A statistical approach for identifying factors governing streamflow recession behaviour

• Published in: Hydrological processes 2022-10, Vol.36 (10), p.n/a
• Main Authors: Li, Hongyi, Ameli, Ali
• Format: Article
• Language: English
• Subjects: Aridity; Catchment area; catchment hydrologic function; Catchment scale; Catchments; Climate change; Equivalence; hydrograph recession parameters; Hydrology; large sample hydrology; Nonlinear systems; Nonlinearity; Parameter identification; Parameters; prediction in ungauged basins; Rainfall; random forests; Recession; Slopes; Snow; Snow-water equivalent; Snowmelt; Spatial variability; Spatial variations; statistical feature importance; Statistics; Storage; storage‐release relation; Stream discharge; Stream flow; streamflow recession nonlinearity
• ISSN: 0885-6087; 1099-1085
• DOI: 10.1002/hyp.14718

Catchment storage‐release relation has been widely studied using the parameters of streamflow recession analysis. The governing factors of the recession parameters are poorly understood, particularly in snow‐dominated regions. Here, we tailor a newly developed statistical approach, marginal contribution feature importance, to a hydrologic context, and couple it with random forests, in order to investigate the governing physical and climatic factors of catchment recession behaviour. The coupled approach can incorporate the interactions among catchment climatic and physical attributes. In a large sample hydrology study, we identify and compare the governing factors of recession parameters, in rainfall versus snowmelt‐dominated catchments, and in medium‐size versus large catchments, across more than 1000 catchments in United States and Canada. Results show that streamflow recession behaviour, particularly recession nonlinearity, strongly depend on belowground attributes and slope in rain‐dominated medium size catchments, and strongly depend on slope and annual maximum snow water equivalent in snow‐dominated medium size catchments. As catchment scale increases (>1000 km2), the attributes related to the magnitude and timing of input water (e.g., water surplus, aridity index, maximum snow water equivalent) dictates the streamflow recession behaviour and the importance of belowground attributes is dropped. Furthermore, recession nonlinearity generally increases with an increase in catchment size. The findings of this study help improve our understanding of the governing factors and the interpretation of the spatial variability of recession behaviours. Such understanding could inform the development of a generalizable process‐based framework for estimating the sensitivity of catchment storage‐release relation to climate change in different environmental settings. Feature importance scores and rankings for recession nonlinearity (b exponent). (a) Medium size rain‐dominated catchments, (b) Medium size snow‐dominated catchments and (c) Large size catchments. Colours refer to different attribute categories: blue for climate, green for soil and bedrock, and red for topography.