Comparing the performances of WRF QPF and PERSIANN-CCS QPEs in karst flood simulation and forecasting by coupling the Karst-Liuxihe model

Long-term rainfall data are crucial for flood simulations and forecasting in karst regions.However,in karst areas,there is often a lack of suitable precipitation data available to build distributed hydrological models to forecast karst floods.Quantitative precipitation forecasts(QPFs)and estimates(QPEs)could provide rational methods to acquire the available precipitation data for karst areas.Furthermore,coupling a physically based hydrological model with QPFs and QPEs could greatly enhance the performance and extend the lead time of flood forecasting in karst areas.This study served two main purposes.One purpose was to compare the performance of the Weather Research and Forecasting(WRF)QPFs with that of the Precipitation Estimations through Remotely Sensed Information based on the Artificial Neural Network-Cloud Classification System(PERSIANN-CCS)QPEs in rainfall forecasting in karst river basins.The other purpose was to test the feasibility and effective application of karst flood simulation and forecasting by coupling the WRF and PERSIANN models with the Karst-Liuxihe model.The rainfall forecasting results showed that the precipitation distributions of the 2 weather models were very similar to the observed rainfall results.However,the precipitation amounts forecasted by WRF QPF were larger than those measured by the rain gauges,while the quantities forecasted by the PERSIANN-CCS QPEs were smaller.A postprocessing algorithm was proposed in this paper to correct the rainfall estimates produced by the two weather models.The flood simulations achieved based on the postprocessed WRF QPF and PERSIANN-CCS QPEs coupled with the Karst-Liuxihe model were much improved over previous results.In particular,coupling the postprocessed WRF QPF with the Karst-Liuxihe model could greatly extend the lead time of flood forecasting,and a maximum lead time of 96 h is adequate for flood warnings and emergency responses,which is extremely important in flood simulations and forecasting.

Simulation of Flood in the Thach Han-Ben Hai River Basin(Vietnam)under the Influence of the Upstream Reservoir System

The central provinces in Vietnam always suffer from the negative impacts of floods every year,especially in the downstream areas.Quang Tri province in the TBRB(Thach Han-Ben Hai River Basin)is one of the provinces suffering heavy damage caused by floods.A 1-dimensional hydrodynamic model was researched and applied connecting 2 dimensions in the MIKE model set(MIKE FLOOD)to simulate inundation level,inundation time,flood flow velocity for communes in TBRB.Simulation results for 111 communes in Quang Tri province show that:39 communes(35%)are not flooded;3 communes are flooded below 0.5 m;15 communes are flooded from 0.5-1.0 m,flooding time is about 1 day;30 communes are flooded from 1.0-2.0 m,inundation time is about 2 days;30 communes are flooded over 2.0 m,flooded for about 3 days,especially 3 communes are flooded over 4.0 m.This result helps to develop flood prevention plans for localities in the province.

Field based index of flood vulnerability (IFV): A new validation technique for flood susceptible models

The flood hazard management is one of the major challenges in the floodplain regions worldwide.With the rise in population growth and the spread of infrastructural development,the level of risk has increased over time.Therefore,the prediction of flood susceptible area is a key challenge for the adoption of management plans.Flood susceptibility modeling is technically a common work,but it is still a very tough job to validate flood susceptible models in a very rigorous and scientific manner.Therefore,the present work in the Atreyee River Basin of India and Bangladesh was planned to establish artificial neural network(ANN),radial basis function(RBF),random forest(RF)and their ensemble-based flood susceptibility models.The flood susceptible models were constructed based on nine flood conditioning parameters.The flood susceptibility models were validated in a conventional way using the receiver operating curve(ROC).To validate the flood-susceptible models,a two dimensional(2D)hydraulic flood simulation model was developed.Also,the index of flood vulnerability model was developed and applied for validating the flood susceptible models,which was a very unique way to validate the predictive models.Friedman test and Wilcoxon Signed rank test were employed to compare the generated flood susceptible models.Results showed that 11.95%-12.99%of the entire basin area(10188.4 km^(2))comes under very high flood-susceptible zones.Accuracy evaluation results have shown that the performance of ensemble flood susceptible models outperforms other standalone machine learning models.The flood simulation model and IFV model were also spatially adjusted with the flood susceptibility models.Therefore,the present study recommended for the ensemble flood susceptibility prediction and IFV based validation along with conventional ways.

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 ac-cording to food simulations under seven scheduling scenarios.This helps to regulate the spatial flood diversion caused by the LEFCP during extreme precipitation.

