Statistical modelling of century-long precipitation and temperature extremes in Himachal Pradesh, India: generalized extreme value approach and return level estimation
Abstract Climate variability, particularly, that of temperature and precipitation, has received a great deal of attention worldwide. In the present work, we have carried out a thorough statistical modelling of precipitation and temperature extremes by taking century-long (1901–2002) datasets into co...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ahuja, Vineet [verfasserIn] Pandey, Chhavi P. [verfasserIn] Joshi, Lokesh K. [verfasserIn] Nandan, Hemwati [verfasserIn] Pathak, Parmanand P. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© Indian Association for the Cultivation of Science 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Indian journal of physics - Springer India, 2009, 98(2023), 8 vom: 20. Dez., Seite 2653-2664 |
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| Übergeordnetes Werk: |
volume:98 ; year:2023 ; number:8 ; day:20 ; month:12 ; pages:2653-2664 |
| Links: |
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| DOI / URN: |
10.1007/s12648-023-03011-4 |
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SPR056259506 |
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| 520 | |a Abstract Climate variability, particularly, that of temperature and precipitation, has received a great deal of attention worldwide. In the present work, we have carried out a thorough statistical modelling of precipitation and temperature extremes by taking century-long (1901–2002) datasets into consideration for Himachal Pradesh, India. Globally, the patterns of extreme weather events have changed from longer and hotter heat waves to heavier rains. A fragile ecosphere, climatic change, and the unique geology of Himachal Pradesh have all led to a sharp increase in natural disasters in the hill state over time. The climate of the state varies by altitude. Since precipitation and temperature are the main driving forces behind climate change, quantifying these two variables over a range of return periods is crucial in policymaking for minimizing the potential harm brought on by variations in these atmospheric elements. The statistical modelling of the meteorological data and probabilistic forecasting were done using extreme value theory (EVT), an advanced statistical modelling approach that is widely acknowledged. The generalized extreme value (GEV) analysis approach of EVT is utilized for the probabilistic forecasting of the extremities. The extreme values and their probable occurrence are estimated once every 50, 80, 100, 120, 200, 250, 300, and 500 years, respectively. By estimating the probabilities of extreme events, we can better understand the potential risks and impacts of climate change and develop strategies to manage and mitigate these risks. | ||
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10.1007/s12648-023-03011-4 doi (DE-627)SPR056259506 (SPR)s12648-023-03011-4-e DE-627 ger DE-627 rakwb eng 530 VZ Ahuja, Vineet verfasserin aut Statistical modelling of century-long precipitation and temperature extremes in Himachal Pradesh, India: generalized extreme value approach and return level estimation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Indian Association for the Cultivation of Science 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Climate variability, particularly, that of temperature and precipitation, has received a great deal of attention worldwide. In the present work, we have carried out a thorough statistical modelling of precipitation and temperature extremes by taking century-long (1901–2002) datasets into consideration for Himachal Pradesh, India. Globally, the patterns of extreme weather events have changed from longer and hotter heat waves to heavier rains. A fragile ecosphere, climatic change, and the unique geology of Himachal Pradesh have all led to a sharp increase in natural disasters in the hill state over time. The climate of the state varies by altitude. Since precipitation and temperature are the main driving forces behind climate change, quantifying these two variables over a range of return periods is crucial in policymaking for minimizing the potential harm brought on by variations in these atmospheric elements. The statistical modelling of the meteorological data and probabilistic forecasting were done using extreme value theory (EVT), an advanced statistical modelling approach that is widely acknowledged. The generalized extreme value (GEV) analysis approach of EVT is utilized for the probabilistic forecasting of the extremities. The extreme values and their probable occurrence are estimated once every 50, 80, 100, 120, 200, 250, 300, and 500 years, respectively. By estimating the probabilities of extreme events, we can better understand the potential risks and impacts of climate change and develop strategies to manage and mitigate these risks. Extreme weather (dpeaa)DE-He213 Extreme value theory (dpeaa)DE-He213 Generalized extreme value distribution (dpeaa)DE-He213 Himachal Pradesh (dpeaa)DE-He213 Himalaya (dpeaa)DE-He213 Pandey, Chhavi P. verfasserin aut Joshi, Lokesh K. verfasserin aut Nandan, Hemwati verfasserin aut Pathak, Parmanand P. verfasserin aut Enthalten in Indian journal of physics Springer India, 2009 98(2023), 8 vom: 20. Dez., Seite 2653-2664 (DE-627)606030921 (DE-600)2508021-0 0974-9845 nnns volume:98 year:2023 number:8 day:20 month:12 pages:2653-2664 https://dx.doi.org/10.1007/s12648-023-03011-4 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 98 2023 8 20 12 2653-2664 |
