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IntroductionValidation of urban flood prediction models requires accurate observations of flood extents and depths. Different methods are used to validate model predictions depending on the type of observed flood data. Satellite imagery and aerial photos can be used to determine flood extent at certain times during a flood event. Some satellite imagery exists for urban areas, but infrequent revisit times and locations limit their utility (Neal et al. 2009; Werner et al. 2005). Social-media data and news reports/photos are growing sources of data for validation and have the potential to provide vast volumes of flood-related information. However, the information in the photos and reports must be converted into useable forms, such as relating the pictured flood level to a local depth at a specific location and time of occurrence. This often requires visits to the pictured site and painstaking photo interpretation and data entry procedures (e.g., Macchione et al. 2019). Nonetheless, photographic high-water mark (HWM) data provide value as shown by Noh et al. (2019), Yu et al. (2016), Xing et al. (2019), Blumberg et al. (2015), Fohringer et al. (2015), Kutija et al. (2014), and McDougall and Temple-Watts (2012).Debris lines left on the ground at the flooded edge [i.e., wrack lines; Neal et al. (2009)] provide an estimate of maximum flood extent and also give an indication of maximum surface water elevation. High-water marks (HWMs) such as mud lines on trees or buildings can be surveyed to provide point estimates of maximum surface water elevation. For evaluating predicted flood extents, binary pixel-wise metrics, such as the critical success index derived from contingency tables, have been used to quantify the error between predicted and observed wet/dry computational cells (e.g., Wing et al. 2019; Yu and Lane 2006). However, Stephens et al. (2014) noted biases in these metrics and recommended further exploration. Moreover, consensus does not exist in the literature on the best approach to evaluate simulated and observed HWMs. When both the simulated and observed high water indicates an above-ground depth at a specific location, researchers apply traditional measures, such as mean error, root mean square error (RMSE), correlation, and bias, to quantify the vertical error (e.g., Wing et al. 2019; Xing et al. 2019; Yu et al. 2016; Hartnet and Nash 2017; Nguyen et al. 2016; Blumberg et al. 2015; Horritt et al. 2010; Neal et al. 2009; Mignot et al. 2006). However, in modeling studies, it is possible that the predicted flood extent does not reach the location of an observed high-water mark. In such instances, it is not clear how to compute a goodness-of-fit metric. To the best of our knowledge, only a few studies addressed this issue. Savage et al. (2016), Smith et al. (2015), and Neal et al. (2009) computed the vertical difference between the high-water mark elevation and the water surface elevation in the nearest wet cell. In another approach, Hunter et al. (2005) computed the vertical difference between the high-water mark elevation and the digital elevation model (DEM) elevation.Additional complexities emerge when using point-surveyed HWMs to evaluate models having different discretizations and computational element sizes. Fig. 1 illustrates these issues using the two models in the present study: the Weather Research Forecasting Model Hydrologic Extension (WRF-Hydro) (Gochis et al. 2020) and ADHydro (Ogden et al. 2015). ADHydro is an unstructured mesh model that uses smaller elements where more topographic detail is needed, such as channel-overbank boundaries, while larger elements are used to represent areas requiring less detail. In contrast, WRF-Hydro was applied at a ∼10-m grid resolution throughout the study domain. In Fig. 1, the underlying DEM is the 10-m USGS National Elevation Dataset (NED). The notations SUG_16, 17, and 18 indicate the location of three surveyed HWMs along Sugar Creek. The thick blue line represents the stream channel vector. The red triangles are the ADHydro mesh elements encompassing the HWMs. Fig. 1 clearly shows the size variation among ADHydro mesh elements and the typical size difference between the ADHydro mesh elements and the WRF-Hydro 10-m grid cells. Because both models utilize the same underlying DEM, differences in elevations between the two models should be small and a function of the underlying grid structure and element size differences. That being said, elevation differences between the models are a source of uncertainty and could introduce error, especially when larger ADHydro mesh elements span areas of high elevation variability in the DEM.These uncertainties give rise to such questions as the following: •How does one evaluate predicted water depths originating from models having different underlying grid mesh structures and element sizes?