Compound Flooding in River-Urban-Coastal Environments: Multi-factorial Drivers and Modeling Considerations

Compound Flooding in River-Urban-Coastal Environments: Multi-factorial Drivers and Modeling Considerations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Compound Flooding in River-Urban-Coastal Environments: Multi-factorial Drivers and Modeling Considerations Arslaan Khalid, Celso Ferreira, Jason Elliott This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3866206/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the National Capital Region, existing coastal flood guidance systems frequently underestimate total water levels (TWL), leading to inaccurate flood predictions. Flood forecasting in this region, located at the confluence of two major rivers (Potomac and Anacostia) with tidal connections to the Chesapeake Bay, faces TWL under-predictions due to missing physical processes, limited integration of hydrological and hydrodynamic models, and simplified operational model frameworks. This study introduces an integrated TWL framework using a high-resolution two-dimensional coastal storm surge model (ADCIRC) that includes multiple flood drivers (storm tide, river flows, urban runoff, and local wind forcing) as one-way input boundary conditions in the tidal Potomac River. This framework accurately replicates historical events based on observed data, with validations indicating a 0.1 m under-prediction at the NOAA Washington, DC tide station (WASD), representing a -5% deviation from observed maximum water levels. Through hypothetical simulations for 25-, 50-, and 100-year return periods, we emphasize the substantial impact of individual flood drivers. Local winds had the smallest impact on water levels at WASD compared to downstream storm surge from the Chesapeake Bay (Lewisetta, VA). Upstream major river discharges elevate water levels beyond the National Weather Service (NWS) major flooding level by 0.9 m, further amplified to 1.4 m above the threshold when urban discharges occur simultaneously in the National Capital Region. Unlike prior studies, our work offers a comprehensive evaluation of individual flood drivers' influence on TWL modeling, underscoring the imperative need for their inclusion in the framework to accurately estimate river, coastal, and compound floods in estuarine metropolitan areas. Total Water Level Washington DC Tidal Potomac ADCIRC Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Key Points Current flood forecast systems based solely on ocean surge dynamics underestimate water levels near National Capital Region Strong local winds could raise water levels to NWS’s moderate flooding level at Washington DC High river discharge causes the water level in the Tidal Potomac to surge 0.9 meters above the NWS’s major flooding level in Washington DC. High River discharge combined with urban runoff can amplify flooding to 1.4 m above NWS’s major flooding level in Washington DC 1. Introduction Coastal cities in the mid-Atlantic region of the United States face numerous flood risks arising from astronomical tides, storm surges, precipitation, river discharge, and wind impact (Depietri et al., 2018 ; Ghanbari et al., 2021 ; Herdman et al., 2018 ). The combination of these factors often leads to compound flooding, a phenomenon that intensifies flooding impacts and causes significant socioeconomic losses (Bermúdez et al., 2021 ; Zscheischler et al., 2018 ). Recent estimations suggest that compound flooding events cost around 132 billion USD globally in 2021 and are projected to rise to approximately 158 trillion USD by 2050 (Jongman et al., 2012 ). Moreover, with the ongoing rise in global mean sea level, compound flooding is expected to become increasingly significant in coastal cities worldwide (Howat et al., 2007 ; Rahmstorf, 2017 ; Sweet et al., 2017 ; Wahl et al., 2015 ). Compound flooding, in our study characterized by the simultaneous occurrence of riverine, coastal, and meteorological flooding, poses a significant threat to river-urban-coastal environments (Dresback et al., 2013 ; Ghanbari et al., 2021 ; Santiago-Collazo et al., 2019 ). This flooding phenomenon results from the combined effects of low-pressure systems near coastal regions causing sea-level rise and accompanying frontal systems leading to excessive precipitation (Dresback et al., 2013 ). The mechanisms driving such flooding can arise from single meteorological events, a sequence of rapid independent events, or concurrent occurrences, thereby amplifying the flood hazard (Santiago-Collazo et al., 2019 ). The National Capital Region, including Washington, DC, is located at the confluence of two major rivers (Potomac and Anacostia) and experiences tidal oscillations from Chesapeake Bay. This region has witnessed an increase in the frequency and intensity of storms, leading to heavy precipitation events, rising sea levels, and elevated water temperatures in the Bay (Atkinson et al., 2012 ; Bennett, 2021 ; Zhong et al., 2008 ). In addition to heavy rainfall and stronger storm surges, the area is susceptible to "King tides," exceptionally high tides exacerbating flooding vulnerability in susceptible areas (Loftis & Forrest, 2018). Local factors such as near-surface winds (Mashriqui et al., 2014 ; Möller et al., 2001 ) and stormwater runoff (Walsh et al., 2012 ) play significant roles in generating local water level changes. Furthermore, regional variations in water levels can occur during summer seasons due to thermal expansion of water, even without discernible weather events (Boesch et al., 2018 ; Cronin et al., 2019 ; Reay & Erdle, 2011 ). Recent studies have indicated that the weakening of Gulf Streams and Florida Currents during tropical cyclones can intensify flooding potential in the mid-Atlantic coastal areas of the United States by amplifying the propagation of higher storm surges to upstream tidal regions (Ezer et al., 2013 ). Accurately simulating compound floods or total water levels (TWL) in riverine and coastal areas is a significant challenge for current coastal and hydraulic models. Existing models typically address specific flood drivers, limiting their capability to account for the complex interactions between tides, storm surge, sea-level rise, wind, and river discharge. Although some models accurately estimate storm surge impacts, they lack the ability to simulate streamflow, surface-runoff flow, and subsurface flow (Loveland et al., 2021 ). Other hydraulic models effectively simulate rainfall-runoff processes for small watershed scales but have limitations considering large-scale coastal processes like storm surge generation (Brunner, 2002 ; Downer & Ogden, 2004 ). To overcome the limitations of current models, recent studies have focused on using coupled frameworks that integrate multiple models to simulate compound floods accurately (Loveland et al., 2021 ). However, the application of these frameworks for flood forecasting remains limited due to computational costs and potential model instability (Ikeuchi et al., 2017 ). Operational models employed by the National Oceanic and Atmospheric Administration (NOAA) and the National Weather Service (NWS) also have limitations in accurately representing flood drivers and hydrodynamic processes, resulting in high uncertainties in total water level forecasts and inadequate incorporation of urban runoff and local wind forcing (Mashriqui et al., 2014 ; Thomas et al., 2022 ). Therefore, the primary objective of this study is to comprehensively evaluate the role of compound flooding drivers in the Potomac River near Washington, D.C., using the Advanced Circulation (ADCIRC) model. Specifically, this research aims to (a) assess the accuracy of the high-resolution ADCIRC model in reproducing historical water levels for riverine, coastal, and compound flooding events; (b) quantify the changes in local water levels at the Washington, D.C. tide gauge resulting from different levels of storm surge, river discharge, urban runoff, and local winds, both independently and in combination; and (c) establish a ranking or priority system for improving modeling accuracy in compound flooding simulation in the tidal area of the Potomac River. The study will involve calibrating the ADCIRC model to recreate and validate historical water levels at the Washington, D.C. tide gauge, followed by testing the TWL modeling framework for hypothetical input forcing scenarios representing various return period levels of flood drivers. The results of this study will contribute to a better understanding of compound flooding dynamics, improve flood forecasting, and aid in the development of effective mitigation strategies in river-urban-coastal environments, particularly in the National Capital Region. 2. Methods To evaluate the applicability of an integrated framework for total water level modeling in upstream tidal areas, we established a dedicated hydrodynamic numerical modeling domain specifically for the Potomac River. The ADCIRC coastal model was utilized for this purpose. The framework underwent calibration to accurately simulate astronomical tides and water level variations driven by riverine discharge, as described in section 2.2. Furthermore, the model's performance was assessed by comparing its results with six historical flooding events recorded at the Washington, DC NOAA station (section 2.3). This validation process ensured that the model accurately captures the simultaneous effects of multiple flood drivers. Subsequently, our focus shifted to quantifying the changes in water levels resulting from individual flood drivers, as well as their co-occurrence, using return period events (section 2.4). Finally, we introduced a ranking system that evaluates the influence of each flood driver on localized water level changes. This ranking system was established through return period event simulations (section 2.5). 2.1. Study Area This study focuses on Washington, DC, specifically its location at the confluence of the Potomac and Anacostia River. NOAA maintains a water level recording station in Washington, DC (referred to as WASD), which has documented an increase in the frequency of minor-level flooding days from 24 in 2010 to 43 in 2020. Minor-level flooding, as defined by the National Weather Service (NWS), refers to incidents with minimal to no property damage but the potential for some public threat. The Potomac River, the largest tributary of the Chesapeake Bay, spans approximately 166 km from Washington, DC to the Chesapeake Bay. Figure 1 displays the location of the Potomac River, which experiences tidal fluctuations due to its connection with the Chesapeake Bay downstream. However, the tidal influence diminishes near the Little Falls Pump station (LFMD), which serves as a United States Geological Survey (USGS) river discharge gauge upstream of Chain Bridge. LFMD has a total drainage area of 29,940 square kilometers (km 2 ) and exhibits an average daily flow of 334 cubic meters per second (m 3 /s). During heavy rainfall events, the flow can exceed 600 m 3 /s, as indicated by the NWS's Action Stage. On the eastern side of Washington, DC, the Anacostia River joins the Potomac near the Navy Yard (WNDC). The Anacostia River is shallower and narrower than the Potomac River (McDowell, 2016), and the average daily flow near Bladensburg is significantly smaller (< 4 m 3 /s) compared to the inflow from LFMD due to its drainage area of 315 km 2 (Huanxin et al., 1997 ). Moreover, several small urban streams discharge into the Potomac River between Chain Bridge and Fort Washington Park, as indicated by the blue arrows in Fig. 1. The drainage areas of these streams range from 104 to 521 km 2 . The mean tidal range at the Washington, DC station is approximately 0.9 meters (m), with a tidal phase lagging 5 hours (h) behind Lewisetta (LWTV) and 11.5 h behind Hampton Roads at Sewells Point (SWPV), which is situated at the mouth of the Chesapeake Bay (NOAA tides and currents). Figure 1 Map of study area. a) Overview map indicates the location of the study area in reference to Chesapeake Bay. Locations of two NOAA recording stations (Lewisetta and Swells Point) used for model validations is shown as black diamonds. b) Computational Model (Red Polygon) domain in Potomac River. Downstream boundary condition is represented as a blue line near Lewisetta NOAA gage (LWTV). Other NOAA gages used for model validations are shown as black diamonds. NOAA tide predictions are shown as light blue squares. Locations of stream flow gages is indicated using orange triangles. c) Shows an insert focusing on Washington, DC. Purple arrows indicate location of river inflows whereas blue arrows show location of urban flows or lateral flows. Figure 1 illustrates the entire modeling domain of the study area, including three boundary conditions: (1) major upstream river boundaries based on observed discharge data from LFMD on the west, as well as the Northeast and Northwest Anacostia River USGS streamflow gauges (NWMD and NEMD) on the east, shown as purple arrows; (2) small stream flow boundaries based on available USGS observed discharge, represented by blue arrows; and (3) a downstream boundary (stage hydrograph) based on water observations collected at the NOAA Lewisetta, VA gauge (LWTV). Water level observations and predicted tides from NOAA stations were acquired from the Center for Operational Oceanographic Products and Services (NOAA Tides and Currents) to assess the performance of the model. The positions of the NOAA water level and tide stations are indicated in Fig. 1b, represented by black and light blue dots, respectively. A comprehensive account of each recording station utilized in this study can be found in Table A1 of the appendix . 2.2. Modeling Framework In this study, we employed a model framework whose schematic structure is illustrated in Fig. 2 . The components of the model framework were interconnected in an offline mode through boundary linkage. The coastal contribution, particularly astronomical tides and storm surges (referred to as storm tide), were provided by the downstream input location. The river inflow locations accounted for the contribution from rainfall-runoff processes. Additionally, the urban inflow was considered as lateral flows into the model domain. To incorporate local changes in water levels at the NOAA Washington, DC tide gage, we utilized local wind speed observations from the NOAA Washington, DC meteorological station and the Washington National Airport (WAS) recording station. These observations were uniformly interpolated over the entire modeling domain. 2.2.1. Advanced Circulation (ADCIRC) The Advanced Circulation (ADCIRC) model, originally developed by Luettich et al. in 1992 (R. A. Luettich et al., 1992 ), is a finite-element hydrodynamic model that employs the generalized wave continuity equation (GWCE). By solving these equations on an unstructured computational grid in both space and time, the ADCIRC model can effectively simulate the dynamics of open water bodies such as oceans, lakes, and rivers. The model is capable of accurately capturing the effects of various driving forces, including astronomical tides, coastal storms, and river inflows. Its versatility has made it a popular choice for modeling historical storm surges and forecasting floods (Blain et al., 2010 ; Dresback et al., 2013 ; Funakoshi et al., 2012 ; J. L. Garzon et al., 2018 ; Hanson et al., 2013 ; Khalid & Ferreira, 2020 ; Shen et al., 2006 ). In our study, we utilized the two-dimensional, depth-integrated version of ADCIRC, known as ADCIRC-2DDI, operating in the barotropic mode with a constant density assumption. This configuration allowed us to simulate the combined impacts of astronomical tides, river inflows, urban runoff, local winds, and storm surges on the overall water levels. ADCIRC is implemented using FORTRAN and is an open-source numerical model that benefits from extensive documentation available in both the published scientific literature (R. Luettich & Westerink, 2004a ) and the official ADCIRC website ( http://adcirc.org/ ). 2.2.2. Model Input Forcing The study utilizes four primary input variables for model simulation, namely downstream water levels, upstream major river discharges, stream flows, and wind observations obtained from the WASD, which covers the entire modeling domain. The downstream boundary is determined based on observed water levels at the LWTV gauge. In cases where LWTV data is unavailable, data from the SWPV gauge is used instead, adjusted for a time lag of -5 hours. The upstream discharge boundary is derived from daily-observed flow data at the USGS Little Falls Pump Station gage (LFMD), which is interpolated to hourly flow using spline interpolation to ensure model stability. Additionally, urban runoff (defined as lateral flows) is considered as a boundary condition, but only when available for historical validation events. Prior to 2008, wind observations at the WASD station were not accessible. Therefore, wind data from the Washington National Airport meteorological station were utilized, with adjustments made by converting from a 5 m elevation to a 10 m elevation using a multiplier of 1.09, as described by Hsu et al. (Hsu et al., 1994 ). No wind observations were available for model validation before 1937. It is important to acknowledge that due to the interpolation of daily to hourly data and the use of proxy data when observed data is absent, there is a potential for uncertainty in the model results during the validation process. 