Statistical Analysis and Stochastic Modelling of Hydrological Extremes
Hydrological extremes have become a major concern because of their devastating consequences and their increased risk as a result of climate change and the growing concentration of people and infrastructure in high-risk zones. The analysis of hydrological extremes is challenging due to their rarity a...
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| Ձևաչափ: | Online |
| Լեզու: | անգլերեն |
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MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Խորագրեր: | |
| Առցանց հասանելիություն: | 42709 |
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Չկան պիտակներ, Եղեք առաջինը, ով նշում է այս գրառումը!
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| _version_ | 1869529561361809408 |
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| author | Tabari, Hossein |
| author_browse | Tabari, Hossein |
| author_facet | Tabari, Hossein |
| author_sort | Tabari, Hossein |
| collection | Directory of Open Access Books |
| description | Hydrological extremes have become a major concern because of their devastating consequences and their increased risk as a result of climate change and the growing concentration of people and infrastructure in high-risk zones. The analysis of hydrological extremes is challenging due to their rarity and small sample size, and the interconnections between different types of extremes and becomes further complicated by the untrustworthy representation of meso-scale processes involved in extreme events by coarse spatial and temporal scale models as well as biased or missing observations due to technical difficulties during extreme conditions. The complexity of analyzing hydrological extremes calls for robust statistical methods for the treatment of such events. This Special Issue is motivated by the need to apply and develop innovative stochastic and statistical approaches to analyze hydrological extremes under current and future climate conditions. The papers of this Special Issue focus on six topics associated with hydrological extremes: Historical changes in hydrological extremes; Projected changes in hydrological extremes; Downscaling of hydrological extremes; Early warning and forecasting systems for drought and flood; Interconnections of hydrological extremes; Applicability of satellite data for hydrological studies. |
| format | Online |
| id | doab-20.500.12854ir-59997 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-599972024-04-09T11:42:04Z Statistical Analysis and Stochastic Modelling of Hydrological Extremes Tabari, Hossein QC851-999 Q1-390 artificial neural network downscaling innovative methods reservoir inflow forecasting simulation extreme events climate variability sparse monitoring network weighted mean analogue sampling errors precipitation drought indices discrete wavelet SWSI hyetograph trends climate change SIAP Kabul river basin Hurst exponent extreme rainfall evolutionary strategy the Cauca River hydrological drought global warming least square support vector regression polynomial normal transform TRMM satellite data Fiji heavy storm flood regime compound events random forest uncertainty seasonal climate forecast INDC pledge Pakistan wavelet artificial neural network HBV model temperature APCC Multi-Model Ensemble meteorological drought flow regime high resolution rainfall clausius-clapeyron scaling statistical downscaling ENSO forecasting variation analogue machine learning extreme rainfall analysis hydrological extremes multivariate modeling monsoon non-stationary support vector machine ANN model stretched Gaussian distribution drought prediction non-normality statistical analysis extreme precipitation exposure drought analysis extreme value theory streamflow flood management thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RB Earth sciences::RBP Meteorology and climatology Hydrological extremes have become a major concern because of their devastating consequences and their increased risk as a result of climate change and the growing concentration of people and infrastructure in high-risk zones. The analysis of hydrological extremes is challenging due to their rarity and small sample size, and the interconnections between different types of extremes and becomes further complicated by the untrustworthy representation of meso-scale processes involved in extreme events by coarse spatial and temporal scale models as well as biased or missing observations due to technical difficulties during extreme conditions. The complexity of analyzing hydrological extremes calls for robust statistical methods for the treatment of such events. This Special Issue is motivated by the need to apply and develop innovative stochastic and statistical approaches to analyze hydrological extremes under current and future climate conditions. The papers of this Special Issue focus on six topics associated with hydrological extremes: Historical changes in hydrological extremes; Projected changes in hydrological extremes; Downscaling of hydrological extremes; Early warning and forecasting systems for drought and flood; Interconnections of hydrological extremes; Applicability of satellite data for hydrological studies. 2021-02-12T04:30:55Z 2021-02-12T04:30:55Z 2019-12-09 16:10:12 2019 book 42709 9783039216659 9783039216642 https://directory.doabooks.org/handle/20.500.12854/59997 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/1751 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03921-665-9 10.3390/books978-3-03921-665-9 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039216659 9783039216642 294 open access |
