Flood Simulation Results Research Articles

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.

How does high resolution topography affect flood simulation at watershed scale: A case study in a small watershed based on tilt photography

The data source and resolution of DEM will affect the extraction of watershed features and flood simulation precision. In this paper, based on the 7 cm resolution DEM of the Liulin watershed obtained by tilt photography technology, the nearest neighbor interpolation, bilinear interpolation, and cubic convolution methods were used to resample DEMs with 1m and 30m resolution, and 6 resampled DEMs were obtained. The Aster 30m resolution DEM was downloaded from the geospatial data cloud. The watershed features were extracted from the 7 DEMs and compared. Based on these DEMs, the HEC-HMS model was established. 20 flood events were selected in the Liulin watershed for model calibration and verification. The results showed that the resampling method has little effect on the topographic features, but there were some differences between the resampling and downloaded DEM topographic features. The flood simulation results of the 1m resolution DEM-based HEC-HMS model are slightly better than that of the 30m resolution DEM-based model. The resampled 30m resolution DEM-based model performed identically with Aster 30m resolution DEM-based model. Therefore, an Aster 30m DEM can be used for flood simulation in a watershed that lacks measured high-precision topographic data.

Urban flood risk assessment characterizing the relationship among hazard, exposure, and vulnerability.

Risk assessment is an effective means to alleviate urban flood disasters and has attracted the attention of many studies. However, most previous studies about urban flood risk assessment often focused more on urban inundation area and depth, less on the inter-relationship of the components of risk. In this study, an urban flood risk assessment approach that characterizes the relationship among the three components of risk "hazard-exposure-vulnerability" (H-E-V) is developed. Firstly, eleven flood risk indicators are selected based on the flood simulation results of urban flood model and statistical data to establish the urban flood risk assessment index system. Then, the combination of analytic hierarchy process (AHP) and entropy weight method is employed to determine the weight of each indicator and the comprehensive urban flood risk is assessed. Most importantly, the coupling coordination degree model (CCDM) is used to reveal the relationship among H-E-V. After applying this method to Haikou city, China, the results show that the comprehensive effect and the coupling coordination degrees among H-E-V have a multidimensional impact on urban flood risk. For example, some sub-catchments, although at high risk of flooding, may experience a potential waste of resources. Urban flood assessment can be made more detailed and three-dimensional by comparing hazard, exposure, and vulnerability horizontally. Understanding and grasping the internal relationships among these three risk components can help implement flood prevention measures, optimize the allocation of flood prevention resources, and effectively reduce urban flood risks.

Evaluating the effects of water exchange between surface rivers and karst aquifers on surface flood simulations at different watershed scales

The complex subsurface conditions and spatial anisotropy of water-bearing media in karst basins result in much more variable runoff generation and confluence processes and water circulation than those in nonkarst basins. Therefore, accurate flood forecasting is more difficult in karst areas, and it is challenging to accurately quantify the water exchange relationship between surface rivers and karst aquifers. To provide key technical support for accurate flood forecasts in karst basins, this study improved a distributed physical hydrologic model, the Karst-Liuxihe model, by improving the runoff generation and confluence algorithms and the parameter optimization algorithm to simulate surface floods in three different karst watersheds in southern China. Additionally, in the model calculations, this study focused on the influence of water exchange on surface flood simulations in changing watersheds. The results showed that for the largest basin, Xijiang Basin (353,100 km2), considering or not considering the water exchange between the surface river and karst aquifer had little effect on the surface flood simulation results. However, this water exchange had a great effect on the surface simulations for the smallest basin, the Beijiang Basin (1,700 km2). For instance, the simulated average peak flow showed a 29 % difference, and the total amount of flooding showed a 25 % difference between dry years and normal years. Therefore, when forecasting surface floods in small karst areas, the water exchange relationship between surface rivers and karst aquifers must be considered. The results showed that the impact of groundwater on surface rivers can be disregarded when forecasting surface floods in the large Xijiang karst basin and can be treated directly as surface flood forecasting in nonkarst areas. The application of this strategy will greatly simplify the cost of hydrologic modelling in the Xijiang basin. By improving the Karst-Liuxihe model, the accuracy of its flood simulation and general applicability in karst basins were greatly enhanced. The improvement in the karst hydrological model in this study provides a good tool for flood simulation and forecasting in the karst regions of Southwest China.