2.3. Model Setup The unstructured computational grid for the ADCIRC model was created using the automated mesh generator, OceanMesh2D (Roberts et al., 2019 ). To accurately represent the coastline in the numerical grid, the high-resolution coastline data from the Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG) (Wessel & Smith, 1996 ) was manually updated within the study domain. The open ocean boundary, located near LWTV (Fig. 1), was defined as a non-periodic, time-varying elevation boundary condition. By placing this ocean boundary near LWTV, we were able to incorporate coastal forcing directly from NOAA's station, minimizing the error in tidal predictions resulting from low-resolution Global Tidal Models. For validation storms, the river discharge boundary was added as a time-dependent flux boundary condition at LFMD. Since ADCIRC interprets flow as flux (Pandey et al., 2021 ), the discharge data was converted to m 2 /s using the width between the flow boundary nodes. The topography data in the localized model was extracted from the USGS 1/9 arc-second Digital Elevation Model (DEM), while bathymetry data was obtained from various sources including NOAA nautical charts (Austin, 2005 ), NOAA National Centers for Environmental Information (NCEI) (Caldwell et al., 2015 ), and the Coastal National Elevation Database (CoNED) (Thatcher et al., 2016 ) during the calibration phase to determine the most accurate bathymetry. All elevation and bathymetry datasets were in meters and vertically adjusted to the North American Vertical Datum (NAVD88) before being used in the ADCIRC model. The land cover in the modeling setup was based on the National Land Cover dataset (NLCD) of 2019 (Dewitz, 2021 ). ADCIRC's f13 utility ( https://adcirc.org/ ) was employed to generate nodal attributes such as mannings_n_at_sea_floor , primitive_weighting_in_continuity_equation , surface_canopy_coefficient , and surface_directional_effective_roughness_length . In our study, the simulations included various ADCIRC model features such as the wetting and drying algorithm, non-linear bottom friction, advection, finite amplitude terms, convective acceleration, and the time derivative of convective acceleration. 2.4. Model Calibration To achieve the study objective, we performed four set of model simulations to determine the best ADCIRC modeling setup to simulate astronomical tides within the study area. These experiments focused on testing different configurations of four modeling input parameters namely, numerical mesh resolution, bathymetry data source, bottom friction, and eddy horizontal viscosity. The testing range of values for calibration of bottom friction variable manning’s n in waterways (0.01 to 0.02), eddy horizontal viscosity (0 to 40) and grid resolution for bathymetric representation in channels (500 m to < 50m) were based on test values provided by previous literature (Bacopoulos & Hagen, 2017 ; Bakhtyar et al., 2020 ; Bastidas et al., 2016 ; J. Garzon & Ferreira, 2016 ; Kerr et al., 2013 ; Mied et al., 2006 ; Thomas et al., 2021 ). For bathymetry testing, we used three publicly available data sources for Potomac River (Modified NOAA nautical charts, NCEI, CONED) along with a modified mosaic of available bathymetry datasets (Merged DEM). Table 1 lists parameter-testing values used for each calibration test. Figure A1 in appendix provides the comparison of model bathymetries. The downstream ocean boundary was set as NOAA predicted astronomical tides from LWTV starting at January 1, 2020 and ending at January 30, 2020, and each calibration test simulation lasted for 30 days. Table 1 Calibration Parameters and associated average percentage error for each test in Experiment 1 Parameters Identifier Description Nodal information Numerical Model Resolution low_noCh Low resolution with no defined channels NC: 6253, MiG: 450, MaG: 550 low_Ch Low resolution with defined channels NC 8068, MiG : 350, MaG : 450 high_noCh High resolution with no defined channels NC: 133101, MiG: 60, MaG: 225 high_Ch high-resolution numerical mesh (high_Ch) NC: 276647, MiG:15, MaG: 200 Bathymetry Datasets B1 Modified NOAA nautical charts (NOAA) Same as high_Ch B2 National Centers for Environmental Information (NCEI) Same as high_Ch B3 Coastal National Elevation Database (CoNED) Same as high_Ch B4 Merged DEM Same as high_Ch Bottom Friction (Manning’s n) M1 0.02 Same as high_Ch M2 0.015 Same as high_Ch M3 0.013 Same as high_Ch M4 0.01 Same as high_Ch Horizontal Eddy Viscosity (m 2 /s) E1 0.5 Same as high_Ch E2 1 Same as high_Ch E3 5 Same as high_Ch E4 10 Same as high_Ch * NC: Total ADCIRC node count; * MiG: ADCIRC minimum node spacing (meters); * MaG: ADCIRC maximum node spacing (meters) 2.5. Model Validation For model validation, we employed the optimal mesh resolution, bathymetry sources, bottom friction, and eddy horizontal viscosity parameters established during the model calibration phase. We conducted simulations for six historical storm events using available data on river discharge, ocean water levels, and meteorological conditions as boundary inputs to assess the model's accuracy in replicating historical total water levels at the NOAA Washington, DC tide gauge, often referred to as WASD. These case studies encompass two historical events for each flooding source category: River, Coastal, and Compound. A "River" flood event is characterized by a predominant influence of the upstream discharge from the USGS Little Falls Pump Station gauge (LFMD) on the observed water level increase at WASD. In contrast, a "Coastal" flood event results from a pronounced storm tide signal recorded at LWTV, which propagates upstream to affect WASD. Coastal floods typically involve relatively low discharge from the LFMD gauge (below 4000 m3/s). Lastly, a "Compound" flood occurs when both high river discharge and elevated coastal water levels coincide, leading to a "Minor" flooding alert issued by the NWS (defined as 0.85 m water level above NAVD88 at WASD). These model validation events were simulated for approximately 23 days, with the simulations commencing at least 10 days prior to the observed peak water levels at WASD to allow for model initialization and enhanced stability. Table 2 provides a detailed breakdown of event types, observed maximum water levels, wind conditions, discharge data, and event timing. Additional information regarding the selected validation events can be found below. Table 2 Validation Events for evaluating recreation of historical water levels Events Type Year Dates Avail. Data Flooding Level Observed Maximum Water level (m) Discharge (cfs) Wind speeds (m/s) Great flood of 1936 River 1936 03/14 − 03/24 Q, H* Major 2.79 14500 - Hurricane Agnes River 1996 01/14 − 01/25 Q, H*, W+ Major 2.22 9458 14.9 April 2011 Compound 2011 04/08 − 04/27 Q, H, W Moderate 1.35 4585 10.5 May 2014 Compound 2014 05/08 − 05/27 Q, H, W Moderate 1.33 4073 10.4 Hurricane Isabel Coastal 2003 09/15 − 09/27 Q, H, W+ Major 2.7 4348 20.1 TS Ernesto Coastal 2006 08/28 − 09/05 Q, H, W+ Major 1.61 513 14.9 Q = Discharge at LFMD, H = Water levels at LWTV, H* =Water levels at SWPV, W = Wind speeds at WASD, W + = Wind speeds at DCA In the early 1900s, riverine floods were common in the Washington, DC area and led to significant historical floods, including the Chesapeake-Potomac Hurricane of 1933, the Great Potomac Flood of 1936, the Flood of 1942, and Hurricane Agnes in 1972 (National Capital Planning Commission, 2008 ). The USGS Little Falls Pump Station gauge has continuously recorded daily river discharge since the 1930s. The Great Flood of 1936, resulting from snowmelt and heavy rainfall, saw LFMD discharge exceed 14,500 m 3 /s, causing severe flooding in Washington, DC, with observed water levels over 2.79 m above NAVD88 (Winters, 2018 ). The Blizzard of 1996, characterized by heavy snow followed by rain, led to flash floods, with LFMD recording a peak discharge of 9,260 m 3 /s and resulting in a maximum water level of 2.04 m above NAVD88 at the WASD gauge station. Coastal flooding in Washington, DC is primarily caused by strong storm tide signals from the NOAA Lewisetta station in the Chesapeake Bay. Two notable coastal events, Hurricane Isabel in 2003 and Tropical Storm Ernesto in 2006, caused flooding at WASD due to recorded water levels exceeding 1 m at LWTV. Coastal floods at WASD are generally smaller than riverine floods, except for Hurricane Isabel, which led to "major" flooding at WASD with levels reaching 2.7 m above NAVD88 (Montgomery et al., 2006 ). Tropical Storm Ernesto generated a record storm tide of 1.5 m above NAVD88 but resulted in moderate flooding at WASD, reaching 1.61 m above NAVD88 (Knabb et al., 2006 ). Compound floods in Washington, DC result from simultaneous high river discharge and coastal water levels. Major compound flood events occurred in 1937 and 1972, but they were not simulated due to data unavailability. Two distinct compound events in April 2011 and May 2014 were used for validation, combining a large storm tide from LWTV with high river discharge (> 4,500 m 3 /s) from LFMD, resulting in water levels at WASD rising to 1.35 and 1.3 m above NAVD88, classified as moderate flooding by the NWS. Wind speeds during both events exceeded 10 m/s, with gusts reaching up to 15 m/s at WASD. 2.6. Model Forcing Sensitivity To assess the impact of various flood drivers on the total water level in the Potomac River, we conducted a sensitivity analysis that encompassed the following scenarios: 1) downstream boundary conditions (storm tide); 2) upstream boundary conditions (major river discharges); 3) lateral boundary conditions (urban runoff); 4) combined upstream and lateral boundary conditions; and 5) local-scale wind forcing. Each scenario involved unsteady ADCIRC simulations spanning 15 days, which included a 3-day spin-up period. These simulations were compared to a baseline scenario representing calm day conditions, driven by tidal influences alone. For the downstream boundary conditions, we applied storm tide values based on NOAA's calculated 25-, 50-, and 100-year return periods at LWTV, resulting in surge values of 1.1 m, 1.2 m, and 1.3 m above the NAVD88 datum, respectively. To isolate the influence of downstream boundary conditions, we excluded stream flow and local wind forcing from these simulations. To quantify the impact of upstream major river flows, we conducted simulations with discharge inputs based on stream flows corresponding to 25-, 50-, and 100-year return periods at LFMD and Anacostia river gages, sourced from USGS StreamStats analysis (Ries III et al., 2017 ). For analyzing the effects of urban runoff, we introduced discharge inputs as lateral flows at various urban stream locations along the Potomac River, with return period flows of 25-, 50-, and 100-year, also based on USGS StreamStats analysis. We further performed simulations with combined flows from major rivers and urban runoff to evaluate the cumulative impact on water levels when both factors contributed to Potomac River flow. In these combined forcing simulations, no time lag was considered to represent worst-case compound flooding conditions. Lastly, to investigate the influence of local wind changes on water level variations at WASD, we conducted tests with eight wind directions (N, NE, E, SE, S, SW, W, and NW) and wind magnitudes ranging from 5 to 35 m/s, with a peak duration of 12 hours. These wind magnitudes were based on the wind records at WASD and the WAS meteorological station, taking into account predominant wind directions and magnitudes exceeding 35 m/s. Table 3 Flow characteristic for major rivers and urban runoffs Full name Code Drainage Area (km 2 ) Max measured flow (m 3 /s) 25 year return period 50 year return period 100 year return period Major Rivers Little Falls at Potomac River RF1 29940 13705 8680 10752 12908 Bladensburg at Anacostia River RF2 239 349 233 317 580 Urban Runoffs Rock Creek LF1 521 77 330 417 518 Four Mile Run LF2 122 36 308 390 483 Oxon Run LF3 98 - 121 160 209 Cameron Run LF4 228 116 349 426 512 Broad Creek LF5 173 35 166 218 283 Piscataway Creek LF6 420 127 231 300 384 Little Hunting Creek LF7 65 - 23 31 39 The input conditions employed in these scenarios are theoretical and represent simplified representations of actual situations, spanning from typical daily weather to severe weather extremes. These scenarios serve the purpose of enabling a more precise assessment of the influence of each flood driver on total water levels. They underscore the significance of either incorporating or omitting specific factors to attain accuracy in total water level modeling in the Potomac River, particularly at the WASD tide gauge. 2.7. Ranking Flood Drivers Following the aforementioned experiments, we conducted a detailed analysis to isolate the percentage change in water levels above normal daily tides at the WASD gauge station. This allowed us to quantify the individual contributions of each flood driver. In this process, flood drivers that exhibited the highest percentage change in a given return period experiment were assigned a higher rank in comparison to the other flood drivers. This ranking system provides valuable insights into the relative impact of different drivers on total water levels at WASD. 2.8. Model Evaluation For a quantitative assessment of the modeled water levels, we employ various statistical metrics, which encompass bias, mean absolute error (MAE), and percentage change. Bias assesses the model's propensity to either overestimate or underestimate peak water levels. MAE represents the average of all absolute errors computed during the storm's duration. Percentage change is employed to gauge the variation in the modeled peak water level in comparison to the maximum observed water level at WASD. These evaluation metrics are defined as follows: $$Bias= {x}_{mod} -{x}_{obs}$$ $$MAE= \frac{1}{n}{\sum }_{i=1}^{n}| {x}_{mod} -{x}_{obs}|$$ $$Percent Change= \frac{{x}_{mod} -{x}_{obs}}{{x}_{obs}}* 100$$ Where \({x}_{mod}\) and \({x}_{obs}\) are the modeled and observed water level at a given recording station at a given time step. The \(n\) is the number of time steps in hours for error calculation. 3. Results and Discussions 3.1. Model Calibration 3.1.1. Tidal Hydrodynamics Results from experiment 1, which focused on model calibration, are depicted in Fig. 1 as a bar plot of MAE for eight selected NOAA tide recording stations. The average MAE across all recording stations is represented by horizontal lines in Fig. 4 . The findings reveal the following: 1) The use of Modified NOAA Nautical DEM for bathymetry resulted in the smallest MAE; 2) A high-resolution mesh with clearly defined channels (high_Ch) demonstrated the lowest MAE; 3) Employing a Manning’s n roughness value of 0.01 in open water for bottom friction yielded the smallest MAE for modeled tides; and 4) An ESLM value of 0.5 produced the smallest MAE while maintaining model stability. In Fig. 4 , Panel a illustrates that the ADCIRC model with the highest resolution and well-defined channels (high_Ch) resulted in the smallest MAE. This indicates that to accurately replicate local hydrodynamic processes, the model grid resolution must be sufficiently fine, particularly in the channels, to enhance conveyance and reduce errors. Previous studies have shown that higher resolution grids with improved channel representation can lead to a 5–10% increase in tidal amplitude (Blanton et al., 2004 ) and alter tidal propagation and resonance (Bacopoulos & Hagen, 2017 ). Panel b of Fig. 4 demonstrates that the source of bathymetry also influences tidal simulations. For instance, at stations such as WASD, NCEI, and NOAA, the MAE values are 0.07 m, while for NCEI bathymetry, the MAE values become significantly larger along the Anacostia river stations (KMLD and ELMD). This highlights the critical role of accurate bathymetry in ensuring precise hydrodynamic simulations, as uncertainties in bathymetric data can lead to misrepresentations of estuarine morphology and subsequently impact current and water level conditions (Sohrt et al., 2021 ). Panel c of Fig. 4 displays the MAE associated with four simulations of horizontal eddy viscosity (ESLM). Similar MAE values are observed at tide stations up to ALVA, but decrease with smaller values of ESLM for upstream stations. The ESLM parameter governs hydraulic losses within ADCIRC simulations, and smaller values allow for more accurate computation of hydraulic losses based on ADCIRC grid resolution (Dill, 2007 ; R. Luettich & Westerink, 2004b ). Our calibration results for ESLM testing align with previous findings (J. Garzon & Ferreira, 2016 ), indicating that tidal amplitude and current are sensitive to ESLM values. Similar to other calibration parameters, Manning’s n roughness parameter also impacts the MAE of simulated tides in the Potomac River. Panel d of Fig. 4 shows that the smallest MAE for WASD and accompanying stations is observed at a Manning’s n value of 0.01 for waterways, which controls bottom friction by offering less resistance against the bottom. This supports previous research (J. Garzon & Ferreira, 2016 ; Passeri et al., 2012 ) highlighting the sensitivity of bottom friction formulation to tidal amplitude. Our calibration testing provides insights into the use of lower range values for Manning’s n roughness and ESLM, in combination with high-resolution mesh with defined channels and bathymetry obtained from CONED. As a result, the calibrated version of the ADCIRC model is used for model validations and return period simulations in the following sections. 