| Autor: |
Huimin Wang, Songbai Song, Gengxi Zhang, Olusola O. Ayantoboc |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Journal of Hydrology: Regional Studies, Vol 47, Iss , Pp 101374- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2214-5818 |
| DOI: |
10.1016/j.ejrh.2023.101374 |
| Popis: |
Study region: Weihe River Basin of China Study focus: In recent decades, changing environments destroyed the natural structure of streamflow, making accurate streamflow prediction challenging. This study develops a multi-regime Markov-switching Generalized Autoregressive Conditional Heteroskedasticity (MS-GARCH) model to predict daily streamflow time series with structural breaks (SB), named ARIMA (Autoregressive Integrated Moving Average)-MS-GARCH model. Consequently, the multi-regime ARIMA-MS-GARCH model is compared with other classical single-regime ARIMA-GARCH models to evaluate whether and to what extent it improves streamflow prediction accuracy. New hydrology insights for the region: There exist structural breaks in the daily streamflow time series, and the number of breakpoints at each station varies. The GARCH model ignores the description of volatility aggregation in the daily streamflow time series (α |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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