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There is a general belief that the oil industry has its activities incompatible with the concept of sustainable development. However, the development of humanity has been based on power consumption; much of them are provided by this industry. With the new exploratory oil & gas (O&G) frontier in Brazil, the pre-salt, it is possible to envision that the operating segment will be very present in the country, as well as their negative impacts to the environment and its tons of waste that will be generated. This paper seeks to promote reflection on the theme discussing the concept of life-cycle thinking (LCT). The application of life-cycle thinking to waste management brings a differential for long-term reduction of pollution, by facilitating the analysis of various aspects including the necessary infrastructure for managing legal matters (i.e., licensing) and logistics. LCT still promotes a better understanding for the general public, regulators, operators and companies providing services. It even allows companies in Brazil to be better prepared for the challenges related to the pollution generated by O&G offshore exploration wastes.

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Antecedent moisture conditions are essential in explaining differences in the translation of flood-producing precipitation to floods. This study proposes an empirical residual-oriented antecedent precipitation index (RAPI) to estimate and further link antecedent moisture conditions with flood predictive uncertainty. By developing a fully kernel-based residual error model without functional presumptions, the proposed RAPI is calibrated as the regressor of the deterministic model residual. Furthermore, the MI-LXPM algorithm is applied to search for optimal parameters in mixed-integer constraints. The rationality of the proposed framework is demonstrated by its application to a case study in South-East China. The quality of probabilistic streamflow predictions is then quantified using reliability, precision, and the NSE of the prediction mean. The results show that the RAPI closely connects to the uncertainty of hourly flood prediction as a proxy of antecedent soil moisture. The influence of RAPI is mainly on the precision and unbiasedness of flood prediction. Compared with the deterministic model output, the RAPI provides a better flood prediction of small to median flood events as a regressor. Along with the proposed date-driven residual error model, the framework can be applied to any pre-calibrated hydrological model and help modelers achieve high-quality probability flood prediction.