No description available.

This study focuses on two main broad areas of active research on climate: climate variability and climate change and their implications on regional precipitation characteristics. All the analysis is carried out for a climate change-sensitive region, the state of Florida, USA. The focus of the climate variability analysis is to evaluate the influence of individual and coupled phases (cool and warm) of Atlantic multidecadal oscillation (AMO) and El Niäno southern oscillation (ENSO) on regional precipitation characteristics. The two oscillations in cool and warm phases modulate each other which have implications on flood control and water supply in the region. Extreme precipitation indices, temporal distribution of rainfall within extreme storm events, dry and wet spell transitions and antecedent conditions preceding extremes are evaluated. Kernel density estimates using Gaussian kernel for distribution-free comparative analysis and bootstrap sampling-based confidence intervals are used to compare warm and cool phases of different lengths. Depth-duration-frequency (DDF) curves are also developed using generalized extreme value (GEV) distributions characterizing the extremes. ... This study also introduces new approaches to optimally select the predictor variables which help in modeling regional precipitation and further provides a mechanism to select an optimum spatial resolution to downscale the precipitation projections. New methods for correcting the biases in monthly downscaled precipitation projections are proposed, developed and evaluated in this study. The methods include bias corrections in an optimization framework using various objective functions, hybrid methods based on universal function approximation and new variants. / by Aneesh Goly. / Thesis (Ph.D.)--Florida Atlantic University, 2013. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

The use of cross-correlation analysis on spring discharge and precipitation data in karst aquifer basins has been used for many years to develop a conceptual understanding of an aquifer and estimate aquifer properties. However, to this point, the application of these processes has relied on gaged precipitation at discrete locations. The use of spatially varying precipitation data and cross-correlation analysis provides a means of spatially characterizing recharge locations on a karst aquifer. NEXRAD provides a spatial estimate of precipitation based by combining reflectivity measurements from radar stations and traditional precipitation gages. This study combines NEXRAD precipitation data with spring discharge data to develop maps of contributing areas for two karst springs in Central Texas. By calculating the cross-correlation of each NEXRAD measurement to spring flow data for the same period of time a map showing the locations hydraulically connected to the spring can be developed. Both numerical experiments and field applications were conducted as part of the study. The numerical experiments conducted by Padilla and Pulido-Bosch are revisited using the numerical groundwater model MODFLOW. This allowed the introduction of spatially varying parameters into the model. The results show that spatially varying parameters can be inferred based on the results cross-correlation of spatially varying precipitation with respect to a single spring discharge location. Also, contributing area maps are prepared for both Barton Springs and Jacob’s Well. Barton Springs has a precise estimate of the recharge area. The current map of the recharge area and the NEXRAD derived map show good agreement with the cross-correlation results. Conversely, Jacob’s Well has not been sufficiently studied to delineate a contributing area map. This study provides an preliminary estimate of the area contributing to flow at Jacob’s Well. Finally, the development of these maps can also be applied to the construction of regional groundwater models. An application of this methodology with the groundwater availability model for the Barton Springs portion of the Edward’s aquifer is introduced. The application of spatial cross-correlation analysis to constrain recharge in the model showed a reduction in the objective function with respect to discharge at Barton Springs of 15%. / text

Indiana University-Purdue University Indianapolis (IUPUI) / Heat is one of the most important factors in our atmosphere for precipitation (thunderstorm) formation. Thermal energy from local urban land-cover is also a common source of heat in the lower atmosphere. This phenomenon is known as the urban heat island effect (UHI) and is identified as a substantial cause to a changing climate in surface weather modification. The proceeding study investigates this connection between the UHI and surface weather using remote sensing platforms A ten-year analysis of the Indianapolis UHI and thunderstorms were studied from the summer months of May, June, July, August and September (MJJAS) from 2002 until 2011. LANDSAT space borne satellite technology and land-surface based weather radar technology was used in this analysis for thunderstorm investigation. Precipitation areas identified from land-based NEXRAD WSR-88D technology were used to identify convection from non-synoptic forcing and non-normal surface diurnal heating scenarios. Only convection appearing from the UHI were studied and analyzed. Results showed twenty-one events over eighteen days with the year 2005 and 2011 having the largest frequency of events. The month of August had the largest concentration with seven events during the late afternoon hours. UHI results showed July had the largest heat island magnitude with April and September having the lowest magnitude in UHI temperatures. Three events of the twenty-one storm paths did not had a significant mean temperature difference in the heat island below the storm reflectivity. The nineteen storm paths that were significant had a warmer area underneath storm path development by an average 6.2°C than surrounding areas. UHI initiation points showed twelve of the twenty-one events having statistically significant differences between 2 km initiation areas and the rest of the study areas. Land-cover results showed low intensity developed areas had the most land-cover type (48%) in the 2km initiation buffer regions.