Study on Urban Rainfall–Runoff Model under the Background of Inter-Basin Water Transfer

The imbalance of water supply and demand forces many cities to transfer water across basins, which changes the original “rainfall–runoff” relationship in urban basins. Long-term hydrological simulation of urban basins requires a tool that comprehensively considers the relationship of “rainfall–runoff” and the background of inter-basin water transfer. This paper combines the rainfall–runoff model, the GR3 model, with the background of inter-basin water transfer to simulate the hydrological process of Huangtaiqiao basin (321 km2) in Jinan city, Shandong Province, China for 18 consecutive years with a 1 h time step. Twenty-one flood simulation results of different scales over 18 years were selected for statistical analysis. By comparing the simulation results of the GR3 model and the measured process, the results were verified by multiple evaluation indicators (the Nash–Sutcliffe efficiency coefficient, water relative error, the relative error of flood peak flow, and difference of peak arrival time) at different time scales. It was found that the simulation results of the GR3 model after inter-basin water transfer were considered to be in good agreement with the measured data. This study proves the long-term impact of inter-basin water transfer on rainfall–runoff processes in an urban basin, and the GR3-ibwt model can better simulate the hydrological processes of urban basins, providing a new perspective and method.

Improving the performance of city-scale hydrodynamic flood modelling through a GIS-based DEM correction method

Flood modelling can provide useful information to support flood risk assessment and management. The accuracy of flood simulation results is highly dependent on the quality of input data. In particular, digital elevation models (DEMs) may directly influence the performance of flood predictions and improper representation of complex urban features including buildings and bridges may lead to incorrect prediction of flooding paths and extents, and consequently miscalculate flood risk. In this work, a geographic information system (GIS)-based correction method is proposed to make modifications in high-resolution DEMs by adding building complexes and removing unphysical representations of bridges for a more realistic description of flood paths in considering the flow connectivity in intensely urbanized areas and with the objective of obtaining more accurate flood simulation results. The proposed DEM correction method is applied to support large-scale urban flood modelling in Fuzhou City, China, using an established hydrodynamic flood model known as High-Performance Integrated hydrodynamic Modelling System (HiPIMS). Comparisons are made to the simulation results with and without the DEM improvements using the proposed correction method. The results demonstrate that correct representation of the artificial structures in the urban DEM can significantly improve the flood simulation results.

Elaborate simulations and forecasting of the effects of urbanization on karst flood events using the improved Karst-Liuxihe model

Urbanization, especially land use pattern changes, has a great impact on natural flood events in a karst basin. Quantitative simulations of the effects of urbanization on karst flood events and forecasts of future evolution trends are difficult based on the current hydrological models. These models often require a large amount of data when used in karst areas due to the complex model structure and model parameters. To overcome the limitations of model applications, a physically based and fully distributed karst hydrological model, i.e., the improved Karst-Liuxihe (IKL) model, was proposed in this study to simulate and forecast karst flood events under the influence of urbanization. This IKL model was developed through the overall improvement of the Karst-Liuxihe (KL) model. The main additions were the improved runoff generation algorithm and the underground river confluence module. The karst flood simulation results of the IKL model were much better than those of the KL model: the average values of the Nash–Sutcliffe coefficient, correlation coefficient, and coefficient of the water balance increased by 23%, 23% and 26%, respectively, while the process relative error, flood peak flow relative error, and flood peak flow time error decreased by 21%, 22%, and 3 h, respectively, which confirmed that the improvements to the model were effective and feasible. Therefore, this paper used the IKL model to simulate karst flood events and annual runoff under the influence of urbanization based on 4 extreme land use scenarios. In addition, the model effectively forecasted the future runoff in 2030, 2040, and 2050. The results indicated that the critical proportion of urbanized area throughout the watershed area was 45%. When the urban proportion exceeded 45%, waterlogging could occur in the study area.

