Comparing the performances of WRF QPF and PERSIANN-CCS QPEs in karst flood simulations and forecasting with a new Karst-Liuxihe model

Abstract. Long-term, available rainfall data are very important for karst flood simulations and forecasting. However, in karst areas, there is often a lack of effective precipitation available to build distributed hydrological models. Forecasting karst floods is highly challenging. Quantitative precipitation forecasts (QPF) and estimates (QPEs) could provide rational methods to acquire the available precipitation results for karst areas. Furthermore, coupling a physically-based hydrological model with the QPF and QPEs felicitously could largely enhance the performance and extend the lead time of floods forecasting in karst areas, the performance of coupling the Weather Research and Forecasting Quantitative Precipitation Forecast (WRF QPF) and Precipitation Estimations through Remotely Sensed Information based on the Artificial Neural Network-Cloud Classification System (PERSIANN-CCS QPEs) with a new fully distributed and physical hydrological model, the Karst-Liuxihe model in flood simulations and forecasting in karst area. This study served 2 main purposes: one purpose is to compare the performances of WRF QPF and PERSIANN-CCS QPEs for rainfall forecasting in karst river basins. The other purpose is to test the effective feasibility and application of the karst flood simulation and forecasting by coupling the 2 weather models with a new Karst-Liuxihe model. The new Karst-Liuxihe model improved the structure of the model by adding the karst mechanism based on the Liuxihe model as follows: (1) Refine the model structure and put forward the concept of karst hydrological response units (KHRUs) in the model. The KHRU, as the smallest unit of the Karst-Liuxihe model, is defined in this paper to be suitable for karst basins; (2) Increase the calculations of water movement rules in the epikarst zone and underground river, such as the division of slow flow and rapid flow in the epikarst zone and the exchange of water flow between the karst fissures and conduit systems; thus, the convergence of the underground runoff calculation method is improved to be suitable for karst water-bearing media; and (3) Add some necessary hydrogeological parameters in the coupled model to reflect the true conditions of rainfall-runoff in the karst underlying surface. Moreover, the flood detention and peak clipping effects due to the upstream karst depressions during flooding were considered and reasonably calculated in the coupled model. The flood detention effect can affect the peak flow time error simulated in the model and make the true peak flow appear later; the flood peak clipping effect can affect the flood peak flow relative errors and the simulation errors of floods volume. The consideration of these 2 factors in the model makes the flood simulations and forecasting effects more credible. The rainfall forecasting result show that the precipitation distribution of the 2 weather models was very similar compared with the observed rainfall result. However, the precipitation amounts forecasted by WRF QPF were larger than that measured by the rain gauges, while the quantities were smaller by the PERSIANN-CCS QPEs. A postprocessing algorithm was adopted in this paper to correct the rainfall results by the 2 weather models. The karst flood simulation and forecasting results showed that the flood peak flow simulations were better by coupling the Karst-Liuxihe model with the PERSIANN-CCS QPEs, and coupling the Karst-Liuxihe model with WRF QPF could extend the lead time of flood forecasting largely, as a maximum lead time of 96 hours can provide an adequate amount of time for flood warnings and emergency responses. The satisfying and rational karst flood simulation evaluation indices proved that coupling the 2 weather models with the new Karst-Liuxihe model could be effectively used for karst river basins, which provides great practical application prospects for karst flood simulations and forecasting. In addition, the postprocessing method used to revise the 2 weather models in this paper is feasible and effective, and this method can largely improve the coupled model application effectiveness and prospect in karst river basins.

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Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model

Hydrology and Earth System Sciences ◽

10.5194/hess-23-1505-2019 ◽

2019 ◽

Vol 23 (3) ◽

pp. 1505-1532 ◽

Cited By ~ 1

Author(s):

Ji Li ◽

Daoxian Yuan ◽

Jiao Liu ◽

Yongjun Jiang ◽

Yangbo Chen ◽

...

