Evaluation of typhoon‐induced rainfall using nonparametric Monte Carlo simulation and locally weighted polynomial regression

Typhoons in Korea are the major causes of natural disasters in the Korean peninsula. In this study, rainfall generated by typhoons was quantitatively analysed using various statistical methods. First, the frequency analysis of rainfall induced by typhoons was carried out to calculate the design rainfall. Second, the frequency analysis of simulated rainfall derived by nonparametric Monte Carlo simulation (NMCS) was performed to evaluate the uncertainty of rainfall caused by typhoons. Third, the regression relationship between the physical characteristic factors of typhoons and rainfall was established by locally weighted polynomial regression (LWPR), and the characteristic factors of typhoons were simulated. The simulated characteristic factors were then used to estimate rainfall and to calculate the design rainfall by typhoons. Comparative analyses of design rainfalls as estimated using various statistical methods were performed. The LWPR showed good performance in terms of reproducing typhoon characteristics. Therefore, the combined NMCS and LWPR method suggested in this study can be used as a supplementary technique for assessing extreme rainfall with climate change and reflected variability. Copyright © 2010 John Wiley & Sons, Ltd.     

Comparing threshold definition techniques for rainfall‐induced landslides: A national assessment using radar rainfall

Translational landslides and debris flows are often initiated during intense or prolonged rainfall. Empirical thresholds aim to classify the rain conditions that are commonly associated with landslide occurrence and therefore improve understating of these hazards and predictive ability. Objective techniques that are used to determine these thresholds are likely to be affected by the length of the rain record used, yet this is not routinely considered. Moreover, remotely sensed spatially continuous rainfall observations are under‐exploited. This study compares and evaluates the effect of rain record length on two objective threshold selection techniques in a national assessment of Scotland using weather radar data. Thresholds selected by ‘threat score’ are sensitive to rain record length whereas, in a first application to landslides, ‘optimal point’ (OP) thresholds prove relatively consistent. OP thresholds increase landslide detection and may therefore be applicable in early‐warning systems. Thresholds combining 1‐ and 12‐day antecedence variables best distinguish landslide initiation conditions and indicate that Scottish landslides may be initiated by lower rain accumulation and intensities than previously thought. © 2017 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.     

The performance of grass filter strips in controlling high‐concentration suspended sediment from overland flow under rainfall/non‐rainfall conditions

There is little information on the performance of vegetative filter strips (VFS) in filtering high‐concentration sediment from subcritical overland flow. Flume experiments on simulated grass strips were conducted using combinations of three slope gradients (3°, 9° and 15°), five 1‐m‐wide slope positions (from upslope to downslope), two flow rates (60 and 20 L min^‐1 m^‐1) and sediment concentrations of 100–300 kg m^‐3 under simulated rainfall and non‐rainfall conditions. The results showed that sediment deposition efficiency increased with VFS width as a power function. Rainfall significantly reduced sediment deposited within VFS. Higher sediment concentration corresponded to a larger sediment deposition load but reduced deposition efficiency. Flow rate had a negative effect on deposition efficiency but no effect on deposition load. Sediments were more easily deposited at the upper slope position than downslope, and the upper slope position had a higher percentage of coarse sediments. The deposited sediment had significantly greater median diameters (D₅₀) than the inflow sediment. A greater proportion of coarse sediments larger than 25 µm in diameter were deposited, and particles smaller than 1 µm and of 10–25 µm had a better deposition performance than particles of 1–10 µm. Rainfall reduced the deposited sediment D₅₀ at a slope gradient of 3° and had no significant influence on it at 9° or 15°. A higher sediment concentration led to a smaller D₅₀ of the deposited sediment. Rainfall had no significant effect on overland flow velocity. Both the deposited sediment load and D₅₀ decreased with increasing flow velocity, and flow velocity was the most sensitive factor impacting sediment deposition. The results from this study should be useful to control sediment flowing into rivers in areas with serious soil erosion. Copyright © 2013 John Wiley & Sons, Ltd.     

