Use of meteorological data for identification of drought

Karinki, Ravi Kiran; Sahoo, Sanat Nalini

doi:10.1080/09715010.2018.1564075

Cited by 7 publications

(4 citation statements)

References 31 publications

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“…According to Kumar et al [56], the distinct intra-seasonal variability of 3-month SPI and IDMI was seen in 2016, which might be ascribed to the receipt of around 62.0% lower rainfall than the mean rainfall of Tamil Nadu's northeast monsoon season. The dry condition might indicate stressed vegetation due to a lack of water [71]. In an investigation, the SPI values plummeted from 0.03 to −0.15, indicating an extremely dry condition triggered by insufficient precipitation [81].…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, 2015 was observed to have mild drought in the western, high altitude, and hilly and north western zones of Tamil Nadu, and the remaining zones were classified under no drought. The same methodology to identify the drought years was followed by Karinki and Sahoo [71]. Furthermore, the rainfall deviation map was generated for each district to know the district-wise drought severity condition.…”

Section: Assessment Of Meteorological Drought Based On Rainfall Depar...mentioning

confidence: 99%

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Evaluation of SPI and Rainfall Departure Based on Multi-Satellite Precipitation Products for Meteorological Drought Monitoring in Tamil Nadu

Pazhanivelan

Vellingiri

Samykannu

et al. 2023

Water

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The prevalence of the frequent water stress conditions at present was found to be more frequent due to increased weather anomalies and climate change scenarios, among other reasons. Periodic drought assessment and subsequent management are essential in effectively utilizing and managing water resources. For effective drought monitoring/assessment, satellite-based precipitation products offer more reliable rainfall estimates with higher accuracy and spatial coverage than conventional rain gauge data. The present study on satellite-based drought monitoring and reliability evaluation was conducted using four high-resolution precipitation products, i.e., IMERGH, TRMM, CHIRPS, and PERSIANN, during the northeast monsoon season of 2015, 2016, and 2017 in the state of Tamil Nadu, India. These four precipitation products were evaluated for accuracy and confidence level by assessing the meteorological drought using standard precipitation index (SPI) and by comparing the results with automatic weather station (AWS) and rain gauge network data-derived SPI. Furthermore, considering the limited number of precipitation products available, the study also indirectly addressed the demanding need for high-resolution precipitation products with consistent temporal resolution. Among different products, IMERGH and TRMM rainfall estimates were found equipollent with the minimum range predictions, i.e., 149.8, 32.07, 80.05 mm and 144.31, 34.40, 75.01 mm, respectively, during NEM of 2015, 2016, and 2017. The rainfall data from CHIRPS were commensurable in the maximum range of 1564, 421, and 723 mm in these three consequent years (2015 to 2017) compared to AWS data. CHIRPS data recorded a higher per cent of agreement (>85%) compared to AWS data than other precipitation products in all the agro-climatic zones of Tamil Nadu. The SPI values were positive > 1.0 during 2015 and negative < −0.99 for 2016 and 2017, indicating normal/wet and dry conditions in the study area, respectively. This study highlighted discrepancies in the capability of the precipitation products IMERGH and TRMM estimates for low rainfall conditions and CHIRPS estimates in high rainfall regimes.

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Section: Discussionmentioning

confidence: 99%

Section: Assessment Of Meteorological Drought Based On Rainfall Depar...mentioning

confidence: 99%

Evaluation of SPI and Rainfall Departure Based on Multi-Satellite Precipitation Products for Meteorological Drought Monitoring in Tamil Nadu

Pazhanivelan

Vellingiri

Samykannu

et al. 2023

Water

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“…The Percent of Normal (PN) was defined as a percent of the rainfall to the normal rainfall where the normal rainfall was commonly determined from a long term mean or median rainfall [20]. The calculation for PN could be calculated as [21]:…”

Section: Percent Of Normal Index (Pn)mentioning

confidence: 99%

“…The resulted indexes of PN then must be transformed into the standard normal distribution to make similar to the numerical format of SPI. The drought level of PN Index is grouped into normal conditions (>80%), slightly drought (70%-80%), moderately drought (55%-70%), severely drought (40% -55%), and extremely drought (< 40%) [21].…”

Section: Percent Of Normal Index (Pn)mentioning

confidence: 99%

Meteorological Drought and its Relationship with Southern Oscillation Index (SOI)

Harisuseno

2020

Civ Eng J

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Drought monitoring, including its severity, spatial, and duration is essential to enhance resilience towards drought, particularly for overcoming drought risk management and mitigation plan. The present study has an objective to examine the suitability of the Standardized Precipitation Index (SPI) and Percent of Normal Index (PN) on assessing drought event by analyzing their relationship with the Southern Oscillation Index (SOI). The monthly rainfall data over twenty years of the observation period were used as a basis for data input in the drought index calculation. The statistical association analyses, included the Pearson Correlation (r), Kendal tau (τ), and Spearman rho (rs) used to assess the relationship between the monthly drought indexes and SOI. The present study confirmed that the SPI showed a more consistent and regular pattern relationship with SOI basis which was indicated by a moderately high determination coefficient (R2) of 0.74 and the magnitude of r, τ, and rs that were of 0.861, 0.736, and 0.896, respectively. Accordingly, the SPI showed better compatibility than the PN for estimating drought characteristics. The study also revealed that the SOI data could be used as a variable to determine the reliability of drought index results.

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Monitoring of agricultural drought in semi-arid ecosystem of Peninsular India through indices derived from time-series CHIRPS and MODIS datasets

Sandeep

Reddy

Jegankumar

et al. 2021

Ecological Indicators

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Use of meteorological data for identification of drought

Cited by 7 publications

References 31 publications

Evaluation of SPI and Rainfall Departure Based on Multi-Satellite Precipitation Products for Meteorological Drought Monitoring in Tamil Nadu

Evaluation of SPI and Rainfall Departure Based on Multi-Satellite Precipitation Products for Meteorological Drought Monitoring in Tamil Nadu

Meteorological Drought and its Relationship with Southern Oscillation Index (SOI)

Monitoring of agricultural drought in semi-arid ecosystem of Peninsular India through indices derived from time-series CHIRPS and MODIS datasets

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