scholarly journals Investigating Rainfall Trend and Monitoring Meteorological Drought in a Himalayan Watershed of India

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Alok Kumar Mishra ◽  
◽  
Anil Kumar ◽  
Keyword(s):  
Meteorological Drought ◽  
Rainfall Trend

Satellite Data Based Soil Water Index as a Tool to Monitor Droughts and Agricultural Crisis -An Example of Yavatmal District, Maharashtra, India

2020 ◽  
Vol 4 (1-2) ◽  
pp. 12-18
Author(s):  
Vijendra Boken
Keyword(s):  
Soil Water ◽  
Satellite Data ◽  
Suicide Rate ◽  
Crop Yields ◽  
Meteorological Drought ◽  
Agricultural Drought ◽  
Coefficient Of Correlation ◽  
Water Index ◽  
Farmer Suicides ◽  
June July

Yavatmal is one of the drought prone districts in Maharashtra state of India and has witnessed an agricultural crisis to the extent that hundreds of its farmers have committed suicides in recent years. Satellite data based products have previously been used globally for monitoring and predicting of drought, but not for monitoring their extreme impacts that may include farmer-suicides. In this study, the performance of the Soil Water Index (SWI) derived from the surface soil moisture estimated by the European Space Agency’s Advanced Scatterometer (ASCAT) is assessed. Using the 2007-2015 data, it was found that the relationship of the SWI anomaly was bit stronger (coefficient. of correlation = 0.59) with the meteorological drought or precipitation than with the agricultural drought or crop yields of major crops (coefficient. of correlation = 0.50).  The farmer-suicide rate was better correlated with the SWI anomaly averaged annually than with the SWI anomaly averaged only for the monsoon months (June, July, August, and September). The correlation between the SWI averaged annually increased to 0.89 when the averages were taken for three years, with the highest correlation occurring between the suicide rate and the SWI anomaly averaged for three years. However, a positive relationship between SWI and the suicide rate indicated that drought was not a major factor responsible for suicide occurrence and other possible factors responsible for suicide occurrence need to examine in detail.

scholarly journals Estimation of SPEI Meteorological Drought using Machine Learning Algorithms

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ali Mokhtar ◽  
Mohammadnabi Jalali ◽  
Ahmed Elbeltagi ◽  
Nadhir Al-Ansari ◽  
Karam Alsafadi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Meteorological Drought ◽  
Machine Learning Algorithms

Geospatial analysis of meteorological drought impact on Southern Africa biomes

2020 ◽  
Vol 42 (6) ◽  
pp. 2155-2173
Author(s):  
Farai Maxwell Marumbwa ◽  
Moses Azong Cho ◽  
Paxie W Chirwa
Keyword(s):  
Southern Africa ◽  
Meteorological Drought ◽  
Geospatial Analysis ◽  
Drought Impact

Attribution of trends in meteorological drought during 1960–2016 over the Loess Plateau, China

2021 ◽  
Vol 31 (8) ◽  
pp. 1123-1139
Author(s):  
Mengyao Guo ◽  
Dunxian She ◽  
Liping Zhang ◽  
Lingcheng Li ◽  
Zong-Liang Yang ◽  
...  
Keyword(s):  
Loess Plateau ◽  
Meteorological Drought ◽  
The Loess Plateau

scholarly journals Kecenderungan dan Perubahan Hujan Ekstrem Harian di Pulau Madura

2020 ◽  
Vol 18 (1) ◽  
pp. 89-96
Author(s):  
Ahmad Nur Akma Juangga Fura ◽  
Retno Utami Agung Wiyono ◽  
Indarto Indarto
Keyword(s):  
Flood Hazard ◽  
Parametric Method ◽  
Extreme Rainfall ◽  
Kendall Test ◽  
Random Processes ◽  
Rain Gauge ◽  
Rainfall Trend ◽  
Significance Level ◽  
High Level ◽  
Crossing Test

Madura subject to a high level of flood hazard. One of the main causes of flood is extreme rainfall. Global warming generates changes in the amount of extreme rainfall. This research is conducted to identify and to analyze the trends, changes, and randomness of 24-hour extreme rainfall data on Madura Island. The method used is a non-parametric method which includes the Median Crossing test, the Mann-Kendall test, and the Rank-Sum test at the significance level of α =0.05. The analysis was carried out on 31 rain gauge stations. The recording period observed is between 1991-2015. The results of the analysis show that based on the Median Crossing test, most rainfall stations have data originating from random processes. The result shows also that the maximum 24-hour extreme rainfall trend is significantly decreased in a few locations, while for the majority of other stations have no experience a significant trend.

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