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10.1007/s12648-023-03011-4 doi (DE-627)SPR056259506 (SPR)s12648-023-03011-4-e DE-627 ger DE-627 rakwb eng 530 VZ Ahuja, Vineet verfasserin aut Statistical modelling of century-long precipitation and temperature extremes in Himachal Pradesh, India: generalized extreme value approach and return level estimation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Indian Association for the Cultivation of Science 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Climate variability, particularly, that of temperature and precipitation, has received a great deal of attention worldwide. In the present work, we have carried out a thorough statistical modelling of precipitation and temperature extremes by taking century-long (1901–2002) datasets into consideration for Himachal Pradesh, India. Globally, the patterns of extreme weather events have changed from longer and hotter heat waves to heavier rains. A fragile ecosphere, climatic change, and the unique geology of Himachal Pradesh have all led to a sharp increase in natural disasters in the hill state over time. The climate of the state varies by altitude. Since precipitation and temperature are the main driving forces behind climate change, quantifying these two variables over a range of return periods is crucial in policymaking for minimizing the potential harm brought on by variations in these atmospheric elements. The statistical modelling of the meteorological data and probabilistic forecasting were done using extreme value theory (EVT), an advanced statistical modelling approach that is widely acknowledged. The generalized extreme value (GEV) analysis approach of EVT is utilized for the probabilistic forecasting of the extremities. The extreme values and their probable occurrence are estimated once every 50, 80, 100, 120, 200, 250, 300, and 500 years, respectively. By estimating the probabilities of extreme events, we can better understand the potential risks and impacts of climate change and develop strategies to manage and mitigate these risks. Extreme weather (dpeaa)DE-He213 Extreme value theory (dpeaa)DE-He213 Generalized extreme value distribution (dpeaa)DE-He213 Himachal Pradesh (dpeaa)DE-He213 Himalaya (dpeaa)DE-He213 Pandey, Chhavi P. verfasserin aut Joshi, Lokesh K. verfasserin aut Nandan, Hemwati verfasserin aut Pathak, Parmanand P. verfasserin aut Enthalten in Indian journal of physics Springer India, 2009 98(2023), 8 vom: 20. Dez., Seite 2653-2664 (DE-627)606030921 (DE-600)2508021-0 0974-9845 nnns volume:98 year:2023 number:8 day:20 month:12 pages:2653-2664 https://dx.doi.org/10.1007/s12648-023-03011-4 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 98 2023 8 20 12 2653-2664 |
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10.1007/s12648-023-03011-4 doi (DE-627)SPR056259506 (SPR)s12648-023-03011-4-e DE-627 ger DE-627 rakwb eng 530 VZ Ahuja, Vineet verfasserin aut Statistical modelling of century-long precipitation and temperature extremes in Himachal Pradesh, India: generalized extreme value approach and return level estimation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Indian Association for the Cultivation of Science 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Climate variability, particularly, that of temperature and precipitation, has received a great deal of attention worldwide. In the present work, we have carried out a thorough statistical modelling of precipitation and temperature extremes by taking century-long (1901–2002) datasets into consideration for Himachal Pradesh, India. Globally, the patterns of extreme weather events have changed from longer and hotter heat waves to heavier rains. A fragile ecosphere, climatic change, and the unique geology of Himachal Pradesh have all led to a sharp increase in natural disasters in the hill state over time. The climate of the state varies by altitude. Since precipitation and temperature are the main driving forces behind climate change, quantifying these two variables over a range of return periods is crucial in policymaking for minimizing the potential harm brought on by variations in these atmospheric elements. The statistical modelling of the meteorological data and probabilistic forecasting were done using extreme value theory (EVT), an advanced statistical modelling approach that is widely acknowledged. The generalized extreme value (GEV) analysis approach of EVT is utilized for the probabilistic forecasting of the extremities. The extreme values and their probable occurrence are estimated once every 50, 80, 100, 120, 200, 250, 300, and 500 years, respectively. By estimating the