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Modelling in ungauged catchments has always been a complicated issue due to scarcity of the requisite data. Therefore, hydrologists have to rely on indirect techniques of estimation. A novel approach is hereby presented for solar radiation estimation in ungauged catchments using readily available datasets of temperature and precipitation. The rationale is that the extraterrestrial radiation is attenuated not only by different atmospheric processes but also by weather phenomena. A comparison of four clear sky radiation models was made and the best model output together with temperature and precipitation data was analysed using the gamma test for best input combination and data length selection. This led to the development of a non-linear artificial neural network model for solar radiation estimation. Results show a good correlation between the observed and estimated values, depicting the usefulness of gamma test in model development. The study is novel in the sense that this is the first time the gamma test has been used for hourly solar radiation estimation in ungauged catchments and hence extends their solar radiation records. Moreover, the regionalisation of the proposed model to other catchments would also aid solar radiation estimation in such catchments.

Designed for rainstorms and flooding, hydrosystems are largely based on local rainfall Intensity-Duration-Frequency (IDF) curves which include nonstationary components accounting for climate variability. IDF curves are commonly calculated using downscaling outputs from General Circulation Models (GCMs) or Regional Circulation Models (RCMs). However, the downscaling procedures used in most studies are based on one specific time scale (e.g., 1 h) and generally ignore scale-driven uncertainty. This study analyzes the uncertainties in IDF curves stemming from RCM downscaling ratios for four representative weather stations in the United Kingdom. We constructed a series of IDF curves using distribution-based scaling bias-correction technology and a statistical downscaling method to explore the scale-driven uncertainty of IDF curves. The results revealed considerable scale-induced uncertainty of IDF curves for short durations and long return periods; however, there was no clear correlation with the mean storm intensity of the IDF curves of different RCM ensemble members for each duration and return period. The scale-driven uncertainty of IDF curves, which may be propagated or enhanced through hydrometeorological applications, is critical and cannot be ignored in the hydrosystem design process; therefore, a multi-scale method to derive IDF curves must be developed.

Rainfall–runoff is a complicated nonlinear process and many data mining tools have demonstrated their powerful potential in its modelling but still there are many unsolved problems. This paper addresses a mostly ignored area in hydrological modelling: data time interval for models. Modern data collection and telecommunication technologies can provide us with very high resolution data with extremely fine sampling intervals. We hypothesise that both too large and too small time intervals would be detrimental to a model's performance, which has been illustrated in the case study. It has been found that there is an optimal time interval which is different from the original data time interval (i.e. the measurement time interval). It has been found that the data time interval does have a major impact on the model's performance, which is more prominent for longer lead times than for shorter ones. This is highly relevant to flood forecasting since a flood modeller usually tries to stretch his/her model's lead time as far as possible. If the selection of data time interval is not considered, the model developed will not be performing at its full potential. The application of the Gamma Test and Information Entropy introduced in this paper may help the readers to speed up their data input selection process.

Model input data selection is a complicated process, especially for non-linear dynamic systems. The questions on which inputs should be used and how long the training data should be for model development have been hard to solve in practice. Despite the importance of this subject, there have been insufficient reports in the published literature about inter-comparison between different model input data selection techniques. In this study, several methods (i.e. the Gamma test, entropy theory, AIC (Akaike's information criterion)/BIC (Bayesian information criterion) have been explored with the aid of non-linear models of LLR (local linear regression) and ANN (artificial neural networks). The methodology is tested in estimation of solar radiation in the Brue Catchment of England. It has been found that the conventional model selection tools such as AIC/BIC failed to demonstrate their functionality. Although the entropy theory is quite powerful and efficient to compute, it failed to pick up the best input combinations. On the other hand, it is very encouraging to find that the new Gamma test was able to choose the best input selection. However, it is surprising to note that the Gamma test significantly underestimated the required training data while the entropy theory did a better job in this aspect. This is the first study to compare the performance of those techniques for model input selections and still there are many unsolved puzzles. Copyright © 2009 John Wiley & Sons, Ltd.

