Abstract
Abstract. Tracer data have been successfully used for hydrograph separation in glacierized basins. However, in these basins uncertainties of the hydrograph separation are large and are caused by the spatiotemporal variability in the tracer signatures of water sources, the uncertainty of water sampling, and the mixing model uncertainty. In this study, we used electrical conductivity (EC) measurements and two isotope signatures (δ18O and δ2H) to label the runoff components, including groundwater, snow and glacier meltwater, and rainfall, in a Central Asian glacierized basin. The contributions of runoff components (CRCs) to the total runoff and the corresponding uncertainty were quantified by two mixing approaches, namely a traditional end-member mixing approach (abbreviated as EMMA) and a Bayesian end-member mixing approach. The performance of the two mixing approaches was compared in three seasons that are distinguished as the cold season, snowmelt season, and glacier melt season. The results show the following points. (1) The Bayesian approach generally estimated smaller uncertainty ranges for the CRC when compared to the EMMA. (2) The Bayesian approach tended to be less sensitive to the sampling uncertainties of meltwater than the EMMA. (3) Ignoring the model uncertainty caused by the isotope fractionation likely led to an overestimated rainfall contribution and an underestimated meltwater share in the melt seasons. Our study provides the first comparison of the two end-member mixing approaches for hydrograph separation in glacierized basins and gives insight into the application of tracer-based mixing approaches in similar basins.
Highlights
Glaciers and snowpack store a large amount of fresh water in glacierized basins, providing an important water source for downstream human societies and ecosystems (Barnett et al, 2005; Viviroli et al, 2007; He et al, 2014; Penna et al, 2016)
(1) The Bayesian approach generally estimated smaller uncertainty ranges for the contributions of runoff components (CRCs) when compared to the end-member mixing approach (EMMA). (2) The Bayesian approach tended to be less sensitive to the sampling uncertainties of meltwater than the EMMA. (3) Ignoring the model uncertainty caused by the isotope fractionation likely led to an overestimated rainfall contribution and an underestimated meltwater share in the melt seasons
The contributions of runoff components (CRCs) to the total runoff were estimated for three seasons, i.e., cold season, snowmelt, and glacier melt seasons
Summary
Glaciers and snowpack store a large amount of fresh water in glacierized basins, providing an important water source for downstream human societies and ecosystems (Barnett et al, 2005; Viviroli et al, 2007; He et al, 2014; Penna et al, 2016). Seasonal meltwater and rainfall play significant roles in shaping the magnitude and timing of runoff in these basins (Rahman et al, 2015; Pohl et al, 2017). Quantifying the seasonal contributions of the runoff components (CRCs), including groundwater, snowmelt, glacier melt, and rainfall, to the total runoff is highly necessary for understanding the dynamics of water resources in glacierized basins under the current climate warming (La Frenierre and Mark, 2014; Penna et al, 2014; He et al, 2015). He et al.: Comparing Bayesian and traditional end-member mixing approaches
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