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

Author:

Gutierrez Pacheco Sebastian,Lagacé Robert,Hugron Sandrine,Godbout Stéphane,Rochefort Line

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 (R2 = 0.95, RMSE = 2.48 cm and the lowest AIC), followed by the general linear method (R2 = 0.92, RMSE = 3.10 cm) and support vector machines method (R2 = 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).

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Transformers-based time series forecasting for piezometric level prediction;2022 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS);2022-05-25

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