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Establishing and Calibrating the Model of a Coastal Aquifer with Limited Data for Assessing the Safety of the Groundwater Exploitation

Author:
Ziogas, Alexandros I., Kaleris, Vassilios K.
Source:
Water resources management 2019 v.33 no.8 pp. 2693-2709
ISSN:
0920-4741
Subject:
aquifers, base flow, computer software, groundwater, groundwater recharge, hydrologic models, rivers, saltwater intrusion, temporal variation, water table, wells
Abstract:
Reliable assessment of the groundwater safety in coastal aquifers is efficiently supported by the use of reasonable conceptual groundwater models, which take into consideration the density driven flow and are sufficiently calibrated. A main issue in establishing the model of the aquifer studied here, in which the groundwater level exhibits significant fluctuations within the year, is the estimation of the temporal variation of the time depended input data, i.e. the groundwater recharge from the river crossing the aquifer, the precipitation, the boundary inflows and the groundwater extractions. To address this problem we estimated initial distributions for the aforementioned components of the groundwater budget from related hydrological and operational data, as it is the river recharge, the precipitation height, the base flow from neighboring formations and the operational time schedule of pumping wells, and then we defined multiplicative coefficients that scale the initial distributions to the temporal variation of the corresponding input. For the simulations, the software SEAWAT for density driven flow is used. The calibration is performed in two steps, i.e. a manual and an automatic one performed by using the code PEST combined with SEAWAT. The manual calibration has been used for adjusting the conceptual model and estimating reasonable initial values for the automatic calibration. The groundwater safety is assessed by estimating the temporal variation of the saltwater intrusion and by defining the capture zones of the exploitation wells using the code MODPATH. The study gives insight into the sequence of the assumptions required to tackle the lack of data.
Agid:
6489483