Calibration of an oscillating nozzle‐type rainfall simulator

Nozzle‐type rainfall simulators are commonly used in hydrologic and soil erosion research. Simulated rainfall intensity, originating from the nozzle, increases as the distance between the point of measurement and the source is decreased. Hence, rainfall measured using rain gauges would systematically overestimate the rainfall received at the ground level. A simple model was developed to adjust rainfall measured anywhere under the simulator to plot‐wide average rainfall at the ground level. Nozzle height, plot width, gauge diameter and height, and gauge location are required to compute this adjustment factor. Results from 15 runs at different rain intensities and durations, and with different rain gauge layouts, showed that a simple average of measured rain would overestimate the plot‐wide rain by about 20 per cent. Using the adjustment factor to convert measured rainfall for individual gauges before averaging improved the estimate of plot‐wide rainfall considerably. For the 15 runs considered, overall discrepancy between actual and measured rain is reduced to less than 1 per cent with a standard error of 0·97 mm. This model can be easily tested in the ?eld by comparing rainfall depths of different sized gauges. With the adjustment factor they should all give very similar values. Copyright © 2003 John Wiley & Sons, Ltd.     

Evaluation of remote‐sensing‐based rainfall products through predictive capability in hydrological runoff modelling

The emergence of regional and global satellite‐based rainfall products with high spatial and temporal resolution has opened up new large‐scale hydrological applications in data‐sparse or ungauged catchments. Particularly, distributed hydrological models can benefit from the good spatial coverage and distributed nature of satellite‐based rainfall estimates (SRFE). In this study, five SRFEs with temporal resolution of 24 h and spatial resolution between 8 and 27 km have been evaluated through their predictive capability in a distributed hydrological model of the Senegal River basin in West Africa. The main advantage of this evaluation methodology is the integration of the rainfall model input in time and space when evaluated at the sub‐catchment scale. An initial data analysis revealed significant biases in the SRFE products and large variations in rainfall amounts between SRFEs, although the spatial patterns were similar. The results showed that the Climate Prediction Center/Famine Early Warning System (CPC‐FEWS) and cold cloud duration (CCD) products, which are partly based on rain gauge data and produced specifically for the African continent, performed better in the modelling context than the global SRFEs, Climate Prediction Center MORPHing technique (CMORPH), Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). The best performing SRFE, CPC‐FEWS, produced good results with values of R²_NS between 0·84 and 0·87 after bias correction and model recalibration. This was comparable to model simulations based on traditional rain gauge data. The study highlights the need for input specific calibration of hydrological models, since major differences were observed in model performances even when all SRFEs were scaled to the same mean rainfall amounts. This is mainly attributed to differences in temporal dynamics between products. Copyright © 2009 John Wiley & Sons, Ltd.     

Quantifying rainfall controls on catchment‐scale landslide erosion in Taiwan

Landslide erosion is a dominant hillslope process and the main source of stream sediment in tropical, tectonically active mountain belts. In this study, we quantified landslide erosion triggered by 24 rainfall events from 2001 to 2009 in three mountainous watersheds in Taiwan and investigated relationships between landslide erosion and rainfall variables. The results show positive power‐law relations between landslide erosion and rainfall intensity and cumulative rainfall, with scaling exponents ranging from 2·94 to 5·03. Additionally, landslide erosion caused by Typhoon Morakot is of comparable magnitude to landslide erosion caused by the Chi‐Chi Earthquake (M_W = 7·6) or 22–24 years of basin‐averaged erosion. Comparison of the three watersheds indicates that deeper landslides that mobilize soil and bedrock are triggered by long‐duration rainfall, whereas shallow landslides are triggered by short‐duration rainfall. These results suggest that rainfall intensity and watershed characteristics are important controls on rainfall‐triggered landslide erosion and that severe typhoons, like high‐magnitude earthquakes, can generate high rates of landslide erosion in Taiwan. Copyright © 2012 John Wiley & Sons, Ltd.     