probabilities of extreme events, we can better understand the potential risks and impacts of climate change and develop strategies to manage and mitigate these risks. Extreme weather (dpeaa)DE-He213 Extreme value theory (dpeaa)DE-He213 Generalized extreme value distribution (dpeaa)DE-He213 Himachal Pradesh (dpeaa)DE-He213 Himalaya (dpeaa)DE-He213 Pandey, Chhavi P. verfasserin aut Joshi, Lokesh K. verfasserin aut Nandan, Hemwati verfasserin aut Pathak, Parmanand P. verfasserin aut Enthalten in Indian journal of physics Springer India, 2009 98(2023), 8 vom: 20. Dez., Seite 2653-2664 (DE-627)606030921 (DE-600)2508021-0 0974-9845 nnns volume:98 year:2023 number:8 day:20 month:12 pages:2653-2664 https://dx.doi.org/10.1007/s12648-023-03011-4 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 98 2023 8 20 12 2653-2664 |
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10.1007/s12648-023-03011-4 doi (DE-627)SPR056259506 (SPR)s12648-023-03011-4-e DE-627 ger DE-627 rakwb eng 530 VZ Ahuja, Vineet verfasserin aut Statistical modelling of century-long precipitation and temperature extremes in Himachal Pradesh, India: generalized extreme value approach and return level estimation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Indian Association for the Cultivation of Science 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Climate variability, particularly, that of temperature and precipitation, has received a great deal of attention worldwide. In the present work, we have carried out a thorough statistical modelling of precipitation and temperature extremes by taking century-long (1901–2002) datasets into consideration for Himachal Pradesh, India. Globally, the patterns of extreme weather events have changed from longer and hotter heat waves to heavier rains. A fragile ecosphere, climatic change, and the unique geology of Himachal Pradesh have all led to a sharp increase in natural disasters in the hill state over time. The climate of the state varies by altitude. Since precipitation and temperature are the main driving forces behind climate change, quantifying these two variables over a range of return periods is crucial in policymaking for minimizing the potential harm brought on by variations in these atmospheric elements. The statistical modelling of the meteorological data and probabilistic forecasting were done using extreme value theory (EVT), an advanced statistical modelling approach that is widely acknowledged. The generalized extreme value (GEV) analysis approach of EVT is utilized for the probabilistic forecasting of the extremities. The extreme values and their probable occurrence are estimated once every 50, 80, 100, 120, 200, 250, 300, and 500 years, respectively. By estimating the probabilities of extreme events, we can better understand the potential risks and impacts of climate change and develop strategies to manage and mitigate these risks. Extreme weather (dpeaa)DE-He213 Extreme value theory (dpeaa)DE-He213 Generalized extreme value distribution (dpeaa)DE-He213 Himachal Pradesh (dpeaa)DE-He213 Himalaya (dpeaa)DE-He213 Pandey, Chhavi P. verfasserin aut Joshi, Lokesh K. verfasserin aut Nandan, Hemwati verfasserin aut Pathak, Parmanand P. verfasserin aut Enthalten in Indian journal of physics Springer India, 2009 98(2023), 8 vom: 20. Dez., Seite 2653-2664 (DE-627)606030921 (DE-600)2508021-0 0974-9845 nnns volume:98 year:2023 number:8 day:20 month:12 pages:2653-2664 https://dx.doi.org/10.1007/s12648-023-03011-4 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 98 2023 8 20 12 2653-2664 |
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10.1007/s12648-023-03011-4 doi (DE-627)SPR056259506 (SPR)s12648-023-03011-4-e DE-627 ger DE-627 rakwb eng 530 VZ Ahuja, Vineet verfasserin aut Statistical modelling of century-long precipitation and temperature extremes in Himachal Pradesh, India: generalized extreme value approach and return level estimation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Indian Association for the Cultivation of Science 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Climate variability, particularly, that of temperature and precipitation, has received a great deal of attention worldwide. In the present work, we have carried out a thorough statistical modelling of precipitation and temperature extremes by taking century-long (1901–2002) datasets into consideration for Himachal Pradesh, India. Globally, the patterns of extreme weather events have changed from longer and hotter heat waves to heavier rains. A fragile ecosphere, climatic change, and the unique geology of Himachal Pradesh have all led to a sharp increase in natural disasters in the hill state over time. The climate of the state varies by altitude. Since precipitation and temperature are the main driving forces behind climate change, quantifying these two variables over a range of return periods is crucial in policymaking for minimizing the potential harm brought on by variations in these atmospheric elements. The statistical modelling of the meteorological data and probabilistic forecasting were done using extreme value theory (EVT), an advanced statistical