A coupled atmospheric–hydrologic modeling system with variable grid sizes for rainfall–runoff simulation in semi-humid and semi-arid watersheds: how does the coupling scale affects the results?

Abstract. The coupled atmospheric–hydrologic modeling system is an effective way to improve the accuracy of rainfall–runoff modeling and extend the lead time in real-time flood forecasting. The aim of this study is to explore the appropriate coupling scale of the coupled atmospheric–hydrologic modeling system, which is established by the Weather Research and Forecasting (WRF) model and the gridded Hebei model with three different sizes (1 km×1 km, 3 km×3 km and 9 km×9 km). The Hebei model is a conceptual rainfall–runoff model designed to describe a mixed runoff generation mechanism, including both storage excess and infiltration excess, in the semi-humid and semi-dry area of northern China. The soil moisture storage capacity and infiltration capacity of different grids in the gridded Hebei model are obtained and dispersed using the topographic index. The lumped Hebei model is also used to establish the lumped atmospheric–hydrologic coupled system as a reference system. Four 24 h storm events occurring at two small- and medium-scale sub-watersheds in northern China are selected as case studies. Contrastive analyses of the flood process simulations from the gridded and lumped systems are carried out. The results show that the flood simulation results may not always be improved with higher-dimension precision and more complicated system, and the grid size selection has a strong relationship with the rainfall evenness. For the storm events with uniform spatial distribution, the coupling scale has less impact on flood simulation results, and the lumped system also performs well. For the storm events with uneven spatiotemporal distribution, the corrected rainfall can improve the simulation results significantly, and higher resolution leads to better flood process simulation. The results can help to establish the appropriate coupled atmospheric–hydrologic modeling system to improve the flood forecasting accuracy.

The Use of LiDAR-Derived DEM in Flood Applications: A Review

Flood occurrence is increasing due to escalated urbanization and extreme climate change; hence, various studies on this issue and methods of flood monitoring and mapping are also increasing to reduce the severe impacts of flood disasters. The advancement of current technologies such as light detection and ranging (LiDAR) systems facilitated and improved flood applications. In a LiDAR system, a laser emits light that travels to the ground and reflects off objects like buildings and trees. The reflected light energy returns to the sensor, whereby the time interval is recorded. Since the conventional methods cannot produce high-resolution digital elevation model (DEM) data, which results in low accuracy of flood simulation results, LiDAR data are extensively used as an alternative. This review aims to study the potential and the applications of LiDAR-derived DEM in flood studies. It also provides insight into the operating principles of different LiDAR systems, system components, and advantages and disadvantages of each system. This paper discusses several topics relevant to flood studies from a LiDAR-derived DEM perspective. Furthermore, the challenges and future perspectives regarding DEM LiDAR data for flood mapping and assessment are also reviewed. This study demonstrates that LiDAR-derived data are useful in flood risk management, especially in the future assessment of flood-related problems.

Data-Driven Approach for the Rapid Simulation of Urban Flood Prediction

Flooding due to the increase of heavy rainfall caused even larger damage in metropolitan areas. Therefore, numerical simulation and probabilistic models have been used for flood prediction, but the methodologies for real-time flood prediction by drainage district in metropolitan areas are still not sufficient. In this study, a flood scenario database was established by using one- and two-dimensional hydraulic analysis models to propose a realtime urban flood prediction method by drainage districts in metropolitan areas. Flood prediction models were constructed for each drainage district through the Nonlinear Auto-Regressive with eXogenous inputs and Self-Organizing Map (NARX-SOM). Suggested prediction model is a data-driven model because it is based on flood database which composed with diverse flood simulation results. To evaluate the predictive capacity of the models, flood prediction was performed for the actual heavy rainfall in 2010 and 2011 that caused severe flooding in Seoul, Republic of Korea. Flood prediction models for a total of 24 drainage districts were constructed, and it was found that the goodness of fit on the flood area ranged from 68.7 to 89.7%. In terms of the expected inundation map, the predictive power was found to be high when the SOM result with 5 × 5 dimension was mainly used. Through this study, it was possible to identify the predictive capability of the NARX-SOM flood prediction algorithm. The time for inundation map prediction for each area was within two minutes, but the one- and two-dimensional flood simulation usually takes 60–80 minutes. Moreover, when the calculated goodness of fit was examined, the proposed method was found to be a practical methodology that can be helpful in improving flood response capabilities.

