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Improving the SM2RAIN-derived rainfall estimation using Bayesian optimization

Miao, Linguang ; Wei, Zushuai ; Zhong, Yanmei and Duan, Zheng LU (2023) In Journal of Hydrology 622.
Abstract

The rainfall product derived from the SM2RAIN (Soil Moisture to Rain) algorithm has been widely used. However, there is still a large uncertainty partly due to the soil moisture input and parameters estimation of the SM2RAIN algorithm, which limits the application of the model in alpine regions. Here, the SM2RAIN-BayesOpt algorithm was developed by integrating the SM2RAIN algorithm and Bayesian optimization to improve the estimation of parameters (Z, a, b, Tbase, Tpot), subsequently incorporating SMAP Level-3 soil moisture products for rainfall estimation. The performance of the SM2RAIN-BayesOpt algorithm was evaluated based on observed rainfall data under different environmental conditions in three typical alpine... (More)

The rainfall product derived from the SM2RAIN (Soil Moisture to Rain) algorithm has been widely used. However, there is still a large uncertainty partly due to the soil moisture input and parameters estimation of the SM2RAIN algorithm, which limits the application of the model in alpine regions. Here, the SM2RAIN-BayesOpt algorithm was developed by integrating the SM2RAIN algorithm and Bayesian optimization to improve the estimation of parameters (Z, a, b, Tbase, Tpot), subsequently incorporating SMAP Level-3 soil moisture products for rainfall estimation. The performance of the SM2RAIN-BayesOpt algorithm was evaluated based on observed rainfall data under different environmental conditions in three typical alpine regions, namely Tibetan Plateau, Heihe River Basin, and Shandian River Basin. Moreover, SM2RAIN-BayesOpt, IMERG-V06B, and ERA5 reanalysis rainfall estimates were also compared with in-situ rainfall observations. The results showed that the proposed SM2RAIN-BayesOpt algorithm can obtain more accurate rainfall estimates in all studied areas in terms of different evaluation metrics. It was also found that our proposed SM2RAIN-BayesOpt algorithm performs better in alpine meadows and grassland than in desert and forestland. SM2RAIN-BayesOpt algorithm can considerably improve the accuracy of rainfall estimation, and it is of significant value for rainfall monitoring in alpine regions where observational data are scarce.

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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Bayesian optimization, Rainfall estimation, SM2RAIN, SMAP, Soil moisture
in
Journal of Hydrology
volume
622
article number
129728
publisher
Elsevier
external identifiers
  • scopus:85161539254
ISSN
0022-1694
DOI
10.1016/j.jhydrol.2023.129728
language
English
LU publication?
yes
id
5a64800a-f8af-4357-8013-dd344a2c57b2
date added to LUP
2023-08-15 14:29:52
date last changed
2025-10-14 11:18:07
@article{5a64800a-f8af-4357-8013-dd344a2c57b2,
  abstract     = {{<p>The rainfall product derived from the SM2RAIN (Soil Moisture to Rain) algorithm has been widely used. However, there is still a large uncertainty partly due to the soil moisture input and parameters estimation of the SM2RAIN algorithm, which limits the application of the model in alpine regions. Here, the SM2RAIN-BayesOpt algorithm was developed by integrating the SM2RAIN algorithm and Bayesian optimization to improve the estimation of parameters (Z, a, b, T<sub>base</sub>, T<sub>pot</sub>), subsequently incorporating SMAP Level-3 soil moisture products for rainfall estimation. The performance of the SM2RAIN-BayesOpt algorithm was evaluated based on observed rainfall data under different environmental conditions in three typical alpine regions, namely Tibetan Plateau, Heihe River Basin, and Shandian River Basin. Moreover, SM2RAIN-BayesOpt, IMERG-V06B, and ERA5 reanalysis rainfall estimates were also compared with in-situ rainfall observations. The results showed that the proposed SM2RAIN-BayesOpt algorithm can obtain more accurate rainfall estimates in all studied areas in terms of different evaluation metrics. It was also found that our proposed SM2RAIN-BayesOpt algorithm performs better in alpine meadows and grassland than in desert and forestland. SM2RAIN-BayesOpt algorithm can considerably improve the accuracy of rainfall estimation, and it is of significant value for rainfall monitoring in alpine regions where observational data are scarce.</p>}},
  author       = {{Miao, Linguang and Wei, Zushuai and Zhong, Yanmei and Duan, Zheng}},
  issn         = {{0022-1694}},
  keywords     = {{Bayesian optimization; Rainfall estimation; SM2RAIN; SMAP; Soil moisture}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Journal of Hydrology}},
  title        = {{Improving the SM2RAIN-derived rainfall estimation using Bayesian optimization}},
  url          = {{http://dx.doi.org/10.1016/j.jhydrol.2023.129728}},
  doi          = {{10.1016/j.jhydrol.2023.129728}},
  volume       = {{622}},
  year         = {{2023}},
}