modelling approach that is widely acknowledged. The generalized extreme value (GEV) analysis approach of EVT is utilized for the probabilistic forecasting of the extremities. The extreme values and their probable occurrence are estimated once every 50, 80, 100, 120, 200, 250, 300, and 500 years, respectively. By estimating the probabilities of extreme events, we can better understand the potential risks and impacts of climate change and develop strategies to manage and mitigate these risks. Extreme weather (dpeaa)DE-He213 Extreme value theory (dpeaa)DE-He213 Generalized extreme value distribution (dpeaa)DE-He213 Himachal Pradesh (dpeaa)DE-He213 Himalaya (dpeaa)DE-He213 Pandey, Chhavi P. verfasserin aut Joshi, Lokesh K. verfasserin aut Nandan, Hemwati verfasserin aut Pathak, Parmanand P. verfasserin aut Enthalten in Indian journal of physics Springer India, 2009 98(2023), 8 vom: 20. Dez., Seite 2653-2664 (DE-627)606030921 (DE-600)2508021-0 0974-9845 nnns volume:98 year:2023 number:8 day:20 month:12 pages:2653-2664 https://dx.doi.org/10.1007/s12648-023-03011-4 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 98 2023 8 20 12 2653-2664 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Climate variability, particularly, that of temperature and precipitation, has received a great deal of attention worldwide. In the present work, we have carried out a thorough statistical modelling of precipitation and temperature extremes by taking century-long (1901–2002) datasets into consideration for Himachal Pradesh, India. Globally, the patterns of extreme weather events have changed from longer and hotter heat waves to heavier rains. A fragile ecosphere, climatic change, and the unique geology of Himachal Pradesh have all led to a sharp increase in natural disasters in the hill state over time. The climate of the state varies by altitude. Since precipitation and temperature are the main driving forces behind climate change, quantifying these two variables over a range of return periods is crucial in policymaking for minimizing the potential harm brought on by variations in these atmospheric elements. The statistical modelling of the meteorological data and probabilistic forecasting were done using extreme value theory (EVT), an advanced statistical modelling approach that is widely acknowledged. The generalized extreme value (GEV) analysis approach of EVT is utilized for the probabilistic forecasting of the extremities. The extreme values and their probable occurrence are estimated once every 50, 80, 100, 120, 200, 250, 300, and 500 years, respectively. 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Ahuja, Vineet |
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Ahuja, Vineet ddc 530 misc Extreme weather misc Extreme value theory misc Generalized extreme value distribution misc Himachal Pradesh misc Himalaya Statistical modelling of century-long precipitation and temperature extremes in Himachal Pradesh, India: generalized extreme value approach and return level estimation |
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530 VZ Statistical modelling of century-long precipitation and temperature extremes in Himachal Pradesh, India: generalized extreme value approach and return level estimation Extreme weather (dpeaa)DE-He213 Extreme value theory (dpeaa)DE-He213 Generalized extreme value distribution (dpeaa)DE-He213 Himachal Pradesh (dpeaa)DE-He213 Himalaya (dpeaa)DE-He213 |
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ddc 530 misc Extreme weather misc Extreme value theory misc Generalized extreme value distribution misc Himachal Pradesh misc Himalaya |
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Statistical modelling of century-long precipitation and temperature extremes in Himachal Pradesh, India: generalized extreme value approach and return level estimation |
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Statistical modelling of century-long precipitation and temperature extremes in Himachal Pradesh, India: generalized extreme value approach and return level estimation |
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Ahuja, Vineet Pandey, Chhavi P. Joshi, Lokesh K. Nandan, Hemwati Pathak, Parmanand P. |
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statistical modelling of century-long precipitation and temperature extremes in himachal pradesh, india: generalized extreme value approach and return level estimation |
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Statistical modelling of century-long precipitation and temperature extremes in Himachal Pradesh, India: generalized extreme value approach and return level estimation |
| abstract |
