For this study, 248 overall hydrologic and hydraulic model scenarios were developed (table 2). Because of the similarities among the results of the different combinations of magnitude and duration, the final results were condensed into 14 representative stage conditions. These scenarios included the probabilistic storm simulations and “dry,” “normal,” and “wet” antecedent runoff condition variations. The probabilistic simulated storms are modeled as simple events; that is, they are stand alone and do not take into account possible combined effects that would result from multiple successive storms and runoff peaks. Climate-change conditions were developed based on the Coupled Model Intercomparison Project (CMIP) Climate Data Processing Tool 2.1, which was created by the Federal Highway Administration (Kilgore and others, 2021).

Accurate precipitation data are needed as inputs to the hydrologic model to simulate the resulting runoff and stream profiles. The precipitation data used for model calibration events were from NOAA’s Multi-Radar Multi-Sensor system (Iowa State University of Science and Technology, 2024b). The precipitation data used for the model scenarios were probabilistic precipitation magnitudes associated with storm durations of 15 minutes to 24 hours at a single location within the basin (National Oceanic and Atmospheric Administration, 2024). To more closely align the methodology for processing the precipitation from the radar source with that of the point-source data to be used in practice, the gridded radar data were converted to a time series and averaged within four subbasins in the study area (fig. 1). In practice, precipitation data used for the display and interpretation of the flood-inundation maps would be obtained from the NWS’s ASOS network site at the Lee’s Summit Municipal Airport (Iowa State University of Science and Technology, 2024a).

Probabilistic precipitation events used as inputs to the hydrologic model for the modeled scenarios (table 2) were developed from the NOAA Atlas 14 point-precipitation frequency estimates (National Oceanic and Atmospheric Administration, 2024). The Atlas 14 event duration values were refined to include additional 4-hour duration events for this small study basin. The precipitation values for the 4-hour storms were interpolated by fitting a logarithmic trend line to the 5-, 10-, 15-, and 30-minute and 1-, 2-, 3-, 6-, 12-, and 24-hour duration values for each recurrence interval (fig. 3). The values represent a total amount of rainfall for the specified time duration. The storm distributions, or time series of precipitation based on the Atlas 14 totals, were developed by distributing the precipitation in 5-minute increments over the storm duration using an alternating block distribution approach (Chow and others, 1988).

The volume of precipitation in the HEC–RAS rain-on-grid model exceeding soil infiltration and storage is calculated and transformed into runoff using the Soil Conservation Service CN loss model with initial abstractions (Soil Conservation Service, 1986; Natural Resources Conservation Service, 2004). The infiltration layer in HEC–RAS was created by combining a Soil Survey Geographic database (Natural Resources Conservation Service, 2022) soils layer and the 2021 land cover from the National Land Cover Database (Dewitz, 2023).

For this project, initial abstraction values, representing the amount of soil storage before runoff, were applied to each CN value. The initial abstraction values were chosen from Soil Conservation Service (1986, table 4–1). Using the Soil Conservation Service CN approach, antecedent runoff conditions were divided into three classes: CNI for “dry,” CNII for “normal,” and CNIII for “wet” antecedent runoff conditions (Soil Conservation Service, 1986). All three antecedent runoff conditions were simulated in this study. The initial average, or “normal,” runoff condition (CNII) values used in this model were converted to initial estimates of “dry” conditions (CNI) and “wet” conditions (CNIII) using the following equations from Chow and others (1988):CNI=4.2CNII10−0.058CNII(1)andCNIII=23CNII10+0.13CNII(2)After the initial calculation of the CN values for the “dry” and “wet” antecedent runoff conditions was completed, the values were further refined manually during the calibration process.

In a two-dimensional HEC–RAS hydrologic and hydraulic rain-on-grid model, “each computational cell face is evaluated similar to a cross section and is pre-processed into detailed hydraulic property tables” (U.S. Army Corps of Engineers, 2025). For these types of models, the routing method depends on the equations chosen to run the model because water-surface elevations, velocities, and depths are calculated at each cell face based on the equations chosen (U.S. Army Corps of Engineers, 2025). For this study, the model computations were completed using the shallow water equations method (U.S. Army Corps of Engineers, 2025).

To calibrate the model, antecedent flows were added to the East Fork Little Blue River, tributary A1, and tributary A2. The antecedent flow added to the East Fork Little Blue River was 10 cubic feet per second, and the antecedent flow added to both of the tributaries was 5 cubic feet per second. Adding the antecedent flows generated water-surface elevations that matched the observed water-surface elevations before the simulated storm event began. These antecedent flow values were held constant throughout the calibration simulations. For continuity purposes, these antecedent flows were also included in the probabilistic simulations.

