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Recent findings suggest that abnormal operating conditions of equipment in the oil and gas supply chain represent a large fraction of anthropogenic methane emissions. Thus, effective mitigation of emissions necessitates rapid identification and repair of sources caused by faulty equipment. In addition to advances in sensing technology that allow for more frequent surveillance, prompt and cost-effective identification of sources requires computational frameworks that provide automatic fault detection. Here, we present a changepoint detection algorithm based on a recursive Bayesian scheme that allows for simultaneous emission rate estimation and fault detection. The proposed algorithm is tested on a series of near-field controlled release mobile experiments, with promising results demonstrating successful detection (>90% success rate) of changes in the leak rate when the emission rate is tripled after an abrupt change. Moreover, we show that the statistics of the measurements, such as the coefficient of variation and range are good predictors of the performance of the algorithm. Finally, we describe how this methodology can be easily adapted to suit time-averaged concentration data measured by stationary sensors, thus showcasing its flexibility.

Understanding dynamics of hydrological responses is essential in producing skillful runoff forecast. This can be quantitatively done by tracking changes in hydrology model parameters that represent physical characteristics. In this study, we implement a Bayesian estimation method in continuously estimating hydrology model parameters given observations of rainfall and runoff for small watersheds. The method is coupled with a conceptual hydrology model using a Gamma distribution-based Instantaneous Unit Hydrograph. The whole analytical framework is tested using synthetic data as well as observational data from the Fall Creek watershed. The results show that the Bayesian method can well track the hidden parameters that change inter-annually. Then the model is applied to examine temporal and spatial variability of the rainfall-runoff responses and we find 1) a systematic shift in the rainfall-runoff response for the Fall Creek watershed around 1943 and 2) a statistically significant relationship between rainfall-runoff responses and watershed sizes for selected NY watersheds. Our results demonstrate potential of the Bayesian estimation method as a rapid surveillance tool in monitoring and tracking changes of hydrological responses for small watersheds.