Accounting for the uncertainty of precipitation forecasts and its impacts on probabilistic flood inundation mapping skill

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As hurricane forecasts evolve, spatial discrepancies in precipitation estimates lead to misalignment between forecasted and observed rainfall, affecting flood prediction accuracy. This study presents a methodology for addressing storm mispositioning using an integration of the High Resolution Rapid Refresh (HRRR) NWP data and a 2-D hydrodynamic model to generate flood inundation maps. The analysis, focused on Hurricane Beryl (July 2024), evaluates the impact of multiple storm location scenarios over 24-hour forecast periods with 6-hour intervals. Quantitative Precipitation Forecast (QPF) fields are used as input to the Super-Fast INundations of CoasT (SFINCS) model, applied to the highly urbanized central Houston area. Results show that incorporating HRRR forecasts and spatial displacement of QPF fields improves the correlation with in-situ meteorological and water elevation observations. This method provides a more accurate flood inundation mapping by accounting for uncertainties in the precipitation forecasts. SFINCS model performance metrics, including Kling-Gupta Efficiency (KGE), improved from 0.632 using only forecast data to 0.70 when incorporating the WSE ensemble mean generated from modified QPF fields. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction From an operational perspective, access to reliable precipitation inputs is essential for forecasting future discharge. For this purpose, Quantitative Precipitation Forecasts (QPF) are primarily developed using Numerical Weather Prediction (NWP) models or through meteorologist-guided predictions, such as those produced by the Weather Prediction Center (WPC). Numerous QPF products are available, including the High-Resolution Rapid Refresh (HRRR, Benjamin et al., 2016 ), the North American Mesoscale (NAM; (Janjic, 2003 )), the Global Forecast System (GFS;(Environmental Modeling Center, 2003 )), or the European Centre for Medium-Range Weather Forecasts (ECMWF; (Molteni et al., 1996 )), all with different spatial and temporal coverages, resolutions and lead times. However, the integration of QPFs into the hydrological component is tied to significant challenges, primarily related to uncertainties in the predictive capabilities of numerical models to replicate storm patterns and quantify future streamflow. Consequently, accurately characterizing and quantifying the impact of QPF products on flood forecasts is an imperative but complex task that requires more than a deterministic analysis (Adams and Dymond, 2019a , b ), not only for flow forecasting but also for water surface elevation (WSE) estimation. The outcome of most operational flood forecast systems is often limited to ensembles of discharge forecast hydrographs without the simulation of a physical model to convert streamflow forecasts to flood inundation areas. To provide more actionable forecast information and help inform decision makers, streamflow hydrographs should be converted into flood inundation maps (Cloke and Pappenberger, 2009 ; Jafarzadegan et al., 2023 ). However, challenges remain in improving discharge estimation using Near-Real-Time (NRT) and forecast data, which are essential for accurately informing hydrodynamic or conceptual models for flood inundation mapping (Coelho et al., 2022 , 2025 ). Flood inundation mapping is an essential component of flood risk management, serving as a crucial tool for assessing potential flood extents and guiding emergency response and infrastructure planning. Accurate flood inundation forecasts could help protect lives and property, reducing flood impacts and enhancing community resilience. The quality of forecast inputs, including precipitation and streamflow data, profoundly influences the accuracy of flood inundation models. Errors in these inputs can propagate through the model, introducing significant uncertainties that affect the reliability of forecast results (Bogner and Pappenberger, 2011 ; Pappenberger et al., 2006 ; Rodríguez-Rincón et al., 2015 ; Zappa et al., 2011 ). Moreover, uncertainties stemming from model parameters, grid resolution, and initial conditions can amplify discrepancies between simulated and observed flood extents (Alipour et al., 2022b ; Jafarzadegan et al., 2021b ; Savage et al., 2016 ). Addressing these uncertainties requires not only improved model calibration and validation but also the development of ensemble forecasting approaches that account for variability in forcing data (Jafarzadegan et al., 2021a ). Forecasting flood inundation using hydrodynamic models is particularly complex in regions prone to compound flooding, where multiple drivers such as river flow, storm surge, and intense rainfall converge to amplify flood risks (Gomez et al., 2024 ; Muñoz et al., 2022 ; Santiago-Collazo et al., 2019 ). Accurately simulating these complex events requires refined QPF estimations and the integration of these datasets into hydrodynamic models. However, discrepancies between model-based forecasts and rain gauge observations persist, posing challenges to QPF accuracy in regions susceptible to intense, convective-driven rainfall. Several methodologies have been proposed to correct QPF estimates in terms of storm direction (Hugeback et al., 2023 ; Seo et al., 2018 ; Vergara et al., 2023 ; Yu et al., 2016 , 2018 ). Challenges remain in improving precipitation estimates regarding magnitude and the spatial-temporal evolution of the storm (Adams and Dymond, 2019b ; Carlberg et al., 2020 ). Models like HRRR, with high spatial (3 km) and temporal (1 h) resolutions, offer promising data for real-time and short-term forecasting, despite ongoing limitations in accurately representing storm patterns, spatial coverage, and rainfall intensity. The uncertainty arising from the spatial displacement of precipitation fields has not been fully addressed in flood inundation mapping, specially under compound flood events and in highly urbanized watersheds. This gap presents an opportunity to improve the predictive skill of flood inundation models by accounting for the impact of spatial displacement of precipitation fields in QPF forecasts. This study aims to address this limitation by analyzing QPF displacement errors relative to in-situ observations and Quantitative Precipitation Estimates (QPE), to generate an ensemble of precipitation inputs to the hydrodynamic model. The approach consists of two key steps: first, identifying spatial shifts in QPF based on storm path across various lead times. These ensembles will be integrated into a 2D hydrodynamic model to evaluate their performance in simulating flood depths and extents over Houston, TX. This methodology offers a pathway to operationally enhance QPF accuracy, especially as input to 2D numerical models for predicting flood dynamics. The research article is organized as follows. First, in section 2 the methodology is described considering the description of the SFINCS hydrodynamic framework and HRRR QPF ensemble generation process for precipitation field generation. Section 3 presents the definition of study area along with the datasets used on this research. Section 4 contains the results of the methodology and hydrodynamic simulations for probabilistic flood inundation mapping, and section 5 presents the discussion and conclusion of the findings. 2 Methodology Our designed methodology first focuses on the generation of displaced QPF fields, which are critical for evaluating the impact of spatial shifts in precipitation on compound flood modeling during hurricane events. The QPF displacement process involves systematically shifting the HRRR v4 QPF fields based on the forecasted storm path given the low pressure center accounting for uncertainties in storm positioning. Previous works have demonstrated that QPF forecast from NWP models exhibit displacement errors compared to observations that could be corrected (Adams and Dymond, 2019b ; Kiel et al., 2022 ), and that performing spatial shifts to precipitation storms fields directly impact hydrological forecast accuracy (Carlberg et al., 2020 ; Hugeback et al., 2023 ). In this research, spatial shifts are applied in four directions (forward, backward, right-forward, and left-forward) and at four different distances (10, 20, 30, and 40 kilometers) considering the hurricane forecasted path. This creates ensemble scenarios that represent a range of potential storm trajectories and precipitation distributions, providing multiple precipitation fields for the hydrodynamic model. These shifted QPF fields are then used in the Super-Fast INundation of CoastS (SFINCS) model to simulate flood dynamics across the Houston area in the state of Texas. In the hindcast configuration, initial simulations use historical data, including USGS discharge, NOAA storm tide observations, MRMS QPE and NLDAS forcings ( u-v wind components, and surface pressure fields). These inputs are used to generate restart files for the forecast runs. The hindcast simulations generate flood inundation maps that serve as a baseline for comparison against forecasted maps, in order to evaluate the performance of the forecast simulations. In the forecasting phase, the SFINCS model is run using the HRRR u-v wind components, surface pressure fields, and QPF fields—both the original and the shifted QPFs—as precipitation inputs. The forecast simulations, initialized with the restart files from the hindcast phase, simulate potential future flood events under various storm scenarios. By considering different spatial displacements of the QPF fields, the model provides probabilistic flood inundation maps that account for the uncertainty in precipitation forecasts, storm position, and intensity. These errors that can be propagated through the ensemble modeling and subsequently translated into flood inundation extent and WSE errors in the compound flood event analysis (Abbaszadeh et al., 2022 ). Figure 1 summarizes the primary steps and variables considered within the proposed methodology. 2.1 HRRR QPF shifting The High-Resolution Rapid Refresh (HRRR) model is a critical tool in atmospheric forecasting, providing high-frequency, high-resolution data for weather prediction across the United States (Benjamin et al., 2016 ). The model's lead times vary depending on its version. For versions 1 and 2, the longest lead time is 18 hours, while version 3 extends to 36 hours, and the current version, HRRR v4, offers a 48-hour lead time. For this study HRRR v4 model runs every 6 hours are considered, providing hourly forecast for the first 24 hours at 00, 06, 12, and 18 UTC cycles, starting at Jul/06/2024 00:00 until Jul/09/2024 1800UTC. This study focuses on a 24-hour lead-time window, analyzing short-term forecasts for these four cycles per day to capture high-resolution atmospheric conditions that influence precipitation, wind, and pressure patterns over short intervals. The methodology focuses on shifting the QPF fields to generate an ensemble of precipitation scenarios, each of which is subsequently fed into the hydrodynamic model to simulate resulting flood extents. This process involves the spatial shifting of QPF fields, systematically displaced across a range of directions and distances (10, 20, 30, and 40 kilometers) to capture potential deviations in the storm’s precipitation footprint over time. The storm center is determined based on the location of the lowest value of 'Mean Sea Level Pressure' variable within the HRRR hourly forecast, from which the directional component is calculated as one of the eight primary compass directions, reflecting the trajectory of the storm center over the evaluated lead times. To account for lateral deviations, we include neighboring directions adjacent to the calculated trajectory; for instance, if the trajectory is determined to be north (N), we consider movements in the northeast (NE) and northwest (NW) directions at the same specified distances. Additionally, recognizing that storms may experience accelerations or decelerations that are difficult to forecast, we analyze the opposite direction of the storm trajectory. The rationale for considering these specific displacements is multifaceted. The forward and backward shifts are based on the overall storm trajectory, with uncertainties related to the position of the low-pressure center, which may vary as lead times increase, reflecting the common increase in forecast errors over time in NWP models (Chen et al., 2022 ; Rey and Mulligan, 2021 ). The right-forward displacement is particularly relevant as it addresses the observed tendency for most rainfall to occur in the first quadrant relative to the storm’s trajectory. Tropical cyclones frequently exhibit asymmetric rainfall distributions, with the right-front quadrant typically receiving greater precipitation due to the combination of storm motion and the radial distribution of wind fields (Trepanier and Tucker, 2018 ). Additionally, the left-forward displacement accounts for the shear-stress-drift induced by wind direction and magnitude, recognizing that raindrops do not fall vertically due to the influence of horizontal wind forces. This phenomenon, often referred to as wind-induced drift, can significantly affect the spatial distribution of precipitation (Yan and Bárdossy, 2019 ). The incorporation of this displacement allows for a more nuanced representation of the precipitation field, aligning better with observed rainfall patterns during hurricane events. As a result, a single hourly forecast generates a total of 16 perturbed forecasts in the displacement component, encompassing a range of potential storm behaviors and improving the model’s ability to represent the complexities of precipitation patterns during hurricane events. 