Short-term Flood Research Articles

Research on particle swarm optimization in LSTM neural networks for rainfall-runoff simulation

Flood forecasting is an essential non-engineering measure for flood prevention and disaster reduction. Many models have been developed to study the complex and highly random rainfall-runoff process. In recent years, artificial intelligence methods, such as the artificial neural network (ANN), have attempted to construct rainfall-runoff models. The more advanced deep learning methods of long short-term memory (LSTM) network have been proved to better predict hydrological time series. However, the selection of LSTM hyperparameters in the past mostly relied on the experience of the staff, which often led to failure to achieve the best performance. The aim of this study is to develop a method to improve flood forecast accuracy and lead time. A deep learning neural network model based on LSTM networks and particle swarm optimization (PSO) is proposed in this paper. The PSO algorithm was used to optimize the LSTM hyperparameter to improve the ability to learn data sequence features. The model focuses on the Jingle Watershed in the Fenhe River and the Lushi Watershed in the Luohe River and was used to predict flood processes using rainfall and runoff observation data from stations in the watersheds. We evaluated the performance of the model with the Nash Sutcliffe efficiency coefficient, root mean square error, and bias. The results show that the PSO-LSTM model outperforms the M-EIES, ANN, PSO-ANN, and LSTM at all stations in the watersheds. The PSO-LSTM model improves the flood forecasting accuracy at different lead times, especially for those exceeding 6 h, and has higher prediction accuracy and stability. The PSO-LSTM model could be used to improve accuracy in short-term flood forecast applications.

Short-term flood probability density forecasting using a conceptual hydrological model with machine learning techniques

Making accurate and reliable probability density forecasts of flood processes is fundamentally challenging for machine learning techniques, especially when prediction targets are outside the range of training data. Conceptual hydrological models can reduce rainfall-runoff modelling errors with efficient quasi-physical mechanisms. The Monotone Composite Quantile Regression Neural Network (MCQRNN) is used for the first time to make probability density forecasts of flood processes and serves as a benchmark model, whereas it confronts the drawbacks of overfitting and biased-prediction. Here we propose an integrated model (i.e. XAJ-MCQRNN) that incorporates Xinanjiang conceptual model (XAJ) and MCQRNN to overcome the phenomena of error propagation and accumulation encountered in multi-step-ahead flood probability density forecasts. We consider flood forecasts as a function of rainfall factors and runoff data. The models are evaluated by long-term (2009–2015) 3-hour streamflow series of the Jianxi River catchment in China and rainfall products of the European Centre for Medium-Range Weather Forecasts. Results demonstrated that the proposed XAJ-MCQRNN model can not only outperform the MCQRNN model but also prominently enhance the accuracy and reliability of multi-step-ahead probability density forecasts of flood process. Regarding short-term forecasts in testing stages at four horizons, the XAJ-MCQRNN model achieved higher Nash-Sutcliffe Efficiency but lower Root Mean Square Error values, while improving Coverage Ratio and Relative Bandwidth values in comparison to the MCQRNN model. Consequently, the improvement can benefit the mitigation of the impacts associated with uncertainties of extreme flood and rainfall events as well as promote the accuracy and reliability of flood forecasting and early warning.

Short-Term Flood Control and Long-Term Energy Maximization in Multi-reservoir Systems Using Improved Particle Swarm Optimization

Short-Term Flood Control and Long-Term Energy Maximization in Multi-reservoir Systems Using Improved Particle Swarm Optimization

A Submonthly Surface Water Classification Framework via Gap-Fill Imputation and Random Forest Classifiers of Landsat Imagery

Global surface water classification layers, such as the European Joint Research Centre’s (JRC) Monthly Water History dataset, provide a starting point for accurate and large scale analyses of trends in waterbody extents. On the local scale, there is an opportunity to increase the accuracy and temporal frequency of these surface water maps by using locally trained classifiers and gap-filling missing values via imputation in all available satellite images. We developed the Surface Water IMputation (SWIM) classification framework using R and the Google Earth Engine computing platform to improve water classification compared to the JRC study. The novel contributions of the SWIM classification framework include (1) a cluster-based algorithm to improve classification sensitivity to a variety of surface water conditions and produce approximately unbiased estimation of surface water area, (2) a method to gap-fill every available Landsat image for a region of interest to generate submonthly classifications at the highest possible temporal frequency, (3) an outlier detection method for identifying images that contain classification errors due to failures in cloud masking. Validation and several case studies demonstrate the SWIM classification framework outperforms the JRC dataset in spatiotemporal analyses of small waterbody dynamics with previously unattainable sensitivity and temporal frequency. Most importantly, this study shows that reliable surface water classifications can be obtained for all pixels in every available Landsat image, even those containing cloud cover, after performing gap-fill imputation. By using this technique, the SWIM framework supports monitoring water extent on a submonthly basis, which is especially applicable to assessing the impact of short-term flood and drought events. Additionally, our results contribute to addressing the challenges of training machine learning classifiers with biased ground truth data and identifying images that contain regions of anomalous classification errors.