基于霍顿下渗能力曲线的流域产汇流计算

选择黄河中游山陕区间右岸的一级支流佳芦河为研究对象，采用入黄水文站申家湾1978~2014年的洪水资料，以霍顿下渗能力曲线为基础，结合ArcGIS，分析下渗能力，建立产汇流模型，并进行历史典型洪水模拟。用流域实测降雨量、径流量及前期影响雨量等资料，分析佳芦河下渗能力，建立f~W0~Ft关系，计算出佳芦河流域稳定下渗率为3.1 mm/h。产汇流模块由蒸散发、产流、坡面汇流和河道汇流共四部分组成，分别采用瞬时单位线法和马斯京根法研究坡面汇流和河道汇流，求出产汇流参数。用率定的产汇流参数对典型洪水进行模拟，模拟效果较好，平均洪峰相对误差约为8.3%，洪峰误差均在预报允许范围之内，平均峰现误差约为0.05小时，平均确定性系数为0.77。 Flood data from 1978 to 2014 at Shenjiawan hydrology station, Jialuhe basin located in the Shanxi-Shaanxi Region of the middle Yellow River, are selected in this study. Based on the Horton infiltration capacity curve and ArcGIS, the stable infiltration rate is calculated, and the runoff yield and conflux model is established to simulate history floods. The observed rainfall, runoff and antecedent precipitation are used to analyze the infiltration capacity, and the stable infiltration rate is 3.1 mm/h through the relation of f~W0~Ft. The watershed runoff yield and conflux model is composed of four parts, among which, the overland flow module adopts instantaneous unit method and the river flow module adopts Muskingum flow algorithm. The history flood simulation results are good: the average error of peak time is about 0.05 hour; the error of peak discharge is in the forecasting allowable range, and the deterministic coefficient is 0.77.

Comparison of parameter uncertainty analysis techniques for a TOPMODEL application

Parameter uncertainty in hydrologic modeling is crucial to the flood simulation and forecasting. The Bayesian approach allows one to estimate parameters according to prior expert knowledge as well as observational data about model parameter values. This study assesses the performance of two popular uncertainty analysis (UA) techniques, i.e., generalized likelihood uncertainty estimation (GLUE) and Bayesian method implemented with the Markov chain Monte Carlo sampling algorithm, in evaluating model parameter uncertainty in flood simulations. These two methods were applied to the semi-distributed Topographic hydrologic model (TOPMODEL) that includes five parameters. A case study was carried out for a small humid catchment in the southeastern China. The performance assessment of the GLUE and Bayesian methods were conducted with advanced tools suited for probabilistic simulations of continuous variables such as streamflow. Graphical tools and scalar metrics were used to test several attributes of the simulation quality of selected flood events: deterministic accuracy and the accuracy of 95 % prediction probability uncertainty band (95PPU). Sensitivity analysis was conducted to identify sensitive parameters that largely affect the model output results. Subsequently, the GLUE and Bayesian methods were used to analyze the uncertainty of sensitive parameters and further to produce their posterior distributions. Based on their posterior parameter samples, TOPMODEL’s simulations and the corresponding UA results were conducted. Results show that the form of exponential decline in conductivity and the overland flow routing velocity were sensitive parameters in TOPMODEL in our case. Small changes in these two parameters would lead to large differences in flood simulation results. Results also suggest that, for both UA techniques, most of streamflow observations were bracketed by 95PPU with the containing ratio value larger than 80 %. In comparison, GLUE gave narrower prediction uncertainty bands than the Bayesian method. It was found that the mode estimates of parameter posterior distributions are suitable to result in better performance of deterministic outputs than the 50 % percentiles for both the GLUE and Bayesian analyses. In addition, the simulation results calibrated with Rosenbrock optimization algorithm show a better agreement with the observations than the UA’s 50 % percentiles but slightly worse than the hydrographs from the mode estimates. The results clearly emphasize the importance of using model uncertainty diagnostic approaches in flood simulations.

