Simulating Flash Floods Research Articles

Corrigendum: Comparison of two hydrodynamic models for their rain-on-grid technique to simulate flash floods in steep catchment

Corrigendum: Comparison of two hydrodynamic models for their rain-on-grid technique to simulate flash floods in steep catchment

Comparison of two hydrodynamic models for their rain-on-grid technique to simulate flash floods in steep catchment

In this study, two hydrodynamic models, TELEMAC-2D and HEC-RAS 2D, were compared for their Rain-on-Grid (RoG) technique with a particular focus on runoff generation processes in a small and steep catchment. Curve number (CN) method was applied in both the models to simulate two single storm events up to 20 h of duration, whereas the Green-Ampt Redistribution (GAR) method was additionally applied in HEC-RAS 2D for a multi-peak flood event with sustained flow between the peaks. CN and GAR methods were compared for this flood event, and a sensitivity analysis of the GAR parameters was also done. Moreover, the two models were compared for their calibration process, computational time, mesh size and shape, and model availability, in general, as well as the results including inundated areas, water depth, and velocity. The results indicate that both the models are capable of reproducing short duration single storm floods. NSE and R2 for both models ranged from 0.70 to 0.90 and from 0.93 to 0.95. However, the models struggled to reproduce the long- duration multi-peak flood event. The sensitivity analysis showed that the results are not very sensitive to the two GAR parameters which are responsible to influence the flow of the second peak in the flood event. Neither the CN nor the GAR infiltration method successfully replicated such events because the hydraulic models permanently lose infiltrated water from the domain. The returned sub-surface flow significantly contributes to river flow during these flood events; however, none of the model incorporates a return flow algorithm.

Assessing flash flood inundation from an extreme rainfall event: case study: Wadi Al Jizzi Oman

Abstract Flash floods present a significant risk to urbanized arid regions, and assessing their inundation patterns is crucial for effective disaster management. Extreme hydrologic events due to aridity and climate change are shaping human lives and major activities in numerous countries at an unprecedented pace. This study aims to assess flash floods from extreme storm events in an arid catchment using high-resolution data. The study applied two models on the event of a single storm, namely the IHACRES and AHP models. The observed flow was used for models' validation. The average flow output determined with the IHACRES model was approximately 0.47 m3/s while the flow output resulting from the AHP model was 0.45 m3/s. The efficiency showed that the IHACRES performed better in evaluating extreme events with an average of 0.88 while the AHP model showed an efficiency of 0.68. The quantitative simulation of both models is likely to have good applicability for simulating single storm events in arid catchments. The validated IHACRES and AHP models offer valuable tools for simulating flash flood. The study's outcomes have implications for flood management policy and infrastructure planning, ensuring a more resilient response to extreme flood events in arid regions globally.

Simulating flash floods using remote sensing and GIS-based KW-GIUH hydrological model

Understanding storm flow generation within arid wadis is challenging because of the absence of reliable long-term field measurements. This paper simulates the flash flood runoff within Wadi Billi, Egypt, using remote sensing and GIS-based KW-GIUH model considering the linkage between the watershed’s geomorphological characteristics and its hydrologic response. A morphometric database of 58 parameters for all aspects has been developed using GIS; then, at the sub-basin level, KW-GIUH model has been used to simulate the hydrological response for the storm event of 9th March 2014. The statistical analysis, using Pearson correlation, classified the morphometric parameters according to their hydrological contribution and showed that only 21 parameters are significant. The results lead to isolating the most effective morphometric parameters, and this could be used to optimize the mathematical equations of the hydrological models to be more realistic in representing the physical processes of flash floods. Defining the morphometric parameters at sub-basin level is essential to predict the damages and to forecast the water flow order, which helps in designing a sustainable stormwater system that can protect the downstream areas and use rainwater instead of discharging it to the ocean.