Non‐stationarity in rainfall and temperature in the Murray Darling Basin

Two analyses, one based on multiple regression and the other using the Holt–Winters algorithm, for investigating non‐stationarity in environmental time series are presented. They are applied to monthly rainfall and average maximum temperature time series of lengths between 38 and 108 years, from six stations in the Murray Darling Basin and four cities in eastern Australia. The first analysis focuses on the residuals after fitting regression models which allow for seasonal variation, the Pacific Decadal Oscillation (PDO) and the Southern Oscillation Index (SOI). The models provided evidence that rainfall is reduced during periods of negative SOI, and that the interaction between PDO and SOI pronounces this effect during periods of negative PDO. Following this, there was no evidence of any trend in either the PDO or SOI time series. The residuals from this regression were analysed with a cumulative sum (CUSUM) technique, and the statistical significance was assessed using a Monte Carlo method. The residuals were also analysed for volatility, autocorrelation, long‐range dependence and spatial correlation. For all ten rainfall and temperature time series, CUSUM plots of the residuals provided evidence of non‐stationarity for both temperature and rainfall, after removing seasonal effects and the effects of PDO and SOI. Rainfall was generally lower in the first half of the twentieth century and higher during the second half. However, it decreased again over the last 10 years. This pattern was highlighted with 5‐year moving average plots. The residuals for temperature showed a complementary pattern with increases in temperature corresponding to decreased rainfall. The second analysis decomposed the rainfall and temperature time series into random variation about an underlying level, trend and additive seasonal effects and changes in the level; trend and seasonal effects were tracked using a Holt–Winters algorithm. The results of this analysis were qualitatively similar to those of the regression analysis. Copyright © 2010 John Wiley & Sons, Ltd.     

Using on‐nadir spectral reflectance to detect soil surface changes induced by simulated rainfall and wind tunnel abrasion

The surface susceptibility to erosion (erodibility) is an important component of soil erosion models. Many studies of wind erosion have shown that even relatively small changes in surface conditions can have a considerable effect on the temporal and spatial variability of dust emissions. One of the main difficulties in measuring erodibility is that it is controlled by a number of highly variable soil factors. Collection of these data is often limited in scale because in situ measurements are labour‐intensive and very time‐consuming. To improve wind erosion model predictions over several spatial and temporal scales simultaneously, there is a requirement for a non‐invasive approach that can be used to rapidly assess changes in the compositional and structural nature of a soil surface in time and space. Spectral reflectance of the soil surface appears to meet these desirable requirements and it is controlled by properties that affect the soil erodibility. Three soil surfaces were modified using rainfall simulation and wind tunnel abrasion experiments. Observations of those changes were made and recorded using digital images and on‐nadir spectral reflectance. The results showed clear evidence of the information content in the spectral domain that was otherwise difficult to interpret given the complicated interrelationships between soil composition and structure. Changes detected at the soil surface included the presence of a crust produced by rainsplash, the production of loose erodible material covering a rain crust and the selective erosion of the soil surface. The effect of rainsplash and aeolian abrasion was different for each soil tested and crust abrasion was shown to decrease as rainfall intensity increased. The relative contributions of the eroded material from each soil surface to trapped mixtures of material assisted the erodibility assessment. Ordination analyses within each of two important soil types explained significant amounts of the variation in the reflectance of all wavebands by treatments of the soil and hence changes in the soil surface. The results show that soil surface conditions within a soil type are an underestimated source of variation in the characterization of soil surface erodibility and in the remote sensing of soil. Copyright © 2005 John Wiley & Sons, Ltd.     

Probabilistic assessment of one‐step‐ahead rainfall variation by Split Markov Process