Keyword(s):

River Basin ◽

Relative Error ◽

Hydrological Model ◽

Peak Flow ◽

Coupled Model ◽

Distributed Hydrological Model ◽

Flood Simulation ◽

Flood Prediction ◽

Physically Based ◽

Karst River

Abstract. In general, there are no long-term meteorological or hydrological data available for karst river basins. The lack of rainfall data is a great challenge that hinders the development of hydrological models. Quantitative precipitation estimates (QPEs) based on weather satellites offer a potential method by which rainfall data in karst areas could be obtained. Furthermore, coupling QPEs with a distributed hydrological model has the potential to improve the precision of flood predictions in large karst watersheds. Estimating precipitation from remotely sensed information using an artificial neural network-cloud classification system (PERSIANN-CCS) is a type of QPE technology based on satellites that has achieved broad research results worldwide. However, only a few studies on PERSIANN-CCS QPEs have occurred in large karst basins, and the accuracy is generally poor in terms of practical applications. This paper studied the feasibility of coupling a fully physically based distributed hydrological model, i.e., the Liuxihe model, with PERSIANN-CCS QPEs for predicting floods in a large river basin, i.e., the Liujiang karst river basin, which has a watershed area of 58 270 km2, in southern China. The model structure and function require further refinement to suit the karst basins. For instance, the sub-basins in this paper are divided into many karst hydrology response units (KHRUs) to ensure that the model structure is adequately refined for karst areas. In addition, the convergence of the underground runoff calculation method within the original Liuxihe model is changed to suit the karst water-bearing media, and the Muskingum routing method is used in the model to calculate the underground runoff in this study. Additionally, the epikarst zone, as a distinctive structure of the KHRU, is carefully considered in the model. The result of the QPEs shows that compared with the observed precipitation measured by a rain gauge, the distribution of precipitation predicted by the PERSIANN-CCS QPEs was very similar. However, the quantity of precipitation predicted by the PERSIANN-CCS QPEs was smaller. A post-processing method is proposed to revise the products of the PERSIANN-CCS QPEs. The karst flood simulation results show that coupling the post-processed PERSIANN-CCS QPEs with the Liuxihe model has a better performance relative to the result based on the initial PERSIANN-CCS QPEs. Moreover, the performance of the coupled model largely improves with parameter re-optimization via the post-processed PERSIANN-CCS QPEs. The average values of the six evaluation indices change as follows: the Nash–Sutcliffe coefficient increases by 14 %, the correlation coefficient increases by 15 %, the process relative error decreases by 8 %, the peak flow relative error decreases by 18 %, the water balance coefficient increases by 8 %, and the peak flow time error displays a 5 h decrease. Among these parameters, the peak flow relative error shows the greatest improvement; thus, these parameters are of the greatest concern for flood prediction. The rational flood simulation results from the coupled model provide a great practical application prospect for flood prediction in large karst river basins.

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Flood forecasting in large karst river basin by coupling PERSIANN CCS QPEs with a physically based distributed hydrological model

10.5194/hess-2018-438 ◽

2018 ◽

Author(s):

Ji Li ◽

Daoxian Yuan ◽

Jiao Liu ◽

Yongjun Jiang ◽

Yangbo Chen ◽

...

Keyword(s):

River Basin ◽

Relative Error ◽

Hydrological Model ◽

Peak Flow ◽

Flood Forecasting ◽

Distributed Hydrological Model ◽

Practical Application ◽

Time Error ◽

Flood Simulation ◽

Karst River

Abstract. There is no long-term meteorological or hydrological data in karst river basins to a large extent. Especially lack of typical rainfall data is a great challenge to build a hydrological model. Quantitative precipitation estimates (QPEs) based on the weather satellites could offer a good attempt to obtain the rainfall data in karst area. What's more, coupling QPEs with a distributed hydrological model has the potential to improve the precision for flood forecasting in large karst watershed. Precipitation estimation from remotely sensed information using artificial neural networks-cloud classification system (PERSIANN-CCS) as a technology of QPEs based on satellites has been achieved a wide research results in the world. However, only few studies on PERSIANN-CCS QPEs are in large karst basins and the accuracy is always poor in practical application. In this study, the PERSIANN-CCS QPEs is employed to estimate the hourly precipitation in such a large river basin-Liujiang karst river basin with an area of 58 270 km2. The result shows that, compared with the observed precipitation by rain gauge, the distribution of precipitation by PERSIANN-CCS QPEs has a great similarity. But the quantity values of precipitation by PERSIANN-CCS QPEs are smaller. A post-processed method is proposed to revise the PERSIANN-CCS QPEs products. The result shows that coupling the post-processed PERSIANN-CCS QPEs with a distributed hydrological model-Liuxihe model has a better performance than the result with the initial PERSIANN-CCS QPEs in karst flood simulation. What's more, the coupling model’s performance improves largely with parameter re-optimized with the post-processed PERSIANN-CCS QPEs. The average values of the six evaluation indices including Nash–Sutcliffe coefficient has a 14 % increase, the correlation coefficient has a 14 % increase, process relative error has a 8 % decrease, peak flow relative error has a 18 % decrease, the water balance coefficient has a 7 % increase, and peak flow time error has 25 hours decrease, respectively. Among them, the peak flow relative error and peak flow time error have the biggest improvement, which are the greatest concerned factors in flood forecasting.The rational flood simulation results by the coupling model provide a great practical application prospect for flood forecasting in large karst river basins.

