Flood Simulation Model Research Articles

A tight coupling model for urban flood simulation based on SWMM and TELEMAC-2D and the uncertainty analysis

The urban flood of rainstorm has posed a major threat to human life and property in recent years, seriously affecting the sustainable development of society. The numerical model can simulate how an urban flood develops and moves, and then guide how to reduce the disaster's impact. This study develops a tight coupling model named STUFMS based on Storm Water Management Model (SWMM) and TELEMAC-2D for urban flood simulation. Theoretical and practical cases were respectively applied to verify the reasonability and accuracy of the model. The theoretical case demonstrates that the STUFMS can better simulate the process of water exchange between the surface and rainwater drainage systems while the practical case suggests that the change in water level and the flooded area simulated by the STUFMS are basically consistent with the typical historical rainfall events. The uncertainty analysis revealed that terrain resolution and temporal resolution of rainfall series have a significant impact on the inundation scopes. Generally, the STUFMS model behaved well in simulating the urban flood process, offering a novel tool for numerical modeling of urban flood.

A direct two-way coupling of the hydrologic and 1D-2D hydrodynamic models for watershed flood simulation

A direct two-way coupling of hydrologic and 1D-2D hydrodynamic models (DCM2D) for watershed flood simulation was proposed. This coupling included three models (i.e., fully distributed hydrologic, 1D hydrodynamic, and 2D hydrodynamic models) and three coupling strategies: the bidirectionally coupled hydrologic-2D hydrodynamic module, two-way coupled hydrologic-1D hydrodynamic module (HH1D), and two-way coupled 1D-2D hydrodynamic module (HH2D). The refined river links (RLs) were used to couple the hydrologic and 1D hydrodynamic models, and the exchange discharge between these models was determined using either the weir flow equations or hydrologic model, depending on the flow transitions. Four cases were used to evaluate the proposed DCM2D. The results indicated that the discharge obtained from the proposed HH1D exhibited a high level of consistency with analytical solutions, with NSE of 0.986. This finding supported the validity of the proposed coupling schemes for hydrologic and 1D hydrodynamic models, particularly in scenarios where there were no distinct flow transitions. The DCM2D accurately represented the physical flood process by allowing rainfall runoff discharge into both 2D low-lying inundation regions and 1D rivers. This contrasted with the indirect coupling models (ICM2D), which required runoff to enter the 1D rivers before flowing into the 2D inundation regions. It exhibited reasonable flow directions and achieved satisfactory simulation accuracy, with higher NSE compared to the ICM2D. The proposed DCM2D model can effectively adapt to the real-life flood evolution process and offer practical and reliable solutions for simulating floods in watersheds.

Multi-objective optimisation framework for Blue-Green Infrastructure placement using detailed flood model

Designing city-scale Blue-Green Infrastructure (BGI) for flood risk management requires detailed and robust methods. This is due to the complex interaction of flow pathways and the need to assess cost-benefit trade-offs for various BGI options. This study aims to find a cost-effective BGI placement scheme by developing an improved approach called the Cost OptimisatioN Framework for Implementing blue-Green infrastructURE (CONFIGURE). The optimisation framework integrates a detailed hydrodynamic flood simulation model with a multi-objective optimisation algorithm (Non-dominated Sorting Genetic Algorithm II). The use of a high-resolution flood simulation model ensures the explicit representation of BGI and other land use features to simulate flow pathways and surface flood risk accurately, while the optimisation algorithm guarantees achieving the best cost-benefit trade-offs for given BGI options. The current study uses the advanced CityCAT hydrodynamic flood model to evaluate the efficiency of the optimisation framework and the impact of location and size of permeable interventions on the optimisation process and subsequent cost-benefit trade-offs. This is achieved by dividing permeable surface areas into intervention zones of varying size and quantity. Furthermore, rainstorm events with 100-year and 30-year return periods are analysed to identify any common optimal solutions for different rainfall intensities. Depending on the number of intervention locations, the automated framework reliably achieves optimal BGI implementation solutions in a fraction of the time required to find the best solutions by trialling all possible options. Designing and optimising interventions with smaller sizes but many permeable zones save a good fraction of investment. However, such a design scheme requires more computational time to find optimal options. Furthermore, the optimal spatial configuration of BGI varies with different rainstorm severities, suggesting a need for careful selection of the rainstorm return period. Based on the results, CONFIGURE shows promise in devising sustainable urban flood risk management designs.