Evaporation plays a key role in water resources management in arid and semiarid climatic regions. This is the first time that an artificial neural network (ANN) model is applied to estimate evaporation in a hot and dry region (BWh climate by the Koppen classification). It has been found that ANN works very well at the study site and, further, an integrated ANN and autoregressive with exogeneous inputs can have an improved performance over the traditional ANN. Both models significantly outperformed the two empirical methods. It has been demonstrated that the important weather factors to be included in the model inputs are wind speed, saturation vapor pressure deficit, and relative humidity. This result is different from all those reported in the literature and is interestingly linked with a 1936 study by Anderson, who emphasized the importance of saturation vapor pressure deficit. As evaporation is a nonlinear dynamic process, the selection of suitable input weather variables has been a complicated and tim...

Despite the widespread application of nonlinear mathematical models, comparative studies of different models are still a huge task for modellers. This is because a large number of trial and error processes are needed to develop each model, so the workload will be multiplied into an unmanageable level if many types of models are involved. This study presents an efficient approach by using the Gamma test (GT) to select the input variables and the training data length, so that the trial and error workload can be greatly reduced. The methodology is tested in estimating solar radiation at the Brue catchment, UK. Several nonlinear models have been developed efficiently with the aid of the GT, including local linear regression, multi-layer perceptron (MLP), Elman neural network, neural network auto-regressive model with exogenous inputs (NNARX) and adaptive neuro-fuzzy inference system (ANFIS). This work is only feasible within the time and resources constraint, due to the GT in reducing huge workload of the trial and error process.

Rainfall runoff is a very complicated process due to its nonlinear and multidimensional dynamics, and hence difficult to model. There are several options for a modeller to consider, for example: the type of input data to be used, the length of model calibration (training) data and whether or not the input data be treated as signals with different frequency bands so that they can be modelled separately. This paper describes a new hybrid modelling scheme to answer the above mentioned questions. The proposed methodology is based on a hybrid model integrating wavelet transformation, a modelling engine (Artificial Neural Network) and the Gamma Test. First, the Gamma Test is used to decide the required input data dimensions and its length. Second, the wavelet transformation decomposes the input signals into different frequency bands. Finally, a modelling engine (ANN in this study) is used to model the decomposed signals separately. The proposed scheme was tested using the Brue catchment, Southwest England, as a case study and has produced very positive results. The hybrid model outperforms all other models tested. This study has a wider implication in the hydrological modelling field since its general framework could be applied to other model combinations (e.g., model engine could be Support Vector Machines, neuro-fuzzy systems, or even a conceptual model. The signal decomposition could be carried out by Fourier transformation).

Evaporation, as a major component of the hydrologic cycle, plays a key role in water resources development and management in arid and semi-arid climatic regions. Although there are empirical formulas available, their performances are not all satisfactory due to the complicated nature of the evaporation process and the data availability. This paper explores evaporation estimation methods based on artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques. It has been found that ANN and ANFIS techniques have much better performances than the empirical formulas (for the test data set, ANN R 2 = 0.97, ANFIS R 2 = 0.92 and Marciano R 2 = 0.54). Between ANN and ANFIS, ANN model is slightly better albeit the difference is small. Although ANN and ANFIS techniques seem to be powerful, their data input selection process is quite complicated. In this research, the Gamma test (GT) has been used to tackle the problem of the best input data combination and how many data points should be used in the model calibration. More studies are needed to gain wider experience about this data selection tool and how it could be used in assessing the validation data.

This paper presents a novel technique to monitor continuously the azimuthal pointing accuracy of a weather radar antenna. The technique consists of cross-correlating between modelled and measured echoes from ground clutter in real-time at low elevation angles under precipitation and non-precipitation conditions. The azimuthal angle lag with the maximum cross-correlation indicates the adjustment needed in antenna pointing. The modelled ground clutter echoes were obtained using high-resolution digital elevation model (DEM) data whereas the measured ground clutter echoes can be obtained in real-time using a Bayes classifier, which identifies the clutter echoes in the presence of precipitation. The technique has been successfully tested in the Thurnham radar in Southeast England. This method can be used by data users as well as radar operators. It should complement the traditional methods based on sun measurements. Copyright © 2008 Royal Meteorological Society