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Bi-directional Coupling of an Unstructured Triangular Meshes-based Integrated Hydrodynamic Model for Heterogeneous Feature-based Urban Flood Simulation

10.21203/rs.3.rs-375226/v1 ◽

2021 ◽

Author(s):

Guoqiang Peng ◽

Zhuo Zhang ◽

Tian Zhang ◽

Zhiyao Song ◽

Arif Masrur

Keyword(s):

Water Flow ◽

Urban Areas ◽

Overland Flow ◽

Coupled Model ◽

Flood Simulation ◽

Urban Flood ◽

Essential Condition ◽

Flow Processes ◽

Feature Based ◽

Heterogeneous Feature

Abstract Urban pluvial flash floods have become a matter of widespread concern, as they severely impact people’s lives in urban areas. Hydrological and hydraulic models have been widely used for urban flood management and urban planning. Traditionally, to reduce the complexity of urban flood modelling and simulations, simplification or generalization methods have been used; for example, some models focus on the simulation of overland water flow, and some models focus on the simulation of the water flow in sewer systems. However, the water flow of urban floods includes both overland flow and sewer system flow. The overland flow processes are impacted by many different geographical features in what is an extremely spatially heterogeneous environment. Therefore, this article is based on two widely used models (SWMM and ANUGA) that are coupled to develop a bi-directional method of simulating water flow processes in urban areas. The open source overland flow model uses the unstructured triangular as the spatial discretization scheme. The unstructured triangular-based hydraulic model can be better used to capture the spatial heterogeneity of the urban surfaces. So, the unstructured triangular-based model is an essential condition for heterogeneous feature-based urban flood simulation. The experiments indicate that the proposed coupled model in this article can accurately depict surface waterlogged areas and that the heterogeneous feature-based urban flood model can be used to determine different types of urban flow processes.

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Inclusion of Modified Snow Melting and Flood Processes in the SWAT Model

Water ◽

10.3390/w10121715 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1715 ◽

Cited By ~ 6

Author(s):

Yongchao Duan ◽

Tie Liu ◽

Fanhao Meng ◽

Min Luo ◽

Amaury Frankl ◽

...

Keyword(s):

Assessment Tool ◽

Swat Model ◽

Melting Process ◽

Peak Performance ◽

Maximum Temperature ◽

Snow Melting ◽

Flood Simulation ◽

Flood Peak ◽

Forecasting Method ◽

Time Accuracy

Flooding, one of the most serious natural disasters, poses a significant threat to people’s lives and property. At present, the forecasting method uses simple snowmelt accumulation and has certain regional restrictions that limit the accuracy and timeliness of flood simulation and prediction. In this paper, the influence of accumulated temperature (AT) and maximum temperature (MT) on snow melting was considered in order to (1) reclassify the precipitation categories of the watershed using a separation algorithm of rain and snow that incorporates AT and MT, and (2) develop a new snow-melting process utilizing the algorithm in the Soil and Water Assessment Tool Model (SWAT) by considering the effects of AT and MT. The SWAT model was used to simulate snowmelt and flooding in the Tizinafu River Basin (TRB). We found that the modified SWAT model increased the value of the average flood peak flow by 43%, the snowmelt amounts increased by 45%, and the contribution of snowmelt to runoff increased from 44.7% to 54.07%. In comparison, we concluded the snowmelt contribution to runoff, flood peak performance, flood process simulation, model accuracy, and time accuracy. The new method provides a more accurate simulation technique for snowmelt floods and flood simulation.

