Assessment on the function of reservoirs for flood control during typhoon seasons based on a distributed hydrological model

A grid‐based distributed hydrological model, PDTank model, is used to simulate hydrological processes in the upper Tone River catchment. The Tone River catchment often suffers from heavy rainfall events during the typhoon seasons. The reservoirs located in the catchment play an important role in flood regulation. Through the coupling of the PDTank model and a reservoir module that combines the storage function and operation function, the PDTank model is used for flood forecasting in this study. By comparing the hydrographs simulated using gauging and radar rainfall data, it is found that the spatial variability of rainfall is an important factor for flood simulation and the accuracy of the hydrographs simulated using radar rainfall data is slightly improved. The simulation of the typhoon flood event numbered No. 9 shows that the reservoirs in the catchment attenuate the peak flood discharge by 423·3 m³/s and validates the potential applicability of the distributed hydrological model on the assessment of function of reservoirs for flood control during typhoon seasons. Copyright © 2011 John Wiley & Sons, Ltd.     

A unique flood event in an arid zone

Arid regions generally lack surface water records for developing flood characteristics for hydraulic design purposes. Consequently good records of floods, particularly large ones, are a data asset to arid zone hydrology. A large rainfall and runoff event which occurred on 23 April 1985 has been fully recorded on Wadi Ghat, a 597 km² catchment in southwest Saudi Arabia. Maximum point rainfall intensity was 115.4 mm hr^?1. The peak discharge was 3200 m³ s^?1. Point rainfall has an expected frequency of occurrence in excess of once every 200 years. The peak discharge is expected to occur on an average once in 143 years.     

Coupling the Xinanjiang model to a kinematic flow model based on digital drainage networks for flood forecasting

The Xinanjiang model, which is a conceptual rainfall‐runoff model and has been successfully and widely applied in humid and semi‐humid regions in China, is coupled by the physically based kinematic wave method based on a digital drainage network. The kinematic wave Xinanjiang model (KWXAJ) uses topography and land use data to simulate runoff and overland flow routing. For the modelling, the catchment is subdivided into numerous hillslopes and consists of a raster grid of flow vectors that define the water flow directions. The Xinanjiang model simulates the runoff yield in each grid cell, and the kinematic wave approach is then applied to a ranked raster network. The grid‐based rainfall‐runoff model was applied to simulate basin‐scale water discharge from an 805‐km² catchment of the Huaihe River, China. Rainfall and discharge records were available for the years 1984, 1985, 1987, 1998 and 1999. Eight flood events were used to calibrate the model's parameters and three other flood events were used to validate the grid‐based rainfall‐runoff model. A Manning's roughness via a linear flood depth relationship was suggested in this paper for improving flood forecasting. The calibration and validation results show that this model works well. A sensitivity analysis was further performed to evaluate the variation of topography (hillslopes) and land use parameters on catchment discharge. Copyright © 2009 John Wiley & Sons, Ltd.     

Derivation of a curve number and kinematic-wave based flood frequency distribution

Abstract

The physically-based flood frequency models use readily available rainfall data and catchment characteristics to derive the flood frequency distribution. In the present study, a new physically-based flood frequency distribution has been developed. This model uses bivariate exponential distribution for rainfall intensity and duration, and the Soil Conservation Service-Curve Number (SCS-CN) method for deriving the probability density function (pdf) of effective rainfall. The effective rainfall-runoff model is based on kinematic-wave theory. The results of application of this derived model to three Indian basins indicate that the model is a useful alternative for estimating flood flow quantiles at ungauged sites.     

