Multisite downscaling of seasonal predictions to daily rainfall characteristics over pacific-andean river basins in Ecuador and peru using a nonhomogeneous hidden markov model

Abstract

© 2016 American Meteorological Society. The seasonal predictability of daily rainfall characteristics is examined over 21 hydrologic units in the Pacific-Andean region of Ecuador and Peru (PAEP) using a nonhomogeneous hidden Markov model (NHMM) and retrospective seasonal information from general circulation models (GCMs). First, a hidden Markov model is used to diagnose four states that play distinct roles in the December-May rainy season. The estimated daily states fall into two wet states, one dry state, and one transitional dry-wet state, and show a systematic seasonal evolution together with intraseasonal and interannual variability. The first wet state represents regionwide wet conditions, while the second one represents north-south gradients. The former could be associated with the annual moisture offshore of the PAEP, thermally driven by the climatological maximum of sea surface temperatures in the Niño-1.2 region. The latter corresponds with the dynamically noisy component of the PAEP rainfall signal, associated with the annual displacement of the intertropical convergence zone. Then, a four-state NHMM is coupled with GCM information to simulate daily sequences at each station. Simulations of the GCM-NHMM approach represent daily rainfall characteristics at station level well. The best skills were found in reproducing the interannual variation of seasonal rainfall amount and mean intensity at the regional-averaged level with correlations equal to 0.60 and 0.64, respectively. At catchment level, the best skills appear over catchments south of 48S, where hydrologically relevant characteristics are well simulated. It is thus shown that the GCM-NHMM approach provides the potential to produce precipitation information relevant for hydrological prediction in this climate-sensitive region.