3.2. Model Validation The ADCIRC model was run for six historical storms, and the water level time series at WASD is shown in Fig. 6 . On average, the modeled water levels were 5% lower than the observed maximum water level at WASD. The peak of the modeled water level was underestimated by a small bias of 0.1 m for all six historical storms during the storm duration. For the two riverine events, the ADCIRC model simulated the increase in water levels caused by the large incoming river discharge from LFMD, with a small underestimation of -0.17 m or -6% of the observed maximum peak (panel a and b of Fig. 6 ). The modeled maximum water level during the 1936 flood showed an increase of nearly 2.5 m above normal daily tide as a result of incoming river discharge. The modeled water level for WASD during the 1996 event also showed an underestimation of -0.09, which translated to a -4% change compared to the observed peak. No time lag was observed in the modeled peak at WASD for 1936 river flood, however, time lag of -1 hour occurred during Blizzard of 1996. This underestimation could have resulted from non-availability of the hourly discharge values for use in the ADCIRC river discharge boundary or inability of ADCIRC to accurately capture discharge-driven water level changes. For the two coastal events, the ADCIRC model clearly simulated the increase in water levels at WASD caused by the strong storm surge signal propagating upstream from LWTV. The modeled peak at WASD was slightly underestimated by -0.08 m for Hurricane Isabel (2003) and − 0.05 m for Tropical Storm Ernesto (2006), which translated to a percentage change of -3% for both events. MAE values of 0.07 and 0.05 was calculated for the duration of the storm for both coastal events. No time lag was observed in the modeled peak at WASD for Hurricane Isabel, however, time lag of -1 hour occurred during Tropical Storm Ernesto. For the two compound events, the ADCIRC model was able to reproduce the peak of the events somewhat accurately when compared with observations, but failed to simulate the increase in water levels prior to the observed peak. An underestimation of -0.1 and − 0.2 m was noted during the peak of the storm, which represented a percentage change of 8 and 14% for the 2011 and 2014 flood events, respectively. In terms of the time lag, ADCIRC modeled maximum water level lagged observation by 14 and 26 hours for 2011 and 2014 compound flood. Table 4 summarizes the model evaluation metrics. Overall, the ADCIRC model consistently underestimated the water level by 0.1 m, which is also equivalent to an average percentage change of -5% for all six historical validation events. The model tended to underrepresent the shape of the maximum water level timeseries during compound flood events, which is likely due to its inability to model baseflow. Our validation results for the Great Flood of 1936 and Hurricane Isabel in 2003 at WASD were similar to those published in an earlier study (Wang et al., 2015 ), in which the authors used the SCHISM model with the upstream river boundary set at LFMD and the downstream boundary set at Colonial Beach (53 km upstream from LWTV). Our modeling setup underestimated the peak water level by 0.17 m and 0.08 m during the Great Flood of 1936 and Hurricane Isabel, respectively, which is higher than our overall model evaluation. The simulated water level peak error during Isabel was also comparable to the ADCIRC model results at WASD published earlier by Mashriqui et al. (Mashriqui et al., 2014 ) (Bias of 0.65 m and phase lag of 4 hours). It is important to note that the model tends to underrepresent the shape of the maximum water level timeseries during compound flood events. This could be due to ADCIRC's inability to model baseflow, which can lead to uncertainties at the peak and recession curve (Loveland et al., 2021 ; Tromble, 2011 ). Despite these limitations, the model validation results show that the integrated modeling framework can reliably simulate historic extreme water levels at WASD. Table 4 Model validation summary for nine historical storms at Washington DC NOAA tide gage Events Type Avail. Data Observed max peak (m) Model max peak (m) Bias (m) Percent Change at peak MAE Storm Duration (m) Observed peak timing Model peak timing Peak Time Diff (hr) Great flood of 1936 River Q, H* 2.79 2.62 -0.17 -6% -0.05 1936-03-20 00:00 1936-03-20 00:00 0 Blizzard of 1996 River Q, H, W+ 2.03 1.95 -0.08 -4% 0.03 1996-01-21 16:00 1996-01-21 15:00 -1 Hurricane Isabel Coastal Q, H, W+ 2.70 2.81 -0.08 -3% 0.07 2003-09-19 09:00 2003-09-19 09:00 0 TS Ernesto Coastal Q, H, W+ 1.61 1.56 -0.05 -3% 0.05 2006-09-02 06:00 2006-09-02 05:00 -1 April 2011 Compound Q, H, W 1.35 1.25 0.10 8% -0.08 2011-04-16 22:00:00 2011-04-17 12:00:00 14 May 2014 Compound Q, H, W 1.30 1.10 -0.20 14% -0.03 2014-05-16 13:00:00 2014-05-17 15:00:00 26 Q = Discharge at LFMD, H = Water levels at LWTV, H* =Water levels at SWPV, W = Wind speeds at WASD, W + = Wind speeds at DCA 3.3. Model Forcing Sensitivity The following subsections quantifies the change in water levels resulting from various flood drivers. 3.3.1. Downstream Boundary Conditions Figure 7 illustrates the elevation in water levels above normal daily tides at several recording stations (from LWTV to WASD) when a storm tide with a return period of 25 to 100 years enters the Potomac River. The magnitude of this increase in water levels becomes consistent at Alexandria (ALVA) and continues to propagate upstream. The results reveal that the maximum elevation in water levels above normal daily tides at WASD during a 100-year return period does not surpass 1.5 meters. During Tropical Storm Ernesto in 2006, a surge of similar magnitude propagated upstream from LWTV (1.47 meters above NAVD88), elevating the water levels at WASD by approximately 1.11 meters above NOAA's predicted astronomical tides. The disparity between the estimated increase and the observed increase due to downstream boundary conditions could potentially be attributed to other flood drivers during Tropical Storm Ernesto. Additionally, the results indicate an approximately 26% increase in storm surge magnitude as the storm surge signal travels from LWTV to WASD (as shown in the right panel of Fig. 7 ). In terms of local variations along the stations in comparison to LWTV, the storm tide with return periods of 25 to 100 years alone results in a 25–27% increase between LWTV and WASD. Given the substantial contribution of water level arising from downstream boundary conditions, it significantly influences the water levels at WASD in comparison to other forcing factors. 3.3.2. Upstream Boundary Conditions of Major River Flows In Fig. 8 , the elevation in water levels resulting from the introduction of a range of return period (25 to 100 years) discharges into a typical tidal simulation in January 2020 is displayed. The effects of individual upstream river discharges are illustrated in panels a and b of Fig. 8 , from the point of discharge input to WASD. Panel a shows that for return periods of 25 to 100 years, an elevation of 8 to 10 meters is observed in comparison to a normal daily tidal simulation. This elevation gradually diminishes as it progresses towards WASD, resulting in an increase of approximately 1.4 to 2.6 meters compared to the normal daily tidal simulation. In contrast to the influence of LFMD, the discharge boundaries from the Anacostia River only yield an increase of 2.2 to 2.7 meters at the boundary station, which decreases to around 0.1 to 0.13 meters at WASD. Given the substantially smaller drainage area of the Anacostia River, the rise in water levels due to Anacostia discharges was expected to be lower for return periods of 25 to 100 years. Panel c portrays the combined impact of both river discharge boundaries from WASD to LWTV. The elevation in water levels at the WASD station resulting from 25 and 100-year return periods is approximately 1.5 to 2.7 meters above normal daily tides. Our model validation for the 1936 Flood demonstrated a slightly smaller increase in water levels (approximately 3 meters above normal tides) at WASD when a 100-year observed return period flow at LFMD inundated the region. It's important to note that during the validation for the 1936 Flood, only LFMD experienced flows exceeding the 100-year return period, while BDMD had no recorded observed flow until 1938. Figure 8 also presents the increase in water level as a function of distance when various return period flows were introduced. This plot suggests that upstream discharge can have an impact as far downstream as Colonial Beach (COVA), which aligns with observations from a previous study (Mashriqui et al., 2014 ). This analysis underscores the significance of incorporating upstream major discharge boundaries, particularly when riverine flows surpass 10 times the average daily flow, equivalent to a 2-year return period, as the model may not accurately capture total water levels in their absence. 3.3.3. Upstream Boundary Conditions from Urban Runoff In Fig. 9 , Panel a illustrates the rise in water levels resulting from the introduction of a range of return period stream flows from tributaries (indicated by blue arrows in Fig. 1) into the Potomac River. A discernible increase ranging from 0.14 to 0.23 meters is observed at WASD. While this increase is notably smaller than that from the Little Falls discharge, it surpasses the influence of the Anacostia discharge at WASD. The simulations of urban discharge emphasize the importance of including it in the TWL modeling to accurately capture local water level fluctuations at WASD. However, the impact from urban discharges becomes negligible near GCMD, signifying the conclusion of urban contribution in a normal daily tidal simulation. 3.3.4. Upstream Boundary Conditions from Combined River flows and Urban Runoff Furthermore, a series of simulations were conducted involving the combination of major river flow and urban runoff for return periods of 25 to 100 years. Panel b of Fig. 9 illustrates the increase in water levels above normal daily tides as a function of distance. Remarkably, the combined effect of major river flow and urban runoff for a 100-year return period results in an additional 0.3 meters of water level increase compared to simulations with major river flows alone (2.7 m). The maximum water level rise above daily tides at WASD reaches 3 meters when both major discharges and urban runoff experience a 100-year return period. While this scenario is hypothetical, the 1936 flood serves as an example of such events. Given the significant increase in water levels at WASD due to combined discharges for all three return periods above normal daily tides, this underscores the importance of incorporating urban drainage alongside major river flows when modeling historical events or developing an integrated total water level forecasting system. Although urban runoff boundaries alone may not exert a significant influence on water levels at WASD, their combination with substantial river discharges will significantly impact the simulated Total Water Level (TWL). 3.3.5. Effect of Local Winds The results obtained from examining the local wind effects for eight primary directions at six stations surrounding WASD are depicted in Fig. 10 . Positive values signify an increase in water levels, while negative values indicate a decrease. Given the WASD station's location, one would anticipate local water level increases when winds blow from the south and decreases when they come from the northwest, pushing water away from the station. Figure 10 clearly illustrates that when winds blow from the north, northeast, west, and northwest directions, they result in a decrease in water levels at the WASD station, with larger decreases as wind magnitudes increase. Conversely, when winds originate from the south, southwest, east, and southeast, an opposite trend is observed, leading to increases in total water levels. These localized changes in total water levels due to local winds align with findings from a previous study using a Delft3D model (Mashriqui et al., 2014 ), which demonstrated that winds exceeding 5.5 m/s from the northwest direction drained water from the Potomac River, thereby reducing water levels at WASD. Our wind analysis highlights the importance of incorporating local wind forcing when it exceeds 10 m/s to accurately capture localized changes in total water level. On the other hand, winds below 10 m/s from the west, north, northeast, and northwest do not elevate water levels at WASD, making forecasts generated by ocean-scale coastal guidance systems, which do not account for local winds, reasonable. An examination of observed winds at WASD reveals that winds exceed 8 m/s at least once every month (Figure A2 in the appendix ). While events with local wind speeds exceeding 10 m/s are less frequent, the absence of local wind forcing in such situations would lead to a significant misrepresentation of the forecasted TWL, as demonstrated in the simulations above. The sensitivity of individual flood drivers to local water level changes is consistent with prior research, as indicated by Wang et al. (Wang et al., 2015 ), which revealed the significant impact of downstream storm tides and upstream river discharge on water levels in the Washington, DC area's tidal estuaries. Likewise, model experiments conducted by Mashriqui et al. (Mashriqui et al., 2014 ) demonstrated the sensitivity of Washington, DC's water levels to wind speeds exceeding 5 m/s, aligning with our findings. However, the influence of urban runoff on total water levels in Washington, DC lacks extensive documentation, limiting our ability to validate its accuracy. 3.4. Ranking Flood Drivers In Fig. 11 , a summary of the significance of individual flood drivers at WASD is presented. Panel a demonstrates that urban runoff boundaries alone do not elevate water levels beyond the "Action" stage for return periods of 25 to 100 years. Conversely, storm surges have the potential to raise maximum water levels at WASD above the "Moderate" flooding level, reaching the "Major" flooding level when a 100-year storm surge travels up the Potomac River from Lewisetta, as observed during TS Ernesto in 2006. Local wind forcing exceeding 20 m/s from the South falls within the "Minor" flooding level, transitioning to "Major" flooding as local winds exceed 25 m/s. Among all the individual flood drivers, the 100-year return period major river discharges are found to have the most significant impact, resulting in "Major" flooding at WASD. The magnitude of LFMD greatly surpasses that of the Anacostia, and the 100-year discharge boundary alone can elevate water levels at WASD to 2.7 meters above NAVD88. Lastly, the maximum water levels caused by the combined river flows and urban discharge illustrate the potential for a major influence on WASD water levels, resulting in "Major" flooding for a 25-year return period. This plot highlights that a 100-year combination of river flows and urban discharge could lead to a substantial flood event at WASD, surpassing the maximum water level recorded at the station. To showcase the influence of each flood driver on normal daily tides at WASD, panel b is presented. The light blue horizontal bars represent the percentage change for a 25-year return period. It's evident that the urban discharge boundary alone has a negligible impact at WASD (12%), while the influence significantly increases for the "Surge only" (225%) and "Major River discharge" (260%) boundaries, as indicated by higher percentage change values. The combined influence, representing major river discharge and urban runoff acting simultaneously, demonstrates a 315% increase above WASD normal tides, marking the most substantial change among all the flood drivers. The same order of percentage change is noted for 50- and 100-year return periods, with the combined influence reaching nearly 600%. The horizontally hatched bars illustrate the impact on local tides as winds of 10 to 35 m/s persist for 12 hours. A 10 m/s increase is calculated as 15%, rising to 140% and 460% for 25 and 35 m/s, respectively. Panel b portrays the percentage influence at WASD for each flood driver analyzed in these experiments and underscores the critical role of including all flood drivers in total water level modeling. The flood drivers examined in this study emerge as primary influencers on water levels at WASD, evident through the percentage change contributions across various return periods. While other factors like thermal expansion of water, offshore swells, and Bay water level changes due to Gulf Stream disturbances may also play a role (Ezer et al., 2013 ), these elements have a more linear effect on modeling accuracy. They primarily elevate mean sea level, thereby significantly affecting Washington, DC water levels when a storm tide travels upstream from LWTV or combined discharges occur in the National Capital Region, or when local winds exceed the 10 m/s threshold. Based on these findings, it is recommended to assign the highest ranking to storm surge and discharge input boundaries from rivers and urban runoff. Although regional weather models offer reasonable resolutions at a scale of 2.5 km, modelers should also pay close attention to localized winds in the region. 