Compounding effects of sea level rise and fluvial flooding Moftakhari, Hamed R.; Salvadori, Gianfausto; AghaKouchak, Amir ...
Proceedings of the National Academy of Sciences - PNAS,
09/2017, Letnik:
114, Številka:
37
Journal Article
Recenzirano
Odprti dostop
Sea level rise (SLR), a well-documented and urgent aspect of anthropogenic global warming, threatens population and assets located in low-lying coastal regions all around the world. Common flood ...hazard assessment practices typically account for one driver at a time (e.g., either fluvial flooding only or ocean flooding only), whereas coastal cities vulnerable to SLR are at risk for flooding from multiple drivers (e.g., extreme coastal high tide, storm surge, and river flow). Here, we propose a bivariate flood hazard assessment approach that accounts for compound flooding from river flow and coastal water level, and we show that a univariate approach may not appropriately characterize the flood hazard if there are compounding effects. Using copulas and bivariate dependence analysis, we also quantify the increases in failure probabilities for 2030 and 2050 caused by SLR under representative concentration pathways 4.5 and 8.5. Additionally, the increase in failure probability is shown to be strongly affected by compounding effects. The proposed failure probability method offers an innovative tool for assessing compounding flood hazards in a warming climate.
Recent and highly accurate topographic data should be used for flood inundation modeling, but this is not always feasible given time and budget constraints so the utility of several on-line digital ...elevation models (DEMs) is examined with a set of steady and unsteady test problems. DEMs are used to parameterize a 2D hydrodynamic flood simulation algorithm and predictions are compared with published flood maps and observed flood conditions. DEMs based on airborne light detection and ranging (LiDAR) are preferred because of horizontal resolution, vertical accuracy (∼0.1
m) and the ability to separate bare-earth from built structures and vegetation. DEMs based on airborne interferometric synthetic aperture radar (IfSAR) have good horizontal resolution but gridded elevations reflect built structures and vegetation and therefore further processing may be required to permit flood modeling. IfSAR and shuttle radar topography mission (SRTM) DEMs suffer from radar speckle, or noise, so flood plains may appear with non-physical relief and predicted flood zones may include non-physical pools. DEMs based on national elevation data (NED) are remarkably smooth in comparison to IfSAR and SRTM but using NED, flood predictions overestimate flood extent in comparison to all other DEMs including LiDAR, the most accurate. This study highlights utility in SRTM as a global source of terrain data for flood modeling.
► Methods to incorporate buildings in an urban dam-break flood model are compared. ► All methods support accurate flood extent and stream flow prediction. ► Data needs, set-up costs, and execution ...costs differ across methods. ► The best model depends on modeling objectives and constraints. ► The anisotropic building porosity model is presented and validated.
Urban areas are vulnerable to major flood damages due to the density of economic and social assets, and there is increasing interest in localized flood intensity predictions to implement flood risk reduction measures. A number of models have been proposed for unsteady flood flows through urban landscapes, but the data needs and complexity are varied and it is not clear that the benefits of added complexity are justified by improved predictive skill. In this study we compare four methods to model unsteady, multi-dimensional flow through urban areas: building resistance (BR), building block (BB), building hole (BH) and building porosity (BP). Each method is applied to the Baldwin Hills, CA urban dam break scenario which offers excellent data for model parameterization, validation and overall performance assessment including observations of flood extent, stream flow, and scour path. Results show that all four methods are capable of high predictive skill for flood extent and stream flow using unique unstructured meshes tailored to exploit the strengths of each approach. However, localized velocities prove more difficult to predict and are sensitive to the building method even in the limit of a very fine grid (ca. 1.5m resolution). In addition, only those methods that account for building geometries (BB, BH and BP) capture building-scale variability in the velocity field. Tradeoffs between predictive skill, execution time, and set-up time are identified suggesting that the best method for a particular application will depend on available data, computing resources, time constraints, and the specific modeling objectives.
PRIMo: Parallel raster inundation model Sanders, Brett F.; Schubert, Jochen E.
Advances in water resources,
April 2019, 2019-04-00, 20190401, Letnik:
126
Journal Article
Recenzirano
•Detailed flood simulation for a whole city is achieved with parallel computing and upscaling.•Hourly flood simulations are completed in a matter of seconds to minutes.•Subcritical, supercritical and ...transcritical flows and compound flooding can be simulated.
