Time Series Analysis of Nonlinear Head Dynamics Using Synthetic Data Generated with a Variably Saturated Model

Time Series Analysis of Nonlinear Head Dynamics Using Synthetic Data Generated with a Variably Saturated Model

Vonk, M.A. (TU Delft Water Resources; Artesia)
Collenteur, Raoul A. (Swiss Federal Institute of Aquatic Science and Technology)
Panday, Sorab (GSI Environmental Inc.)
Schaars, Frans (Artesia)
Bakker, M. (TU Delft Water Resources)

2024

The performance of time series models is assessed using synthetic head series simulated with a numerical model that solves Richards' equation for variably saturated flow. Heads were simulated in a homogeneous unconfined aquifer between two parallel canals; measured daily precipitation and potential evaporation are specified at the land surface and root water uptake is simulated. The head response to a precipitation event is nonlinear and depends on the saturation degree and rainfall before and after the precipitation event while evaporation reduction occurs during summers. Synthetic series were generated for 27 years and three different soil types; the unsaturated zone thickness varies between 0 and >5 m. The synthetic head series were simulated with a linear and nonlinear time series model. Performance of a linear time series model with four parameters, using a scaled Gamma response, gave R² values ranging from 0.67 to 0.96. The nonlinear time series model with five parameters simulates recharge using a root zone reservoir after which the head response to recharge is simulated with a scaled Gamma response function. The nonlinear time series model was able to simulate all synthetic head series very well with R² values above 0.9 for almost all models. The head response of the nonlinear model to a single precipitation event compares well to the response of the variably saturated groundwater model. The provided scripts may be used to simulate synthetic head series for other climates or for systems with additional complexity to assess the performance of other data-driven models.

http://resolver.tudelft.nl/uuid:30e25352-08b7-4f2e-a96d-2412cac4a272

https://doi.org/10.1111/gwat.13403

0017-467X

Groundwater

Institutional Repository

journal article

© 2024 M.A. Vonk, Raoul A. Collenteur, Sorab Panday, Frans Schaars, M. Bakker