4. Conclusion Real-time flood forecasting in upstream tidal regions presents a formidable challenge, given the intricate and ever-changing interplay of numerous flood drivers. This study offers a comprehensive evaluation of the diverse flood drivers that are important to accurately modeling total water levels in upstream tidal rivers, with the Tidal Potomac River serving as a representative case. This river represents well the complex physical dynamics from ocean tides, freshwater inflows, urban runoff, and local wind impacts on hydrodynamics. Existing operational coastal guidance systems often fall short in predicting water levels accurately in such complex environments. The study area holds national significance, as the National Capital Region, situated at the confluence of the Potomac River and the Anacostia River, faces an escalating threat of flooding. To address these challenges, we employed the ADCIRC-2DDI model to simulate these interactions and established a meticulously calibrated and validated model configuration for the Potomac River. Model validation, conducted for six historical events, confirmed the model's ability to confidently replicate historical water levels at WASD, albeit with a slight underestimation of 0.1 m, equivalent to a -5% variation when compared to observed water levels. Through a range of hypothetical boundary forcing scenarios, we demonstrated the substantial impact of downstream boundaries, upstream river discharges, local urban runoff, and wind forcing, as expressed in terms of percentage change relative to a standard tidal day in January 2020. For example, the downstream boundary propagating from LWTV could elevate water levels at WASD by up to 300% in comparison to calm tidal days, particularly for a 100-year return period. Upstream major river discharges exhibited an approximately 500% change, which escalates to 600% in the presence of simultaneous urban discharge within the National Capital Region. Similarly, we showcased that local winds exceeding 20 m/s can result in a significant water level variation at WASD, reaching almost 140%. These findings underscore the critical importance of considering combined discharges along with local wind factors, a facet often overlooked in total water level forecasting for the Potomac River near Washington, DC, which has contributed to the underestimation of flood levels. Consequently, forecasting systems for such tidal rivers must comprehensively incorporate all flood drivers. We underscore the value of a dedicated framework for total water level modeling tailored to the intricacies of complex tidal rivers near Washington DC. By emphasizing the inclusion of all these boundary forcings (tides, storm surge, river discharge, urban runoff, and local winds), we highlight the potential to enhance the accuracy of total water level predictions, particularly for forecasting flooding events, including coastal, river, and compound flooding. Such a modeling framework, featuring one-way input boundary contributions, should be prioritized by other coastal modeling groups when forecasting total water levels. Declarations Acknowledgements The funding for this research was provided by the Virginia Sea Grant Program [Grant # NA18OAR4170083], and the Flood Hazards Research Lab (FHRL) at George Mason University generously offered research resources. The opinions, findings, conclusions, or recommendations presented in this material are solely those of the authors and do not necessarily represent the views of the NWS or NOAA. The authors extend their gratitude to Mr. Jason Elliott at NWS National Water Center for sharing valuable insights into NWS flood forecast models and operations. The research benefited from access to Extreme Science and Engineering Discovery Environment (XSEDE) STAMPEDE2 resources under allocation ID TG-BCS130009, supported by the National Science Foundation [grant number ACI-1548562] (Towns et al., 2014). Additionally, the authors appreciate the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing high-performance computing resources, which significantly contributed to the model calibration results outlined in this paper (http://www.tacc.utexas.edu). Authors Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Arslaan Khalid. The first draft of the manuscript was written by Arslaan Khalid and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding The funding for this research was provided by the Virginia Sea Grant Program [Grant # NA18OAR4170083], and the Flood Hazards Research Lab (FHRL) at George Mason University generously offered research resources. The research benefited from access to Extreme Science and Engineering Discovery Environment (XSEDE) STAMPEDE2 resources under allocation ID TG-BCS130009, supported by the National Science Foundation [grant number ACI-1548562] (Towns et al., 2014). Availability of data and materials All model analyses conducted in this study were performed on behalf of the Mason Flood Hazards Research Lab, and the results are stored on local servers. The modeling outputs can be made available for non-commercial, academic research purposes upon request from the corresponding author. The hydrodynamic coastal storm surge model, ADCIRC, is accessible for non-commercial, academic research purposes through contact with Crystal Fulcher at the University of North Carolina ( [email protected] ). The integrated modeling framework utilized in this research is based on the iFLOOD paper recently published and can be viewed on the iFLOOD web portal (https://iflood.vse.gmu.edu/map). 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ADCIRC: an advanced three-dimensional circulation model for shelves coasts and estuaries, report 1: theory and methodology of ADCIRC-2DDI and ADCIRC-3DL . Dredging Research Program Technical Report DRP-92-6, U.S. Army Engineers Waterways Experiment Station, Vicksburg, MS, . Mashriqui, H. S., Halgren, J. S., & Reed, S. M. (2014). A 1D River Hydraulic Model for Operational Flood Forecasting in the Tidal Potomac: Evaluation for Freshwater, Tidal, and Wind Driven Events 4 5 6. Retrieved from http://www.nws.noaa.gov/oh/hrl/modelcalibration/6. Hydraulic Model Calibration/potomac_modeling_JHE.pdf Mied, R. P., Donato, T. F., & Friedrichs, C. T. (2006). Eddy generation in the tidal Potomac River. Estuaries and Coasts , 29 (6), 1067–1080. https://doi.org/10.1007/BF02781810 Möller, O. O., Castaing, P., Salomon, J.-C., & Lazure, P. (2001). The influence of local and non-local forcing effects on the subtidal circulation of Patos Lagoon. Estuaries , 24 (2), 297–311. https://doi.org/10.2307/1352953 Montgomery, M. T., Bell, M. M., Aberson, S. D., & Black, M. L. (2006). Hurricane Isabel (2003): New Insights into the Physics of Intense Storms. Part I: Mean Vortex Structure and Maximum Intensity Estimates. Bulletin of the American Meteorological Society , 87 (10), 1335–1348. https://doi.org/10.1175/BAMS-87-10-1335 National Capital Planning Commission. (2008). J A N U A R Y 2 0 0 8 Flooding and Stormwater in. Retrieved from www.ncpc.gov Pandey, S., Rao, A. D., & Haldar, R. (2021). Modeling of Coastal Inundation in Response to a Tropical Cyclone Using a Coupled Hydraulic HEC-RAS and ADCIRC Model. Journal of Geophysical Research: Oceans , 126 (7), e2020JC016810. https://doi.org/10.1029/2020JC016810 Passeri, D., Hagen, S. C., Smar, D., Alimohammadi, N., Risner, A., & White, R. (2012). Sensitivity of an ADCIRC Tide and Storm Surge Model to Manning’s n. Proceedings of the International Conference on Estuarine and Coastal Modeling , 457–475. https://doi.org/10.1061/9780784412411.00027 Rahmstorf, S. (2017). Rising hazard of storm-surge flooding. Proceedings of the National Academy of Sciences of the United States of America , 114 (45), 11806–11808. https://doi.org/10.1073/PNAS.1715895114/ASSET/29033B16-5B3F-47EA-8DD1-AA8FE6EB386A/ASSETS/GRAPHIC/PNAS.1715895114FIG01.JPEG Reay, W. G., & Erdle, S. Y. (2011). W&M ScholarWorks W&M ScholarWorks Reports Sea Level Rise: Local Fact Sheet for the Middle Peninsula, Virginia Sea Level Rise: Local Fact Sheet for the Middle Peninsula, Virginia. https://doi.org/10.25773/d9j4-4n85 Ries III, K. G., Newson, J. K., Smith, M. J., Guthrie, J. D., Steeves, P. A., Haluska, T. L., et al. (2017). StreamStats, version 4 . Roberts, K. J., Pringle, W. J., & Westerink, J. J. (2019). OceanMesh2D 1.0: MATLAB-based software for two-dimensional unstructured mesh generation in coastal ocean modeling. Geoscientific Model Development , 12 (5), 1847–1868. https://doi.org/10.5194/gmd-12-1847-2019 Santiago-Collazo, F. L., Bilskie, M. V., & Hagen, S. C. (2019). A comprehensive review of compound inundation models in low-gradient coastal watersheds. Environmental Modelling and Software , 119 (June), 166–181. https://doi.org/10.1016/j.envsoft.2019.06.002 Shen, J., Wang, H., Sisson, M., & Gong, W. (2006). Storm tide simulation in the Chesapeake Bay using an unstructured grid model. Estuarine, Coastal and Shelf Science , 68 (1), 1–16. https://doi.org/10.1016/j.ecss.2005.12.018 Sohrt, V., Hein, S. S. V., Nehlsen, E., Strotmann, T., & Fröhle, P. (2021). Model Based Assessment of the Reflection Behavior of Tidal Waves at Bathymetric Changes in Estuaries. Water 2021, Vol. 13, Page 489 , 13 (4), 489. https://doi.org/10.3390/W13040489 Sweet, W. V, Kopp, R. E., Weaver, C. P., Obeysekera, J., Horton, R. M., Thieler, E. R., & Zervas, C. (2017). Global and Regional Sea Level Rise Scenarios for the United States. NOAA/NOS Center for Operational Oceanographic Products and Services. Thatcher, C. A., Brock, J. C., Danielson, J. J., Poppenga, S. K., Gesch, D. B., Palaseanu-Lovejoy, M. E., et al. (2016). Creating a Coastal National Elevation Database (CoNED) for science and conservation applications. Journal of Coastal Research , (76), 64–74. Thomas, A., Dietrich, ; J C, Asce, M., Dawson, ; C N, & Luettich, R. A. (2021). Effects of Model Resolution and Coverage on Storm-Driven Coastal Flooding Predictions. https://doi.org/10.1061/(ASCE) Thomas, A., Dietrich, ; J C, Asce, M., Dawson, ; C N, & Luettich, R. A. (2022). Effects of Model Resolution and Coverage on Storm-Driven Coastal Flooding Predictions. Journal of Waterway, Port, Coastal, and Ocean Engineering , 148 (1), 04021046. https://doi.org/10.1061/(ASCE)WW.1943-5460.0000687 Towns, J., Cockerill, T., Dahan, M., Foster, I., Gaither, K., Grimshaw, A., et al. (2014). XSEDE: Accelerating scientific discovery. Computing in Science and Engineering , 16 (5), 62–74. https://doi.org/10.1109/MCSE.2014.80 Tromble, E. (2011). Advances using the ADCIRC hydrodynamic model: Parameter estimation and aspects of coupled hydrologic-hydrodynamic flood inundation modeling . Retrieved from https://search.proquest.com/openview/7a0cab241d1aad63bb90fa4aee656399/1?pq-origsite=gscholar&cbl=18750 Wahl, T., Jain, S., Bender, J., Meyers, S. D., & Luther, M. E. (2015). Increasing risk of compound flooding from storm surge and rainfall for major US cities. Nature Climate Change , 5 (12), 1093–1097. https://doi.org/10.1038/nclimate2736 Walsh, C. J., Fletcher, T. D., & Burns, M. J. (2012). Urban Stormwater Runoff: A New Class of Environmental Flow Problem. PLOS ONE , 7 (9), 1–10. https://doi.org/10.1371/journal.pone.0045814 Wang, H. V, Loftis, J. D., Forrest, D., Smith, W., & Stamey, B. (2015). Modeling Storm Surge and Inundation in Washington, DC, during Hurricane Isabel and the 1936 Potomac River Great Flood. Journal of Marine Science and Engineering , 3 (3), 607–629. https://doi.org/10.3390/jmse3030607 Wessel, P., & Smith, W. H. F. (1996). A global, self-consistent, hierarchical, high-resolution shoreline database. Journal of Geophysical Research: Solid Earth , 101 (B4), 8741–8743. https://doi.org/10.1029/96jb00104 Winters, M. A. (2018). DC Winters. National Weather Service . Zhong, L., Li, M., & Foreman, M. G. G. (2008). Resonance and sea level variability in Chesapeake Bay. Continental Shelf Research , 28 (18), 2565–2573. Zscheischler, J., Westra, S., Van Den Hurk, B. J. J. M., Seneviratne, S. I., Ward, P. J., Pitman, A., et al. (2018). Future climate risk from compound events. Nature Climate Change 2018 8:6 , 8 (6), 469–477. https://doi.org/10.1038/s41558-018-0156-3 Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3866206","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268695664,"identity":"34ffcf06-dae6-4550-ae3f-02801ded784b","order_by":0,"name":"Arslaan Khalid","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-1950-6266","institution":"George Mason University","correspondingAuthor":true,"prefix":"","firstName":"Arslaan","middleName":"","lastName":"Khalid","suffix":""},{"id":268695665,"identity":"800b9364-fc5a-43ee-b7d7-d3b3bb61d114","order_by":1,"name":"Celso Ferreira","email":"","orcid":"","institution":"George Mason University","correspondingAuthor":false,"prefix":"","firstName":"Celso","middleName":"","lastName":"Ferreira","suffix":""},{"id":268695666,"identity":"7f80af93-c390-4cde-a7b7-a2554cf4656f","order_by":2,"name":"Jason Elliott","email":"","orcid":"","institution":"NOAA NWS: National Weather Service","correspondingAuthor":false,"prefix":"","firstName":"Jason","middleName":"","lastName":"Elliott","suffix":""}],"badges":[],"createdAt":"2024-01-15 10:45:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3866206/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3866206/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50120355,"identity":"23b58544-48b9-4a53-aa7c-8e889731cef3","added_by":"auto","created_at":"2024-01-24 19:33:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4302039,"visible":true,"origin":"","legend":"\u003cp\u003eMap of study area. a) Overview map indicates the location of the study area in reference to Chesapeake Bay. Locations of two NOAA recording stations (Lewisetta and Swells Point) used for model validations is shown as black diamonds. b) Computational Model (Red Polygon) domain in Potomac River. Downstream boundary condition is represented as a blue line near Lewisetta NOAA gage (LWTV). Other NOAA gages used for model validations are shown as black diamonds. NOAA tide predictions are shown as light blue squares. Locations of stream flow gages is indicated using orange triangles. c) Shows an insert focusing on Washington, DC. Purple arrows indicate location of river inflows whereas blue arrows show location of urban flows or lateral flows.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3866206/v1/987057ade09e128a63769d6e.png"},{"id":50121100,"identity":"5ab3c519-e166-4a9f-abce-a1b84f9a18d6","added_by":"auto","created_at":"2024-01-24 19:41:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57098,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated total water level modeling schema\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3866206/v1/6d904451cbb1fe4692e1de7a.png"},{"id":50120354,"identity":"e0f296b0-d2f3-4f21-b52a-58cd8ca8b61d","added_by":"auto","created_at":"2024-01-24 19:33:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":295010,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4 MAE at individual stations for Experiment 1\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3866206/v1/3a7c72881891e781316d2b8b.png"},{"id":50120360,"identity":"c0aeb8ce-5363-4dc2-a298-2483d95d5831","added_by":"auto","created_at":"2024-01-24 19:33:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":866411,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 6 Time series of simulated maximum water level at WASD during six historical events. panels a and b show river floods, panels c and d show coastal floods and lastly, panels e and f show Compound flood events. Red solid line represent modeled time series, whereas observed water level are shown as dotted blue line. Upstream river discharge (U/S) is shown in green dashed line shows and downstream (D/S) stage is plotted as a black dotted line.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3866206/v1/0fd63374b4d77448c70debf4.png"},{"id":50121101,"identity":"68215214-dff0-47f9-8e92-d8f202799dd1","added_by":"auto","created_at":"2024-01-24 19:41:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":241319,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 7 Change in water levels magnitude (above normal daily tides) in the Potomac River due to downstream boundary conditions located at LWTV. Left panel shows the percent change relative to LWTV for 25 to 100 year return period.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3866206/v1/d0b4cedc930cdd54ef31b00f.png"},{"id":50120362,"identity":"659bfd68-7669-4db9-8907-fdbdd127f19b","added_by":"auto","created_at":"2024-01-24 19:33:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":473281,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 8 Change in water levels magnitude (above normal daily tides) in the Potomac River due to upstream river discharge boundaries from Little Falls Pump station and Anacostia River gages. Panel shows changes from LFMD to WASD due to Little Falls river discharge only, whereas panel b show changes from BDMD to WASD due to Anacostia river discharge only. Panel c shows the combined influence of both river discharge from WASD to most downstream station at LWTV.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3866206/v1/9fb0339f6494fc8415f4a7c3.png"},{"id":50121102,"identity":"f94ab120-5d6d-496f-b2d3-03b7573bb2b5","added_by":"auto","created_at":"2024-01-24 19:41:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":489697,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 9 Change in water levels magnitude (above normal daily tides) in the Potomac River due to urban discharge boundaries (panel a), and combined urban and major river discharges (panel b).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3866206/v1/f9c65151a1ee1fe861df6681.png"},{"id":50120356,"identity":"25446939-c100-4976-a2ea-289c031c9e65","added_by":"auto","created_at":"2024-01-24 19:33:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":478206,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 10 Change in water levels above normal tides at WASD and nearby stations due to local winds forcing in eight directions.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-3866206/v1/325d68f901cd975adba974b8.png"},{"id":50120359,"identity":"80a03327-4bc5-4dc8-a96d-3c7cddd97980","added_by":"auto","created_at":"2024-01-24 19:33:20","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":484589,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 11 Maximum water levels at WASD resulting from various flood drivers. Horizontal lines represent maximum water levels recorded at WASD during historical events.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-3866206/v1/5ac0bc780d05fbded5fa87a6.png"},{"id":50821694,"identity":"0c68d94f-a271-4aa1-8ea9-cc0fb86eb812","added_by":"auto","created_at":"2024-02-07 21:42:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3353089,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3866206/v1/fcebd7ad-4ab8-410d-b93d-866c543ed436.pdf"},{"id":50120357,"identity":"a3500da2-1254-4f96-a00b-4b8490f29a94","added_by":"auto","created_at":"2024-01-24 19:33:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2363413,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-3866206/v1/58d43de3d8f305ee790eca21.docx"}],"financialInterests":"","formattedTitle":"Compound Flooding in River-Urban-Coastal Environments: Multi-factorial Drivers and Modeling Considerations","fulltext":[{"header":"Key Points","content":"\u003col\u003e\n \u003cli\u003eCurrent flood forecast systems based solely on ocean surge dynamics underestimate water levels near National Capital Region\u003c/li\u003e\n \u003cli\u003eStrong local winds could raise water levels to NWS\u0026rsquo;s moderate flooding level at Washington DC\u003c/li\u003e\n \u003cli\u003eHigh river discharge causes the water level in the Tidal Potomac to surge 0.9 meters above the NWS\u0026rsquo;s major flooding level in Washington DC.