Simulation of flood inundation at metric resolution is important for making hazard information useful to a wide range of end-users involved in flood risk management, and addressing the alarming increase in flood losses that have been observed over recent decades. However, high data volumes and computational demands make this challenging over large spatial extents comparable to the metropolitan areas of major cities where flood impacts are concentrated, especially for time-sensitive applications such as forecasting and repetitive simulation for uncertainty assessment. Additionally, several factors present difficulties for numerical solvers including combinations of steep and flat topography that promote transcritical flows, the need to resolve flow in relatively narrow features such as drainage channels and roadways in urban areas which channel flood water during extreme events, and the need to depict compound hazards resulting from the interaction of pluvial, fluvial and coastal flooding. A new flood inundation model is presented here to address these challenges. The Parallel Raster Inundation Model (PRIMo) solves the shallow-water equations on an upscaled grid that is far coarser than the underlying raster digital topographic model (DTM), and uses a subgrid modeling approach so that the solution benefits from DTM-scale topographic data. Additionally, an approximate Riemann solver is applied in an innovative way to integrate fluxes between cells, as needed to update the solution by the finite volume method, which makes the method applicable to subcritical, supercritical and transcritical flows. PRIMo is implemented using a two-dimensional domain decomposition approach to Single Process Multiple Data (SPMD) parallel computing, and overlapping communications and computations are implemented to yield ideal parallel scaling for well-balanced test cases. With both a subgrid model and ideal parallel scaling, the model can scale to meet the demands of any application. Several benchmarks are presented to demonstrate predictive skill and the potential for timely, whole-city, metric-resolution flooding simulations. Limitations of the methods and opportunities for improvements are also presented.
•An improved integral porosity shallow water model for urban floods is presented.•Model formulation corrects fundamental errors in characteristic wave speeds.•A novel momentum dissipation mechanism ...is presented.•Model advantages shown with benchmark test cases and a field-scale application.
With CPU times 2 to 3 orders of magnitude smaller than classical shallow water-based models, the shallow water equations with porosity are a promising tool for large-scale modelling of urban floods. In this paper, a new model formulation called the Dual Integral Porosity (DIP) model is presented and examined analytically and computationally with a series of benchmark tests. The DIP model is established from an integral mass and momentum balance whereby both porosity and flow variables are defined separately for control volumes and boundaries, and a closure scheme is introduced to link control volume- and boundary-based flow variables. Previously developed Integral Porosity (IP) models were limited to a single set of flow variables. A new transient momentum dissipation model is also introduced to account for the effects of sub-grid scale wave action on porosity model solutions, effects which are validated by fine-grid solutions of the classical shallow-water equations and shown to be important for achieving similarity in dam-break solutions. One-dimensional numerical test cases show that the proposed DIP model outperforms the IP model, with significantly improved wave propagation speeds, water depths and discharge calculations. A two-dimensional field scale test case shows that the DIP model performs better than the IP model in mapping the floods extent and is slightly better in reproducing the anisotropy of the flow field when momentum dissipation parameters are calibrated.
Nuisance flooding (NF) refers to low levels of inundation that do not pose significant threats to public safety or cause major property damage, but can disrupt routine day‐to‐day activities, put ...added strain on infrastructure systems such as roadways and sewers, and cause minor property damage. NF has received some attention in the context of low‐lying coastal cities exposed to increasingly higher high tides, a consequence of sea level rise, which exceeds the heights of coastal topography. However, low levels of flooding are widespread and deserve greater attention. Here a simple, quantitative definition of NF is proposed based on established flood intensity thresholds for flood consequences (e.g., pedestrian safety, property damage, and health risks). Based on a wide range of literature including hydrology, transportation, public health risk, and safety impacts, we define NF based on depth >3 cm and <10 cm, regardless of the source. This definition of NF is not limited to high tide flooding but rather is inclusive of all possible flood drivers including pluvial, fluvial, and oceanic and can capture trends in NF resulting from trends in, and compounding effects of, flood drivers. Furthermore, we also distinguish between NF as a process and NF as an event, which is important for linking NF to societal impacts and developing effective policy interventions and mitigation strategies. Potential applications and implications of NF monitoring are also presented.
Key Points
Focus on extreme events has led to less attention given to nuisance (minor) floods (NF)
We propose a process‐based threshold applicable to regions with short periods of flood monitoring records
This threshold is useful for characterizing the spatial distributions of flood severity
Urban flooding from extreme precipitation and storm surge is a growing threat to cities, and detailed forecasts of urban inundation are needed for emergency response. We present a mechanistic ...framework to simulate flood inundation over metropolitan‐wide areas at fine resolution (3 m). A dual‐grid shallow‐water model is used to overcome computational bottlenecks, and an application to Hurricane Harvey focused on pluvial flooding provides a multi‐dimensional assessment of predictive skill. A hindcast model is shown to simulate peak stage across 41 stream gages with a mean absolute error (MAE) of 0.63 m, and hourly stage levels over a 5‐day period with a median MAE and Nash‐Sutcliffe Efficiency (NSE) of 0.74 m and 0.55, respectively. Peak flood level across 228 high water marks (HWMs) were captured with an MAE of 0.69 m. A forecast model forced by Quantitative Precipitation Forecast data is shown to be only marginally less accurate than the hindcast model. Peak stage is simulated with an MAE of 0.86 m, hourly stage is captured with a median MAE and NSE of 0.90 m and 0.41, respectively, and HWMs are captured with an MAE of 0.77 m. The forecast system also achieves hit rates of 90% and 73% predicting distress calls and FEMA damage claims, respectively, based on simulated flood depth. These results demonstrate the potential to operationally forecast pluvial flood inundation in the U.S. with the timeliness and accuracy needed for early warning, and we also highlight future research needs.