In this paper, Split Markov Process (SMP) is developed to assess one‐step‐ahead variation of daily rainfall at a rain gauge station. SMP is an advancement of general Markov Process and specially developed for probabilistic assessment of change in daily rainfall magnitude. The approach is based on a first‐order Markov chain to simulate daily rainfall variation at a point through state/sub‐state transitional probability matrix (TPM). The state/sub‐state TPM is based on the historical transitions from a particular state to a particular sub‐state, which is the basic difference between SMP and general Markov Process. The cumulative state/sub‐state TPM is represented in a contour plot at different probability levels. The developed cumulative state/sub‐state TPM is used to assess the possible range of rainfall in next time step, in a probabilistic sense. Application of SMP is investigated for daily rainfall at four rain gauge stations – Khandwa, Jabalpur, Sambalpur, and Puri, located at various parts in India. There are 99 years of record available out of which approximately 80% of data are used for calibration, and 20% of data are used to assess the performance. Thus, 80 years of daily monsoon rainfall is used to develop the state/sub‐state TPM, and 19 years data are used to investigate its performance. Model performance is assessed in terms of hit rate (HR), false alarm rate (FAR), and percentage captured. It is found that percentage captured is maximum for Khandwa (70%) and minimum for Sambalpur (44%) whereas hit rate is maximum for Sambalpur and minimum for Khandwa (73%). FAR is around 30% or below for Jabalpur, Sambalpur, and Puri. FAR is maximum for Khandwa (37%). Overall, the assessed range, particularly the upper limit, provides a quantification possible extreme value in the next time step, which is a very useful information to tackle the extreme events, such as flooding, water logging and so on. Copyright © 2011 John Wiley & Sons, Ltd.     

Wavelet–copula‐based mutual information for rainfall forecasting applications

Under a climate change, the physical factors that influence the rainfall regime are diverse and difficult to predict. The selection of skilful inputs for rainfall forecasting models is, therefore, more challenging. This paper combines wavelet transform and Frank copula function in a mutual information‐based input variable selection (IVS) for non‐linear rainfall forecasting models. The marginal probability density functions (PDFs) of a set of potential rainfall predictors and the rainfall series (predictand) were computed using a wavelet density estimator. The Frank copula function was applied to compute the joint PDF of the predictors and the predictand from their marginal PDFs. The relationship between the rainfall series and the potential predictors was assessed based on the mutual information computed from their marginal and joint PDFs. Finally, the minimum redundancy maximum relevance was used as an IVS stopping criterion to determine the number of skilful input variables. The proposed approach was applied to four stations of the Nigerien Sahel with rainfall series spanning the period 1950–2016 by considering 24 climate indices as potential predictors. Adaptive neuro‐fuzzy inference system, artificial neural networks, and random forest‐based forecast models were used to assess the skill of the proposed IVS method. The three forecasting models yielded satisfactory results, exhibiting a coefficient of determination between 0.52 and 0.69 and a mean absolute percentage error varying from 13.6% to 21%. The adaptive neuro‐fuzzy inference system performed better than the other models at all the stations. A comparison made with KDE‐based mutual information showed the advantage of the proposed wavelet–copula approach.     

Estimation of rainfall from infrared‐microwave satellite data for basin‐scale hydrologic modelling

The infrared‐microwave rainfall algorithm (IMRA) was developed for retrieving spatial rainfall from infrared (IR) brightness temperatures (TBs) of satellite sensors to provide supplementary information to the rainfall field, and to decrease the traditional dependency on limited rain gauge data that are point measurements. In IMRA, a SLOPE technique (ST) was developed for discriminating rain/no‐rain pixels through IR image cloud‐top temperature gradient, and 243K as the IR threshold temperature for minimum detectable rainfall rate. IMRA also allows for the adjustment of rainfall derived from IR‐TB using microwave (MW) TBs. In this study, IMRA rainfall estimates were assessed on hourly and daily basis for different spatial scales (4, 12, 20, and 100 km) using NCEP stage IV gauge‐adjusted radar rainfall data, and daily rain gauge data. IMRA was assessed in terms of the accuracy of the rainfall estimates and the basin streamflow simulated by the hydrologic model, Sacramento soil moisture accounting (SAC‐SMA), driven by the rainfall data. The results show that the ST option of IMRA gave accurate satellite rainfall estimates for both light and heavy rainfall systems while the Hessian technique only gave accurate estimates for the convective systems. At daily time step, there was no improvement in IR‐satellite rainfall estimates adjusted with MW TBs. The basin‐scale streamflow simulated by SAC‐SMA driven by satellite rainfall data was marginally better than when SAC‐SMA was driven by rain gauge data, and was similar to the case using radar data, reflecting the potential applications of satellite rainfall in basin‐scale hydrologic modelling. Copyright © 2010 John Wiley & Sons, Ltd.     