2.2 Hydrodynamic modeling We use SFINCS model, a 2D hydrodynamic modeling tool that efficiently simulates compound flood events by integrating multiple flooding mechanism (Lee, 2025 ; Leijnse et al., 2021 ; Sebastian et al., 2021 ). In hurricane conditions, compound flooding is particularly complex due to the interactions between rainfall, rising river discharges, and coastal surges, each contributing to amplified flooding hazard. The SFINCS model is configured to capture these interactions over a high-resolution grid, allowing for detailed spatial representation of flood depths and extents. To enhance the accuracy of the model, the setup incorporates boundary conditions from observed main river discharges, still water surface elevations for surge influence, and high-resolution meteorological data, ensuring that the major contributors to compound flooding are represented in both hindcast and forecast modes. SFINCS model solves Shallow Water Equations. The choice of SFINCS is particularly advantageous due to its computational efficiency, which allows simulating high-resolution flood dynamics with lower computational complexities and the use of High Performance Computing (HPC) facilities for the parallelization of multiple runs at the same time. This feature supports the generation of multiple simulation runs, which is critical for ensemble-based probabilistic flood analysis performed in this study. The SFINCS modeling process follows a two-step approach. The first step involves modeling water surface elevation and extent using near-real-time products or current observations and is referred to as the hindcast simulation. This hindcast provides the necessary water surface elevation states, which are then used to generate the restart files every 6 hours, essential for initializing the forecast simulation. HRRR NWP forecast data (precipitation, surface pressure, wind components) are applied at every 6 hours cycle initialized at 00UTC for 24h lead time. The forecast simulations include both the original HRRR QPF fields and the 16 spatially shifted QPF fields, which are used to simulate multiple potential precipitation scenarios, generating a total of 17 members, used in the computation of the ensemble mean for comparison of final results of flood inundation depths and extent. 3 Study Area and Data This study focuses on the City of Houston, Texas, located in Harris County—a densely populated urban area highly susceptible to flood risks, especially during extreme compound flood events like Hurricane Harvey in 2017 (Gori et al., 2020 ; Huang et al., 2021 ; Samadi et al., 2025 ). The case study centers on Hurricane Beryl in July 2024, a significant tropical system that generated multiple flooding along this city during the 2024 hurricane season. Situated near Galveston Bay, the coastal proximity of Houston not only subjects it to storm surge influences but also allows for the convergence of fluvial and atmospheric conditions that can drive compound flooding events (Valle-Levinson et al., 2020 ). The city's extensive network of bayous, creeks, and the lower Trinity River all play crucial roles in the complex flood dynamics that characterize the Galveston Bay (Gori et al., 2019 ; Juan et al., 2020 ; Peeples et al., 2023 ), combining riverine flooding from upstream discharge with coastal influences such as storm surge during hurricane conditions. Houston’s position along the Gulf Coast, a region prone to frequent tropical storms and hurricanes, underscores its vulnerability to compound flooding. The Gulf’s warm waters contribute to rapid cyclone intensification, increasing the likelihood of high rainfall and strong surge impacts for coastal cities like Houston (Alipour et al., 2022a ; Radfar et al., 2024 ; Trepanier and Tucker, 2018 ). 3.1 Hydrodynamic model setup The SFINCS model (version 2.0.3 Cauberg) is configured in subgrid mode proper characterization of terrain complexities in urban areas like Houston, while maintaining a reasonable computational times (Sebastian et al., 2021 ; Van Ormondt et al., 2024). The subgrid model has 524472 active cells that have flux grid of 45 by 45 meters, corresponding to 15 raster pixels of 3-meter resolution on each side. The NCEI Continuously Updated Digital Elevation Model (CUDEM) Bathymetric and Topographic DEM, with a 1/9 arc-second resolution (National Centers for Environmental Information, 2014) is used as the topography data. The study area is highly urbanized and there is no topographic or bathymetric information under all the bridges or other hydraulic structures. Topographic adjustments were made to guarantee and preserve the hydraulic characteristics of the streams, improving model stability and accuracy of the results against observations (Gomez et al., 2024 ; Hamidi et al., 2024 ). For unsteady flow analysis, an hourly simulation time window is defined between July 06/2024 00UTC to July 09/2024 00UTC. The hydrodynamic model setup incorporates multiple datasets to capture the compound flooding processes in Houston, with boundary and forcing data sources selected to represent riverine, coastal, and precipitation-driven flooding mechanisms accurately (Fig. 3 and Table 1 ). 3.2 Precipitation and atmospheric forcings For the initial base simulation, the hourly MRMS QPE (Zhang et al., 2016 ) is used as the main precipitation forcing and the NLDAS (Xia et al., 2009 ) is used for hourly u-v winds and surface pressure fields. This base model was defined as a first step for calibration purposes of Manning roughness coefficients for the land cover surfaces defined. The calibrated model serves as the generator of restart files for the forecast using HRRR QPF simulations every 6-hour cycle. The simulations in forecast configuration are set over 24-hour lead time in 6-hour cycles initialized at 00UTC. The primary input data for precipitation and atmospheric conditions is obtained from the HRRR v4 model through Herbie python package (Blaylock, 2024 ). The HRRR dataset provides Quantitative Precipitation Forecasts (QPF) along with u-v wind fields and surface pressure at a relatively high spatial (3 km) and temporal (1-hour) resolution. These fields are integral to capturing the evolving intensity and movement of rainfall and atmospheric pressure changes during the hurricane event, offering high-resolution input data that can improve the spatial accuracy of flood predictions. Infiltration processes are critical in flood mitigation as they determine how much rainfall is absorbed into the soil and how much becomes surface runoff. Factors such as soil type, vegetation cover, and land use affect the ground’s ability to infiltrate water, directly influencing flood risk. When the infiltration capacity is exceeded during high-intensity rainfall, excess water leads to surface runoff and potential flooding, making it essential to account for infiltration in flood modeling. The SFINCS model incorporates infiltration using the SCS Curve Number (CN) method to calculate a runoff coefficient, which helps determine runoff volume. For this study, the Global Curve Number Dataset (GCN250, Jaafar and Ahmad, 2019 ), representing average antecedent runoff conditions (ARC II), is used as input to define the curve numbers within the study area. 3.3 Discharge and tidal forcings River discharge data for model boundary conditions is sourced from the U.S. Geological Survey (USGS), with specific attention to discharge measurements from rivers surrounding Houston. This discharge data, combined with HRRR precipitation inputs, forms the hydrological basis of the model, simulating riverine inflows essential to compound flood events. To characterize the coastal ocean boundary, hourly still water surface elevation data is obtained from NOAA for the Morgans Point station (Station ID: 8770613). This data reflects on the combined influence of astronomical tidal conditions and storm surge, further refining the simulation of compound flooding under hurricane conditions. Hourly river discharge data from the U.S. Geological Survey ( 2016 ) is used for most of the significant streams along Houston.. Table 1 summarizes the boundary conditions applied to the SFINCS model and Fig. 3 depicts the location of the discharge and WSE stations. Table 1 Boundary conditions summary ID Gauge station name Source Code Use 0 Morgans Point NOAA 8770613 Still water surface elevation downstream 1 Sims Bayou at Houston USGS 08075500 Discharge 2 Brays Bayou at Houston USGS 08075000 Discharge 3 Buffalo Bayou at Houston USGS 08074000 Discharge 4 San Jacinto River nr Sheldon USGS 08072050 Discharge 5 Garners Bayou nr Humble USGS 08076180 Discharge 6 Greens Bayou nr Houston USGS 08075900 Discharge 7 Whiteoak Bayou at Houston USGS 08074500 Discharge 8 Hunting Bayou USGS 08075763 Discharge 9 Little Whiteoak Bayou at Trimble St USGS 08074540 Discharge 10 Berry Bayou at Nevada USGS 08075605 Discharge 11 Halls Bayou USGS 08076500 Discharge 12 Goose Ck nr Mcnair USGS 08067520 Discharge 3.4 Validation data The model’s outputs are validated using multiple observational datasets, with WSE and rainfall distribution as key indicators. WSE data are obtained from 16 stations of the Harris County Flood Warning System (HCFWS) distributed throughout the modeling region, enabling a comprehensive comparison between simulated and observed flood elevations. Additionally, rainfall data from 184 stations across Harris County, provided by the HCFWS, offer extensive ground-based precipitation measurements for direct comparison with the HRRR QPF fields. These ground-truth validation datasets are crucial for identifying discrepancies in forecasted rainfall intensities and distributions. Specifically, the HCFWS precipitation data are used to validate magnitude and displacement correlation, as these in-situ measurements provide the necessary foundation for correcting the HRRR QPF fields. The MRMS dataset is used as a spatial verification tool to assess the distribution of rainfall, particularly in capturing the presence of rain bands, which are characteristic of high-rotation convective storms. 4 Results 4.1 Storm characterization Hurricane Beryl was a significant tropical cyclone that posed significant threats to the Gulf Coast of United States, particularly impacting the City of Houston. Hurricane Beryl was an earliest-forming Category 4 and 5 hurricane on record, and it was only the second storm in history to reach this intensity in the month of July. It holds the strongest maximum sustained winds for an Atlantic hurricane prior to August on record. The storm ultimately impacted the Houston area and generated 8 to 12 inches of precipitation, along with maximum totals of 14.99 inches, reported over HCFWS rain gages network (Beven II et al., 2025 ). The storm’s trajectory, documented using HURDAT2 data (National Hurricane Center, 2024 ), shows a path that tracked northwestward across the Gulf of Mexico before curving inland over southeastern Texas. Figure 4 illustrates (a) the overall position of Beryl’s storm center and (b) a zoomed-in view of the storm path over southeastern Texas, which includes Houston. Overlaid on the zoom-in trajectory is the 24-hour forecasted paths generated from the HRRR model, plotted as green lines at each 6-hour cycle. These forecasted paths represent the storm center hourly positions based on the lowest ‘Mean Sea Level Pressure’ values within each hourly HRRR forecast, providing insights into forecast accuracy and the potential spatial discrepancies in hurricane trajectory predictions. Hurricane rain bands are curved formations of clouds and thunderstorms that spiral outward from the eye wall, producing intense bursts of rain, wind, and sometimes tornadoes. At an hourly scale, HRRR QPF does not accurately capture the spatial position and intensity of rainbands. MRMS, with higher temporal and spatial resolution in observations, delineates the rainbands with greater fidelity, whereas HRRR often misplaces these bands, leading to inaccuracies in both position and magnitude. This discrepancy indicates that uncertainties in HRRR forecasts, especially during tropical cyclone events, are partially attributable to spatial misalignment with the observed precipitation distribution. These findings suggest that HRRR forecast errors could be mitigated by implementing a spatial shift adjustment in the QPF. By applying systematic spatial corrections, we can potentially improve the alignment of forecasted and observed precipitation fields, enhancing the accuracy of precipitation inputs in hydrodynamic flood models. This adjustment framework provides a pathway for refining model-based QPFs to better capture the spatial dynamics of hurricane rain bands and reduce forecast uncertainty. Figure 5 shows the bias between the HRRR QPF and MRMS QPE in the first row, for three different forecasts initialized at various hours and considering different lead times. The second row presents the bias for the same forecasts after applying the spatial shifting procedure. It is evident that the spatial shifting of QPF fields reduces both overestimations and underestimations in the forecasts by improving the alignment of high-intensity rainbands during hurricane conditions. The effectiveness of these adjustments is influenced by the hurricane's initialization location and its evolution over forecast lead times. The uncertainty in the direction and distance of the spatial shift can be affected by additional variables, including the model's parameterization, which impacts the storm's evolution. For the 24-hour period evaluated, shifts greater than 40 kilometers show a decrease in the correlation between forecasted values and observations at rain gauges. This suggests that larger displacement distances result in a less