\u003c/li\u003e\n \u003cli\u003eHigh River discharge combined with urban runoff can amplify flooding to 1.4 m above NWS\u0026rsquo;s major flooding level in Washington DC\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eCoastal cities in the mid-Atlantic region of the United States face numerous flood risks arising from astronomical tides, storm surges, precipitation, river discharge, and wind impact (Depietri et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ghanbari et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Herdman et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The combination of these factors often leads to compound flooding, a phenomenon that intensifies flooding impacts and causes significant socioeconomic losses (Berm\u0026uacute;dez et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zscheischler et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Recent estimations suggest that compound flooding events cost around 132\u0026nbsp;billion USD globally in 2021 and are projected to rise to approximately 158 trillion USD by 2050 (Jongman et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Moreover, with the ongoing rise in global mean sea level, compound flooding is expected to become increasingly significant in coastal cities worldwide (Howat et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Rahmstorf, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sweet et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wahl et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCompound flooding, in our study characterized by the simultaneous occurrence of riverine, coastal, and meteorological flooding, poses a significant threat to river-urban-coastal environments (Dresback et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ghanbari et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Santiago-Collazo et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This flooding phenomenon results from the combined effects of low-pressure systems near coastal regions causing sea-level rise and accompanying frontal systems leading to excessive precipitation (Dresback et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The mechanisms driving such flooding can arise from single meteorological events, a sequence of rapid independent events, or concurrent occurrences, thereby amplifying the flood hazard (Santiago-Collazo et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe National Capital Region, including Washington, DC, is located at the confluence of two major rivers (Potomac and Anacostia) and experiences tidal oscillations from Chesapeake Bay. This region has witnessed an increase in the frequency and intensity of storms, leading to heavy precipitation events, rising sea levels, and elevated water temperatures in the Bay (Atkinson et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Bennett, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhong et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In addition to heavy rainfall and stronger storm surges, the area is susceptible to \"King tides,\" exceptionally high tides exacerbating flooding vulnerability in susceptible areas (Loftis \u0026amp; Forrest, 2018). Local factors such as near-surface winds (Mashriqui et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; M\u0026ouml;ller et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and stormwater runoff (Walsh et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) play significant roles in generating local water level changes. Furthermore, regional variations in water levels can occur during summer seasons due to thermal expansion of water, even without discernible weather events (Boesch et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cronin et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Reay \u0026amp; Erdle, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Recent studies have indicated that the weakening of Gulf Streams and Florida Currents during tropical cyclones can intensify flooding potential in the mid-Atlantic coastal areas of the United States by amplifying the propagation of higher storm surges to upstream tidal regions (Ezer et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccurately simulating compound floods or total water levels (TWL) in riverine and coastal areas is a significant challenge for current coastal and hydraulic models. Existing models typically address specific flood drivers, limiting their capability to account for the complex interactions between tides, storm surge, sea-level rise, wind, and river discharge. Although some models accurately estimate storm surge impacts, they lack the ability to simulate streamflow, surface-runoff flow, and subsurface flow (Loveland et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Other hydraulic models effectively simulate rainfall-runoff processes for small watershed scales but have limitations considering large-scale coastal processes like storm surge generation (Brunner, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Downer \u0026amp; Ogden, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo overcome the limitations of current models, recent studies have focused on using coupled frameworks that integrate multiple models to simulate compound floods accurately (Loveland et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the application of these frameworks for flood forecasting remains limited due to computational costs and potential model instability (Ikeuchi et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Operational models employed by the National Oceanic and Atmospheric Administration (NOAA) and the National Weather Service (NWS) also have limitations in accurately representing flood drivers and hydrodynamic processes, resulting in high uncertainties in total water level forecasts and inadequate incorporation of urban runoff and local wind forcing (Mashriqui et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Thomas et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, the primary objective of this study is to comprehensively evaluate the role of compound flooding drivers in the Potomac River near Washington, D.C., using the Advanced Circulation (ADCIRC) model. Specifically, this research aims to (a) assess the accuracy of the high-resolution ADCIRC model in reproducing historical water levels for riverine, coastal, and compound flooding events; (b) quantify the changes in local water levels at the Washington, D.C. tide gauge resulting from different levels of storm surge, river discharge, urban runoff, and local winds, both independently and in combination; and (c) establish a ranking or priority system for improving modeling accuracy in compound flooding simulation in the tidal area of the Potomac River. The study will involve calibrating the ADCIRC model to recreate and validate historical water levels at the Washington, D.C. tide gauge, followed by testing the TWL modeling framework for hypothetical input forcing scenarios representing various return period levels of flood drivers. The results of this study will contribute to a better understanding of compound flooding dynamics, improve flood forecasting, and aid in the development of effective mitigation strategies in river-urban-coastal environments, particularly in the National Capital Region.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eTo evaluate the applicability of an integrated framework for total water level modeling in upstream tidal areas, we established a dedicated hydrodynamic numerical modeling domain specifically for the Potomac River. The ADCIRC coastal model was utilized for this purpose. The framework underwent calibration to accurately simulate astronomical tides and water level variations driven by riverine discharge, as described in section 2.2. Furthermore, the model's performance was assessed by comparing its results with six historical flooding events recorded at the Washington, DC NOAA station (section 2.3). This validation process ensured that the model accurately captures the simultaneous effects of multiple flood drivers. Subsequently, our focus shifted to quantifying the changes in water levels resulting from individual flood drivers, as well as their co-occurrence, using return period events (section 2.4). Finally, we introduced a ranking system that evaluates the influence of each flood driver on localized water level changes. This ranking system was established through return period event simulations (section 2.5).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Area\u003c/h2\u003e \u003cp\u003eThis study focuses on Washington, DC, specifically its location at the confluence of the Potomac and Anacostia River. NOAA maintains a water level recording station in Washington, DC (referred to as WASD), which has documented an increase in the frequency of minor-level flooding days from 24 in 2010 to 43 in 2020. Minor-level flooding, as defined by the National Weather Service (NWS), refers to incidents with minimal to no property damage but the potential for some public threat.\u003c/p\u003e \u003cp\u003eThe Potomac River, the largest tributary of the Chesapeake Bay, spans approximately 166 km from Washington, DC to the Chesapeake Bay. Figure\u0026nbsp;1 displays the location of the Potomac River, which experiences tidal fluctuations due to its connection with the Chesapeake Bay downstream. However, the tidal influence diminishes near the Little Falls Pump station (LFMD), which serves as a United States Geological Survey (USGS) river discharge gauge upstream of Chain Bridge.\u003c/p\u003e \u003cp\u003eLFMD has a total drainage area of 29,940 square kilometers (km\u003csup\u003e2\u003c/sup\u003e) and exhibits an average daily flow of 334 cubic meters per second (m\u003csup\u003e3\u003c/sup\u003e/s). During heavy rainfall events, the flow can exceed 600 m\u003csup\u003e3\u003c/sup\u003e/s, as indicated by the NWS's Action Stage. On the eastern side of Washington, DC, the Anacostia River joins the Potomac near the Navy Yard (WNDC). The Anacostia River is shallower and narrower than the Potomac River (McDowell, 2016), and the average daily flow near Bladensburg is significantly smaller (\u0026lt;\u0026thinsp;4 m\u003csup\u003e3\u003c/sup\u003e/s) compared to the inflow from LFMD due to its drainage area of 315 km\u003csup\u003e2\u003c/sup\u003e (Huanxin et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, several small urban streams discharge into the Potomac River between Chain Bridge and Fort Washington Park, as indicated by the blue arrows in Fig.\u0026nbsp;1. The drainage areas of these streams range from 104 to 521 km\u003csup\u003e2\u003c/sup\u003e. The mean tidal range at the Washington, DC station is approximately 0.9 meters (m), with a tidal phase lagging 5 hours (h) behind Lewisetta (LWTV) and 11.5 h behind Hampton Roads at Sewells Point (SWPV), which is situated at the mouth of the Chesapeake Bay (NOAA tides and currents).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 1 Map of study area. a) Overview map indicates the location of the study area in reference to Chesapeake Bay. Locations of two NOAA recording stations (Lewisetta and Swells Point) used for model validations is shown as black diamonds. b) Computational Model (Red Polygon) domain in Potomac River. Downstream boundary condition is represented as a blue line near Lewisetta NOAA gage (LWTV). Other NOAA gages used for model validations are shown as black diamonds. NOAA tide predictions are shown as light blue squares. Locations of stream flow gages is indicated using orange triangles. c) Shows an insert focusing on Washington, DC. Purple arrows indicate location of river inflows whereas blue arrows show location of urban flows or lateral flows.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFigure 1 illustrates the entire modeling domain of the study area, including three boundary conditions: (1) major upstream river boundaries based on observed discharge data from LFMD on the west, as well as the Northeast and Northwest Anacostia River USGS streamflow gauges (NWMD and NEMD) on the east, shown as purple arrows; (2) small stream flow boundaries based on available USGS observed discharge, represented by blue arrows; and (3) a downstream boundary (stage hydrograph) based on water observations collected at the NOAA Lewisetta, VA gauge (LWTV).\u003c/p\u003e \u003cp\u003eWater level observations and predicted tides from NOAA stations were acquired from the Center for Operational Oceanographic Products and Services (NOAA Tides and Currents) to assess the performance of the model. The positions of the NOAA water level and tide stations are indicated in Fig.\u0026nbsp;1b, represented by black and light blue dots, respectively. A comprehensive account of each recording station utilized in this study can be found in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003eA1\u003c/span\u003e of the \u003cspan refid=\"Sec25\" class=\"InternalRef\"\u003eappendix\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Modeling Framework\u003c/h2\u003e \u003cp\u003eIn this study, we employed a model framework whose schematic structure is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The components of the model framework were interconnected in an offline mode through boundary linkage. The coastal contribution, particularly astronomical tides and storm surges (referred to as storm tide), were provided by the downstream input location. The river inflow locations accounted for the contribution from rainfall-runoff processes. Additionally, the urban inflow was considered as lateral flows into the model domain. To incorporate local changes in water levels at the NOAA Washington, DC tide gage, we utilized local wind speed observations from the NOAA Washington, DC meteorological station and the Washington National Airport (WAS) recording station. These observations were uniformly interpolated over the entire modeling domain.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Advanced Circulation (ADCIRC)\u003c/h2\u003e \u003cp\u003eThe Advanced Circulation (ADCIRC) model, originally developed by Luettich et al. in 1992 (R. A. Luettich et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), is a finite-element hydrodynamic model that employs the generalized wave continuity equation (GWCE). By solving these equations on an unstructured computational grid in both space and time, the ADCIRC model can effectively simulate the dynamics of open water bodies such as oceans, lakes, and rivers. The model is capable of accurately capturing the effects of various driving forces, including astronomical tides, coastal storms, and river inflows. Its versatility has made it a popular choice for modeling historical storm surges and forecasting floods (Blain et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Dresback et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Funakoshi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; J. L. Garzon et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hanson et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Khalid \u0026amp; Ferreira, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shen et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn our study, we utilized the two-dimensional, depth-integrated version of ADCIRC, known as ADCIRC-2DDI, operating in the barotropic mode with a constant density assumption. This configuration allowed us to simulate the combined impacts of astronomical tides, river inflows, urban runoff, local winds, and storm surges on the overall water levels. ADCIRC is implemented using FORTRAN and is an open-source numerical model that benefits from extensive documentation available in both the published scientific literature (R. Luettich \u0026amp; Westerink, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004a\u003c/span\u003e) and the official ADCIRC website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://adcirc.org/\u003c/span\u003e\u003cspan address=\"http://adcirc.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Model Input Forcing\u003c/h2\u003e \u003cp\u003eThe study utilizes four primary input variables for model simulation, namely downstream water levels, upstream major river discharges, stream flows, and wind observations obtained from the WASD, which covers the entire modeling domain. The downstream boundary is determined based on observed water levels at the LWTV gauge. In cases where LWTV data is unavailable, data from the SWPV gauge is used instead, adjusted for a time lag of -5 hours. The upstream discharge boundary is derived from daily-observed flow data at the USGS Little Falls Pump Station gage (LFMD), which is interpolated to hourly flow using spline interpolation to ensure model stability. Additionally, urban runoff (defined as lateral flows) is considered as a boundary condition, but only when available for historical validation events. Prior to 2008, wind observations at the WASD station were not accessible. Therefore, wind data from the Washington National Airport meteorological station were utilized, with adjustments made by converting from a 5 m elevation to a 10 m elevation using a multiplier of 1.09, as described by Hsu et al. (Hsu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). No wind observations were available for model validation before 1937. It is important to acknowledge that due to the interpolation of daily to hourly data and the use of proxy data when observed data is absent, there is a potential for uncertainty in the model results during the validation process.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Model Setup\u003c/h2\u003e \u003cp\u003eThe unstructured computational grid for the ADCIRC model was created using the automated mesh generator, OceanMesh2D (Roberts et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To accurately represent the coastline in the numerical grid, the high-resolution coastline data from the Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG) (Wessel \u0026amp; Smith, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) was manually updated within the study domain. The open ocean boundary, located near LWTV (Fig.\u0026nbsp;1), was defined as a non-periodic, time-varying elevation boundary condition. By placing this ocean boundary near LWTV, we were able to incorporate coastal forcing directly from NOAA's station, minimizing the error in tidal predictions resulting from low-resolution Global Tidal Models.\u003c/p\u003e \u003cp\u003eFor validation storms, the river discharge boundary was added as a time-dependent flux boundary condition at LFMD. Since ADCIRC interprets flow as flux (Pandey et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the discharge data was converted to m\u003csup\u003e2\u003c/sup\u003e/s using the width between the flow boundary nodes. The topography data in the localized model was extracted from the USGS 1/9 arc-second Digital Elevation Model (DEM), while bathymetry data was obtained from various sources including NOAA nautical charts (Austin, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), NOAA National Centers for Environmental Information (NCEI) (Caldwell et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and the Coastal National Elevation Database (CoNED) (Thatcher et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) during the calibration phase to determine the most accurate bathymetry. All elevation and bathymetry datasets were in meters and vertically adjusted to the North American Vertical Datum (NAVD88) before being used in the ADCIRC model.