Uncertainty analysis for snow cover and runoff modeling in a high mountain catchment with scare data
Abstract
It is generally difficult to accurately simulate snowmelt in high mountain areas due to limited meteorological data. One solution to this problem is to use data in lower elevation, which are relatively richer and of better quality, to facilitate simulation of snow cover and runoff dynamics in high mountain regions. The modeling results however are subject to substantial uncertainty. The area of interest in this study is the Karuxung River catchment in south Tibetan Plateau, and the catchment containing fifty glaciers covers an area of 286 km2 with elevation ranges from 4550 m above sea level (asl) to 7200 m asl at the summit. Based on the MODIS 8-day maximum snow cover data, and the meteorological and hydraulic observation data from two weather stations and one hydrological station in the low elevation (4040 m, 4432 m, and 4550 m asl., respectively), the snow cover area (SCA) model and runoff model were built using an altitude zone based temperature-index model. The two models were calibrated using data during 2003-2005, and validated using data in 2006. The uncertainty analysis is focused on three parameters in the SCA and five parameters in the runoff model. Response surfaces of least-square objective function for these parameters are estimated, and Markov chain Monte Carlo (MCMC) simulation is conducted to quantify the parametric uncertainty. This research not only enriches snowmelt modeling method in high mountain catchment with scarce gauges but also provide insights on better prediction of water availability based on the model uncertainty analysis.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2013
- Bibcode:
- 2013AGUFM.H41G1329G
- Keywords:
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- 1873 HYDROLOGY Uncertainty assessment;
- 1842 HYDROLOGY Irrigation;
- 1812 HYDROLOGY Drought