Inferring Subsurface Preferential Flow Features From a Wavelet Analysis of Hydrological Signals in the Shale Hills Catchment

Hu Liu, Yang Yu, Wenzhi Zhao, Li Guo, Jintao Liu, Qiyue Yang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Preferential flow (PF)-dominated soil structure is often considered a unique system consisting of micropores and macropores and thus supposed to provide dual-pore filtering effects on hydrological signals, through which smoothing effects are likely to be stronger for matrix flow and weaker for PF via macropores. By using time series of hydrological signals (precipitation, canopy interception, throughfall, soil moisture, evapotranspiration, water storage in soil and groundwater, and catchment discharge) propagating through the Shale Hills Catchments and representative soil series, the filtering effects of the catchment and soil profiles were tested through the wavelet analysis. Typical filtering effects, that is, the characteristic of the role of soil and the groundwater table as a buffer, were observed from the wavelet spectrum, gradually smoothing off the precipitation signal. The hypothesized dual-pore style filtering effects of the soil profile were also confirmed through the coherence spectra and phase differences, rendering them applicable for possible use as “fingerprints” of PF to infer subsurface flow features. We found that PF dominates the catchment's discharge response at the scales from 3 to 12 days, which contributes to the catchment discharge mainly as subsurface lateral flow at upper or middle soil horizons. Through subsurface PF pathways, even the hilltop is likely hydrologically connected to the valley floor, building connections with or making contributions to the catchment discharge. This study highlights the potential of wavelet analysis for retrieving and characterizing subsurface flow processes based on the revealed dual-pore filtering effects of the soil system. Although some limitations and uncertainties still exist, we believe that wavelet methods provide a highly potential but underexplored approach to studying subsurface hydrology.

Original languageEnglish (US)
Article numbere2019WR026668
JournalWater Resources Research
Volume56
Issue number11
DOIs
StatePublished - Nov 2020

All Science Journal Classification (ASJC) codes

  • Water Science and Technology

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