Stochastic Precipitation Generation for the Potomac River Basin Using Hidden Markov Models

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CT2020Team1.pdf (1.51 MB)

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http://hpcf-files.umbc.edu/research/papers/CT2020Team1.pdf

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http://hdl.handle.net/11603/19254

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technical reports
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Gerson C. Kroiz et al., Stochastic Precipitation Generation for the Potomac River Basin Using Hidden Markov Models, http://hpcf-files.umbc.edu/research/papers/CT2020Team1.pdf

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UMBC High Performance Computing Facility (HPCF)

Abstract

A daily precipitation generator based on hidden Markov models (HMM) using satellite precipitation estimates is studied for the Potomac river basin in Eastern USA over the wet season months of July to September. GPM-IMERG data between 2001–2018 is used for the study, which at a 0.1◦ × 0.1◦ spatial resolution results in 387 grid points across the basin. A 4-state model has been considered for the state process, and the semi-continuous emission distribution for precipitation at each location is modeled using a mixture comprising a delta function at 0 and two Gamma distributions. The underestimation of the observed spatial correlations between the grid points based on this model is noted, and the HMM is extended using Gaussian copulas to generate spatially correlated precipitation amounts. Performance of this model is examined in terms of dry and wet day stretches, spatial correlations between grid points, and extreme precipitation events. The HMM with Gaussian copulas (HMM-GC) is shown to outperform the classical HMM formulation for precipitation generation when using remote sensing data in the Potomac river basin.