•How does one assess model performance when a predicted neighborhood water depth magnitude is approximately equal to the surveyed HWM depth but is spatially shifted?•Likewise, how does one evaluate a modeled water depth that matches the extent of the HWM but not the magnitude?To address these questions, this paper presents novel methods for evaluating model predictions of flood depths at surveyed high-water marks. These techniques account for differences in model element discretization and size when comparing simulated flood depths to surveyed HWMs. We also developed a novel approach to qualitatively analyze inundation predictions at the locations of flood-damaged structures and crowd-sourced observations of flooded locations. The work in this study is part of a more complete evaluation of two hyper-resolution models (HRMs) for predicting urban flooding (Smith et al. 2020). To the best of our knowledge, our evaluation is among the most comprehensive whole-city studies to date, considering the number of storm events and corresponding observations of surveyed HWMs, flood damage locations, and crowd-sourced locations of flooding.DataForcing DataWe used the NWS Analysis of Record for Calibration (AORC; Kitzmiller et al. 2014) as the primary meteorological forcing for ADHydro and WRF-Hydro. The AORC is a multidecade, internally-consistent data set of precipitation, temperature, solar radiation (shortwave and longwave downward at the surface), dew point, wind vectors, and terrain-level pressure. AORC data cover the contiguous US (CONUS) at a 1-km grid resolution and have an hourly time interval. Noting the flashy hydrograph response of the Sugar Creek stream gages, we utilized the 2 and 5 min precipitation estimates from the NWS Multiradar Multisensor System (MRMS; Zhang et al. 2016) to time disaggregate the hourly AORC precipitation estimates into 15 min intervals.We selected two extreme storms to evaluate the models’ ability to simulate flood inundation. The flood of record in August 2008 resulted from over 28 cm of rain falling over the entire basin in a 36–48 h period. We also selected a large convective event in August 2011 in which 7–15 cm fell in 3–4 h. Hereafter, we refer to these events by year, e.g., the 2008 and 2011 events.Geographic DataAnticipating that there would be future expanded applications of HRMs, we selected only static geographic data sets that had national coverage. The 10 m National Elevation Data set (NED; USGS 2017) was selected to represent topography and define flow directions. Street vectors were derived using Open Street Map and used to define major urban flow paths for both models. We used the 2011 USGS National Land Cover Dataset (NLCD; Wickham et al. 2017) for land use and land cover. Soil texture information was taken from the Soil Survey Geographic (SSURGO) data set (Soil Survey Staff 2021). We chose to forego strict definitions of building footprints to define surface flow paths. Finally, we used the National Hydrography Dataset Plus (NHD+) version 2 data to define the location of the channel network (Moore et al. 2019). Storm sewer networks were not modeled. Cross-section data were available but not used due to our self-imposed limitation to use only data sets with national coverage. Instead, both ADHydro and WRF-Hydro used empirical stream-order relationships to define channel shape parameters.Observed High-Water MarksThe CMSWS provided three types of observed HWMs. The first type was surveyed HWMs collected by a local engineering firm on behalf of CMSWS. The vertical datum used was NAVD 88, and the horizontal datum was NAD83/2007. Map projection data utilized the North Carolina 3200 projection. Categorically, CMSWS rated the surveyed HWMs as good, fair, or poor. However, no additional information was available regarding how this rating was determined. The types of surveyed HWMs were mud lines, wrack lines, debris lines, seed lines, stain lines, and witness marks. Second, flood damage depths were geo-encoded parcel locations of damage in which interior surveys were conducted to determine the depth of water within the structure. The structures impacted include private, business, and utility locations. The residential structures impacted include single-family homes, apartments, condos, and townhomes. Business structures include retail, offices, and warehouses. Recorded water depths were relative depths measured within the structure. Considering 2008 and 2011 together, average damage depths ranged from approximately 43–78 cm depending on the type of measurement (i.e., living area, crawl space, etc). Third, flooded streets were geo-encoded locations where flooding was observed. Of the three types of inundation data, flooded streets are the least quantitative and should be considered subjective. No indication of water depth was provided. Sources of the observations were witnesses, news reports, emergency management (such as police, fire, and local government), and photographs of flooding. Appendix I presents examples of the three types of HWMs from the Charlotte