Estimation of the Time of Concentration with High-Resolution GIS Data: Limitations of Existing Methods and Analysis of New Methods

Variations in the computation of times of concentration making use of the velocity method originate from various degrees of discretization along the longest flowpath in the watershed. We reviewed an idealized system for which an analytical solution could possibly be produced. After that, we analyzed a dataset compiled from watersheds across the State of Maryland, for which the noticed time of concentration was known. In the two cases we reveal that the time of concentration estimate rises with the degree of discretization. A couple of different models have been created which show good predictive agreement with the observed time of concentration. One strategy uses, gradually varied flow concepts to permit velocity to vary more realistically along the discretized flowpath. And the second method utilizes a regression technique to guide the merging of GIS pixel-based flowpath elements into larger segments. Advantages and restrictions of each method are outlined in the context of future application in Maryland and in other places…


Chapter 1: Introduction
1.1 Introduction
1.2 Time of Concentration and Discharge
1.3 Research Goal and Objectives
Chapter 2: Literature Review
2.1 Background
2.2 Observed Time of Concentration
2. 3 Reference Time of Concentration for State of Maryland by Thomas et al (2002)
Chapter 3: Velocity Methods for Calculating Time of Concentration within GIS: Problems and Advantages
3.1 Experiment with an Idealized System
3.2 Velocity Method Time of Concentration:” Pixel-based” and “Single-segment” Time of Concentration
3.3 Velocity Method Calculation for Time of Concentration: Longitudinal Flow
Discretization, Bankfull Flow Assumption and Sensitivity Analysis
3.4 Maryland Time of Concentration Dataset from Thomas et al. (2002)
3.5 Quality of Predicting the Time of Concentration: Goodness-of-Fit Statistics
Chapter 4: Modeling and Data Analysis
4. 1 Gradually Varied Flow Analysis
4.1.1 Background
4.1.2 Methodology: Gradually Varied Flow Analysis
4.1.3 HEC-RAS vs. HEC-2 Modeling
4.1.4 Results and Discussion: Gradually Varied Flow Analysis
4.2 Time-area Unit Hydrograph Analysis
4.2.1 Introduction
4.2.2 Time-area Methods Development SCS-theory based Time-Area Method Volume-based Time-Area Method Unit Hydrograph Time-Area Method
4.2.3 Time-Area Methods Results
4.3 A Statistical Approach for Merging Sections along the Longest Flow Path
Chapter 5: Results and Discussion
5.1 Comparison of Proposed Methods
5.2 Discussion of Best Performing Models
5.3 Model Limitations
Chapter 6: Conclusions

Source: University of Maryland

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