accurate alignment of the precipitation fields, reducing the incorporation of non-significant QPF fields into the hydrodynamic model. The analysis of discrepancies between the HRRR forecast and observations across 180 HCFWS rain gauges was conducted using a heatmap plot comparing the correlation of observed and forecasted precipitation values for both the original HRRR forecast and the 16 displacements considering lead times of 0-6h, 0-12h, 0-18h, and the full 24 hours (Fig. 6 ). Significant variability in the correlation coefficient was observed depending on the lead time evaluated. For the forecasts initialized on Jul-07 at 12:00 and 18:00 UTC the correlation values were predominantly below 0.5, with the lowest values approaching 0 in the first 12 hours (18:00 UTC F0-F12) across all directions. The best correlations, however, were observed when shifting the QPF fields in the backward (B) direction to the storm trajectory over the entire 24-hour period, compared to other directions and the original forecast. This indicates potential mispositioning of the storm in the HRRR forecast, as shown in Fig. 5 (first column), where the smallest spatial error was observed relative to the MRMS dataset. On Jul-08, when Hurricane Beryl made landfall, the correlation between forecasted and observed precipitation improved, particularly for the forward (F) and forward-right (F-R) displacements. For the critical cycles (00, 06, and 12 UTC), the best correlations were observed with the F-R direction, and the correlation reduced as the displacement distance increased in the backward (B) direction. The results show that certain directional shifts and distances can reduce the errors between observed precipitation and HRRR forecasts. Overall, it was observed that forecasts initialized earlier (Jul-07 18:00 UTC and before) showed the lowest correlation values improving for the backward displacement in this case. Later forecasts, closer to the hurricane's impact on the study area, exhibited higher correlations, highlighting the improvement in forecast accuracy as the storm's position became more defined. This finding highlights the importance of accounting for storm dynamics in flood inundation forecast modeling, and that further research is warranted to systematically adjust the storm position over time for improved forecast accuracy. Specifically, the analysis demonstrates variability in forecast accuracy depending on the storm trajectory and the distance of displacement. Some combinations of direction and distance yield improved correspondence between observed and forecasted values, but no consistent trend emerges across all forecast cycles. This highlights the complexity of spatial discrepancies inherent in QPFs, suggesting that spatial perturbations can enhance model performance but must be applied judiciously to effectively address the uncertainty in hurricane forecasting. This analysis provides valuable insights into optimizing QPF applications for hydrodynamic modeling and underscores the importance of ongoing evaluations of forecast accuracy across diverse meteorological scenarios. 4.2 FIM results The evaluation of SFINCS model performance in validation stations is measured through different metrics, including Kling-Gupta Efficiency (KGE) (Kling et al., 2012 ), Root Mean Square Error (RMSE) and the probabilistic metrics normalized root mean square error ratio (NRR) and Reliability. The NRR, as explained by DeChant and Moradkhani, ( 2012 ) was used to measure the ensemble spread and assess the statistical distinction between the ensemble mean and the ensemble spread. A larger spread indicates higher uncertainty in the ensemble predictions, whereas a smaller spread suggests greater confidence in the ensemble mean. Reliability was employed to evaluate the fit of the Q-Q quantile plot to a uniform distribution. A value of 1 indicates perfect uniformity, meaning that the observed and forecasted quantiles are closely aligned, while a value of 0 represents the maximum deviation from uniformity (DeChant and Moradkhani, 2012 ). These metrics provide a quantitative assessment of the ability of generated ensembles to capture uncertainty in storm trajectories and precipitation, and their agreement with observed data in flood inundation results. The formulations of these metrics, which collectively provide insights into different facets of model accuracy, are summarized in Table 2 . These metrics serve as quantitative measures to assess the capability of the model capturing the observed variations in water surface elevation for all the ensembles generated for Hurricane Beryl event under the approach generated. Table 2. Summary of performance metrics used in this study The results of the SFINCS model are presented through hydrographs depicting water surface elevations at validation stations. These hydrographs facilitate a comprehensive comparison among various simulation scenarios, including observed data, hydrodynamic model outputs utilizing MRMS QPE (this output is considered in this case the best hydrodynamic result possible from a hindcast perspective), results derived from the original HRRR forecasts, ensemble simulations generated from shifted QPFs, and the ensemble mean. The Fig. 9 shows the water surface elevation results for four of the stations considered in validation dataset for the consecutive forecast cycles. In the early forecast cycles, significant variability is observed in the WSE results across different stations, reflecting the uncertainty inherent in storm location and precipitation predictions as lead time increases. This variability is particularly pronounced when considering multiple ensemble scenarios, which show a larger impact on WSE results, contributing to both overestimations and underestimations. The discrepancies between forecasted and observed WSE values are most evident in the initial forecast periods, where model performance is relatively poor compared to both the observed data and hindcast results. For instance, in some cases, differences between ensembles reach up to 2 meters in WSE (first two rows in Fig. 7 ). These large variations highlight the substantial uncertainty in the early forecast, which stems from storm mispositioning and the challenge of accurately predicting the magnitude and spatial distribution of precipitation. As the forecast initialization approaches the hurricane's impact on the Houston area, the variability in WSE results decreases, and the predictions become more uniform. In these later forecast cycles, the variations in WSE are primarily more uniform, with smaller discrepancies between ensemble members. This indicates that, as the storm moves closer to the region, the forecast uncertainty reduces, and the model begins to more accurately capture the dynamics of the storm in terms of flood inundation generation. When comparing the forecast results with the flooding thresholds from the AHPS (Advanced Hydrologic Prediction Service) and the HCFWS (Harris County Flood Warning System) in Fig. 7 , it is seen that the original HRRR forecast, when applied to hydrodynamic model, does not always align with the observed flooding categories. This misalignment underscores the value of considering multiple spatial variations of the storm in the modeling process. By applying ensembles that account for different storm positions, this approach enables a broader range of flood scenarios, which improves flood inundation mapping accuracy. Moreover, the ability to define the number of ensemble members that fall within different flooding categories provides a more robust decision-making tool for flood risk assessment. This analysis highlights the importance of incorporating spatial variability into operational forecasting models, as it offers a better understanding of flood risk and enhances the ability to manage uncertainties in flood prediction. Figure 8 presents boxplots for performance metrics including KGE, NSE, and MBE for WSE alongside probabilistic metrics NRR and Reliability, evaluated across different 16 WSE validation stations. For several stations, the KGE and NSE performance metrics are low when using the original HRRR forecast, particularly for forecasts initialized on Jul-07 06:00 UTC and Jul-07 12:00 UTC. However, these performance metrics significantly improve when considering the ensemble mean. Notably, the variability in the results decreases and the average values increase across the 16 validation stations in the study area, with KGE improving from 0.632 to 0.70 (median from 0.81 to 0.823), NSE from − 0.186 to 0.32 (median from 0.80 to 0.86), and MBE from 0.035m to 0.019m (median from 0.062m to 0.04m). For the MBE results, it is observed that the mean tends to approach zero, with positive variability indicated by the range of the boxplots for most forecast cycles. As the forecast lead time approaches the end of the evaluation period, MBE decreases towards negative values, as seen in several of the hydrographs for forecasts initialized on Jul-09 12:00 UTC. Both the original HRRR forecast, and the ensemble mean underestimating the observed water levels at that time. For probabilistic metrics, the NRR boxplots show that for most forecast initializations, the ensemble spread in WSE generated through SFINCS has values higher than 1, indicating that the ensemble spread is limited and that the forecast uncertainty is not fully captured. This suggests that the ensemble range is too narrow, likely due to insufficient variations in the storm's trajectory and precipitation uncertainty. To improve the representation of forecast uncertainty imposed by precipitation input to the hydrodynamic model, it is necessary to increase the spread of ensemble, which can be achieved by incorporating additional displacement scenarios in both the spatial directions and the magnitude of precipitation. It is important to note that this is just one component of the overall uncertainty. Other factors, such as errors in other forecasted forcing data and model parameters also contribute to the total uncertainty in flood inundation modeling forecasting (Abbaszadeh et al., 2022 ; Muñoz et al., 2024 ). For the Reliability metric, boxplots range from 0.5 to 0.9, averaging a value of 0.72, indicating moderate reliability in the probabilistic forecast. This moderate value indicates that while the forecast is reasonably reliable, there is room for improvement in aligning the probabilistic forecast with the observed outcomes. A higher reliability could be achieved by considering a broader set of displacement scenarios, accounting for both the uncertainty in the direction of the storm and the variability in precipitation magnitude. By expanding the ensemble to include more diverse precipitation and storm position scenarios, the spread would increase, leading to a smaller NRR and a higher reliability. The proposed methodology for shifting tropical cyclones offers valuable improvements to flood inundation mapping. It could be further enhanced by integrating other perturbation techniques for input variables, which would improve the probabilistic generation of scenarios and boundary conditions for both hydrological and hydrodynamic models. The ensemble scenarios, which incorporate spatially displaced QPF fields, offer a range of potential outcomes that more effectively capture the uncertainties inherent in storm location and rainfall intensity. By simulating multiple precipitation scenarios, the ensemble approach helps mitigate the biases observed in the original HRRR forecast. The ensemble simulations provide a probabilistic representation of WSE that aligns more closely with observed conditions, particularly in the later forecast cycles when the storm's trajectory is better defined. This variability in the ensemble results provides valuable insight into the potential flood extents, allowing for a more comprehensive flood risk assessment. The use of precipitation ensembles enables the generation of water depth maps (Fig. 9 a), which present the average values of the ensemble results, as well as probability maps for flood extents (Fig. 9 b), considering a 10 centimeters water depth threshold for flooded and non-flooded conditions. These maps provide a more accurate and comprehensive representation of the uncertainty in flood modeling, supporting decision-making related to flood risk management. Contingency maps (Fig. 9 c) are also computed to evaluate the performance of the ensemble mean compared to the hindcast. The map shows a large agreement, indicating that the ensemble mean is capable of closely matching the observed flood conditions in many areas. However, some areas exhibit false positives, where the ensemble mean forecast overestimates flood extents. Notably, these false positives are concentrated in certain regions, reflecting areas where the ensemble model predicts flooding that did not occur in the hindcast, and match some portions of map in Fig. 9 b with relatively lower flood probability. False negative values are nearly absent, suggesting that the ensemble approach is particularly effective in predicting flood events but may occasionally overestimate flood extents. The water depth differences between the ensemble mean and the hindcast, shown in Fig. 9 d, shows that the differences are generally small, with values within the range of ± 0.5 meters across most of the study area. Discrepancies are observed in specific areas, in the upper watershed regions, the ensemble mean tends to underestimate the water depth, whereas closer to the coastal areas, the differences between the hindcast and ensemble mean become more pronounced, with the ensemble mean slightly overestimating the water depths in these areas. The maps in Fig. 9 correspond to results initialized on July 8, 2024, at 00:00 UTC (F18), with MRMS results for the same day at 18:00 UTC, providing a comprehensive comparison between forecasted and observed flood extents and water depths. Overall, the ensemble approach significantly improves the alignment of predicted WSE values with observed data, highlighting the potential of ensemble simulations in addressing forecasted storm uncertainty in flood inundation predictions. 