scholarly journals Meteorological drought analysis using Standardized Precipitation Index over Luni River Basin in Rajasthan, India

2020 ◽  
Vol 2 (9) ◽  
Author(s):  
Jayanta Das ◽  
Amiya Gayen ◽  
Piu Saha ◽  
Sudip Kumar Bhattacharya
Keyword(s):  
River Basin ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Precipitation Index ◽  
Drought Analysis

Drought analysis using multi-scale standardized precipitation index in the Han River Basin, China

2011 ◽  
Vol 12 (6) ◽  
pp. 483-494 ◽  
Author(s):  
Yue-ping Xu ◽  
Sheng-ji Lin ◽  
Yan Huang ◽  
Qin-qing Zhang ◽  
Qi-hua Ran
Keyword(s):  
River Basin ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Drought Analysis ◽  
Multi Scale ◽  
Han River ◽  
Han River Basin

scholarly journals Meteorological Drought Analysis Using Standardized Precipitation Index

2020 ◽  
Vol 15 (3) ◽  
pp. 477-486
Author(s):  
Parthsarthi Pandya ◽  
Rohit Kumarkhaniya ◽  
Ravina Parmar ◽  
Piyush Ajani
Keyword(s):  
Time Scales ◽  
Natural Hazard ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Precipitation Index ◽  
Yield Prediction ◽  
Areal Extent ◽  
Cotton Yield ◽  
Drought Analysis ◽  
Mild Drought

Drought is a natural hazard which is challenging to quantify in terms of severity, duration, areal extent and impact. The present study was aimed to assess the meteorological drought for Junagadh (Gujarat), India using Standardized Precipitation Index (SPI) and evaluate its correlation with the productivity of Groundnut and Cotton. The SPI was computed for eight durations including monthly (June to August each), 3 monthly (June to August and July to September) and 6 monthly (June to November) time scales for the year1988 to 2018. The results revealed that 54% to 67% of years suffered from drought for SPI-1. Drought years based on SPI-3 and SPI-6 were 48 % to 58%. Among all the eight durations, mild drought was the most dominant drought category. Years 1993, 1999, 2002 and 2012 experienced the most severe droughts for Junagadh. Severe droughts were observed only for SPI-1 (July), SPI-3 and SPI-6. No extreme drought was witnessed in Junagadh. Correlation of groundnut yield with SPI was higher as compared to cotton for all time scales. Kharif groundnut and cotton yield were better correlated with SPI-3 and SPI-6 for Junagadh with significant correlation coefficient ranging from 0.57 to 0.79 for groundnut and 0.46 to 0.56 for cotton. Among monthly SPI, the significantly highest correlation was found for June (0.59) for groundnut and September (0.48) for cotton. The SPI-3 and SPI-6 shown ability to quantify the drought and also shown the potential of yield prediction.

scholarly journals Evaluation of meteorological drought using the Standardized Precipitation Index (SPI) in the High Ziz River basin, Morocco

2019 ◽  
Vol 19 (3) ◽  
pp. 125-135 ◽  
Author(s):  
Khadija Diani ◽  
Ilias Kacimi ◽  
Mahmoud Zemzami ◽  
Hassan Tabyaoui ◽  
Ali Torabi Haghighi
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Significant Trend ◽  
Precipitation Index ◽  
Annual Rainfall ◽  
Complex Nature ◽  
Positive Trend ◽  
The Standardized Precipitation Index

Abstract One of the adverse impacts of climate change is drought, and the complex nature of droughts makes them one of the most important climate hazards. Drought indices are generally used as a tool for monitoring changes in meteorological, hydrological, agricultural and economic conditions. In this study, we focused on meteorological drought events in the High Ziz river Basin, central High Atlas, Morocco. The application of drought index analysis is useful for drought assessment and to consider methods of adaptation and mitigation to deal with climate change. In order to analyze drought in the study area, we used two different approaches for addressing the change in climate and particularly in precipitation, i) to assess the climate variability and change over the year, and ii) to assess the change within the year timescale (monthly, seasonally and annually) from 1971 to 2017. In first approach, precipitation data were used in a long time scale e.g. annual and more than one-year period. For this purpose, the Standardized Precipitation Index (SPI) was considered to quantify the rainfall deficit for multiple timescales. For the second approach, trend analysis (using the Mann-Kendall (M-K) test) was applied to precipitation in different time scales within the year. The results showed that the study area has no significant trend in annual rainfall, but in terms of seasonal rainfall, the magnitude of rainfall during summer revealed a positive significant trend in three stations. A significant negative and positive trend in monthly rainfall was observed only in April and August, respectively.

scholarly journals Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models

Climate ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 28
Author(s):  
Anurag Malik ◽  
Anil Kumar ◽  
Priya Rai ◽  
Alban Kuriqi
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Inference ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Vital Role ◽  
Precipitation Index ◽  
Visual Interpretation ◽  
Inference System ◽  
Drought Prediction ◽  
Optimal Functioning

Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning of irrigation systems, risk management, drought readiness, and alleviation. In this work, Artificial Intelligence (AI) models, comprising Multi-layer Perceptron Neural Network (MLPNN) and Co-Active Neuro-Fuzzy Inference System (CANFIS), and regression, model including Multiple Linear Regression (MLR), were investigated for multi-scalar Standardized Precipitation Index (SPI) prediction in the Garhwal region of Uttarakhand State, India. The SPI was computed on six different scales, i.e., 1-, 3-, 6-, 9-, 12-, and 24-month, by deploying monthly rainfall information of available years. The significant lags as inputs for the MLPNN, CANFIS, and MLR models were obtained by utilizing Partial Autocorrelation Function (PACF) with a significant level equal to 5% for SPI-1, SPI-3, SPI-6, SPI-9, SPI-12, and SPI-24. The predicted multi-scalar SPI values utilizing the MLPNN, CANFIS, and MLR models were compared with calculated SPI of multi-time scales through different performance evaluation indicators and visual interpretation. The appraisals of results indicated that CANFIS performance was more reliable for drought prediction at Dehradun (3-, 6-, 9-, and 12-month scales), Chamoli and Tehri Garhwal (1-, 3-, 6-, 9-, and 12-month scales), Haridwar and Pauri Garhwal (1-, 3-, 6-, and 9-month scales), Rudraprayag (1-, 3-, and 6-month scales), and Uttarkashi (3-month scale) stations. The MLPNN model was best at Dehradun (1- and 24- month scales), Tehri Garhwal and Chamoli (24-month scale), Haridwar (12- and 24-month scales), Pauri Garhwal (12-month scale), Rudraprayag (9-, 12-, and 24-month), and Uttarkashi (1- and 6-month scales) stations, while the MLR model was found to be optimal at Pauri Garhwal (24-month scale) and Uttarkashi (9-, 12-, and 24-month scales) stations. Furthermore, the modeling approach can foster a straightforward and trustworthy expert intelligent mechanism for projecting multi-scalar SPI and decision making for remedial arrangements to tackle meteorological drought at the stations under study.

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