Thesis
Connecting the level of detail in spatial discretization of a watershed with peak flow predictions in a distributed model
University of Iowa
Master of Science (MS), University of Iowa
Autumn 2025
DOI: 10.25820/etd.008248
Abstract
Flood forecasting is essential to protect our communities, reducing risk and avoiding infrastructure damage. Distributed hydrological models are often used for this purpose, usually after being calibrated at gauged watershed, these results are then extrapolated to ungauged ones, which are the majority in the U.S. However, this extrapolation typically overlooks the limitations carried into the model by the loss of detail in the spatial representation of river networks. Model calibration hides this structural limitation by giving more weight to other variables rather than the detail of representation. Although we have little information about these limitations and implications, prior studies have shown that less detail of the river network can impact the performance of the forecast affecting the predictive reliability of the model when its applied to other regions. This study investigates the importance of model discretization scales (DS), which is the level of spatial detail used to divide a watershed into hillslopes and river links, can affect the watershed characteristics and simulated flow responses. I analyzed the Smoky Hills watershed (Kansas, U.S.), here I compared geomorphological features like width function, saturated hydraulic conductivity, average slope distribution, travel time to streams, and stream network density and simulated flows across six DS ranging from fine (accumulated area of 0.1 ??2 closer to reality and therefore our benchmark – BDS) and coarser (70 ??2 corresponding to USGS HUCs 12 and HYDRUS average hillslope areas). For each scale, I approached the simulation by using 110 uniform rainfall events with a constant runoff formulation of the Hillslope Link Model (HLM). Model outputs were evaluated at 400 random control points across the watershed. Our results show that coarser DS can alter important geomorphological watershed characteristics that can carry issues into the simulations. Also, simulation shows that as the DS increases, peak flows decrease while time-to-peak significantly increases, indicating slower and longer hydrographs. Finally, it was seen that an optimization of the routing parameters can help emulate the results of our DSB (benchmark) but in some cases this parametrization used unnatural values for the zone of study, weighing on the importance of the river network in the models. Our results raise concerns about using pre-imposed discretization scales and highlight potential limitations when extrapolating flood forecasts to ungauged watersheds. This study highlights the need to account for discretization effect when developing a model and transferring it across regions and scales
Details
- Title: Subtitle
- Connecting the level of detail in spatial discretization of a watershed with peak flow predictions in a distributed model
- Creators
- Simón Martinez Rendon
- Contributors
- Witold Krajewski (Advisor)Nicolas Velásquez Giron (Advisor)Humberto Vergara (Committee Member)
- Resource Type
- Thesis
- Degree Awarded
- Master of Science (MS), University of Iowa
- Degree in
- Civil and Environmental Engineering (Water Resources)
- Date degree season
- Autumn 2025
- DOI
- 10.25820/etd.008248
- Publisher
- University of Iowa
- Number of pages
- xi, 66 pages
- Copyright
- Copyright 2025 Simón Martinez Rendon
- Language
- English
- Date submitted
- 12/07/2025
- Description illustrations
- Illustrations, graphs, charts, tables
- Description bibliographic
- Includes bibliographical references (pages 64-66).
- Public Abstract (ETD)
Flood forecasts are important to keep people safe and protect our cities structures. To generate these forecasts, scientists run computer models to simulate how rain transforms into river flow. Usually, the model is tuned and organized using data from locations with historical flow information and then applied to places without information, which are the majority in the country. Our study points out that with this approach, if the river network is not represented with fine detail, forecasts can be misleading, even if the model fits the gauged places. I tested this in Kansa s Smoky Hill watershed by making a comparison between six levels of river network detail from very fine (close to reality) to very coarse. I ran the model with 110 rainfall events and analyzed the model across 400 locations in the basin. Results showed that in coarse models, flood peaks tended to be smaller and arrived later. Finally, I saw that tuning the model routing settings could sometimes help mimic the fine detail results but only by using unrealistic values raising a concern about the importance of river network detail in models and the limitations we are facing today.
- Academic Unit
- Civil and Environmental Engineering
- Record Identifier
- 9985135349002771
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