Spatial variability of rainfall on a sub‐kilometre scale

The variability of rainfall in space and time is an essential driver of many processes in nature but little is known about its extent on the sub‐kilometre scale, despite many agricultural and environmental experiments on this scale. A network of 13 tipping‐bucket rain gauges was operated on a 1·4 km² test site in southern Germany for four years to quantify spatial trends in rainfall depth, intensity, erosivity, and predicted runoff. The random measuring error ranged from 10% to 0·1% in case of 1 mm and 100 mm rainfall, respectively. The wind effects could be well described by the mean slope of the horizon at the stations. Except for one station, which was excluded from further analysis, the relative differences due to wind were in maximum ±5%. Gradients in rainfall depth representing the 1‐km² scale derived by linear regressions were much larger and ranged from 1·0 to 15·7 mm km^?1 with a mean of 4·2 mm km^?1 (median 3·3 mm km^?1). They mainly developed during short bursts of rain and thus gradients were even larger for rain intensities and caused a variation in rain erosivity of up to 255% for an individual event. The trends did not have a single primary direction and thus level out on the long term, but for short‐time periods or for single events the assumption of spatially uniform rainfall is invalid on the sub‐kilometre scale. The strength of the spatial trend increased with rain intensity. This has important implications for any hydrological or geomorphologic process sensitive to maximum rain intensities, especially when focusing on large, rare events. These sub‐kilometre scale differences are hence highly relevant for environmental processes acting on short‐time scales like flooding or erosion. They should be considered during establishing, validating and application of any event‐based runoff or erosion model. Copyright © 2009 John Wiley & Sons, Ltd.     

A semi‐theoretical model of canopy rainfall interception for a broad‐leaved tree

Through a series of simulation experiments in the laboratory on the broad‐leaved tree Acer mono Maxim, we obtain interception datasets of individual events under different rainfall intensities and leaf area indexes (LAIs). Based on the data, the relationship between rainfall intensity and maximum interception of per unit LAI is quantified. The variation of interception with canopy wetness index is also identified. Hence, an interception model, in which interception is calculated using rainfall intensity and LAI, is constructed with consideration of canopy wetness. Finally, according to the validation experiments, it is concluded that the precision of the model is 92·7%. Copyright © 2007 John Wiley & Sons, Ltd.     

Addressing uncertainty in reflectivity‐rainfall relations in mountain watersheds during summer convection

The use of precipitation estimates from weather radar reflectivity has become widespread in hydrologic predictions. However, uncertainty remains in the use of the nonlinear reflectivity–rainfall (Z‐R) relation, in particular for mountainous regions where ground validation stations are often lacking, land surface data sets are inaccurate and the spatial variability in many features is high. In this study, we assess the propagation of rainfall errors introduced by different Z‐R relations on distributed hydrologic model performance for four mountain basins in the Colorado Front Range. To do so, we compare spatially integrated and distributed rainfall and runoff metrics at seasonal and event time scales during the warm season when convective storms dominate. Results reveal that the basin simulations are quite sensitive to the uncertainties introduced by the Z‐R relation in terms of streamflow, runoff mechanisms and the water balance components. The propagation of rainfall errors into basin responses follows power law relationships that link streamflow uncertainty to the precipitation errors and streamflow magnitude. Overall, different Z‐R relations preserve the spatial distribution of rainfall relative to a reference case, but not the precipitation magnitude, thus leading to large changes in streamflow amounts and runoff spatial patterns at seasonal and event scales. Furthermore, streamflow errors from the Z‐R relation follow a typical pattern that varies with catchment scale where higher uncertainties exist for intermediate‐sized basins. The relatively high error values introduced by two operational Z‐R relations (WSR‐57 and NEXRAD) in terms of the streamflow response indicate that site‐specific Z‐R relations are desirable in the complex terrain region, particularly in light of other uncertainties in the modelling process, such as model parameter values and initial conditions. Copyright © 2012 John Wiley & Sons, Ltd.     

