Search for: [Keywords = "standardized precipitation index \(SPI\)"]

Drought is an extreme event that causes great economic and environmental damage. The main objective of this study is to evaluate sensitivity, characterization and propagation of drought in the Upper Blue Nile. Drought indices: standardized precipitation index (SPI) and the recently developed standardized reconnaissance drought index (RDIst) are applied for five weather stations from 1980 to 2015 to evaluate RDIst applicability in the Upper Blue Nile. From our analysis both SPI and RDIst applied for 3-, 6-, 12 month of time scales follow the same trend, but in some time steps the RDIst varies with small-er amplitude than SPI. The severity and longer duration of drought compared with others periods of meteorological drought is found in the years 1984, 2002, 2009, 2015 including five weather stations and entire Upper Blue Nile. For drought rela-tionships the correlation analysis is made across the time scales to evaluate the relationship between meteorological drought (SPI), soil moisture drought (SMI), and hydrological drought (SRI). We found that the correlation between three indices (SPI, SMI and SRI) at different time scales the 24-month time scale is dominant and are given by 0.82, 0.63 and 0.56.

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Authors and Affiliations

Abebe Kebede

Jaya Prakash Raju

Diriba Korecha

Samuel Takele

Melessew Nigussie

Weather drought index prediction using the support vector regression in the Ansegmir Watershed, Upper Moulouya, Morocco

My Hachem Bekri Abdellah El Hmaidi Habiba Ousmana El Mati El Faleh Mohamed Berrada Kamal El Aissaoui Ali Essahlaoui Abdelhadi El Ouali

Journal of Water and Land Development | 2021 | No 50 | 187-194 | DOI: 10.24425/jwld.2021.138174

Keywords Ansgemir watershed drought forecast modelling standardized precipitation index (SPI) standardized precipitation-evapotranspiration index (SPEI) support vectors regression (SVR)

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Abstract

The purpose of this study is to develop mathematical models based on artificial intelligence: Models based on the support vectors regression (SVR) for drought forecast in the Ansegmir watershed (Upper Moulouya, Morocco). This study focuses on the prediction of the temporal aspect of the two drought indices (standardized precipitation index – SPI and standardized precipitation-evapotranspiration index – SPEI) using six hydro-climatic variables relating to the period 1979–2013. The model SVR3-SPI: RBF, ε = 0.004, C = 20 and γ = 1.7 for the index SPI, and the model SVR3-SPEI: RBF ε = 0.004, C = 40 and γ = 0.167 for the SPEI index are significantly better in comparison to other models SVR1, SVR2 and SVR4. The SVR model for the SPI index gave a correlation coefficient of R = 0.92, MSE = 0.17 and MAE = 0.329 for the learning phase and R = 0.90, MSE = 0.18 and MAE = 0.313 for the testing phase. As for the SPEI index, the overlay is slightly poorer only in the case of the SPI index between the observed values and the predicted ones by the SVR model. It shows a very small gap between the observed and predicted values. The correlation coefficients R = 0.88 for the learning, R = 0.86 for testing remain higher and corresponding to a quadratic error average MSE = 0.21 and MAE = 0.351 for the learning and MSE = 0.21 and MAE = 0.350 for the testing phase. The prediction of drought by SVR model remain useful and would be extremely important for drought risk management.

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Authors and Affiliations

My Hachem Bekri

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ORCID:

Abdellah El Hmaidi

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ORCID:

Habiba Ousmana

e-mail:

ORCID:

El Mati El Faleh

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ORCID:

Mohamed Berrada

e-mail:

ORCID:

Kamal El Aissaoui

e-mail:

ORCID:

Ali Essahlaoui

e-mail:

ORCID:

Abdelhadi El Ouali

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ORCID:

Moulay Ismail University, Faculty of Sciences, B.P. 11201, Zitoune, 50070, Meknes, Morocco

Assessment of agricultural drought based on CHIRPS data and SPI method over West Papua – Indonesia

Arif Faisol Indarto Indarto Elida Novita Budiyono Budiyono

Journal of Water and Land Development | 2022 | No 52 | 44-52 | DOI: 10.24425/jwld.2021.139942

Keywords agricultural drought Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data Standardized Precipitation Index (SPI) method West Papua – Indonesia

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Abstract

This study aims to utilise Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data and Standardised Precipitation Index (SPI) method to assess agricultural drought in West Papua, Indonesia. The data used in this study is monthly CHIRPS data acquired from 1996 to 2019, daily precipitation data recorded from 1996 to 2019 from the five climatological stations in West Papua, Indonesia located at Sorong, Fakfak, Kaimana, Manokwari, and South Manokwari. 3-month SPI or quarterly SPI are used to assess agricultural drought, i.e., SPI January–March, SPI February–April, SPI March-May, SPI April–June, SPI May–July, SPI June–August, SPI July–September, SPI August–October, SPI September–November, and SPI October–December. The results showed that in 2019 agricultural drought in West Papua was moderately wet to severely dry. The most severely dry occurred in September– December periods. Generally, CHIRPS data and SPI methods have an acceptable accuracy in generating drought information in West Papua with an accuracy of 53% compared with climate data analysis. Besides, the SPI from CHIRPS data processing has a moderate correlation with climate data analysis with an average R2 = 0.51.

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Bibliography

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Authors and Affiliations

Arif Faisol

e-mail:

ORCID:

Indarto Indarto

e-mail:

ORCID:

Elida Novita

Budiyono Budiyono

University of Papua, Faculty of Agricultural Technology, Jl. Gn. Salju, Manokwari, West Papua 98314, Indonesia
University of Jember, Faculty of Agricultural Technology, Jember, East Java, Indonesia
University of Papua, Faculty of Agriculture, Manokwari, West Papua, Indonesia

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Drought monitoring in the Seybouse basin (Algeria) over the last decades

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Drought sensitivity characteristics and relationships between drought indices over Upper Blue Nile basin

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Weather drought index prediction using the support vector regression in the Ansegmir Watershed, Upper Moulouya, Morocco

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Assessment of agricultural drought based on CHIRPS data and SPI method over West Papua – Indonesia

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