Hydrological Sciences Journal Contents 2002

Abstract

Flood frequency estimation is crucial in both engineering practice and hydrological research. Regional analysis of flood peak discharges is used for more accurate estimates of flood quantiles in ungauged or poorly gauged catchments. This is based on the identification of homogeneous zones, where the probability distribution of annual maximum peak flows is invariant, except for a scale factor represented by an index flood. The numerous applications of this method have highlighted obtaining accurate estimates of index flood as a critical step, especially in ungauged or poorly gauged sections, where direct estimation by sample mean of annual flood series (AFS) is not possible, or inaccurate. Therein indirect methods have to be used. Most indirect methods are based upon empirical relationships that link index flood to hydrological, climatological and morphological catchment characteristics, developed by means of multi-regression analysis, or simplified lumped representation of rainfall–runoff processes. The limits of these approaches are increasingly evident as the size and spatial variability of the catchment increases. In these cases, the use of a spatially-distributed, physically-based hydrological model, and time continuous simulation of discharge can improve estimation of the index flood. This work presents an application of the FEST-WB model for the reconstruction of 29 years of hourly streamflows for an Alpine snow-fed catchment in northern Italy, to be used for index flood estimation. To extend the length of the simulated discharge time series, meteorological forcings given by daily precipitation and temperature at ground automatic weather stations are disaggregated hourly, and then fed to FEST-WB. The accuracy of the method in estimating index flood depending upon length of the simulated series is discussed, and suggestions for use of the methodology provided.

Editor D. Koutsoyiannis     

Regional flood frequency analysis using Bayesian generalized least squares: a comparison between quantile and parameter regression techniques

Regression‐based regional flood frequency analysis (RFFA) methods are widely adopted in hydrology. This paper compares two regression‐based RFFA methods using a Bayesian generalized least squares (GLS) modelling framework; the two are quantile regression technique (QRT) and parameter regression technique (PRT). In this study, the QRT focuses on the development of prediction equations for a flood quantile in the range of 2 to 100 years average recurrence intervals (ARI), while the PRT develops prediction equations for the first three moments of the log Pearson Type 3 (LP3) distribution, which are the mean, standard deviation and skew of the logarithms of the annual maximum flows; these regional parameters are then used to fit the LP3 distribution to estimate the desired flood quantiles at a given site. It has been shown that using a method similar to stepwise regression and by employing a number of statistics such as the model error variance, average variance of prediction, Bayesian information criterion and Akaike information criterion, the best set of explanatory variables in the GLS regression can be identified. In this study, a range of statistics and diagnostic plots have been adopted to evaluate the regression models. The method has been applied to 53 catchments in Tasmania, Australia. It has been found that catchment area and design rainfall intensity are the most important explanatory variables in predicting flood quantiles using the QRT. For the PRT, a total of four explanatory variables were adopted for predicting the mean, standard deviation and skew. The developed regression models satisfy the underlying model assumptions quite well; of importance, no outlier sites are detected in the plots of the regression diagnostics of the adopted regression equations. Based on ‘one‐at‐a‐time cross validation’ and a number of evaluation statistics, it has been found that for Tasmania the QRT provides more accurate flood quantile estimates for the higher ARIs while the PRT provides relatively better estimates for the smaller ARIs. The RFFA techniques presented here can easily be adapted to other Australian states and countries to derive more accurate regional flood predictions. Copyright © 2011 John Wiley & Sons, Ltd.     

A modelling framework for evaluation of the hydrological impacts of nature‐based approaches to flood risk management,with application to in‐channel interventions across a 29‐km2 scale catchment in the United Kingdom

Nature‐based approaches to flood risk management are increasing in popularity. Evidence for the effectiveness at the catchment scale of such spatially distributed upstream measures is inconclusive. However, it also remains an open question whether, under certain conditions, the individual impacts of a collection of flood mitigation interventions could combine to produce a detrimental effect on runoff response. A modelling framework is presented for evaluation of the impacts of hillslope and in‐channel natural flood management interventions. It couples an existing semidistributed hydrological model with a new, spatially explicit, hydraulic channel network routing model. The model is applied to assess a potential flood mitigation scheme in an agricultural catchment in North Yorkshire, United Kingdom, comprising various configurations of a single variety of in‐channel feature. The hydrological model is used to generate subsurface and surface fluxes for a flood event in 2012. The network routing model is then applied to evaluate the response to the addition of up to 59 features. Additional channel and floodplain storage of approximately 70,000 m³ is seen with a reduction of around 11% in peak discharge. Although this might be sufficient to reduce flooding in moderate events, it is inadequate to prevent flooding in the double‐peaked storm of the magnitude that caused damage within the catchment in 2012. Some strategies using features specific to this catchment are suggested in order to improve the attenuation that could be achieved by applying a nature‐based approach.     

