Teegavarapu, Ramesh

A comprehensive study is conducted to evaluate global sea levels for trends and variations due to climate change and variability by using non-parametric methods. Individual and coupled effects of inter-annual ENSO, decadal PDO, multi-decadal AMO, and quasi-decadal NAO on sea levels are evaluated. Combined influences of different phases (cool or warm) of PDO, AMO, and NAO influences and ENSO are also evaluated. The results from this study showed that sea level at 60% of the sites is increasing with time with all four oscillations impacting global sea levels. AMO warm phase individually and PDO warm combined with La-Niña phase contribute to higher sea levels throughout the world. Trends and variations in sea levels are noted to be spatially non-uniform. Understanding and quantifying climate variability influenced variations in sea levels and assessment of long-term trends enables protection of coastal regions of the world from sea level rise.

Spatial and temporal interpolation methods are commonly used methods for estimating missing precipitation rain gauge data based on values recorded at neighboring gauges. However, these interpolation methods have not been comprehensively checked for their ability to preserve time series characteristics. Assessing the preservation of time series characteristics helps achieving a threshold criteria of length of gaps in a data set that is acceptable to be filled. This study evaluates the efficacy of optimal weighting interpolation for estimation of missing data in preserving time series characteristics. Rain gauges in the state of Kentucky are used as a case study. Several model performance measures are also evaluated to validate the filling model; followed by time series characteristics to evaluate the accuracy of estimation and preservation of precipitation data characteristics. This study resulted in a definition of region-specific threshold of the maximum length of gaps allowed in a data set at five percent.

The concept of Intensity Duration Frequency (IDF) relationship curve presents crucial design contribution for several decades under the assumption of a stationary climate, the frequency and intensity of extreme rainfall nonetheless seemingly increase worldwide. Based on the research conducted in recent years, the greatest increases are likely to occur in short-duration storms lasting less than a day, potentially leading to an increase in the magnitude and frequency of flash floods. The trend analysis of the precipitation influencing the climate variability and extreme rainfall in the state of Florida is conducted in this study. Since these local changes are potentially or directly related to the surrounding oceanic-atmospheric oscillations, the following oscillations are analyzed or highlighted in this study: Atlantic Multi-Decadal Oscillation (AMO), El Niño Southern Oscillation (ENSO), and Pacific Decadal Oscillations (PDO). Collected throughout the state of Florida, the precipitation data from rainfall gages are grouped and analyzed based on type of duration such as short-term duration or minute, in hourly and in daily period. To assess statistical associations based on the ranks of the data, the non-parametric tests Kendall’s tau and Spearman’s rho correlation coefficient are used to determine the orientation of the trend and ultimately utilize the testing results to determine the statistical significance of the analyzed data. The outcome of the latter confirms with confidence whether there is an increasing or decreasing trend in precipitation depth in the State of Florida. The main emphasis is on the influence of rainfall extremes of short-term duration over a period of about 50 years. Results from both Spearman and Mann-Kendall tests show that the greatest percentage of increase occurs during the short rainfall duration period. The result highlights a tendency of increasing trends in three different regions, two of which are more into the central and peninsula region of Florida and one in the continental region. Given its topography and the nature of its water surface such as the everglades and the Lake Okeechobee, Florida experience a wide range of weather patterns resulting in frequent flooding during wet season and drought in the dry season.

Comprehensive evaluation of changes in streamflow extremes and characteristics
due to climate change and variability is the main focus of this study. Available
streamflow data at several gaging stations in least anthropologically affected watersheds
of the Southeastern Gulf-Atlantic Region, were used for this analysis. To evaluate
influences due to climate change, nonparametric trend tests were applied to annual and
monthly extremes, while considering seasonality, along with changes in streamflow
characteristics. To understand climate variability influences, streamflow data is
partitioned in to cool and warm phases of four oceanic and atmospheric oscillations
known to have an effect on hydroloclimatology of the region: El Niño-Southern
Oscillation (ENSO), Pacific Decadal Oscillation (PDO); Atlantic Multi-decadal
Oscillation (AMO); and North Atlantic Oscillation (NAO). Generally, results showed
decreasing trends in overall streamflow extremes, as well as spatially varying, temporally non-uniform influences of climate variability on streamflow extremes and characteristics.

Trends in streamflow extremes at a regional scale linked to the possible influences of four major oceanic-atmospheric oscillations are analyzed in this study. Oscillations considered include: El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), and North Atlantic Oscillation (NAO). The main emphasis is low flows in the South-Atlantic Gulf region of the United States. Several standard drought indices of low flow extremes during two different phases (warm/positive and cool/negative) of these oscillations are evaluated. Long-term streamflow data at 43 USGS sites in the region from the Hydro-Climatic Data Network that are least affected by anthropogenic influences are used for analysis. Results show that for ENSO, low flow indices were more likely to occur during La Niña phase; however, longer deficits were more likely during El Niño phase. Results also show that for PDO (AMO), all (most) low flow indices occur during the cool (warm) phase.

Three major teleconnections, Atlantic Multidecadal Oscillation (AMO), North Atlantic
Oscillation (NAO), and the Pacific Decadal Oscillation (PDO), in warm and cool phases,
effect precipitation in Florida. The effects of the oscillation phases on the precipitation
characteristics are analyzed by using long-term daily precipitation data, on different
temporal (annual, monthly, and daily) and spatial scales, utilizing numerous indices, and
techniques. Long-term extreme precipitation data for 9 different durations is used to
examine the effects of the oscillation phases on the rainfall extremes, by employing
different parametric and non-parametric statistical tests, along with Depth-Duration-
Frequency analysis. Results show that Florida will experience higher rainfall when AMO
is in the warm phase, except in the panhandle and south Florida, while PDO cool phase is
positively correlated with precipitation, except for the southern part of the peninsula.

Climate models are common tools for developing design standards in the hydrologic field; however,
these models contain uncertainties in multi-model and scenario selections. Along with these uncertainties,
biases can be attached to the models. Such biases and uncertainties can present difficulties in predicting
future extremes. These hydrologic extremes are believed to be non-stationary in character. Only in the
recent past have model users come to terms that the current hydrologic designs are no longer relevant due
to their assumption of stationarity. This study describes a systematic method of selecting a best fit model in
relationship to location and time, along with the use of that best fit model for evaluation of future extremes.
Rain gage stations throughout Florida are used to collect daily precipitation data used in extreme precipitation and quantitative indices. Through these indices conclusions are made on model selection and
future extremes, as they relate to hydrologic designs.