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Bivariate Hydrologic Risk Assessment of Flood Episodes using the Notation of Failure Probability

Civil Engineering Journal ◽

10.28991/cej-2020-03091599 ◽

2020 ◽

Vol 6 (10) ◽

pp. 2002-2023

Author(s):

Shahid Latif ◽

Firuza Mustafa

Keyword(s):

Failure Probability ◽

Goodness Of Fit ◽

Peak Flow ◽

Gaussian Copula ◽

Dependence Structure ◽

Distribution Analysis ◽

Return Periods ◽

Goodness Of Fit Test ◽

Flood Peak ◽

Flood Volume

Floods are becoming the most severe and challenging hydrologic issue at the Kelantan River basin in Malaysia. Flood episodes are usually thoroughly characterized by flood peak discharge flow, volume and duration series. This study incorporated the copula-based methodology in deriving the joint distribution analysis of the annual flood characteristics and the failure probability for assessing the bivariate hydrologic risk. Both the Archimedean and Gaussian copula family were introduced and tested as possible candidate functions. The copula dependence parameters are estimated using the method-of-moment estimation procedure. The Gaussian copula was recognized as the best-fitted distribution for capturing the dependence structure of the flood peak-volume and peak-duration pairs based on goodness-of-fit test statistics and was further employed to derive the joint return periods. The bivariate hydrologic risks of flood peak flow and volume pair, and flood peak flow and duration pair in different return periods (i.e., 5, 10, 20, 50 and 100 years) were estimated and revealed that the risk statistics incrementally increase in the service lifetime and, at the same instant, incrementally decrease in return periods. In addition, we found that ignoring the mutual dependency can underestimate the failure probabilities where the univariate events produced a lower failure probability than the bivariate events. Similarly, the variations in bivariate hydrologic risk with the changes of flood peak in the different synthetic flood volume and duration series (i.e., 5, 10, 20, 50 and 100 years return periods) under different service lifetimes are demonstrated. Investigation revealed that the value of bivariate hydrologic risk statistics incrementally increases over the project lifetime (i.e., 30, 50, and 100 years) service time, and at the same time, it incrementally decreases in the return period of flood volume and duration. Overall, this study could provide a basis for making an appropriate flood defence plan and long-lasting infrastructure designs. Doi: 10.28991/cej-2020-03091599 Full Text: PDF

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Flood forecasting that considers the impact of hydraulic projects by an improved TOPMODEL model in the Wudaogou basin, Northeast China

Water Science & Technology Water Supply ◽

10.2166/ws.2016.075 ◽

2016 ◽

Vol 16 (5) ◽

pp. 1467-1476 ◽

Cited By ~ 3

Author(s):

Yong Peng ◽

Jinggang Chu ◽

Xinguo Sun ◽

Huicheng Zhou ◽

Xiaoli Zhang

Keyword(s):

Water Conservation ◽

Northeast China ◽

Storage Capacity ◽

Flood Simulation ◽

Soil Moisture Storage ◽

Moisture Storage ◽

Flood Simulations ◽

Model Parameter Uncertainty ◽

The Impact ◽

Engineering Projects

Many hydraulic projects such as reservoirs, ponds and paddy fields as well as soil and water conservation engineering projects have been constructed to improve utilization of water resources upstream of the Wudaogou station basin in Northeast China in recent years. As a result, the local hydrological characteristics of the basin and the flood runoff and process have been changed. These changes in the basin characteristics make basin hydrological forecasting more difficult. In order to model and assess this situation, the TOPMODEL, which includes the dynamic soil moisture storage capacity (DSMSC-TOPMODEL), is used in this study to simulate the flood impact of hydraulic projects. Furthermore, the Bayesian method is used to evaluate model parameter uncertainty and assess the TOPMODEL's performance over the basin. Flood simulation results show that accuracy is significantly improved when the stock version of TOPMODEL is replaced with DSMSC-TOPMODEL, with the qualified ratio of forecasting runoff yield increasing from 65% in the former to 88% in the latter. Moreover, these flood simulations are more suitable for helping observers visualize the process.

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Assessing the factors governing the ability to predict late-spring flooding in cold-region mountain basins

Hydrology and Earth System Sciences ◽

10.5194/hess-24-2141-2020 ◽

2020 ◽

Vol 24 (4) ◽

pp. 2141-2165 ◽

Cited By ~ 1

Author(s):

Vincent Vionnet ◽

Vincent Fortin ◽

Etienne Gaborit ◽

Guy Roy ◽

Maria Abrahamowicz ◽

...

