Current Search:   Teegavarapu, Ramesh S. V. (x)

View All Items

  • CSV Spreadsheet
(1 - 6 of 6)
EVALUATION OF INFLUENCES OF THE EL NIÑO-SOUTHERN OSCILLATION (ENSO) EVENTS ON CHANGES IN TEMPERATURE EXTREMES AND RESIDENTIAL ENERGY CONSUMPTION IN SOUTH FLORIDA
Title
EVALUATION OF INFLUENCES OF THE EL NIÑO-SOUTHERN OSCILLATION (ENSO) EVENTS ON CHANGES IN TEMPERATURE EXTREMES AND RESIDENTIAL ENERGY CONSUMPTION IN SOUTH FLORIDA.
Creator
Thakker, Kuntal S., Teegavarapu, Ramesh S. V., Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description
El Niño Southern Oscillation (ENSO) occurrences have a well-established impact on regional hydroclimatic variability and alterations in crucial climatic factors such as temperature and precipitation. The impact of ENSO on temperature extremes can cause fluctuations in energy consumption, leading to the need for energy utilities to implement more effective management measures. This study aims to evaluate the potential impacts of El Niño Southern Oscillation (ENSO) events on local temperature...
Show moreEl Niño Southern Oscillation (ENSO) occurrences have a well-established impact on regional hydroclimatic variability and alterations in crucial climatic factors such as temperature and precipitation. The impact of ENSO on temperature extremes can cause fluctuations in energy consumption, leading to the need for energy utilities to implement more effective management measures. This study aims to evaluate the potential impacts of El Niño Southern Oscillation (ENSO) events on local temperature patterns & extremes and residential energy usage in South Florida. The region of focus consists of three Counties: Miami-Dade, Broward, and Palm Beach. The impact of ENSO occurrences on temperature is assessed by analyzing long-term monthly average, minimum, and maximum temperature data from numerous weather stations in these counties, spanning from 1961 to 2018. The study analyzes variations of monthly electricity usage data acquired from a local power utility company (e.g., Florida Power & Light) and temperature data from 2001 to 2018. Temporal frames that align with the three phases of ENSO (namely warm, cool, and neutral) are employed to assess variations in temperature and energy consumption. Nonparametric hypothesis tests are employed to validate statistically significant variations in temperature and residential energy consumption across the stages of ENSO. This study aims to analyze the potential regional and temporal impacts of ENSO episodes on temperature and residential energy consumption in South Florida. Initial findings indicate that the non-uniform distribution of temperature, affected by El Niño and La Niña occurrences, impacts the amount of energy consumed by households in South Florida.
Show less
Date Issued
2024
PURL
http://purl.flvc.org/fau/fd/FA00014493
Subject Headings
Energy consumption, Florida, South, Climate change, El Niño Current, La Niña Current
Format
Document (PDF)
INFLUENCES OF CLIMATE CHANGE AND VARIABILITY ON BASEFLOWS
Title
INFLUENCES OF CLIMATE CHANGE AND VARIABILITY ON BASEFLOWS.
Creator
Chen, Hao, Teegavarapu, Ramesh S. V., Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description
Baseflow is the portion of the streamflow that is sustained between precipitation events, fed to streams by delayed pathways. Baseflow estimation and evaluation are two critical and essential tasks for water quality and quantity, drought management, water supply, and groundwater protection. In this research study, the influences of climate change and variability on baseflow derived from hundreds of watersheds in the continental United States are evaluated. Baseflows are estimated using...