Hydrological modelling is a complicated procedure and there are many tough questions facing all modellers: what input data should be used? how much data is required? and what model should be used? In this paper, the gamma test (GT) has been used for the first time in modelling one of the key hydrological components: solar radiation. The study aimed to resolve the questions about the relative importance of input variables and to determine the optimum number of data points required to construct a reliable smooth model. The proposed methodology has been studied through the estimation of daily solar radiation in the Brue Catchment, the UK. The relationship between input and output in the meteorological data sets was achieved through error variance estimation before the modelling using the GT. This work has demonstrated how the GT helps model development in nonlinear modelling techniques such as local linear regression (LLR) and artificial neural networks (ANN). It was found that the GT provided very useful information for input data selection and subsequent model development. The study has wider implications for various hydrological modelling practices and suggests further exploration of this technique for improving informed data and model selection, which has been a difficult field in hydrology in past decades.

This paper describes an application of SVM over the Bird Creek catchment and addresses some important issues in developing and applying SVM in flood forecasting. It has been found that, like artificial neural network models, SVM also suffers from over-fitting and under-fitting problems and the over-fitting is more damaging than under-fitting. This paper illustrates that an optimum selection among a large number of various input combinations and parameters is a real challenge for any modellers in using SVMs. A comparison with some benchmarking models has been made, i.e. Transfer Function, Trend and Naive models. It demonstrates that SVM is able to surpass all of them in the test data series, at the expense of a huge amount of time and effort. Unlike previous published results, this paper shows that linear and nonlinear kernel functions (i.e. RBF) can yield superior performances against each other under different circumstances in the same catchment. The study also shows an interesting result in the SVM response to different rainfall inputs, where lighter rainfalls would generate very different responses to heavier ones, which is a very useful way to reveal the behaviour of a SVM model.

Quantization is a process by where continuous signals are transformed into discrete values. It is an important part of the signal processing involved in using weather radar. Technological advances have made it easier to increase the number of quantization levels, as witnessed by the replacement of a 3 bit system by an 8 bit system by the UK Meteorological Office. Research has been conducted in the past demonstrating the error statistics of quantized rainfall, although these studies have used real radar data. The novelty of this study is in using synthetic rain, generated with a Poisson cluster model to represent hourly rainfall, and subsequently disaggregated using a fractal cascade to a fine 5 min time scale. The advantage of this approach is the length of time series that can be generated far outweighs the limited duration of historical rainfall series, especially at such fine time scales. This provides sufficient rainfall data, especially high intensity rainfall, to say something statistically significant about the error statistics. The models are parameterised for different months and also for a non-seasonal set. Rainfall is then generated for a summer case, a winter case, and for the non-seasonal case. It is discovered that the error distribution varies significantly as the parameters change for 3 bit rainfall. This error distribution is relatively constant for 8 bit data, within its working range (up to 126 mm/h). At a fine time scale, such high intensity events are not uncommon. This knowledge is useful when investigating historical radar data at lower quantization levels, for the purpose of flood frequency analysis, and remains relevant, especially, if as some studies have shown, the occurrence of high intensity storms is likely to increase.

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

Bias correction is a necessary post-processing procedure in order to use regional climate model (RCM) simulated local climate variables as the input data for hydrological models owing to systematic errors of RCMs. Most of present bias correction methods adjust statistical properties between observed and simulated data on the basis of calendar periods, e.g. month or season. However, this matching statistic is only a necessary condition, not a sufficient condition, as temporal distribution of the precipitation between observed and simulated data is ignored. This study suggests an improved bias correction scheme that considers not only statistical properties but also the temporal distribution between the time series of observed and modelled data. The ratio of the observed precipitation to simulated precipitation is used to compare the behaviour between the observed and modelled precipitation data, and three criteria are proposed when dividing bias correction periods: (1) underestimation of precipitation, (2) stability of /underestimation of precipitation, (2) stability of precipitation ratio and (3) oscillation of precipitation ratio. The results show that the output of the proposed bias correction method follows the trend of the observed precipitation better than that of the conventional bias correction method. This study indicates that temporal distribution should not be ignored when choosing a comparison period for bias correction. However, the study is only a preliminary attempt to address this important issue, and we hope it will stimulate more research activities to improve the methodology. Future efforts on several unsolved problems have been suggested such as how to find out the optimal group number to avoid the overfitting and underfitting conditions.