Keyword(s):

Soil Texture ◽

Peak Flow ◽

River Basins ◽

Late Spring ◽

Atmospheric Forcing ◽

Cold Region ◽

Flooding Event ◽

Southern Alberta ◽

Precipitation Analysis ◽

Headwater Basins

Abstract. From 19 to 22 June 2013, intense rainfall and concurrent snowmelt led to devastating floods in the Canadian Rockies, foothills and downstream areas of southern Alberta and southeastern British Columbia, Canada. Such an event is typical of late-spring floods in cold-region mountain headwater, combining intense precipitation with rapid melting of late-lying snowpack, and represents a challenge for hydrological forecasting systems. This study investigated the factors governing the ability to predict such an event. Three sources of uncertainty, other than the hydrological model processes and parameters, were considered: (i) the resolution of the atmospheric forcings, (ii) the snow and soil moisture initial conditions (ICs) and (iii) the representation of the soil texture. The Global Environmental Multiscale hydrological modeling platform (GEM-Hydro), running at a 1 km grid spacing, was used to simulate hydrometeorological conditions in the main headwater basins of southern Alberta during this event. The GEM atmospheric model and the Canadian Precipitation Analysis (CaPA) system were combined to generate atmospheric forcing at 10, 2.5 and 1 km over southern Alberta. Gridded estimates of snow water equivalent (SWE) from the Snow Data Assimilation System (SNODAS) were used to replace the model SWE at peak snow accumulation and generate alternative snow and soil moisture ICs before the event. Two global soil texture datasets were also used. Overall 12 simulations of the flooding event were carried out. Results show that the resolution of the atmospheric forcing affected primarily the flood volume and peak flow in all river basins due to a more accurate estimation of intensity and total amount of precipitation during the flooding event provided by CaPA analysis at convection-permitting scales (2.5 and 1 km). Basin-averaged snowmelt also changed with the resolution due to changes in near-surface wind and resulting turbulent fluxes contributing to snowmelt. Snow ICs were the main sources of uncertainty for half of the headwater basins. Finally, the soil texture had less impact and only affected peak flow magnitude and timing for some stations. These results highlight the need to combine atmospheric forcing at convection-permitting scales with high-quality snow ICs to provide accurate streamflow predictions during late-spring floods in cold-region mountain river basins. The predictive improvement by inclusion of high-elevation weather stations in the precipitation analysis and the need for accurate mountain snow information suggest the necessity of integrated observation and prediction systems for forecasting extreme events in mountain river basins.

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Bias-corrected climate projections for South Asia from Coupled Model Intercomparison Project-6

Scientific Data ◽

10.1038/s41597-020-00681-1 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Vimal Mishra ◽

Udit Bhatia ◽

Amar Deep Tiwari

Keyword(s):

Climate Change ◽

Impact Assessment ◽

South Asia ◽

General Circulation ◽

Coupled Model ◽

General Circulation Models ◽

River Basins ◽

Climate Projections ◽

Model Intercomparison ◽

Intercomparison Project

Abstract Climate change is likely to pose enormous challenges for agriculture, water resources, infrastructure, and livelihood of millions of people living in South Asia. Here, we develop daily bias-corrected data of precipitation, maximum and minimum temperatures at 0.25° spatial resolution for South Asia (India, Pakistan, Bangladesh, Nepal, Bhutan, and Sri Lanka) and 18 river basins located in the Indian sub-continent. The bias-corrected dataset is developed using Empirical Quantile Mapping (EQM) for the historic (1951–2014) and projected (2015–2100) climate for the four scenarios (SSP126, SSP245, SSP370, SSP585) using output from 13 General Circulation Models (GCMs) from Coupled Model Intercomparison Project-6 (CMIP6). The bias-corrected dataset was evaluated against the observations for both mean and extremes of precipitation, maximum and minimum temperatures. Bias corrected projections from 13 CMIP6-GCMs project a warmer (3–5°C) and wetter (13–30%) climate in South Asia in the 21st century. The bias-corrected projections from CMIP6-GCMs can be used for climate change impact assessment in South Asia and hydrologic impact assessment in the sub-continental river basins.