5 Discussion and Conclusions The precipitation forecasts generated by the HRRR model presents significant challenges in accurately representing precipitation associated with hurricane-driven convective storms, particularly when compared to MRMS QPE and in-situ observations. While the HRRR model provides high-resolution forecasts, it tends to overestimate precipitation, particularly during high-intensity rainfall events. This discrepancy is especially noticeable in regions such as Houston, where localized heavy rainfall and highly urbanized watershed can lead to substantial hydrodynamic impacts. These overestimations are attributed to misalignments between forecasted rainfall bands and the actual storm trajectory, as well as higher precipitation magnitudes. In this study, the focus was on spatial displacement of the QPF fields. The QPF fields were displaced based on the evolving position of the storm’s low-pressure center to generate multiple precipitation scenarios. These spatial shifts allowed for a better alignment between forecasted and observed precipitation, improving the model's accuracy in capturing storm dynamics. The spatial shifts in the precipitation field played a critical role in improving forecast accuracy. By incorporating shifts based on the trajectory of the storm, derived from the forecasted movement of the low-pressure center, the alignment of the forecasted rain bands with actual precipitation patterns was enhanced. This adjustment led to a more accurate representation of the storm's precipitation footprint, and results demonstrated that spatial shifts improved the correlation between forecasted and observed precipitation values for particular directions over different lead times previous to the impact of the hurricane. Short-range forecasts were especially valuable, as they provided more stable predictions of storm location and trajectory, leading to more reliable precipitation forecasts. As lead times increased, however, forecast uncertainty also increased, highlighting the importance of short-term forecasting in the accurate prediction of hurricane-driven precipitation. Despite the improvements achieved through spatial displacement, operational forecasting challenges, such as data latency in QPE and rain gauge measurements, remained a key issue. The latency of QPE data impacts on the real-time availability of forecasts, thus affecting computational times and delaying flood inundation predictions. The selection of QPE data for base modeling and restart file generation also plays a crucial role, as these datasets influence the water surface elevation WSE and flood depth results. While the focus of this study was on spatial displacement of QPF fields, the integration of QPE products such as IMERG or NCEP Stage IV, which offer high-quality precipitation estimates over broader areas, could further enhance the accuracy of model simulations by improving the precipitation input for hydrodynamic models. These QPE datasets, known for their reliability in providing real-time and post-event rainfall estimates, are valuable for correcting discrepancies between forecasted and observed precipitation. In addition to QPE products, the use of alternative QPF sources from other NWP models such as NAM, GFS, NBM, ECMWF, or RRFS could provide valuable insights into the uncertainty of precipitation forecasts. Each of these models has different parameterizations and physical assumptions that influence precipitation predictions. Using these diverse QPF sources in combination could improve the robustness of flood inundation modeling, as the different parameterizations would likely lead to variations in predicted WSE and flood extents, thus providing a more comprehensive assessment of flood risk. The SFINCS model was employed to generate probabilistic flood inundation maps based on multiple HRRR QPFs for Hurricane Beryl, focusing on a 24-hour lead time window. The SFINCS model integrates river discharge, surface elevation, and QPF inputs to simulate flood behavior under different precipitation scenarios. The use of ensemble simulations, generated from spatially displaced QPF fields, enabled the estimation of flood extents and depths under varying storm conditions. In future studies, more advanced techniques, such as Bayesian Model Averaging (BMA) and its integration with copulas (Gomez et al., 2024 ) can be implemented to enhance the probabilistic flood mapping skill into flood dynamics. 6 Data Availability All the data used in this study, including the gauge discharge, water stage data, and the DEMs, are publicly available from the USGS, NOAA, and Harris County Flood Warning System websites, respectively. All precipitation data used in this study are publicly available on their respective websites. Declarations Data Availability All the data used in this study, including the gauge discharge, water stage data, and the DEMs, are publicly available from the USGS, NOAA, and Harris County Flood Warning System websites, respectively. All precipitation data used in this study are publicly available on their respective websites. Author Contribution FG, KJ and HM conceptualized the study. FG implemented the methodology, conducted formal analysis, generated results, and wrote the original draft. KJ, HMF and HM edited the original draft and suggested formal analysis. HM –supervised the project, conducted funding acquisition,. Competing Interest The authors declare that they have no conflict of interest. Acknowledgements This study was partially supported by the USACE ERDC, contract no. W912HZ202005. 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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-6688922","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":458917048,"identity":"6a783a25-7b09-4bcb-b580-0397949da0a4","order_by":0,"name":"Francisco Javier Gomez Diaz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYBAC9oYDYJqHH0gcQIiz4dbCcwCiTkaygXgtENrG4ACKOD4tjMcvPrpRUcdjfPzsw0M3/tjkMUgkP2D4UHYYtxaGM8XGOWfYeMzOpBsczm1LK2aQSDNgnHEOtxZ7hjNp0rltPDxmN9gYDuc2HE5skEgwYOZtw2sLUMs/CR7jGUAtOX9AWtI/MP/Fq+X4MencBgMeAwmQFjaQlhwDZkb8tjAb5xxL4JE4k8YA8ktiG8+bgoM959Jxa5E4/vBxTk2dPX/7MebPOX9sEvvZ0zc++FFmjVMLg8QZA1QBUIwcwK0eCPjbH+CVHwWjYBSMglHAAACasFYd3vDOrQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2435-6207","institution":"The University of Alabama","correspondingAuthor":true,"prefix":"","firstName":"Francisco","middleName":"Javier Gomez","lastName":"Diaz","suffix":""},{"id":458917049,"identity":"3fa07a3b-1ebd-485f-b1b8-be402b2ab8be","order_by":1,"name":"Keighobad Jafarzadegan","email":"","orcid":"","institution":"Oklahoma State University","correspondingAuthor":false,"prefix":"","firstName":"Keighobad","middleName":"","lastName":"Jafarzadegan","suffix":""},{"id":458917050,"identity":"44b27023-31a1-4713-b7ab-f3acb144c324","order_by":2,"name":"Hamed Moftakhari","email":"","orcid":"","institution":"The University of Alabama","correspondingAuthor":false,"prefix":"","firstName":"Hamed","middleName":"","lastName":"Moftakhari","suffix":""},{"id":458917051,"identity":"728cddd1-bd2d-40f3-a2a4-7edd3988cbc8","order_by":3,"name":"Hamid Moradkhani","email":"","orcid":"","institution":"The University of Alabama","correspondingAuthor":false,"prefix":"","firstName":"Hamid","middleName":"","lastName":"Moradkhani","suffix":""}],"badges":[],"createdAt":"2025-05-17 20:54:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6688922/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6688922/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83245496,"identity":"a3dafcb6-1f91-4a4c-b58f-49db8fa48b3d","added_by":"auto","created_at":"2025-05-21 17:06:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97582,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the proposed methodology for probabilistic flood inundation mapping using spatial displaced QPF fields. The upper section shows the generation of hindcast flood inundation maps and restart files generation. The lower section shows the generation of forecasted flood maps using HRRR forcing. Blue boxes represent 2D time series datasets, green boxes are input data to initial model generation. Yellow box is a points-time series boundary condition.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6688922/v1/042910e70721bd917da89946.jpg"},{"id":83245497,"identity":"0bf3767c-2743-4016-8bc4-aba3f3bdeed1","added_by":"auto","created_at":"2025-05-21 17:06:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":68804,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of the proposed storm displacement\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6688922/v1/a9572a35ad79594955b46b16.jpg"},{"id":83246035,"identity":"918fa44d-27e6-4faa-bbeb-1c0677833950","added_by":"auto","created_at":"2025-05-21 17:14:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":186498,"visible":true,"origin":"","legend":"\u003cp\u003eSFINCS model domain, topographic data and location of boundary conditions and validation stations over the domain. Coordinates are in UTM 15N\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6688922/v1/cea07d7a592118ead430ba53.jpg"},{"id":83246034,"identity":"fe582ba7-ea4e-45e3-9484-674ae9ba5c41","added_by":"auto","created_at":"2025-05-21 17:14:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":109497,"visible":true,"origin":"","legend":"\u003cp\u003eThe trajectory of Hurricane Beryl over the Gulf of Mexico and zoom over study area. Red labels show direction and date, green lines represent the HRRR low pressure center position over time for different forecast cycles.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6688922/v1/3cce05d04f550a26131fa4e3.jpg"},{"id":83245502,"identity":"a8ec9357-d200-4e85-85f5-abc542388c89","added_by":"auto","created_at":"2025-05-21 17:06:29","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":138365,"visible":true,"origin":"","legend":"\u003cp\u003eHourly error comparison of MRMS QPE and HRRR QPF for identification of systematic biases in storm rainbands pattern and examples of errors after displacement.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6688922/v1/921fd4be83be3f4bfad107f2.jpg"},{"id":83246036,"identity":"a3640745-5bf7-45f7-8332-d2f9b513b958","added_by":"auto","created_at":"2025-05-21 17:14:29","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":129970,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of correlation coefficients of HRRR QPF fields generated for the cycles evaluated against Harris County Flood Warning Systems rain gauges over 6, 12, 18 and 24h lead times. F is the shifting in the forward storm trajectory, F-R is the shifting in forward right direction, F-L is the shifting the forward left direction and B is the shifting in the opposite direction to storm path\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6688922/v1/e7521b11427c3b7c087901f1.jpg"},{"id":83246601,"identity":"66cb53b0-a3ec-48a8-8d42-83e9cab84841","added_by":"auto","created_at":"2025-05-21 17:30:29","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":157345,"visible":true,"origin":"","legend":"\u003cp\u003eHydrographs of simulated water surface elevation (WSE) by the SFINCS model for hindcast (blue), using original HRRR QPF (red), shifted QPFs (grey), and ensemble mean (green) along with the observed WSE values for Hurricane Beryl. Each column represents the WSE result at a given validation stations for different forecast initialization. Given the Advanced Hydrologic Prediction Service (AHPS) classification MaF: Major Flooding, MoF: Moderate Flooding, MiF: Minor Flooding, A: Action. For HCWFS gauges FP: Flooding possible, FL: Flooding Likely\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6688922/v1/f8b5f569038537b2cc2c0ea5.jpg"},{"id":83246037,"identity":"c4141a8d-dd96-4985-9e47-a7cfc9c0c28b","added_by":"auto","created_at":"2025-05-21 17:14:29","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":105705,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots of KGE, NSE and MBE performance metrics for WSE simulation over the validation stations considering original HRRR QPF (red) and the ensemble mean (blue). Grey boxplots summarize NRR and Reliability metrics of WSE results considering the ensembles generated over different 24h-forecast initialization.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6688922/v1/fd709201ac183311f18a47d3.jpg"},{"id":83245505,"identity":"ea9aab6f-0231-4faa-999c-1391e63ac21c","added_by":"auto","created_at":"2025-05-21 17:06:29","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":171600,"visible":true,"origin":"","legend":"\u003cp\u003eResults of probabilistic flood inundation map for Hurricane Beryl event over Houston area. (a) The ensemble means water depth map, (b) probabilistic flood extent map using all ensemble members, c) contingency map of ensemble mean results against MRMS hindcast, d) flood depth difference between MRMS and ensemble mean. All maps with forecast initialized at Jul/08/2024 00 UTC F18, MRMS results for Jul/08/2024 18UTC.