Development of Short-term Flood Forecast Using ARIMA

The aim of this study is to use the Box-Jenkins method to build a flood forecast model by analysing real-time flood parameters for Pengkalan Rama, Melaka river, hereafter known as Sungai Melaka. The time series was tested for stationarity using the Augmented Dickey-Fuller (ADF) and differencing method to render a non-stationary time series stationary from 1 July 2020 at 12:00am to 30th July 2020. A utocorrelation (ACF) and partial autocorrelation (PACF) functions was measured and observed using visual observation to identify the suitable model for water level time series. The parameter Akaike Information Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were used to find the best ARIMA model (BIC). ARIMA (2, 1, 3) was the best ARIMA model for the Pengkalan Rama, with an AIC of 5653.7004 and a BIC of 5695.209. The ARIMA (2, 1, 3) model was used to produce a lead forecast of up to 7 hours for the time series. The model's accuracy was tested by comparing the original and forecast sequences by using Pearson r and R squared. The ARIMA model appears to be adequate for Sungai Melaka, according to the findings of this study. Finally, the ARIMA model provides an appropriate short-term water level forecast with a lead forecast of up to 7 hours. As a result, the ARIMA model is undeniably ideal for river flooding.

Comparative study of very short-term flood forecasting using physics-based numerical model and data-driven prediction model

Reliable hourly flood forecasting using weather radar rainfall data for early warning system is essential for reducing natural disaster risk during extreme typhoon events. This study proposed a novel approach integrated with physics-based WASH123D and HEC-HMS models to forecast 1 h ahead flood level in the Fengshan Creek basin, northern Taiwan. The comparison was done with data-driven support vector machine (SVM) model, and performances were assessed by using statistical indicators (root mean square error, correlation coefficient, the error of time to peak flood level, the error of peak flood). Four typhoons and two plum rain events (with 620 data sets) were selected for the process of model calibration and validation. The model performs better when it used quantitative precipitation estimate radar data rather than rain gauge data. Results of using 1 h ahead quantitative precipitation forecast (QPF) as input for flood forecasting were encouraging but not feasible to use directly for early flood warning system due to errors in peak flood levels and timing. Therefore, the improvement in accuracy of 1 h ahead flood forecasting was done using physics-based approach and SVM model. The systematic comparison revealed that the SVM model is an attractive way out to improve the accuracy of QPF forecasted flood levels but unable to fully describe the flood level patterns in terms of timings and flood peaks, while the results obtained by the physics-based approach were accurate and much better than the SVM model. The approach fully described the physics of hydrograph patterns and outputs have exactly the same 1 h ahead predictions, in excellent agreement with observations. The reliable and accurate reflections of timing and amount of flood peaks in all selected typhoons by a newly developed physics-based approach with its operational nature are recommended to use by the government in the future for early warning to reduce the flood impacts during typhoon events.

Deep Learning with Long Short Term Memory Based Sequence-to-Sequence Model for Rainfall-Runoff Simulation

Accurate runoff prediction is one of the important tasks in various fields such as agriculture, hydrology, and environmental studies. Recently, with massive improvements of computational system and hardware, the deep learning-based approach has recently been applied for more accurate runoff prediction. In this study, the long short-term memory model with sequence-to-sequence structure was applied for hourly runoff predictions from 2015 to 2019 in the Russian River basin, California, USA. The proposed model was used to predict hourly runoff with lead time of 1–6 h using runoff data observed at upstream stations. The model was evaluated in terms of event-based performance using the statistical metrics including root mean square error, Nash-Sutcliffe Efficiency, peak runoff error, and peak time error. The results show that proposed model outperforms support vector machine and conventional long short-term memory models. In addition, the model has the best predictive ability for runoff events, which means that it can be effective for developing short-term flood forecasting and warning systems. The results of this study demonstrate that the deep learning-based approach for hourly runoff forecasting has high predictive power and sequence-to-sequence structure is effective method to improve the prediction results.