Hydrological and erosional response to natural rainfall in a semi‐arid area of south‐east Spain

A better knowledge of soil erosion by water is essential for planning effective soil and water conservation practices in semi‐arid Mediterranean environments. The special climatic and hydrological characteristics of these areas, however, make accurate soil loss predictions difficult, particularly in the absence of minimal data. Two zero‐order experimental microcatchments (328–759 m²), representative of an extensive semi‐arid watershed with a high potential erosion risk in the south‐east of Spain, were selected and monitored for 3 years (1991–93) in order to provide information on the hydrological and erosional response. A pluviogram and hydrograph recorded data at 1‐min intervals during each storm, after which the soil loss was collected and the particle size of the sediment was analysed. Runoff coefficients of about 9% and soil losses of between 84·83 and 298·9 g m^?2 year^?1 were observed in the area. Rapid response times (geometric mean values lower than 2 h) and low runoff thresholds (mean values between 3·5 to 5·9 mm) were the norm in the experimental areas. A rain intensity of over 15 mm h^?1 was considered as ‘erosive rainfall’ in these areas because of the total soil loss and the transport capacity of the overland flow. Differences in pore‐size distribution explained the different hydrological responses observed between areas. The erosional response was more complex and basically seemed to be determined by soil aggregate stability and topographical properties. A greater proportion of finer particles in the eroded material than in the soil matrix indicated selective erosion and the transport of finer material. Copyright © 2001 John Wiley & Sons, Ltd.     

Flood simulation using the gauge‐adjusted radar rainfall and physics‐based distributed hydrologic model

This paper reports the results of an investigation into flood simulation by areal rainfall estimated from the combination of gauged and radar rainfalls and a rainfall–runoff model on the Anseong‐cheon basin in the southern part of Korea. The spatial and temporal characteristics and behaviour of rainfall are analysed using various approaches combining radar and rain gauges: (1) using kriging of the rain gauge alone; (2) using radar data alone; (3) using mean field bias (MFB) of both radar and rain gauges; and (4) using conditional merging technique (CM) of both radar and rain gauges. To evaluate these methods, statistics and hyetograph for rain gauges and radar rainfalls were compared using hourly radar rainfall data from the Imjin‐river, Gangwha, rainfall radar site, Korea. Then, in order to evaluate the performance of flood estimates using different rainfall estimation methods, rainfall–runoff simulation was conducted using the physics‐based distributed hydrologic model, Vflo^?. The flood runoff hydrograph was used to compare the calculated hydrographs with the observed one. Results show that the rainfall field estimated by CM methods improved flood estimates, because it optimally combines rainfall fields representing actual spatial and temporal characteristics of rainfall. Copyright © 2008 John Wiley & Sons, Ltd.     

Is a chaotic multi‐fractal approach for rainfall possible?

An Erratum has been published for this article in Hydrological Processes 15 (12) 2001, 2381–2382. Applications of the ideas gained from fractal theory to characterize rainfall have been one of the most exciting areas of research in recent times. The studies conducted thus far have nearly unanimously yielded positive evidence regarding the existence of fractal behaviour in rainfall. The studies also revealed the insufficiency of the mono‐fractal approaches to characterizing the rainfall process in time and space and, hence, the necessity for multi‐fractal approaches. The assumption behind multi‐fractal approaches for rainfall is that the variability of the rainfall process could be directly modelled as a stochastic (or random) turbulent cascade process, since such stochastic cascade processes were found to generically yield multi‐fractals. However, it has been observed recently that multi‐fractal approaches might provide positive evidence of a multi‐fractal nature not only in stochastic processes but also in, for example, chaotic processes. The purpose of the present study is to investigate the presence of both chaotic and fractal behaviours in the rainfall process to consider the possibility of using a chaotic multi‐fractal approach for rainfall characterization. For this purpose, daily rainfall data observed at the Leaf River basin in Mississippi are studied, and only temporal analysis is carried out. The autocorrelation function, the power spectrum, the empirical probability distribution function, and the statistical moment scaling function are used as indicators to investigate the presence of fractal, whereas the presence of chaos is investigated by employing the correlation dimension method. The results from the fractal identification methods indicate that the rainfall data exhibit multi‐fractal behaviour. The correlation dimension method yields a low dimension, suggesting the presence of chaotic behaviour. The existence of both multi‐fractal and chaotic behaviours in the rainfall data suggests the possibility of a chaotic multi‐fractal approach for rainfall characterization. Copyright © 2001 John Wiley & Sons, Ltd.     