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6688922/v1/d28c1f7d87526fbeeb3176f0.jpg"},{"id":86194448,"identity":"5bf6b887-7476-4cc1-9540-dc386ae0b04d","added_by":"auto","created_at":"2025-07-07 20:47:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1865753,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6688922/v1/82793e13-c9bd-42d6-86f6-094f189a924b.pdf"}],"financialInterests":"","formattedTitle":"Accounting for the uncertainty of precipitation forecasts and its impacts on probabilistic flood inundation mapping skill","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eFrom an operational perspective, access to reliable precipitation inputs is essential for forecasting future discharge. For this purpose, Quantitative Precipitation Forecasts (QPF) are primarily developed using Numerical Weather Prediction (NWP) models or through meteorologist-guided predictions, such as those produced by the Weather Prediction Center (WPC). Numerous QPF products are available, including the High-Resolution Rapid Refresh (HRRR, Benjamin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the North American Mesoscale (NAM; (Janjic, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e)), the Global Forecast System (GFS;(Environmental Modeling Center, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2003\u003c/span\u003e)), or the European Centre for Medium-Range Weather Forecasts (ECMWF; (Molteni et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1996\u003c/span\u003e)), all with different spatial and temporal coverages, resolutions and lead times. However, the integration of QPFs into the hydrological component is tied to significant challenges, primarily related to uncertainties in the predictive capabilities of numerical models to replicate storm patterns and quantify future streamflow. Consequently, accurately characterizing and quantifying the impact of QPF products on flood forecasts is an imperative but complex task that requires more than a deterministic analysis (Adams and Dymond, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003eb\u003c/span\u003e), not only for flow forecasting but also for water surface elevation (WSE) estimation. The outcome of most operational flood forecast systems is often limited to ensembles of discharge forecast hydrographs without the simulation of a physical model to convert streamflow forecasts to flood inundation areas. To provide more actionable forecast information and help inform decision makers, streamflow hydrographs should be converted into flood inundation maps (Cloke and Pappenberger, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Jafarzadegan et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, challenges remain in improving discharge estimation using Near-Real-Time (NRT) and forecast data, which are essential for accurately informing hydrodynamic or conceptual models for flood inundation mapping (Coelho et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFlood inundation mapping is an essential component of flood risk management, serving as a crucial tool for assessing potential flood extents and guiding emergency response and infrastructure planning. Accurate flood inundation forecasts could help protect lives and property, reducing flood impacts and enhancing community resilience. The quality of forecast inputs, including precipitation and streamflow data, profoundly influences the accuracy of flood inundation models. Errors in these inputs can propagate through the model, introducing significant uncertainties that affect the reliability of forecast results (Bogner and Pappenberger, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Pappenberger et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Rodr\u0026iacute;guez-Rinc\u0026oacute;n et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zappa et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Moreover, uncertainties stemming from model parameters, grid resolution, and initial conditions can amplify discrepancies between simulated and observed flood extents (Alipour et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e; Jafarzadegan et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Savage et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Addressing these uncertainties requires not only improved model calibration and validation but also the development of ensemble forecasting approaches that account for variability in forcing data (Jafarzadegan et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eForecasting flood inundation using hydrodynamic models is particularly complex in regions prone to compound flooding, where multiple drivers such as river flow, storm surge, and intense rainfall converge to amplify flood risks (Gomez et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mu\u0026ntilde;oz et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Santiago-Collazo et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Accurately simulating these complex events requires refined QPF estimations and the integration of these datasets into hydrodynamic models. However, discrepancies between model-based forecasts and rain gauge observations persist, posing challenges to QPF accuracy in regions susceptible to intense, convective-driven rainfall. Several methodologies have been proposed to correct QPF estimates in terms of storm direction (Hugeback et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Seo et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vergara et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Challenges remain in improving precipitation estimates regarding magnitude and the spatial-temporal evolution of the storm (Adams and Dymond, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e; Carlberg et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Models like HRRR, with high spatial (3 km) and temporal (1 h) resolutions, offer promising data for real-time and short-term forecasting, despite ongoing limitations in accurately representing storm patterns, spatial coverage, and rainfall intensity.\u003c/p\u003e \u003cp\u003eThe uncertainty arising from the spatial displacement of precipitation fields has not been fully addressed in flood inundation mapping, specially under compound flood events and in highly urbanized watersheds. This gap presents an opportunity to improve the predictive skill of flood inundation models by accounting for the impact of spatial displacement of precipitation fields in QPF forecasts. This study aims to address this limitation by analyzing QPF displacement errors relative to in-situ observations and Quantitative Precipitation Estimates (QPE), to generate an ensemble of precipitation inputs to the hydrodynamic model. The approach consists of two key steps: first, identifying spatial shifts in QPF based on storm path across various lead times. These ensembles will be integrated into a 2D hydrodynamic model to evaluate their performance in simulating flood depths and extents over Houston, TX. This methodology offers a pathway to operationally enhance QPF accuracy, especially as input to 2D numerical models for predicting flood dynamics. The research article is organized as follows. First, in section 2 the methodology is described considering the description of the SFINCS hydrodynamic framework and HRRR QPF ensemble generation process for precipitation field generation. Section 3 presents the definition of study area along with the datasets used on this research. Section 4 contains the results of the methodology and hydrodynamic simulations for probabilistic flood inundation mapping, and section 5 presents the discussion and conclusion of the findings.\u003c/p\u003e"},{"header":"2 Methodology","content":"\u003cp\u003eOur designed methodology first focuses on the generation of displaced QPF fields, which are critical for evaluating the impact of spatial shifts in precipitation on compound flood modeling during hurricane events. The QPF displacement process involves systematically shifting the HRRR v4 QPF fields based on the forecasted storm path given the low pressure center accounting for uncertainties in storm positioning. Previous works have demonstrated that QPF forecast from NWP models exhibit displacement errors compared to observations that could be corrected (Adams and Dymond, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e; Kiel et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and that performing spatial shifts to precipitation storms fields directly impact hydrological forecast accuracy (Carlberg et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hugeback et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this research, spatial shifts are applied in four directions (forward, backward, right-forward, and left-forward) and at four different distances (10, 20, 30, and 40 kilometers) considering the hurricane forecasted path. This creates ensemble scenarios that represent a range of potential storm trajectories and precipitation distributions, providing multiple precipitation fields for the hydrodynamic model. These shifted QPF fields are then used in the Super-Fast INundation of CoastS (SFINCS) model to simulate flood dynamics across the Houston area in the state of Texas. In the hindcast configuration, initial simulations use historical data, including USGS discharge, NOAA storm tide observations, MRMS QPE and NLDAS forcings (\u003cem\u003eu-v\u003c/em\u003e wind components, and surface pressure fields). These inputs are used to generate restart files for the forecast runs. The hindcast simulations generate flood inundation maps that serve as a baseline for comparison against forecasted maps, in order to evaluate the performance of the forecast simulations.\u003c/p\u003e \u003cp\u003eIn the forecasting phase, the SFINCS model is run using the HRRR \u003cem\u003eu-v\u003c/em\u003e wind components, surface pressure fields, and QPF fields\u0026mdash;both the original and the shifted QPFs\u0026mdash;as precipitation inputs. The forecast simulations, initialized with the restart files from the hindcast phase, simulate potential future flood events under various storm scenarios. By considering different spatial displacements of the QPF fields, the model provides probabilistic flood inundation maps that account for the uncertainty in precipitation forecasts, storm position, and intensity. These errors that can be propagated through the ensemble modeling and subsequently translated into flood inundation extent and WSE errors in the compound flood event analysis (Abbaszadeh et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the primary steps and variables considered within the proposed methodology.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 HRRR QPF shifting\u003c/h2\u003e \u003cp\u003eThe High-Resolution Rapid Refresh (HRRR) model is a critical tool in atmospheric forecasting, providing high-frequency, high-resolution data for weather prediction across the United States (Benjamin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The model's lead times vary depending on its version. For versions 1 and 2, the longest lead time is 18 hours, while version 3 extends to 36 hours, and the current version, HRRR v4, offers a 48-hour lead time. For this study HRRR v4 model runs every 6 hours are considered, providing hourly forecast for the first 24 hours at 00, 06, 12, and 18 UTC cycles, starting at Jul/06/2024 00:00 until Jul/09/2024 1800UTC. This study focuses on a 24-hour lead-time window, analyzing short-term forecasts for these four cycles per day to capture high-resolution atmospheric conditions that influence precipitation, wind, and pressure patterns over short intervals.\u003c/p\u003e \u003cp\u003eThe methodology focuses on shifting the QPF fields to generate an ensemble of precipitation scenarios, each of which is subsequently fed into the hydrodynamic model to simulate resulting flood extents. This process involves the spatial shifting of QPF fields, systematically displaced across a range of directions and distances (10, 20, 30, and 40 kilometers) to capture potential deviations in the storm\u0026rsquo;s precipitation footprint over time. The storm center is determined based on the location of the lowest value of 'Mean Sea Level Pressure' variable within the HRRR hourly forecast, from which the directional component is calculated as one of the eight primary compass directions, reflecting the trajectory of the storm center over the evaluated lead times.\u003c/p\u003e \u003cp\u003eTo account for lateral deviations, we include neighboring directions adjacent to the calculated trajectory; for instance, if the trajectory is determined to be north (N), we consider movements in the northeast (NE) and northwest (NW) directions at the same specified distances. Additionally, recognizing that storms may experience accelerations or decelerations that are difficult to forecast, we analyze the opposite direction of the storm trajectory. The rationale for considering these specific displacements is multifaceted. The forward and backward shifts are based on the overall storm trajectory, with uncertainties related to the position of the low-pressure center, which may vary as lead times increase, reflecting the common increase in forecast errors over time in NWP models (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rey and Mulligan, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe right-forward displacement is particularly relevant as it addresses the observed tendency for most rainfall to occur in the first quadrant relative to the storm\u0026rsquo;s trajectory. Tropical cyclones frequently exhibit asymmetric rainfall distributions, with the right-front quadrant typically receiving greater precipitation due to the combination of storm motion and the radial distribution of wind fields (Trepanier and Tucker, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, the left-forward displacement accounts for the shear-stress-drift induced by wind direction and magnitude, recognizing that raindrops do not fall vertically due to the influence of horizontal wind forces. This phenomenon, often referred to as wind-induced drift, can significantly affect the spatial distribution of precipitation (Yan and B\u0026aacute;rdossy, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The incorporation of this displacement allows for a more nuanced representation of the precipitation field, aligning better with observed rainfall patterns during hurricane events. As a result, a single hourly forecast generates a total of 16 perturbed forecasts in the displacement component, encompassing a range of potential storm behaviors and improving the model\u0026rsquo;s ability to represent the complexities of precipitation patterns during hurricane events.