Time Series Model for Standardized Precipitation Index in the Ping River Basin of Chiang Mai Province

2020 ◽  
Vol 31 (2) ◽  
Author(s):  
Tanachot Chaito ◽  
Manad Khamkong
Keyword(s):  
Time Series ◽  
River Basin ◽  
Standardized Precipitation Index ◽  
Time Series Model ◽  
Precipitation Index ◽  
Chiang Mai ◽  
Chiang Mai Province

scholarly journals Modeling, prediction and trend assessment of drought in Iran using standardized precipitation index

2018 ◽  
Vol 10 (1) ◽  
pp. 181-196 ◽  
Author(s):  
Mehdi Bahrami ◽  
Samira Bazrkar ◽  
Abdol Rassoul Zarei
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Occurrence Frequency ◽  
Natural Phenomenon ◽  
Drought Severity ◽  
Data Series ◽  
Climate Data ◽  
Seasonal Drought

Abstract Drought as an exigent natural phenomenon, with high frequency in arid and semi-arid regions, leads to enormous damage to agriculture, economy, and environment. In this study, the seasonal Standardized Precipitation Index (SPI) drought index and time series models were employed to model and predict seasonal drought using climate data of 38 Iranian synoptic stations during 1967–2014. In order to model and predict seasonal drought ITSM (Interactive Time Series Modeling) statistical software was used. According to the calculated seasonal SPI, within the study area, drought severity classes 4 and 3 had the greatest occurrence frequency, while classes 6 and 7 had the least occurrence frequency. Results indicated that the best fitted models were Moving-Average or MA (5) Innovations and MA (5) Hannan-Rissenen, with 60.53 and 15.79 percentage, respectively. On the other hand, results of the prediction as well, indicated that drought class 4 with the highest percentages, was the most abundant class over the study area and drought class 7 was the least frequent class. According to results of trend analysis, without attention to significance of them, observed seasonal SPI data series (1967–2014), in 84.21% of synoptic stations had a negative trend, but this percentage changes to 86.84% when studying the combination of observed and predicted simultaneously (1967–2019).

scholarly journals Drought and climate change assessment using Standardized Precipitation Index (SPI) for Sarawak River Basin

2019 ◽  
Vol 11 (4) ◽  
pp. 956-965 ◽  
Author(s):  
C. H. J. Bong ◽  
J. Richard
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Drought Index ◽  
Standardized Precipitation Index ◽  
Recent Decade ◽  
Kendall Test ◽  
Precipitation Index ◽  
Irrigated Agriculture ◽  
Drought Event ◽  
Monthly Precipitation

Abstract Severe droughts in the year 1998 and 2014 in Sarawak due to the strong El Niño has impacted the water supply and irrigated agriculture. In this study, the Standardized Precipitation Index (SPI) was used for drought identification and monitoring in Sarawak River Basin. Using monthly precipitation data between the year 1975 and 2016 for 15 rainfall stations in the basin, the drought index values were obtained for the time scale of three, six and nine months. Rainfall trend for the years in study was also assessed using the Mann–Kendall test and Sen's slope estimator and compared with the drought index. Findings showed that generally there was a decreasing trend for the SPI values for the three time scales, indicating a higher tendency of increased drought event throughout the basin. Furthermore, it was observed that there was an increase in the numbers of dry months in the recent decade for most of the rainfall stations as compared to the previous 30 to 40 years, which could be due to climate change. Findings from this study are valuable for the planning and formulating of drought strategies to reduce and mitigate the adverse effects of drought.

scholarly journals Application of Markov chains to Standardized Precipitation Index (SPI) in São Francisco River Basin

2019 ◽  
Vol 14 (3) ◽  
pp. 1
Author(s):  
Esdras Adriano Barbosa dos Santos ◽  
Tatijana Stosic ◽  
Ikaro Daniel de Carvalho Barreto ◽  
Laélia Campos ◽  
Antonio Samuel Alves da Silva
Keyword(s):  
Markov Chains ◽  
River Basin ◽  
Arid Region ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Severe Drought ◽  
Sao Francisco River ◽  
São Francisco River ◽  
Semi Arid Region ◽  
Semi Arid

This work evaluated dry and rainy conditions in the subregions of the São Francisco River Basin (BHSF) using the Standardized Precipitation Index (SPI) and Markov chains. Each subregion of the BHSF has specific physical and climatic characteristics. The data was obtained from the National Water Agency (ANA), collected by four pluviometric stations (representative of each subregion), covering 46 years of data, from 1970 to 2015. The SPI was calculated for the time scales of six and twelve months and transition probabilities were obtained using the Markov chain. Transition matrices showed that, at both scales, if the climate conditions were severe drought or rainy, switching to another class would be unlikely in the short term.  Correlating this information with the probabilities of the stationary distribution, it was possible to find the regions that are most likely to be under rainy or dry weather in the future. The recurrence times calculated for the stations that belong to the semi-arid region were smaller when compared to the value of the return period of the representative station of Upper São Francisco that has higher levels of precipitation, confirming the predisposition of the semi-arid region to present greater chances of future periods of drought.

scholarly journals Wavelet and cuckoo search-support vector machine conjugation for drought forecasting using Standardized Precipitation Index (case study: Urmia Lake, Iran)

2018 ◽  
Vol 20 (4) ◽  
pp. 975-988 ◽  
Author(s):  
Mehdi Komasi ◽  
Soroush Sharghi ◽  
Hamid R. Safavi
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Standardized Precipitation Index ◽  
Cuckoo Search ◽  
Precipitation Index ◽  
Urmia Lake ◽  
Support Vector ◽  
Drought Forecasting ◽  
Svm Algorithm ◽  
Svm Model

Abstract In this study, wavelet-support vector machine (WSVM) is proposed for drought forecasting using the Standardized Precipitation Index (SPI). In this way, the SPI time series of Urmia Lake watershed is decomposed to multiple frequency time series by wavelet transform. Then, these time sub-series are applied as input data to the support vector machine (SVM) model to forecast drought. Also, a cuckoo search (CS)-based approach is proposed for parameter optimization of SVM, finding the best initial constant parameters of the SVM algorithm. The obtained results indicate that the radial basis function (RBF)-kernel function of the SVM algorithm has high efficiency in the SPI modeling, resulting in a determination coefficient (DC) of 0.865 in verification step. In the WSVM model, the Coif1, which is considered as a mother wavelet function with decomposition level of five, shows a better performance with DC of 0.954 in verification step, revealing that the proposed hybrid WSVM model outperforms the single SVM model in forecasting SPI time series. Also, DC of cuckoo search-support vector machine (CS-SVM) is calculated to be 0.912 in verification step, indicating the fact that the proposed CS-SVM model shows better efficiency than single SVM model.

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