Quantifying Hydrological Uncertainties under Climate Change using
High-Resolution Numerical Models
Abstract
Modelling the response of hydrological processes to the changing climate
requires the use of a chain of numerical models, each of which
contributes some degree of uncertainty to the final outputs. As a
result, hydrological projections, despite the progressive increase in
the accuracy of the models along the chain, can still display high
levels of uncertainty, especially at small temporal and spatial scales.
The randomness intrinsic to climate phenomena, known as internal climate
variability, is also a component contributing to the uncertainty of the
hydrological projections. Unlike the uncertainties emerging from the
climate and hydrological models, the internal climate variability is
irreducible. In this work, we quantify and partition the uncertainty of
hydrological processes in two mountainous catchments in Switzerland,
emerging from climate models and internal variability, across a broad
range of scales. To that end, we used high-resolution ensembles of
climate and hydrological data, produced by a two-dimensional weather
generator (AWE-GEN-2d) and a distributed hydrological model
(Topkapi-ETH). We quantified the uncertainty in hydrological projections
towards the end of the century through the estimation of the values of
signal-to-noise ratios (STNR). We found small STNR values
(<-1) in the projection of annual streamflow for most
sub-catchments in both study sites that are dominated by the large
natural variability of precipitation (explains ~70% of
total uncertainty). Furthermore, we investigated in detail specific
hydrological components that are critical in the model chain. For
example, snowmelt or liquid precipitation exhibits robust change
signals, which translates into high STNR values for streamflow during
warm seasons and at higher elevations, together with a larger
contribution of climate model uncertainty, suggesting that an
improvement of the involved models has the potential of significantly
narrowing the uncertainty. In contrast, extreme flows show low STNR
values due to large internal climate variability across all elevations,
which limits the possibility of narrowing their estimation uncertainty
in a warming climate.