An original approach combining biogeochemical signatures and a mixing model to discriminate spatial runoff-generating sources in a peri-urban catchment

Hydrograph separation using biogeochemical data is a commonly used method for the vertical decomposition of flow into surface, subsurface and groundwater contributions. However, its application to the spatial decomposition of flow remains limited, despite its potential to identify contributions linked to geological, pedological, and land use characteristics, as well as anthropogenic contaminant sources. In this study, a Bayesian mixing model was applied to the Ratier peri-urban sub-catchment of the OTHU Yzeron observatory. Eight runoff-generating sources were identified and sampled, including different land uses (e.g. forest, grassland, agricultural areas), a colluvium aquifer, and urban point discharges (e.g. sewer system, urban and road surface runoff). A wide range of biogeochemical parameters were analysed including classical (i.e., major chemical compounds, dissolved metals) and innovative tracers (i.e., characteristics of dissolved organic matter, microbial indicators). Streamwater samples collected under contrasting hydro-meteorological conditions revealed distinct source signatures and highly variable contributions, with wastewater dominating under dry weather and rapid surface runoff during summer storms. Using these results, we improved a previously designed perceptual hydrological model of the Ratier and Mercier catchments, at the hillslope scale, which highlighted the potential of spatial tracer-based decomposition in addition to classical vertical hydrological separation. More broadly, this study demonstrates the potential of such mixing model, using classical but also more innovative tracers, to provide insights for validating distributed hydrological models and to anticipate the influence of land use, urbanisation, and climate changes on runoff generation.