| spellingShingle | QC851-999 Q1-390 artificial neural network downscaling innovative methods reservoir inflow forecasting simulation extreme events climate variability sparse monitoring network weighted mean analogue sampling errors precipitation drought indices discrete wavelet SWSI hyetograph trends climate change SIAP Kabul river basin Hurst exponent extreme rainfall evolutionary strategy the Cauca River hydrological drought global warming least square support vector regression polynomial normal transform TRMM satellite data Fiji heavy storm flood regime compound events random forest uncertainty seasonal climate forecast INDC pledge Pakistan wavelet artificial neural network HBV model temperature APCC Multi-Model Ensemble meteorological drought flow regime high resolution rainfall clausius-clapeyron scaling statistical downscaling ENSO forecasting variation analogue machine learning extreme rainfall analysis hydrological extremes multivariate modeling monsoon non-stationary support vector machine ANN model stretched Gaussian distribution drought prediction non-normality statistical analysis extreme precipitation exposure drought analysis extreme value theory streamflow flood management thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RB Earth sciences::RBP Meteorology and climatology Tabari, Hossein Statistical Analysis and Stochastic Modelling of Hydrological Extremes |
| title | Statistical Analysis and Stochastic Modelling of Hydrological Extremes |
| title_full | Statistical Analysis and Stochastic Modelling of Hydrological Extremes |
| title_fullStr | Statistical Analysis and Stochastic Modelling of Hydrological Extremes |
| title_full_unstemmed | Statistical Analysis and Stochastic Modelling of Hydrological Extremes |
| title_short | Statistical Analysis and Stochastic Modelling of Hydrological Extremes |
| title_sort | statistical analysis and stochastic modelling of hydrological extremes |
| topic | QC851-999 Q1-390 artificial neural network downscaling innovative methods reservoir inflow forecasting simulation extreme events climate variability sparse monitoring network weighted mean analogue sampling errors precipitation drought indices discrete wavelet SWSI hyetograph trends climate change SIAP Kabul river basin Hurst exponent extreme rainfall evolutionary strategy the Cauca River hydrological drought global warming least square support vector regression polynomial normal transform TRMM satellite data Fiji heavy storm flood regime compound events random forest uncertainty seasonal climate forecast INDC pledge Pakistan wavelet artificial neural network HBV model temperature APCC Multi-Model Ensemble meteorological drought flow regime high resolution rainfall clausius-clapeyron scaling statistical downscaling ENSO forecasting variation analogue machine learning extreme rainfall analysis hydrological extremes multivariate modeling monsoon non-stationary support vector machine ANN model stretched Gaussian distribution drought prediction non-normality statistical analysis extreme precipitation exposure drought analysis extreme value theory streamflow flood management thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RB Earth sciences::RBP Meteorology and climatology |
| topic_facet | QC851-999 Q1-390 artificial neural network downscaling innovative methods reservoir inflow forecasting simulation extreme events climate variability sparse monitoring network weighted mean analogue sampling errors precipitation drought indices discrete wavelet SWSI hyetograph trends climate change SIAP Kabul river basin Hurst exponent extreme rainfall evolutionary strategy the Cauca River hydrological drought global warming least square support vector regression polynomial normal transform TRMM satellite data Fiji heavy storm flood regime compound events random forest uncertainty seasonal climate forecast INDC pledge Pakistan wavelet artificial neural network HBV model temperature APCC Multi-Model Ensemble meteorological drought flow regime high resolution rainfall clausius-clapeyron scaling statistical downscaling ENSO forecasting variation analogue machine learning extreme rainfall analysis hydrological extremes multivariate modeling monsoon non-stationary support vector machine ANN model stretched Gaussian distribution drought prediction non-normality statistical analysis extreme precipitation exposure drought analysis extreme value theory streamflow flood management thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RB Earth sciences::RBP Meteorology and climatology |
| url | 42709 |
| work_keys_str_mv | AT tabarihossein statisticalanalysisandstochasticmodellingofhydrologicalextremes |