basin. Table 1 presents the number of observed HWMs obtained for this study. Examination of the 2011 flood damage (100 values) and flooded streets data (1,951 values) revealed that the latitude/longitude location information between the two types was redundant. As such, the 100 flood damage depths in 2011 were treated as a subset of the 1,951 flooded street observations. Hereafter, we use the term flood damage/flooded streets to refer to this group of observations.Table 1. Number and type of high-water mark observations for the two storm eventsTable 1. Number and type of high-water mark observations for the two storm eventsType of HWM20082011Surveyed HWM13141Flood damage locations373100Flooded streetsnone1,951Model ApplicationWe set up ADHydro and WRF-Hydro on the Sugar Creek basin to generate predictions of maximum flow depths in each computational element to compare to the observed high water information for each storm. Noting the major role that streets play in routing urban floods (e.g., Schubert and Sanders 2012), we created the mesh for ADHydro so that major streets were defined as impervious flow paths (e.g., Gallegos et al. 2009). For WRF-Hydro, the DEM corresponding to major streets was artificially lowered to ensure that flow followed street directions. After these steps, the median area of the ADHydro irregular mesh elements for the entire Charlotte basin was 1,922  m2 or ∼44  m on a regular grid side. The basin-wide ratio of ADHydro median element sizes to WRF-Hydro grid cells was ∼16∶1. Along the channel segments, the median area of the ADHydro mesh elements was 1,579  m2 or approximately 40 m on a regular grid side. Thus, near the channels, the ratio of ADHydro median mesh element size to WRF-Hydro grid cell size was approximately 13∶1. Trapezoidal channel dimensions for both models were defined using empirical stream order relationships that could be applied nationally.Surveyed HWM data are not available everywhere in the US; thus, we did not use these measurements for model calibration. Our goal was to calibrate model parameters using only nationally-available USGS observed hydrographs to get the hydrograph volume correct and, subsequently, to determine how well the models performed for simulating observed HWMs. Interested readers are referred to the study by Smith et al. (2020) for details regarding model calibration, simulation run periods, initial conditions, and analysis of simulated hydrographs. In this study, we focus on the analysis of inundation results from versions of ADHydro and WRF-Hydro that were calibrated to fit observed hydrographs.The constraint to use only nationally-available data sets in our underlying feasibility study (Smith et al. 2020) precluded the explicit modeling of buildings, microtopography, storm sewer networks, and cross sections, which likely impacted the simulation accuracy. Nonetheless, the choice of which urban features to model and how to model them must be considered in light of trade-offs in computational time, expected accuracy, and model complexity. Moreover, we still do not know how much physical complexity a flood inundation model needs to address a given problem (Neal et al. 2012). Modelers are cautioned regarding the expectation that increased modeling resolution and complexity will necessarily result in greater accuracy (Dottori et al. 2013). Modeling choices must also consider project goals, end-user requirements, data availability, preprocessing demands, and implementation effort (Schubert and Sanders 2012). These considerations are important to the NWS for the operational implementation of models at a national scale. For example, end users of NWS flood forecasts, such as emergency managers, often want actionable depth information presented in general ranges as they consider what level of response is necessary, such as signage, road closures, and rescue operations.We present several examples of the trade-off between model complexity (e.g., buildings, storm sewers, and cross sections) and project goals. Horritt et al. (2010) and Gallegos et al. (2009) determined that excessive computational demands with two-dimensional (2D) hydraulic models precluded the use of mesh sizes needed to resolve buildings. Yu et al. (2016) neglected buildings given the project scope and goals. Wing et al. (2019) did not model buildings, streets, or storm sewers in their city-scale evaluation of a 2D hydraulic model and a simple GIS-based approach for Hurricane Harvey in Houston. Even when buildings are modeled, simulation results can be contradictory and confounding. For example, Neal et al. (2009) found that RMSE errors in HWM simulations were slightly worse when buildings were modeled compared to the no-building scenario. Similarly, Grimley et al. (2017) found that representing buildings in the terrain model resulted in slightly worse results in basin outlet hydrograph simulation compared to the no-building case. On the other hand, Schubert and Sanders (2012) found