The elevation data used in the HEC–RAS model were obtained from a 1-meter DEM that was derived from light detection and ranging (lidar) data collected by Quantum Spatial, Inc., between April and December 2020 (U.S. Geological Survey, 2021a). As per USGS quality level 2 data-accuracy specifications (ver. 1.2; Heidemann, 2018), the lidar data required a nonvegetated vertical accuracy of a maximum of 30 centimeters at the 95th percentile. For the DEM used in this study, the nonvegetated vertical accuracy had a tested value of 8.12 centimeters at the 95th percentile confidence level. Channel bathymetry at the reference location was verified by the USGS using channel bottom elevations the USGS hydrologist obtained while making streamflow measurements.

In the hydraulic model of the East Fork Little Blue River and its tributaries, 21 hydraulic structures are represented. These 21 structures include 3 four-lane road crossings and 3 extended pipe culverts and have the potential to affect water-surface elevations during flooding along the stream. The rest of the culverts and bridges are two-lane road crossings. All bridge-geometry information is contained in the hydraulic models used for analyses and provided in Atkinson (2026).

Hydraulic analysis requires the estimation of energy losses that result from frictional resistance exerted by a channel on flow. These energy losses are quantified by the Manning’s roughness coefficient (n-value). Initial n-values were refined on the basis of field observations, aerial imagery, and the land-cover data downloaded from Dewitz (2023). During the calibration process, the initial n-values were varied by flow and adjusted until the differences between simulated and observed water-surface elevations at the calibration locations (fig. 1) were minimized. Calibration regions were added to simulate the channel bottom throughout the reach and the overbank areas near the pressure transducer locations EFLB2 and EFLB3. The final n-values ranged from 0.015 for paved areas to 0.16 for wooded areas. The channel n-value was 0.05. The channel coefficient was consistent along the length of the East Fork Little Blue River channel and all tributaries. The highest n-values were detected in wooded residential areas. Some of these areas were in the overbank area, but others were scattered throughout nearby parks and fields. The lowest n-values were in open parking lot areas and on the streets and cul-de-sacs.

The HEC–RAS analysis for this study was completed using the two-dimensional unsteady-state flow computation option. Inflow boundary conditions consisted of (1) precipitation time series applied to the entire study basin using the “precipitation” boundary condition and (2) constant flow hydrographs applied to the channels within the study basin using the “flow hydrograph” boundary condition. The input hydrographs for the HEC–RAS model scenarios were generated using a range of precipitation frequency-duration events, as detailed in the “Precipitation Data” section. The model used the original shallow water equations, Eulerian-Lagrangian method to calculate the water-surface elevations (U.S. Army Corps of Engineers, 2024). Normal depth, based on an estimated average water-surface slope of 0.01 foot per foot, was used as the downstream boundary condition.

The HEC–RAS model was calibrated to observed streamflow-stages collected during the April 28, 2024; May 2, 2024, and July 1, 2024, events at the reference point RF1 and to observed continuous water-surface elevations at the pressure transducer locations (fig. 1). Although the July 1 event was considered the primary event because the hydrograph pattern of the event followed a pattern similar to the modeled storms, the model was also calibrated to the other events to capture the full range of antecedent moisture conditions. Model calibration was completed by adjusting CN values until the results of the computations closely agreed with the observed water-surface elevations with a primary focus of calibration at the reference point RF1 (fig. 4A–C). In limited cases, the channel terrain was modified to provide a uniform channel. The channel and tributaries are small and narrow; consequently, the pixelation of the lidar-derived terrain could result in unreasonable fluctuations in water-surface levels.

Simulated water-surface elevations generally were within 0.51 ft of observed values, with the exception of the location of pressure transducer EFLB2 (fig. 1). For the July 1 event, the primary event, the difference between the observed and simulated water-surface elevation at the reference point RF1 was 0.06 ft (table 3). The differences during the July 1 event were 0.33 ft, 0.72 ft, and 0.10 ft for EFLB1, EFLB2, and EFLB3, respectively. Water-surface elevation comparisons between the simulated storms and the observed water-surface elevations at the EFLB1 location are shown in figure 4A–C. Simulated flood peaks for the three calibration events were within 23.2 percent, and the timing of the flood peaks was within 37 minutes of the observed peaks (table 4).