Spatial and temporal scaling of extreme rainfall in the United Kingdom

AbstractExtreme rainfall estimates for ungauged areas contribute to improved resilience to flooding. This study applies a sub‐daily rainfall scaling method to annual daily maximum rainfall series from 102 UK weather stations. We analyse resultant parameters by season, homogenous rainfall region, urban area and geographic factors to investigate how scaling varies temporally and spatially. Dummy regression models are built using these variables to predict the sub‐daily scaling parameter for any location in the United Kingdom. Estimated rainfall intensities are validated with observations and yield Mean Absolute Errors of 3.0, 1.9 and 0.9 mm/h for 1‐, 2‐ and 6‐h events, respectively. We also demonstrate intensity‐duration‐frequency curves at a site in Oxfordshire for scaled and observed data and find that 1‐ to 6‐h, 20‐year rainfall intensities are estimated to be within 9.4%. With such unified scaling relationships, it is possible to derive extreme rainfall for specified durations and return periods at ungauged locations. According to our cross‐validation of estimated and observed intensities, more than 88% of sites fall within 10% error bounds. This method offers a means of generating design rainfall series as input to flood simulation models to evaluate pluvial flood risks in urban areas.

ANALISIS BANJIR SUNGAI MENGGUNAKAN MODEL HEC-RAS DI WILAYAH DAS TABANIO KABUPATEN TANAH LAUT PROVINSI KALIMANTAN SELATAN

This study aims to determine the amount of water runoff and water depth in the Tabanio Watershed, Tanah Laut Regency. The study was conducted using the HEC-RAS software to generate a flood simulation model. Data analysis included Digital Elevation Model for National Scale (DEMNAS) data, flood discharge data, rainfall data, river bathymetry, and tidal data. DEMNAS data was obtained from the website of the National Geospatial Information Agency (BIG), rainfall data was obtained from the Center for Hydrometeorology and Remote Sensing (CHRS) website. Rainfall data analysis involves selecting the maximum daily rainfall value to determine the flood discharge. The hourly flood discharge is calculated using the Synthetic Unit Hydrograph method with the Nakayasu approach. River bathymetry data is processed as additional data for DEM, with interval recording done every 0.2 seconds. Tide data was obtained through prediction based on the location constant closest to the research area and analyzed using the Pasut.exe software for a period of 6 days. Based on the results of flood analysis research using the HEC-RAS model in the Tabanio Watershed, Tanah Laut Regency, South Kalimantan Province, it can be concluded that the model results show the water depth and area of water runoff at maximum discharge. as follows: less than 0.5 meters with a very low category covering an area of 4,168.86 hectares, 0.51 to 1.5 meters with a low category covering an area of 6,417.79 hectares, 1.51 to 2.5 meters with a medium category covering an area of 1,987, 25 hectares, 2.51 to 3.5 meters with a high category of 741.47 hectares, and above 3.51 meters with a very high category of 113.69 hectares. So that the total water runoff reaches 13,429.05 hectares.

