Drought analysis in the Tons River Basin, India during 1969-2008

Meshram, Sarita Gajbhiye; Gautam, Randhir; Kahya, Ercan

doi:10.1007/s00704-017-2129-2

Cited by 19 publications

(8 citation statements)

References 33 publications

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“…For example, the SPI is easy to calculate and use in quantifying the precipitation deficit on different timescales. Hence, a number of researchers have employed this method in order to analyse spatial and temporal variability of droughts in various regions of the world (Bonaccorso et al ., ; Sirdas and Sen, ; Mishra and Desai, ; Vicente‐Serrano, ; Chen et al ., ; Keskin et al ., ; Turkes and Tatli, ; Edossa et al ., ; Koutroulis et al ., ; Dash et al ., ; Ford and Labosier, ; Gocic and Trajkovic, ; Bayissa et al ., ; Zhang et al ., ; Meshram et al ., ). A major reason of spatial and temporal variability of droughts could be attributed to the effects of well‐known large‐scale atmospheric oscillation patterns (e.g., North Atlantic Oscillation [NAO], El Niño‐Southern Oscillation [ENSO] and Arctic Oscillation [AO]).…”

Section: Introductionmentioning

confidence: 97%

Evaluation of spatial and temporal relationships between large‐scale atmospheric oscillations and meteorological drought indexes in Turkey

Tosunoğlu

Can

Kahya

2018

Intl Journal of Climatology

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Spatial and temporal linkages between large-scale atmospheric oscillations, namely, North Atlantic Oscillation (NAO), Southern Oscillation (SO) and North Sea-Caspian Pattern (NCP), and meteorological droughts in Turkey were investigated in this study. The corresponding oscillation indexes (NAOI, SOI and NCPI) were considered as monthly time indicators of the oscillations while the Standard Precipitation Index (SPI) obtained from 148 stations was used to define meteorological droughts. The suitability of various probability distributions was evaluated to obtain precise estimations of SPI values. Correlation analyses were then conducted to assess spatial and temporal distribution of the relationship between the oscillation and drought indexes. The linkages between the NAOI, NCPI and SPI were found to be more significant at the lag-0, while the SOI had significant positive and negative correlations with SPI series at lag-1-2. Moreover, our results revealed that the NAO is more dominant in the west and in the central Anatolia regions while the NCP has more influence on the northern and eastern regions.

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Section: Introductionmentioning

confidence: 97%

Evaluation of spatial and temporal relationships between large‐scale atmospheric oscillations and meteorological drought indexes in Turkey

Tosunoğlu

Can

Kahya

2018

Intl Journal of Climatology

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“…Despite this, individual efforts to assessing drought in South Asia (Sigdel & Ikeda, 2010;Shakya & Yamaguchi, 2010;Kafle, 2014;Dahal et al, 2016;Masud, Khaliq, & Wheater, 2015;Abuzar et al, 2017;Carabine et al, 2014;Miyan, 2015;Aadhar & Mishra, 2017) using different drought indices have taken place. From these previous studies, the most frequent droughts evidenced across South Asian nations are attributed to variations in rainfall volume (i.e., absence), onset, and distribution pattern during the main monsoon season (Hore, Werner, & Maskey, 2019;Meshram, Gautam, & Kahya, 2018;Miyan, 2015;Regmi, 2010;Imbulana, 2010). In India, the 2014-2015 drought, which has a return duration of 542 years (Mishra, Aadhar, Asoka, Pai, & Kumar, 2016), impacted 330 million people and resulted in a severe water shortage.…”

Section: Introductionmentioning

confidence: 99%

Historical drought and its trend in South Asia: Spatial and temporal analysis 2000-2020

Kafle¹,

Khaitu²,

Gyawali³

et al. 2023

APN SCI BULL

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Every year, South Asian countries suffer from declining agricultural outputs due to climate extremes such as floods and droughts. Recurrent droughts have depressed rural economies and enhanced widespread hunger and human migration to South Asian cities (Miyan, 2015). Due to climatic changes, the region is projected to experience rising temperatures and more frequent extreme weather events in the long term (Trenberth et al., 2014). Accurate predictions of drought, its impact, and early detection facilities are not present in most South Asian countries due to a lack of sufficient hydro-meteorological datasets, poor access to satellite products, and shortages of well-trained staff. This study seeks to address these deficiencies scientifically by analysing historical drought conditions on a regional scale using open-access satellite products. The Drought Severity Index (DSI) has been employed to assess meteorological droughts from 2000 to 2020 and to prepare drought severity maps for the South Asian region. Results from DSI were further compared with the Standardised Precipitation Index (SPI) in Nepal and Bangladesh. The results identified pre-monsoon months as the driest period in South Asian countries experiencing severe to moderate drought.

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“…Subash et al (2012) analyzed the variability and trend of annual and monthly monsoon rainfall for the different districts of Bihar. Rainfall analysis for crop planning was carried out in different regions of the country as reported by Mandal et al (2013), Sunit Das et al (2015, Shonam Sharma and Prasoon Kumar (2017), Meshram et al (2018), Panda A and Sahu N (2019) and Deoli V and Rana (2019). In this context, a similar attempt was made to analyze the rainfall variability in month, season and annual wise for Bidar region of Karnataka.…”

Section: Introductionmentioning

confidence: 99%

Study of Rainfall Distribution Pattern and Its Variability in Bidar Region, Karnataka, India

VISHWANATH¹,

VENKATARAMANA²,

PRABHAKARAN³

2022

Asian.Jr.of.Microbiol.Biotech.Env.Sc.

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A case study has been done to study the rainfall distribution pattern and its variability for the selection of crops and cropping pattern of Bidar region, Karnataka with a predominant pigeon pea and sugarcane cropping systems. The study revealed that the overall mean annual rainfall of Aurad was 846 mm, which was distributed as 673.7 mm, 101.6 mm, 60.2 mm and 10.7 mm in monsoon, post monsoon, summer and winter respectively. The average annual rainfall of Bhalki was 874.7 mm with average rainy days of 51 days. The mean annual rainfall of Humnabad is 797 mm with 27 per cent variability spread over 50 mean rainy days. The analysis of rainfall data indicate that the average annual rainfall for the Basavakalyan was 759 mm spread over with a mean rainy days of 49.9 days. The mean annual rainfall for Bidar is 937.3 mm with coefficient of variation of 22.2 per cent indicated that the annual rainfall was more or less stable over the years. Within the rainy season, August was the highest rainfall contributing month (21.3 per cent) followed by July (19.9 per cent). Rainfall during monsoon season and its variability govern the cropping system of Bidar. There is an ample scope for rain water harvesting from July to September which can be utilized as crop saving irrigation as well as per sowing irrigation for succeeding rabi crops which are generally sown on residual soil moisture.

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Drought analysis in the Tons River Basin, India during 1969-2008

Cited by 19 publications

References 33 publications

Evaluation of spatial and temporal relationships between large‐scale atmospheric oscillations and meteorological drought indexes in Turkey

Evaluation of spatial and temporal relationships between large‐scale atmospheric oscillations and meteorological drought indexes in Turkey

Historical drought and its trend in South Asia: Spatial and temporal analysis 2000-2020

Study of Rainfall Distribution Pattern and Its Variability in Bidar Region, Karnataka, India

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