Increase of Future Summer Rainfall in the Middle and Lower Reach of the Yangtze River Basin Projected With a Nonhomogeneous Hidden Markov Model

Guo, Lianyi; Jiang, Zhihong; Li, Laurent; Wang, Huijun

doi:10.1029/2021gl097325

Cited by 1 publication

(1 citation statement)

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“…For instance, Panda et al (2022) only selected the first few components explaining 95% variance for each predictor. Guo et al (2022) used a combination of the first 26 components of four predictors to run the NHMM downscaling. Liu et al (2011) used two indicators, that is, the explained variances and partial correlation to select the predictors from 20 candidate variables for daily precipitation downscaling.…”

Section: Introductionmentioning

confidence: 99%

A stacked ensemble learning and non‐homogeneous hidden Markov model for daily precipitation downscaling and projection

Jiang,

Cioffi,

Conticello

et al. 2023

Hydrological Processes

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Global circulation models (GCMs) are routinely used to project future climate conditions worldwide, such as temperature and precipitation. However, inputs with a finer resolution are required to drive impact‐related models at local scales. The non‐homogeneous hidden Markov model (NHMM) is a widely used algorithm for the precipitation statistical downscaling for GCMs. To improve the accuracy of the traditional NHMM in reproducing spatiotemporal precipitation features of specific geographic sites, especially extreme precipitation, we developed a new precipitation downscaling framework. This hierarchical model includes two levels: (1) establishing an ensemble learning model to predict the occurrence probabilities for different levels of daily precipitation aggregated at multiple sites and (2) constructing a NHMM downscaling scheme of daily amount at the scale of a single rain gauge using the outputs of ensemble learning model as predictors. As the results obtained for the case study in the central‐eastern China (CEC), show that our downscaling model is highly efficient and performs better than the NHMM in simulating precipitation variability and extreme precipitation. Finally, our projections indicate that CEC may experience increased precipitation in the future. Compared with ~26 years (1990–2015), the extreme precipitation frequency and amount would significantly increase by 21.9%–48.1% and 12.3%–38.3%, respectively, by the late century (2075–2100) under the Shared Socioeconomic Pathway 585 climate scenario.

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Section: Introductionmentioning

confidence: 99%