Validating quantitative precipitation forecast for the Flood Meteorological Office,Patna region during 2011–2014

In order to issue an accurate warning for flood, a better or appropriate quantitative forecasting of precipitation is required. In view of this, the present study intends to validate the quantitative precipitation forecast (QPF) issued during southwest monsoon season for six river catchments (basin) under the flood meteorological office, Patna region. The forecast is analysed statistically by computing various skill scores of six different precipitation ranges during the years 2011–2014. The analysis of QPF validation indicates that the multi-model ensemble (MME) based forecasting is more reliable in the precipitation ranges of 1–10 and 11–25 mm. However, the reliability decreases for higher ranges of rainfall and also for the lowest range, i.e., below 1 mm. In order to testify synoptic analogue method based MME forecasting for QPF during an extreme weather event, a case study of tropical cyclone Phailin is performed. It is realized that in case of extreme events like cyclonic storms, the MME forecasting is qualitatively useful for issue of warning for the occurrence of floods, though it may not be reliable for the QPF. However, QPF may be improved using satellite and radar products.     

Variation of debris flow/flood formation conditions at the watershed scale in the Wenchuan Earthquake area

A channelized debris flow/flood generally originates from initial gully erosion by superficial runoff that evolves rapidly into massive erosion of the channel bed. Knowledge of the formation conditions of such events is crucial for accurate forecasting, and determination of rainfall and runoff thresholds for such hazards is a primary concern following a strong earthquake. This work proposed a framework for debris flow/flood formation at the watershed scale in two watersheds (area: 2.4 and 32.4 km²) in the Wenchuan Earthquake area (China). The critical runoff and rainfall conditions required for debris flow/flood formation were simulated and their annual variations investigated. Ultimately, the runoff conditions required for debris flow/flood formation in the two studied watersheds were calculated on an annual basis and found to increase in time. Similarly, following consideration of three different rainfall types, critical rainfall conditions were proposed that also showed an increasing tendency. The increase of rainfall and runoff conditions for debris flow/flood formation is attributable to both the recovery of vegetation and the reduction of source materials. In comparison with actual monitored flow behaviors and previously proposed rainfall thresholds, the results showed strong consistency and high forecasting efficiency.

     

Combination of GIS and mathematical modeling to predict floods in semiarid areas: case of Rheraya watershed (Western High Atlas,Morocco)

The Tinitine sub-watershed of Rheraya is renowned for its semiarid climate, irregular supply of water flow, and its historical floods; for these reasons, it is classified as vulnerable areas during a rainfall event. We conducted this study to propose flood prediction models adapted to this risky zone. For this, a hydrological method of flood forecasting rainfall-runoff used to determine a model conforms to the semiarid basin. This model is based on the articulation of the series production and transfer function. The goal of the production function is to determine the portion of the rainfall runoff, which is performed by two approaches that differ in their structures: (1) the first approach is based on the mathematical model of Horton; (2) the second approach is based on the determination of the part of the rain contributes to runoff and obtained by a spatial map of runoff coefficient (GIS). The transfer function is based on the two models of Clark and Nash, rainy efficient routes to the catchment outlet from a unit hydrograph. The characteristic parameters of these models are obtained through the application of mathematical optimization methods such as genetic algorithms. Thereby, the coupling function producing Horton (identified by the parameters: initial infiltration?=?15.03, final infiltration?=?0.3, and phi?=?0.45) and Clark transfer function (identified by the parameters C_A?=?0.21 and C_B?=?0.79) was given very satisfying results, mainly the low difference between observed and simulated hydrograph and Nash coefficient which is about 85%. This shows the interest of this coupling model in flood forecasting.     

A Probability-Based Renewal Rainfall Model for Flow Forecasting

In real-time flood warning systems, sufficient lead-time is important for people to take suitable actions. Rainfall forecasting is one of the ways commonly used to extend the lead-time for catchments with short response time. However, an accurate forecast of rainfall is still difficult for hydrologists using the present deterministic model. Therefore, a probability-based rainfall forecasting model, based on Markov chain, was proposed in this study. The rainfall can be forecast one to three hours in advance for a specified nonexceeding probability using the transition probability matrix of rainfall state. In this study, the nonexceeding probability, which was hourly updated on the basis of development or decay of rainfall processes, was taken as a dominant variable parameter. The accuracy of rainfall forecasting one to three hours in advance is concluded from the application of this model to four recording rain gauges. A lumped rainfall-runoff forecasting model derived from a transfer function was further applied in unison with this rainfall forecasting model to forecast flows one to four hours in advance. The results of combination of these two models show good performance with agreement between the observed and forecast hydrographs.     