Development and Application of a Real-Time Flood Forecasting System (RTFlood System) in a Tropical Urban Area: A Case Study of Ramkhamhaeng Polder, Bangkok, Thailand

In urban areas of Thailand, and especially in Bangkok, recent flash floods have caused severe damage and prompted a renewed focus to manage their impacts. The development of a real-time warning system could provide timely information to initiate flood management protocols, thereby reducing impacts. Therefore, we developed an innovative real-time flood forecasting system (RTFlood system) and applied it to the Ramkhamhaeng polder in Bangkok, which is particularly vulnerable to flash floods. The RTFlood system consists of three modules. The first module prepared rainfall input data for subsequent use by a hydraulic model. This module used radar rainfall data measured by the Bangkok Metropolitan Administration and developed forecasts using the TITAN (Thunderstorm Identification, Tracking, Analysis, and Nowcasting) rainfall model. The second module provided a real-time task management system that controlled all processes in the RTFlood system, i.e., input data preparation, hydraulic simulation timing, and post-processing of the output data for presentation. The third module provided a model simulation applying the input data from the first and second modules to simulate flash floods. It used a dynamic, conceptual model (PCSWMM, Personal Computer version of the Stormwater Management Model) to represent the drainage systems of the target urban area and predict the inundation areas. The RTFlood system was applied to the Ramkhamhaeng polder to evaluate the system’s accuracy for 116 recent flash floods. The result showed that 61.2% of the flash floods were successfully predicted with accuracy high enough for appropriate pre-warning. Moreover, it indicated that the RTFlood system alerted inundation potential 20 min earlier than separate flood modeling using radar and local rain stations individually. The earlier alert made it possible to decide on explicit flood controls, including pump and canal gate operations.

An Alternative Algorithm for Simulating Flash Flood

Most of the approaches in numerical modeling techniques are based on the Eulerian coordinate system. This approach faces difficulty in simulating flash flood front propagation. This paper shows an alternative method that implements a numerical modeling technique based on the Lagrangian coordinate system to simulate the water of debris flow. As for the interaction with the riverbed, the simulation uses an Eulerian coordinate system. The method uses the conservative and momentum equations of water and sediment mixture in the Lagrangian form. Source terms represent deposition and erosion. The riverbed in the Eulerian coordinate system interacts with the flow of the mixture. At every step, the algorithm evaluates the relative position of moving nodes of the flow part to the fixed nodes of the riverbed. Computation of advancing velocity and depth uses the riverbed elevation, slope data, and the bed elevation change computation uses the erosion or deposition data of the flow on the moving nodes. Spatial discretization is implementing the Galerkin method. Furthermore, temporal discretization is implementing the forward difference scheme. Test runs show that the algorithm can simulate downward, upward, and reflected backward 1-D flow cases. Two-D model tests and comparisons with SIMLAR software show that the algorithm works in simulating debris flow.

Flash flood modeling in the data-poor basin: A case study in Matina River Basin

Forecasting flooding hazards is a very effective non-engineering measure for flood control. Presently, the commonly used forecasting method in simulating flash flood events is through a watershed hydrological model. Many Asia-Pacific countries like the Philippines are prone to frequent hydrometeorological hazards such as tropical cyclones, resulting in frequent heavy rainfall events. However, most rivers in the many basins lack water flow observation data, which makes it challenging to use lumped and data-driven models for flash flood forecasting. With the continuous progress of remote sensing (RS) and geographic information system (GIS) techniques, the physically-based distributed hydrological model (PBDHMs) has rapidly advanced. PBDHMs can directly determine the model parameters according to the underlying surface characteristics from remotely-sensed data, which makes it possible for flood forecasting in areas with little to virtually no data. In this study, the Matina River basin in Davao City was selected as a case study in simulating a small data-poor basin in the region. The Liuxihe model was used to formulate a forecasting scheme and simulated the past flash flood events. The results show that there is a good correspondence between the past heavy rainfall events and their corresponding simulated river discharges. The results conform to the hydrological regularities, which can be used for flood forecasting and can serve as a baseline for the development of a flood forecasting system in the rivers of Davao City and elsewhere.

SHETRAN and HEC HMS Model Evaluation for Runoff and Soil Moisture Simulation in the Jičinka River Catchment (Czech Republic)

Due to the improvement of computation power, in recent decades considerable progress has been made in the development of complex hydrological models. On the other hand, simple conceptual models have also been advanced. Previous studies on rainfall–runoff models have shown that model performance depends very much on the model structure. The purpose of this study is to determine whether the use of a complex hydrological model leads to more accurate results or not and to analyze whether some model structures are more efficient than others. Different configurations of the two models of different complexity, the Système Hydrologique Européen TRANsport (SHETRAN) and Hydrologic Modeling System (HEC-HMS), were compared and evaluated in simulating flash flood runoff for the small (75.9 km2) Jičinka River catchment in the Czech Republic. The two models were compared with respect to runoff simulations at the catchment outlet and soil moisture simulations within the catchment. The results indicate that the more complex SHETRAN model outperforms the simpler HEC HMS model in case of runoff, but not for soil moisture. It can be concluded that the models with higher complexity do not necessarily provide better model performance, and that the reliability of hydrological model simulations can vary depending on the hydrological variable under consideration.

