BOKU - Universität für Bodenkultur Wien - Forschungsinformationssystem

Logo BOKU-Forschungsportal

Gewählte Publikation:

Laaha, G; Parajka, J; Viglione, A; Koffler, D; Haslinger, K; Schoner, W; Zehetgruber, J; Bloschl, G.
(2016): A three-pillar approach to assessing climate impacts on low flows
HYDROL EARTH SYST SC. 2016; 20(9): 3967-3985. FullText FullText_BOKU

Abstract:
The objective of this paper is to present a framework for assessing climate impacts on future low flows that combines different sources of information, termed pillars. To illustrate the framework three pillars are chosen: (a) extrapolation of observed low-flow trends into the future, (b) rainfall-runoff projections based on climate scenarios and (c) extrapolation of changing stochastic rainfall characteristics into the future combined with rainfall-runoff modelling. Alternative pillars could be included in the overall framework. The three pillars are combined by expert judgement based on a synoptic view of data, model outputs and process reasoning. The consistency/inconsistency between the pillars is considered an indicator of the certainty/uncertainty of the projections. The viability of the framework is illustrated for four example catchments from Austria that represent typical climate conditions in central Europe. In the Alpine region where winter low flows dominate, trend projections and climate scenarios yield consistently increasing low flows, although of different magnitudes. In the region north of the Alps, consistently small changes are projected by all methods. In the regions in the south and south-east, more pronounced and mostly decreasing trends are projected but there is disagreement in the magnitudes of the projected changes. The process reasons for the consistencies/inconsistencies are discussed. For an Alpine region such as Austria the key to understanding low flows is whether they are controlled by freezing and snowmelt processes, or by the summer moisture deficit associated with evaporation. It is argued that the three-pillar approach offers a systematic framework of combining different sources of information aimed at more robust projections than that obtained from each pillar alone.
Autor*innen der BOKU Wien:
Koffler Daniel
Laaha Gregor
Zehetgruber Judith



Altmetric:
© BOKU Wien Impressum