An effective bias correction procedure using gauge measurement is a significant step for radar data processing to reduce the systematic error in hydrological applications. In these bias correction methods, the spatial matching of precipitation patterns between radar and gauge networks is an important premise. However, the wind-drift effect on radar measurement induces an inconsistent spatial relationship between radar and gauge measurements as the raindrops observed by radar do not fall vertically to the ground. Consequently, a rain gauge does not correspond to the radar pixel based on the projected location of the radar beam. In this study, we introduce an adjustment method to incorporate the wind-drift effect into a bias correlation scheme. We first simulate the trajectory of raindrops in the air using downscaled three-dimensional wind data from the weather research and forecasting model (WRF) and calculate the final location of raindrops on the ground. The displacement of rainfall is then estimated and a radar–gauge spatial relationship is reconstructed. Based on this, the local real-time biases of the bin-average radar data were estimated for 12 selected events. Then, the reference mean local gauge rainfall, mean local bias, and adjusted radar rainfall calculated with and without consideration of the wind-drift effect are compared for different events and locations. There are considerable differences for three estimators, indicating that wind drift has a considerable impact on the real-time radar bias correction. Based on these facts, we suggest bias correction schemes based on the spatial correlation between radar and gauge measurements should consider the adjustment of the wind-drift effect and the proposed adjustment method is a promising solution to achieve this.

Soil moisture has been widely recognized as a key variable in hydro-meteorological processes and plays an important role in hydrological modelling. Remote sensing techniques have improved the availability of soil moisture data, however, most previous studies have only focused on the evaluation of retrieved data against point-based observations using only one overpass (i.e., the ascending orbit). Recently, the global Level-3 soil moisture dataset generated from Soil Moisture and Ocean Salinity (SMOS) observations was released by the Barcelona Expert Center. To address the aforementioned issues, this study is particularly focused on a basin scale evaluation in which the soil moisture deficit is derived from a three-layer Xinanjiang model used as a hydrological benchmark for all comparisons. In addition, both ascending and descending overpasses were analyzed for a more comprehensive comparison. It was interesting to find that the SMOS soil moisture accuracy did not improve with time as we would have expected. Furthermore, none of the overpasses provided reliable soil moisture estimates during the frozen season, especially for the ascending orbit. When frozen periods were removed, both overpasses showed significant improvements (i.e., the correlations increased from r = −0.53 to r = −0.65 and from r = −0.62 to r = −0.70 for the ascending and descending overpasses, respectively). In addition, it was noted that the SMOS retrievals from the descending overpass consistently were approximately 11.7% wetter than the ascending retrievals by volume. The overall assessment demonstrated that the descending orbit outperformed the ascending orbit, which was unexpected and enriched our knowledge in this area. Finally, the potential reasons were discussed.

An accurate recession curve model is important for separating individual flow events, which is especially difficult over catchments in regions with a maritime climate where frequent rainfall events cause the flows to rise before they reach the baseflow level. The traditional recession curve equations are based on static linear and nonlinear reservoir models. These models work quite well for ground water dominated recession curves, but not so well when the direct runoff is significant in the recession part. In this study, a new modelling methodology is explored based on self-adaptive parameters in the linear and nonlinear reservoir models. It has been found that the adaptive forms performed better than the static ones, especially when a window for the adaptive parameter estimation is properly selected. While the nonlinear adaptive model had better accuracy over the linear one, it could become unstable if its window is too narrow, indicating that more research work is needed to find an useful pattern for the window size. A comparison between the recession curve models and PDM model (a rainfall-runoff model) has shown that they agreed quite well in most winter events, but less so in the summer.

