Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, BrazilModèles de simulation stochastique de séries chronologiques combinées avec la géostatistique pour prédire des scénarios piézométriques dans une région d’affleurement du système aquifère du Guarani au BrésilSimulación estocástica de modelos de series temporales combinados con geoestadística para predecir escenarios de niveles freáticos en un área aflorante del Sistema Acuífero Guaraní, Brasil利用与地质统计学相结合的时间序列随机模型来预测水位的变化:以巴西Guarani含水层系统出露地区为例Simulação estocástica de modelos de séries temporais combinados com geoestatística para prever cenários de níveis freáticos numa área de afloramento do Sistema Aquífero do Guarani, Brasil

Stochastic methods based on time-series modeling combined with geostatistics can be useful tools to describe the variability of water-table levels in time and space and to account for uncertainty. Monitoring water-level networks can give information about the dynamic of the aquifer domain in both dimensions. Time-series modeling is an elegant way to treat monitoring data without the complexity of physical mechanistic models. Time-series model predictions can be interpolated spatially, with the spatial differences in water-table dynamics determined by the spatial variation in the system properties and the temporal variation driven by the dynamics of the inputs into the system. An integration of stochastic methods is presented, based on time-series modeling and geostatistics as a framework to predict water levels for decision making in groundwater management and land-use planning. The methodology is applied in a case study in a Guarani Aquifer System (GAS) outcrop area located in the southeastern part of Brazil. Communication of results in a clear and understandable form, via simulated scenarios, is discussed as an alternative, when translating scientific knowledge into applications of stochastic hydrogeology in large aquifers with limited monitoring network coverage like the GAS.