FLOOD EVENTS AS GEOMORPHIC THRESHOLDS FOR CHANNEL BED LEVEL CHANGE

Channel bed levels are highly dynamic in time and across space, constantly adjusting under the influence of natural and anthropogenic controls. The aim of this study was to assess the role of flood events recorded between 1994 and 2018 in the bed elevation changes documented on the major tributaries of Trotuș River (Eastern Carpathians). To this purpose, the data from fifteen gauging stations was used, based on which the evolution trends of channel bed levels were determined for the investigated channel reaches. The role of geomorphic threshold was assigned to stream power. The daily maximum and total values of this parameter were used to explain the bed elevation shifts. The average channel bed deepening rate was 2.2 cm/yr. Depending on the variation trend of channel bed levels, three evolution patterns could be distinguished in relation to the reference year: (i) a category where channel degradation was nearly continuous; (ii) another one where initial aggradation of the channel bed occurred, ensued by continuous degradation, without reverting to the initial state, and (iii) a group with oscillating aggradation-degradation evolution resulting in the eventual recovery of the initial state. The recovery time for changes triggered by major flood events ranged between one and nearly 10 years, whereas in the case of shifts generated by high frequency, low magnitude events, the recovery time was maximum one year. Over short term flood events drive the direction and intensity of channel bed elevation adjustments.

Flood forecasting using quantitative precipitation forecasts and hydrological modeling in the Sebeya catchment, Rwanda

AbstractThe absence of a viable flood early warning system for the Sebeya River catchment continues to impede government efforts towards improving community preparedness, the reduction of flood impacts and relief. This paper reports on a recent study that used satellite data, quantitative precipitation forecasts and the rainfall–runoff model for short-term flood forecasting in the Sebeya catchment. The global precipitation measurement product was used as a satellite rainfall product for model calibration and validation and forecasted European Centre Medium-Range Weather Forecasts (ECMWF) rainfall products were evaluated to forecast flood. Model performance was evaluated by the visual examination of simulated hydrographs, observed hydrographs and a number of performance indicators. The real-time flow forecast assessment was conducted with respect to three different flood warning threshold levels for a 3–24-h lead time. The result for a 3-h lead time showed 72% of hits, 7.5% of false alarms and 9.5% of missed forecasts. The number of hits decreased, as the lead time increased. This study did not consider the uncertainties in observed data, and this can influence the model performance. This work provides a base for future studies to establish a viable flood early warning system in the study area and beyond.

Towards an ensemble-based short-term flood forecasting using an event-based flood model- incorporating catchment-average estimates of soil moisture

Extreme floods pose a threat to life and property and accurate and reliable flood forecasts are required to mitigate the consequences of these events. In this paper, we propose a novel Monte-Carlo based framework that utilizes an ensemble-based short-term flood forecasting model. An event-based rainfall-runoff model is selected due to its simplicity and wide use by industry practitioners. However, a challenge with event models is that they are unable to account for the initial condition of the catchment wetness at the commencement of a particular rainfall event. To address this issue, we used independent catchment-average estimates of soil moisture to estimate catchment losses at the commencement and over the duration of the event. The adopted framework enables uncertainty in catchment losses and rainfall depth and patterns to be quantified and incorporated in forecasts. The accuracy and reliability of stochastic components of this framework are evaluated using ensemble verification measures. Later, these measures are used to evaluate four scenarios based on different combinations of deterministic and stochastic conditions. In the stochastic scenarios, the uncertainty of the flood forecasts is reliably quantified (88% reliability on average). The results show that operationally available estimates of catchment wetness can be used to account for uncertainty from event-based loss parameters, and that the uncertainty from average forecast rainfall depth is considerably more important than the uncertainty from forecast losses and the spatio-temporal patterns of the rainfall. This study has shown that event-based models have the potential to be applied within a probabilistic framework to operationally generate probabilistic flood forecasts.