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Hydrodynamic modeling\u003c/h2\u003e \u003cp\u003eWe use SFINCS model, a 2D hydrodynamic modeling tool that efficiently simulates compound flood events by integrating multiple flooding mechanism (Lee, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Leijnse et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sebastian et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In hurricane conditions, compound flooding is particularly complex due to the interactions between rainfall, rising river discharges, and coastal surges, each contributing to amplified flooding hazard. The SFINCS model is configured to capture these interactions over a high-resolution grid, allowing for detailed spatial representation of flood depths and extents. To enhance the accuracy of the model, the setup incorporates boundary conditions from observed main river discharges, still water surface elevations for surge influence, and high-resolution meteorological data, ensuring that the major contributors to compound flooding are represented in both hindcast and forecast modes.\u003c/p\u003e \u003cp\u003eSFINCS model solves Shallow Water Equations. The choice of SFINCS is particularly advantageous due to its computational efficiency, which allows simulating high-resolution flood dynamics with lower computational complexities and the use of High Performance Computing (HPC) facilities for the parallelization of multiple runs at the same time. This feature supports the generation of multiple simulation runs, which is critical for ensemble-based probabilistic flood analysis performed in this study.\u003c/p\u003e \u003cp\u003eThe SFINCS modeling process follows a two-step approach. The first step involves modeling water surface elevation and extent using near-real-time products or current observations and is referred to as the hindcast simulation. This hindcast provides the necessary water surface elevation states, which are then used to generate the restart files every 6 hours, essential for initializing the forecast simulation. HRRR NWP forecast data (precipitation, surface pressure, wind components) are applied at every 6 hours cycle initialized at 00UTC for 24h lead time. The forecast simulations include both the original HRRR QPF fields and the 16 spatially shifted QPF fields, which are used to simulate multiple potential precipitation scenarios, generating a total of 17 members, used in the computation of the ensemble mean for comparison of final results of flood inundation depths and extent.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Study Area and Data","content":"\u003cp\u003eThis study focuses on the City of Houston, Texas, located in Harris County\u0026mdash;a densely populated urban area highly susceptible to flood risks, especially during extreme compound flood events like Hurricane Harvey in 2017 (Gori et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Samadi et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The case study centers on Hurricane Beryl in July 2024, a significant tropical system that generated multiple flooding along this city during the 2024 hurricane season. Situated near Galveston Bay, the coastal proximity of Houston not only subjects it to storm surge influences but also allows for the convergence of fluvial and atmospheric conditions that can drive compound flooding events (Valle-Levinson et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The city's extensive network of bayous, creeks, and the lower Trinity River all play crucial roles in the complex flood dynamics that characterize the Galveston Bay (Gori et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Juan et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Peeples et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), combining riverine flooding from upstream discharge with coastal influences such as storm surge during hurricane conditions. Houston\u0026rsquo;s position along the Gulf Coast, a region prone to frequent tropical storms and hurricanes, underscores its vulnerability to compound flooding. The Gulf\u0026rsquo;s warm waters contribute to rapid cyclone intensification, increasing the likelihood of high rainfall and strong surge impacts for coastal cities like Houston (Alipour et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Radfar et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Trepanier and Tucker, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Hydrodynamic model setup\u003c/h2\u003e \u003cp\u003eThe SFINCS model (version 2.0.3 Cauberg) is configured in subgrid mode proper characterization of terrain complexities in urban areas like Houston, while maintaining a reasonable computational times (Sebastian et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Van Ormondt et al., 2024). The subgrid model has 524472 active cells that have flux grid of 45 by 45 meters, corresponding to 15 raster pixels of 3-meter resolution on each side.\u003c/p\u003e \u003cp\u003eThe NCEI Continuously Updated Digital Elevation Model (CUDEM) Bathymetric and Topographic DEM, with a 1/9 arc-second resolution (National Centers for Environmental Information, 2014) is used as the topography data. The study area is highly urbanized and there is no topographic or bathymetric information under all the bridges or other hydraulic structures. Topographic adjustments were made to guarantee and preserve the hydraulic characteristics of the streams, improving model stability and accuracy of the results against observations (Gomez et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hamidi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For unsteady flow analysis, an hourly simulation time window is defined between July 06/2024 00UTC to July 09/2024 00UTC. The hydrodynamic model setup incorporates multiple datasets to capture the compound flooding processes in Houston, with boundary and forcing data sources selected to represent riverine, coastal, and precipitation-driven flooding mechanisms accurately (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Precipitation and atmospheric forcings\u003c/h2\u003e \u003cp\u003eFor the initial base simulation, the hourly MRMS QPE (Zhang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) is used as the main precipitation forcing and the NLDAS (Xia et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) is used for hourly \u003cem\u003eu-v\u003c/em\u003e winds and surface pressure fields. This base model was defined as a first step for calibration purposes of Manning roughness coefficients for the land cover surfaces defined. The calibrated model serves as the generator of restart files for the forecast using HRRR QPF simulations every 6-hour cycle.\u003c/p\u003e \u003cp\u003eThe simulations in forecast configuration are set over 24-hour lead time in 6-hour cycles initialized at 00UTC. The primary input data for precipitation and atmospheric conditions is obtained from the HRRR v4 model through Herbie python package (Blaylock, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The HRRR dataset provides Quantitative Precipitation Forecasts (QPF) along with \u003cem\u003eu-v\u003c/em\u003e wind fields and surface pressure at a relatively high spatial (3 km) and temporal (1-hour) resolution. These fields are integral to capturing the evolving intensity and movement of rainfall and atmospheric pressure changes during the hurricane event, offering high-resolution input data that can improve the spatial accuracy of flood predictions.\u003c/p\u003e \u003cp\u003eInfiltration processes are critical in flood mitigation as they determine how much rainfall is absorbed into the soil and how much becomes surface runoff. Factors such as soil type, vegetation cover, and land use affect the ground\u0026rsquo;s ability to infiltrate water, directly influencing flood risk. When the infiltration capacity is exceeded during high-intensity rainfall, excess water leads to surface runoff and potential flooding, making it essential to account for infiltration in flood modeling. The SFINCS model incorporates infiltration using the SCS Curve Number (CN) method to calculate a runoff coefficient, which helps determine runoff volume. For this study, the Global Curve Number Dataset (GCN250, Jaafar and Ahmad, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), representing average antecedent runoff conditions (ARC II), is used as input to define the curve numbers within the study area.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Discharge and tidal forcings\u003c/h2\u003e \u003cp\u003eRiver discharge data for model boundary conditions is sourced from the U.S. Geological Survey (USGS), with specific attention to discharge measurements from rivers surrounding Houston. This discharge data, combined with HRRR precipitation inputs, forms the hydrological basis of the model, simulating riverine inflows essential to compound flood events. To characterize the coastal ocean boundary, hourly still water surface elevation data is obtained from NOAA for the Morgans Point station (Station ID: 8770613). This data reflects on the combined influence of astronomical tidal conditions and storm surge, further refining the simulation of compound flooding under hurricane conditions.\u003c/p\u003e \u003cp\u003eHourly river discharge data from the U.S. Geological Survey (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) is used for most of the significant streams along Houston.. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the boundary conditions applied to the SFINCS model and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e depicts the location of the discharge and WSE stations.\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\u003eBoundary conditions summary\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGauge station name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUse\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMorgans Point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNOAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8770613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStill water surface elevation downstream\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSims Bayou at Houston\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e08075500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrays Bayou at Houston\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e08075000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuffalo Bayou at Houston\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e08074000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSan Jacinto River nr Sheldon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e08072050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGarners Bayou nr Humble\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e08076180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreens Bayou nr Houston\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e08075900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhiteoak Bayou at Houston\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e08074500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHunting Bayou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e08075763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLittle Whiteoak Bayou at Trimble St\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e08074540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBerry Bayou at Nevada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e08075605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHalls Bayou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e08076500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGoose Ck nr Mcnair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e08067520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Validation data\u003c/h2\u003e \u003cp\u003eThe model\u0026rsquo;s outputs are validated using multiple observational datasets, with WSE and rainfall distribution as key indicators. WSE data are obtained from 16 stations of the Harris County Flood Warning System (HCFWS) distributed throughout the modeling region, enabling a comprehensive comparison between simulated and observed flood elevations. Additionally, rainfall data from 184 stations across Harris County, provided by the HCFWS, offer extensive ground-based precipitation measurements for direct comparison with the HRRR QPF fields. These ground-truth validation datasets are crucial for identifying discrepancies in forecasted rainfall intensities and distributions. Specifically, the HCFWS precipitation data are used to validate magnitude and displacement correlation, as these in-situ measurements provide the necessary foundation for correcting the HRRR QPF fields. The MRMS dataset is used as a spatial verification tool to assess the distribution of rainfall, particularly in capturing the presence of rain bands, which are characteristic of high-rotation convective storms.