Abstract Climate variability, particularly, that of temperature and precipitation, has received a great deal of attention worldwide. In the present work, we have carried out a thorough statistical modelling of precipitation and temperature extremes by taking century-long (1901–2002) datasets into consideration for Himachal Pradesh, India. Globally, the patterns of extreme weather events have changed from longer and hotter heat waves to heavier rains. A fragile ecosphere, climatic change, and the unique geology of Himachal Pradesh have all led to a sharp increase in natural disasters in the hill state over time. The climate of the state varies by altitude. Since precipitation and temperature are the main driving forces behind climate change, quantifying these two variables over a range of return periods is crucial in policymaking for minimizing the potential harm brought on by variations in these atmospheric elements. The statistical modelling of the meteorological data and probabilistic forecasting were done using extreme value theory (EVT), an advanced statistical modelling approach that is widely acknowledged. The generalized extreme value (GEV) analysis approach of EVT is utilized for the probabilistic forecasting of the extremities. The extreme values and their probable occurrence are estimated once every 50, 80, 100, 120, 200, 250, 300, and 500 years, respectively. By estimating the probabilities of extreme events, we can better understand the potential risks and impacts of climate change and develop strategies to manage and mitigate these risks. © Indian Association for the Cultivation of Science 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Climate variability, particularly, that of temperature and precipitation, has received a great deal of attention worldwide. In the present work, we have carried out a thorough statistical modelling of precipitation and temperature extremes by taking century-long (1901–2002) datasets into consideration for Himachal Pradesh, India. Globally, the patterns of extreme weather events have changed from longer and hotter heat waves to heavier rains. A fragile ecosphere, climatic change, and the unique geology of Himachal Pradesh have all led to a sharp increase in natural disasters in the hill state over time. The climate of the state varies by altitude. Since precipitation and temperature are the main driving forces behind climate change, quantifying these two variables over a range of return periods is crucial in policymaking for minimizing the potential harm brought on by variations in these atmospheric elements. The statistical modelling of the meteorological data and probabilistic forecasting were done using extreme value theory (EVT), an advanced statistical modelling approach that is widely acknowledged. The generalized extreme value (GEV) analysis approach of EVT is utilized for the probabilistic forecasting of the extremities. The extreme values and their probable occurrence are estimated once every 50, 80, 100, 120, 200, 250, 300, and 500 years, respectively. By estimating the probabilities of extreme events, we can better understand the potential risks and impacts of climate change and develop strategies to manage and mitigate these risks. © Indian Association for the Cultivation of Science 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Climate variability, particularly, that of temperature and precipitation, has received a great deal of attention worldwide. In the present work, we have carried out a thorough statistical modelling of precipitation and temperature extremes by taking century-long (1901–2002) datasets into consideration for Himachal Pradesh, India. Globally, the patterns of extreme weather events have changed from longer and hotter heat waves to heavier rains. A fragile ecosphere, climatic change, and the unique geology of Himachal Pradesh have all led to a sharp increase in natural disasters in the hill state over time. The climate of the state varies by altitude. Since precipitation and temperature are the main driving forces behind climate change, quantifying these two variables over a range of return periods is crucial in policymaking for minimizing the potential harm brought on by variations in these atmospheric elements. The statistical modelling of the meteorological data and probabilistic forecasting were done using extreme value theory (EVT), an advanced statistical modelling approach that is widely acknowledged. The generalized extreme value (GEV) analysis approach of EVT is utilized for the probabilistic forecasting of the extremities. The extreme values and their probable occurrence are estimated once every 50, 80, 100, 120, 200, 250, 300, and 500 years, respectively. By estimating the probabilities of extreme events, we can better understand the potential risks and impacts of climate change and develop strategies to manage and mitigate these risks. © Indian Association for the Cultivation of Science 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
Statistical modelling of century-long precipitation and temperature extremes in Himachal Pradesh, India: generalized extreme value approach and return level estimation |
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https://dx.doi.org/10.1007/s12648-023-03011-4 |
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Pandey, Chhavi P. Joshi, Lokesh K. Nandan, Hemwati Pathak, Parmanand P. |
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Pandey, Chhavi P. Joshi, Lokesh K. Nandan, Hemwati Pathak, Parmanand P. |
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| score |
7.3991566 |