Study on the sensitive ecological flow of mountainous river under the action of the weir group

Mountainous rivers are the key area of weir-building for water and hydroenergy storage with abundant energy resources. In view of the intensive construction of river weirs in the mountainous areas of Zhejiang Province, with the aim of meeting fish migration demand and maintaining the longitudinal continuous flow of rivers, this paper put forward the idea and method of determining sensitive ecological flow. Taking the typical section of Majinxi, a tributary of the Qiantang River as an example, the sensitive ecological flow based on the suitable flow rate of fish and the submerged flow of the weirs were calculated by hydrodynamic method. On the basis of the analysis of the hydrologic characteristics of the studied river reach, the criteria for determining effective flood pulses was put forward, and the assessment of ecological flow satisfaction was carried out. The research showed that in order to eliminate the barrier effect brought by the weir group, the short-term flood peak flow was large and low satisfied. It was proposed that fish paths or ladders should be added to the dam, or the form and density of the dam could be optimized to reduce the demand for sensitive flow.

Accuracy assessment of heavy rainfall forecasting in the Western Caucasus with ICON-Eu regional atmospheric model for short-term flood forecasting

Heavy rainfalls and rain-induced floods regularly cause substantial damage in the Western Caucasus region. In this study, we assess the accuracy of heavy rainfall forecasting with ICON-Eu regional atmospheric model (developed by the weather service of Germany) and the possibility of its use as input data for rain flood prediction. The main conclusion is that the forecast accuracy is strongly determined by the nature of the rainfall in question (mainly convective or triggered by synoptic-scale processes) and the season of the year. The ICON-Eu model systematically underestimates (by 2-3 times) the precipitation amount in the warm season (April-September) and almost never reproduces local convective heavy rainfall events. Therefore, its forecasts for short-term prediction of summertime rain floods have low efficiency. On the other hand, in the cold season (October-March) the model adequately reproduces heavy precipitation events, with some underestimation of the maximum precipitation amount and overestimation of the coverage area. These forecasts can be used to improve the short-term prediction of flash floods on the rivers of the Black Sea coast of the Caucasus during the period from October to March. In addition, we have performed a detailed analysis of the heavy rainfall and a related flood event which occurred on August 17-18, 2019 by comparing hourly observed precipitation with ICON-Eu and Cosmo-Ru forecasts, as well as applying WRF simulation and rainfall-runoff modelling.

Spatio-temporal analysis of flood data from South Carolina

To investigate the relationship between flood gage height and precipitation in South Carolina from 2012 to 2016, we built a conditional autoregressive (CAR) model using a Bayesian hierarchical framework. This approach allows the modelling of the main spatio-temporal properties of water height dynamics over multiple locations, accounting for the effect of river network, geomorphology, and forcing rainfall. In this respect, a proximity matrix based on watershed information was used to capture the spatial structure of gage height measurements in and around South Carolina. The temporal structure was handled by a first-order autoregressive term in the model. Several covariates, including the elevation of the sites and effects of seasonality, were examined, along with daily rainfall amount. A non-normal error structure was used to account for the heavy-tailed distribution of maximum gage heights. The proposed model captured some key features of the flood process such as seasonality and a stronger association between precipitation and flooding during summer season. The model is able to forecast short term flood gage height which is crucial for informed emergency decision. As a byproduct, we also developed a Python library to retrieve and handle environmental data provided by some main agencies in the United States. This library can be of general usefulness for studies requiring rainfall, flow, and geomorphological information over specific areas of the conterminous US.

Modeling of the Summer 2019 Disastrous Floods on the Iya River (Irkutsk Oblast)

This paper discusses the results from investigating the factors that led to the formation of a catastrophic flood in June 2019 on the Iya river in the city of Tulun, Irkutsk oblast, as well as from calculating and assessing the characteristics of these floods. An analysis of the meteorological environments of flood formation is performed by using the ICON global weather forecast model. The trends in hydrometeorological characteristics for the period 1966–2019 are analyzed. The study revealed an increase in the June precipitation amount by 36–61% (31–46 mm), and negative trends in the maximum water discharges making up 24–37% from May to September. Using data on the water level and historical Q(H) curves, the maximum daily water discharge on June 29, 2019 was estimated at 6800 ± 700 m3/s. A schematization of the Iya river basin is carried out, the parameters are systematized and the distributed Hydrograph hydrological model is verified. For three subbasins within the Iya river basin, the median value of the Nash–Sutcliff criterion was 0.69 for the period 1970–1996. The flood characteristics are simulated and assessed using two sources of meteorological data. According to data from weather stations, the maximum daily discharge was 6570 m3s. This value falls within the range of estimates of observed values but its formation is delayed by 1 day. According to the simulation based on the ICON model data, the maximum 3-hour discharge reached 5260 m3/s, or at least 840 m3/s lower than the observed value; however, the simulated flood hydrograph coincides with observed one for the time intervals. The results of the study confirm a possibility for a short-term flood forecast using the method of deterministic hydrological modeling.