Flood frequency analysis of Turkey using L‐moments method

The index flood procedure coupled with the L‐moments method is applied to the annual flood peaks data taken at all stream‐gauging stations in Turkey having at least 15‐year‐long records. First, screening of the data is done based on the discordancy measure (D_i) in terms of the L‐moments. Homogeneity of the total geographical area of Turkey is tested using the L‐moments based heterogeneity measure, H, computed on 500 simulations generated using the four parameter Kappa distribution. The L‐moments analysis of the recorded annual flood peaks data at 543 gauged sites indicates that Turkey as a whole is hydrologically heterogeneous, and 45 of 543 gauged sites are discordant which are discarded from further analyses. The catchment areas of these 543 sites vary from 9·9 to 75121 km² and their mean annual peak floods vary from 1·72 to 3739·5 m³ s^?1. The probability distributions used in the analyses, whose parameters are computed by the L‐moments method are the general extreme values (GEV), generalized logistic (GLO), generalized normal (GNO), Pearson type III (PE3), generalized Pareto (GPA), and five‐parameter Wakeby (WAK). Based on the L‐moment ratio diagrams and the |Z_dist|‐statistic criteria, the GEV distribution is identified as the robust distribution for the study area (498 gauged sites). Hence, for estimation of flood magnitudes of various return periods in Turkey, a regional flood frequency relationship is developed using the GEV distribution. Next, the quantiles computed at all of 543 gauged sites by the GEV and the Wakeby distributions are compared with the observed values of the same probability based on two criteria, mean absolute relative error and determination coefficient. Results of these comparisons indicate that both distributions of GEV and Wakeby, whose parameters are computed by the L‐moments method, are adequate in predicting quantile estimates. Copyright © 2011 John Wiley & Sons, Ltd.     

Effects of Forest Fires on Flood Frequency Curves in a Mediterranean Catchment/Effets d'incendies de forêt sur les courbes de fréquence de crue dans un bassin versant Méditerranéen

Abstract

The effect of land-use change on the flood frequency curve (FFC) in a natural catchment is analysed. To achieve this, a simple methodology for the derivation of FFCs in land-use change scenarios is proposed. The adopted methodology, using a stochastic model in Monte Carlo simulation of FFCs, was found to provide a useful framework for detecting changes in flood magnitudes in both pre- and post-fire conditions. In particular, the importance of the antecedent soil moisture condition in the determination of the flood frequency distribution was analysed. The analysis of FFCs for pre- and post-fire conditions shows an increase in the average value of Curve Number and a decrease in the catchment time lag. The derivation of FFCs shows a clear increase in flood quantiles. For the post-fire conditions, the FFC exhibits higher quantiles of the peak discharges showing a reduction in frequency of occurrence. This variation is more significant for low-return period quantiles than for high-return period quantiles. The results of the catchment studies reported here support the hypothesis that the hydrological response of the watershed changes as a result of fire, especially during the first years following a fire event.     

Climate-change driven increased flood magnitudes and frequency in the British uplands: Geomorphologically informed scientific underpinning for upland flood-risk management

