Stochastic model for drought forecasting for Bundelkhand region in Central India
In the present study, standardized precipitation index (SPI) series at 3-month, 6-month, 9-month, 12-month and 24month time scale has been used to assess the vulnerability of meteorological drought in the Bundelkhand region of Central India. SPI values revealed that the droughts in the region over t...
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| Format: | Article |
| Language: | English |
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Indian Council of Agricultural Research
2014-02-01
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| Series: | The Indian Journal of Agricultural Sciences |
| Subjects: | |
| Online Access: | https://epubs.icar.org.in/index.php/IJAgS/article/view/38045 |
| _version_ | 1827804641753038848 |
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| author | N M ALAM P K MISHRA C JANA PARTHA PRATIM ADHIKARY |
| author_facet | N M ALAM P K MISHRA C JANA PARTHA PRATIM ADHIKARY |
| author_sort | N M ALAM |
| collection | DOAJ |
| description | In the present study, standardized precipitation index (SPI) series at 3-month, 6-month, 9-month, 12-month and 24month time scale has been used to assess the vulnerability of meteorological drought in the Bundelkhand region of Central India. SPI values revealed that the droughts in the region over the study period vary from moderately high to extremely high. Suitable linear stochastic model, viz. seasonal and non-seasonal autoregressive integrated moving average (ARIMA) developed to predict drought at different time scale. The best model was selected based on minimum Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC). Statistical analysis revealed that non-seasonal ARIMA model was appropriate for 3-month SPI series while seasonal ARIMA models have been found promising for SPI series at 6-, 9,12 and 24-month time scale. Parameter estimation step indicates that the estimated model parameters are significantly different from zero. The predicted data using the best ARIMA model were compared to the observed data for model validation purpose in which the predicted data show reasonably good agreement with the actual data. Hence the models were applied to forecast drought in the Bundelhand region up to 3 months advanced with good accuracy. |
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| format | Article |
| id | doaj.art-598fb3c1e3ab4ae991def39febd9ed56 |
| institution | Directory Open Access Journal |
| issn | 0019-5022 2394-3319 |
| language | English |
| last_indexed | 2024-03-11T21:14:52Z |
| publishDate | 2014-02-01 |
| publisher | Indian Council of Agricultural Research |
| record_format | Article |
| series | The Indian Journal of Agricultural Sciences |
| spelling | doaj.art-598fb3c1e3ab4ae991def39febd9ed562023-09-29T04:46:53ZengIndian Council of Agricultural ResearchThe Indian Journal of Agricultural Sciences0019-50222394-33192014-02-0184210.56093/ijas.v84i2.38045Stochastic model for drought forecasting for Bundelkhand region in Central IndiaN M ALAM0P K MISHRA1C JANA2PARTHA PRATIM ADHIKARY3Central Soil and Water Conservation Research and Training Institute, Dehradun, Uttarakhand 248 195Central Soil and Water Conservation Research and Training Institute, Dehradun, Uttarakhand 248 195Central Soil and Water Conservation Research and Training Institute, Dehradun, Uttarakhand 248 195Central Soil and Water Conservation Research and Training Institute, Dehradun, Uttarakhand 248 195In the present study, standardized precipitation index (SPI) series at 3-month, 6-month, 9-month, 12-month and 24month time scale has been used to assess the vulnerability of meteorological drought in the Bundelkhand region of Central India. SPI values revealed that the droughts in the region over the study period vary from moderately high to extremely high. Suitable linear stochastic model, viz. seasonal and non-seasonal autoregressive integrated moving average (ARIMA) developed to predict drought at different time scale. The best model was selected based on minimum Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC). Statistical analysis revealed that non-seasonal ARIMA model was appropriate for 3-month SPI series while seasonal ARIMA models have been found promising for SPI series at 6-, 9,12 and 24-month time scale. Parameter estimation step indicates that the estimated model parameters are significantly different from zero. The predicted data using the best ARIMA model were compared to the observed data for model validation purpose in which the predicted data show reasonably good agreement with the actual data. Hence the models were applied to forecast drought in the Bundelhand region up to 3 months advanced with good accuracy.https://epubs.icar.org.in/index.php/IJAgS/article/view/38045Auto regressive integrated moving averageDroughtLinear stochastic modelSeasonal auto regressive integrated moving averageStandardized Precipitation Index |
| spellingShingle | N M ALAM P K MISHRA C JANA PARTHA PRATIM ADHIKARY Stochastic model for drought forecasting for Bundelkhand region in Central India The Indian Journal of Agricultural Sciences Auto regressive integrated moving average Drought Linear stochastic model Seasonal auto regressive integrated moving average Standardized Precipitation Index |
| title | Stochastic model for drought forecasting for Bundelkhand region in Central India |
| title_full | Stochastic model for drought forecasting for Bundelkhand region in Central India |
| title_fullStr | Stochastic model for drought forecasting for Bundelkhand region in Central India |
| title_full_unstemmed | Stochastic model for drought forecasting for Bundelkhand region in Central India |
| title_short | Stochastic model for drought forecasting for Bundelkhand region in Central India |
| title_sort | stochastic model for drought forecasting for bundelkhand region in central india |
| topic | Auto regressive integrated moving average Drought Linear stochastic model Seasonal auto regressive integrated moving average Standardized Precipitation Index |
| url | https://epubs.icar.org.in/index.php/IJAgS/article/view/38045 |
| work_keys_str_mv | AT nmalam stochasticmodelfordroughtforecastingforbundelkhandregionincentralindia AT pkmishra stochasticmodelfordroughtforecastingforbundelkhandregionincentralindia AT cjana stochasticmodelfordroughtforecastingforbundelkhandregionincentralindia AT parthapratimadhikary stochasticmodelfordroughtforecastingforbundelkhandregionincentralindia |