Estimation of Daily Water Table Level with Bimonthly Measurements in Restored Ombrotrophic Peatland

Sebastian Gutierrez Pacheco, Robert Lagacé, Sandrine Hugron, Stéphane Godbout and Line Rochefort
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Sebastian Gutierrez Pacheco: Faculté des Sciences de L’agriculture et de L’alimentation, Université Laval, Québec City, QC G1V 0A6, Canada
Robert Lagacé: Faculté des Sciences de L’agriculture et de L’alimentation, Université Laval, Québec City, QC G1V 0A6, Canada
Sandrine Hugron: Faculté des Sciences de L’agriculture et de L’alimentation, Université Laval, Québec City, QC G1V 0A6, Canada
Stéphane Godbout: Institut de Recherche et de Développement en Agroenvironnement, Quebec City, QC G1P 3W8, Canada
Line Rochefort: Faculté des Sciences de L’agriculture et de L’alimentation, Université Laval, Québec City, QC G1V 0A6, Canada

Sustainability, 2021, vol. 13, issue 10, 1-21

Abstract: Daily measurements of the water table depth are sometimes needed to evaluate the influence of seasonal water stress on Sphagnum recolonization in restored ombrotrophic peatlands. However, continuous water table measurements are often scarce due to high costs and, as a result, water table depth is more commonly measured manually bimonthly with daily logs in few reference wells. A literature review identified six potential methods to estimate daily water table depth with bimonthly records and daily measurements from a reference well. A new estimation method based on the time series decomposition (TSD) is also presented. TSD and the six identified methods were compared with the water table records of an experimental peatland site with controlled water table regime located in Eastern Canada. The TSD method was the best performing method (R 2 = 0.95, RMSE = 2.48 cm and the lowest AIC), followed by the general linear method (R 2 = 0.92, RMSE = 3.10 cm) and support vector machines method (R 2 = 0.91, RMSE = 3.24 cm). To estimate daily values, the TSD method, like the six traditional methods, requires daily data from a reference well. However, the TSD method does not require training nor parameter estimation. For the TSD method, changing the measurement frequency to weekly measurements decreases the RMSE by 16% (2.08 cm); monthly measurements increase the RMSE by 13% (2.80 cm).

Keywords: water table monitoring; water table depth fluctuation; groundwater hydrograph; Sphagnum moss; data-driven regression; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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