scholarly journals STATISTICAL UNCERTAINTY IN DROUGHT FORECASTING USING MARKOV CHAINS AND THE STANDARD PRECIPITATION INDEX (SPI).

2021 ◽  
Vol 28 ◽  
Author(s):  
Ályson Brayner Sousa Estácio ◽  
Samiria Maria Oliveira da Silva ◽  
Francisco de Assis Souza Filho
Keyword(s):  
Markov Chains ◽  
Precipitation Index ◽  
Statistical Uncertainty ◽  
Drought Forecasting ◽  
Standard Precipitation Index

scholarly journals A new early warning drought index for Ethiopia

2018 ◽  
Vol 9 (3) ◽  
pp. 624-630
Author(s):  
Yonas Tadesse ◽  
Aklilu Amsalu ◽  
Paolo Billi ◽  
Massimiliano Fazzini
Keyword(s):  
Early Warning ◽  
Drought Index ◽  
Aridity Index ◽  
Precipitation Index ◽  
Z Score ◽  
Standard Precipitation Index ◽  
Eastern Ethiopia ◽  
Rain Gauges ◽  
Dire Dawa ◽  
Seeding Time

Abstract This study investigates the occurrence of droughts in the Dire Dawa area of eastern Ethiopia. A new index based on the rainfall delay (Rd) with respect to the expected onset (and traditional) seeding time and other indices, i.e., the aridity index and the Z-score, alternatives to the Standard Precipitation Index (SPI), are used to test the validity of the new Rd index in identifying severe droughts extending back to 1955. Although only data of rain gauges located in the district of Dire Dawa were used, they proved, albeit with different accuracies, able to identify nation-wide droughts.

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 Analysis of Short-Term Droughts in the Mewar Region of Rajasthan by Standard Precipitation Index

2017 ◽  
Vol 6 (6) ◽  
pp. 182-192
Author(s):  
K.A. Basamma ◽  
R.C. Purohit ◽  
S.R. Bhakar ◽  
Mahesh Kothari ◽  
R.R. Joshi ◽  
...  
Keyword(s):  
Precipitation Index ◽  
Standard Precipitation Index ◽  
Short Term

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.

scholarly journals Analysing The Feature of Meteorological Drought in North East Ethiopia: The Case of Meket and Wadla Districts in North Wello Zone, Amhara Region

2021 ◽  
Author(s):  
Mesfin Anteneh
Keyword(s):  
Time Scale ◽  
Rainy Season ◽  
Meteorological Drought ◽  
Precipitation Index ◽  
Trend Test ◽  
Drought Event ◽  
Standard Precipitation Index ◽  
North East ◽  
Short Rainy Season ◽  
History Of

Abstract The study was undertaken to investigate the magnitude, frequency and trends of drought incidence in North Wello Zone, northeast Ethiopia using monthly rainfall record for the period 1987 to 2017 of Meket and Wadla station. Standard precipitation index and Mann–Kendal test were used to analyse drought event and trends of drought occurrences, respectively. Drought Index Calculator used to analyse standard precipitation index. The coefficient variation of the study area for Meket was (21.2%), while for Wadla it was (53%) which showed high inter-annual variability. It was established that both studied stations experienced drought episodes in 1987, 1988, 1991, 1992, 1994, 1998, 1999, 2001, 2006, 2014 and 2015, drought years in the history of Ethiopia. The year 2006 was the most severe and distinct-wide extreme drought episode in both studied stations which standard precipitation index values -2.14 at Wadla and -2.01 at Meket station. The frequency of drought number of years which experienced negative standardize precipitation index values in the total time series of 30 years observed for all time scale at both station is 50 percent and above. The drought magnitude of different time scale varied from slight to extreme severe in the studied stations. The Mann–Kendal trend test shows except two-month timescale at Wadla station, all timescales were not statistically significant (P<0.05). Generally increasing tendencies of drought were observed during main rainy season and decreasing tendencies of drought during short rainy season and annual scale observed in the study area.

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