that the inclusion of buildings is important for modeling local scale velocities and depths but less important for the simulation of hydrographs and flood extents.Regarding the importance of defining urban microtopography, Fewtrell et al. (2011) conducted a benchmarking study using two variants of a hydraulic model. Spatial resolutions of 25 cm, 50 cm, 1 m, 2 m, and 5 m were used to define the microtopography (e.g., curbs, road camber, etc.) on a very small 0.11  km2 basin. Such modeling resolutions required the use of vehicle-mounted light detection and ranging (LiDAR) units as airborne LiDAR has been incapable of providing the resolution needed to define urban microtopography (Ozdemir et al. 2013). Furthermore, proprietary software was needed to process the LiDAR data. Clearly, such efforts are nearly impossible at present and in the near future for city-scale operationally-viable forecasting in urban areas across the US.Studies have shown (e.g., Rafieeinasab et al. 2015; Schumann et al. 2011; Ogden et al. 2011) that in severe rainfall events, such as the two used in our study, the capacity of the subsurface drainage network pales in comparison to the flow conveyed by surface features. Moreover, it is nearly impossible to model all storm sewers in a city-wide domain in the time appropriate for operational forecasting. As a result, decisions must be made as to what level of simplification of the storm sewer network needs to be made to meet project goals (e.g., Habibi and Seo 2018; Leitao et al. 2010). Indeed, the immense complexity of the storm sewer network argues for simplicity as a first modeling step, as in our case (Gallegos et al. 2009).It is well known that cross-section shape and spacing can have large influences on the extent and depth of flood inundation. Among others, Ali et al. (2015), Cook and Merwade (2009), and Fewtrell et al. (2011) noted differences in flood inundation extents and depths when using cross sections derived from topographic data of various resolutions.ResultsResults at Surveyed HWMsWe computed maximum depths in each computational element to compare to the total of 172 surveyed HWMs and over 2,000 flood-damage/flooded-street observations. For WRF-Hydro, the maximum depth data files consisted of overland and channel flow depths. The ADHydro team submitted maximum depths in all nonchannel computational mesh elements. These depths should be taken into account when interpreting the results. For brevity, we focus on the calibrated results. The uncalibrated results are presented by Smith et al. (2020).Table 2 presents the analysis of predicted inundation depths at surveyed HWMs. Column 2 lists the six types of predicted maximum depths. The reference value refers to the maximum depth for the model element which contains the surveyed HWM. The other types (maximum, mean, median, IDW, and areal mean) are depths computed using the predicted maximum depths in all model elements residing within the areal sectors which contain each surveyed HWM. Note that the maximum category most likely included flow depths in channels for WRF-Hydro. There are 131 surveyed HWMs for the 2008 event and 41 for the 2011 event. Of these, 73 and 15 were above-ground HWMs for 2008 and 2011, respectively.Table 2. RMSE and MAE errors for simulated depths at surveyed HWMsTable 2. RMSE and MAE errors for simulated depths at surveyed HWMsModel 1Calculated Depth 2RMSE 3MAE 4RMSE 5MAE 6RMSE 7MAE 8RMSE 9MAE 10 1Reference0.910.570.530.320.980.810.880.83 2Maximum3.162.531.501.182.962.200.990.85 3Mean0.760.580.460.370.790.620.670.61 4Median0.840.570.520.321.000.850.850.81 5IDW0.830.570.510.330.920.750.830.79 6Areal Weighted Mean0.780.580.470.360.810.630.690.63 7Reference1.500.961.501.011.581.081.321.04 8Maximum3.552.943.222.933.082.592.922.71 9Mean1.801.401.691.421.611.221.251.04 10Median2.001.451.891.471.9001.421.501.13 11IDW1.451.051.641.271.360.931.350.98 12Areal Weighted Mean1.651.251.651.311.471.051.370.95Table 2 presents root mean square errors and mean absolute errors (MAE) between the predicted and observed HWM depths. We computed these errors for the 2008 and 2011 storm events for the reference depth and the five areal sector computed depths. An observed depth of zero was used in the case of surveyed on-ground HWMs. Columns 3–6 present the errors at all surveyed HWMs, while columns 7–10 highlight the errors for only above-ground surveyed HWMs. Values in bold font indicate the smallest errors in a column.The reference results (Row 1) in Table 2 indicate that WRF-Hydro generated lower RMSE and MAE values than ADHydro (Row 7) at all locations of surveyed HWMs. All the various areal sector RMSE and MAE values support this result by assessing the predicted depths at and in the vicinity of the surveyed HWM. In six of eight columns, the best overall results were generated by the WRF-Hydro mean depth, as seen by the values in bold in Row 3.Considering the within-model results, it would be reasonable to expect that a model’s