Physics-informed neural networks as surrogate models of hydrodynamic simulators

In response to growing concerns surrounding the relationship between climate change and escalating flood risk, there is an increasing urgency to develop precise and rapid flood prediction models. Although high-resolution flood simulations have made notable advancements, they remain computationally expensive, underscoring the need for efficient machine learning surrogate models. As a result of sparse empirical observation and expensive data collection, there is a growing need for the models to perform effectively in ‘small-data’ contexts, a characteristic typical of many scientific problems. This research combines the latest developments in surrogate modelling and physics-informed machine learning to propose a novel Physics-Informed Neural Network-based surrogate model for hydrodynamic simulators governed by Shallow Water Equations. The proposed method incorporates physics-based prior information into the neural network structure by encoding the conservation of mass into the model without relying on calculating continuous derivatives in the loss function. The method is demonstrated for a high-resolution inland flood simulation model and a large-scale regional tidal model. The proposed method outperforms the existing state-of-the-art data-driven approaches by up to 25 %. This research demonstrates the benefits and robustness of physics-informed approaches in surrogate modelling for flood and hydroclimatic modelling problems.

Spatial diversion and coordination of flood water for an urban flood control project in Suzhou, China

Suzhou City, located in the Yangtze River Delta in China, is prone to flooding due to a complex combination of natural factors, including its monsoon climate, low elevation, and tidally influenced position, as well as intensive human activities. The Large Encirclement Flood Control Project (LEFCP) was launched to cope with serious floods in the urban area. This project changed the spatiotemporal pattern of flood processes and caused spatial diversion of floods from the urban area to the outskirts of the city. Therefore, this study developed a distributed flood simulation model in order to understand this transition of flood processes. The results revealed that the LEFCP effectively protected the urban areas from floods, but the present scheduling schemes resulted in the spatial diversion of floods to the outskirts of the city. With rainstorm frequencies of 10.0% to 0.5%, the water level differences between two representative water level stations (Miduqiao (MDQ) and Fengqiao (FQ)) located inside and outside the LEFCP area, ranged from 0.75 m to 0.24 m and from 1.80 m to 1.58 m, respectively. In addition, the flood safety margin at MDQ and the duration with the water level exceeding the warning water level at FQ ranged from 0.95 m to 0.43 m and from 4 h to 22 h, respectively. Rational scheduling schemes for the hydraulic facilities of the LEFCP in extreme precipitation cases were developed according to flood simulations under seven scheduling scenarios. This helps to regulate the spatial flood diversion caused by the LEFCP during extreme precipitation.

STFS-urban: Spatio-temporal flood simulation model for urban areas

Amidst escalating urbanization and increasing extreme climatic events, strengthening flood resilience strategies in global cities has become imperative. This study introduces an innovative spatiotemporal urban flood simulation model that seamlessly integrates diverse refined and multi-spatiotemporal scales, ranging from 7.5 to 60 min and 100–2000 m, respectively. The model comprises multi-scale radar rainfall inversion (MRI), fine-grained coupled flood simulation model (FGCFS), and transformer-CNN flood prediction (TCFP) modules. Employing the Nanjing urban area as a case study, the model's efficacy is subjected to rigorous assessment. The advantages derived from integrated refinement coupling and boundary conditions through FGCFS and TCFP are accentuated. Impressively, the results underscore the robust performance of radar rainfall inversion across most scales, revealing a correlation coefficient surpassing 0.8 and a root-mean-square error of under 5.2 mm. FGCFS achieves optimal simulated water depth changes at 7.5 min × 500 m resolution, with the Nash efficiency coefficient exceeding 0.69 (0.94 at YS observation point and 0.89 at SXM observation point), alongside percentage deviations below 12.89 (3.59 at SXM observation point and 2.42 at XJL observation point). TCFP's learning proficiency is showcased through error convergence to 0.002 m after twenty iterations, particularly suitable for resolutions below 4 m. Notably, both FGCFS and TCFP demonstrate efficient utilization of resources, enabling streamlined simulations across varying data resolutions. Consequently, our study propels a sophisticated framework harmonizing multi-scale data integration, refinement coupling, and dynamic allocation. Our work extends beyond practical solutions, offering a glimpse into the future of flood simulation modeling, and reaffirming its pivotal role within the realm of environmental research and management.