Digital elevation models (or maps) (DEMs) are increasingly becoming available to consulting engineers and academic researchers for delineating catchment boundaries. There are two important questions to be answered in using DEMs for generating those boundaries: first, how good are the computer-generated boundaries and second, what map resolution should be used from a host of data products on the market? This paper describes some explorations of these two topical issues through a case study for the Brue catchment in the south-west of England. The study concludes that computer-generated boundaries failed to delineate accurately or reliably the catchment border with all the available digital maps tested. This was mainly because the computer cannot pick up man-made features (highways, ditches, etc.), in addition to data quality and algorithm problems. With respect to map resolution, it has been found that although the discrepancy of the delineation increases with the grid size (i.e. poor resolution maps would generate lower-quality boundaries), there is a threshold which defines a zone where the clear relationship between the map resolution and discrepancy becomes very unstable. This is a very important conclusion since it indicates that higher map resolution may produce poorer results than lower map resolution, which is quite serious in practical projects for map users who face increased map costs, longer computing time and potentially poorer results. In the end, the paper proposes an integrated technique that uses the strengths of both manual and computer methods to produce an optimised boundary.

A comparative modelling of two catchments of similar sizes in Taiwan and England is described. In the study, despite its success in many Taiwanese catchments, including the Yan-Shui Creek catchment in this study, the distributed model GBDM was initially found unsuitable when applied to the Brue catchment in South West England. However, the simulations are much better after revising the infiltration capacity. Further exploration reveals several interesting findings. (1) The infiltration computation based on soil characteristics and classifications is unreliable in the model. Other factors, such as climate, farming practice and vegetation cover, could have a much more significant impact. (2) The application of the GBDM far away from its 'home country' unveils a possible weakness of such a model for being 'underfitting'. The fact that an 'adjustment factor' added in the model could improve both its calibration and validation may indicate that there is a room to improve the GBDM structure for catchments outside Taiwan. (3) The study illustrates the difficulty in creating a universal distributed model that could suit all possible hydrological environments, under the constraint of model parameter parsimony to minimize the 'equifinality' problem.

This article presents an algorithm to correct the increase in reflectivity in the melting layer using weather radars. The correction starts with a fuzzy logic system (FLS) to classify rain, snow and melting snow using the reflectivity factor, the differential reflectivity, the linear depolarisation ratio and the height of the measurement from the ground surface. Using the output of the FLS, a correction is performed on the bright band contaminated reflectivity data assuming an idealised vertical reflectivity profile typical from stratiform precipitation and obtained from a considerable number of observations at S-band frequencies. Copyright © 2005 Royal Meteorological Society

This paper describes an exploration in using SVM (Support Vector Machine) models, which were initially developed in the Machine Learning community, in flood forecasting, with the focus on the identification of a suitable model structure and its relevant parameters for rainfall runoff modelling. SVM has been applied in many fields and has a high success rate in classification tasks such as pattern recognition, OCR, etc. The applications of SVM in regression of time series are relatively new and they are more problematic in comparison with classifications. This study found that exhaustive search of an optimum model structure and its parameter space is prohibitive due to their sheer size and unknown characteristics. Some parameters are very sensitive and can increase the CPU load tremendously (and hence result in very long computation times). All these make it very difficult to efficiently identify SVM models, which has been carried out by manual operations in all study cases so far. The paper further explored the relationships among various model structures (ξ-SV or ν-SV regression), kernel functions (linear, polynomial, radial basis and sigmoid), scaling factor, model parameters (cost C, epsilon) and composition of input vectors. These relationships should be able to provide useful information for more effective model identification in the future. The unit response curve from SVM was compared with a transfer function model and it is found that a TF model outperforms SVM in short-range predictions. It is still unclear how the unit response curve could be utilised for model identification processes and future exploration in this area is needed.

Open Source, in which the source code to software is freely shared and improved upon, has recently risen to prominence as an alternative to the more usual closed approach to software development. A number of high profile projects, such as the Linux operating system kernel and the Apache web server, have demonstrated that Open Source can be technically effective, and companies such as Cygnus Solutions (now owned by Red Hat) and Zope Corporation have demonstrated that it is possible to build successful companies around open source software. Open Source could have significant benefits for hydroinformatics, encouraging widespread interoperability and rapid development. In this paper we present a brief history of Open Source, a summary of some reasons for its effectiveness, and we explore how and why Open Source is of particular interest in the field of hydroinformatics. We argue that for technical, scientific and business reasons, Open Source has a lot to offer.