best results would be achieved by the reference simulation at the HWM. For ADHydro, the reference depth (Row 7) did indeed generate the lowest RMSE and MAE errors in three cases, as seen in columns four through six. The IDW sector depth (Row 11) resulted in the lowest RMSE and MAE values in three other cases (columns 3, 7, and 8). For WRF-Hydro, none of the reference depths achieved the lowest RMSE and MAE values. Rather, the lowest WRF-Hydro values in six out of eight columns resulted from the mean sector depth (Row 3). The areal weighted mean depth achieved the second-lowest results (columns 3, 5, and 7–10). Our results in Table 2 suggest that the predictive strength of WRF-Hydro was achieved by mean or areal weighted mean depths rather than reference depths at the specific HWM locations. In contrast, ADHydro’s best results were achieved at or near the HWM locations using reference and IDW depths, respectively.The overall lower values of RMSE and MAE from WRF-Hydro compared to ADHydro in Table 2 are likely the result of two factors. First, because it is limited to a one-way coupling between overland and channel flow, WRF-Hydro frequently underestimated inundation depths. This resulted in lower RMSE and MAE values when observed depths were also shallow. Second, channel capacities in ADHydro were likely underestimated, causing that model to more frequently overpredict inundations depths by an amount greater than what WRF-Hydro underestimated them. This is supported by the general improvement in ADHydro’s RMSE and MAE values when surveyed on-ground watermarks are removed (21 out of 24 cases in columns 7–10). Contrary to ADHydro, when the surveyed on-ground watermarks were removed, WRF-Hydro’s RMSE and MAE values typically worsened (20 out of 24 cases in columns 7–10). Quantitatively, WRF-Hydro may be producing slightly lower RMSE and MAE values overall, but qualitatively, we believe ADHydro more realistically predicts inundation near the surveyed HWM and provides additional actionable information.We present further analyses of predicted depths at surveyed HWMs in Table 3. This table shows the percentage of hits each model recorded at the surveyed HWMs. A hit occurs when the model predicts a maximum depth that is greater than the observed depth multiplied by a threshold. Hits are computed using the reference depth and the areal weighted mean depth of the sector containing the surveyed HWM. We prescribed hit categories with depth thresholds of 10%, 30%, and 50% of the observed high water depth. For example, suppose the surveyed observed depth is 1.0 m. At the 10% threshold, a model must compute a depth greater than 1.0  m×10% or 0.1 m to record a hit. Note that for on-ground HWMs, a hit is counted anytime the flow depth is greater than zero. We interpret Table 3 as follows. The WRF-Hydro value of 39 in column 5 means that at 39% of the 131 surveyed HWMs, WRF-Hydro predicted a maximum depth which was equal to or greater than 50% of the observed high water depth.Table 3. Percentage of hits at surveyed HWMs by depth threshold.Table 3. Percentage of hits at surveyed HWMs by depth threshold.Model 1Calculated Depth 210% 330% 450% 510% 630% 750% 810% 930% 1050% 1110% 1230% 1350% 14WRF-HydroReference494339464444372619700Areal weighted mean897969806663816347602013ADHydroReference 978983807371958170533327Areal weighted mean10098969898981009693939393Considering first the reference case, the results in Table 3 indicate ADHydro computed the highest number of hits at all depth thresholds. Looking at the case for all HWMs (columns 3–8) for each depth threshold, ADHydro predicted exceedance depths at nearly twice the number of HWMs compared to WRF-Hydro. The differences are greater when only above-ground HMWs are considered (columns 9–14). Similarly, ADHydro areal weighted mean flood depths exceeded the thresholds at a greater number of HWM locations than WRF-Hydro. These results indicate ADHydro properly placed floodwaters at HWM locations more often than WRF-Hydro. ADHydro also computed more hits than WRF-Hydro for uncalibrated simulations of surveyed HWMs (Smith et al. 2020).Results at Flood-Damage/Flooded-Street LocationsTable 4 presents the analysis of simulated depths at property parcels where damage surveys were conducted, and photos and news outlets reported flooding. We used five threshold depths (<2.54, ≥2.54, ≥5.08, ≥15.24, and ≥30.48  cm) to calculate hit flooded percentages for the parcel polygons. We computed the parcel-threshold hits or the percentage of parcels at which the models predicted a maximum depth greater than a threshold depth.Table 4. Percentage of hits for different depth thresholds at flood-damaged/flood-damage/flooded-street locationsTable 4. Percentage of hits for different depth thresholds at flood-damaged/flood-damage/flooded-street locationsModelThreshold depth%Hits%Hits20082011373 flood reports1,951 flood reports280 parcels1,144 parcelsWRF-Hydro<2.54  cm31%84%≥2.54  cm69%16%≥5.08  cm61%12%≥15.24  