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NERTO Students

Engela Sthapit

Engela Sthapit
PhD, Civil Engineering, Graduate




Internship Location: ESRL/NOAA Boulder, CO

Internship Date: Spring 2020


Profile:

Sthapit holds a BS in Environmental Science and dual MS in Civil Engineering – Water Resources emphasis and Biological Sciences. Before joining NOAA CESSRST, she worked in the energy and environmental field for 10 years where she gained valuable industry experience.



NERTO Research Project Title:

Understanding the snow representation in the Noah-MP Model through a single column experiment

NERTO Project Details :

Snow plays an important role in the water balance since the input of melted snow during spring seasons contributes an important fraction of total runoff in snow-dominated watersheds. Water managers require accurate and precise estimates of snowpack to estimate runoff from snow dominant watersheds.

The objective was to analyze the representation of snow in the WRF-Hydro Model and one of its embedded land surface models (LSM), Noah-MP. The goals were to: 1) Test various forcing datasets for an idealized, single-column representation of Noah-MP. Datasets tested will likely include the NLDAS (used currently for retrospective NWM simulations), the AORC (developed by NOAA specifically for NWM forcing), and a ‘perfect’ forcing dataset from the CREST-Snow Analysis and Field Experiment. Comparison of the snow-specific performance of Noah-MP with these different forcing datasets will provide insight into physical reasons underlying model deficiencies. 2) Perform sensitivity analysis of snow parameters in the Noah-MP LSM. These sensitivity experiments will first be conducted for the idealized, single-column version of Noah-MP, and then, if time permits, for a cutout of the NWM-configured WRF-Hydro across the Aroostook River watershed in Caribou, Maine. For the latter, WRF-Hydro Model streamflow will be compared with the USGS streamflow for the selected watershed, and Noah-MP snow will be compared with selected satellite snow data products.



NERTO Outcomes:

I learned how to run Noah-MP and WRF-Hydro model.

Value of NERTO to the Line Office:

The work provided NOAA/ESRL with a better understanding of the differenced in the forcing datasets, snow parameters of NOAH-LSM and select satellite snow products. WRF-Hydro with the NOAH-MP LSM serves as the modeling framework of the National Water Model (NWM), therefore this work will help for future improvements to the NWM.

NERTO Skills:

My programming skills in python has improved a great deal.