Date of Award


Degree Type


Degree Name

Doctor of Philosophy in Biological and Environmental Sciences



First Advisor

Soni Pradhanang


Hydrological regime and nutrient dynamics are associated with hydrological setting of any watershed. Human interventions have direct and indirect impacts on these hydrologic setting and watershed response. The increasing anthropogenic pressure has continued to degrade the water quality. This may lead to eutrophication that can seriously harm the water quality and aquatic ecosystems (Vitousek 1997).

The effective mitigation strategies in land and water management practices should have good understand of water and nutrient fluxes in watershed and relating them to spatio-temporal hydrologic factors (Liang et al., 2020).

The best approaches of the management strategies should always start from monitoring and modeling of hydrology of the area concerned. Monitoring enables to understand a baseline of current status and trends in water quantity and quality (Whitehead et al., 2019). The assessment of long term nutrient load has large degree of uncertainties (Zhang & Hirsch, 2019). High frequency water quality measurement is the best suited approach bridge this knowledge gap.

The current and future climate stresses along with the changing land cover and land use should be understood to address this issue. The future climate projections suggest increase in extreme precipitation events, prolonged drought regime and increased temperature that can impact hydrologic behavior.

Hydrological model at watershed scale are best suited approached to perform water quality and quantity simulation (Clark et al., 2015). This is also pivotal in understanding the watershed response under various hydrologic and climate settings.

Physically based hydrological model has enabled understanding of event based and long term response of watershed. Water quality has been modeling has been hindered due to infrequent grab samples. Therefore, the water management systems understand all these hurdles and develop robust measure to understand hydrologic responses with current and future climate stresses (Nilawar and Waikar 2019).

Therefore, the objective of this dissertation is to develop water quantity and quality model calibrated against water quality parameters derived from high frequency optimal sensor to simulate discharge and nutrient under current and future climate stresses. In order to achieve this objective, I developed a multi criteria selection and downscaling of climate model from the GCM, employed machine learning model and classical statistical method to measure water quality parameters and developed a hydrological model in SWAT to simulate hydrological behavior and response at current and future climate stresses.



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