arXiv subjects

Jeeban Panthi

Publications and source records attributed to Jeeban Panthi.

3 recordsLinked to original sources

arXiv2023

Nepal Himalaya Offers Considerable Potential for Pumped Storage Hydropower

There is a pressing need for a transition from fossil fuel to renewable energy to meet the increasing energy demands and reduce greenhouse gas (GHG) emissions. The Himalayas possess substantial renewable energy potential that can be harnessed through hydropower projects due to its peculiar topographic characteristics and abundant water resources. However, the current exploitation rate is low owing to the predominance of run-of-river hydropower systems to support the power system. The utility-scale storage facility is crucial in the load scenario of an integrated power system to manage diurnal variation, peak demand, and penetration of intermittent energy sources. In this study, we first identify the potential of pumped storage hydropower across Nepal (a central Himalayan country) under multiple configurations by pairing lakes, hydropower projects, rivers, and available flat terrains. We then identify technically feasible pairs from those of potential locations. Infrastructural, environmental, operational, and other technical constraints govern the choice of feasible locations. We find the flat land-to-river configuration most promising than other configurations. Our results provide insight into the potential of pumped storage hydropower and are of practical importance in planning sustainable power systems in the Himalayas and beyond.

stat.AP↗

arXiv2021

Bayesian Characterization of Uncertainties Surrounding Fluvial Flood Hazard Estimates

Fluvial floods drive severe risk to riverine communities. There is a strong evidence of increasing flood hazards in many regions around the world. The choice of methods and assumptions used in flood hazard estimates can impact the design of risk management strategies. In this study, we characterize the expected flood hazards conditioned on the uncertain model structures, model parameters and prior distributions of the parameters. We construct a Bayesian framework for river stage return level estimation using a nonstationary statistical model that relies exclusively on Indian Ocean Dipole Index. We show that ignoring uncertainties can lead to biased estimation of expected flood hazards. We find that the considered model parametric uncertainty is more influential than model structures and model priors. Our results highlight the importance of incorporating uncertainty in river stage estimates, and are of practical use for informing water infrastructure designs in a changing climate.

stat.AP↗

arXiv2020

Weather Radar in Nepal: Opportunities and Challenges in Mountainous Region

Extreme rainfall is one of the major causes of natural hazards (for example flood, landslide, and debris flow) in the central Himalayan region, Nepal. The performance of strategies to manage these risks relies on the accuracy of quantitative rainfall estimates. Rain gauges have traditionally been used to measure the amount of rainfall at a given location. The point measurement often misrepresents the basin estimates, because of limited density and high spatial variability of rainfall fields across the Himalayas. The Department of Hydrology and Meteorology (DHM), Nepal has planned to install a network of three weather radars that cover the entire country. So far, the first weather radar has been installed in 2019 in the western region of the country. Two more radars will be added for the planned radar network in the near future covering the central and eastern regions of the country. Here we introduce the first installed weather radar in Nepal. We highlight both the opportunities and challenges with the radar observation in the mountainous regions. Radar rainfall estimates across the Himalayas are critical to issue severe weather warnings; forecast floods and landslides; and inform decision making in a broad range of sectors, including water and energy, construction, transportation, and agriculture.

physics.ao-ph↗