Spatial and temporal analysis of drought in Manjalar sub-basin of Vaigai in Tamil Nadu using standardized precipitation index

Janapriya, S.; Bosu, S. Santhana; Kannan, Balaji; Kokilavani, S.

doi:10.31018/jans.v8i2.845

Cited by 4 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prediction of agricultural drought can be replicated best by calculating the SPI value based on 2-3 months time scale [23] - [25]. In this study, two threshold limits for Agricultural drought were estimated, one by considering the cumulative…”

Section: Threshold Limit For Calculation Of Agricultural Droughtmentioning

confidence: 99%

Spatio-Temporal Assessment of Drought using Effective Drought Index (EDI) and Standardized Precipitation Index (SPI) During Monsoon Months in Cuttack District, Odisha, India

Mishra*,

Choudhary,

Padhi*

et al. 2019

IJRTE

View full text Add to dashboard Cite

were also estimated for agricultural drought during monsoon months in this study. The threshold limit for agricultural drought varied from 553.9 to 706.3, 516.2 to 722.8, 614.1 to 687.4 and 586.0 to 702. mm based on EDI 3 Aug, SPI 3 Aug, EDI 3 Sep and SPI 3 Sep value respectively. This drought analysis will be helpful for implementing various strategies of water management and crop planning for different blocks of Cutttack District.

show abstract

Section: Threshold Limit For Calculation Of Agricultural Droughtmentioning

confidence: 99%

Spatio-Temporal Assessment of Drought using Effective Drought Index (EDI) and Standardized Precipitation Index (SPI) During Monsoon Months in Cuttack District, Odisha, India

Mishra*,

Choudhary,

Padhi*

et al. 2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…Drought assessment is usually performed using drought indices, which can allow the transformation of large amounts of data into quantitative information required for several applications, such as precise forecasting of drought, reporting drought levels, and planning contingency (Janapriya et al, 2016). Over 150 drought indices have been introduced by various researchers worldwide (Alahacoon & Edirisinghe, 2022).…”

Section: Introductionmentioning

confidence: 99%

Drought characterization: A systematic literature review

Choukri,

Naimi,

Chikhaoui

2023

Sains Tanah J. Soil Sci. Agroclimatology

View full text Add to dashboard Cite

<p>This study examined the worsening severity of global droughts caused by climate change. However, the multiple definitions and varied range of drought indices pose challenges in effectively monitoring and assessing the prevalence and severity of droughts. This study aims to give a comprehensive overview of the various drought definitions found in the literature and how they have evolved based on their applications. Specifically, the focus was to shed light on the dynamic nature of drought characterization and offer insights into the factors that shaped its conceptualization over time. Within this context, this study explored three primary categories of drought indices: climatic, remote sensing, and composite. Each category was discussed in relation to its utility in specific fields, such as meteorological, agricultural, and hydrological drought assessments, along with an analysis of their strengths and limitations. Furthermore, this study presents modified meteorological drought indices that have been adapted to better monitor agricultural droughts. Additionally, the authors used geographic information systems to create a map showing the distribution of drought-related publications globally over the past decade. The findings showed that countries with arid and semi-arid climates are more actively involved in drought research, highlighting their particular interest and concern regarding the subject matter. The implications of this study emphasize the urgent need for immediate and coordinated efforts to address the escalating issue of droughts caused by climate change. By improving monitoring and assessment methods and focusing on tailored strategies in vulnerable regions, it is possible to mitigate the far-reaching consequences of drought and to build more resilient communities and ecosystems.</p>

show abstract

“…Accordingly, this study recommended that ARIMA models can be very useful tools for drought forecasting that can help water resources managers and planners to take precautions considering the severity of drought in advance. Janapriya et al (2016) conducted a spatial and temporal analysis of drought in Manjalur sub basin of Vaigai in Tamil Nadu using SPI. The SPI was calculated by using monthly mean precipitation data from 1982 to 2012.…”

Section: Introductionmentioning

confidence: 99%

Drought Forecast Using Arima Model for the Standardized Precipitation Index (Spi) and Precipitation Data

Dakak¹,

Saleh²,

Elnikhely³

2021

International Journal of Civil Engineering and Technology (Ijciet)

View full text Add to dashboard Cite

Drought forecasting is considering an important tool to help the decision makers. Standardized Precipitation Index (SPI) is a tool, which was primarily developed to identify meteorological drought and wet events by using only series of monthly rainfall. The autoregressive integrated moving average (ARIMA) models were developed to fit and forecasting both the SPI series and precipitation data. As the land station data at the selected stations in Ethiopia is not available/ cost expensive after the year 2010, therefore the satellite image data, which is available and less costly, is used after correction using the available land data. The land station data for the stations which have more than 30 years land data recorded are used to correct the deviation in the satellite image data. After comparing the land station data and the satellite image data three stations are selected. The selected stations have overlap between both satellite and land stations data for more than 16 years. SPSS program was used to analyses and forecasting the data. As a result, it is concluded that forecasting using SPSS program is recommending for both drought and precipitation using ARIMA model.

show abstract

Spatial and temporal analysis of drought in Manjalar sub-basin of Vaigai in Tamil Nadu using standardized precipitation index

Cited by 4 publications

References 7 publications

Spatio-Temporal Assessment of Drought using Effective Drought Index (EDI) and Standardized Precipitation Index (SPI) During Monsoon Months in Cuttack District, Odisha, India

Spatio-Temporal Assessment of Drought using Effective Drought Index (EDI) and Standardized Precipitation Index (SPI) During Monsoon Months in Cuttack District, Odisha, India

Drought characterization: A systematic literature review

Drought Forecast Using Arima Model for the Standardized Precipitation Index (Spi) and Precipitation Data

Contact Info

Product

Resources

About