Bayesian hydrograph separation in a minimally gauged alpine volcanic watershed in central Chile

Abstract

This study examines the question of how much information one can extract from a tracer-based hydrograph separation in a remote and minimally gaged alpine catchment in Chile. We combine PCA-based endmember mixing analysis to identify the sources of flow contribution to the Diguillín River with a hierarchical Bayesian mixing model to integrate spatial and temporal variability in endmember concentration and quantify the source contributions to streamflow over time. The PCA-analysis shows that precipitation isotopes do not vary by elevation (e.g. snow and rainfall had identical signatures) but vary significantly by season, and that a third endmember is necessary to bound streamflow variability at the basin outlet, which was not captured by our field sampling. One of the main advantages of Bayesian methods is the quasi-machine learning capabilities, where we treated the third endmember as a parameter from which the mixing model could both estimate proportional contributions as well as posterior estimates for the tracer concentrations.The two tracer, three endmember hydrograph separation revealed groundwater to be the largest and precipitation (rain and snow) to be the smallest contributor, on average, to streamflow with the third unknown endmember contributing around 40% of streamflow during the Winter wet season. We hypothesize that interflow is occurring as the third endmember in the Alto Diguillín subwatershed, based on inferred tracer values and the presence of alluvium atop impermeable bedrock along certain reaches. More work is necessary to observe and sample these flowpaths, which was not possible during this study. The results of this work have implications for water resource management, since groundwater sustains the majority of streamflow in the Diguillín, and climate change will impact the timing and quantity of baseflow and interflow. Overall, we demonstrate the utility of combining PCA with Bayesian statistical modeling and inference to extract maximum information from a limited field dataset in a remote alpine catchment. The findings of this work can guide future water management in the Diguillín, but also provide clear questions for future research.