TYPHOON JEBI-INDUCED FLOOD MODELING DUE TO WAVE OVERTOPPING/RUNUP AND REVERSE FLOW AT KANSAI AIRPORT

Typhoon-induced high waves and precipitations cause severe flood damage in low-lying coastal areas. Typhoon Jebi (2018) induced a compound flood at Kansai Airport by stormy waves, and seawater reversed flow through sewer systems. However, the cause of damage at Kansai Airport has been presented incompletely in various studies that analyzed individual flood factors and simply summed each result. A valid method for simulating the compound flood has not yet been established due to insufficient discussions. It means that we might encounter serious difficulties in adequately addressing the potential compound flood risk in the future. Therefore, this study validates the applicability of the compound flood simulation model by reproducing the flooding process of Kansai Airport. A series of numerical experiments were conducted to reproduce Jebi-induced inland flood at Kansai Airport by using a coupled coastal flood model of the storm surge, high wave, wave overtopping/runup, precipitation, and sewer backflow (Jo et al., 2021).

An urban hydrological model for flood simulation in piedmont cities: Case study of Jinan City, China

Urbanization has great potential to adversely affect the local hydrological cycle, as well as result in severe ecological and environmental problems. Piedmont cities have a special topography and complicated overland flow processes, thus creating challenges for the simulation of urban flooding/waterlogging processes. The TVGM-USWM model, a new distributed urban hydrological model that considers the flow routing in urban drainage systems and complicated overland flow processes, was developed in this study. We applied this model to the Huangtaiqiao drainage basin of Jinan City, China and set up two simulation scenarios. The spatial heterogeneity of the proportion of directly connected impervious area (DCIA) and disconnected impervious area (DIA) in sub-catchments was considered in scenario B, but not in scenario A. We conducted sensitivity analysis on model parameters by using the Sobol method. Results showed that: (1) model parameters have a certain degree of uncertainty, and the parameter characterizing the proportion of the DCIA and DIA showed a relatively high sensitivity under rainstorms of different levels and different accuracy assessment standards; (2) The simulation of scenario A was poor (with a mean Nash coefficient of 0.59), which ignored the different flow paths followed by runoff from DCIA and DIA; and (3) Considering the spatial differences in the overland flow process, the prediction accuracy of scenario B significantly increased (with a mean Nash coefficient of 0.82). This indicated the importance of the disconnected impervious area and the lateral water exchange across the impervious-pervious interface on the flood simulation in a piedmont city. Compared to most urban hydrological models, the TVGM-USWM model can represent complex rainfall-runoff mechanisms in urban areas with varied terrain. The findings of this study can provide scientific decision-making for the planning and construction of a sponge city in Jinan City.

Study of Flood Simulation in Small and Medium-Sized Basins Based on the Liuxihe Model

The uneven distribution of meteorological stations in small and medium-sized watersheds in China and the lack of measured hydrological data have led to difficulty in flood simulation and low accuracy in flood forecasting. Traditional hydrological models no longer achieve the forecasting accuracy needed for flood prevention. To improve the simulation accuracy of floods and maximize the use of hydrological information from small and medium-sized watersheds, high-precision hydrological models are needed as a support mechanism. This paper explores the applicability of the Liuxihe model for flood simulation in the Caojiang river basin and we compare flood simulation results of the Liuxihe model with a traditional hydrological model (Xinanjiang model). The results show that the Liuxihe model provides excellent simulation of field floods in Caojiang river basin. The average Nash–Sutcliffe coefficient is 0.73, the average correlation coefficient is 0.9, the average flood peak present error is 0.33, and the average peak simulation accuracy is 93.9%. Compared with the traditional flood hydrological model, the Liuxihe model simulates floods better with less measured hydrological information. In addition, we found that the particle swarm optimization (PSO) algorithm can improve the simulation of the model, and its practical application only needs one representative flood for parameter optimization, which is suitable for areas with little hydrological information. The study can support flood forecasting in the Caojiang river basin and provide a reference for the preparation of flood forecasting schemes in other small and medium-sized watersheds.