Development of a minimalist conceptual numerical model for flood forecasting and management under GIS environment

AbstractThe floods that Algeria has experienced in recent years are among the most significant natural disasters recorded by the country. These disasters, whose amplitude and frequency have tended to become increasingly irregular in space and time, in the current context of global climate change, encourage us to improve our flood management and forecasting strategies, notably through the re-evaluation of protection structure capacities, designed on the basis of hydrological data analyzed by statistical adjustment of past rainfall hazards. The objective of this study is to develop a minimalist conceptual numerical model for flood forecasting and management under GIS environment for the north-east region of Algeria. This model was developed by analyzing hydrographic data that can be adapted to climate data collected in real time, to predict short-term flood hydrographs in all segments of the hydrographic network, based on the Sokolovsky model for construction of synthetic hydrographs, combined with the Horton architecture for basin discretization. We obtained accuracy on past rainfall hazard simulations around 65.2% for peak flow amplitudes and 88.3% for surface runoff base times. This low-cost simple model opens the way to more possibilities in flood management, and can be improved through better spatialization and calibration with more field data.

Operationalizing a flood forecasting decision support system for Ayeyarwady river, Myanmar

ABSTRACT Myanmar is one of the developing countries of South East Asia (SEA), wherein millions of people are exposed to risk of flooding every year. The Department of Meteorology and Hydrology (DMH), Myanmar is thus, bestowed with the responsibility of flood forecasting within the country for early warning. This study presents a new web-based Decision Support System (DSS), for DMH’s use in operational flood forecasting. The new DSS, that has a forecast lead time of 24–72 h, is a significant improvement over the existing forecasting systems used by DMH, which had a lead time of 4–24 h. Moreover, the new DSS couples Hydrological Engineering Centre Hydrological Modelling System (HEC-HMS) with Weather and Research (WRF) rainfall forecast for Ayeyarwady river basin (ARB), the principle river basin of the country. In addition to the hydro-meteorological coupling, core modules of the DSS include rainfall forecasting, bias correction and flow error correction. The hydrological model setup of the DSS was calibrated and validated for the periods 2008–2012 and 2013–2014 with observed rainfall. The DSS was then actively deployed and tested operationally by DMH using bias-corrected WRF rainfall forecasts for the period 2015–2017. Performance of the system is very good in forecasting flows and floods within ARB, and also provides additional lead time to DMH for issuing the flood advisories. Hence, the novel web-based flood forecasting DSS, is an important applied research contribution, that is being successfully used by the operational staff of DMH for short-term flood forecasting in Myanmar and has the potential to improve flood resiliency of similar agencies in the SEA region.

Flood Prediction Using Rainfall-Flow Pattern in Data-Sparse Watersheds

Real-time flood forecasting of small- and medium-sized rivers in areas with scarce hydrological data is an urgent problem that needs to be solved. Traditional hydrological model parameters cannot be fully trained owing to a lack of data; thus, results obtained by such models are not satisfactory. We need a new way to solve the forecasting problem for small- and medium-sized rivers. We found that the time series of some feature variables have evident change trajectories in spatial dimension, and the change of some feature variables in the spatial dimension has a decisive influence on flooding processes, such as the spatial distribution of rainfall. To reflect the change of feature variables in spatial dimension with to solve the problem of the lack of hydrological data, we constructed a rainfall-flow pattern composed of a spatial-temporal dynamic time warping algorithm and multi-feature algorithm to measure the similarity of hydrological time series. In the experimental watersheds, we used rainfall-flow patterns to forecast the short-term flood streamflow, and satisfactory results were obtained. This suggests that the algorithm is suitable for hydrological studies and improves the accuracy of real-time flood forecasting for longer forecast periods.

