A comparison of one‐ and two‐dimensional approaches to modelling flood inundation over complex upland floodplains

A much understudied aspect of flood inundation is examined, i.e. upland environments with topographically complex floodplains. Although the presence of high‐resolution topographic data (e.g. lidar) has improved the quality of river flood inundation predictions, the optimum dimensionality of hydraulic models for this purpose has yet to be fully evaluated for situations of both topographic and topological (i.e. the connectivity of floodplain features) complexity. In this paper, we present the comparison of three treatments of upland flood inundation using: (a) a one‐dimensional (1D) model (HEC‐RAS v. 3·1·2) with the domain defined as series of extended cross‐sections; (b) the same 1D model, but with the floodplain defined by a series of storage cells, hydraulically connected to the main river channel and other storage cells on the floodplain according to floodplain topological characteristics; (c) a two‐dimensional (2D) diffusion wave treatment, again with explicit representation of floodplain structural features. The necessary topographic and topological data were derived using lidar and Ordnance Survey Landline data. The three models were tested on a 6 km upland reach of the River Wharfe, UK. The models were assessed by comparison with measured inundation extent. The results showed that both the extended cross‐section and the storage cell 1D modes were conceptually problematic. They also resulted in poorer model predictions, requiring incorrect parameterization of the main river to floodplain flux in order to approach anything like the level of agreement observed when the 2D diffusion wave treatment was assessed. We conclude that a coupled 1D–2D treatment is likely to provide the best modelling approach, with currently available technology, for complex floodplain configurations. Copyright © 2007 John Wiley & Sons, Ltd.     

A spatial analysis of hydro‐climatic and vegetation condition trends in the Yellow River basin

Stream‐gauge data indicate that the flow of the Yellow River has declined during the past several decades. Zero flow in sections of the river channel, i.e. the Yellow River drying‐up phenomenon, has occurred since the 1970s. In this paper we present an analysis of changes in the spatial patterns of climatic and vegetation condition data in the Yellow River basin based on data from meteorological stations and satellites. The climatic data are from 1960 to 2000 and the vegetation condition data are from 1982 to 2000. The angular‐distance‐weighted interpolation method is used to get climatic data coverage from station observations. The spatial distribution of tendency is detected with Student's t‐test. The spatial patterns of climatic and vegetation condition change was analysed together with the statistical data on human activities. The analysis indicates that the precipitation decreases and temperature increases in most parts of the Yellow River basin, the evaporative demand of the atmosphere decreases in the upper reaches and increases in the lower reaches, and human activities have improved the vegetation condition in the irrigation districts. The Loess Plateau, the Tibetan Plateau, and the irrigation districts are respectively suggested as precipitation, temperature, and human activity hot spots of the Yellow River drying‐up phenomenon. Copyright © 2007 John Wiley & Sons, Ltd.     

A stochastic framework to assess the performance of flood warning systems based on rainfall‐runoff modeling

Nowadays, Flood Forecasting and Warning Systems (FFWSs) are known as the most inexpensive and efficient non‐structural measures for flood damage mitigation in the world. Benefit to cost of the FFWSs has been reported to be several times of other flood mitigation measures. Beside these advantages, uncertainty in flood predictions is a subject that may affect FFWS's reliability and the benefits of these systems. Determining the reliability of advanced flood warning systems based on the rainfall–runoff models is a challenge in assessment of the FFWS performance which is the subject of this study. In this paper, a stochastic methodology is proposed to provide the uncertainty band of the rainfall–runoff model and to calculate the probability of acceptable forecasts. The proposed method is based on Monte Carlo simulation and multivariate analysis of the predicted time and discharge error data sets. For this purpose, after the calibration of the rainfall–runoff model, the probability distributions of input calibration parameters and uncertainty band of the model are estimated through the Bayesian inference. Then, data sets of the time and discharge errors are calculated using the Monte Carlo simulation, and the probability of acceptable model forecasts is calculated by multivariate analysis of data using copula functions. The proposed approach was applied for a small watershed in Iran as a case study. The results showed using rainfall–runoff modeling based on real‐time precipitation is not enough to attain high performance for FFWSs in small watersheds, and it seems using weather forecasts as the inputs of rainfall–runoff models is essential to increase lead times and the reliability of FFWSs in small watersheds. Copyright © 2013 John Wiley & Sons, Ltd.     

