Data-driven time series modeling to support groundwater model development for the Grazer Feld Aquifer, Austria

Alluvial aquifers and springs play a vital role in the water supply of Austria as the main source of drinking water. One such example is the Grazer Feld Aquifer, located in an urban and semi-urban setting in southeast Austria. Urbanisation imposes stresses on aquifers leading to quantitative and qualitative groundwater problems. These problems are commonly addressed by numerical groundwater models. In these models, natural and anthropogenic processes need to be carefully represented. Calibration to groundwater level data disturbed by human activities will fail or produce erroneous parameter estimates if the disturbance is not adequately considered by the model. As a consequence, such model likely result in erroneous predictions and assessments. To account for relevant drivers in the system and examine groundwater level data for the calibration of a numerical groundwater model, we test the application of the 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 objective of this study is twofold. First, time series models were set up and calibrated for each monitoring well in the aquifer, using the stresses identified in the initial hydrogeological assessment (precipitation, evapotranspiration, and river levels). 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). This is achieved by constructing TSMs and conducting a visual and spatial investigation on model results. The process differentiates good fit models from no-good fit models. Then, models, not delivering 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 115 out of 149 observation wells are found to be reasonably simulated by considering recharge from precipitation and, if applicable, river stages as driving forces. For 34 observation wells, however, the models perform less accurately, suggesting other factors, such as construction activities and temporary groundwater abstraction, influencing groundwater level fluctuations during the part or the entire simulation period. Estimated recharge is found to be higher in urban and semi-urban areas compared to agricultural fields. The results from this study will be used in the future development of a numerical groundwater model for the entire aquifer.