Probabilistic Flood Inundation Forecasting Using Rating Curve Libraries

Mapping floods from remote sensing data and quantifying the effects of surface obstruction by clouds and vegetation

In this paper, land cover was predicted from Landsat TM imagery and used to generate spatially-distributed friction coefficients. Flood inundation was then predicted using the raster-based model LISFLOOD-FP, based on friction and three different elevation models. Sensitivity of LISFLOOD-FP to spatially-distributed friction was assessed. It was found that effect of friction on the flood wave is small when compared to the underlying elevation, but is greatest during the recession phase of the hydrograph. Hydraulic models of channel and overland flow allow river discharge to be related to flood inundation extent, by simu- lating flooding based on a scenario discharge. An important boundary condition for such models is surface friction. In particular, floodplain land cover is related to friction that affects the movement of the flood wave. However, the selection of appropriate friction coefficients for hydraulic models is difficult and it is recognised that floodplain friction coefficients have considerable uncertainty and sensitivity associated with them (1). Specific problems are (i) a lack of data sources, (ii) the often coarse spatial resolution of data and (iii) the use of stationary models (e.g., use of average friction over the whole catchment). Remote sensing can provide spatially distributed estimates of land cover, allowing a more informative and accurate representation of floodplain friction in flood inundation mod- els. However, hard classification may not provide sufficiently detailed land cover data at the sub-pixel scale. For example, where Landsat Thematic Mapper (TM) (spatial resolution of 30 m) imagery is used and the flood is only several hundred metres across, hard classification may be inappropriate. There- fore, in this paper, soft classification was used to estimate friction coefficients on a continuous scale for each cell on the floodplain. The objective was then to assess the sensitivity of a flood inundation model to uncertainty in these coefficients.

Abstract. River bathymetry is important for accurate flood inundation modelling but is often unavailable due to the time-intensive and expensive nature of its acquisition. This leads to several proposed and implemented approaches for its estimation. However, the errors in estimations inherent in these methods and how they affect the accuracy of the flood inundation modelling outputs, has not been extensively researched. Hence, to contribute, we investigate the sensitivity of flood predictions to the errors in river slope, width, and bank-full flow used in two formulas – the Uniform Flow and the Conceptual Multivariate Regression – for estimating river bathymetry. In this study, we employed a Monte Carlo framework to introduce random errors into these parameters drawn from a normal distribution with zero mean and a standard deviation set to 10 % of their best estimates. Using this process, we generated 50 simulated river bathymetries for each parameter along with an additional 50 where the errors were applied to all parameters simultaneously. The riverbeds generated from these bathymetries were combined with topographic LiDAR data to create model grids. Each grid was used in the hydrodynamic model LISFLOOD-FP to simulate the 2005 flood event in the Waikanae River area of New Zealand. We assessed the resulting flood inundation predictions for their variability and sensitivity. The results indicate that between two methods, the errors in the parameters in the Uniform Flow formula are associated with greater uncertainty in flood inundation depths and extents compared to the Conceptual Multivariate Regression. Among the parameters, the width errors correspond to the highest uncertainty, while the slope errors correspond to the lowest.

The potential flood inundation extent can be estimated with flood inundation models, which can differ in the level of physical and numerical modelling complexity included in the solution procedure. In recent years, several studies have highlighted the benefits of shock-capturing flood inundation models, particularly when modelling a high Froude number or supercritical flows, or in areas prone to the occurrence of rapidly varying flood events, such as flash floods. Nonetheless, decision makers are often reluctant to implement more complex modelling tools into practical flood inundation modelling studies, unless evidence is provided to establish when such refined modelling tools should be used. The main objective of this study was to determine a general threshold value of the bottom slope that could be used by decision makers as an orientation guide to ascertain when to use a specific type of flood inundation model. The results obtained suggest that in torrential river basins or catchments (i.e., river basins and catchments with a bed slope generally greater than 1%), the flood inundation modelling should be conducted by using a flood inundation model that include shock-capturing algorithms in the model solution procedure.