Upland river systems in the UK are predicted to be prone to the effects of increased flood magnitudes and frequency, driven by climate change. It is clear from recent events that some headwater catchments can be very sensitive to large floods, activating the full sediment system, with implications for flood risk management further down the catchment. We provide a 15-year record of detailed morphological change on a 500-m reach of upland gravel-bed river, focusing upon the geomorphic response to an extreme event in 2007, and the recovery in the decade following. Through novel application of two-dimensional (2D) hydrodynamic modelling we evaluate the different energy states of pre- and post-flood morphologies of the river reach, exploring how energy state adjusts with recovery following the event. Following the 2007 flood, morphological adjustments resulted in changes to the shear stress population over the reach, resulting in higher shear stresses. Although the proportion of shear stresses in excess of those experienced using the pre-flood digital elevation model (DEM) varied over the recovery period, they remained substantially in excess of those experienced pre-2007, suggesting that there is still potential for enhanced bedload transport and morphological adjustment within the reach. Although volumetric change calculated from DEM differencing does indicate a reduction in erosion and deposition volumes in the decade following the flood, we argue that the system still has not fully recovered to the pre-flood state. We further argue that Thinhope Burn, and other similarly impacted catchments in upland environments, may not recover under the wet climatic phase currently being experienced. Hence systems like Thinhope Burn will continue to deliver large volumes of sediment further down river catchments, providing new challenges for flood risk management into the future.     

Changes in flood regime by use of the modified curve number method

Abstract

This paper describes the use of a simple two stage rainfall-runoff model in which a curve number (CN) principle is used to calculate the soil water content and, subsequently, the rainfall contribution to direct runoff and groundwater flow. The maximum soil water retention, S, is used to express various characteristics of a catchment (infiltration rate, soil cover and land use, as in the CN method) relevant to flood formation. Using historical flood events, the model is calibrated, and the statistical distribution parameters of peak flows determined. With the same historical input data scenarios (rainfall), sets of flood hydrographs are simulated for various values of the parameter S, and corresponding distribution parameters of peak flows are determined. This procedure is used to demonstrate possible changes in flood regime to be expected due to changes of the catchment soil properties and its vegetation cover. A case study is presented for the River Hron catchment, area 582 km², in the mountainous region of central Slovakia.     

Impact assessment of climatic and land-use changes on flood runoff in southeast Queensland

Abstract

Over the past century, land-use has changed in southeast Queensland, and when coupled with climatic change, the risk of flooding has increased. This research aims to examine impacts of climate and land-use changes on flood runoff in southeast Queensland, Australia. A rainfall–runoff model, RORB, was calibrated and validated using observed flood hydrographs for one rural and one urbanized catchment, for 1961–1990. The validated model was then used to generate flood hydrographs using projected rainfall based on two climate models: the Geophysical Fluid Dynamics Laboratory Climate Model 2.1 (GFDL CM2.1) and the Conformal-Cubic Atmospheric Model (CCAM), for 2016–2045. Projected daily rainfall for the two contrasting periods was used to derive adjustment factors for a given frequency of occurrence. Two land-use change scenarios were used to evaluate likely impacts. Based on the projected rainfall, the results showed that, in both catchments, future flood magnitudes are unlikely to increase for large flood events. Extreme land-use change would significantly impact flooding in the rural catchment, but not the urbanized catchment.

Editor Z.W. Kundzewicz; Associate editor Y. Gyasi-Agyei     

The effects of river restoration on catchment scale flood risk and flood hydrology

A rising exposure to flood risk is a predicted consequence of increased development in vulnerable areas and an increase in the frequency of extreme weather events due to climate change. In the face of this challenge, a continued reliance on engineered at‐a‐point flood defences is seen as both unrealistic and undesirable. The contribution of ‘soft engineering’ solutions (e.g. riparian forests, wood in rivers) to integrated, catchment scale flood risk management has been demonstrated at small scales but not larger ones. In this study we use reduced complexity hydrological modelling to analyse the effects of land use and channel changes resulting from river restoration upon flood flows at the catchment scale. Results show short sections of river‐floodplain restoration using engineered logjams, typical of many current restoration schemes, have highly variable impacts on catchment‐scale flood peak magnitude and so need to be used with caution as a flood management solution. Forested floodplains have a more general impact upon flood hydrology, with areas in the middle and upper catchment tending to show reductions in peak magnitude at the catchment outflow. The most promising restoration scenarios for flood risk management are for riparian forest restoration at the sub‐catchment scale, representing 20–40% of the total catchment area, where reductions in peak magnitude of up to 19% are observed through de‐synchronization of the timings of sub‐catchment flood waves. Sub‐catchment floodplain forest restoration over 10–15% of total catchment area can lead to reductions in peak magnitude of 6% at 25 years post‐restoration. Copyright © 2016 John Wiley & Sons, Ltd.     

