Preprints
https://doi.org/10.5194/hess-2022-51
https://doi.org/10.5194/hess-2022-51
 
29 Mar 2022
29 Mar 2022
Status: this preprint is currently under review for the journal HESS.

A snow and glacier hydrological model for large catchments – case study for the Naryn River, Central Asia

Sarah Rose Shannon1,2, Anthony Payne1,2, Jim Freer3,2,1, Gemma Coxon1,2, Martina Kauzlaric4, David Kriegel5, and Stephan Harrison6 Sarah Rose Shannon et al.
  • 1Department of Geographical Science, University Road, University of Bristol, BS8 1SS, UK
  • 2Cabot Institute, University of Bristol, Bristol, BS8 1UJ, UK
  • 3University of Saskatchewan, Centre for Hydrology, 116-1151 Sidney Street, Canmore, Alberta, T1W 3G1, Canada
  • 4Universität Bern,Geographisches Institut, Climate Impact Research, Hallerstrasse 12,CH-3012 Bern, Switzerland
  • 5Ingenieurbüro für Grundwasser GmbH, Leipzig, Germany
  • 6College of Life and Environmental Sciences, University of Exeter, Cornwall Campus Penryn, TR10 9EZ, U.K.

Abstract. In this paper we implement a degree day snow and glacier melt model into the Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) model. The purpose is to develop a hydrological model that can be applied to large glaciated and snow-fed catchments, yet is computationally efficient enough to include model uncertainty in streamflow predictions. The model is evaluated by simulating monthly discharge at six gauging stations in the Naryn River catchment (57,833 km2) in Central Asia over the period 1951 to a variable end date between 1980 and 1995 depending on the availability of discharge observations. The spatial distribution of simulated snow cover is validated against MODIS weekly snow extent for the years 2001–2007. Discharge is calibrated by selecting parameter sets using Latin Hypercube sampling and assessing the model performance using six evaluation metrics.

The model shows good performance at simulating monthly discharge for the evaluation period (NSE is 0.74 < NSE < 0.87) and validation period (0.7 < NSE < 0.9) where the range of NSE values represent the 5th–95th percentile prediction limits across the gauging stations. The exception is the Uch-Kurgan station which exhibits a reduction in model performance during the validation period attributed to commissioning of the Toktugal reservoir in 1975 which impacted the observations. The model reproduces the spatial extent in seasonal snow cover well, capturing 86 % of the snow extent on average (2001–2007) for the median ensemble member of the best 0.5 % evaluation simulations, when evaluated against MODIS snow extent.

We establish the present-day contributions of glacier melt, snow melt and rainfall to the total annual discharge and the timing of when these components dominate river flow. The model predicts the observed increase in discharge during the spring (April–May) associated with the onset of snow melting and peak discharge during the summer (June, July and August) associated with glacier melting well. At all stations snow melting is the largest component, followed by the rainfall and the glacier melt component. In August, glacier melting can contribute up to 66 % of the total discharge at the highly glacierised Naryn headwater sub-catchment. The glaciated area predicted by the best 0.5 % evaluation simulations overlap the Landsat observations for the late 1990s and mid-2000s. Despite good predictions for discharge, the model produces a large range of estimates for the glaciated area (680 km2–1,196 km2) (5th–95th percentile limits) at the end of the simulation period. To constrain these estimates further, additional observations such as glacier mass balance, snow depth or snow extent should be used directly to constrain model simulations.

Sarah Rose Shannon et al.

Status: open (until 22 Jun 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-51', Jonathan D Mackay, 21 Apr 2022 reply

Sarah Rose Shannon et al.

Sarah Rose Shannon et al.

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Short summary
We added a snow and glacier melt model to the Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) model. The motivation was to develop a hydrological model that can be applied to large glaciated and snow-fed catchments, yet is computationally efficient enough to include model uncertainty in streamflow predictions. The model is applied to the highly glacierised Naryn River catchment in Central Asia and found to reproduce past changes in streamflow well.