We investigate the ability of hydrological multimodel ensemble predictions to enhance the skill of streamflow forecasts at short‐ to medium‐range timescales. To generate the multimodel ensembles, we implement a new statistical postprocessor, namely, quantile regression‐Bayesian model averaging (QR‐BMA). Quantile regression‐Bayesian model averaging uses quantile regression to bias correct the ensemble streamflow forecasts from the individual models and Bayesian model averaging to optimally combine their probability density functions. Additionally, we use an information‐theoretic measure, namely, conditional mutual information, to quantify the skill enhancements from the multimodel forecasts. We generate ensemble streamflow forecasts at lead times from 1 to 7 days using three hydrological models: (i) Antecedent Precipitation Index‐Continuous, (ii) Hydrology Laboratory‐Research Distributed Hydrologic Model, and (iii) Weather Research and Forecasting Hydrological modeling system. As forcing to the hydrological models, we use weather ensemble forecasts from the National Centers for Environmental Prediction 11‐member Global Ensemble Forecast System Reforecast version 2. The forecasting experiments are performed for four nested basins of the North Branch Susquehanna River, USA. We find that after bias correcting the streamflow forecasts from each model, their skill performance becomes comparable. We find that the multimodel ensemble forecasts have higher skill than the best single‐model forecasts. Furthermore, the skill enhancements obtained by the multimodel ensemble forecasts are found to be dominated by model diversity, rather than by increased ensemble size alone. This result, obtained using conditional mutual information, indicates that each hydrological model contributes additional information to enhance forecast skill. Overall, our results highlight benefits of hydrological multimodel forecasting for improving streamflow predictions.

Flood inundation maps are dependent on the topographic and geomorphologic features of a wadi (drainage basin) in arid regions, which are most susceptible for potential flash flood occurrences, such as in the southwestern part of the Kingdom of Saudi Arabia. It is not possible to control the potential flood hazards by using only technological instruments that forewarn the occurrences or imminence. Additionally, it would be better to prepare flood risk maps so as to delineate the risky areas to educate the administrators and local settlers. The availability of these maps is the key requirement for any urban development that entails land use allocation, identification of dam, tunnel, highway, bridge sites, and infrastructure locations for sustainable future. This paper suggests the necessary steps in flood inundation map preparation after determining the possible flood discharge. For this purpose, a set of critical cross-sections along the possible flood plain are taken in the field with surveying methods and measurements. The calculation of the average flow velocity in each section is calculated according to the cross-section geometric, hydraulic, and material properties. Synthetic rating curves (SRC) are prepared for each cross section, which are very useful especially in arid and semi-arid regions where there are no perennial surface water flows for natural rating curve measurements. All the SRCs appear in the form of power function which relates the flow depth to discharge in a given cross section. It is then possible to calculate the flood depth in the cross section through its SRC. Depending on the cross-section shape, the flood width can be calculated. The connection of a series of widths on a scaled topographic map delineates the flood inundation area. If digital elevation map (DEM) is available, then the SRCs can be integrated with these maps and the flood inundation delineation can be achieved automatically. Since DEMs are not available, the topographic maps are used for this purpose in order to delineate flood inundation areas within wadis Hali and Yiba from the southwestern Kingdom of Saudi Arabia.

Despite the recent developments in continental‐scale streamflow and flood inundation modeling frameworks, effects of time‐specific and spatially explicit storage‐release dynamics of numerous dams and reservoirs remain underexplored. This paper fills this knowledge gap by directly inserting operational daily flow release data at 175 dam locations into a streamflow simulation of ∼1.2 million river reaches in the Mississippi River Basin (MRB), and therefore quantifying the effect of these regulations on streamflow and flood inundation extents. Using a streamflow routing model called the Routing Application for Parallel computatIon of Discharge (RAPID) and flood inundation mapping model called AutoRoute, two simulation scenarios were constructed respectively including and excluding the daily flow releases from those dams and reservoirs for a 10‐year period (2005–2014). Flood inundation maps were simulated for peak flow conditions at a ∼10‐m hyper spatial resolution. Kling‐Gupta efficiency (KGE) values show that streamflow model performance considerably improved when reservoirs were included in the modeled system, varying from 2% in the eastern region to 380% in the drier western region. Despite small variation of streamflow model improvement with reservoir release inclusion in the eastern region of the basin, the flow model was able to better capture observed peak flows. For a 1% change in streamflow, we observe a 0.8% change in estimated flood inundation. Comparisons to three observed flood events in the MRB demonstrate that the flood inundation estimates improve when percent change in streamflow is relatively high. Overall, inclusion of reservoir release resulted in substantial improvement in continental‐scale streamflow and flood inundation mapping.

