Version 1
: Received: 20 February 2017 / Approved: 22 February 2017 / Online: 22 February 2017 (04:26:49 CET)
How to cite:
Corripio, J.; López-Moreno, J. Analysis and Predictability of the Hydrological Response of Mountain Catchments to Heavy Rain on Snow Events: A Case Study in the Spanish Pyrenees. Preprints2017, 2017020080 (doi: 10.20944/preprints201702.0080.v1).
Corripio, J.; López-Moreno, J. Analysis and Predictability of the Hydrological Response of Mountain Catchments to Heavy Rain on Snow Events: A Case Study in the Spanish Pyrenees. Preprints 2017, 2017020080 (doi: 10.20944/preprints201702.0080.v1).
Cite as:
Corripio, J.; López-Moreno, J. Analysis and Predictability of the Hydrological Response of Mountain Catchments to Heavy Rain on Snow Events: A Case Study in the Spanish Pyrenees. Preprints2017, 2017020080 (doi: 10.20944/preprints201702.0080.v1).
Corripio, J.; López-Moreno, J. Analysis and Predictability of the Hydrological Response of Mountain Catchments to Heavy Rain on Snow Events: A Case Study in the Spanish Pyrenees. Preprints 2017, 2017020080 (doi: 10.20944/preprints201702.0080.v1).
Abstract
From June 18 to 19, 2013, the Ésera river in the Pyrenees, Northern Spain, caused widespread damage due to flooding as a result of torrential rains and sustained snowmelt. We estimate the contribution of snow melt to total discharge applying a snow energy balance to the catchment. Precipitation is derived from sparse local measurements and the WRF-ARW model over three nested domains, down to a grid cell size of 2 km. Temperature profiles, precipitation and precipitation gradient are well simulated, although with a possible displacement regarding the observations. Snowpack melting was correctly reproduced and verified in three instrumented sites, and according to satellite images. We found that the hydrological simulations agree well with measured discharge. Snowmelt represented 33% of total runoff during the main flood event and 23% at peak flow. The snow energy balance model indicates that most of the energy for snow melt during the day of maximum precipitation came from turbulent fluxes. This approach forecast correctly peak flow and discharge during normal conditions at least 24h in advance and could give an early warning of the extreme event 2.5 days before.
Subject Areas
ROS; snow; rain; flood; WRF; numerical weather forecast; energy balance; discharge estimation; early alert system
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