Simulating Flash Floods in Small Rivers of the Northern Caucasus with the Use of Data of Automated Hydrometeorological Network

Examples are given for numerical modeling of rain-induced flash floods in mountain areas based on actual and forecast data on precipitation. The calculated hydrographs are compared with the data of hydrometeorological network and calculations of other authors. Two hydrological models were applied: STREAM 2D CUDA, based on numerical solution of the shallow water equations in a two-dimensional (horizontal) formulation using an original algorithm, and KW-GIUH―a Geomorphological Instantaneous Unit Hydrograph model, based on kinematic wave equation. The direct numerical simulation of level regimes at the passage of flood wave and the incorporation of the accumulation of a part of flood flow by reservoirs–traps in the dynamic regime are new scientific results in hydrology. It is concluded that both models can be effectively used to forecast rain floods, in case reliable data on precipitation are available.

Comparing Three Hydrological Models for Flash Flood Simulations in 13 Humid and Semi-humid Mountainous Catchments

Flash flood disaster ranks top among all the natural hazards across the world due to its high frequency, severity and fatality. However, flash flood simulation is still challenging in small and medium-sized catchments with complex orography, flashy hydrological responses and poor observations. Three distributed hydrological models, i.e., TOPModel, HEC and CNFF, are selected to simulate flash floods in seven humid and six semi-humid catchments in China, with consideration of water balance (RER), peak flow rate (REQ) and its occurrence time (TP), hydrograph variation (SNSE) and model uncertainty. Influences of five catchment attributes are further investigated on individual model performances. All three models perform satisfactorily in humid catchments, but less satisfactorily in semi-humid catchments. Water balance is well obtained by CNFF, followed by HEC and TOPModel. Peak flow rate and its occurrence time are most accurately captured by CNFF and HEC, respectively. Hydrograph variations are well reproduced by HEC and CNFF. TOPModel performs well for picking peak flow and hydrograph variation in humid catchments. Uncertainty interval is narrowest for HEC with average relative interval length at 95% confidence level being 0.78 ~ 2.53. Most observations are bracketed by uncertainty intervals for TOPModel (64.79% ~ 91.91% of total). Three model performance indices (i.e., RER, REQ, and SNSE) are mainly affected by drainage area and forest ratio across humid and semi-humid catchments, while TP performance is mainly affected by mean slope in humid catchments.

Simulating flash flood hydrographs and behavior metrics across China: Implications for flash flood management

China frequently suffers from considerable and disastrous flash floods with wide areal coverage and high frequency. Obtaining useful information to support flash flood management and decision-making is challenging for massive flash flood events that vary greatly in spatio-temporal characteristics. In this study, hydrological modelling approach (CNFF) and cluster analysis were integrated to assess simulation reliability of entire flash flood processes including both hydrographs and behavior characteristics in a manner of similarity classification, rather than at event scale. A total of 207 hourly events from 13 mountainous catchments with diverse physiographic and meteorological characteristics across China were selected for study. Representative flash flood types were classified using normalized hydrographs with diverse spatio-temporal patterns by k-means clustering algorithm. For individual flash flood types, simulation reliability of CNFF was assessed in capturing corresponding hydrographs, seven behavior metrics measuring flash flood magnitude, intensity, occurrence time, flood timescale, rates of change and variability, and their uncertainties. Results showed that three (fast, intermediate and slow) flash flood types were identified from all the flash flood events with overall average silhouette index of 0.45. Hourly hydrographs of three flash flood types were well reproduced by CNFF, with absolute average relative error of runoff within 15% and Nash-Sutcliffe Efficiency above 0.55. All the behavior metrics were the most accurately reproduced for slow flash flood type with the least average relative root-mean-square error (0.30), followed by intermediate (0.52) and fast (0.58) types. Moreover, the slow flash flood type had the most reliable but greatest uncertainty interval of both hydrograph and behavior metrics, with average relative interval length being 1.24 and 71.96%, and 93.10% and 100% of observations contained in 95% confidence interval, respectively. This study provided efficient and detailed information for flash flood management, and extended application scope of hydrological models to encompass flash flood types and behavior metrics.

