Applicability of groundwater recharge estimation from time series modeling of groundwater levels in a (semi-)urban area

Groundwater recharge estimates can be obtained from time series of hydrometeorological variables and groundwater levels using a data-driven approach that combines a root-zone model with a lumped-parameter groundwater model. This approach was successfully tested at an agricultural site where lysimeter data of seepage is available (Collenteur et al. 2021). At that site, groundwater levels are assumed to be solely driven by recharge from precipitation. Frequently, however, groundwater levels are affected by other hydrological stresses, for example, resulting from stream-aquifer interaction or direct human impacts such as water abstraction or construction activities. To assess the method under more complex conditions, we evaluate the recharge estimates obtained from the model application to a multitude of groundwater monitoring wells located in the urban area of Graz (Austria) and the semi-urban and agricultural area south of the city (Kokimova et al. 2022). Possible influences from the River Mur, including changes in hydraulic structures, were taken into account in the model, whereas other stresses were insufficiently known and thus ignored. The resulting recharge estimates show a wide range, with values in agricultural areas often plausible. However, at some locations, particularly in the urban area, extraordinarily high values that appear implausible were estimated. Besides the possible impact of unknown water abstraction and construction activities, the simplified representation of the urban recharge processes may explain these findings. Even where the model failed to provide reasonable recharge estimates, the results support the identification of possible local influences on groundwater levels, which should be further investigated to enable a better assessment of groundwater recharge, particularly in the urban area.

Collenteur, R., Bakker, M., Klammler, G., Birk, S. (2021): Estimation of groundwater recharge from groundwater levels using non-linear transfer function noise models and comparison to lysimeter data. Hydrol. Earth Syst. Sci. 25: 2931-2949. doi: 10.5194/hess-25-2931-2021

Kokimova, A., Collenteur, R., Birk, S. (2022): Data-driven time series modeling to support groundwater model development for the Grazer Feld Aquifer. EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7734, https://doi.org/10.5194/egusphere-egu22-7734