Groundwater Level Prediction/Forecasting and Assessment of Uncertainty Using SGS and ARIMA Models: A Case Study in the Bauru Aquifer System (Brazil)

Autor(es)

de Moraes Takafuji, Eduardo Henrique ^[1] ; da Rocha, Marcelo Monteiro ^[1] ; Manzione, Rodrigo Lilla ^[2]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Geosci, IGc, Rua Lago, 562 Cidade Univ, BR-05508080 Sao Paulo, SP - Brazil

^[2] Sao Paulo State Univ FCE UNESP, Sch Sci & Engn, Rua Domingos Costa Lopes, 780 Jd Itaipu, BR-17602496 Tupa, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: NATURAL RESOURCES RESEARCH; v. 28, n. 2, p. 487-503, APR 2019.
• Citações Web of Science: 2

Resumo

Best water management practices should involve the prediction of the availability of groundwater resources. To predict/forecast and consequently manage these water resources, two known methods are discussed: a time series method using the autoregressive integrated moving average (ARIMA) and a geostatistical method using sequential Gaussian simulation (SGS). This study was conducted in the Ecological Station of Santa Barbara (EEcSB), located at the Bauru Aquifer System domain, a substantial water source for the countryside of SAo Paulo State, Brazil. The relevance of this study lies in the fact that the 2013/2014 hydrological year was one of the driest periods ever recorded in SAo Paulo State, which was directly reflected in the groundwater table level behavior. A hydroclimatological network comprising 49 wells was set up to monitor the groundwater table depths at EEcSB to capture this response. The traditional time series has the advantage that it has been created to forecast and the disadvantage that an interpolation method must also be used to generate a spatially distributed map. On the other hand, a geostatistical approach can generate a map directly. To properly compare the results, both methods were used to predict/forecast the groundwater table levels at the next four measured times at the wells' locations. The errors show that SGS achieves a slightly higher level of accuracy and considered anomalous events (e.g., severe drought). Meanwhile, the ARIMA models are considered better for monitoring the aquifer because they achieved the same accuracy level as SGS in the 2-month forecast and a higher precision at all periods and can be optimized automatically by using the Akaike information criterion. (AU)

• Processo FAPESP: 16/09737-4 - Modelagem da variabilidade espaço-temporal em águas subterrâneas a partir de dados de monitoramento de níveis freáticos utilizando funções de covariância
• Beneficiário: Rodrigo Lilla Manzione
• Linha de fomento: Auxílio à Pesquisa - Regular