A comprehensive review of watershed flood simulation model

A comprehensive review of watershed flood simulation model

Development of an integrated socio-hydrological modeling framework for assessing the impacts of shelter location arrangement and human behaviors on flood evacuation processes

Abstract. In many flood-prone areas, it is essential for emergency responders to use advanced computer models to assess flood risk and develop informed flood evacuation plans. However, previous studies have had a limited understanding of how evacuation performance is affected by the arrangement of evacuation shelters (with respect to their number and geographical distribution) and human behaviors (with respect to the heterogeneity of household evacuation preparation times and route-searching strategies). In this study, we develop an integrated socio-hydrological modeling framework that couples (1) a hydrodynamic model for flood simulation, (2) an agent-based model for evacuation management policies and human behaviors, and (3) a transportation model for simulating household evacuation processes in a road network. We apply the model to the Xiong'an New Area and examine household evacuation outcomes for various shelter location plans and human behavior scenarios. The results show that household evacuation processes are significantly affected by the number and geographical distribution of evacuation shelters. Surprisingly, we find that establishing more shelters may not improve evacuation results if the shelters are not strategically located. We also find that low heterogeneity in evacuation preparation times can result in heavy traffic congestion and long evacuation clearance times. If each household selects their own shortest route without considering the effects of other evacuees' route choices, traffic congestion will likely occur, thereby reducing system-level evacuation performance. These results demonstrate the unique functionality of our model with respect to supporting flood risk assessment and advancing our understanding of how multiple management and behavioral factors jointly affect evacuation performance.

Flood Risk Assessment Using TELEMAC-2D Models Integrated with Multi-Index Analysis in Shenzhen River Basin, China

An urban flood simulation model based on TELEMAC-2D was constructed, and the inundation data of two measured rainstorms (7 June 2018 and 16 September 2018) were selected to validate the model. Flooding processes were simulated under 12 designed rainfall scenarios with rainfall return periods of 20, 50 and 100 years and rainfall peak coefficients of 0.2, 0.4, 0.6 and 0.8, respectively. The hazard-vulnerability (H-V) method was used for urban flood risk assessment. The selected hazard factors included inundation depth, flood velocity, elevation and slope. The vulnerability factors included land use type, population density and property distribution. The analytic hierarchy process (AHP) method was used to calculate the weight values of each indicator factor, and ArcGIS software was used for overlay calculation. The results of the analysis show that as the rainfall peak coefficient factor increases, the area of each risk zone increases to varying degrees. The larger the rainfall peak coefficient factor, the more serious the flooding. As the rainfall return period increases, the effect of the rainfall peak coefficient factor of the change in the area of the highest risk zone diminishes. The highest risk zone is the largest within Luohu District (LHD), accounting for 46.38%, 60.92% and 45.54% of the total highest risk area, respectively. As the return period increases, the area of the highest risk zone within Futian District (FTD) increases, but its proportion has a decreasing trend, and the proportion of the highest risk area within Longgang District (LGD) has an increasing trend. The risk zoning map can better reflect the risk distribution of the basin and provide a scientific basis for early warning of flood prevention and drainage in the Shenzhen River basin.

Highlighting the role of the model user and physical interpretation in urban flooding simulation

Abstract Technological evolution survey allowed the broad use of 2D mathematical models for flood simulation. However, it is possible that the answer required for a given problem does not need a 2D approximation or even does not configure a 2D surface solution. Urban flood simulations may fall in this second case, since urban structures may interact with flood flows and introduce discontinuities in the surface solution. This study aims to highlight the discussion about the physical interpretation and the modeller role as key elements in the interpretation and representation of physical systems. To support this proposal, the MODCEL, a Quasi-2D flow-cell model, was used in two different ways: in a detailed raster approach, similarly to usual 2D model uses; and in an alternative conceptual and interpretive way, using larger cells representing homogeneous portions of the territory. The modelling results showed that equivalent responses can be obtained. Although other models can be used and could offer different absolute results, the relative analysis offered sufficient support to the research hypothesis that a physical-based process conducted by a conscient modeller is crucial for model reliability and optimization.

