High uncertainty in 21st century runoff projections from glacierized basins

Abstract

Glacier response to a changing climate and its impact on runoff is understood in general terms, but model-based projections are affected by considerable uncertainties. They originate from the driving climate model, input data quality, and simplifications in the glacio-hydrological model and hamper the reliability of the simulations. Here, an integrative assessment of the uncertainty in 21st century glacier runoff is provided based on experiments using the Glacier Evolution Runoff Model (GERM) applied to the catchment of Findelengletscher, Switzerland. GERM is calibrated and validated in a multi-objective approach and is run using nine Regional Climate Models (RCMs) until 2100. Among others, the hydrological impacts of the RCM downscaling procedure, the winter snow accumulation, the surface albedo and the calculation of ice melt and glacier retreat are investigated. All experiments indicate rapid glacier wastage and a transient runoff increase followed by reduced melt season discharge. However, major uncertainties in, e.g., glacier area loss (À100% to À63%) and the change in annual runoff (À57% to +25% relative to today) by 2100 are found. The impact of model assumptions on changes in August runoff is even higher (À94% to À5%). The spread in RCM results accounts for 20-50% of the overall uncertainty in modeled discharge. Initial ice thickness, the amount and spatial distribution of winter snow and the glacier retreat model have the largest effect on the projections, whereas the RCM downscaling procedure, calibration data quality and the melt model (energy balance vs. degree-day approach) are of secondary importance.

Projected cryospheric and hydrological impacts of 21st century climate change in the Ötztal Alps (Austria) simulated using a physically based approach

“…Only rain falling on bare ground, ice, firn, or already saturated snow is part of the rainfall runoff contribution in this case. Huss et al (2014) assessed the influence of various model assumptions in projections of glacier evolution and runoff in high-mountain catchments. Their results indicate that major uncertainty sources are especially (i) the winter snow accumulation in terms of both volume and spatial distribution, (ii) the approach to account for glacier geometry changes, (iii) the initial glacier ice volume distribution, and (iv) the individual climate projections.…”

“…While there are numerous studies on climate change impacts on the future of glaciers and hydrology for various parts of the European Alps, particularly Switzerland (e.g., Addor et al, 2014;Bosshard et al, 2013;Farinotti et al, 2011;Fatichi et al, 2015;Finger et al, 2012;Horton et al, 2006;Huss et al, 2008Huss et al, , 2014Kobierska et al, 2013;Milano et al, 2015), few such studies exist for Austria. Kuhn and Batlogg (1998) used hypothetical temperature change scenarios to simulate future runoff for nine Austrian catchments with varying glacierization using a simple conceptual water balance model while assuming constant glacier areas.…”

Projecting hydropower production under future climates: a guide for decision‐makers and modelers to interpret and design climate change impact assessments

“…It should be kept in mind that such large-scale analyses based on precipitation, discharge and glacier mass balance data strongly simplify the actual water balance and do not account for groundwater storage changes. The big open question for glacier-dependent HPP is the expected moment of peak water (Gleick and Palaniappan, 2010) and peak annual production due to streamflow from net glacier mass loss Huss et al, 2014). Locally, climate change impacts on glacial hazards (glacial lake outburst, Dussaillant et al, 2010) and on sediment delivery might outweigh the water availability question Gobiet et al, 2014).…”

Imaging spectroscopy to assess the composition of ice surface materials and their impact on glacier mass balance

“…The model allows the determination of seasonal glacier volume changes in water equivalent (w.e.). A detailed description of the used model is given in Huss, Bauder, and Funk (2009) and Huss, Zemp, Joerg, and Salzmann (2014). The model components relevant to our study are briefly summarized hereafter.…”