Simulating Flash Floods Using Geostationary Satellite-Based Rainfall Estimation Coupled with a Land Surface Model

Clarifying hydrologic behavior, especially behavior related to extreme events such as flash floods, is vital for flood mitigation and management. However, discharge and rainfall measurement data are scarce, which is a major obstacle to flood mitigation. This study: (i) simulated flash floods on a regional scale using three types of rainfall forcing implemented in a land surface model; and (ii) evaluated and compared simulated flash floods with the observed discharge. The three types of rainfall forcing were those observed by the Automated Meteorological Data Acquisition System (AMeDAS) (Simulation I), the observed rainfall from the Ministry of Land, Infrastructure and Transportation (MLIT) (Simulation II), and the estimated rainfall from the Multi-purpose Transport Satellite (MTSAT), which was downscaled by AMeDAS rainfall (Simulation III). MLIT rainfall observations have a denser station network over the Ishikari River basin (spacing of approximately 10 km) compared with AMeDAS (spacing of approximately 20 km), so they are expected to capture the rainfall spatial distribution more accurately. A land surface model, the Minimal Advance Treatments of Surface Interaction and Runoff (MATSIRO), was implemented for the flash flood simulation. The river flow simulations were run over the Ishikari river basin at a 1-km grid resolution and a 1-h temporal resolution during August 2010. The statistical performance of the river flow simulations during a flash flood event on 23 and 24 August 2010 demonstrated that Simulation I was reasonable compared with Simulation III. The findings also suggest that the advantages of the MTSAT-based estimated rainfall (i.e., good spatial distribution) can be coupled with the benefit of direct AMeDAS observations (i.e., representation of the true rainfall).

Utilization of Storm Water Management Model for Urban Flood Scenario

Majors cities in India have witnessed huge floods from past few decades. Due to rapid population growth and improper urban planning the chances of creek, localised or flash urban floods have drastically increased. Climatic changes are also a key reason for heavy rainfall that increases the flood volume and depth in a catchment. Modelling of Storm water plays a key role in estimating flood runoff quantity and quality. To check these issues SWMM is used to simulate floods scenario in Urban areas. This hydrological study is carried out to simulate and understand the rainfall runoff characteristics of the study area by using SWMM. It is an effective tool used for simulating flash floods and runoff in urban areas. In this study catchments have been subdivided into 14 parts and modelled for year 2017 rainfall events of 1-hr interval. The present study area is evaluated by importing AutoCAD map of the area in SWMM. Further Rainfall Data is imported as time series in the model. The results depict that the Runoff for the Sub catchments of S1, S2, S3, S9, S13 blocks are maximum. The study states that no nodes are flooded and also no overflow sections. Thus, the selected study area storm network system has been well planned and has enough carrying capacity to carry the simulated rainfall for a prolonged duration.

Bayesian calibration of HEC-HMS model using two different algorithms

The assessment of hydrological model parameters is impacted by uncertainties that may be attributed to several sources of errors. Many methods have been developed and applied to various hydrological models in order to address these uncertainties. The present study aims to apply two uncertainty analysis methods, i.e., generalized uncertainty estimation (GLUE) and differential evolution adaptive metropolis (DREAMzs), for the purpose of evaluating the parametric and predictive uncertainty of the HEC-HMS hydrological model. The HEC-HMS model was coupled to these two approaches in order to simulate flash floods in the Mekerra watershed (Algeria) and to estimate the uncertainty of 14 model parameters which are due to be calibrated. For this, six flood events were selected for the purpose of calibrating and validating the model, two informal likelihood measures L1 and L2 were applied in GLUE and one formal likelihood measure was applied in DREAMzs. Considering the difference between the results, and not the principles of the two methods, the analysis indicated that these parameters can be better identified by DREAMzs algorithm. Indeed, the coefficients of variation of their posterior distributions are generally lower. Compared to GLUE, DREAMzs algorithm is more efficient in exploring the parametric space. With regard to the predictive uncertainty, 95% of the uncertainty intervals were estimated by both methods and then analyzed. The DREAMzs algorithm results suggested a good dynamics of the simulated floods, associated with a significant uncertainty that is due not to the parametric uncertainty but to other sources of errors. The GLUE algorithm generates similar results but does not distinguish between the total uncertainty and parametric uncertainty. Further studies would be desirable to evaluate the impact of other likelihood measures on the simulation results with both algorithms

Evaluation and Bias Correction of Satellite-Based Rainfall Estimates for Modelling Flash Floods over the Mediterranean region: Application to Karpuz River Basin, Turkey

