Improving the SM2RAIN-derived rainfall estimation using Bayesian optimization
(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.
(Less)
- author
- Miao, Linguang ; Wei, Zushuai ; Zhong, Yanmei and Duan, Zheng LU
- organization
- publishing date
- 2023
- 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
- 2024-05-29 11:19:18
@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}}, }