Wavelet analysis of soil moisture measurements for hillslope hydrological processes

Lee, Eun-Hyung; Kim, Sanghyun

doi:10.1016/j.jhydrol.2019.05.023

Cited by 37 publications

(23 citation statements)

References 39 publications

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“…This work has also exhibited the advantages of wavelet analysis over other types of signal analysis, particularly for temporally varying events. Recently, other studies such as Schuler et al (2021); Rodríguez-Murillo and Filella (2020); Lee and Kim (2019) have explored wavelet analysis and wavelet coherence as a tool for characterizing hydrological measurements (see Supporting Information). Future work could also include extending sensor coverage across the peninsula to create a more granular map of lag times.…”

Section: Discussionmentioning

confidence: 99%

Inland Tidal Oscillations Within the Yucatan Peninsula

Coutino

Stastna

McNeill-Jewer

et al. 2021

Geophysical Research Letters

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The Yucatan Peninsula (165,000 km 2 ) encompasses three Mexican states (Yucatan, Quintana Roo, and Campeche) and is composed of Miocene to Late Pleistocene limestone which has undergone intense dissolution through cycles of sea-level change creating a karst topography (Bauer-Gottwein et al., 2011;Perry et al., 2003;Weidie, 1985). The high porosity of the limestone (14%-23%; Beddows et al., 2007) results in few surface water bodies (e.g., lakes), as rainfall quickly infiltrates through the limestone and into the unconfined aquifer which flows toward the coast through networks of caves and fracture zones (Beddows et al., 2007;Emblanch et al., 2003;Stoessell, 1995). The few surface water bodies that do exist are thought to contain draping layers of sediment which act as an aquitard (

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Section: Discussionmentioning

confidence: 99%

Inland Tidal Oscillations Within the Yucatan Peninsula

Coutino

Stastna

McNeill-Jewer

et al. 2021

Geophysical Research Letters

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“…In their analysis of a 16-yr SWC record, Wu et al (2002) determined the time shift in the SWC response to precipitation with depth. Lee and Kim (2019) used WCA for identifying vertical preferential flow events from the time shift in depth-dependent SWC responses between closer and more distant soil horizons.…”

Section: Core Ideasmentioning

confidence: 99%

Wavelet analysis of soil water state variables for identification of lateral subsurface flow: Lysimeter vs. field data

Ehrhardt

Groh

Gerke

2021

Vadose Zone Journal

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Preferential and lateral subsurface flow (LSF) may be responsible for the accelerated transport of water and solutes in sloping agricultural landscapes; however, the process is difficult to observe. One idea is to compare time series of soil moisture observations in the field with those in lysimeters, where flow is vertically oriented. This study aims at identifying periods of deviations in soil water contents and pressure heads measured in the field and in a weighing lysimeter with the same soil profile. Wavelet coherency analysis (WCA) was applied to time series of hourly soil water content and pressure head data (15-, 32-, 60-, 80-, and 140-cm depths) from Colluvic Regosol soil profiles. The phase shifts and periodicities indicated by the WCA plots reflected the response times to rain events in the same depth of lysimeter and field soil. For many rain events and depths, pressure and moisture sensors installed in the field soil responded earlier than those in the lysimeter. This could be explained by either vertical preferential flow or LSF from upper hillslope positions. Vice versa, a faster response in the lysimeter soil could be indicative for vertical preferential flow effects. Dry weather conditions and data gaps limited the number of periods with elevated soil moisture in 2016-2018, in which LSF was likely to occur. The WCA plots comprise all temporal patterns of time shifts and correlations between larger data time series in a condensed form to identify potentially relevant periods for more detailed analyses of subsurface flow dynamics.Abbreviations: COI, cone of influence; FDR, frequency domain reflectometry; LSF, lateral subsurface flow; SWC, soil water content; Vol-%, unit of the volumetric soil water content in volume percentage; WCA, wavelet coherency analysis; WTC, wavelet coherency spectrum.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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“…Wavelet coherence analysis was used here to assess these interactions. It calculates the coherence value of cross-wavelet transform among two time series in the time-frequency domain (Lee and Kim 2019). Detailed theory on wavelet transform can be found in Grinstead et al (2004).…”

Section: -12-wavelet Coherence Analysis In Interaction Analysismentioning

confidence: 99%

“…Detailed theory on wavelet transform can be found in Grinstead et al (2004). In the present case, wavelet coherence between Ts and meteorological variables was calculated as (Lee and Kim 2019):…”

Section: -12-wavelet Coherence Analysis In Interaction Analysismentioning

confidence: 99%

Hourly soil temperature prediction using integrated machine learning methods, GLUE uncertainty analysis, Taguchi search, and wavelet coherence analysis

Seifi¹,

Ehteram²,

Nayebloei³

et al. 2021

Preprint

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In this study, hourly Ts variations at 5, 10, and 30 cm soil depth were investigated and predicted for an arid site (Sirjan) and a semi-humid site (Sanandaj) in Iran. Standalone machine learning models (adaptive neuron fuzzy interface system (ANFIS), support vector machine model (SVM), radial basis function neural network (RBFNN), and multilayer perceptron (MLP)) were hybridized with four optimization algorithms (sunflower optimization (SFO), firefly algorithm (FFA), salp swarm algorithm (SSA), particle swarm optimization (PSO)) to improve prediction accuracy and reduce uncertainty. Uncertainty analysis was performed using generalized likelihood uncertainty estimation (GLUE), while wavelet coherence was used to assess interactions between Ts and meteorological parameters. For the arid site, ANFIS-SFO (RMSE = 1.18oC, MAE = 1.05oC, NSE = 0.93, PBIAS = 7%, and R2 = 0.9998) produced the most accurate performance at 5 cm soil depth. At best, hybridization with SFO (ANFIS-SFO, MLP-SFO, RBFNN-SFO, SVM-SFO) decreased RMSE by 5.6, 18, 18.3, and 18.18 % compared with the respective standalone model. At the semi-humid site, all integrated models showed most accurate performance at 10 cm soil depth, with RMSE for the best model (ANFIS-SFO) increasing by 10.5%, and MAE by 10.1%, from 10 to 30 cm depth. GLUE analysis confirmed that integrating optimization algorithms with machine learning models decreased the uncertainty in Ts predictions. Wavelet coherence analysis demonstrated that air temperature, relative humidity, and solar radiation, but not wind speed, had high coherence with Ts at different soil depths at both sites, and meteorological parameters mostly influenced Ts in upper soil layers.

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Wavelet analysis of soil moisture measurements for hillslope hydrological processes

Cited by 37 publications

References 39 publications

Inland Tidal Oscillations Within the Yucatan Peninsula

Inland Tidal Oscillations Within the Yucatan Peninsula

Wavelet analysis of soil water state variables for identification of lateral subsurface flow: Lysimeter vs. field data

Hourly soil temperature prediction using integrated machine learning methods, GLUE uncertainty analysis, Taguchi search, and wavelet coherence analysis

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