This study investigates the utility of satellite-based rainfall estimates in simulating flash floods in Karpuz River Basin, Turkey, characterized by limited rain gauge network. Global Satellite Mapping of Precipitation (GSMaP) product was evaluated with the rain gauge network at daily and monthly time-scales considering seasonality, elevation zones, extreme events and rainfall intensity thresholds. Statistical analysis indicated that GSMaP shows acceptable linear correlation coefficient with rain gauges, however, suffers from significant underestimation bias. Statistical measures exhibited a remarkable deterioration with increasing elevation-following a linear relationship; for example, percent bias was found to increase by a rate of 11.7% with every 400 m interval. A multiplicative bias correction scheme was devised, and Hydrological River Basin Environmental Assessment Model (Hydro-BEAM) was implemented to simulate flash floods driven by the uncorrected/corrected GSMaP data. Analysis of intensity thresholds revealed that appropriate threshold selection is critically important for the bias correction procedure. The hydrological model was calibrated for flash flood events during October–December 2007 and 2012 and validated during October–December, 2009 and 2010. Flash floods simulations were improved by the local bias correction procedure applied to the GSMaP data, but the degree of improvement varied from one period to another. The results of the study indicate that bias factors incorporating multiple variables such as extreme events and elevation variability have the potential to further improve flood simulations.

Simulating Flash Floods at Hourly Time-Step Using the SWAT Model

Flash floods are natural phenomena with environmental, social and economic impacts. To date, few numerical models are able to simulate hydrological processes at catchment scale at a reasonable time scale to describe flash events with accurate details. Considering a ~810 km2 Mediterranean river coastal basin (southwestern France) as a study case, the objective of the present study was to assess the ability of the sub-daily module of the lumped Soil and Water Assessment Tool (SWAT) to simulate discharge (1) time-continuously, by testing two sub-basin delineation schemes, two catchment sizes, and two output time-steps; and (2) at flood time-scale, by comparing the performances of SWAT to the performances of the event-based fully distributed MARINE model when simulating flash flood events. We showed that there was no benefit of decreasing the size of the minimum drainage area (e.g., from ~15 km2 down to ~1 km2) when delineating sub-basins in SWAT. We also showed that both the MARINE and SWAT models were equally able to reproduce peak discharge, flood timing and volume, and that they were both limited by rainfall and soil data. Hence, the SWAT model appears to be a reliable modelling tool to predict discharge over long periods of time in large flash-flood-prone basins.

Establishing a rainfall threshold for flash flood warnings in China’s mountainous areas based on a distributed hydrological model

Summary Flash flooding is one of the most common natural hazards in China, particularly in mountainous areas, and usually causes heavy damage and casualties. However, the forecasting of flash flooding in mountainous regions remains challenging because of the short response time and limited monitoring capacity. This paper aims to establish a strategy for flash flood warnings in mountainous ungauged catchments across humid, semi-humid and semi-arid regions of China. First, we implement a geomorphology-based hydrological model (GBHM) in four mountainous catchments with drainage areas that ranges from 493 to 1601 km2. The results show that the GBHM can simulate flash floods appropriately in these four study catchments. We propose a method to determine the rainfall threshold for flood warning by using frequency analysis and binary classification based on long-term GBHM simulations that are forced by historical rainfall data to create a practically easy and straightforward approach for flash flood forecasting in ungauged mountainous catchments with drainage areas from tens to hundreds of square kilometers. The results show that the rainfall threshold value decreases significantly with increasing antecedent soil moisture in humid regions, while this value decreases slightly with increasing soil moisture in semi-humid and semi-arid regions. We also find that accumulative rainfall over a certain time span (or rainfall over a long time span) is an appropriate threshold for flash flood warnings in humid regions because the runoff is dominated by excess saturation. However, the rainfall intensity (or rainfall over a short time span) is more suitable in semi-humid and semi-arid regions because excess infiltration dominates the runoff in these regions. We conduct a comprehensive evaluation of the rainfall threshold and find that the proposed method produces reasonably accurate flash flood warnings in the study catchments. An evaluation of the performance at uncalibrated interior points in the four gauged catchments provides results that are indicative of the expected performance at ungauged locations. We also find that insufficient historical data lengths (13 years with a 5-year flood return period in this study) may introduce uncertainty in the estimation of the flood/rainfall threshold because of the small number of flood events that are used in binary classification. A data sample that contains enough flood events (10 events suggested in the present study) that exceed the threshold value is necessary to obtain acceptable results from binary classification.

