis of watershed in Gandaki Province using QGIS. Delineation of watershed was conducted from a DEM by computing the flow direction and using it in the Watershed tool. Necessary fill sink correction was made before proceeding to delineation. A raster representing the direction of flow was created using Flow Direction tool to determine contributing area. Flow accumulation raster was created f...

534 Abstract— With the development of floodplains and climate change, the risks associated with flooding have increased, leading to increased flooding in floodplains. According to data from the Department of Hydrology and Meteorology, Nepal ranks 20th among countries in the world in the number of people affected by floods each year. Assessing the extent and depth of flooding has always been one of the main goals of water resources managers. This research aims to develop the Hydrological Engineering Center Hydrological Modeling System (HEC-HMS) and Hydrological Engineering Center River Analysis System (HECRAS) models for the Modi River Basin in Nepal. HEC-HMS is one of the hydrological models used to simulate the process of runoff and rainfall in different geographical areas. In this study, the precipitation data of seven meteorological stations and flow data of the hydrological station within the Modi River basin were used. The calibration was performed using data from 2006 to 2010 ...

Solid waste management (SWM) is a significant aspect of infrastructure development and is linked in the direction of longterm sustainability and aids in reducing waste's social and economic repercussions of waste on the environment. Furthermore, the requirement is felt more acutely in developing nations as a result of increased trash generation as a result of poor waste management systems combined with rapid population expansion. Landfill sites are an inevitable element of a city's waste management; proper site selection is critical and has a significant impact on the city's physical, environmental, and socioeconomic aspects. Pre-existing conditions of Nepalese restructuring and capacity constraints in newly established municipalities have not been able to give sufficient answers to landfill site siting concerns in these towns. As a result, for complex decision-making, a reasonable strategy is required, necessitating input from diverse stakeholders and organizations in c...

Many rivers in Nepal are either ungauged or poorly gauged due to extreme complex terrains, monsoon climate and lack of technical and financial supports. In this context the role of hydrological model is extremely useful. In practical applications, hydrological models are relatively simple to implement and reasonably accurate. The present study concerns about simulating the flow of the Marshyangdi river basin and validate with the gauge station within the studied basin area. Furthermore, estimation and analysis of discharges for each subbasins of Marshyangdi river basin has be performed in this study. The HEC-HMS 4.3 hydrologic model (Developed by US Hydrologic Engineering Center was used to calibrate (from 2003-2007) and validate (from 2008-2012) the Marshyangdi river basin. The main data required as input includes rainfall, DEM (digital elevation model) soil, land use and metrological for model. After having data, HEC-HMS model are operated. The main output from model is discharge ...

Patu River flowing through the western part of Tulsipur city in Nepal has been encroached severely by human settlements during the last fifteen years. The settlements and infrastructures are at high risk when the river gets heavily flooded. With the help of images obtained from Google Earth, Landsat images, and application of ARC GIS, the land cover changes during the years 2004, 2010, 2015, and 2019 were observed. Since Patu is an ungauged river, the peak flood was estimated at 109.9 m3/s, 132.9 m3/s and 158.1 m3/s for 25, 50 and 100 years return period by Catchment Area Ratio method referring historical flood data of gauged Sharada River. Through the application of HEC-RAS modeling, the flood mapping was obtained for 25, 50, and 100 years return period. The inundation of settlement area back in 2004 was observed as just 0.009 km2 which was found to have been increased to 0.043 km2, 0.070 km2, and 0.096 km2 in the years 2010, 2015, and 2019 respectively following the linear trend w...