长三角地区典型海绵设施水文效益试验研究

开展不同海绵设施在中国长三角气候模式下的水文效益研究, 对增强城市应对内涝能力从而提高城市对变化环境的适应性具有重要科学意义。选择国家首批海绵试点城市镇江海绵基地4种典型海绵设施作为研究案例, 采用径流系数、削减率、削峰率及洪峰流量等指标, 评估场次降雨与海绵设施出流相关性, 分析海绵设施在不同降水量级和降雨雨型下的水文性能, 以及运行时间对海绵设施水文效益的影响。结果表明: ①透水铺装类海绵设施的降雨—径流关系呈单一式; 而绿植类则表现为分段式, 即在场次降水量超过一定临界暴雨量之后关系线发生转折, 其中平均径流系数增加了8.4~38.5倍, 平均削峰率和削减率分别减少了50.4%和44.6%。②暴雨条件下不同海绵设施的产流能力和洪峰流量最大, 对径流总量消减能力及洪峰流量削减能力最弱, 且从暴雨到大雨变化规律比大雨到中雨变化规律更显著。③海绵设施的水文性能受到降雨雨型、平均降雨强度和最大单位降雨强度等因子多重复合影响。除雨水花园外, 其他海绵设施的径流系数对上述影响因子变化最为敏感, 洪峰流量次之, 削减率第3, 削峰率的敏感性显著低于前面三者。④车行透水砖运行1 a后, 其产流能力与洪峰流量分别显著增加1.7~2.1倍和1.9~2.5倍; 径流控制能力显著减弱, 其中消减能力降低了16%。     

An operational flood warning system for poorly gauged basins: demonstration in the Guadalhorce basin (Spain)

This paper deals with the presentation of a flood warning system (GFWS) developed for the specific characteristics of the Guadalhorce basin (3,200 km², SE of Spain), which is poorly gauged and often affected by flash and plain floods. Its complementarity with the European flood alert system (EFAS) has also been studied. At a lower resolution, EFAS is able to provide a flood forecast several days in advance. The GFWS is adapted to the use of distributed rainfall maps (such as radar rainfall estimates), and discharge forecasts are computed using a distributed rainfall–runoff model. Due to the lack of flow measurements, the model parameters calibrated on a small watershed have been transferred in most of the basin area. The system is oriented to provide distributed warnings and fulfills the requirements of ungauged basins. This work reports on the performance of the system on two recent rainfall events that caused several inundations. These results show how the GFWS performed well and was able to forecast the location and timing of flooding. It demonstrates that despite its limitations, a simple rainfall–runoff model and a relatively simple calibration could be useful for event risk management. Moreover, with low resolution and long anticipation, EFAS appears as a good complement tool to improve flood forecasting and compensate for the short lead times of the GFWS.     

全过程联合校正方法在饶河流域洪水预报中的应用研究

为提高饶河流域洪水预报精度,将全过程联合校正(EPJC)方法与三水源新安江(XAJ)模型结合,按一定比例划分洪水预报总误差为各过程误差,基于系统响应理论反演得到面雨量计算误差和模型参数误差,重新输入流域水文模型正演得出校正后的洪水过程.选取洪峰流量相对误差、峰现时间绝对误差、径流深相对误差和确定性系数作为模型评价指标,...     

淮河干流洪水预报系统及其在1991年大洪水的应用

综述了淮河流域概况，淮河干流洪水预报系统物采用的预报方法。１９９１年淮河干流洪水预报采用了降雨径流预报与上，下游站相应流量预防方法相配合，并注重实时水情分析；在行洪区多，行洪后水面宽广而比降又极小的河段的汇流计算，采用了以实测洪水资料绘制的经验蓄曲线为充分发挥的湖泊洪水演算方法。     