Incorporating transmission losses in flash flood routing in ephemeral streams by using the three-parameter Muskingum method

Stream flow in arid and semi-arid regions is usually in a form of flash floods that occur in dry bed ephemeral streams. In this research, the three-parameter Muskingum method has been investigated as a tool for flash flood routing in arid zones using data from Yiba catchment in the Kingdom of Saudi Arabia. The method is general; it takes into account both lateral inflows from side tributaries and also lateral outflow which can represent transmission losses. Applying the methodology for ephemeral streams requires some modifications of the assumptions. The ephemeral stream is considered as a long linear channel with a total movement time of the hydrograph that consists of two parts, the translation time and the redistribution time. The translation time is the travel time of the flood between two stations, while the redistribution time is the lag time that is estimated from the conventional Muskingum method due to the reshaping of the hydrograph. Eight runoff events have been studied for the model parameter estimation phase, while another four events are considered for the verification phase. A spreadsheet model has been developed to implement the methodology and checked with data from the published literature. The method shows very good results; the average RMSE value is 3 m3/s for the hydrographs of the reconstructed events. The correlation coefficients between observed and calculated parameters show relatively high correlation, i.e., 0.99 for the peak discharges, 0.95 for the time lags, and 0.99 for coefficient of transmission losses (the α-parameter). The maximum transmission loss is estimated to be 84 % of the inflow hydrograph form the studied events. A Monte Carlo approach is followed for the verification of the model. It has been found out that the following set of combination of the routing parameter, i.e., a lower limit of d 1, an upper limit of d 2 and a lower limit of d 3 produces satisfactorily good results for the four independent events. Results indicate that the three-parameter Muskingum method exhibits good results in simulating flash flood routing in arid environments.

Simulation of flash floods in ungauged basins using post‐event surveys and numerical modelling

AbstractThe hydrological and hydraulic processes of the flash flood that affected Atrani village (Amalfi Coast of Southern Italy) on 9 September 2010 are analysed using post‐flood surveys and numerical modelling. Observations in this case are particularly challenging because of the small spatial and temporal scales of the event, relative to the sampling characteristics of conventional rain and discharge measurement networks. Given these aspects, some of the relevant characteristics of the event were deduced by analysing field data that were collected immediately after the event. A width function instantaneous unit hydrograph (WFIUH) rainfall‐runoff model is applied to derive a flow hydrograph for the area immediately upstream of the village. This hydrograph is inputted as an upstream boundary condition for the simulation of the propagation processes. A two‐dimensional innovative hydraulic model is used to reproduce the flood within the village. The use of an unstructured mesh enabled a detailed simulation of water flow within the narrow and winding alleyways of the village. The results of the simulations indicate agreement with the field observations. The implemented approach is suitable for simulating flash floods in similar contexts and, therefore, may be used to predict potential future events.

An All-Season Flash Flood Forecasting System for Real-Time Operations

Flash floods can cause extensive damage to both life and property, especially because they are difficult to predict. Flash flood prediction requires high-resolution meteorological observations and predictions, as well as calibrated hydrological models, which should effectively simulate how a catchment filters rainfall inputs into streamflow. Furthermore, because of the requirement of both hydrological and meteorological components in flash flood forecasting systems, there must be extensive data handling capabilities built in to force the hydrological model with a variety of available hydrometeorological data and predictions, as well as to test the model with hydrological observations. The authors have developed a working prototype of such a system, called KINEROS/hsB-SM, after the hydrological models that are used: the Kinematic Erosion and Runoff (KINEROS) and hillslope-storage Boussinesq Soil Moisture (hsB-SM) models. KINEROS is an event-based overland flow and channel routing model that is designed to simulate flash floods in semiarid regions where infiltration excess overland flow dominates, while hsB-SM is a continuous subsurface flow model, whose model physics are applicable in humid regions where saturation excess overland flow is most important. In addition, KINEROS/hsB-SM includes an energy balance snowmelt model, which gives it the ability to simulate flash floods that involve rain on snow. There are also extensive algorithms to incorporate high-resolution hydrometeorological data, including stage III radar data (5 min, 1° by 1 km), to assist in the calibration of the models, and to run the model in real time. The model is currently being used in an experimental fashion at the National Weather Service Binghamton, New York, Weather Forecast Office.