Nonstationarities and At-site Probabilistic Forecasts of Seasonal Precipitation in the East River Basin, China
Abstract Seasonal precipitation changes under the influence of large-scale climate oscillations in the East River basin were studied using daily precipitation data at 29 rain stations during 1959–2010. Seasonal and global models were developed and evaluated for probabilistic precipitation forecastin...
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ftdoajarticles:oai:doaj.org/article:e6bab97def954421896cf2f27e05934c 2023-05-15T15:08:23+02:00 Nonstationarities and At-site Probabilistic Forecasts of Seasonal Precipitation in the East River Basin, China Peng Sun Qiang Zhang Xihui Gu Peijun Shi Vijay P. Singh Changqing Song Xiuyu Zhang 2018-03-01T00:00:00Z https://doi.org/10.1007/s13753-018-0165-x https://doaj.org/article/e6bab97def954421896cf2f27e05934c EN eng SpringerOpen http://link.springer.com/article/10.1007/s13753-018-0165-x https://doaj.org/toc/2095-0055 https://doaj.org/toc/2192-6395 doi:10.1007/s13753-018-0165-x 2095-0055 2192-6395 https://doaj.org/article/e6bab97def954421896cf2f27e05934c International Journal of Disaster Risk Science, Vol 9, Iss 1, Pp 100-115 (2018) China ENSO regimes GAMLSS model Nonstationarity Probabilistic precipitation forecasting Disasters and engineering TA495 article 2018 ftdoajarticles https://doi.org/10.1007/s13753-018-0165-x 2023-01-08T01:31:47Z Abstract Seasonal precipitation changes under the influence of large-scale climate oscillations in the East River basin were studied using daily precipitation data at 29 rain stations during 1959–2010. Seasonal and global models were developed and evaluated for probabilistic precipitation forecasting. Generalized additive model for location, scale, and shape was used for at-site precipitation forecasting. The results indicate that: (1) winter and spring precipitation processes at most stations are nonstationary, while summer and autumn precipitation processes at few of the stations are stationary. In this sense, nonstationary precipitation processes are dominant across the study region; (2) the magnitude of precipitation is influenced mainly by the Arctic Oscillation, the North Pacific Oscillation, and the Pacific Decadal Oscillation (PDO). The El Niño / Southern Oscillation (ENSO) also has a considerable effect on the variability of precipitation regimes across the East River basin; (3) taking the seasonal precipitation changes of the entire study period as a whole, the climate oscillations influence precipitation magnitude, and this is particularly clear for the PDO and the ENSO. The latter also impacts the dispersion of precipitation changes; and (4) the seasonal model is appropriate for modeling spring precipitation, but the global model performs better for summer, autumn, and winter precipitation. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Pacific International Journal of Disaster Risk Science 9 1 100 115 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
China ENSO regimes GAMLSS model Nonstationarity Probabilistic precipitation forecasting Disasters and engineering TA495 |
spellingShingle |
China ENSO regimes GAMLSS model Nonstationarity Probabilistic precipitation forecasting Disasters and engineering TA495 Peng Sun Qiang Zhang Xihui Gu Peijun Shi Vijay P. Singh Changqing Song Xiuyu Zhang Nonstationarities and At-site Probabilistic Forecasts of Seasonal Precipitation in the East River Basin, China |
topic_facet |
China ENSO regimes GAMLSS model Nonstationarity Probabilistic precipitation forecasting Disasters and engineering TA495 |
description |
Abstract Seasonal precipitation changes under the influence of large-scale climate oscillations in the East River basin were studied using daily precipitation data at 29 rain stations during 1959–2010. Seasonal and global models were developed and evaluated for probabilistic precipitation forecasting. Generalized additive model for location, scale, and shape was used for at-site precipitation forecasting. The results indicate that: (1) winter and spring precipitation processes at most stations are nonstationary, while summer and autumn precipitation processes at few of the stations are stationary. In this sense, nonstationary precipitation processes are dominant across the study region; (2) the magnitude of precipitation is influenced mainly by the Arctic Oscillation, the North Pacific Oscillation, and the Pacific Decadal Oscillation (PDO). The El Niño / Southern Oscillation (ENSO) also has a considerable effect on the variability of precipitation regimes across the East River basin; (3) taking the seasonal precipitation changes of the entire study period as a whole, the climate oscillations influence precipitation magnitude, and this is particularly clear for the PDO and the ENSO. The latter also impacts the dispersion of precipitation changes; and (4) the seasonal model is appropriate for modeling spring precipitation, but the global model performs better for summer, autumn, and winter precipitation. |
format |
Article in Journal/Newspaper |
author |
Peng Sun Qiang Zhang Xihui Gu Peijun Shi Vijay P. Singh Changqing Song Xiuyu Zhang |
author_facet |
Peng Sun Qiang Zhang Xihui Gu Peijun Shi Vijay P. Singh Changqing Song Xiuyu Zhang |
author_sort |
Peng Sun |
title |
Nonstationarities and At-site Probabilistic Forecasts of Seasonal Precipitation in the East River Basin, China |
title_short |
Nonstationarities and At-site Probabilistic Forecasts of Seasonal Precipitation in the East River Basin, China |
title_full |
Nonstationarities and At-site Probabilistic Forecasts of Seasonal Precipitation in the East River Basin, China |
title_fullStr |
Nonstationarities and At-site Probabilistic Forecasts of Seasonal Precipitation in the East River Basin, China |
title_full_unstemmed |
Nonstationarities and At-site Probabilistic Forecasts of Seasonal Precipitation in the East River Basin, China |
title_sort |
nonstationarities and at-site probabilistic forecasts of seasonal precipitation in the east river basin, china |
publisher |
SpringerOpen |
publishDate |
2018 |
url |
https://doi.org/10.1007/s13753-018-0165-x https://doaj.org/article/e6bab97def954421896cf2f27e05934c |
geographic |
Arctic Pacific |
geographic_facet |
Arctic Pacific |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
International Journal of Disaster Risk Science, Vol 9, Iss 1, Pp 100-115 (2018) |
op_relation |
http://link.springer.com/article/10.1007/s13753-018-0165-x https://doaj.org/toc/2095-0055 https://doaj.org/toc/2192-6395 doi:10.1007/s13753-018-0165-x 2095-0055 2192-6395 https://doaj.org/article/e6bab97def954421896cf2f27e05934c |
op_doi |
https://doi.org/10.1007/s13753-018-0165-x |
container_title |
International Journal of Disaster Risk Science |
container_volume |
9 |
container_issue |
1 |
container_start_page |
100 |
op_container_end_page |
115 |
_version_ |
1766339754541973504 |