Statistical Analysis and Stochastic Modelling of Hydrological Extremes
Tabari, Hossein
Statistical Analysis and Stochastic Modelling of Hydrological Extremes - MDPI - Multidisciplinary Digital Publishing Institute 2019 - 1 electronic resource (294 p.)
Open Access
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.
Creative Commons
English
books978-3-03921-665-9 9783039216659 9783039216642
10.3390/books978-3-03921-665-9 doi
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
Statistical Analysis and Stochastic Modelling of Hydrological Extremes - MDPI - Multidisciplinary Digital Publishing Institute 2019 - 1 electronic resource (294 p.)
Open Access
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.
Creative Commons
English
books978-3-03921-665-9 9783039216659 9783039216642
10.3390/books978-3-03921-665-9 doi
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