Assessment of GCM simulations of annual and seasonal rainfall and daily rainfall distribution across south‐east Australia

Global warming can potentially lead to changes in future rainfall and runoff and can significantly impact the regional hydrology and future availability of water resources. All the large‐scale climate impact studies use the future climate projections from global climate models (GCMs) to estimate the impact on future water availability. This paper presents results from a detailed assessment to investigate the capability of 15 GCMs to reproduce the observed historical annual and seasonal mean rainfalls, the observed annual rainfall series and the observed daily rainfall distribution across south‐east Australia. The assessment shows that the GCMs can generally reproduce the spatial patterns of mean seasonal and annual rainfalls. However, there can be considerable differences between the mean rainfalls simulated by the GCMs and the observed rainfall. The results clearly show that none of the GCMs can simulate the actual annual rainfall time series or the trend in the annual rainfall. The GCMs can also generally reproduce the observed daily (ranked) rainfall distribution at the GCM scale. The GCMs are ranked against their abilities to reproduce the observed historical mean annual rainfall and daily rainfall distribution, and, based on the combined score, the better GCMs include MPI‐ECHAM5, MIUB, CCCMA_T47, INMCM, CSIRO‐MK3·0, CNRM, CCCMA_T63 and GFDL 2·0 and those with poorer performances are MRI, IPSL, GISS‐AOM, MIROC‐M, NCAR‐PCM1, IAP and NCAR‐CCSM. However, the reduction in the combined score as we move from the best‐ to the worst‐performing GCMs is gradual, and there is no evident cut‐off point or threshold to remove GCMs from climate impact studies. There is some agreement between the results here and many similar studies comparing the performance of GCMs in Australia, but the results are not always consistent and do significantly disagree with several of the studies. Copyright © 2010 John Wiley & Sons, Ltd.     

Traffic‐induced variability in dynamic properties of cable‐stayed bridge

The traffic‐induced variability in the dynamic properties of a cable‐stayed bridge is investigated using ambient vibration measurements. Under a relatively steady wind and temperature environment, the ambient vibration test was conducted on the bridge with normal traffic conditions and totally 24 h acceleration response time histories were recorded. These data are divided into 12 sections with each data section containing 2 h measurements. Thereby the modal variability due to changing traffic loading is investigated through post‐processing of the data in each section in both amplitude and frequency domains. The result indicates that the natural frequencies of the global modes can exhibit as much as 1% variation within a day. The modal amplitudes of each mode as well as the modal deflection at each measurement position vary insignificantly. The damping ratios however are sensitive to the vibration intensity, especially when the deck vibration exceeds a certain level. Copyright © 2002 John Wiley & Sons, Ltd.     

Long‐lead seasonal rainfall forecasting using time‐delay recurrent neural networks: a case study

A temporal artificial neural network‐based model is developed and applied for long‐lead rainfall forecasting. Tapped delay lines and recurrent connections are two different components that are used along with a static multilayer perceptron network to design a time‐delay recurrent neural network. The proposed model is, in fact, a combination of time‐delay and recurrent neural networks. The model is applied in three case studies of the Northwest, West, and Southwest basins of Iran. In addition, an autoregressive moving average with exogenous inputs (ARMAX) model is used as a baseline in order to be compared with the time‐delay recurrent neural networks developed in this study. Large‐scale climate signals, such as sea‐level pressure, that affect the rainfall of the study area are used as the predictors in the models, as well as the persistence between rainfall data. The results of winter‐spring rainfall forecasts are discussed thoroughly. It is demonstrated that in all cases the proposed neural network results in better forecasts in comparison with the statistical ARMAX model. Moreover, it is found that in two of three case studies the time‐delay recurrent neural networks perform better than either recurrent or time‐delay neural networks. The results demonstrate that the proposed method can significantly improve the long‐lead forecast by utilizing a non‐linear relationship between climatic predictors and rainfall in a region. Copyright © 2007 John Wiley & Sons, Ltd.