Show moreBaseflow is the portion of the streamflow that is sustained between precipitation events, fed to streams by delayed pathways. Baseflow estimation and evaluation are two critical and essential tasks for water quality and quantity, drought management, water supply, and groundwater protection. In this research study, the influences of climate change and variability on baseflow derived from hundreds of watersheds in the continental United States are evaluated. Baseflows are estimated using streamflow data from these watersheds that are least affected by anthropogenic influences. In the initial phase of the study, an exhaustive evaluation of four different baseflow separation methods is carried out using streamflow data at several sites from the South Atlantic-Gulf region which includes a geographical region comprising of nine states in the southeastern U.S. Baseflows are estimated at different temporal scales and are used to assess the performances of different methods over a 44-year period starting from the year 1970 and the best method among these methods is selected for further analysis. Assessments of climate change influence on baseflows are then carried out using two nonparametric statistical trend tests (viz., Spearman’s Rho (SR) and Mann-Kendall (MK)). Trends in baseflows are evaluated at 574 sites located within the watersheds in the U.S. that are known to be least impacted by human influences. Trends were determined for annual maximum, mean, and median baseflows for the period 1970-2013. Spatially non-uniform trends and changes in characteristics of baseflows and strong influences of past precipitation events on the baseflow extremes were noted across the continental U.S. Some regions have shown decreasing baseflow trends and these are cause for concern and have severe implications for drought mitigation plans and low-flow management strategies in several watersheds in the U.S. In the final phase of the study influences of climate variability on baseflow manifested through different phases of individual and coupled oceanic and atmospheric oscillations are evaluated. Baseflows at 574 sites separated by temporal windows that coincide with two or more phases of different decadal, quasi-decadal and multi-year oscillations (viz., Pacific decadal oscillation (PDO), North Atlantic oscillation (NAO), Atlantic multidecadal oscillation (AMO), and El Niño-southern oscillation (ENSO)) are evaluated for statistically significant changes using nonparametric statistical hypothesis tests. Results from the study indicate that unlike climate change influences, climate variability effects are noted only in few specific physiographic regions of the U.S. This study documents an exhaustive and comprehensive assessment of changes in baseflows due to changing climate and results from this work can aid in short- and long-term management of low flows at a regional level that supports sustainable aquatic environment and handle droughts effectively.
Show less
Date Issued
2019
PURL
http://purl.flvc.org/fau/fd/FA00013366
Subject Headings
Climatic changes, Streamflow, Base flow (Hydrology)
Format
Document (PDF)
INDUCTIVE AND MODEL-TREE-BASED APPROACHES FOR FORECASTING TEMPERATURE
Title
INDUCTIVE AND MODEL-TREE-BASED APPROACHES FOR FORECASTING TEMPERATURE.
Creator
Schauer, Alexis, Teegavarapu, Ramesh S. V., Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description
Inductive and model-tree (MT) approach-based models are developed and evaluated for forecasting mean, minimum and maximum monthly temperature in this study. The models are developed and tested using long-term historical temperature time series data derived from U.S. Historical Climatology Network at 22 sites located in the state of Florida. Inductive models developed include conceptually simple naïve models to multiple regression models utilizing lagged temperature values, sea surface...
Show moreInductive and model-tree (MT) approach-based models are developed and evaluated for forecasting mean, minimum and maximum monthly temperature in this study. The models are developed and tested using long-term historical temperature time series data derived from U.S. Historical Climatology Network at 22 sites located in the state of Florida. Inductive models developed include conceptually simple naïve models to multiple regression models utilizing lagged temperature values, sea surface temperatures (SSTs), correction factors derived using historical data. A global model using data from all the sites is also developed. The performances of the models were evaluated using observed temperature records and several error and performance measures. A composite measure combining multiple error and performance measures is developed to select the best model. MT approach-based and regression models with SSTs and correction factors along with lagged temperature values are found to be best models for forecasting temperature based on assessments of composite measures and error diagnostics.
Show less
Date Issued
2021
PURL
http://purl.flvc.org/fau/fd/FA00013856
Subject Headings
Temperature, Forecasting--Mathematical models
Format
Document (PDF)
REGULARIZATION MODELS FOR IMPUTATION OF MISSING PRECIPITATION DATA
Title
REGULARIZATION MODELS FOR IMPUTATION OF MISSING PRECIPITATION DATA.
Creator
Azad, Anika, Teegavarapu, Ramesh S. V., Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description
This study focuses on developing optimization models to estimate missing precipitation data at twenty-two sites within Kentucky State. Various optimization formulations and regularization models are explored in this context. The performance of these models is evaluated using a range of performance measures and error metrics for handling missing records. The findings revealed that regularization models performed better than optimization models. This superiority is attributed to their ability...
