The urbanization process of the Kathmandu Valley has a significant impact on LULC change, river runoff, and sediment transport capability. The historical sediment flow pattern indicates that the sediment transport capacity of the basin has increased even when precipitation and river discharge decreased. So, the sediment regression model is developed in this study in relation to discharge, precipitation, and built-up area change. Model parameters are calibrated and validated through the measured sediment discharge of the basin and the performance of the model is evaluated through NSE, PBIAS, and R2. In the future, the sediment transport capacity of the channel is projected for average monthly, maximum, and minimum flow conditions by +4.33%, +6%, and -2.66% respectively per decade due to the rise in the urban area (+6% per decade). Increasing the rigid ground surface through urbanization reduces the sediment generation through the watershed and balances the sediment transport capability, excess erosion is produced in the river channel causing a change in the river morphology. The findings of this study will be useful for planning and management of the river basin and the river structures.

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This study is based on the Bagmati river basin that flows along with the capital city, Kathmandu which is a small and topographically steep basin. Major flood occurring in 1993 and 2002 as stated in the report of DWIDP shows that the basin is subjected to water-induced disaster in monsoon season affecting people and property. This study focuses on the development of a rainfall-runoff model for Bagmati basin in HEC-HMS using the Synthetic Unit Hydrograph (SUH) with Khokana as the outlet. The coefficients for SUH like Lag time coefficient (Ct), peak discharge coefficient (Cp), unit hydrograph widths at 50% and 75% of peak and base time were determined calibrating the Synder’s equation where Ct varies from 0.244 to 1.016 and Cp varies from 0.439 to 0.410. The rainfall-runoff model in HEC-HMS has been calibrated from daily data of 1992-2013 and validated from hourly data for July 2011, August 2012, and July 2013. Furthermore, the model has been tested to compare the discharge for various return periods with the observed ones which are in close agreement. The determination of Peak Maximum Flood (PMF) using the calculated Peak Maximum Precipitation (PMP) is also another application of the model which can be used to design various hydraulic structures. Thus the values of coefficients, Ct and Cp can be used to construct unit hydrograph for the basin. Moreover, the satisfactory performance of the model during calibration and validation proves the applicability of the model in flood forecasting and early warning.

The observation of hydrological as well as meteorological data is very essential for any kind of hydraulic and hydrological study. In Nepal, due to the significant variation of topography and climatic characteristics as well it is necessary to establish the meteorological stations densely, here only 231 meteorological stations are available and are handled by government organizations Department of Hydrology and Meteorology (DHM).  Even though, due to many limitations, the satellite data is very useful for the water resources study. There are so many satellite products available but the performance of these products varies from place to place. In this study, the performance of four satellite products i.e., Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR), Integrated Multi-Satellite Retrievals for GPM (IMERG), and TRMM Multi-satellite Precipitation Analysis (TMPA) all over Nepal are evaluated with different elevation bands. The performance of each product is evaluated by Probability of Detection (POD), Critical Success Index (CSI), Frequency Bias Index (FBI), False Alarm Ratio (FAR), Root Mean Square Error (RMSE), and Percentage Bias (PBIAS). After analysis of each product, the PERSIANN-CDR data set gives a reasonable performance for all elevation bands after bias correction.