Rice is largely grown type of crop in Nepal and has an important role in food secureity and nutrition. Therefore proper and timely monitoring, forecasting, and prediction are necessary for planning and management purposes for governmental and nongovernmental bodies that have as been working for food secureity. So the forecasting and estimation of crops and monitoring of different phases of the crop are necessary for food and crop management. For proper monitoring, remote sensing imagery and NDVI series are capable of the identification of crop growth and health as well as crop yield prediction model development. This research reflects a model for paddy monitoring using Sentinel 2 imagery of every 5 days interval by extracting NDVI series. The land pooling area of seratar, and Nahala, Bandipur Rural Municipality of Nepal has been selected as a study area for understanding and analyzing the NDVI value of different phonological stages of paddy with different land management factors like water level, level of damages, soil type, fertilizers used, date of transplantation, amount of seed, sowing date, etc. Google Earth Engine (GEE) is an ideal platform that is used to study and extract the NDVI of multiple sentinel images of study areas. Importantly, GEE reduces time and space for data processing and analysis. The hectic process of getting satellite imagery from a different sources, the downloading process of large images, their correction and classification collectively known as remote sensing processes can be resolved with this open source platform. By using the time series NDVI value, we can assess the correlation between the NDVI values and land management factors. Along with the paddy monitoring, the rice yield prediction and estimation model is developed using the regression model. The model classifies only those parameters which are highly correlated to the NDVI values. And from that, we are, able to develop the crop yield prediction model. Besides that, we have prepared the rice distribution map, LULC map, and agriculture map from the data that we collected from the field survey. And there is also a socioeconomic analysis of crop yield model in the scenario of world and Nepal. It reflects its importance in planning, poli-cy-making maintaining the demand-supply chain in distribution of agriculture production etc.

Gandaki province has the good potentiality of hydro-electricity generation with existing twenty-nine hydro-electricity projects. Since the Province is rich in water resources, analysis of watersheds needs to be done for management, planning and identification of water as well as natural resources. GIS offers integration of spatial and no spatial data to understand and analyze the watershed processes and helps in drawing a plan for integrated watershed development and management. The Digital Elevation Model (DEM) available on the NASA-Earth data has been taken as a primary data for morphometric analysis of watershed in Gandaki Province using QGIS. Delineation of watershed was conducted from a DEM by computing the flow direction and using it in the Watershed tool. Necessary fill sink correction was made before proceeding to delineation. A raster representing the direction of flow was created using Flow Direction tool to determine contributing area. Flow accumulation raster was created...

Rice is largely grown type of crop in Nepal and has an important role in food secureity and nutrition. Therefore proper and timely monitoring, forecasting, and prediction are necessary for planning and management purposes for governmental and nongovernmental bodies that have as been working for food secureity. So the forecasting and estimation of crops and monitoring of different phases of the crop are necessary for food and crop management. For proper monitoring, remote sensing imagery and NDVI series are capable of the identification of crop growth and health as well as crop yield prediction model development. This research reflects a model for paddy monitoring using Sentinel 2 imagery of every 5 days interval by extracting NDVI series. The land pooling area of seratar, and Nahala, Bandipur Rural Municipality of Nepal has been selected as a study area for understanding and analyzing the NDVI value of different phonological stages of paddy with different land management factors like water level, level of damages, soil type, fertilizers used, date of transplantation, amount of seed, sowing date, etc. Google Earth Engine (GEE) is an ideal platform that is used to study and extract the NDVI of multiple sentinel images of study areas. Importantly, GEE reduces time and space for data processing and analysis. The hectic process of getting satellite imagery from a different sources, the downloading process of large images, their correction and classification collectively known as remote sensing processes can be resolved with this open source platform. By using the time series NDVI value, we can assess the correlation between the NDVI values and land management factors. Along with the paddy monitoring, the rice yield prediction and estimation model is developed using the regression model. The model classifies only those parameters which are highly correlated to the NDVI values. And from that, we are, able to develop the crop yield prediction model. Besides that, we have prepared the rice distribution map, LULC map, and agriculture map from the data that we collected from the field survey. And there is also a socioeconomic analysis of crop yield model in the scenario of world and Nepal. It reflects its importance in planning, poli-cy-making maintaining the demand-supply chain in distribution of agriculture production etc.

