Abstract

AbstractStreamflow is the runoff response integrated in space and time over a complex system involving climatic and catchment physiographic factors. In the Andes, accelerating runoff process understanding is hampered by the inability to quantify heterogeneity of surface and subsurface catchment properties. Here, we present a statistical approach based on regression models and correlation analysis that links hydrological signatures and catchment properties to unveil processes in a set of volcanic mountain catchments (latitude 0°30'N) in Ecuador. The catchments represent form and function diversity in the same hydrological unit. We found that despite of similar atmospheric‐water inputs the water yield in the north‐east region is about 5× larger than in the south‐west region and their flow regimes are asymmetric. The soil‐bedrock interface and lithology exert a first‐order control on hydrologic partitioning, and this allowed us to hypothesize two hydrological mechanisms. Firstly, in the north‐east region, the perennial streamflow is associated with seasonal rainfall patterns, and subsequent drainage processes taking place at the surface and subsurface level. The amount of streamflow is related to landform characteristics, high canopy density and root development of forest as well as water holding capacity of organic soils. From a mechanistic standpoint, the low concentration time, steep slopes and shallow infiltration limited by high‐consolidated deposits of sedimentary and volcanics suggest a lateral movement of the flow. Secondly, in the south‐west region the streamflow regime is mostly groundwater‐dependent and it becomes seasonally enhanced by rainfall. Larger seasonal variations of precipitation and temperature result into enhanced evapotranspiration in the drier months, limiting shallow soil infiltration. Under the soil layers, highly permeable pyroclastic deposits and andesitic lavas promote deep percolation. The results highlight the degree of dissimilarity of hydrological processes in Andean settings, but unravelling their complexity seems plausible using streamflow signatures and causal explanatory models.

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