\u003c/p\u003e \u003cp\u003eThe land cover in the modeling setup was based on the National Land Cover dataset (NLCD) of 2019 (Dewitz, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). ADCIRC's f13 utility (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://adcirc.org/\u003c/span\u003e\u003cspan address=\"https://adcirc.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed to generate nodal attributes such as \u003cem\u003emannings_n_at_sea_floor\u003c/em\u003e, \u003cem\u003eprimitive_weighting_in_continuity_equation\u003c/em\u003e, \u003cem\u003esurface_canopy_coefficient\u003c/em\u003e, and \u003cem\u003esurface_directional_effective_roughness_length\u003c/em\u003e. In our study, the simulations included various ADCIRC model features such as the wetting and drying algorithm, non-linear bottom friction, advection, finite amplitude terms, convective acceleration, and the time derivative of convective acceleration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Model Calibration\u003c/h2\u003e \u003cp\u003eTo achieve the study objective, we performed four set of model simulations to determine the best ADCIRC modeling setup to simulate astronomical tides within the study area. These experiments focused on testing different configurations of four modeling input parameters namely, numerical mesh resolution, bathymetry data source, bottom friction, and eddy horizontal viscosity.\u003c/p\u003e \u003cp\u003eThe testing range of values for calibration of bottom friction variable manning\u0026rsquo;s n in waterways (0.01 to 0.02), eddy horizontal viscosity (0 to 40) and grid resolution for bathymetric representation in channels (500 m to \u0026lt;\u0026thinsp;50m) were based on test values provided by previous literature (Bacopoulos \u0026amp; Hagen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bakhtyar et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bastidas et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; J. Garzon \u0026amp; Ferreira, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kerr et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mied et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Thomas et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For bathymetry testing, we used three publicly available data sources for Potomac River (Modified NOAA nautical charts, NCEI, CONED) along with a modified mosaic of available bathymetry datasets (Merged DEM). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists parameter-testing values used for each calibration test. Figure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003eA1\u003c/span\u003e in \u003cspan refid=\"Sec25\" class=\"InternalRef\"\u003eappendix\u003c/span\u003e provides the comparison of model bathymetries. The downstream ocean boundary was set as NOAA predicted astronomical tides from LWTV starting at January 1, 2020 and ending at January 30, 2020, and each calibration test simulation lasted for 30 days.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCalibration Parameters and associated average percentage error for each test in Experiment 1\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIdentifier\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNodal information\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eNumerical Model Resolution\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow_noCh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow resolution with no defined channels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eNC: 6253, MiG: 450, MaG: 550\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow_Ch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow resolution with defined channels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eNC\u003c/em\u003e 8068, \u003cem\u003eMiG\u003c/em\u003e: 350, \u003cem\u003eMaG\u003c/em\u003e: 450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehigh_noCh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh resolution with no defined channels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eNC: 133101, MiG: 60, MaG: 225\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ehigh_Ch\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ehigh-resolution numerical mesh (high_Ch)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eNC: 276647, MiG:15, MaG: 200\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eBathymetry Datasets\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModified NOAA nautical charts (NOAA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSame as high_Ch\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNational Centers for Environmental Information (NCEI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSame as high_Ch\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eB3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eCoastal National Elevation Database (CoNED)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSame as high_Ch\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMerged DEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSame as high_Ch\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eBottom Friction (Manning\u0026rsquo;s n)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSame as high_Ch\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSame as high_Ch\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSame as high_Ch\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eM4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSame as high_Ch\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eHorizontal Eddy Viscosity (m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/s)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSame as high_Ch\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eE2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSame as high_Ch\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSame as high_Ch\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSame as high_Ch\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e* NC: Total ADCIRC node count; * MiG: ADCIRC minimum node spacing (meters); * MaG: ADCIRC maximum node spacing (meters)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Model Validation\u003c/h2\u003e \u003cp\u003eFor model validation, we employed the optimal mesh resolution, bathymetry sources, bottom friction, and eddy horizontal viscosity parameters established during the model calibration phase. We conducted simulations for six historical storm events using available data on river discharge, ocean water levels, and meteorological conditions as boundary inputs to assess the model's accuracy in replicating historical total water levels at the NOAA Washington, DC tide gauge, often referred to as WASD.\u003c/p\u003e \u003cp\u003eThese case studies encompass two historical events for each flooding source category: River, Coastal, and Compound. A \"River\" flood event is characterized by a predominant influence of the upstream discharge from the USGS Little Falls Pump Station gauge (LFMD) on the observed water level increase at WASD. In contrast, a \"Coastal\" flood event results from a pronounced storm tide signal recorded at LWTV, which propagates upstream to affect WASD. Coastal floods typically involve relatively low discharge from the LFMD gauge (below 4000 m3/s).\u003c/p\u003e \u003cp\u003eLastly, a \"Compound\" flood occurs when both high river discharge and elevated coastal water levels coincide, leading to a \"Minor\" flooding alert issued by the NWS (defined as 0.85 m water level above NAVD88 at WASD). These model validation events were simulated for approximately 23 days, with the simulations commencing at least 10 days prior to the observed peak water levels at WASD to allow for model initialization and enhanced stability.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a detailed breakdown of event types, observed maximum water levels, wind conditions, discharge data, and event timing. Additional information regarding the selected validation events can be found below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValidation Events for evaluating recreation of historical water levels\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAvail. Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFlooding Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eObserved Maximum\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWater level (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDischarge (cfs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWind speeds (m/s)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGreat flood of 1936\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e03/14\u0026thinsp;\u0026minus;\u0026thinsp;03/24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ, H*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMajor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHurricane Agnes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e01/14\u0026thinsp;\u0026minus;\u0026thinsp;01/25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ, H*, W+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMajor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eApril 2011\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e04/08\u0026thinsp;\u0026minus;\u0026thinsp;04/27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ, H, W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMay 2014\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e05/08\u0026thinsp;\u0026minus;\u0026thinsp;05/27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ, H, W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHurricane Isabel\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoastal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e09/15\u0026thinsp;\u0026minus;\u0026thinsp;09/27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ, H, W+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMajor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTS Ernesto\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoastal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e08/28\u0026thinsp;\u0026minus;\u0026thinsp;09/05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ, H, W+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMajor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eQ\u0026thinsp;=\u0026thinsp;Discharge at LFMD, H\u0026thinsp;=\u0026thinsp;Water levels at LWTV, H* =Water levels at SWPV, W\u0026thinsp;=\u0026thinsp;Wind speeds at WASD, W\u003csup\u003e+\u003c/sup\u003e = Wind speeds at DCA\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the early 1900s, riverine floods were common in the Washington, DC area and led to significant historical floods, including the Chesapeake-Potomac Hurricane of 1933, the Great Potomac Flood of 1936, the Flood of 1942, and Hurricane Agnes in 1972 (National Capital Planning Commission, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The USGS Little Falls Pump Station gauge has continuously recorded daily river discharge since the 1930s. The Great Flood of 1936, resulting from snowmelt and heavy rainfall, saw LFMD discharge exceed 14,500 m\u003csup\u003e3\u003c/sup\u003e/s, causing severe flooding in Washington, DC, with observed water levels over 2.79 m above NAVD88 (Winters, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The Blizzard of 1996, characterized by heavy snow followed by rain, led to flash floods, with LFMD recording a peak discharge of 9,260 m\u003csup\u003e3\u003c/sup\u003e/s and resulting in a maximum water level of 2.04 m above NAVD88 at the WASD gauge station.\u003c/p\u003e \u003cp\u003eCoastal flooding in Washington, DC is primarily caused by strong storm tide signals from the NOAA Lewisetta station in the Chesapeake Bay. Two notable coastal events, Hurricane Isabel in 2003 and Tropical Storm Ernesto in 2006, caused flooding at WASD due to recorded water levels exceeding 1 m at LWTV. Coastal floods at WASD are generally smaller than riverine floods, except for Hurricane Isabel, which led to \"major\" flooding at WASD with levels reaching 2.7 m above NAVD88 (Montgomery et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Tropical Storm Ernesto generated a record storm tide of 1.5 m above NAVD88 but resulted in moderate flooding at WASD, reaching 1.61 m above NAVD88 (Knabb et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCompound floods in Washington, DC result from simultaneous high river discharge and coastal water levels. Major compound flood events occurred in 1937 and 1972, but they were not simulated due to data unavailability. Two distinct compound events in April 2011 and May 2014 were used for validation, combining a large storm tide from LWTV with high river discharge (\u0026gt;\u0026thinsp;4,500 m\u003csup\u003e3\u003c/sup\u003e/s) from LFMD, resulting in water levels at WASD rising to 1.35 and 1.3 m above NAVD88, classified as moderate flooding by the NWS. Wind speeds during both events exceeded 10 m/s, with gusts reaching up to 15 m/s at WASD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Model Forcing Sensitivity\u003c/h2\u003e \u003cp\u003eTo assess the impact of various flood drivers on the total water level in the Potomac River, we conducted a sensitivity analysis that encompassed the following scenarios: 1) downstream boundary conditions (storm tide); 2) upstream boundary conditions (major river discharges); 3) lateral boundary conditions (urban runoff); 4) combined upstream and lateral boundary conditions; and 5) local-scale wind forcing. Each scenario involved unsteady ADCIRC simulations spanning 15 days, which included a 3-day spin-up period. These simulations were compared to a baseline scenario representing calm day conditions, driven by tidal influences alone.\u003c/p\u003e \u003cp\u003eFor the downstream boundary conditions, we applied storm tide values based on NOAA's calculated 25-, 50-, and 100-year return periods at LWTV, resulting in surge values of 1.1 m, 1.2 m, and 1.3 m above the NAVD88 datum, respectively. To isolate the influence of downstream boundary conditions, we excluded stream flow and local wind forcing from these simulations.\u003c/p\u003e \u003cp\u003eTo quantify the impact of upstream major river flows, we conducted simulations with discharge inputs based on stream flows corresponding to 25-, 50-, and 100-year return periods at LFMD and Anacostia river gages, sourced from USGS StreamStats analysis (Ries III et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor analyzing the effects of urban runoff, we introduced discharge inputs as lateral flows at various urban stream locations along the Potomac River, with return period flows of 25-, 50-, and 100-year, also based on USGS StreamStats analysis.\u003c/p\u003e \u003cp\u003eWe further performed simulations with combined flows from major rivers and urban runoff to evaluate the cumulative impact on water levels when both factors contributed to Potomac River flow. In these combined forcing simulations, no time lag was considered to represent worst-case compound flooding conditions.\u003c/p\u003e \u003cp\u003eLastly, to investigate the influence of local wind changes on water level variations at WASD, we conducted tests with eight wind directions (N, NE, E, SE, S, SW, W, and NW) and wind magnitudes ranging from 5 to 35 m/s, with a peak duration of 12 hours. These wind magnitudes were based on the wind records at WASD and the WAS meteorological station, taking into account predominant wind directions and magnitudes exceeding 35 m/s.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFlow characteristic for major rivers and urban runoffs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrainage Area\u003c/p\u003e \u003cp\u003e(km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax measured flow (m\u003csup\u003e3\u003c/sup\u003e/s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u0026nbsp;year return period\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50\u0026nbsp;year return period\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100\u0026nbsp;year return period\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eMajor Rivers\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLittle Falls at Potomac River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12908\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBladensburg at Anacostia River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e580\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cem\u003eUrban Runoffs\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRock Creek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFour Mile Run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOxon Run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCameron Run\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBroad Creek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLF5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePiscataway Creek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLF6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLittle Hunting Creek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLF7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe input conditions employed in these scenarios are theoretical and represent simplified representations of actual situations, spanning from typical daily weather to severe weather extremes. These scenarios serve the purpose of enabling a more precise assessment of the influence of each flood driver on total water levels. They underscore the significance of either incorporating or omitting specific factors to attain accuracy in total water level modeling in the Potomac River, particularly at the WASD tide gauge.