Show moreThis study focuses on developing optimization models to estimate missing precipitation data at twenty-two sites within Kentucky State. Various optimization formulations and regularization models are explored in this context. The performance of these models is evaluated using a range of performance measures and error metrics for handling missing records. The findings revealed that regularization models performed better than optimization models. This superiority is attributed to their ability to reduce model complexity while enhancing overall performance. The study underscores the significance of regularization techniques in improving the accuracy and efficiency of precipitation data estimation.
Show less
Date Issued
2024
PURL
http://purl.flvc.org/fau/fd/FA00014432
Subject Headings
Rain and rainfall, Precipitation (Meteorology), Missing data (Statistics), Machine learning
Format
Document (PDF)
ESTIMATING MISSING PRECIPITATION RECORDS USING TREE-BASED APPROACHES
Title
ESTIMATING MISSING PRECIPITATION RECORDS USING TREE-BASED APPROACHES.
Creator
Nguyen, Thu, Teegavarapu, Ramesh S. V., Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description
Missing rainfall records happens frequently in many areas, and making precipitation estimation has been a challenge due to the spatial-temporal variability of the parameter. Model tree (MT), regression tree (RT), and ensemble approach models were developed and evaluated for estimating missing precipitation values in this research study. The selection of stations using correlation coefficient and similar distribution, and variation of data used to build the model were applied in this study....
Show moreMissing rainfall records happens frequently in many areas, and making precipitation estimation has been a challenge due to the spatial-temporal variability of the parameter. Model tree (MT), regression tree (RT), and ensemble approach models were developed and evaluated for estimating missing precipitation values in this research study. The selection of stations using correlation coefficient and similar distribution, and variation of data used to build the model were applied in this study. Proposed models were developed and tested using daily rainfall data from 1971 to 2016 at twenty-two stations in Kentucky, U.S.A. The model results were analyzed and evaluated using error and performance measures. The results indicated that MT-based and ensemble models produce a better estimation of missing rainfall than regression trees. The MT-based model was able to estimate missing rainfall accurately without needing objective selection of stations and using minimal calibration data to build the model.
Show less
Date Issued
2024
PURL
http://purl.flvc.org/fau/fd/FA00014414
Subject Headings
Precipitation (Meteorology), Estimating techniques, Missing data (Statistics)
Format
Document (PDF)
FAU Climate Change Initiative Priority Theme: Research, Engineering, and Adaption to a Change Climate
Title
FAU Climate Change Initiative Priority Theme: Research, Engineering, and Adaption to a Change Climate.
Creator
Berry, Leonard, Koch, Marguerite, Center for Environmental Studies, Benscoter, Brian, Comas, Xavier, Devlin, Donna, Fadiman, Maria, Gerstein, E., Herzing, Denise L., Hindle, Tobin, Milton, Sarah L., Oleinik, Anton E., Proffitt, C. Edward, Restrepo, Jorge I., Root, Tara L., Wyneken, Jeanette, Xie, Zhixiao, Zhang, Xing-Hai, Esnard, Ann-Margaret, Mitsova, Diana, Murley, J., Vos, J., Escaleras, Monica, Mehallis, M., Shaw, Eric H., Hardman, Guillermo [John], Lambert, Julie, Thomas, G., Arockiasamy, Madasamy, Bloetscher, Frederick, Carvalho, G., Dhanak, Manhar R., Frisk, George V., Kaisar, Evangelos I., Kalva, Hari, Meeroff, Daniel E., Rodriguez, Jarice, Scarlatos, Panagiotis (Pete) D., Shankar, Ravi, Teegavarapu, Ramesh, Brown, Clifford T., McAfee, Francis, Widener, Patricia, Dalgleish, Fraser R., Hanisak, M. Dennis, McMulloch, S., O'Corry-Crowe, Gregory, Pomponi, Shirley A., Reed, John K., Scarpa, John, Voss, Joshua, Heimlich, Barry N., Alvarez, R., Jolley, J., Edwards, A., Charles E. Schmidt College of Science, Harbor Branch Oceanographic Institute, College of Business, Dorothy F. Schmidt College of Arts and Letters, College of Education, College of Engineering and Computer Science
Date Issued
2010
PURL
http://purl.flvc.org/fau/fd/FA00003457
Format
Citation