Hydraulic models play an important role in determining flood inundation areas. When considering a wide array of one‐ (1D) and two‐dimensional (2D) hydraulic models, selecting an appropriate model and its calibration are crucial in an accurate prediction of flood inundation. This study compares the performance of four commonly used 1D and 2D hydraulic models, including HEC‐RAS 1D, HEC‐RAS 2D, LISFLOOD‐FP diffusive, and LISFLOOD‐FP subgrid, with respect to their model structure and their sensitivity to surface roughness characterisation. Application of these models to four study reaches with different river geometry and roughness characterisation shows that for a given set of roughness condition, the geometry, including the sinuosity, reach length and floodplain width, does not affect the performance of a 1D or 2D model. Overall, the performance of a 1D model is comparable to the 2D models used in the study, with the 2D models showing slightly better results. The performance of 2D models is affected by low channel roughness, and it improves with increasing channel roughness that enables more water to enter into the floodplain. On the contrary, the performance of 1D model is positively affected with increasing floodplain roughness. When the models are evaluated for uniform versus distributed roughness characterisation in the floodplain, the uniform surface characterisation provides the best results compared to the distributed roughness characterisation.

This work aims at improving the accuracy of ensemble streamflow forecasts at short-to-medium ranges with the conditional bias-penalized regression (CBPR)-aided Meteorological Ensemble Forecast Processor (MEFP) and streamflow data assimilation (DA). To assess the potential impact of the CBPR-aided MEFP and streamflow DA, or CBPR-DA, 20-yr hindcast experiments were carried out using the Global Ensemble Forecast System version 12 reforecast dataset for 46 locations in the service areas of 11 River Forecast Centers of the US NWS. The results show that, relative to the current practice of using the MEFP and no DA, or MEFP-NoDA, CBPR-DA improves the accuracy of ensemble forecasts of 3-day flow over lead times of 0 to 3 days by over 40% for 4 RFCs and by over 20% for 9 of the 11 RFCs. The margin of improvement is larger where the predictability of precipitation is larger and the hydrologic memory is stronger. As the lead time increases, the margin of improvement decreases but still exceeds 10% for the prediction of 14-day flow over lead times of 0 to 14 days for all but 3 RFCs.

An ability to quantify the reliability of probabilistic flood inundation predictions is a requirement not only for guiding model development but also for their successful application. Probabilistic flood inundation predictions are usually produced by choosing a method of weighting the model parameter space, but previous study suggests that this choice leads to clear differences in inundation probabilities. This study aims to address the evaluation of the reliability of these probabilistic predictions. However, a lack of an adequate number of observations of flood inundation for a catchment limits the application of conventional methods of evaluating predictive reliability. Consequently, attempts have been made to assess the reliability of probabilistic predictions using multiple observations from a single flood event. Here, a LISFLOOD-FP hydraulic model of an extreme (>1 in 1000 years) flood event in Cockermouth, UK, is constructed and calibrated using multiple performance measures from both peak flood wrack mark data and aerial photography captured post-peak. These measures are used in weighting the parameter space to produce multiple probabilistic predictions for the event. Two methods of assessing the reliability of these probabilistic predictions using limited observations are utilized; an existing method assessing the binary pattern of flooding, and a method developed in this paper to assess predictions of water surface elevation. This study finds that the water surface elevation method has both a better diagnostic and discriminatory ability, but this result is likely to be sensitive to the unknown uncertainties in the upstream boundary condition. Copyright © 2015 John Wiley & Sons, Ltd.

Calculation and visualization of flood inundation based on a topographic triangle network

A system that utilizes an ensemble of streamflow forecasts as input to the Truckee-Carson RiverWare © operational forecast model has been developed to provide operators and stakeholders with an ensemble of operational forecasts. This ensemble of operational forecasts allows users to estimate uncertainty and perform risk-based analyses to assist with operational decisions. The ensemble of streamflow forecasts are provided by the River Forecast Center's Ensemble Streamflow Prediction program which generates the forecasts based upon current hydrologic conditions and historic meteorology records. The streamflow forecasts are input hydrographs for the operational forecast model. The operational forecast model is run once for each trace of the ensemble streamflow forecast. During each run the model simulates daily operation of the river according to current basin policy. Select output is then collected from each run to be analyzed statistically, providing estimates of risk and uncertainty in the operational forecast. The RiverWare © Multiple Run Management tool is employed to facilitate the efficient use of the ensemble forecasts for daily operational decisions. This tool provides an automated process for performing multiple model runs with varying input and compiling select output within one file. An overview of the process and a sample application on Lake Tahoe precautionary release operations will be presented.