Event-based classification for global study of river flood generating processes

Better understanding of which processes generate floods in a catchment can improve flood frequency analysis and potentially climate change impacts assessment. However, current flood classification methods are either not transferable across locations or do not provide event-based information. We therefore developed a location-independent, event-based flood classification methodology that is applicable in different climates and returns a classification of all flood events, including extreme ones. We use precipitation time series and very simply modelled soil moisture and snowmelt as inputs for a decision tree. A total of 113,635 events in 4155 catchments worldwide were classified into one of five hydro-climatological flood generating processes: short rain, long rain, excess rainfall, snowmelt and a combination of rain and snow. The new classification was tested for its robustness and evaluated with available information; these two tests are often lacking in current flood classification approaches. According to the evaluation, the classification is mostly successful and indicates excess rainfall as the most common dominant process. However, the dominant process is not very informative in most catchments, as there is a high at-site variability in flood generating processes. This is particularly relevant for the estimation of extreme floods which diverge from their usual flood generation pattern, especially in the United Kingdom, Northern France, Southeastern United States, and India.     

Process controls on the statistical flood moments ‐ a data based analysis

In this paper, the controls of different indicators on the statistical moments (i.e. mean annual flood (MAF), coefficient of variation (CV) and skewness (CS)) of the maximum annual flood records of 459 Austrian catchments are analysed. The process controls are analysed in terms of the correlation of the flood moments within five hydrologically homogeneous regions to two different types of indicators. Indicators of the first type are static catchment attributes, which are associated with long‐term observations such as mean annual precipitation, the base flow index, and the percentage of catchment area covered by a geological unit or soil type. Indicators of the second type are dynamic catchment attributes that are associated with the event scale. Indicators of this type used in the study are event runoff coefficients and antecedent rainfall. The results indicate that MAF and CV are strongly correlated with indicators characterising the hydro‐climatic conditions of the catchments, such as mean annual precipitation, long‐term evaporation and the base flow index. For the catchments analysed, the flood moments are not significantly correlated with static catchment attributes representing runoff generation, such as geology, soil types, land use and the SCS curve number. Indicators of runoff generation that do have significant predictive power for flood moments are dynamic catchment attributes such as the mean event runoff coefficients and mean antecedent rainfall. The correlation analysis indicates that flood runoff is, on average, more strongly controlled by the catchment moisture state than by event rainfall. Copyright © 2008 John Wiley & Sons, Ltd.     

PAI-OFF: A new proposal for online flood forecasting in flash flood prone catchments

The Process Modelling and Artificial Intelligence for Online Flood Forecasting (PAI-OFF) methodology combines the reliability of physically based, hydrologic/hydraulic modelling with the operational advantages of artificial intelligence. These operational advantages are extremely low computation times and straightforward operation. The basic principle of the methodology is to portray process models by means of ANN. We propose to train ANN flood forecasting models with synthetic data that reflects the possible range of storm events. To this end, establishing PAI-OFF requires first setting up a physically based hydrologic model of the considered catchment and – optionally, if backwater effects have a significant impact on the flow regime – a hydrodynamic flood routing model of the river reach in question. Both models are subsequently used for simulating all meaningful and flood relevant storm scenarios which are obtained from a catchment specific meteorological data analysis. This provides a database of corresponding input/output vectors which is then completed by generally available hydrological and meteorological data for characterizing the catchment state prior to each storm event. This database subsequently serves for training both a polynomial neural network (PoNN) – portraying the rainfall–runoff process – and a multilayer neural network (MLFN), which mirrors the hydrodynamic flood wave propagation in the river. These two ANN models replace the hydrological and hydrodynamic model in the operational mode. After presenting the theory, we apply PAI-OFF – essentially consisting of the coupled “hydrologic” PoNN and “hydrodynamic” MLFN – to the Freiberger Mulde catchment in the Erzgebirge (Ore-mountains) in East Germany (3000 km²). Both the demonstrated computational efficiency and the prediction reliability underline the potential of the new PAI-OFF methodology for online flood forecasting.     