基于相关系数和Fisher最优分割法的汛期分期研究

从径流形成原理出发,提出了采用降雨径流相关系数作为汛期分期指标的基本思路。以陕西省石头河水库1954~2007年的日降水和日流量资料为基础,采用主成分分析法对影响时段径流的因素进行了分析,筛选出了影响不同时段径流量的主要因素,计算了主要影响因素与时段径流量的相关系数,采用Fisher最优分割法将石头河水库汛期划分为汛前过渡期(4月1日~5月20日)、前汛期(5月21日~7月10日)、主汛期(7月11日~8月10日)、后汛期(8月11日~9月20日)和汛后过渡期(9月21日~10月31日),并结合区域气候特点和实际发生的洪水对成果进行了合理性论证。该方法资料要求低,计算简便,具有一定的推广应用价值。     

Regional modelling of rainfall-induced runoff using hydrological model by incorporating plant cover effects: case study in Kelantan,Malaysia

The objective of this paper is to develop a spatial temporal runoff modelling of local rainfall patterns effect on the plant cover hilly lands in Kelantan River Basin. Rainfall interception loss based on leaf area index, loss/infiltration on the ground surface, and runoff calculation were considered as the main plant cover effects on the runoff volume. In this regard, a hydrological and geotechnical grid-based regional model (integrated model) was performed using Microsoft Excel^® and GIS framework system for deterministic modelling of rainfall-induced runoff by incorporating plant cover effects. The infiltration process of the current model was integrated with the precipitation distribution method and rainfall interception approach while the runoff analysis of integrated model was employed based on loss/infiltration water on the ground surface with consideration of water interception loss by canopy and the remaining surface water. In the following, the spatial temporal analysis of rainfall-induced runoff was performed using 10 days of hourly rainfall events at the end of December 2014 in Kelantan River Basin. The corresponding changes in pressure head and consequent rate of infiltration were calculated during rainfall events. Subsequently, flood volume is computed using local rainfall patterns, along with water interception loss and the remaining surface water in the study area. The results showed the land cover changes caused significant differences in hydrological response to surface water. The increase in runoff volume of the Kelantan River Basin is as a function of deforestation and urbanization, especially converting the forest area to agricultural land (i.e. rubber and mixed agriculture).     

Quantitative precipitation forecasting over Narmada Catchment

Quantitative precipitation forecasting (QPF) has been attempted over the Narmada Catchment following a statistical approach. The catchment has been divided into five sub-regions for the development of QPF models with a maximum lead-time of 24 hours. For this purpose the data of daily rainfall from 56 raingauge stations, twice daily observations on different surface meteorological parameters from 28 meteorological observatories and upper air data from 11 aerological stations for the nine monsoon seasons of 1972–1980 have been utilized. The horizontal divergence, relative vorticity, vertical velocity and moisture divergence are computed using the kinematic method at different pressure levels and used as independent variables along with the rainfall and surface meteorological parameters. Multiple linear regression equations have been developed using the stepwise procedure separately with actual and square root and log-transformed rainfall using 8-year data (1972–1979). When these equations were verified with an independent data for the monsoon season of 1980, it was found that the transformed rainfall equations fared much better compared to the actual rainfall equations. The performance of the forecasts of QPF model compared to the climatological and persistence forecasts has been assessed by computing the verification scores using the forecasts for the monsoon season of 1980.     

Forecasting for flood warning

Advances in flood forecasting have been constrained by the difficulty of estimating rainfall continuously over space, for catchment-, national- and continental-scale areas. This has had a concomitant impact on the choice of appropriate model formulations for given flood-forecasting applications. Whilst weather radar used in combination with raingauges – and extended to utilise satellite remote-sensing and numerical weather prediction models – have offered the prospect of progress, there have been significant problems to be overcome. These problems have curtailed the development and adoption of more complete distributed model formulations that aim to increase forecast accuracy. Advanced systems for weather radar display and processing, and for flood forecast construction, are now available to ease the task of implementation. Applications requiring complex networks of models to make forecasts at many locations can be undertaken without new code development and be readily revised to take account of changing requirements. These systems make use of forecast-updating procedures that assimilate data from telemetry networks to improve flood forecast performance, at the same time coping with the possibility of data loss. Flood forecasting systems that integrate rainfall monitoring and forecasting with flood forecasting and warning are now operational in many areas. Present practice in flood modelling and forecast updating is outlined from a UK perspective. Challenges for improvement are identified, particularly against a background of greater access to spatial datasets on terrain, soils, geology, land-cover, and weather variables. Representing the effective runoff production and translation processes operating at a given grid or catchment scale may prove key to improved flood simulation, and robust application to ungauged basins through physics-based linkages with these spatial datasets. The need to embrace uncertainty in flood-warning decision-making is seen as a major challenge for the future. To cite this article: R.J. Moore et al., C. R. Geoscience 337 (2005).     

