Flood simulation using different sources of rainfall in the Huong River,Vietnam / Simulation d'inondation à l'aide de différentes sources d'information pluviométrique dans le bassin de la Rivière Huong,Vietnam     

《水文科学杂志》2013,58(5):909-917

Abstract

The possibility of simulating flooding in the Huong River basin, Vietnam, was examined using quantitative precipitation forecasts at regional and global scales. Raingauge and satellite products were used for observed rainfall. To make maximum use of the spatial heterogeneity of the different types of rainfall data, a distributed hydrological model was set up to represent the hydrological processes. In this way, streamflow simulated using the rainfall data was compared with that observed in situ. The forecast on a global scale showed better performance during normal flow peak simulations than during extreme events. In contrast, it was found that during an extreme flood peak, the use of regional forecasts and satellite data gives results that are in close agreement with results using raingauge data. Using the simulated overflow volumes recorded at the control point downstream, inundation areas were then estimated using topographic characteristics. This study is the first step in developing a future efficient early warning system and evacuation strategy.  相似文献   

Prediction and modelling of rainfall–runoff during typhoon events using a physically-based and artificial neural network hybrid model     

Chih-Chieh Young  Wen-Cheng Liu 《水文科学杂志》2013,58(12):2102-2116

Abstract

The accurate prediction of hourly runoff discharge in a watershed during heavy rainfall events is of critical importance for flood control and management. This study predicts n-h-ahead runoff discharge in the Sandimen basin in southern Taiwan using a novel hybrid approach which combines a physically-based model (HEC-HMS) with an artificial neural network (ANN) model. Hourly runoff discharge data (1200 datasets) from seven heavy rainfall events were collected for the model calibration (training) and validation. Six statistical indicators (i.e. mean absolute error, root mean square error, coefficient of correlation, error of time to peak discharge, error of peak discharge and coefficient of efficiency) were employed to evaluate the performance. In comparison with the HEC-HMS model, the single ANN model, and the time series forecasting (ARMAX) model, the developed hybrid HEC-HMS–ANN model demonstrates improved accuracy in recursive n-h-ahead runoff discharge prediction, especially for peak flow discharge and time.  相似文献   

Flood modelling using the distributed event-based SCS-LR model in the Mediterranean Real Collobrier catchment     

Son Nguyen 《水文科学杂志》2013,58(11):1351-1369

ABSTRACT

Event-based models are often used for flood prediction because they require fewer data than more complex models and account for a small number of parameters. We present the performance of such a model in simulating Mediterranean floods, with a focus on the initialization and on the impact of the rainfall uncertainties on the calibration of the model. The distributed event-based parsimonious Soil Conservation Service Lag-and-Route (SCS-LR) model was applied in the Real Collobrier catchment, France, which has a very high density of raingauges. The initial condition of the model was highly correlated with predictors, such as baseflow or soil water content. A reduction in the raingauge density can markedly change the calibration of the model. As the density of raingauges is generally low in most catchments, the uncertainties associated with rainfall measurement are thus expected either to mask the actual accuracy of the model, or to alter the model parameters.  相似文献   

Satellite-based rainfall estimation for river flow forecasting in Africa. I: Rainfall estimates and hydrological forecasts     

《水文科学杂志》2013,58(4):567-584

Abstract

Reliable, real-time river flow forecasting in Africa on a time scale of days can provide enormous humanitarian and economic benefits. This study investigates the feasibility of using daily rainfall estimates based on cold cloud duration (CCD) derived from Meteosat thermal infrared imagery as input to a simple rainfall—runoff model and also whether such estimates can be improved by the inclusion of information from numerical weather prediction (NWP) analysis models. The Bakoye catchment in Mali, West Africa has been used as a test area. The data available for the study covered the main months of the rainy season for three years. The rainfall estimates were initially validated against gauge data. Improvements in quality were observed when information relating to African Easterly Wave phase and storm type was included in a multiple linear regression (MR) algorithm. The estimates were also tested by using them as input to a rainfall—runoff model. When contemporaneous calibrations from raingauges were available for calibration, both CCD-only and MR rainfall estimates gave more accurate river flow forecasts than when using raingauge data alone. In the absence of contemporaneous calibrations, the performance was reduced but the MR did better than the CCDonly input in all years. The use of satellite-derived vegetation index did not improve the quality of the river flow forecasts.  相似文献   

Intraseasonal predictability of the duration of flooding above National Weather Service flood warning levels across the U.S. Midwest     

Andrea Neri  Gabriele Villarini  Francesco Napolitano 《水文研究》2020,34(23):4505-4511