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Ranking Flood Drivers\u003c/h2\u003e \u003cp\u003eFollowing the aforementioned experiments, we conducted a detailed analysis to isolate the percentage change in water levels above normal daily tides at the WASD gauge station. This allowed us to quantify the individual contributions of each flood driver. In this process, flood drivers that exhibited the highest percentage change in a given return period experiment were assigned a higher rank in comparison to the other flood drivers. This ranking system provides valuable insights into the relative impact of different drivers on total water levels at WASD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Model Evaluation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFor a quantitative assessment of the modeled water levels, we employ various statistical metrics, which encompass bias, mean absolute error (MAE), and percentage change. Bias assesses the model's propensity to either overestimate or underestimate peak water levels. MAE represents the average of all absolute errors computed during the storm's duration. Percentage change is employed to gauge the variation in the modeled peak water level in comparison to the maximum observed water level at WASD. These evaluation metrics are defined as follows:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$Bias= {x}_{mod} -{x}_{obs}$$\u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Equb\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$MAE= \\frac{1}{n}{\\sum }_{i=1}^{n}| {x}_{mod} -{x}_{obs}|$$\u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Equc\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$Percent Change= \\frac{{x}_{mod} -{x}_{obs}}{{x}_{obs}}* 100$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{mod}\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{obs}\\)\u003c/span\u003e\u003c/span\u003e are the modeled and observed water level at a given recording station at a given time step. The \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(n\\)\u003c/span\u003e\u003c/span\u003e is the number of time steps in hours for error calculation.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussions","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Model Calibration\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. Tidal Hydrodynamics\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eResults from experiment 1, which focused on model calibration, are depicted in Fig.\u0026nbsp;1 as a bar plot of MAE for eight selected NOAA tide recording stations. The average MAE across all recording stations is represented by horizontal lines in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The findings reveal the following: 1) The use of Modified NOAA Nautical DEM for bathymetry resulted in the smallest MAE; 2) A high-resolution mesh with clearly defined channels (high_Ch) demonstrated the lowest MAE; 3) Employing a Manning\u0026rsquo;s n roughness value of 0.01 in open water for bottom friction yielded the smallest MAE for modeled tides; and 4) An ESLM value of 0.5 produced the smallest MAE while maintaining model stability.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Panel a illustrates that the ADCIRC model with the highest resolution and well-defined channels (high_Ch) resulted in the smallest MAE. This indicates that to accurately replicate local hydrodynamic processes, the model grid resolution must be sufficiently fine, particularly in the channels, to enhance conveyance and reduce errors. Previous studies have shown that higher resolution grids with improved channel representation can lead to a 5\u0026ndash;10% increase in tidal amplitude (Blanton et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and alter tidal propagation and resonance (Bacopoulos \u0026amp; Hagen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePanel b of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrates that the source of bathymetry also influences tidal simulations. For instance, at stations such as WASD, NCEI, and NOAA, the MAE values are 0.07 m, while for NCEI bathymetry, the MAE values become significantly larger along the Anacostia river stations (KMLD and ELMD). This highlights the critical role of accurate bathymetry in ensuring precise hydrodynamic simulations, as uncertainties in bathymetric data can lead to misrepresentations of estuarine morphology and subsequently impact current and water level conditions (Sohrt et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePanel c of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays the MAE associated with four simulations of horizontal eddy viscosity (ESLM). Similar MAE values are observed at tide stations up to ALVA, but decrease with smaller values of ESLM for upstream stations. The ESLM parameter governs hydraulic losses within ADCIRC simulations, and smaller values allow for more accurate computation of hydraulic losses based on ADCIRC grid resolution (Dill, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; R. Luettich \u0026amp; Westerink, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2004b\u003c/span\u003e). Our calibration results for ESLM testing align with previous findings (J. Garzon \u0026amp; Ferreira, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), indicating that tidal amplitude and current are sensitive to ESLM values.\u003c/p\u003e \u003cp\u003eSimilar to other calibration parameters, Manning\u0026rsquo;s n roughness parameter also impacts the MAE of simulated tides in the Potomac River. Panel d of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that the smallest MAE for WASD and accompanying stations is observed at a Manning\u0026rsquo;s n value of 0.01 for waterways, which controls bottom friction by offering less resistance against the bottom. This supports previous research (J. Garzon \u0026amp; Ferreira, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Passeri et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) highlighting the sensitivity of bottom friction formulation to tidal amplitude.\u003c/p\u003e \u003cp\u003eOur calibration testing provides insights into the use of lower range values for Manning\u0026rsquo;s n roughness and ESLM, in combination with high-resolution mesh with defined channels and bathymetry obtained from CONED. As a result, the calibrated version of the ADCIRC model is used for model validations and return period simulations in the following sections.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Model Validation\u003c/h2\u003e \u003cp\u003eThe ADCIRC model was run for six historical storms, and the water level time series at WASD is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003e. On average, the modeled water levels were 5% lower than the observed maximum water level at WASD. The peak of the modeled water level was underestimated by a small bias of 0.1 m for all six historical storms during the storm duration.\u003c/p\u003e \u003cp\u003eFor the two riverine events, the ADCIRC model simulated the increase in water levels caused by the large incoming river discharge from LFMD, with a small underestimation of -0.17 m or -6% of the observed maximum peak (panel a and b of Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The modeled maximum water level during the 1936 flood showed an increase of nearly 2.5 m above normal daily tide as a result of incoming river discharge. The modeled water level for WASD during the 1996 event also showed an underestimation of -0.09, which translated to a -4% change compared to the observed peak. No time lag was observed in the modeled peak at WASD for 1936 river flood, however, time lag of -1 hour occurred during Blizzard of 1996. This underestimation could have resulted from non-availability of the hourly discharge values for use in the ADCIRC river discharge boundary or inability of ADCIRC to accurately capture discharge-driven water level changes.\u003c/p\u003e \u003cp\u003eFor the two coastal events, the ADCIRC model clearly simulated the increase in water levels at WASD caused by the strong storm surge signal propagating upstream from LWTV. The modeled peak at WASD was slightly underestimated by -0.08 m for Hurricane Isabel (2003) and \u0026minus;\u0026thinsp;0.05 m for Tropical Storm Ernesto (2006), which translated to a percentage change of -3% for both events. MAE values of 0.07 and 0.05 was calculated for the duration of the storm for both coastal events. No time lag was observed in the modeled peak at WASD for Hurricane Isabel, however, time lag of -1 hour occurred during Tropical Storm Ernesto.\u003c/p\u003e \u003cp\u003eFor the two compound events, the ADCIRC model was able to reproduce the peak of the events somewhat accurately when compared with observations, but failed to simulate the increase in water levels prior to the observed peak. An underestimation of -0.1 and \u0026minus;\u0026thinsp;0.2 m was noted during the peak of the storm, which represented a percentage change of 8 and 14% for the 2011 and 2014 flood events, respectively. In terms of the time lag, ADCIRC modeled maximum water level lagged observation by 14 and 26 hours for 2011 and 2014 compound flood.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the model evaluation metrics. Overall, the ADCIRC model consistently underestimated the water level by 0.1 m, which is also equivalent to an average percentage change of -5% for all six historical validation events. The model tended to underrepresent the shape of the maximum water level timeseries during compound flood events, which is likely due to its inability to model baseflow.\u003c/p\u003e \u003cp\u003eOur validation results for the Great Flood of 1936 and Hurricane Isabel in 2003 at WASD were similar to those published in an earlier study (Wang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), in which the authors used the SCHISM model with the upstream river boundary set at LFMD and the downstream boundary set at Colonial Beach (53 km upstream from LWTV). Our modeling setup underestimated the peak water level by 0.17 m and 0.08 m during the Great Flood of 1936 and Hurricane Isabel, respectively, which is higher than our overall model evaluation. The simulated water level peak error during Isabel was also comparable to the ADCIRC model results at WASD published earlier by Mashriqui et al. (Mashriqui et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) (Bias of 0.65 m and phase lag of 4 hours).\u003c/p\u003e \u003cp\u003eIt is important to note that the model tends to underrepresent the shape of the maximum water level timeseries during compound flood events. This could be due to ADCIRC's inability to model baseflow, which can lead to uncertainties at the peak and recession curve (Loveland et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tromble, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Despite these limitations, the model validation results show that the integrated modeling framework can reliably simulate historic extreme water levels at WASD.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel validation summary for nine historical storms at Washington DC NOAA tide gage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAvail. Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObserved max peak (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel max peak (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBias (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePercent Change at peak\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMAE Storm Duration (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eObserved peak timing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eModel peak timing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePeak Time Diff (hr)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGreat flood of 1936\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ, H*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e1936-03-20 00:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c10\"\u003e \u003cp\u003e1936-03-20 00:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlizzard of 1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ, H, W+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e1996-01-21 16:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c10\"\u003e \u003cp\u003e1996-01-21 15:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHurricane Isabel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoastal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ, H, W+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e2003-09-19 09:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c10\"\u003e \u003cp\u003e2003-09-19 09:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTS Ernesto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoastal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ, H, W+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e2006-09-02 06:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c10\"\u003e \u003cp\u003e2006-09-02 05:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApril 2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ, H, W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e2011-04-16 22:00:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c10\"\u003e \u003cp\u003e2011-04-17 12:00:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMay 2014\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ, H, W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e2014-05-16 13:00:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c10\"\u003e \u003cp\u003e2014-05-17 15:00:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eQ\u0026thinsp;=\u0026thinsp;Discharge at LFMD, H\u0026thinsp;=\u0026thinsp;Water levels at LWTV, H* =Water levels at SWPV, W\u0026thinsp;=\u0026thinsp;Wind speeds at WASD, W\u003csup\u003e+\u003c/sup\u003e = Wind speeds at DCA\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Model Forcing Sensitivity\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe following subsections quantifies the change in water levels resulting from various flood drivers.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Downstream Boundary Conditions\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates the elevation in water levels above normal daily tides at several recording stations (from LWTV to WASD) when a storm tide with a return period of 25 to 100 years enters the Potomac River. The magnitude of this increase in water levels becomes consistent at Alexandria (ALVA) and continues to propagate upstream. The results reveal that the maximum elevation in water levels above normal daily tides at WASD during a 100-year return period does not surpass 1.5 meters. During Tropical Storm Ernesto in 2006, a surge of similar magnitude propagated upstream from LWTV (1.47 meters above NAVD88), elevating the water levels at WASD by approximately 1.11 meters above NOAA's predicted astronomical tides. The disparity between the estimated increase and the observed increase due to downstream boundary conditions could potentially be attributed to other flood drivers during Tropical Storm Ernesto. Additionally, the results indicate an approximately 26% increase in storm surge magnitude as the storm surge signal travels from LWTV to WASD (as shown in the right panel of Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In terms of local variations along the stations in comparison to LWTV, the storm tide with return periods of 25 to 100 years alone results in a 25\u0026ndash;27% increase between LWTV and WASD. Given the substantial contribution of water level arising from downstream boundary conditions, it significantly influences the water levels at WASD in comparison to other forcing factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Upstream Boundary Conditions of Major River Flows\u003c/h2\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the elevation in water levels resulting from the introduction of a range of return period (25 to 100 years) discharges into a typical tidal simulation in January 2020 is displayed. The effects of individual upstream river discharges are illustrated in panels a and b of Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e8\u003c/span\u003e, from the point of discharge input to WASD.\u003c/p\u003e \u003cp\u003ePanel a shows that for return periods of 25 to 100 years, an elevation of 8 to 10 meters is observed in comparison to a normal daily tidal simulation. This elevation gradually diminishes as it progresses towards WASD, resulting in an increase of approximately 1.4 to 2.6 meters compared to the normal daily tidal simulation. In contrast to the influence of LFMD, the discharge boundaries from the Anacostia River only yield an increase of 2.2 to 2.7 meters at the boundary station, which decreases to around 0.1 to 0.13 meters at WASD. Given the substantially smaller drainage area of the Anacostia River, the rise in water levels due to Anacostia discharges was expected to be lower for return periods of 25 to 100 years. Panel c portrays the combined impact of both river discharge boundaries from WASD to LWTV. The elevation in water levels at the WASD station resulting from 25 and 100-year return periods is approximately 1.5 to 2.7 meters above normal daily tides. Our model validation for the 1936 Flood demonstrated a slightly smaller increase in water levels (approximately 3 meters above normal tides) at WASD when a 100-year observed return period flow at LFMD inundated the region. It's important to note that during the validation for the 1936 Flood, only LFMD experienced flows exceeding the 100-year return period, while BDMD had no recorded observed flow until 1938. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e8\u003c/span\u003e also presents the increase in water level as a function of distance when various return period flows were introduced. This plot suggests that upstream discharge can have an impact as far downstream as Colonial Beach (COVA), which aligns with observations from a previous study (Mashriqui et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This analysis underscores the significance of incorporating upstream major discharge boundaries, particularly when riverine flows surpass 10 times the average daily flow, equivalent to a 2-year return period, as the model may not accurately capture total water levels in their absence.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3. Upstream Boundary Conditions from Urban Runoff\u003c/h2\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e9\u003c/span\u003e, Panel a illustrates the rise in water levels resulting from the introduction of a range of return period stream flows from tributaries (indicated by blue arrows in Fig.