A novel non-homogeneous hidden Markov model for simulating and predicting monthly rainfall
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ORIGINAL PAPER
A novel non-homogeneous hidden Markov model for simulating and predicting monthly rainfall Hui Wang 1 & Tirusew Asefa 2 & Abhra Sarkar 3 Received: 21 April 2020 / Accepted: 25 October 2020 # Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract Monthly-to-seasonal precipitation forecasts are important in water resource management. Hidden Markov models (HMM) are widely applied in precipitation simulation due to its simplicity and advancements in associated computing techniques. HMMs, however, lack the flexibility to accommodate external factors in the dynamics. In this study, we consider a novel semiparametric Bayesian non-homogeneous hidden Markov model (BNHMM) that can explicitly incorporate the effects of observed covariates, e.g., climate index, on the state transition probabilities as well as the observation emission distributions. The proposed approach is tested for three rainfall stations in the service area of Tampa Bay Water, a regional water supply agency in the Southeastern United States. The BNHMM is first examined to simulate historical monthly rainfall data from the period 1979 to 2016. It is then evaluated in a retrospective mode to generate 3-month-ahead precipitation forecasts. Results indicate that the proposed BNHMM can capture historical rainfall properties well, and it is a promising alternative in providing operational rainfall forecasts. Although large-scale atmospheric and oceanic teleconnections, e.g., ENSO, have a strong influence in regional rainfall at the seasonal time scale, especially for the winter and early spring; its influence on monthly scale rainfall is limited. Potential improvement in forecasting performance is also discussed.
1 Introduction The statistical characterization and modeling of rainfall at different temporal scales are crucial to support hydrologic study and develop decision tools for water resource management. As a key component of the hydrologic cycle, it is often used as an input to hydrologic models to simulate other attributes and processes, e.g., streamflow and evapotranspiration. Rainfall observation and forecasts can facilitate decision-making in water resource planning and management. Rainfall forecasts of short-duration extreme nature, for instance, are often utilized by emergency managers to mitigate possible damage caused by stormwater and rapid flooding (Toth et al. 2000).
* Hui Wang [email protected] 1
Principal Water Resources Systems Engineer, Tampa Bay Water, 2575 Enterprise Road, Clearwater, FL 33763, USA
2
System Decision Support, Tampa Bay Water, Clearwater, FL 33763, USA
3
Department of Statistics and Data Sciences, The University of Texas at Austin, Austin, TX 78712, USA
Medium-range rainfall forecasts, e.g., weekly, monthly, and seasonal time scales, are employed by water resource managers to develop adaptive operational rules meeting human water demand and ecological needs (Wang and Liu 2013; Wang et al. 2015). Alternatively, rainfall cycle prediction at annual to decadal time scale is useful for w
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