Flood occurrence hazard forecasting based on geographical information system

The application of Geographical Information system (GIS) in modeling flood and its prediction in catchments offers considerable potential. Several examples illustrate simple GIS techniques to produce flood hazard indices or its zonation using hydrologic-type models. Existing flood models can also be loosely coupled to a GIS, such as the HMS (Hydrological Modeling System) model. Forethermore, models can be fully integrated into a GIS by embedded coupling, such as the SCS (Soil Conservation Service) model. Installation of flood forecasting systems in watersheds with incomplete hydrometric data may reduce the flood-induced damages. In this study Geographical Information system used to up to date the watershed data and estimation of SCS model parameters which is sensible to considered the real time flood forecasting in Kasilian catchment of Mazandaran province. The main aim of this paper is to investigate the possibility of the linkage between GIS with a comprehensive hydrologic model, especially HMS. The use of GIS could produce a suitable agreement between observed results (extracted rainfall and runoff data of 1992, 1995 and 1996 from the related stations) with the calculated results of the hydrological model. The obtained results from rainfall-runoff process simulations of the model in this research showed that submergibility of the main watershed, Kasillian, does not depend on the outlet discharge rate of each one of its watershed independently. But it is related to how those two outlet hydrographs from main river watershed are combined. The model is capable of showing the flood characteristics temporally and spatially in each cross section of the channel network.     

洪水预报产流误差的动态系统响应曲线修正方法

为提高实时洪水预报精度,提出了一种基于动态系统响应曲线洪水预报误差修正新方法。该方法将动态系统响应曲线引入洪水预报误差修正中,建立一种向误差源头追溯的动态反馈修正模型。此修正方法将新安江模型产流以下的部分作为响应系统, 用线性差分近似代替非线性系统响应函数的偏导数值,得到时段产流量所对应的系统响应曲线。用实测流量和计算流量之间的差值作为信息,使用最小二乘估计原理,对产流量进行修正, 再用修正后的产流量重新计算出流过程。该修正方法分别用理想案例和王家坝流域进行检验,结果证明此方法效果比传统二阶自回归模型有明显提高。     

Particle swarm optimization feedforward neural network for modeling runoff

The rainfall-runoff relationship is one of the most complex hydrological phenomena. In recent years, hydrologists have successfully applied backpropagation neural network as a tool to model various nonlinear hydrological processes because of its ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets. However, the backpropagation neural network convergence rate is relatively slow and solutions can be trapped at local minima. Hence, in this study, a new evolutionary algorithm, namely, particle swarm optimization is proposed to train the feedforward neural network. This particle swarm optimization feedforward neural network is applied to model the daily rainfall-runoff relationship in Sungai Bedup Basin, Sarawak, Malaysia. The model performance is measured using the coefficient of correlation and the Nash-Sutcliffe coefficient. The input data to the model are current rainfall, antecedent rainfall and antecedent runoff, while the output is current runoff. Particle swarm optimization feedforward neural network simulated the current runoff accurately with R = 0.872 and E² = 0.775 for the training data set and R = 0.900 and E²= 0.807 for testing data set. Thus, it can be concluded that the particle swarm optimization feedforward neural network method can be successfully used to model the rainfall-runoff relationship in Bedup Basin and it could be to be applied to other basins.     

Determination of potential runoff coefficient for Al-Baha Region,Saudi Arabia using GIS

The curve number (CN) is a hydrologic parameter used to describe the stormwater runoff potential for drainage areas, and it is a function of land use, soil type, and soil moisture. This study was conducted to estimate the potential runoff coefficient (PRC) using geographic information system (GIS) based on the area’s hydrologic soil group, land use, and slope and to determine the runoff volume. The soil map for the study area was developed using GPS data carried on to identify the soil texture to be used in building a soil hydrological groups map. Unsupervised and supervised classifications were done to Landsat 5/7 TM/ETM image to generate land-use and land-cover map. This map was reclassified into four main classes (forest, grass and shrub, cropland, and bare soil). Slope map for Al-Baha was generated from a 30-m digital elevation model. The GIS technique was used to combine the previous three maps into one map to generate PRC map. Annual runoff depth is derived based on the annual rainfall surplus and runoff coefficient per pixel using raster calculator tool in ArcGIS. An indication that in the absence of reliable ground measurements of rainfall product, it can satisfactorily be applied to estimate the spatial rainfall distribution based on values of R and R ² (0.9998) obtained. Annual runoff generation from the study area ranged from 0 to 82 % of the total rainfall. Rainfall distribution in the study area shows the wise use of identifying suitable sites for rainwater harvesting, where most of the constructed dams are located in the higher rainfall areas.     