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Storm characterization\u003c/h2\u003e\n \u003cp\u003eHurricane Beryl was a significant tropical cyclone that posed significant threats to the Gulf Coast of United States, particularly impacting the City of Houston. Hurricane Beryl was an earliest-forming Category 4 and 5 hurricane on record, and it was only the second storm in history to reach this intensity in the month of July. It holds the strongest maximum sustained winds for an Atlantic hurricane prior to August on record. The storm ultimately impacted the Houston area and generated 8 to 12 inches of precipitation, along with maximum totals of 14.99 inches, reported over HCFWS rain gages network (Beven II et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). The storm\u0026rsquo;s trajectory, documented using HURDAT2 data (National Hurricane Center, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), shows a path that tracked northwestward across the Gulf of Mexico before curving inland over southeastern Texas. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates (a) the overall position of Beryl\u0026rsquo;s storm center and (b) a zoomed-in view of the storm path over southeastern Texas, which includes Houston. Overlaid on the zoom-in trajectory is the 24-hour forecasted paths generated from the HRRR model, plotted as green lines at each 6-hour cycle. These forecasted paths represent the storm center hourly positions based on the lowest \u0026lsquo;Mean Sea Level Pressure\u0026rsquo; values within each hourly HRRR forecast, providing insights into forecast accuracy and the potential spatial discrepancies in hurricane trajectory predictions.\u003c/p\u003e\n \u003cp\u003eHurricane rain bands are curved formations of clouds and thunderstorms that spiral outward from the eye wall, producing intense bursts of rain, wind, and sometimes tornadoes. At an hourly scale, HRRR QPF does not accurately capture the spatial position and intensity of rainbands. MRMS, with higher temporal and spatial resolution in observations, delineates the rainbands with greater fidelity, whereas HRRR often misplaces these bands, leading to inaccuracies in both position and magnitude. This discrepancy indicates that uncertainties in HRRR forecasts, especially during tropical cyclone events, are partially attributable to spatial misalignment with the observed precipitation distribution. These findings suggest that HRRR forecast errors could be mitigated by implementing a spatial shift adjustment in the QPF. By applying systematic spatial corrections, we can potentially improve the alignment of forecasted and observed precipitation fields, enhancing the accuracy of precipitation inputs in hydrodynamic flood models. This adjustment framework provides a pathway for refining model-based QPFs to better capture the spatial dynamics of hurricane rain bands and reduce forecast uncertainty.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the bias between the HRRR QPF and MRMS QPE in the first row, for three different forecasts initialized at various hours and considering different lead times. The second row presents the bias for the same forecasts after applying the spatial shifting procedure. It is evident that the spatial shifting of QPF fields reduces both overestimations and underestimations in the forecasts by improving the alignment of high-intensity rainbands during hurricane conditions. The effectiveness of these adjustments is influenced by the hurricane\u0026apos;s initialization location and its evolution over forecast lead times. The uncertainty in the direction and distance of the spatial shift can be affected by additional variables, including the model\u0026apos;s parameterization, which impacts the storm\u0026apos;s evolution. For the 24-hour period evaluated, shifts greater than 40 kilometers show a decrease in the correlation between forecasted values and observations at rain gauges. This suggests that larger displacement distances result in a less accurate alignment of the precipitation fields, reducing the incorporation of non-significant QPF fields into the hydrodynamic model.\u003c/p\u003e\n \u003cp\u003eThe analysis of discrepancies between the HRRR forecast and observations across 180 HCFWS rain gauges was conducted using a heatmap plot comparing the correlation of observed and forecasted precipitation values for both the original HRRR forecast and the 16 displacements considering lead times of 0-6h, 0-12h, 0-18h, and the full 24 hours (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Significant variability in the correlation coefficient was observed depending on the lead time evaluated. For the forecasts initialized on Jul-07 at 12:00 and 18:00 UTC the correlation values were predominantly below 0.5, with the lowest values approaching 0 in the first 12 hours (18:00 UTC F0-F12) across all directions. The best correlations, however, were observed when shifting the QPF fields in the backward (B) direction to the storm trajectory over the entire 24-hour period, compared to other directions and the original forecast. This indicates potential mispositioning of the storm in the HRRR forecast, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e (first column), where the smallest spatial error was observed relative to the MRMS dataset. On Jul-08, when Hurricane Beryl made landfall, the correlation between forecasted and observed precipitation improved, particularly for the forward (F) and forward-right (F-R) displacements. For the critical cycles (00, 06, and 12 UTC), the best correlations were observed with the F-R direction, and the correlation reduced as the displacement distance increased in the backward (B) direction. The results show that certain directional shifts and distances can reduce the errors between observed precipitation and HRRR forecasts. Overall, it was observed that forecasts initialized earlier (Jul-07 18:00 UTC and before) showed the lowest correlation values improving for the backward displacement in this case. Later forecasts, closer to the hurricane\u0026apos;s impact on the study area, exhibited higher correlations, highlighting the improvement in forecast accuracy as the storm\u0026apos;s position became more defined. This finding highlights the importance of accounting for storm dynamics in flood inundation forecast modeling, and that further research is warranted to systematically adjust the storm position over time for improved forecast accuracy. Specifically, the analysis demonstrates variability in forecast accuracy depending on the storm trajectory and the distance of displacement. Some combinations of direction and distance yield improved correspondence between observed and forecasted values, but no consistent trend emerges across all forecast cycles. This highlights the complexity of spatial discrepancies inherent in QPFs, suggesting that spatial perturbations can enhance model performance but must be applied judiciously to effectively address the uncertainty in hurricane forecasting. This analysis provides valuable insights into optimizing QPF applications for hydrodynamic modeling and underscores the importance of ongoing evaluations of forecast accuracy across diverse meteorological scenarios.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 FIM results\u003c/h2\u003e\n \u003cp\u003eThe evaluation of SFINCS model performance in validation stations is measured through different metrics, including Kling-Gupta Efficiency (KGE) (Kling et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e), Root Mean Square Error (RMSE) and the probabilistic metrics normalized root mean square error ratio (NRR) and Reliability. The NRR, as explained by DeChant and Moradkhani, (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) was used to measure the ensemble spread and assess the statistical distinction between the ensemble mean and the ensemble spread. A larger spread indicates higher uncertainty in the ensemble predictions, whereas a smaller spread suggests greater confidence in the ensemble mean. Reliability was employed to evaluate the fit of the Q-Q quantile plot to a uniform distribution. A value of 1 indicates perfect uniformity, meaning that the observed and forecasted quantiles are closely aligned, while a value of 0 represents the maximum deviation from uniformity (DeChant and Moradkhani, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). These metrics provide a quantitative assessment of the ability of generated ensembles to capture uncertainty in storm trajectories and precipitation, and their agreement with observed data in flood inundation results. The formulations of these metrics, which collectively provide insights into different facets of model accuracy, are summarized in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. These metrics serve as quantitative measures to assess the capability of the model capturing the observed variations in water surface elevation for all the ensembles generated for Hurricane Beryl event under the approach generated.\u003c/p\u003e\n \u003cp\u003eTable 2. Summary of performance metrics used in this study\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" style=\"width: 810px; height: 412.332px;\" width=\"810\" height=\"412.332\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThe results of the SFINCS model are presented through hydrographs depicting water surface elevations at validation stations. These hydrographs facilitate a comprehensive comparison among various simulation scenarios, including observed data, hydrodynamic model outputs utilizing MRMS QPE (this output is considered in this case the best hydrodynamic result possible from a hindcast perspective), results derived from the original HRRR forecasts, ensemble simulations generated from shifted QPFs, and the ensemble mean. The Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e shows the water surface elevation results for four of the stations considered in validation dataset for the consecutive forecast cycles.\u003c/p\u003e\n \u003cp\u003eIn the early forecast cycles, significant variability is observed in the WSE results across different stations, reflecting the uncertainty inherent in storm location and precipitation predictions as lead time increases. This variability is particularly pronounced when considering multiple ensemble scenarios, which show a larger impact on WSE results, contributing to both overestimations and underestimations. The discrepancies between forecasted and observed WSE values are most evident in the initial forecast periods, where model performance is relatively poor compared to both the observed data and hindcast results. For instance, in some cases, differences between ensembles reach up to 2 meters in WSE (first two rows in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). These large variations highlight the substantial uncertainty in the early forecast, which stems from storm mispositioning and the challenge of accurately predicting the magnitude and spatial distribution of precipitation.\u003c/p\u003e\n \u003cp\u003eAs the forecast initialization approaches the hurricane\u0026apos;s impact on the Houston area, the variability in WSE results decreases, and the predictions become more uniform. In these later forecast cycles, the variations in WSE are primarily more uniform, with smaller discrepancies between ensemble members. This indicates that, as the storm moves closer to the region, the forecast uncertainty reduces, and the model begins to more accurately capture the dynamics of the storm in terms of flood inundation generation.\u003c/p\u003e\n \u003cp\u003eWhen comparing the forecast results with the flooding thresholds from the AHPS (Advanced Hydrologic Prediction Service) and the HCFWS (Harris County Flood Warning System) in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, it is seen that the original HRRR forecast, when applied to hydrodynamic model, does not always align with the observed flooding categories. This misalignment underscores the value of considering multiple spatial variations of the storm in the modeling process. By applying ensembles that account for different storm positions, this approach enables a broader range of flood scenarios, which improves flood inundation mapping accuracy. Moreover, the ability to define the number of ensemble members that fall within different flooding categories provides a more robust decision-making tool for flood risk assessment. This analysis highlights the importance of incorporating spatial variability into operational forecasting models, as it offers a better understanding of flood risk and enhances the ability to manage uncertainties in flood prediction.