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Sciences in Cold and Arid Regions ›› 2020, Vol. 12 ›› Issue (2): 95-103.doi: 10.3724/SP.J.1226.2020.00095.

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Spatial and temporal transferability of Degree-Day Model and Simplified Energy Balance Model: a case study

HuiLin Li1,2()   

  1. 1.State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
    2.Tianshan Glaciological Station, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
  • Received:2020-01-05 Accepted:2020-02-10 Online:2020-04-30 Published:2020-04-27
  • Contact: HuiLin Li E-mail:lihuilin@lzb.ac.cn

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Abstract:

Glacier mass balance, the difference between accumulation and ablation at the glacier surface, is the direct reflection of the local climate regime. Under global warming, the simulation of glacier mass balance at the regional scale has attracted increasing interests. This study selects Urumqi Glacier No. 1 as the testbed for examining the transferability in space and time of two commonly used glacier mass balance simulation models: i.e., the Degree-Day Model (DDM) and the simplified Energy Balance Model (sEBM). Four experiments were carried out for assessing both models' temporal and spatial transferability. The results show that the spatial transferability of both the DDM and sEBM is strong, whereas the temporal transferability of the DDM is relatively weak. For all four experiments, the overall simulation effect of the sEBM is better than that of the DDM. At the zone around Equilibrium Line Altitude (ELA), the DDM performed better than the sEBM. Also, the accuracy of parameters, including the lapse rate of air temperature and vertical gradient of precipitation at the glacier surface, is of great significance for improving the spatial transferability of both models.

Key words: Degree-Day Model, Simplified Energy Balance Model, temporal and spatical transferability, Urumqi Glacier No. 1

Figure 1

Topography and mass balance observational network of UG1"

Figure 2

Schematics of the framework for assessing model temporal and spatial transferability"

Table 1

Calibrated model parameters of the DDM"

ParametersValueUnit
DDF for ice15mm/(d?°C)
DDF for snow1.8mm/(d?°C)
Lapse rate for air temperature0.0052°C/m
Vertical gradient for precipitation26mm/100 m

Figure 3

(a-e) Comparison between the observed and DDM-simulated mass balance of the west branch from 2001 through 2005; (f) Correlation between the observed and simulated data"

Figure 4

Comparison between the observed and DDM-simulated mass balance of the east branch from 2006 through 2018: (a) at each stake or altitude; (b) for each year; (c) correlation for the data in the entire period; (d) for 2007 when the simulated annual mass balance was the closest to the observed; and (e) for 2017 when the discrepancy between the simulated and observed was the highest"

Table 2

Calibrated model parameters of the sEBM"

ParametersValueUnit
c01-129W/m2
c02-0.007W/m3
c121W/(m2?K)
c20W/(m2?K2)
Lapse rate for air temperature0.006°C/m
Vertical gradient for precipitation22mm/100 m

Figure 5

(a-e) Comparison between the observed and sEBM-simulated mass balance at each stake of the west branch from 2001 through 2005; (f) Correlation between the observed and simulated mass balance"

Figure 6

Comparison between the observed and sEBM-simulated mass balance of the east branch from 2006 through 2018: (a) at each stake or altitude; (b) for each year; (c) correlation for the data in the entire period; (d) for 2014 when the simulated annual mass balance was the closest to the observed; and (e) for 2016 when the discrepancy between the simulated and observed was the highest"

Figure 7

Error analyses for all four experiments in this study: (a) absolute error, and (b) relative error"

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