基于GPU加速技术的非结构流域雨洪数值模型

为高效高精度地模拟流域雨洪过程,应用动力波法求解二维圣维南方程,并耦合水文过程,建立了包含流域降雨产流、汇流、下渗以及洪水演进等过程的高性能流域雨洪数值模型。该模型的优势在于使用非结构网格,可较好地处理不规则边界,准确贴合复杂地形表面,使得模型能精确计算模拟流域雨洪过程,同时引入GPU技术加速计算,使得大尺度流域雨洪计算成为可能。最后,将模型应用于V型经典算例及2个实际流域雨洪算例,所得结果与实测吻合较好,计算所用时间较短,表明该模型可以快速且精确模拟流域雨洪过程。研究结果有助于实现对实际流域雨洪灾害进行合理高效的预测,为应急抢险工作提供有力支撑。     

Rainfall-Runoff Modeling using Clustering and Regression Analysis for the River Brahmaputra Basin

In this research, k-means, agglomerative hierarchical clustering and regression analysis have been applied in hydrological real time series in the form of patterns and models, which gives the fruitful results of data analysis, pattern discovery and forecasting of hydrological runoff of the catchment. The present study compares with the actual field data, predicted value and validation of statistical yields obtained from cluster analysis, regression analysis with ARIMA model. The seasonal autoregressive integrated moving average (SARIMA) and autoregressive integrated moving average (ARIMA) models is investigated for monthly runoff forecasting. The different parameters have been analyzed for the validation of results with casual effects. The comparison of model results obtained by K-means & AHC have very close similarities. Result of models is compared with casual effects in the same scenario and it is found that the developed model is more suitable for the runoff forecasting. The average value of R² determined is 0.92 for eight ARIMA models. This shows more accuracy of developed ARIMA model under these processes. The developed rainfall runoff models are highly useful for water resources planning and development.     

Evaluation of several techniques for estimating stormwater runoff in arid watersheds

Several traditional techniques have been used for estimating storm-water runoff from ungauged watersheds. Some of these techniques were applied to watersheds of Rashadia in south-eastern desert of Jordan. When engineers apply rainfall-runoff models for hydrologic design, there are difficulties in defining and quantifying peak discharges that are required to design different types of hydraulic structures. The lack of data presents major difficulties for rainfall-runoff modeling in arid regions. These regions have characteristically high rainfall intensity and consequent flash floods. The specific objectives of this study are: (1) apply synthetic hydrographs for estimating peak discharges from limited hydrological data. (2) Evaluate the reliability of six techniques to accurately estimate storm-water runoff; and, to evaluate the runoff that is required to design hydraulic structures such as bridges, culverts and dams. (3) Estimate the flood resulting from direct runoff after subtracting all the loses such as: the infiltration, interflow and evaporation. (4) Develop a simple regression relationship between peak flow discharges and catchment areas. The results show that there is uncertainty in determining the accuracy of storm-water volume, this is due to several methods were utilizing the estimation the hydrographs base time, but promising results in predicting the peak flow discharge.     

Quantifying the uncertainty of semiarid flash floods using generalized likelihood uncertainty estimation

This paper aims to address the question of how the parameter uncertainty associated with a mixed conceptual and physical based rainfall-runoff model (AFFDEF) has influences on flood simulation of the semiarid Abolabbas catchment (284 km²), in Iran. AFFDEF was modified and coupled with the generalized likelihood uncertainty estimation (GLUE) algorithm to simulate four flash flood events. Analysis suggests that AFFDEF parameters showed non-unique posterior distributions depending on the magnitudes and duration of flash flood events. Model predictive uncertainty was heavily dominated by error and bias in soil antecedent moisture condition that led to large storage effect in simulation. Overall, multiplying parameter for the infiltration reservoir capacity and multiplying parameter for the interception reservoir capacity along with potential runoff contributing areas were identified the key model parameters and more influential on flood simulation. Results further revealed that uncertainty was satisfactorily quantified for the event with low to moderate flood magnitudes while high magnitude event exhibited unsatisfactory result.