The Nash-Sutcliffe efficiency (NSE) coefficient (Nash and Sutcliffe, 1970) was used to assess model fit. Values of NSE can vary from negative infinity (−∞) to 1. Values of 1 correspond to a perfect match between simulated and observed time series, whereas values less than 0 indicate the observed average is a better predictor than the simulated values. Because the model does not account for changing hydrologic response conditions during the event, even though the first peaks hit within calibration limits, later peaks had greater variability. Hydrographs for all the calibration events are shown in figure 4A–C, and time-of-peak comparisons for all events are listed in table 4. The NSE values of the calibrated models for the April 28, 2024; May 2, 2024; and July 1, 2024, storms were about 0.84, 0.81, and 0.94, respectively, indicating a good fit for all storms (table 5). Overall, the results demonstrate that the model is capable of simulating accurate water levels throughout the reach at varying antecedent moisture conditions.

All the probabilistic storm scenarios (table 2) were run using the model for “dry,” “normal,” and “wet” antecedent runoff conditions, and the peak water-surface elevations at the reference point RF1 were ranked in order from smallest to largest. A rating curve that was initially developed using the discrete streamflow measurements at the reference point RF1 was extended using the HEC–RAS model results so that inundation maps could be produced and associated streamflows estimated for water-surface elevations that exceeded the maximum observed condition. A representative scenario was then selected for each 1-ft water-surface elevation interval between 884 ft at the reference point (corresponding to the approximate bankfull condition at EFLB1) and 897 ft above the North American Vertical Datum of 1988 (water level corresponding to the 1,000-year recurrence interval, 24-hour event with “wet” antecedent runoff conditions). This 14-ft range of water-surface elevations corresponds to the approximate bankfull condition at EFLB1, or 3 ft of depth at the reference point RF1, to 16 ft of depth at the reference point RF1 (table 6). Inundation extents were then processed using Esri ArcGIS Pro (Esri, 2024) for these 14 selected scenarios. Each water-surface profile is intended to represent 1 ft of depth; specifically, the scenario representing a water-surface elevation of 886 ft is intended to represent the inundation extents for water-surface elevations ranging from 885.5 to 886.5 ft. The resulting flood-inundation maps indicate the maximum water-surface elevation attained at each location throughout the longitudinal reach for the corresponding precipitation magnitude and duration. A rating curve that was initially developed using the discrete streamflow measurements at the reference point RF1 was extended using the HEC–RAS model results. Simulated results were used to determine approximate flows at the reference point RF1 for each of the water-surface elevations (table 6).

In addition to flood-inundation extents, depth grids, water-surface elevation grids, and velocities at key locations downstream from bridges and culverts were derived from the model. The depth grid maps indicate the approximate depth of water within the inundated area, and the water-surface elevation grids indicate the approximate water-surface elevation within the inundated area for each of the 14 selected scenarios. The depths and water-surface elevations are provided to the user as a range of 1 ft; for example, a calculated flood depth of 7.5 ft would be shown on the mapper as 7–8 ft, and a calculated water-surface elevation of 941.45 ft would be shown on the mapper as 940.9–941.9 ft. The depth grids and the water-surface elevation grids incorporate lowest adjacent grade data. The lowest adjacent grades are the bare-earth surfaces nearest the buildings (Federal Emergency Management Agency, 2020). When a building is within the inundation extent, the flood depth and water-surface elevations over the building area would be an estimate of the flood depth and water level inside the structure, excluding any implemented elevating or flood-proofing measures. Finally, velocities downstream from bridges and culverts were derived from the model for each of the 14 selected scenarios. Similar to the depth grids and water-surface elevation grids, velocities are shown to users as a range; for example, a calculated velocity of 4.4 feet per second would be shown as 4–5 feet per second.

The climate-change scenarios were developed using the Federal Highway Administration’s CMIP Climate Data Processing Tool 2.1 (Kilgore and others, 2021). This online software tool was developed to “take Localized Constructed Analogs (LOCA) downscaled CMIP5 data and process the information into statistical and other variables that can be used in planning and design with greater ease” (Kilgore and others, 2021, p. i). Using the CMIP tool, users can select a geographic area of interest, a period of interest, and an emissions condition, called the representative concentration pathway (RCP), to determine adjustment factors applied to “existing conditions” precipitation to obtain projected precipitation conditions.

