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Measurement, analysis and modeling of extreme precipitation events linked to floods is vital in understanding changing climate impacts and variability. This book provides methods for assessment of the trends in these events and their impacts. It also provides a basis to develop procedures and guidelines for climate-adaptive hydrologic engineering. Academic researchers in the fields of hydrology, climate change, meteorology, environmental policy and risk assessment, and professionals and policy-makers working in hazard mitigation, water resources engineering and climate adaptation will find this an invaluable resource. This volume is the first in a collection of four books on flood disaster management theory and practice within the context of anthropogenic climate change. The others are: Floods in a Changing Climate: Hydrological Modeling by P. P. Mujumdar and D. Nagesh Kumar, Floods in a Changing Climate: Inundation Modeling by Giuliano Di Baldassarre and Floods in a Changing Climate: Risk Management by Slodoban Simonović.

Understanding the influences of Atlantic multidecadal oscillation (AMO) and El Niño southern oscillation (ENSO) on regional precipitation extremes and characteristics in the state of Florida is the focus of this study. Exhaustive evaluations of individual and combined influences of these oscillations using, descriptive indices-based assessment of statistically significant changes in rainfall characteristics, identification of spatially varying influences of oscillations on dry and wet spell transition states, antecedent precipitation prior to extreme events, intraevent temporal distribution of precipitation and changes in temporal occurrences of extremes including dry/wet cycles are carried out. Rain gage and gridded precipitation data analysis using parametric hypothesis tests confirm statistically significant changes in the precipitation characteristics from one phase to another of each oscillation and also in coupled phases. Spatially nonuniform and uniform influences of AMO and ENSO, respectively, on precipitation are evident. AMO influences vary in peninsular and continental parts of Florida and the warm (cool) phase of AMO contributes to increased precipitation extremes during wet (dry) season. The influence of ENSO is confined to dry season with El Niño (La Niña) contributing to increase (decrease) in extremes and total precipitation. Wetter antecedent conditions preceding daily extremes are dominant in AMO warm phase compared to the cool and are likely to impact design floods in the region. AMO influence on dry season precipitation extremes is noted for ENSO neutral years. The two oscillations in different phases modulate each other with seasonal and spatially varying impacts and implications on flood control and water supply in the region.

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Deterministic and stochastic weighting methods are the most frequently used methods for estimating missing rainfall values. These methods may not always provide accurate estimates due to their inability to completely characterize the spatial and temporal variability of rainfall.A new association rule mining (ARM) based spatial interpolation approach is proposed, developed and investigated in the current study to estimate missing precipitation values at a gauging station. As an integrated approach this methodology combines the power of data mining techniques and spatial interpolation approaches. Data mining concepts are used to extract and formulate rules based on spatial and temporal associations among observed precipitation data series. The rules are then used to improve the precipitation estimates obtained from spatial interpolation methods. A stochastic spatial interpolation technique and three deterministic weighting methods are used as interpolation methods in the current study. Historical daily precipitation data obtained from 15 rain gauging stations from a temperate climatic region (Kentucky, USA) are used to test this approach and derive conclusions about its efficacy for estimating missing precipitation data. Results suggest that the use of association rule mining in conjunction with a spatial interpolation technique can improve the precipitation estimates.

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