The effect of a hydrological model structure and rainfall data on the accuracy of flood description in an upland catchment

Abstract The effect of a hydrological model structure and rainfall data on the accuracy of flood description in an upland catchment. The aim of this paper was to determine the influence of a hydrological model structure and rainfall- -related data on flood parameters obtained from a simulation. The study included an upland river Stobnica, right tributary of the Wisłok. The following assumptions were investigated: (i) the greater number of rainfall stations, the more accurate a flood description, i.e. the resulting hydrograph much better describes the actual flood, (ii) a distributed parameter model provides a more precise description of a catchment response to rainfall than a lumped parameter model. All calculations were performed using HEC-HMS 3.4 software. The analyses showed that increasing the number of rainfall stations slightly improved the model performance (by on average 4.1%). Furthermore, it was showed that in the catchment characterized by low topographical variability and stable land use, more reliable flood simulation results were obtained in the lumped parameter model than in the distributed parameter model. Considering the calibration process slightly improved the model performance, irrespective of its structure and the number of rainfall stations. Multivariate analysis of variance (MANOVA) revealed that the resulting differences in the model efficiency for individual variants were not significant. Considering limited empirical evidence on rainfall-runoff episodes, uncertainty of these results is probably high and thus they should be treated as a starting point for further studies.

CO2 Miscible Simulation for Magnetic Resonance Imaging Coreflood Tests

CO2 miscible flooding is one of main technologies in both the secondary and tertiary floods for Enhanced Oil Recovery (EOR). In addition, it is also an important alternative for a geological CO2 storage. In the recent twenty years, the fundamental research for visualized CO2-EOR has been launched by using mainly the Magnetic Resonance Imaging (MRI). In this study, we simulated a CO2 miscible model by using Eclipse reservoir simulation software based on the MRI coreflood test. We modified the numerical model of CO2 miscible flooding by changing the parameters to improve the CO2 miscible flood simulation results.

Flood Simulation of Upriver District Considering an Influence of Backwater

This study aims to predict inundation and flood-stricken areas more accurately by simulating flood damage caused by reversible flow of rain water in the upper water system through precise 3D terrain model and backwater output. For the upstream of the South Han-River, precise 3D terrain model was established by using aerial LiDAR data and backwater by area was output by applying the storm events of 2002 including the history of flood damage. The 3D flood simulation was also performed by using GIS Tool and for occurrence of related rainfall events, inundation events of the upriver region of water system was analyzed. In addition, the results of flood simulation using backwater were verified by making the inundation damage map for the relevant area and comparing it with flood simulation's results. When comparing with the results of the flood simulation applying uniformly the gauging station's water surface elevation used for the existing flood simulation, it is found that the results of the flood simulation using backwater are close to the actual inundation damage status. Accordingly, the causes of flood occurred in downstream of water system and upstream that has different topographic characteristics could be investigated and applying the simulation with backwater is proved more proper in order to procure accuracy of the flood simulation for the upriver region.

Effects of DEM sources on hydrologic applications

Digital elevation model (DEM) data have been used to derive hydrological features which serve as inputs to various models. Currently, elevation data are available from several major sources and at different spatial resolutions: the National Elevation Dataset (NED), Shuttle Radar Topography Mission (SRTM) data, and the Light Detection and Ranging (LIDAR) data. This article shows the variability of analysis results from these data sources and of different scales. A standard algorithm in GIS was used to extract river networks from these DEM data for part of Kansas. Extracted networks were compared with the one derived from the National Hydrography Dataset (NHD). Flood simulation results obtained from various sets of DEM data were also compared. Using higher resolution DEMs can derive more accurate river networks, but highest resolution data may not necessarily offer the best results, depending on the resolutions selected to compare the extracted networks. DEM spatial resolution may have minor impacts on flood simulation results, but inundation areas from flood simulations vary significantly across different DEM data sources. Results highlight the caveats on using DEM-derived river network data for hydrologic applications and the difficulties in reconciling differences among elevation data from various sources and of different resolutions.