Glacio-hydrological melt and run-off modelling: application of a limits of acceptability framework for model comparison and selection

Abstract

Abstract. Glacio-hydrological models (GHMs) allow us to develop an understanding of how future climate change will affect river flow regimes in glaciated watersheds. A variety of simplified GHM structures and parameterisations exist, yet the performance of these are rarely quantified at the process level or with metrics beyond global summary statistics. A fuller understanding of the deficiencies in competing model structures and parameterisations and the ability of models to simulate physical processes require performance metrics utilising the full range of uncertainty information within input observations. Here, the glacio-hydrological characteristics of the Virkisá River basin in southern Iceland are characterised using 33 signatures derived from observations of ice melt, snow coverage and river discharge. The uncertainty of each set of observations is harnessed to define the limits of acceptability (LOA), a set of criteria used to objectively evaluate the acceptability of different GHM structures and parameterisations. This framework is used to compare and diagnose deficiencies in three melt and three run-off-routing model structures. Increased model complexity is shown to improve acceptability when evaluated against specific signatures but does not always result in better consistency across all signatures, emphasising the difficulty in appropriate model selection and the need for multi-model prediction approaches to account for model selection uncertainty. Melt and run-off-routing structures demonstrate a hierarchy of influence on river discharge signatures with melt model structure having the most influence on discharge hydrograph seasonality and run-off-routing structure on shorter-timescale discharge events. None of the tested GHM structural configurations returned acceptable simulations across the full population of signatures. The framework outlined here provides a comprehensive and rigorous assessment tool for evaluating the acceptability of different GHM process hypotheses. Future melt and run-off model forecasts should seek to diagnose structural model deficiencies and evaluate diagnostic signatures of system behaviour using a LOA framework.

Groundwater–glacier meltwater interaction in proglacial aquifers

“…The mean summer river flow over 3 years of continuous monitoring (2011-2014) ranged from 5.3 to 7.9 m 3 s −1 ; and significant river flow occurred in winter (mean 1.6-2.4 m 3 s −1 ). Isotopic studies (MacDonald et al, 2016), validated by numerical modelling (Mackay et al, 2018), demonstrate that summer river flows are governed by glacier ice melt, and that winter flows are a combination of glacier meltwater, local precipitation and groundwater flow. The Virkisjökull sandur falls from 100 to 50 m a.s.l.…”

“…Glacier meltwater rivers in Alaska can potentially lose half their annual flow to groundwater (Liljedahl et al, 2017). Groundwater can comprise 15 %-75 % of winter river flows in glacial catchments in the European Alps, Canadian Rockies, Peruvian Andes and Iceland (Malard et al, 1999;Hood et al, 2006;Bury et al, 2011;McKenzie et al, 2014;Baraer et al, 2015;MacDonald et al, 2016). Direct experimental studies of groundwater in glacial environments are rare (Vincent et al, 2019): e.g.…”

“…Ice melt from glaciers, which cover ∼ 11 % of Iceland, provides an estimated third of total river runoff (Björnson and Pálsson, 2008), but glacier retreat across Iceland (Sigurðsson et al, 2007) is forecast to produce significant changes in glacial catchment hydrology (Aðalgeirsdóttir et al, 2011). The British Geological Survey (BGS), in collaboration with Veðurstofa Íslands (the Icelandic Meteorological Office), has studied the Virkisjökull catchment since 2009, monitoring rapid glacier retreat (Bradwell et al, 2013), retreat mechanisms (Phillips et al, 2013(Phillips et al, , 2014, and researching glacial meltwater hydrology (Mac-Donald et al, 2016;Flett et al, 2017;Mackay et al, 2018).…”

Information content of snow hydrological signatures based on streamflow, precipitation and air temperature

“…It is possible that the temperature‐streamflow slopes, and , and the date of the streamflow regime maxima, t Q max , are sensitive to the average total snow storage: larger volumes of snow storage should induce larger volumes of snow melt, larger catchment scale snow melt rates and a slower change of snow melt affected area; as a consequence, we expect larger streamflow regime peaks, and hence smaller and , as well as later streamflow peak dates, that is, larger t Q max . As the volumes of snow storage are expected to be strongly correlated to elevation (e.g., Blöschl, Kirnbauer, & Gutknecht, 1991; Mackay et al, 2018; Tennant et al, 2017), we verify if these signatures are correlated to catchment average elevations.…”

“…Some widely used hydrological signatures such as the baseflow index, quantiles or slopes derived from the flow duration curve, recession constants, monthly streamflow average, and so on have been used in catchments where snow processes play an important role (e.g., Hingray, Schaefli, Mezghani, & Hamdi, 2010; Kelleher, Wagener, & McGlynn, 2015; Mackay et al, 2018; Safeeq & Hunsaker, 2016; Sawicz et al, 2011; Todorović, Stanić, Vasilić, & Plavsic, 2019). To some extent, these signatures successfully identify dominant driving mechanisms in streamflow generation involving snow accumulation and melt processes.…”