How time series modeling can support groundwater model development: A case study of the Grazer Feld, Austria.

Ainur Kokimova1, Raoul Collenteur1, Steffen Birk1
1 Institute of Earth Sciences, NAWI Graz Geocenter, University of Graz, Graz, Austria​

V 16.4 in Freie Themen

23.03.2022, 11:00-11:15, HS 1

According to the 2018 United Nations’ Report on world urbanization, Europe is one of the most urbanized regions in the world with 74% of dwellers residing in cities (UN 2019). The projected future increase of population in urban areas leads to the rise of stresses put on groundwater resources (Morris et al. 2007). Such stresses further cause quantitative and qualitative groundwater problems. To solve these issues and support decision-making, numerical groundwater models are commonly applied tools. In an urban setting, natural and anthropogenic processes need to be carefully represented in such models. A calibration to groundwater level data disturbed by human activities causes the model to perform differently in comparison to natural groundwater level fluctuations, thus leading to erroneous predictions and assessments. An adequate groundwater model needs to account for the relevant drivers and responses originating from these drivers. This study explores the application of time series analysis as an additional and preliminary step in a general numerical groundwater modeling framework. The results of time series models (TSM) contribute to the understanding of spatiotemporal aquifer dynamics and main driving forces as advocated by Bakker and Schaars (2019). The approach is tested for an unconfined aquifer located in a semi-urban and urban area of southeastern Austria, the Grazer Feld Aquifer. The objective of this study is twofold. First, we identify the drivers that should be included in the groundwater model. Second, we create a calibration data set that flags groundwater level observations that were caused by human temporal activities (e.g., pumping, irrigation, and/or dam construction) that are not included in the numerical groundwater model. This is achieved by constructing TSMs and conducting a visual and spatial investigation on model results. The process helps to differentiate good fit models from no-good fit models. Then, models, that do not deliver a good fit, are checked for missing driving forces by engaging local stakeholders. Once the process is characterized, the period with unexplained groundwater level change is marked. The groundwater level fluctuations of 88 out of 149 observation wells are found to be reasonably simulated by considering recharge from precipitation, evapotranspiration and, if applicable, river stages as driving forces. For 61 of observation wells, however, the models perform less accurately, suggesting that other factors, such as temporary groundwater abstractions at construction sites or for irrigation purposes, are influencing the groundwater level fluctuations during (part of) the simulation period. The results from this study will be used in the future development of a numerical groundwater model for the entire aquifer.



Bakker, M. and Schaars, F. (2019), Solving Groundwater Flow Problems with Time Series Analysis: You May Not Even Need Another Model. Groundwater, 57: 826-833. https://doi.org/10.1111/gwat.12927

Morris, B., Rueedi, J., Cronin, A.A., Diaper, C. and DeSilva, D. (2007), Using linked process models to improve urban groundwater management: an example from Doncaster England. Water and Environment Journal, 21: 229-240. https://doi.org/10.1111/j.1747-6593.2006.00067.x

United Nations, Department of Economic and Social Affairs, Population Division (2019). World Urbanization Prospects: The 2018 Revision (ST/ESA/SER.A/420). New York: United Nations



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