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Assessment of Flood Extremes Using Downscaled CMIP5 High-Resolution Ensemble Projections of Near-Term Climate for the Upper Thu Bon Catchment in Vietnam

Water ◽

10.3390/w11040634 ◽

2019 ◽

Vol 11 (4) ◽

pp. 634 ◽

Cited By ~ 1

Author(s):

Do Nam ◽

Tran Hoa ◽

Phan Duong ◽

Duong Thuan ◽

Dang Mai

Keyword(s):

High Resolution ◽

Return Period ◽

Coupled Model ◽

Daily Rainfall ◽

Flood Peak ◽

Basin Scale ◽

The Future ◽

Near Term ◽

Baseline Climate ◽

Ensemble Projections

Exploring potential floods is both essential and critical to making informed decisions for adaptation options at a river basin scale. The present study investigates changes in flood extremes in the future using downscaled CMIP5 (Coupled Model Intercomparison Project—Phase 5) high-resolution ensemble projections of near-term climate for the Upper Thu Bon catchment in Vietnam. Model bias correction techniques are utilized to improve the daily rainfall simulated by the multi-model climate experiments. The corrected rainfall is then used to drive a calibrated supper-tank model for runoff simulations. The flood extremes are analyzed based on the Gumbel extreme value distribution and simulation of design hydrograph methods. Results show that the former method indicates almost no changes in the flood extremes in the future compared to the baseline climate. However, the later method explores increases (approximately 20%) in the peaks of very extreme events in the future climate, especially, the flood peak of a 50-year return period tends to exceed the flood peak of a 100-year return period of the baseline climate. Meanwhile, the peaks of shorter return period floods (e.g., 10-year) are projected with a very slight change. Model physical parameterization schemes and spatial resolution seem to cause larger uncertainties; while different model runs show less sensitivity to the future projections.

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Impact of the models structure uncertainty in flood simulations: Simbach case study

10.5194/egusphere-egu2020-15216 ◽

2020 ◽

Author(s):

Qing Lin ◽

Jorge Leandro ◽

Markus Disse ◽

Daniel Sturm

Keyword(s):

Flash Flood ◽

Drainage System ◽

Model Structure ◽

Flood Simulation ◽

Hydraulic Models ◽

Uncertainty Factor ◽

Model Component ◽

Model Structure Uncertainty ◽

Flood Simulations ◽

Model Structures

The quantification of model structure uncertainty on hydraulic models is very important for flash flood simulations. The choice of an appropriate model structure complexity and assessment of the impacts due to infrastructure failure can have a huge impact on the simulation results. To assess the risk of flash floods, coupled hydraulic models, including 1D-sewer drainage and 2D-surface run-off models are required for urban areas because they include the bidirectional water exchange, which occurs between sewer and overland flow in a city [1]. By including various model components, we create different model structures. For example, modelling the inflow to the city with the 2D surface-runoff or with the delineated 1D model; including the sewer system or use a surrogate as an alternative; modifying the connectivity of manholes and pumps; or representing the drainage system failures during flood events. As the coupling pattern becomes complex, quantifying the model structure uncertainty is essential for the model structure evaluation. If one model component leads to higher model uncertainty, it is reasonable to conclude that the new component has a large impact in our model and therefore needs to be accounted for; if one component has a less impact in the overall uncertainty, then the model structure can be simplified, by removing that model component. In this study, we set up seven different model structures [2] for the German city of Simbach. By comparison with two inflow calculation types (1D-delineated inflow or 2D-catchment), the existence of drainage system and infrastructure failures, the Model Uncertainty Factor (MUF) is calculated to quantify the model structure uncertainties and further trade-off values with Parameter Uncertainty Factor (PUF) [3]. Finally, we can obtain a more efficient hydraulic model with the essential model structure for urban flash flood simulation. &#160; <ol>1. Leandro, J., Chen, A. S., Djordjevic, S., and Dragan, S. (2009). "A comparison of 1D/1D and 1D/2D coupled hydraulic models for urban flood simulation." Journal of Hydraulic Engineering-ASCE, 6(1):495-504.</ol> <ol>2. Leandro, J., Schumann, A., and Pfister, A. (2016). A step towards considering the spatial heterogeneity of urban, key features in urban hydrology flood modelling. J. Hydrol., Elsevier, 535 (4), 356-365.</ol> <ol>3. Van Zelm, R., Huijbregts, M.A.J. (2013). Quantifying the trade-off between parameter and model structure uncertainty in life cycle impact assessment, Environ. Sci. Technol., 47(16), pp. 9274-9280.</ol> &#160;

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