The climate-change scenarios and CMIP tool were used to simulate the 100-year, 24-hour storm and the 100-year, 6-hour storm for all three antecedent moisture conditions. Projected precipitation amounts for varying future time and emission conditions were 4–7 percent greater than existing conditions (table 7).

After the scenarios were run, the modeled peak values at the reference point RF1 (fig. 1) were recorded. Overall, the increase in water-surface elevation at RF1 because of the increased precipitation is less than 0.5 ft, although increases are greater for the 100-year, 24-hour scenarios than the 100-year, 6-hour scenarios (table 8). However, although increases are larger for the 100-year, 24-hour scenarios, the changes in flood-inundation extents are similar for each scenario (fig. 6A and B).

The geospatial data files of developed flood-inundation polygons and corresponding depth grids, water-surface elevation rasters, depth of water above lowest adjacent grade, and velocity data are available in Atkinson (2026). Additionally, a flood-inundation mapping interface (City of Lee’s Summit, 2025) has been established to make the fully processed USGS flood-inundation study information available to the public. The website links to a mapping application that provides map libraries and detailed information on flood extents and depths for modeled sites.

Determining an appropriate flood-inundation map to reference for a particular event begins with monitoring rainfall. At the time of this report, the Lee’s Summit mapping application, precipitation totals are gathered every 10 minutes from the NWS’s ASOS network station at the Lee’s Summit Municipal Airport. For the mapping application associated with this study, these data can be downloaded through Iowa State University’s Environmental Mesonet (Iowa State University of Science and Technology, 2024a) and shown on a mapping application. Forecasted precipitation is based on the quantitative precipitation forecast for the area and is available from the NWS’s text products (National Weather Service, 2025b).

The mapping application provides the opportunity to show current conditions or a user-specified rainfall. Based on the rainfall option chosen by the user or the recently downloaded current and forecasted precipitation, the hydrologic response of various precipitation events is used to determine the flood-inundation map shown to the user of the application. For example, if current conditions are showing a rainfall amount closely corresponding to 3.44 inches (in.) of rain falling in 2 hours, which is equivalent to the 25-year, 2-hour storm, then the mapping application would show the flood-inundation extents corresponding to the map related to the peak value produced for this particular storm at the reference point RF1 (fig. 1). Alternatively, the user can opt to choose a rainfall depth and time combination to view the resulting flood-inundation extents.

The response of the East Fork Little Blue River and tributaries to a rainstorm that is associated with a precipitation magnitude and duration and represented by the selected flood-inundation map will also vary depending on the selected antecedent runoff condition (“dry,” “normal,” or “wet”). In the mapping application, the antecedent runoff condition associated with the current condition is determined based on awareness of hydroclimatic conditions in the basin preceding an event. The current antecedent moisture conditions are based on the flash-flood guidance for a particular basin. The flash-flood guidance value for the basin is determined based on the soil moisture conditions before a storm event. The current value is downloaded from the NWS’s application programming interface (National Weather Service, 2025a) and shown on the flood-inundation mapping application (City of Lee’s Summit, 2025). Alternatively, users may manually select an antecedent moisture condition to pair with a manually selected rainfall amount and duration.

In calibrating the models for this study, the “normal” antecedent runoff condition was observed in the summer (July 1, 2024, event) with full vegetation cover with an antecedent 7-day rainfall of 2.87 in., mostly (2.72 in.) from one storm about 5 days before the July 1 event. The maximum daily air temperatures, in degrees Fahrenheit, were in the 80s and 90s for 4 weeks before the event. The “wet” condition calibration event was in April under growing vegetation with an antecedent 7-day rainfall of 1.62 in., mostly from a 1.46-in. event 2 days before April 28th. Maximum daily temperatures in the preceding month were mostly in the 50s, 60s, and low 70s, and a few days were in the 80s. “Dry” antecedent runoff conditions with no initial abstractions were observed for the May 2, 2024, event with still growing vegetation cover, and antecedent rainfall after the April 28 event was about 0.55 in. Maximum daily temperatures for these few days varied from the high 60s to mid-80s. Based on these conditions, the antecedent moisture conditions seem most affected by the rainfall and temperatures immediately before the calibration event. The application draws from the NWS’s application programming interface to update the estimated antecedent moisture condition each hour (National Weather Service, 2025a).