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e presents boxplots for performance metrics including KGE, NSE, and MBE for WSE alongside probabilistic metrics NRR and Reliability, evaluated across different 16 WSE validation stations. For several stations, the KGE and NSE performance metrics are low when using the original HRRR forecast, particularly for forecasts initialized on Jul-07 06:00 UTC and Jul-07 12:00 UTC. However, these performance metrics significantly improve when considering the ensemble mean. Notably, the variability in the results decreases and the average values increase across the 16 validation stations in the study area, with KGE improving from 0.632 to 0.70 (median from 0.81 to 0.823), NSE from \u0026minus;\u0026thinsp;0.186 to 0.32 (median from 0.80 to 0.86), and MBE from 0.035m to 0.019m (median from 0.062m to 0.04m). For the MBE results, it is observed that the mean tends to approach zero, with positive variability indicated by the range of the boxplots for most forecast cycles. As the forecast lead time approaches the end of the evaluation period, MBE decreases towards negative values, as seen in several of the hydrographs for forecasts initialized on Jul-09 12:00 UTC. Both the original HRRR forecast, and the ensemble mean underestimating the observed water levels at that time. For probabilistic metrics, the NRR boxplots show that for most forecast initializations, the ensemble spread in WSE generated through SFINCS has values higher than 1, indicating that the ensemble spread is limited and that the forecast uncertainty is not fully captured. This suggests that the ensemble range is too narrow, likely due to insufficient variations in the storm\u0026apos;s trajectory and precipitation uncertainty. To improve the representation of forecast uncertainty imposed by precipitation input to the hydrodynamic model, it is necessary to increase the spread of ensemble, which can be achieved by incorporating additional displacement scenarios in both the spatial directions and the magnitude of precipitation. It is important to note that this is just one component of the overall uncertainty. Other factors, such as errors in other forecasted forcing data and model parameters also contribute to the total uncertainty in flood inundation modeling forecasting (Abbaszadeh et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mu\u0026ntilde;oz et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). For the Reliability metric, boxplots range from 0.5 to 0.9, averaging a value of 0.72, indicating moderate reliability in the probabilistic forecast. This moderate value indicates that while the forecast is reasonably reliable, there is room for improvement in aligning the probabilistic forecast with the observed outcomes. A higher reliability could be achieved by considering a broader set of displacement scenarios, accounting for both the uncertainty in the direction of the storm and the variability in precipitation magnitude. By expanding the ensemble to include more diverse precipitation and storm position scenarios, the spread would increase, leading to a smaller NRR and a higher reliability. The proposed methodology for shifting tropical cyclones offers valuable improvements to flood inundation mapping. It could be further enhanced by integrating other perturbation techniques for input variables, which would improve the probabilistic generation of scenarios and boundary conditions for both hydrological and hydrodynamic models.\u003c/p\u003e\n \u003cp\u003eThe ensemble scenarios, which incorporate spatially displaced QPF fields, offer a range of potential outcomes that more effectively capture the uncertainties inherent in storm location and rainfall intensity. By simulating multiple precipitation scenarios, the ensemble approach helps mitigate the biases observed in the original HRRR forecast. The ensemble simulations provide a probabilistic representation of WSE that aligns more closely with observed conditions, particularly in the later forecast cycles when the storm\u0026apos;s trajectory is better defined. This variability in the ensemble results provides valuable insight into the potential flood extents, allowing for a more comprehensive flood risk assessment.\u003c/p\u003e\n \u003cp\u003eThe use of precipitation ensembles enables the generation of water depth maps (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ea), which present the average values of the ensemble results, as well as probability maps for flood extents (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eb), considering a 10 centimeters water depth threshold for flooded and non-flooded conditions. These maps provide a more accurate and comprehensive representation of the uncertainty in flood modeling, supporting decision-making related to flood risk management. Contingency maps (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ec) are also computed to evaluate the performance of the ensemble mean compared to the hindcast. The map shows a large agreement, indicating that the ensemble mean is capable of closely matching the observed flood conditions in many areas. However, some areas exhibit false positives, where the ensemble mean forecast overestimates flood extents. Notably, these false positives are concentrated in certain regions, reflecting areas where the ensemble model predicts flooding that did not occur in the hindcast, and match some portions of map in Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eb with relatively lower flood probability. False negative values are nearly absent, suggesting that the ensemble approach is particularly effective in predicting flood events but may occasionally overestimate flood extents.\u003c/p\u003e\n \u003cp\u003eThe water depth differences between the ensemble mean and the hindcast, shown in Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ed, shows that the differences are generally small, with values within the range of \u0026plusmn;\u0026thinsp;0.5 meters across most of the study area. Discrepancies are observed in specific areas, in the upper watershed regions, the ensemble mean tends to underestimate the water depth, whereas closer to the coastal areas, the differences between the hindcast and ensemble mean become more pronounced, with the ensemble mean slightly overestimating the water depths in these areas. The maps in Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e correspond to results initialized on July 8, 2024, at 00:00 UTC (F18), with MRMS results for the same day at 18:00 UTC, providing a comprehensive comparison between forecasted and observed flood extents and water depths. Overall, the ensemble approach significantly improves the alignment of predicted WSE values with observed data, highlighting the potential of ensemble simulations in addressing forecasted storm uncertainty in flood inundation predictions.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5 Discussion and Conclusions","content":"\u003cp\u003eThe precipitation forecasts generated by the HRRR model presents significant challenges in accurately representing precipitation associated with hurricane-driven convective storms, particularly when compared to MRMS QPE and in-situ observations. While the HRRR model provides high-resolution forecasts, it tends to overestimate precipitation, particularly during high-intensity rainfall events. This discrepancy is especially noticeable in regions such as Houston, where localized heavy rainfall and highly urbanized watershed can lead to substantial hydrodynamic impacts. These overestimations are attributed to misalignments between forecasted rainfall bands and the actual storm trajectory, as well as higher precipitation magnitudes. In this study, the focus was on spatial displacement of the QPF fields. The QPF fields were displaced based on the evolving position of the storm\u0026rsquo;s low-pressure center to generate multiple precipitation scenarios. These spatial shifts allowed for a better alignment between forecasted and observed precipitation, improving the model's accuracy in capturing storm dynamics.\u003c/p\u003e \u003cp\u003eThe spatial shifts in the precipitation field played a critical role in improving forecast accuracy. By incorporating shifts based on the trajectory of the storm, derived from the forecasted movement of the low-pressure center, the alignment of the forecasted rain bands with actual precipitation patterns was enhanced. This adjustment led to a more accurate representation of the storm's precipitation footprint, and results demonstrated that spatial shifts improved the correlation between forecasted and observed precipitation values for particular directions over different lead times previous to the impact of the hurricane. Short-range forecasts were especially valuable, as they provided more stable predictions of storm location and trajectory, leading to more reliable precipitation forecasts. As lead times increased, however, forecast uncertainty also increased, highlighting the importance of short-term forecasting in the accurate prediction of hurricane-driven precipitation.\u003c/p\u003e \u003cp\u003eDespite the improvements achieved through spatial displacement, operational forecasting challenges, such as data latency in QPE and rain gauge measurements, remained a key issue. The latency of QPE data impacts on the real-time availability of forecasts, thus affecting computational times and delaying flood inundation predictions. The selection of QPE data for base modeling and restart file generation also plays a crucial role, as these datasets influence the water surface elevation WSE and flood depth results. While the focus of this study was on spatial displacement of QPF fields, the integration of QPE products such as IMERG or NCEP Stage IV, which offer high-quality precipitation estimates over broader areas, could further enhance the accuracy of model simulations by improving the precipitation input for hydrodynamic models. These QPE datasets, known for their reliability in providing real-time and post-event rainfall estimates, are valuable for correcting discrepancies between forecasted and observed precipitation. In addition to QPE products, the use of alternative QPF sources from other NWP models such as NAM, GFS, NBM, ECMWF, or RRFS could provide valuable insights into the uncertainty of precipitation forecasts. Each of these models has different parameterizations and physical assumptions that influence precipitation predictions. Using these diverse QPF sources in combination could improve the robustness of flood inundation modeling, as the different parameterizations would likely lead to variations in predicted WSE and flood extents, thus providing a more comprehensive assessment of flood risk.\u003c/p\u003e \u003cp\u003eThe SFINCS model was employed to generate probabilistic flood inundation maps based on multiple HRRR QPFs for Hurricane Beryl, focusing on a 24-hour lead time window. The SFINCS model integrates river discharge, surface elevation, and QPF inputs to simulate flood behavior under different precipitation scenarios. The use of ensemble simulations, generated from spatially displaced QPF fields, enabled the estimation of flood extents and depths under varying storm conditions. In future studies, more advanced techniques, such as Bayesian Model Averaging (BMA) and its integration with copulas (Gomez et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) can be implemented to enhance the probabilistic flood mapping skill into flood dynamics.\u003c/p\u003e"},{"header":"6 Data Availability","content":"\u003cp\u003eAll the data used in this study, including the gauge discharge, water stage data, and the DEMs, are publicly available from the USGS, NOAA, and Harris County Flood Warning System websites, respectively. All precipitation data used in this study are publicly available on their respective websites.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data used in this study, including the gauge discharge, water stage data, and the DEMs, are publicly available from the USGS, NOAA, and Harris County Flood Warning System websites, respectively. All precipitation data used in this study are publicly available on their respective websites.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFG, KJ and HM conceptualized the study. FG implemented the methodology, conducted formal analysis, generated results, and wrote the original draft. KJ, HMF and HM edited the original draft and suggested formal analysis. HM \u0026ndash;supervised the project, conducted funding acquisition,.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was partially supported by the USACE ERDC, contract no. 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Bull Am Meteorol Soc 97:621\u0026ndash;638. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/BAMS-D-14-00174.1\u003c/span\u003e\u003cspan address=\"10.1175/BAMS-D-14-00174.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"","lastPublishedDoi":"10.21203/rs.3.rs-6688922/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6688922/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUncertainty in operational weather forecasts, particularly in predicting storm location and precipitation patterns, presents challenges for flood inundation mapping. As hurricane forecasts evolve, spatial discrepancies in precipitation estimates lead to misalignment between forecasted and observed rainfall, affecting flood prediction accuracy. This study presents a methodology for addressing storm mispositioning using an integration of the High Resolution Rapid Refresh (HRRR) NWP data and a 2-D hydrodynamic model to generate flood inundation maps. The analysis, focused on Hurricane Beryl (July 2024), evaluates the impact of multiple storm location scenarios over 24-hour forecast periods with 6-hour intervals. Quantitative Precipitation Forecast (QPF) fields are used as input to the Super-Fast INundations of CoasT (SFINCS) model, applied to the highly urbanized central Houston area. Results show that incorporating HRRR forecasts and spatial displacement of QPF fields improves the correlation with in-situ meteorological and water elevation observations. This method provides a more accurate flood inundation mapping by accounting for uncertainties in the precipitation forecasts. SFINCS model performance metrics, including Kling-Gupta Efficiency (KGE), improved from 0.632 using only forecast data to 0.70 when incorporating the WSE ensemble mean generated from modified QPF fields.\u003c/p\u003e","manuscriptTitle":"Accounting for the uncertainty of precipitation forecasts and its impacts on probabilistic flood inundation mapping skill","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-21 17:06:24","doi":"10.21203/rs.3.rs-6688922/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":"396fd3c5-b8a1-44f6-ba25-557cedb09a1d","owner":[],"postedDate":"May 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T20:39:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-21 17:06:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6688922","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6688922","identity":"rs-6688922","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}