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Data acquisition by automatic monitoring allows obtaining a large number of data, supporting a better understanding of the monitored region. The use of automated discharge measurement enables a better understanding of floods, the relationship between surface and groundwater flow rate values, and aquifer recharge. However, automatic instruments processing and storage may fail, leading to missing values in some intervals of the recorded time series. These missing values may be replaced by estimates from the regional flow rate or other statistical approximations. For evolved karst systems, however, those techniques may not be adequate due to their rapid discharge responses to rainfall. The aim of this paper is to develop a method able to estimate fluviometric monitoring missing values, based on time series correlation for correlated data. These estimates were obtained from automated monitoring through pressure transducers in 6 streams in a region of approximately 505 km2, predominantly covered by the carbonate and metapellitic Neoproterozoic rocks of the Bambuí Group. The proposed method is composed of four sequential steps: computing the streamflow-data autocorrelation and the cross-correlation of pluviometry with the flow rate; calculating the precipitation fraction that directly contributes to the discharge; fitting of a linear relationship between pluviometry and the monitored daily discharge, to calculate discharge values on days when automatic measurements failed; and approximation of the calculated and monitored discharge values, using a number of statistical criteria. The results show the maturity of the karst aquifer system, with fast ground-water flow and low storage, well-calibrated stage-discharge rating curves for the 2016/2017 hydrological year and that the values estimated by proposed methodology present a deviation of less than 9% over the monitored data.

Method to complete flow rate data in automatic fluviometric stations in the karst system of Lagoa Santa area, MG, Brazil