Developing an ensemble hydrologic prediction system is essential for reservoir operations and flood early warning. However, efforts to build hydrologic ensemble prediction systems considering the influence of reservoirs have been lacking in India. We examine the potential of the Extended Range Forecast System (ERFS, 16 ensemble members) and Global Ensemble Forecast System (GEFS, 21 ensemble members) forecast for streamflow prediction in India using the Narmada River basin as a testbed. We use the Variable Infiltration Capacity (VIC) with reservoir operations (VIC-Res) scheme to simulate the daily river flow at four locations in the Narmada basin. We examined the streamflow forecast skills of the ERFS forecast for the period 2003–2018 at 1–32 day lead. We compared the streamflow forecast skills of raw meteorological forecasts from ERFS and GEFS at a 1–10 day lead for the summer monsoon (June–September) 2019–2020. The ERFS forecast underestimated extreme precipitation against the observations compared to the GEFS during the summer monsoon of 2019–2020. However, both the forecast products showed better skills for minimum and maximum temperatures than precipitation. Ensemble streamflow forecast from the GEFS performed better than the ERFS during 2019–2020. The performance of the GEFS based ensemble streamflow forecast declines after five days lead. Overall, the GEFS ensemble streamflow forecast can provide reliable skills at a 1–5 day lead. Our findings provide directions for developing a flood early warning system based on ensemble streamflow prediction considering the influence of reservoirs in India.

Developing an ensemble hydrologic prediction system is essential for reservoir operations and flood early warning. However, efforts to build hydrologic ensemble prediction systems considering the influence of reservoirs have been lacking in India. We examine the potential of the Extended Range Forecast System (ERFS, 16 ensemble members) and Global Ensemble Forecast System (GEFS, 21 ensemble members) forecast for streamflow prediction in India using the Narmada River basin as a testbed. We use the Variable Infiltration Capacity (VIC) with reservoir operations (VIC-Res) scheme to simulate the daily river flow at four locations in the Narmada basin. We examined the streamflow forecast skills of the ERFS forecast for the period 2003–2018 at 1–32 day lead. We compared the streamflow forecast skills of raw meteorological forecasts from ERFS and GEFS at a 1–10 day lead for the summer monsoon (June–September) 2019–2020. The ERFS forecast underestimated extreme precipitation against the observations compared to the GEFS during the summer monsoon of 2019–2020. However, both the forecast products showed better skills for minimum and maximum temperatures than precipitation. Ensemble streamflow forecast from the GEFS performed better than the ERFS during 2019–2020. The performance of the GEFS based ensemble streamflow forecast declines after five days lead. Overall, the GEFS ensemble streamflow forecast can provide reliable skills at a 1–5 day lead. Our findings provide directions for developing a flood early warning system based on ensemble streamflow prediction considering the influence of reservoirs in India.

The Geomorphic Flood Index (GFI) model can show flood inundation in a watershed based on flood events. Flood inundation can occur because of the backwater effect in the river channel. The different water levels from the river and tide that occur downstream may temporarily disturb the water depth and flow velocity. Because of this disturbance, a backwater effect might occur. The case study is located at the Plumbon River in Semarang City, which has the potential for a backwater effect in the river downstream. Backwater is a hydraulic phenomenon that occurs in rivers and can be simulated using a hydrodynamic model, as it can show the relationship between river discharge and tides. The conventional GFI method does not consider hydraulic effects, such as backwater effects, as part of the calculation. Therefore, modifications will be inserted into the calculation of the hydraulic effect on the modelling of flood inundation. The main purpose of this study is to build a modified GFI method that includes backwater analysis in its modelling to predict the backwater effect that occurs downstream of rivers and its impact on inundation. This study will help rapidly predict flood inundation.