Plain Language Summary
Major cities across the U.S. and globally are experiencing severe flooding that impacts large populations of people, disrupts economies and livelihoods, and causes extensive damage. While short‐term weather forecasts are now able to predict the extreme precipitation, storm surge, and/or streamflow which create conditions conducive to urban flooding, forecasting of local flood inundation on a street‐by‐street or house‐by‐house basis is not generally available. Here, we present a new modeling system capable of making street‐level forecasts of flood inundation with lead times of hours to several days. We report the level of accuracy in terms of hydrologic skill and the ability to predict distress and damage within the built environment. We also show that the modeling system runs sufficiently fast to support timely decision‐making. This study reports information that cities across the U.S. and elsewhere can use to develop forecast systems useful for damage avoidance and public safety.
Key Points
A mechanistic framework is presented for flood inundation forecasting at 3 m resolution and metropolitan scale
Hurricane Harvey forecast shows sub‐meter accuracy for high water marks and executes 26 times faster than real‐time
Framework demonstrates capacity to forecast human impacts including distress and damage
Nonstationary extreme value analysis (NEVA) can improve the statistical representation of observed flood peak distributions compared to stationary (ST) analysis, but management of flood risk relies ...on predictions of out‐of‐sample distributions for which NEVA has not been comprehensively evaluated. In this study, we apply split‐sample testing to 1250 annual maximum discharge records in the United States and compare the predictive capabilities of NEVA relative to ST extreme value analysis using a log‐Pearson Type III (LPIII) distribution. The parameters of the LPIII distribution in the ST and nonstationary (NS) models are estimated from the first half of each record using Bayesian inference. The second half of each record is reserved to evaluate the predictions under the ST and NS models. The NS model is applied for prediction by (1) extrapolating the trend of the NS model parameters throughout the evaluation period and (2) using the NS model parameter values at the end of the fitting period to predict with an updated ST model (uST). Our analysis shows that the ST predictions are preferred, overall. NS model parameter extrapolation is rarely preferred. However, if fitting period discharges are influenced by physical changes in the watershed, for example from anthropogenic activity, the uST model is strongly preferred relative to ST and NS predictions. The uST model is therefore recommended for evaluation of current flood risk in watersheds that have undergone physical changes. Supporting information includes a MATLAB® program that estimates the (ST/NS/uST) LPIII parameters from annual peak discharge data through Bayesian inference.
Key Points
Stationary predictions of flood peak distributions are preferred, overall
Extrapolation of the nonstationary model parameter trend rarely improves the stationary prediction, even if an observed trend continues
Using the most recent nonstationary parameters to predict with an updated stationary model is preferred for physically changing watersheds
•Compound flood hazard levels computed for tidal channels or estuaries.•Bivariate statistical and hydrodynamic modeling is linked.•Spatially distributed flood hazard levels computed for return period ...T.•Method accounts for statistical and physical compounding effects.
A method to link bivariate statistical analysis and hydrodynamic modeling for flood hazard estimation in tidal channels and estuaries is presented and discussed for the general case where flood hazards are linked to upstream riverine discharge Q and downstream ocean level, H. Using a bivariate approach, there are many possible combinations of Q and H that jointly reflect a specific return period, T, raising questions about the best choice as boundary forcing in a hydrodynamic model. We show, first of all, how possible Q and H values depend on whether the definition of T corresponds to the probability of exceedance of “H OR Q” or “H AND Q”. We also show that flood hazards defined by “OR” return periods are more conservative than “AND” return periods. Finally, we introduce a new composite water surface profile to represent the spatially distributed hazard for return period T. The composite profile synthesizes hydrodynamic model results from the “AND” hazard scenario and two scenarios based on traditional univariate analysis, a “Marginal Q” scenario and a “Marginal H” scenario.
•A DEM processing method for fully distributed depression integrated hydrologic modeling is presented.•Method identifies ponds well and removes noise with minimal distortion to the landscape.•The ...integration of depressions significantly alters hydrologic simulation.
Land surface depressions play a central role in the transformation of rainfall to ponding, infiltration and runoff, yet digital elevation models (DEMs) used by spatially distributed hydrologic models that resolve land surface processes rarely capture land surface depressions at spatial scales relevant to this transformation. Methods to generate DEMs through processing of remote sensing data, such as optical and light detection and ranging (LiDAR) have favored surfaces without depressions to avoid adverse slopes that are problematic for many hydrologic routing methods. Here we present a new topographic conditioning workflow, Depression-Preserved DEM Processing (D2P) algorithm, which is designed to preserve physically meaningful surface depressions for depression-integrated and efficient hydrologic modeling. D2P includes several features: (1) an adaptive screening interval for delineation of depressions, (2) the ability to filter out anthropogenic land surface features (e.g., bridges), (3) the ability to blend river smoothing (e.g., a general downslope profile) and depression resolving functionality. From a case study in the Goodwin Creek Experimental Watershed, D2P successfully resolved 86% of the ponds at a DEM resolution of 10 m. Topographic conditioning was achieved with minimum impact as D2P reduced the number of modified cells from the original DEM by 51% compared to a conventional algorithm. Furthermore, hydrologic simulation using a D2P processed DEM resulted in a more robust characterization on surface water dynamics based on higher surface water storage as well as an attenuated and delayed peak streamflow.
PDF
