Use of hydroclimatic forecasts for improved water management in central Texas

Accurate seasonal to interannual streamflow forecasts based on climate information are critical for optimal management and operation of water resources systems. Considering most water supply systems are multipurpose, operating these systems to meet increasing demand under the growing stresses of climate variability and climate change, population and economic growth, and environmental concerns could be very challenging. This study was to investigate improvement in water resources systems management through the use of seasonal climate forecasts. Hydrological persistence (streamflow and precipitation) and large-scale recurrent oceanic-atmospheric patterns such as the El Niño/Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), the Atlantic Multidecadal Oscillation (AMO), the Pacific North American (PNA), and customized sea surface temperature (SST) indices were investigated for their potential to improve streamflow forecast accuracy and increase forecast lead-time in a river basin in central Texas. First, an ordinal polytomous logistic regression approach is proposed as a means of incorporating multiple predictor variables into a probabilistic forecast model. Forecast performance is assessed through a cross-validation procedure, using distributions-oriented metrics, and implications for decision making are discussed. Results indicate that, of the predictors evaluated, only hydrologic persistence and Pacific Ocean sea surface temperature patterns associated with ENSO and PDO provide forecasts which are statistically better than climatology. Secondly, a class of data mining techniques, known as tree-structured models, is investigated to address the nonlinear dynamics of climate teleconnections and screen promising probabilistic streamflow forecast models for river-reservoir systems. Results show that the tree-structured models can effectively capture the nonlinear features hidden in the data. Skill scores of probabilistic forecasts generated by both classification trees and logistic regression trees indicate that seasonal inflows throughout the system can be predicted with sufficient accuracy to improve water management, especially in the winter and spring seasons in central Texas. Lastly, a simplified two-stage stochastic economic-optimization model was proposed to investigate improvement in water use efficiency and the potential value of using seasonal forecasts, under the assumption of optimal decision making under uncertainty. Model results demonstrate that incorporating the probabilistic inflow forecasts into the optimization model can provide a significant improvement in seasonal water contract benefits over climatology, with lower average deficits (increased reliability) for a given average contract amount, or improved mean contract benefits for a given level of reliability compared to climatology. The results also illustrate the trade-off between the expected contract amount and reliability, i.e., larger contracts can be signed at greater risk.

Determining bridge deck deterioration through the use of 3D photogrammetry

The bridge inspection industry has yet to utilize a rapidly growing technology that shows promise to help improve the inspection process. This thesis investigates the abilities that 3D photogrammetry is capable of providing to the bridge inspector for a number of deterioration mechanisms. The technology can provide information about the surface condition of some bridge components, primarily focusing on the surface defects of a concrete bridge which include cracking, spalling and scaling. Testing was completed using a Canon EOS 7D camera which then processed photos using AgiSoft PhotoScan to align the photos and develop models. Further processing of the models was done using ArcMap in the ArcGIS 10 program to view the digital elevation models of the concrete surface. Several experiments were completed to determine the ability of the technique for the detection of the different defects. The cracks that were able to be resolved in this study were a 1/8 inch crack at a distance of two feet above the surface. 3D photogrammetry was able to be detect a depression of 1 inch wide with 3/16 inch depth which would be sufficient to measure any scaling or spalling that would be required be the inspector. The percentage scaled or spalled was also able to be calculated from the digital elevation models in ArcMap. Different camera factors including the distance from the defects, number of photos and angle, were also investigated to see how each factor affected the capabilities. 3D photogrammetry showed great promise in the detection of scaling or spalling of the concrete bridge surface.