cm44%4%≥30.48  cm21%1%ADHydro<2.54  cm1%43%≥2.54  cm99%57%≥5.08  cm92%36%≥15.24  cm84%21%≥30.48  cm76%15%In the case of WRF-Hydro simulations of the 2008 event, 31% of the parcel polygons had a predicted mean areal maximum water depth less than 2.54 cm, and 21% of the parcels had a predicted water depth greater than 30.48 cm. For the 2008 event, ADHydro achieved higher percentages of hits for deeper thresholds compared to WRF-Hydro. For example, 76% of the 280 parcels had a predicted maximum depth greater than 30.48 cm compared to 21% for WRF-Hydro.For the 2011 event, ADHydro also achieved more hits at the deeper thresholds than WRF-Hydro. Interestingly, both models generated more hits for the <2.54  cm threshold in 2011 than 2008. For WRF-Hydro, 84% of the parcels in 2011 had a computed depth of less than 2.54 cm compared to 31% in 2008. ADHydro generated max depths less than 2.54 cm in 43% of the cases in 2011, compared to only 1% in 2008.The results in Table 4 indicate WRF-Hydro tended to generate many instances of minimal flood depths (<2.54  cm) in 2008 and 2011 at locations where flooding was observed. Such large numbers of minimal depths are suspect, given the nature of the observations and storm severity. On the other hand, ADHydro computes a greater number of hits at all thresholds deeper than 2.54 cm. Hits at deeper thresholds seem to be realistic given the nature of the flooded streets/flood damage observations.Fig. 5 shows the spatial distribution of a subset of the 1,144 flooded property parcels for the 2011 storm described in Table 4. The preponderance of black parcels in Fig. 5 shows that WRF-Hydro had many more cases of flood depths less than 2.54 cm compared to ADHydro. The large number of parcel inundation depths less than 2.54 cm for WRF-Hydro seems unrealistic because 7–15 cm of rain fell within a period of 3–4 h.Conclusions and RecommendationsThis paper presents the application of novel techniques for the analysis of simulations of high water observations. We compared predicted maximum depths at 172 surveyed high-water marks, 373 locations of flooded structures, and nearly 2,000 observed flooded locations to evaluate the models’ ability to simulate inundation. In terms of data abundance, our study is among the most comprehensive reported in the literature to date.Simulation results were somewhat mixed between models, highlighting the need to examine multiple metrics when evaluating models. WRF-Hydro achieved lower values of RMSE and MAE when comparing simulated and surveyed HWM depths, but we surmise that this is attributed to shallower computed water depths when observed depths are also shallow. The marked improvement for ADHydro values of RMSE and MAE when removing on-ground HWMs suggests that WRF-Hydro skews this result in cases of shallow water depths. On the other hand, ADHydro more frequently generated significant inundation when compared to WRF-Hydro for all depth thresholds at surveyed high-water marks. In addition, ADHydro more often predicted flood inundation at locations with observed flood damage and/or street inundation. Thus, we conclude ADHydro properly predicted inundation more often than WRF-Hydro.Evaluation of simulated inundation depths and extents is complex. Our spatial analyses attempted to account for differences in model discretizations and computational element sizes and to distinguish model performance, assuming that the model predicted flooding in the vicinity of the HWMs. The techniques were predicated on an analysis of NED 10-m grid elevations, which showed minimal topographic variation in most of the areal sectors. Given the data constraints, modeling assumptions, and purpose of the study, we believe the analysis techniques helped distinguish model performance differences and identify model deficiencies. The analysis methods in our study are broadly applicable for validating and intercomparing urban flood inundation models.Further work is recommended to diagnose the surveyed HWM results. We used highly accurate surveyed HWMs in conjunction with the 10-m NED DEM. Future work could use the surveyed HWM data in conjunction with the 1-m LiDAR DEM available for Charlotte, NC, in the hope of achieving more accurate results (e.g., Neal et al. 2009). Two LiDAR DEM versions are available: 8–9  points/m2 and 30  points/m2 (Josh McSwain, CMSWS, personal communication, August 27, 2020).Both models defined channel geometry using stream-order scaling relationships. Using available surveyed cross-section information would likely have benefited both models. Surveyed cross sections were available for the Sugar Creek basin but not used as we desired to explore model performance using only data sets having national coverage.Future related studies should be limited to those models having a two-way coupling of overbank and channel flow. ADHydro contained a two-way coupling between the overland and channel routing components. 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