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(Reference retrieved automatically from SciELO through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Physical-based time series model applied on water table depths dynamics characteristics simulation

Full text
Author(s):
Rodrigo Lilla Manzione [1]
Total Authors: 1
Affiliation:
[1] Universidade Estadual Paulista - Brasil
Total Affiliations: 1
Document type: Journal article
Source: RBRH; v. 23, 2018-07-10.
Abstract

ABSTRACT Time series modelling applied to study water table depths monitoring data is an elegant way to model irregular and continuous data. When successive observations are dependent, future values may be predicted from past observations, and target parameters can be estimated. These may include expected values of groundwater levels, or probabilities that critical levels are exceeded at certain times or during certain periods. These target parameters are estimated with the purpose of obtaining characteristics of the development of a certain domain in time and such characteristics can, for instance, be extrapolated to future situations. In a system identification approach, is it possible to establish the dynamic relationship between water table perturbations and climatological events, vegetation, hydrogeological local conditions, management and groundwater abstraction. The aim of this work was demonstrate the use of a physical-based time series model to stablish the relationship between precipitation and water table depths from hydrogeological monitoring data. The results enabled to infer about water table dynamics even when it is affected by different climatological patterns, simulating mean, maximum and minimum states. (AU)

FAPESP's process: 14/04524-7 - Monitoring water table depths at Bauru Aquifer System in a conservation reserv in Águas de Santa Bárbara, SP - Brazil
Grantee:Rodrigo Lilla Manzione
Support type: Regular Research Grants
FAPESP's process: 16/09737-4 - MODELLING GROUNDWATER SPATIO-TEMPORAL VARIABILITY FROM WATER TABLE MONITORING DATA USING COVARIANCE FUNCTIONS
Grantee:Rodrigo Lilla Manzione
Support type: Regular Research Grants