\u0026nbsp;1) into the Potomac River. A discernible increase ranging from 0.14 to 0.23 meters is observed at WASD. While this increase is notably smaller than that from the Little Falls discharge, it surpasses the influence of the Anacostia discharge at WASD. The simulations of urban discharge emphasize the importance of including it in the TWL modeling to accurately capture local water level fluctuations at WASD. However, the impact from urban discharges becomes negligible near GCMD, signifying the conclusion of urban contribution in a normal daily tidal simulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.3.4. Upstream Boundary Conditions from Combined River flows and Urban Runoff\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFurthermore, a series of simulations were conducted involving the combination of major river flow and urban runoff for return periods of 25 to 100 years. Panel b of Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e9\u003c/span\u003e illustrates the increase in water levels above normal daily tides as a function of distance. Remarkably, the combined effect of major river flow and urban runoff for a 100-year return period results in an additional 0.3 meters of water level increase compared to simulations with major river flows alone (2.7 m). The maximum water level rise above daily tides at WASD reaches 3 meters when both major discharges and urban runoff experience a 100-year return period. While this scenario is hypothetical, the 1936 flood serves as an example of such events. Given the significant increase in water levels at WASD due to combined discharges for all three return periods above normal daily tides, this underscores the importance of incorporating urban drainage alongside major river flows when modeling historical events or developing an integrated total water level forecasting system. Although urban runoff boundaries alone may not exert a significant influence on water levels at WASD, their combination with substantial river discharges will significantly impact the simulated Total Water Level (TWL).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.3.5. Effect of Local Winds\u003c/h2\u003e \u003cp\u003eThe results obtained from examining the local wind effects for eight primary directions at six stations surrounding WASD are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e10\u003c/span\u003e. Positive values signify an increase in water levels, while negative values indicate a decrease. Given the WASD station's location, one would anticipate local water level increases when winds blow from the south and decreases when they come from the northwest, pushing water away from the station. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e10\u003c/span\u003e clearly illustrates that when winds blow from the north, northeast, west, and northwest directions, they result in a decrease in water levels at the WASD station, with larger decreases as wind magnitudes increase. Conversely, when winds originate from the south, southwest, east, and southeast, an opposite trend is observed, leading to increases in total water levels. These localized changes in total water levels due to local winds align with findings from a previous study using a Delft3D model (Mashriqui et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which demonstrated that winds exceeding 5.5 m/s from the northwest direction drained water from the Potomac River, thereby reducing water levels at WASD. Our wind analysis highlights the importance of incorporating local wind forcing when it exceeds 10 m/s to accurately capture localized changes in total water level. On the other hand, winds below 10 m/s from the west, north, northeast, and northwest do not elevate water levels at WASD, making forecasts generated by ocean-scale coastal guidance systems, which do not account for local winds, reasonable. An examination of observed winds at WASD reveals that winds exceed 8 m/s at least once every month (Figure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003eA2\u003c/span\u003e in the \u003cspan refid=\"Sec25\" class=\"InternalRef\"\u003eappendix\u003c/span\u003e). While events with local wind speeds exceeding 10 m/s are less frequent, the absence of local wind forcing in such situations would lead to a significant misrepresentation of the forecasted TWL, as demonstrated in the simulations above.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe sensitivity of individual flood drivers to local water level changes is consistent with prior research, as indicated by Wang et al. (Wang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which revealed the significant impact of downstream storm tides and upstream river discharge on water levels in the Washington, DC area's tidal estuaries. Likewise, model experiments conducted by Mashriqui et al. (Mashriqui et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) demonstrated the sensitivity of Washington, DC's water levels to wind speeds exceeding 5 m/s, aligning with our findings. However, the influence of urban runoff on total water levels in Washington, DC lacks extensive documentation, limiting our ability to validate its accuracy.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Ranking Flood Drivers\u003c/h2\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e11\u003c/span\u003e, a summary of the significance of individual flood drivers at WASD is presented. Panel a demonstrates that urban runoff boundaries alone do not elevate water levels beyond the \"Action\" stage for return periods of 25 to 100 years. Conversely, storm surges have the potential to raise maximum water levels at WASD above the \"Moderate\" flooding level, reaching the \"Major\" flooding level when a 100-year storm surge travels up the Potomac River from Lewisetta, as observed during TS Ernesto in 2006. Local wind forcing exceeding 20 m/s from the South falls within the \"Minor\" flooding level, transitioning to \"Major\" flooding as local winds exceed 25 m/s. Among all the individual flood drivers, the 100-year return period major river discharges are found to have the most significant impact, resulting in \"Major\" flooding at WASD. The magnitude of LFMD greatly surpasses that of the Anacostia, and the 100-year discharge boundary alone can elevate water levels at WASD to 2.7 meters above NAVD88. Lastly, the maximum water levels caused by the combined river flows and urban discharge illustrate the potential for a major influence on WASD water levels, resulting in \"Major\" flooding for a 25-year return period. This plot highlights that a 100-year combination of river flows and urban discharge could lead to a substantial flood event at WASD, surpassing the maximum water level recorded at the station.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo showcase the influence of each flood driver on normal daily tides at WASD, panel b is presented. The light blue horizontal bars represent the percentage change for a 25-year return period. It's evident that the urban discharge boundary alone has a negligible impact at WASD (12%), while the influence significantly increases for the \"Surge only\" (225%) and \"Major River discharge\" (260%) boundaries, as indicated by higher percentage change values. The combined influence, representing major river discharge and urban runoff acting simultaneously, demonstrates a 315% increase above WASD normal tides, marking the most substantial change among all the flood drivers. The same order of percentage change is noted for 50- and 100-year return periods, with the combined influence reaching nearly 600%. The horizontally hatched bars illustrate the impact on local tides as winds of 10 to 35 m/s persist for 12 hours. A 10 m/s increase is calculated as 15%, rising to 140% and 460% for 25 and 35 m/s, respectively. Panel b portrays the percentage influence at WASD for each flood driver analyzed in these experiments and underscores the critical role of including all flood drivers in total water level modeling.\u003c/p\u003e \u003cp\u003eThe flood drivers examined in this study emerge as primary influencers on water levels at WASD, evident through the percentage change contributions across various return periods. While other factors like thermal expansion of water, offshore swells, and Bay water level changes due to Gulf Stream disturbances may also play a role (Ezer et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), these elements have a more linear effect on modeling accuracy. They primarily elevate mean sea level, thereby significantly affecting Washington, DC water levels when a storm tide travels upstream from LWTV or combined discharges occur in the National Capital Region, or when local winds exceed the 10 m/s threshold. Based on these findings, it is recommended to assign the highest ranking to storm surge and discharge input boundaries from rivers and urban runoff. Although regional weather models offer reasonable resolutions at a scale of 2.5 km, modelers should also pay close attention to localized winds in the region.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eReal-time flood forecasting in upstream tidal regions presents a formidable challenge, given the intricate and ever-changing interplay of numerous flood drivers. This study offers a comprehensive evaluation of the diverse flood drivers that are important to accurately modeling total water levels in upstream tidal rivers, with the Tidal Potomac River serving as a representative case. This river represents well the complex physical dynamics from ocean tides, freshwater inflows, urban runoff, and local wind impacts on hydrodynamics. Existing operational coastal guidance systems often fall short in predicting water levels accurately in such complex environments. The study area holds national significance, as the National Capital Region, situated at the confluence of the Potomac River and the Anacostia River, faces an escalating threat of flooding.\u003c/p\u003e \u003cp\u003eTo address these challenges, we employed the ADCIRC-2DDI model to simulate these interactions and established a meticulously calibrated and validated model configuration for the Potomac River. Model validation, conducted for six historical events, confirmed the model's ability to confidently replicate historical water levels at WASD, albeit with a slight underestimation of 0.1 m, equivalent to a -5% variation when compared to observed water levels.\u003c/p\u003e \u003cp\u003eThrough a range of hypothetical boundary forcing scenarios, we demonstrated the substantial impact of downstream boundaries, upstream river discharges, local urban runoff, and wind forcing, as expressed in terms of percentage change relative to a standard tidal day in January 2020. For example, the downstream boundary propagating from LWTV could elevate water levels at WASD by up to 300% in comparison to calm tidal days, particularly for a 100-year return period. Upstream major river discharges exhibited an approximately 500% change, which escalates to 600% in the presence of simultaneous urban discharge within the National Capital Region. Similarly, we showcased that local winds exceeding 20 m/s can result in a significant water level variation at WASD, reaching almost 140%. These findings underscore the critical importance of considering combined discharges along with local wind factors, a facet often overlooked in total water level forecasting for the Potomac River near Washington, DC, which has contributed to the underestimation of flood levels. Consequently, forecasting systems for such tidal rivers must comprehensively incorporate all flood drivers.\u003c/p\u003e \u003cp\u003eWe underscore the value of a dedicated framework for total water level modeling tailored to the intricacies of complex tidal rivers near Washington DC. By emphasizing the inclusion of all these boundary forcings (tides, storm surge, river discharge, urban runoff, and local winds), we highlight the potential to enhance the accuracy of total water level predictions, particularly for forecasting flooding events, including coastal, river, and compound flooding. Such a modeling framework, featuring one-way input boundary contributions, should be prioritized by other coastal modeling groups when forecasting total water levels.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funding for this research was provided by the Virginia Sea Grant Program [Grant # NA18OAR4170083], and the Flood Hazards Research Lab (FHRL) at George Mason University generously offered research resources. The opinions, findings, conclusions, or recommendations presented in this material are solely those of the authors and do not necessarily represent the views of the NWS or NOAA. The authors extend their gratitude to Mr. Jason Elliott at NWS National Water Center for sharing valuable insights into NWS flood forecast models and operations. The research benefited from access to Extreme Science and Engineering Discovery Environment (XSEDE) STAMPEDE2 resources under allocation ID TG-BCS130009, supported by the National Science Foundation [grant number ACI-1548562] (Towns et al., 2014). Additionally, the authors appreciate the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing high-performance computing resources, which significantly contributed to the model calibration results outlined in this paper (http://www.tacc.utexas.edu).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Arslaan Khalid. The first draft of the manuscript was written by Arslaan Khalid and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funding for this research was provided by the Virginia Sea Grant Program [Grant # NA18OAR4170083], and the Flood Hazards Research Lab (FHRL) at George Mason University generously offered research resources. The research benefited from access to Extreme Science and Engineering Discovery Environment (XSEDE) STAMPEDE2 resources under allocation ID TG-BCS130009, supported by the National Science Foundation [grant number ACI-1548562] (Towns et al., 2014).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e \u003c/strong\u003eAll model analyses conducted in this study were performed on behalf of the Mason Flood Hazards Research Lab, and the results are stored on local servers. The modeling outputs can be made available for non-commercial, academic research purposes upon request from the corresponding author. The hydrodynamic coastal storm surge model, ADCIRC, is accessible for non-commercial, academic research purposes through contact with Crystal Fulcher at the University of North Carolina ([email protected]). The integrated modeling framework utilized in this research is based on the iFLOOD paper recently published and can be viewed on the iFLOOD web portal (https://iflood.vse.gmu.edu/map). Historical observational data for winds and water levels were obtained from the NOAA tides and currents database (https://api.tidesandcurrents.noaa.gov/api/prod/), while streamflow data was accessible online through the USGS water database (https://waterdata.usgs.gov/nwis). The streamflow data at a specific return period was computed using the online StreamStats server, which can be accessed at https://streamstats.usgs.gov/ss/. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors give their consent to participate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors give their consent to publish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAtkinson, L. P., Ezer, T., \u0026amp; Smith, E. (2012). 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Future climate risk from compound events. \u003cem\u003eNature Climate Change 2018 8:6\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(6), 469\u0026ndash;477. https://doi.org/10.1038/s41558-018-0156-3\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Total Water Level, Washington, DC, Tidal Potomac, ADCIRC","lastPublishedDoi":"10.21203/rs.3.rs-3866206/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3866206/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the National Capital Region, existing coastal flood guidance systems frequently underestimate total water levels (TWL), leading to inaccurate flood predictions. Flood forecasting in this region, located at the confluence of two major rivers (Potomac and Anacostia) with tidal connections to the Chesapeake Bay, faces TWL under-predictions due to missing physical processes, limited integration of hydrological and hydrodynamic models, and simplified operational model frameworks. This study introduces an integrated TWL framework using a high-resolution two-dimensional coastal storm surge model (ADCIRC) that includes multiple flood drivers (storm tide, river flows, urban runoff, and local wind forcing) as one-way input boundary conditions in the tidal Potomac River. This framework accurately replicates historical events based on observed data, with validations indicating a 0.1 m under-prediction at the NOAA Washington, DC tide station (WASD), representing a -5% deviation from observed maximum water levels. Through hypothetical simulations for 25-, 50-, and 100-year return periods, we emphasize the substantial impact of individual flood drivers. Local winds had the smallest impact on water levels at WASD compared to downstream storm surge from the Chesapeake Bay (Lewisetta, VA). Upstream major river discharges elevate water levels beyond the National Weather Service (NWS) major flooding level by 0.9 m, further amplified to 1.4 m above the threshold when urban discharges occur simultaneously in the National Capital Region. Unlike prior studies, our work offers a comprehensive evaluation of individual flood drivers' influence on TWL modeling, underscoring the imperative need for their inclusion in the framework to accurately estimate river, coastal, and compound floods in estuarine metropolitan areas.\u003c/p\u003e","manuscriptTitle":"Compound Flooding in River-Urban-Coastal Environments: Multi-factorial Drivers and Modeling Considerations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-24 19:33:15","doi":"10.21203/rs.3.rs-3866206/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9942152d-5d7b-4285-8a36-dc0dfb3e41e6","owner":[],"postedDate":"January 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-07T21:34:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-24 19:33:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3866206","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3866206","identity":"rs-3866206","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}