Understanding land use and cover change impacts on run‐off and sediment load at flood events on the Loess Plateau,China

The Loess Plateau has been experiencing large‐scale land use and cover changes (LUCCs) over the past 50 years. It is well known about the significant decreasing trend of annual streamflow and sediment load in the catchments in this area. However, how surface run‐off and sediment load behaved in response to LUCC at flood events remained a research question. We investigated 371 flood events from 1963 to 2011 in a typical medium‐sized catchment within the Plateau in order to understand how LUCC affected the surface run‐off generation and sediment load and their behaviours based on the analysis of return periods. The results showed that the mean annual surface run‐off and sediment load from flood events accounted for 49.6% and 91.8% of their mean annual totals. The reduction of surface run‐off and associated sediment yield in floods explained about 85.0% and 89.2% of declines in the total annual streamflow and sediment load, respectively. The occurrences of flood events and peak sediment concentrations greater than 500 kg/m³ showed a significantly downward trend, yet the counterclockwise loop events still dominated the flood event processes in the catchment. The results suggest that LUCC over the past 50 years resulted in significant changes in the water balance components and associated soil erosion and sediment transportation in the catchment. This was achieved mainly by reducing surface run‐off and sediment yield during floods with return period of less than 5 years. Run‐off–sediment load behaviour during the extreme events with greater than 10‐year return periods has not changed. Outcomes from this study are useful in understanding the eco‐hydrological processes and assisting the sustainable catchment management and land use planning on the Loess Plateau, and the methodologies are general and applicable to similar areas worldwide.     

Comparison of regional index flood estimation procedures based on the extreme value type I distribution

 A comparison of different methods for estimating T-year events is presented, all based on the Extreme Value Type I distribution. Series of annual maximum flood from ten gauging stations at the New Zealand South Island have been used. Different methods of predicting the 100-year event and the connected uncertainty have been applied: At-site estimation and regional index-flood estimation with and without accounting for intersite correlation using either the method of moments or the method of probability weighted moments for parameter estimation. Furthermore, estimation at ungauged sites were considered applying either a log-linear relationship between at-site mean annual flood and catchment characteristics or a direct log-linear relationship between 100-year events and catchment characteristics. Comparison of the results shows that the existence of at-site measurements significantly diminishes the prediction uncertainty and that the presence of intersite correlation tends to increase the uncertainty. A simulation study revealed that in regional index-flood estimation the method of probability weighted moments is preferable to method of moment estimation with regard to bias and RMSE.     

Improving bank erosion modelling at catchment scale by incorporating temporal and spatial variability

Bank erosion can contribute a significant portion of the sediment budget within temperate catchments, yet few catchment scale models include an explicit representation of bank erosion processes. Furthermore, representation is often simplistic resulting in an inability to capture realistic spatial and temporal variability in simulated bank erosion. In this study, the sediment component of the catchment scale model SHETRAN is developed to incorporate key factors influencing the spatio‐temporal rate of bank erosion, due to the effects of channel sinuosity and channel bank vegetation. The model is applied to the Eden catchment, north‐west England, and validated using data derived from a GIS methodology. The developed model simulates magnitudes of total catchment annual bank erosion (617–4063 t y^‐1) within the range of observed values (211–4426 t yr^‐1). In addition, the model provides both greater inter‐annual and spatial variability of bank eroded sediment generation when compared with the basic model, and indicates a potential 61% increase of bank eroded sediment as a result of temporal flood clustering. The approach developed within this study can be used within a number of distributed hydrologic models and has general applicability to temperate catchments, yet further development of model representation of bank erosion processes is required. © 2017 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.