Streamflow Predictions in a Small Urban–Rural Watershed: The Effects of Radar Rainfall Resolution and Urban Rainfall–Runoff Dynamics

Abstract

The authors predicted streamflow in an urban–rural watershed using a nested regional–local modeling approach for the community of Manchester, Iowa, which is downstream of a largely rural watershed. The nested model coupled the hillslope-link model (HLM), used to simulate the upstream rural basins, and XPSWMM, which was used to simulate the more complex rainfall–runoff dynamics and surface and subsurface drainage in the urban areas, making it capable of producing flood maps at the street level. By integrating these models built for different purposes, we enabled fast and accurate simulation of hydrological processes in the rural basins while also modeling the flows in an urban environment. Using the model, we investigated how the spatial and temporal resolution of radar rainfall inputs can affect the modeled streamflow. We used a combination of three radar rainfall products to capture the uncertainty of rainfall estimation in the model results. Our nested model was able to simulate the hydrographs and timing and duration above the threshold known to result in nuisance flooding in Manchester. The spatiotemporal resolution the radar rainfall input to the model impacted the streamflow outputs of the regional, local, and nested models differently depending on the storm event.

Determining the Relative Contributions of Runoff and Coastal Processes to Flood Exposure across the Carolinas during Hurricane Florence

“…The model can be further improved by testing the sensitivity of the results to higher spatial resolution dataset for soil infiltration (e.g., through finer resolution of soil data with the Soil Survey Geographic Database (SSURGO)), the explicit inclusion of levees (e.g., using levee data from the USACE National Levee Database (NLD)), large reservoirs (e.g., using levees/weirs in combination with culvert structure with specified rating curve), and bridge piers (e.g., through increased Manning's n values). The uncertainty in the meteorologic forcing inputs could also be further explored by using an ensemble of wind and rainfall inputs (e.g., Stage IV radar-rainfall, ERA5 reanalysis) (Grimley et al, 2020). Lastly, this study could be expanded to include additional types of meteorologic events, tropical and non-tropical, to better delineate the areas that experience compound flooding.…”

On the Role of Atmospheric Simulations Horizontal Grid Spacing for Flood Modeling

“…Precipitation is the main driver used by hydrologic models to simulate the response of the river basins during ood events. Without an adequate representation of precipitation it is expected that the hydrologic response is biased, leading to an overestimation or underestimation of discharge (Fang and Pomeroy 2016;Bennett et al 2018;Berghuijs et al 2019;Grimley et al 2020). Although these biases can be caused by other components of the hydrologic modeling, precipitation has a very important role (e.g.…”