A review of low‐cost space‐borne data for flood modelling: topography,flood extent and water level

During the last two decades, remote sensing data have led to tremendous progress in advancing flood inundation modelling. In particular, low‐cost space‐borne data can be invaluable for large‐scale flood studies in data‐scarce areas. Various satellite products yield valuable information such as land surface elevation, flood extent and water level, which could potentially contribute to various flood studies. An increasing number of research studies have been dedicated to exploring those low‐cost data towards building, calibration and evaluation, and remote‐sensed information assimilation into hydraulic models. This paper aims at reviewing these recent scientific efforts on the integration of low‐cost space‐borne remote sensing data with flood modelling. Potentials and limitations of those data in flood modelling are discussed. This paper also introduces the future satellite missions and anticipates their likely impacts in flood modelling. Copyright © 2015 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.     

A multi‐temporal analysis of AMSR‐E data for flood and discharge monitoring during the 2008 flood in Iowa

The objective of this work is to demonstrate the potential of using passive microwave data to monitor flood and discharge conditions and to infer watershed hydraulic and hydrologic parameters. The case study is the major flood in Iowa in summer 2008. A new Polarisation Ratio Variation Index (PRVI) was developed based on a multi‐temporal analysis of 37 GHz satellite imagery from the Advanced Microwave Scanning Radiometer (AMSR‐E) to calculate and detect anomalies in soil moisture and/or inundated areas. The Robust Satellite Technique (RST) which is a change detection approach based on the analysis of historical satellite records was adopted. A rating curve has been developed to assess the relationship between PRVI values and discharge observations downstream. A time‐lag term has been introduced and adjusted to account for the changing delay between PRVI and streamflow. Moreover, the Kalman filter has been used to update the rating curve parameters in near real time. The temporal variability of the b exponent in the rating curve formula shows that it converges toward a constant value. A consistent 21‐day time lag, very close to an estimate of the time of concentration, was obtained. The agreement between observed discharge downstream and estimated discharge with and without parameters adjustment was 65 and 95%, respectively. This demonstrates the interesting role that passive microwave can play in monitoring flooding and wetness conditions and estimating key hydrologic parameters. Copyright © 2011 John Wiley & Sons, Ltd.     

A model approach for in- and outflow calculation of upper lake constance: —An investigation of a 60 year time span and observations about the flood of 1999

The revised empirical model for in- and outflow calculation of Upper Lake Constance has provided satisfying results supported by measured values. The given model was implemented to simulate total water inputs of the lake during the period from 1941 to 2000 with emphasis on the flood conditions of 1999. Analysis of annual water input development reveals a tendency toward slight increases until the 1960s. Thereafter, a reduction in inputs can be noted. This trend probably continues to hold true to present. Weather conditions of given individual years have caused distinct fluctuations to the water budget.Unusual meteorological conditions led to extreme flooding in early May of 1999. Daily water inputs of up to 200 mio m³ generated the highest water levels ever observed for this time of the year. Continual extraordinarily high water inputs occurring from February until July and then again from September until the end of 1999 resulted in the second largest annual total water input recorded since 1941.     

Experimental characterization,design and modelling of the RBRL seismic‐isolation system for lightweight structures

The Rolling‐Ball Rubber‐Layer (RBRL) system was developed to enable seismic isolation of lightweight structures, such as special equipment or works of art, and is very versatile, a great range of equivalent natural frequencies and coefficients of damping being achievable through choice of the system parameters. The necessity to have a simple and effective design procedure has led to a new parametric experimentation at Tun Abdul Razak Research Centre (TARRC) on the rolling behaviour of the RBRL system and load–deflection behaviour of the recentering springs. The experimental results, together with theories for the rolling resistance of a loaded steel ball on a thin rubber layer and the lateral load–deflection behaviour of cylindrical rubber springs, are used to develop a general design method for the RBRL system, which allows the system to be tailored to the specific application. Sinusoidal test results are presented for the small‐deflection behaviour of the system, influenced by the presence of a viscoelastic depression on the rubber tracks beneath each ball, and an amplitude‐dependent time‐domain model is proposed, based on these results and on the steady‐state behaviour of the system. The model is validated through comparison with previously performed shaking‐table tests. Attention is here restricted to uniaxial behaviour. Copyright © 2016 John Wiley & Sons, Ltd.