Unpreparedness is often the main cause of the economic and social damages caused by floods. To mitigate these impacts, short-term forecasting has been the focus of several studies during the past decades; however, less effort has been paid to flood predictions at longer lead times. Here, we use forecasts by six models from the North American Multi-Model Ensemble project with a lead time from 0.5 to 9.5 months to predict the seasonal duration of floods above four National Weather Service flood categories (“action,” “flood,” “moderate” and “major”). We focus on 202 U.S. Geological Survey gage stations across the U.S. Midwest and use a statistical framework which considers precipitation, temperature, and antecedent wetness conditions as predictors. We find that the prediction skill of the duration of floods for the “action” and “flood” categories is overall low, largely because of the low accuracy of the climate forecasts rather than of the errors introduced by the statistical models. The prediction skill slightly improves when considering the shortest lead times (i.e., from 0.5 to 2.5 months) during spring in the Northern Great Plains, where antecedent wetness conditions play an important role in influencing the generation of floods. It is very difficult to draw strong conclusions with respect to the “moderate” and “major” flood categories because of the limited number of available events.  相似文献   

Precipitation forecasts for rainfall runoff predictions. A case study in poorly gauged Ribb and Gumara catchments,upper Blue Nile,Ethiopia     

《Physics and Chemistry of the Earth》2013

Flow forecasting in poorly gauged, flood-prone Ribb and Gumara sub-catchments of the Blue Nile was studied with the aim of testing the performance of Quantitative Precipitation Forecasts (QPFs). Four types of QPFs namely MM5 forecasts with a spatial resolution of 2 km; the Maximum, Mean and Minimum members (MaxEPS, MeanEPS and MinEPS where EPS stands for Ensemble Prediction System) of the fixed, low resolution (2.5 by 2.5 degrees) National Oceanic and Atmospheric Administration Global Forecast System (NOAA GFS) ensemble forecasts were used. Both the MM5 and the EPS were not calibrated (bias correction, downscaling (for EPS), etc.). In addition, zero forecasts assuming no rainfall in the coming days, and monthly average forecasts assuming average monthly rainfall in the coming days, were used. These rainfall forecasts were then used to drive the Hydrologic Engineering Center’s–Hydrologic Modeling System, HEC–HMS, hydrologic model for flow predictions. The results show that flow predictions using MaxEPS and MM5 precipitation forecasts over-predicted the peak flow for most of the seven events analyzed, whereas under-predicted peak flow was found using zero- and monthly average rainfall. The comparison of observed and predicted flow hydrographs shows that MM5, MaxEPS and MeanEPS precipitation forecasts were able to capture the rainfall signal that caused peak flows. Flow predictions based on MaxEPS and MeanEPS gave results that were quantitatively close to the observed flow for most events, whereas flow predictions based on MM5 resulted in large overestimations for some events. In follow-up research for this particular case study, calibration of the MM5 model will be performed. The overall analysis shows that freely available atmospheric forecasting products can provide additional information on upcoming rainfall and peak flow events in areas where only base-line forecasts such as no-rainfall or climatology are available.  相似文献   

The relationship between rainfall inputs and flood generation in south–east Spain     

L. J. Bracken  N. J. Cox  J. Shannon 《水文研究》2008,22(5):683-696

Rainfall and flood data are relatively sparse in semi‐arid areas; hence there have been relatively few investigations into the relationships between rainfall inputs and flood generation in these environments. Previous work has shown that flood properties are influenced by a combination of precipitation characteristics including amount, intensity, duration and spatial distribution. Therefore floods may be produced by high intensity, short duration storms, or longer duration, low intensity rainfall. Most of this research has been undertaken in small catchments in either hyper‐arid or relatively high rainfall Mediterranean climates. This paper presents results from a 6 year data record in south‐east Spain from research conducted in two basins, the Rambla Nogalte (171 km²) and the Rambla de Torrealvilla (200 km²). Data cover an area of approximately 500 km² and an annual average rainfall of 300 mm. At coarse temporal resolutions gauges spread over large areas record similar patterns of rainfall, although spells of rain show much more complexity; pulses of rain within storms can vary considerably in total rainfall, intensity and duration over the same area. The analysis for south‐east Spain shows that most storms occur over a period of less than 24 h, but that the number of rainfall events declines as the duration exceeds 8 h. This is at odds with data on floods for the study area suggesting that they are produced by storms lasting longer than 18 h. However, one flood event was produced by a very short (15 min) storm with high intensity rainfall. Most floods tended to occur in May/June or September, which coincides with wetter months of the year (September, October, December and May). Floods are also more highly related to the total rainfall occurring in a spell of rain, than to intensity. The complexity of storm rainfall increases with the storm total, which makes it difficult to generalize on the importance of rainfall intensity for flood generation. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