Numerical algorithm of distributed TOPKAPI model and its application

The TOPKAPI （TOPographic Kinematic APproximation and Integration） model is a physically based rainfall-runoff model derived from the integration in space of the kinematic wave model. In the TOPKAPI model, rainfall-runoff and runoff routing processes are described by three nonlinear reservoir differential equations that are structurally similar and describe different hydrological and hydraulic processes. Equations are integrated over grid cells that describe the geometry of the catchment, leading to a cascade of nonlinear reservoir equations. For the sake of improving the model＇s computation precision, this paper provides the general form of these equations and describes the solution by means of a numerical algorithm, the variable-step fourth-order Runge-Kutta algorithm. For the purpose of assessing the quality of the comprehensive numerical algorithm, this paper presents a case study application to the Buliu River Basin, which has an area of 3 310 km^2, using a DEM （digital elevation model） grid with a resolution of 1 km. The results show that the variable-step fourth-order Runge-Kutta algorithm for nonlinear reservoir equations is a good approximation of subsurface flow in the soil matrix, overland flow over the slopes, and surface flow in the channel network, allowing us to retain the physical properties of the original equations at scales ranging from a few meters to 1 km.

Evaluating performance dependency of a geomorphologic instantaneous unit hydrograph-based hydrological model on DEM resolution

The digital elevation model(DEM)is a type of model that has been widely used in terrain analysis and hydrological modeling.DEM resolution influences the hydrological and geomorphologic features of delineated catchments and consequently affects hydrological simulations.This study investigated the impacts of DEM resolution on the performance of the XAJ-GIUH hydrological model,a model coupling the widely used Xinanjiang(XAJ)hydrological model with the geomorphologic instantaneous unit hydrograph(GIUH),in flood simulations in small and medium-sized catchments.To test the model performance,the model parameters were calibrated at a fine DEM resolution(30 m)and then directly transferred to the simulation runs using coarser DEMs.Afterwards,model recalibration was conducted at coarser DEM resolutions.In the simulation runs with the model parameters calibrated at the 30-m resolution,the DEM resolution slightly affected the overall shape of the simulated flood hydrographs but presented a greater impact on the simulated peak discharges in the two study catchments.The XAJ-GIUH model consistently underestimated the peak discharges when the DEM resolution became coarser.The qualified ratio of peak simulations decreased by 35%when the DEM resolution changed from 30 m to 600 m.However,model recalibration produced comparable model per-formances when DEMs with different resolutions were used.This study showed that the impact of DEM resolution on model performance can be mitigated by model recalibration to some extent,if the DEM resolution is not too coarse.

A non-uniform grid approach for high-resolution flood inundation simulation based on GPUs

In view of the frequent occurrence of floods due to climate change, and the fact that a large calculation domain, with complex land types, is required for solving the problem of the flood simulations, this paper proposes an optimized non-uniform grid model combined with a high-resolution model based on the graphics processing unit (GPU) acceleration to simulate the surface water flow process. For the grid division, the topographic gradient change is taken as the control variable and different optimization criteria are designed according to different land types. In the numerical model, the Godunov-type method is adopted for the spatial discretization, the TVD-MUSUL and Runge-Kutta methods are used to improve the model’s spatial and temporal calculation accuracies, and the simulation time is reduced by leveraging the GPU acceleration. The model is applied to ideal and actual case studies. The results show that the numerical model based on a non-uniform grid enjoys a good stability. In the simulation of the urban inundation, approximately 40%–50% of the urban average topographic gradient change to be covered is taken as the threshold for the non-uniform grid division, and the calculation efficiency and accuracy can be optimized. In this case, the calculation efficiency of the non-uniform grid based on the optimized parameters is 2–3 times of that of the uniform grid, and the approach can be adopted for the actual flood simulation in large-scale areas.

Hydrometeorological Modeling of Limpopo River Basin in Mozambique with TOPMODEL and Remote Sensing

The Limpopo River basin (LRB) is known for its vulnerability to floods, high rates of evapotranspiration, and droughts that cause significant losses to the local community. The present study aimed to perform simulations of flood events occurring in two Mozambican sub-basins of LRB, namely Chókwè and Xai-Xai from 2000 to 2015 with TOPography-based hydrological MODEL (TOPMODEL) and satellite remote sensing data. As input in TOPMODEL, data from two high-resolution global satellite-based precipitation products: Climate Prediction Center MORPHing technique (CMORPH) and Integrated Multi-Satellite Retrievals for the Global Precipitation Mission (GPM) algorithm (IMERG), 8-day MOD16 evapotranspiration product and surface runoff data estimated by Global Land Data Assimilation System (GLDAS) were used. The sensitivity tests of TOPMODEL parameters were applied using the Monte Carlo simulation. Calibration and validation of the model were performed by the Shuffled Complex Evolution (SCE-UA) method and were evaluated with the Kling-Gupta Efficiency (KGE) index. The results indicated that simulations with the GPM-IMERG (KGE: 0.59 and 0.65) tended to underestimate the stream flows, while with the CMORPH product the performance was much better (KGE: 0.66 and 0.77) in both sub-basins. Thus, TOPMODEL can help to develop flood monitoring systems from satellite remotely sensed data in similar regions of Mozambique.