New Algorithm for Rain Cell Identification and Tracking in Rainfall Event Analysis

This study proposes a new algorithm termed rain cell identification and tracking (RCIT) to identify and track rain cells from high resolution weather radar data. Previous algorithms have limitations when tracking non-consequent rain cells owing to their use of maximum correlation coefficient methods and their lack of an alternative way to handle the variation stages of rain cells during their life cycles. To address these deficiencies, various methods are implemented in the new algorithm. These include the particle image velocimetry (PIV) method for motion estimation and the rain cell matching rule to obtain the stage changes of rain cells. High resolution (5 min and 1 km) radar data from three rainy days over the German federal state North Rhine Westphalia (NRW) are used in this study. The performance of the identification module for the new algorithm is accessed by two object-oriented verification methods: structure–amplitude–location (SAL) and geometric index, while the performance of the tracking module is compared with TREC and SCOUT tracking algorithms and evaluated by the contingency table verification approach. Results suggest that the performance of the new algorithm is better than reference tracking method. Application of the RCIT algorithm to the selected cases shows that the inner structure of rainfall events in the experimental region present extreme value distributions, with most rainfall events having a short duration with less intensity. The new algorithm can effectively capture the stage changes of rain cells during their life cycles. The proposed algorithm can serve as the basis for further hydro-meteorological applications such as spatial and temporal analysis of rainfall events and short-term flood forecasting.

Risks Analysis and Response of Forecast-Based Operation for Ankang Reservoir Flood Control

With the improvement of short-term flood forecasting and short-term rainfall forecast accuracy, as well as the advance of hydrological and meteorological information collection and collation methods, the reservoir flood regulation method taking rainfall or inflow forecast into consideration is gaining more and more attention. As the index of Forecast-Based Operation (FBO), the forecasted factor plays an important part in determining success or failure of FBO due to its uncertainty and accuracy. In this study, possible risk sources were analyzed considering the process and the characteristics of reservoir flood regulation firstly, and the uncertainty of the forecast information and the FBO risks were discussed based on hypothesis testing. Then, combined with the case study of applying FBO on Ankang Reservoir, in which the forecasted net rainfall was selected as the index of the FBO rules, the probability distribution of the forecasted net rainfall errors was derived as the basis of risk analysis. Finally, FBO risk analysis was conducted based on Monte Carlo method for several real flood processes, while a simulation was also carried out with the Conventional Operation (CO) for contrast. The results indicate that the maximum risk was reduced more than half when FBO was adopted. Consequently, the possible remedial measures were put forward in the case of invalid forecast happened based on simulation and the analysis of the principle of flood regulation. The conclusions and methods in this research provide ideas for real-time flood regulation and risk management of reservoirs.

Adaptive Blending Method of Radar-Based and Numerical Weather Prediction QPFs for Urban Flood Forecasting

Preparing proper disaster prevention measures is important for decreasing the casualties and property losses resulting from floods. One of the most efficient measures in this regard is real-time flood forecasting using quantitative precipitation forecasts (QPFs) based on either short-term radar-based extrapolation or longer-term numerical weather prediction. As both methods have individual advantages and limitations, in this study we developed a new real-time blending technique to improve the accuracy of rainfall forecasts for hydrological applications. We tested the hydrological applicability of six QPFs used for urban flood forecasting in Seoul, South Korea: the McGill Algorithm for Prediction Nowcasting by Lagrangian Extrapolation (MAPLE), KOrea NOwcasting System (KONOS), Spatial-scale Decomposition method (SCDM), Unified Model Local Data Assimilation and Prediction System (UM LDAPS), and Advanced Storm-scale Analysis and Prediction System (ASAPS), as well as our proposed blended approach based on the assumption that the error of the previously predicted rainfall is similar to that of current predicted rainfall. We used the harmony search algorithm to optimize real-time weights that would minimize the root mean square error between predicted and observed rainfall for a 1 h lead time at 10 min intervals. We tested these models using the Storm Water Management Model (SWMM) and Grid-based Inundation Analysis Model (GIAM) to estimate urban flood discharge and inundation using rainfall from the QPFs as input. Although the blended QPF did not always have the highest correlation coefficient, its accuracy varied less than that of the other QPFs. In addition, its simulated water depth in pipe and spatial extent were most similar to observed inundated areas, demonstrating the value of this new approach for short-term flood forecasting.