The estimated flood-inundation maps are shown in sufficient detail so that preparations for flooding and decisions for emergency response could be completed efficiently. Depending on the flood magnitude, roadways are shown as shaded (inundated and likely impassable) or not shaded (dry and likely passable) to facilitate emergency planning and use. Bridges are shaded (that is, shown as inundated) when the flood stage exceeds the elevation of the bridge deck. A shaded building should not be interpreted to mean that the structure is completely submerged; rather, the lowest adjacent grade bare-earth surfaces near the building are inundated (Federal Emergency Management Agency, 2020). In these instances, the water depth near the building would be an estimate of the water level inside the structure, unless elevating or flood-proofing measures had been implemented. For this study of the East Fork Little Blue River and associated tributaries, the mapping application shows water-surface elevations and water depths. Additionally, the mapping application shows approximate velocities just downstream from selected bridge locations.

The flood-inundation maps should not be used for navigation, regulatory, permitting, or other legal purposes. The USGS provides these maps “as is” for a quick reference, emergency planning tool but assumes no legal liability or responsibility resulting from the use of this information.

Although the flood-inundation maps represent the boundaries of inundation areas with a distinct line, some uncertainty is associated with these maps. The flood boundaries shown were estimated based on water stages at a selected reference location based on the hydrologic response from selected precipitation magnitudes and durations. Water-surface elevations along the stream reach were estimated by unsteady-state hydraulic modeling, assuming unobstructed flow, and used streamflows and hydrologic conditions anticipated at the East Fork Little Blue River main stem, tributary A1, and tributary A2. The rain-on-grid model reflects the land-cover characteristics and any bridge, culvert, dam, levee, or other hydraulic structures existing as of June 2023. Unique meteorological factors (timing and distribution of precipitation) may cause actual streamflows along the modeled reach to vary from those assumed during a flood, which may lead to deviations in the water-surface elevations and inundation boundaries shown. Additional areas may be flooded because of unanticipated conditions such as changes in the streambed elevation or roughness, backwater into major tributaries along a river main stem, or backwater from localized debris or ice jams. The accuracy of the flood water extent portrayed on these maps will vary with the accuracy of the DEM used to simulate the land surface. Of the 14 flood-inundation map scenarios, 8 exceed the largest streamflow measurement made at the East Fork Little Blue River reference location. This highest measurement corresponds to a water-surface elevation of 888.7 ft, which corresponds to the 889-ft stage map; therefore, additional uncertainty for flood-inundation maps exists at water-surface elevations greater than 889 ft.

The user should be aware of additional uncertainties that may be inherent or factored into the simulation of flood peaks using rainfall-runoff simulations. A rain-on-grid model was used to simulate flood peaks associated with various probabilistic precipitation amounts, and the precipitation was assumed to follow a defined temporal distribution over the duration of the event with an even spatial distribution over the basin. The actual temporal and spatial distribution of precipitation may vary, thereby affecting the timing and magnitude of the predicted flood peak in the main stem or one of the tributaries; thus, a single “simple” storm was simulated with a near-base-flow starting condition to develop the flood-inundation maps. For multiple compounding precipitation events, the user should take into consideration that the starting streamflow condition may be considerably higher than base flow used in the simulations, and therefore, the peak condition also will be higher than simulated conditions. Other sources of uncertainty will arise from the selection of the appropriate antecedent runoff condition and the occurrence of atypical precipitation events including rainfall on frozen ground or on a substantial existing snowpack, all of which may affect the timing and magnitude of the predicted flood peak.

For this project, the cooperator wanted to incorporate some additional components into the final flood-inundation map dashboard (City of Lee’s Summit, 2025) beyond the standard inundation extents and water depths (U.S. Geological Survey, 2018). These enhancements include indicating depth of inundation above the lowest adjacent grade of structures within the floodplain, indicating water-surface elevation in addition to water depth at a selected location, and indicating maximum water velocity at selected locations. For this project, the lowest adjacent grade, or bare-earth surfaces near a building (Federal Emergency Management Agency, 2020), values were used to indicate whether or not a particular structure is likely to be flooded.

The Cybersecurity & Infrastructure Security Agency of the Federal Government defines several sectors of critical facilities (Cybersecurity & Infrastructure Security Agency, 2025). For this project, critical infrastructure within the study basin was included in the flood-inundation mapping display. For the purpose of this study, critical infrastructure was defined as emergency services; water and wastewater treatment plants; healthcare facilities, including pharmacies; government facilities, including schools; and any chemical and manufacturing facilities. Several businesses and churches are also within the basin area, although most of the basin contains residential properties.