Transient groundwater modelling with spatio-temporally variable fluxes in a complex aquifer system: new approach in defining boundary conditions for a transient flow model
Το έργο με τίτλο Transient groundwater modelling with spatio-temporally variable fluxes in a complex aquifer system: new approach in defining boundary conditions for a transient flow model από τον/τους δημιουργό/ούς Psarropoulou Effie T., Karatzas Giorgos διατίθεται με την άδεια Creative Commons Αναφορά Δημιουργού 4.0 Διεθνές
Βιβλιογραφική Αναφορά
E.T. Psarropoulou and G.P. Karatzas,"Transient groundwater modelling with spatio-temporally variable fluxes in a complex aquifer system: new approach in defining boundary conditions for a transient flow model,"
Civil Engineering and Environmental Systems, vol. 29, no. 1, pp. 1-21, 2012. doi:10.1080/10286608.2011.637622
https://doi.org/10.1080/10286608.2011.637622
Long-term water resource planning requires both spatial and temporal information on groundwater recharge in order to properly manage not only water use and exploitation but also land-use allocation and development. In this context, a three-dimensional transient numerical model is developed to support management of groundwater resources and decision analysis for the coastal plain of Corinthos in Greece. Transient simulations usually are used to analyse time-dependent problems such as climate change and groundwater-level change. Among transient models, the most reliable but also the least explored, probably due to the demanding input data requirements, are the models with temporally variable recharge, also termed as fully transient models. In these models, the temporal variability of heads is dependent not only on the temporal variability of aquifer storage but also on the temporal variability of fluxes. In order to arrive at a credible set of flux data as boundary conditions for the numerical model, a sinusoidal equation is developed to simulate the missing water-level data and to forecast the future sequential records of the expected recharge. The results of the developed model reveal that at least two periodicities can be identified in the observed subsurface head-level data