Review of Urban Flood Resilience: Insights from Scientometric and Systematic Analysis.

In recent decades, climate change is exacerbating meteorological disasters around the world, causing more serious urban flood disaster losses. Many solutions in related research have been proposed to enhance urban adaptation to climate change, including urban flooding simulations, risk reduction and urban flood-resistance capacity. In this paper we provide a thorough review of urban flood-resilience using scientometric and systematic analysis. Using Cite Space and VOS viewer, we conducted a scientometric analysis to quantitively analyze related papers from the Web of Science Core Collection from 1999 to 2021 with urban flood resilience as the keyword. We systematically summarize the relationship of urban flood resilience, including co-citation analysis of keywords, authors, research institutions, countries, and research trends. The scientometric results show that four stages can be distinguished to indicate the evolution of different keywords in urban flood management from 1999, and urban flood resilience has become a research hotspot with a significant increase globally since 2015. The research methods and progress of urban flood resilience in these four related fields are systematically analyzed, including climate change, urban planning, urban system adaptation and urban flood-simulation models. Climate change has been of high interest in urban flood-resilience research. Urban planning and the adaptation of urban systems differ in terms of human involvement and local policies, while more dynamic factors need to be jointly described. Models are mostly evaluated with indicators, and comprehensive resilience studies based on traditional models are needed for multi-level and higher performance models. Consequently, more studies about urban flood resilience based on local policies and dynamics within global urban areas combined with fine simulation are needed in the future, improving the concept of resilience as applied to urban flood-risk-management and assessment.

Comparison of flood simulation capabilities of a hydrologic model and a machine learning model

AbstractMachine learning models have been widely used for flood simulation. Few studies have compared the flood simulation capabilities of machine learning models and hydrologic models. This study compared the flood simulation capabilities of the SIMHYD hydrologic model and the LSTM machine learning model in 232 basins with different climate conditions. The results show that although the LSTM model significantly outperforms the SIMHYD model in the calibration period, it has significant performance degradation in the validation period. Basin characteristics had limited impacts on the performance difference between the LSTM model and the SIMHYD model. The extension of the calibration period improves the performance of the LSTM model, while it has a limited impact on the performance of the SIMHYD model. Thus, machine learning models are recommended for simulating floods if enough training data are available, otherwise, hydrologic models could be a better choice. This study is helpful to the choice of flood simulation models in different situations.

Determination of building flood risk maps from LiDAR mobile mapping data

With increasing urbanization, flooding is a major challenge for many cities today. Based on forecast precipitation, topography, and pipe networks, flood simulations can provide early warnings for areas and buildings at risk of flooding. Basement windows, doors, and underground garage entrances are common places where floodwater can flow into a building. Some buildings have been prepared or designed considering the threat of flooding, but others have not. Therefore, knowing the heights of these facade openings helps to identify places that are more susceptible to water ingress. However, such data is not yet readily available in most cities. Traditional surveying of the desired targets may be used, but this is a very time-consuming and laborious process. Instead, mobile mapping using LiDAR (light detection and ranging) is an efficient tool to obtain a large amount of high-density 3D measurement data. To use this method, it is required to extract the desired facade openings from the data in a fully automatic manner.This research presents a new process for the extraction of windows and doors from LiDAR mobile mapping data. Deep learning object detection models are trained to identify these objects. Usually, this requires to provide large amounts of manual annotations. In this paper, we mitigate this problem by leveraging a rule-based method. In a first step, the rule-based method is used to generate pseudo-labels. A semi-supervised learning strategy is then applied with three different levels of supervision. The results show that using only automatically generated pseudo-labels, the learning-based model outperforms the rule-based approach by 14.6% in terms of F1-score. After five hours of human supervision, it is possible to improve the model by another 6.2%.By comparing the detected facade openings' heights with the predicted water levels from a flood simulation model, a map can be produced which assigns per-building flood risk levels. Thus, our research provides a new geographic information layer for fine-grained urban emergency response. This information can be combined with flood forecasting to provide a more targeted disaster prevention guide for the city's infrastructure and residential buildings. To the best of our knowledge, this work is the first attempt to achieve such a large scale, fine-grained building flood risk mapping.

