Graduation Year


Document Type




Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Civil and Environmental Engineering

Major Professor

Mahmood Nachabe, Ph.D.

Committee Member

Tirusew Asefa, Ph.D.

Committee Member

Philip van Beynen, Ph.D.

Committee Member

Benjamin Jacob, Ph.D.

Committee Member

Rangachar Kasturi, Ph.D.

Committee Member

Kenneth Trout, Ph.D.


El Nino Southern Oscillation, GCM, Markov Chain, Performance Metrics, Water Supply


Climate change is a global concern as it may affect many aspects of life, including water supply. A tool used to model climate change’s impacts is called a General Circulation Model (GCM). GCMs project future scenarios including temperature and precipitation, but these are designed at a coarse resolution and require downscaling for employment for regional hydrologic modeling. There is a vast amount of research on downscaling and bias-correcting GCMs data, but it is unknown whether these techniques alter precipitation signals embedded in these models or reproduce climate states that are viable for water resource planning and management. Using the Tampa, Florida region for the case study, the first part of the research investigated 1) whether GCM and the downscaled, bias-corrected data were able to replicate important historical climate states; and 2) if climate state and/or transition probabilities in raw GCMs were preserved or lost in translation in the corrected downscaled data. This has an important implication in understanding the limitations of bias-correction methods and shortcomings of future projection scenarios. Results showed that the GCM, and downscaled and bias-corrected data did a poor job in capturing historical climate states for wet or dry states as well as the variability in precipitation including some extremes associated with El Niño events. Additionally, the corrected products ended up creating different cycles compared to the original GCMs. Since the corrected products did not preserve GCMs historical transition probabilities, more than likely similar types of deviations will occur for “future” predictions and therefore another correction could be applied if desired to reproduce the degree of spatial persistence of atmospheric features and climatic states that are hydrologically important.

Furthermore, understanding the sustainability of water supply systems in a changing climate is required for undertaking adaptation measures. Many water suppliers employ GCMs to examine climate change’s effect on hydrologic variables such as precipitation, but little is known on the propagation of mismatch errors in downscaled products through cascade of hydrologic and systems models. The second study examined how deviations in downscaled GCMs precipitation propagated into streamflow and reservoir simulation models by using key performance metrics. Findings exhibited that simulations better reproduced the resilience metric, but failed to capture reliability, vulnerability and sustainability metrics. Discrepancies were attributed to multiple factors including variances in GCMs precipitation and streamflow cumulative distribution functions, and divergences in serial correlation and system memory.

Finally, the last study examined multiple models, emission scenarios and an ensemble to obtain a range of possible implications on reservation operations for time periods 2030-2053, 2054-2077 and 2077-2100 since the future emission trajectory is uncertain. Currently there are four Representative Concentration Pathways (RCPs) as defined by the IPCC’s fifth Assessment Report which provides time-dependent projections based on different forecasted greenhouse gas emission and land use changes. For this research Representative Concentration Pathways (RCPs) 4.0, 6.0 and 8.5 were examined. Scenarios were evaluated utilizing reliability, resilience, vulnerability and sustainability performance metrics and compared to a historical baseline. Findings exhibited that RCP 4.5, the lower end of emission scenario, improved reservoir reliability and resilience over time. Conversely, RCP 8.5, highest emissions, resulted in a steady decline of all metrics by 2100. Although vulnerability increased by 2100 for all emission scenarios, on average RCP 4.5 was less vulnerable. Investigation of permits and adjustments to capture extreme flows might be necessary to combat climate changes and precipitation inputs along with improvements to atmospheric emissions, which correlated with system recuperation with time.