2019 CSDMS meeting-089


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Building a distributed and physically based supraglacial meltwater routing model with Landlab to explore lag between peak meltwater production and discharge

Celia Trunz, University of Arkansas Fayetteville Arkansas, United States. celia.trunz@gmail.com
Matthew Covington, University of Arkansas Fayetteville Arkansas, United States. mcoving@uark.edu

The impact of supraglacial meltwater on the motion of the Greenland Ice Sheet is strongly correlated to spatial and temporal variability of meltwater input. Meltwater infiltrates the bed through moulins and can reduce effective pressure and, consequently, accelerate the ice. However, the subglacial conduit system evacuates the water and can adapt to accommodate different water inputs. The timing of water infiltration impacts the ability of the system to reach equilibrium state. With the progression of the equilibrium line higher up on the ice under warming climate, it is essential to predict how increased meltwater is going to affect ice motion. Understanding these processes will reduce uncertainty in global sea level rise predictions. Temporal variability of meltwater input is difficult to measure on the ice sheet due to the difficulties in instrumenting constantly melting stream beds. Therefore, glacier dynamic models rely on surface mass balance models to simulate the discharge. Those models usually neglect spatial properties of the drainage basin and are not able to reproduce the peak meltwater discharge in supraglacial streams. Lags between peak melt and peak discharge vary from one stream to another, and factors influencing the delay between peak melt and peak discharge have not been thoroughly explored. For this reason, we propose to build a distributed and physically based model using Landlab to reproduce flow routing on the Greenland Ice Sheet. This model will produce discharge values on a grid using three grid layers that calculate: 1) meltwater production, 2) flow direction, and 3) water displacement velocity. Model inputs will be weather, elevation, and snow coverage data. This model will enable us to explore and extract the main parameters influencing lags and predict the spatial pattern of infiltration lags at an ice sheet scale.