Flood-type trend analysis for alpine catchments     

Anna E. Sikorska-Senoner  Jan Seibert 《水文科学杂志》2020,65(8):1281-1299

ABSTRACT

In many places, magnitudes and frequencies of floods are expected to increase due to climate change. To understand these changes better, trend analyses of historical data are helpful. However, traditional trend analyses do not address issues related to shifts in the relative contributions of rainfall versus snowmelt floods, or in the frequency of a particular flood type. We present a novel approach for quantifying such trends in time series of floods using a fuzzy decision tree for event classification and applied it to maximal annual and seasonal floods in 27 alpine catchments for the period 1980–2014. Trends in flood types were studied with Sen’s slope and double mass curves. Our results reveal a decreasing number of rain-on-snow and an increasing number of short rainfall events in all catchments, with flash floods increasing in smaller catchments. Overall, the results demonstrate the value of incorporating a fuzzy flood-type classification into flood trend analyses.  相似文献   

Exploring the effect of data assimilation by WRF‐3DVar for numerical rainfall prediction with different types of storm events     

Jia Liu  Michaela Bray  Dawei Han 《水文研究》2013,27(25):3627-3640

The mesoscale Numerical Weather Prediction (NWP) model is gaining popularity among the hydrometeorological community in providing high‐resolution rainfall forecasts at the catchment scale. Although the performance of the model has been verified in capturing the physical processes of severe storm events, the modelling accuracy is negatively affected by significant errors in the initial conditions used to drive the model. Several meteorological investigations have shown that the assimilation of real‐time observations, especially the radar data can help improve the accuracy of the rainfall predictions given by mesoscale NWP models. The aim of this study is to investigate the effect of data assimilation for hydrological applications at the catchment scale. Radar reflectivity together with surface and upper‐air meteorological observations is assimilated into the Weather Research and Forecasting (WRF) model using the three‐dimensional variational data‐assimilation technique. Improvement of the rainfall accumulation and its temporal variation after data assimilation is examined for four storm events in the Brue catchment (135.2 km²) located in southwest England. The storm events are selected with different rainfall distributions in space and time. It is found that the rainfall improvement is most obvious for the events with one‐dimensional evenness in either space or time. The effect of data assimilation is even more significant in the innermost domain which has the finest spatial resolution. However, for the events with two‐dimensional unevenness of rainfall, i.e. the rainfall is concentrated in a small area and in a short time period, the effect of data assimilation is not ideal. WRF fails in capturing the whole process of the highly convective storm with densely concentrated rainfall in a small area and a short time period. A shortened assimilation time interval together with more efficient utilisation of the weather radar data might help improve the effectiveness of data assimilation in such cases. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

Uncertainties in real‐time flood forecasting with neural networks     

Dawei Han  Terence Kwong  Simon Li 《水文研究》2007,21(2):223-228

Although artificial neural networks (ANNs) have been applied in rainfall runoff modelling for many years, there are still many important issues unsolved that have prevented this powerful non‐linear tool from wide applications in operational flood forecasting activities. This paper describes three ANN configurations and it is found that a dedicated ANN for each lead‐time step has the best performance and a multiple output form has the worst result. The most popular form with multiple inputs and single output has the average performance. In comparison with a linear transfer function (TF) model, it is found that ANN models are uncompetitive against the TF model in short‐range predictions and should not be used in operational flood forecasting owing to their complicated calibration process. For longer range predictions, ANN models have an improved chance to perform better than the TF model; however, this is highly dependent on the training data arrangement and there are undesirable uncertainties involved, as demonstrated by bootstrap analysis in the study. To tackle the uncertainty issue, two novel approaches are proposed: distance analysis and response analysis. Instead of discarding the training data after the model's calibration, the data should be retained as an integral part of the model during its prediction stage and the uncertainty for each prediction could be judged in real time by measuring the distances against the training data. The response analysis is based on an extension of the traditional unit hydrograph concept and has a very useful potential to reveal the hydrological characteristics of ANN models, hence improving user confidence in using them in real time. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

Updating Real-Time Flood Forecasting Using a Fuzzy Rule-Based Model/Mise à Jour de Prévision de Crue en Temps Réel Grâce à un Modèle à Base de Règles Floues     

《水文科学杂志》2013,58(2)

Abstract

An updating technique is a tool to update the forecasts of mathematical flood forecasting model based on data observed in real time, and is an important element in a flood forecasting model. An error prediction model based on a fuzzy rule-based method was proposed as the updating technique in this work to improve one- to four-hour-ahead flood forecasts by a model that is composed of the grey rainfall model, the grey rainfall—runoff model and the modified Muskingum flow routing model. The coefficient of efficiency with respect to a benchmark is applied to test the applicability of the proposed fuzzy rule-based method. The analysis reveals that the fuzzy rule-based method can improve flood forecasts one to four hours ahead. The proposed updating technique can mitigate the problem of the phase lag in forecast hydrographs, and especially in forecast hydrographs with longer lead times.  相似文献