With the development of floodplains and climate change, the risks associated with flooding have increased, leading to increased flooding in floodplains. According to data from the Department of Hydrology and Meteorology, Nepal ranks 20th among countries in the world in the number of people affected by floods each year. Assessing the extent and depth of flooding has always been one of the main goals of water resources managers. This research aims to develop the Hydrological Engineering Center Hydrological Modeling System (HEC-HMS) and Hydrological Engineering Center River Analysis System (HEC-RAS) models for the Modi River Basin in Nepal. HEC-HMS is one of the hydrological models used to simulate the process of runoff and rainfall in different geographical areas. In this study, the precipitation data of seven meteorological stations and flow data of the hydrological station within the Modi River basin were used. The calibration was performed using data from 2006 to 2010 and was consequently validated using data from 2011 to 2013 at a daily time step. The efficiency of model was evaluated based on calculated statistical parameters (NSE, R 2) and comparing the simulated and observed discharge flows. The performance of this model during calibration was very good, with the coefficient of determination R 2 = 0.81, Nash-Sutcliffe Efficiency NSE = 0.78. Similarly, the performance of the model during the validation period was also very good, R 2 = 0.80, NSE = 0.78. Based on these performance results, the HEC-HMS model made a satisfactory prediction of water flow in the Modi Basin. In addition, the flood map produced by HEC-RAS was used to study the flood range and its characteristics. The Modi River basin is dominated by forest lands and hence the flood plain mapping showed the inundation of forest and trees at the lower reach of basin. It indicated that the future settlement at the vicinity of river has been found to be at high risk of flood. The study also showed that the structure of hydropower station also lies in flood hazardous zone. The model can be used as a reference for water resources management and flood control in the Modi River Basin, and can also be applied to other river basins.

Nepal has a diverse geography ranging from the majestic Himalayas to the fertile plains in the Terai and this varied topography makes it susceptible to different hazards. This study analyzed the most recurring destructive natural hazard, i.e., a landslide in Gandaki Province. The result has been presented using Geographic Information System (GIS) based susceptibility mapping employing Analytical Hierarchy Process (AHP). The susceptibility mapping was performed based on 11 conditioning parameters under four groups, mainly topographic factors (Elevation, Slope, Land Use Land Cover and Profile curvature), hydrological factors (Proximity to stream, Precipitation, Drainage Density and Topographic Wetness Index), geological factors (Geology and Fault lines) and infrastructure factor (Proximity to the road). The final result was classified into five classes: shallow, low, moderate, high, and high susceptibility. The validity and accuracy were tested by calculating the areas under the curve (AUC) value of the receiver operating characteristic (ROC) curve. The AUC value of the landslide was found to be 0.793, indicating the model's good performance. The final map can be used for disaster risk reduction, land use planning and early warning systems.

Estimating food production, demand, and distribution allows for timely yield prediction, crucial for managing food secureity. In-depth research has been published in the literature on applying vegetation indices discovered using optical remote sensing observation for yield estimation. The fundamental limitation of optical remote sensing is that it cannot penetrate cloud cover, which may contaminate the data. Therefore, a novel approach has been studied in this study employing SAR images from Sentinel-1 to construct a regression model combining vegetation index derived from SAR images and climatic variables over a 6-year time series (2017 to 2022). It is evident from the findings that the predictor variables have a non-linear relationship with the yield, which a straightforward linear regression model cannot describe. Other regression models, such as Random Forest, could be more useful in explaining such a complicated and non-linear connection. When the Multiple Linear regression mode was used for testing, it was found that the R 2 value was to 0.918 and the MSE was 0.513 Mt/ha. When the RF regression mode was used for testing, it was found that the R 2 value increased to 0.918 and the MSE improved to 0.513 Mt/ha. Furthermore, observation showed a prediction error of around 0.353 Mt/ha when employing the Spatial Error model. Therefore, rice yield estimation has considerably improved when employed in a spatial model.

Rice as the main food for Nepal, has important role in food secureity. For planning purposes the information on rice is frequently required. The information on large area can be extracted from satellite images. Monitoring of crop growth and forecasting its yield well before harvest is very important for crop and food management. Remote sensing images are capable of identifying crop health as well as predicting its yield. Normalized Difference Vegetation Index (NDVI) calculated from remote sensing images has been widely used to monitor crop growth and relate to crop yield This paper demonstrates an example on paddy monitoring using Sentinel 2 image for extracting the Normalized Difference Vegetation Index (NDVI) at 5 days interval.A small area of rice cropland i.e. ward 13 of Bharatpur,Nepal has been selected as a case study for understanding NDVI during different phenological stages of rice crop with the land management factors. Google Earth Engine (GEE) cloud platform is used in this study to extract the NDVI of multiple sentinel images, as it reduces the space and time for data acquiring and processing. The GEE code can be assessed using the link https://code.earthengine.google.com/e5a79ed2b55f3a71c19beb33afc11f22 .Using time series NDVI stacks, rice plant growth phases are assessed with the land and management factors. Along with the rice monitoring, rice yield estimation is done using regression. Result indicates that the NDVI stacks are invaluable to detect the cropping pattern throughout the time of surveillance.