Simulation of Karst Floods with a Hydrological Model Improved by Meteorological Model Coupling

Abstract Karst basins are prone to rapid flooding because of their geomorphic complexity and exposed karst landforms with low infiltration rates. Accordingly, simulating and forecasting floods in karst regions can provide important technical support for local flood control. The study area, the Liujiang karst river basin, is the most well-developed karst areas in South China, and its many mountainous areas lack rainfall gauges, limiting the availability of precipitation information. Quantitative precipitation forecast (QPF) from the Weather Research and Forecasting (WRF) Model and quantitative precipitation estimation (QPE) from the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) can offer reliable precipitation estimates. Here, the distributed Karst–Liuxihe (KL) model was successfully developed from the terrestrial Liuxihe model, as reflected in improvements to its underground structure and confluence algorithm. Compared with other karst distributed models, the KL model has a relatively simple structure and small modeling data requirements, which are advantageous for flood prediction in karst areas lacking hydrogeological data. Our flood process simulation results suggested that the KL model agrees well with observations and outperforms the Liuxihe model. The average Nash coefficient, correlation coefficient, and water balance coefficient increased by 0.24, 0.19, and 0.20, respectively, and the average flood process error, flood peak error, and peak time error decreased by 13%, 11%, and 2 h, respectively. Coupling the WRF Model and PERSIANN-CCS with the KL model yielded a good performance in karst flood simulation and prediction. Notably, coupling the WRF and KL models effectively predicted the karst flood processes and provided flood prediction results with a lead time of 96 h, which is important for flood warning and control. Significance Statement The WRF Model and PERSIANN-CCS can provide precipitation data for mountainous karst areas lacking rainfall gauges, and their rainfall results are forecasted effectively to reduce the uncertainty of input precipitation data. Then, the PERSIANN-CCS QPEs and WRF QPF are coupled with the improved KL model for karst flood simulation and prediction. This coupled model worked well in karst basins.

Towards Coupling of 1D and 2D Models for Flood Simulation—A Case Study of Nilwala River Basin, Sri Lanka

The Nilwala river basin is prone to frequent flooding during the southwest monsoon and second intermonsoon periods. Several studies have recommended coupling 1D and 2D models for flood modelling as they provide sufficient descriptive information of floodplains with greater computational efficiency. This study aims to couple a 1D hydrological model (HEC-HMS) with a 2D hydraulic model (iRIC) to simulate flooding in the Nilwala river basin. Hourly rainfall and streamflow data of three flood events were used for calibration and validation of HEC-HMS. The model performed exceptionally well considering the Nash–Sutcliffe coefficient, percent bias, and root mean square error. The flood event of May 2017 was simulated on iRIC using the streamflow hydrographs modelled by HEC-HMS. An overall accuracy of 81.5% was attained when the simulated extent was compared with the surveyed flood extent. The accuracy of the simulated flood depth was assessed using the observed water level at Tudawa gauging station, which yielded an NSE of 0.94, PBIAS of −4.28, RMSE of 0.18 and R2 of 0.95. Thus, the coupled model provided an accurate estimate of the flood extent and depth and can be further developed for hydrological flood forecasting on a regional scale.