A modern real time flood forecasting system requires itsmathematical model(s) to handle highly complex rainfall runoffprocesses. Uncertainty in real time flood forecasting willinvolve a variety of components such as measurement noise fromtelemetry systems, inadequacy of the models, insufficiency ofcatchment conditions, etc. Probabilistic forecasting is becomingmore and more important in this field. This article describes a novel attempt to use a Fuzzy Logic approach for river flow modelling based on fuzzy decision trees. These trees are learntfrom data using the MA-ID3 algorithm. This is an extension of Quinlan's ID3 and is based on mass assignments. MA-ID3 allows for the incorporation of fuzzy attribute and class values intodecision trees aiding generalisation and providing a framework for representing linguistic rules. The article showed that with only five fuzzy labels, the FDT model performed reasonably welland a comparison with a Neural Network model (Back Propagation)was carried out. Furthermore, the FDT model indicated that therainfall values of four or five days before the prediction time are regarded as more informative to the prediction than the morerecent ones. Although its performance is not as good as the neural network model in the test case, its glass box nature couldprovide some useful insight about the hydrological processes.

Reference Evapotranspiration (ETo) and soil moisture deficit (SMD) are vital for understanding the hydrological processes, particularly in the context of sustainable water use efficiency in the globe. Precise estimation of ETo and SMD are required for developing appropriate forecasting systems, in hydrological modeling and also in precision agriculture. In this study, the surface temperature downscaled from Weather Research and Forecasting (WRF) model is used to estimate ETo using the boundary conditions that are provided by the European Center for Medium Range Weather Forecast (ECMWF). In order to understand the performance, the Hamon’s method is employed to estimate the ETo using the temperature from meteorological station and WRF derived variables. After estimating the ETo, a range of linear and non-linear models is utilized to retrieve SMD. The performance statistics such as RMSE, %Bias, and Nash Sutcliffe Efficiency (NSE) indicates that the exponential model (RMSE = 0.226; %Bias = −0.077; NSE = 0.616) is efficient for SMD estimation by using the Observed ETo in comparison to the other linear and non-linear models (RMSE range = 0.019–0.667; %Bias range = 2.821–6.894; NSE = 0.013–0.419) used in this study. On the other hand, in the scenario where SMD is estimated using WRF downscaled meteorological variables based ETo, the linear model is found promising (RMSE = 0.017; %Bias = 5.280; NSE = 0.448) as compared to the non-linear models (RMSE range = 0.022–0.707; %Bias range = −0.207–−6.088; NSE range = 0.013–0.149). Our findings also suggest that all the models are performing better during the growing season (RMSE range = 0.024–0.025; %Bias range = −4.982–−3.431; r = 0.245–0.281) than the non−growing season (RMSE range = 0.011–0.12; %Bias range = 33.073–32.701; r = 0.161–0.244) for SMD estimation.

Accurate estimation of point rainfall at ungauged locations from the measurements at surrounding sites is critical in obtaining a continuous surface of rainfall information. This can be accomplished through numerous interpolation methods, which have different strengths and weaknesses. The accuracy of the resulting continuous surface of rainfall information depends on the density of the point data and observational errors, which in turn affect the integrity of hydrological studies that utilize the data as input. In this study, four interpolation methods—Thiessen polygon, inverse distance weighting (IDW), thin plate, and Kriging—were evaluated at an experimental catchment in South West England at three gauge densities through the leave-one-out cross-validation (LOOCV) method. The numbers of rain gauges used for the three densities were 49, 28, and 10, which were translated to 2.75, 4.82, and 13.5 km2 per gauge since the area of the catchment was 135.2 km2. The gauge density was found to have an ef...

Reproducibility and repeatability of experiments are the fundamental prerequisites that allow researchers to validate results and share hydrological knowledge, experience and expertise in the light of global water management problems. Virtual laboratories offer new opportunities to enable these prerequisites since they allow experimenters to share data, tools and pre-defined experimental procedures (i.e. protocols). Here we present the outcomes of a first collaborative numerical experiment undertaken by five different international research groups in a virtual laboratory to address the key issues of reproducibility and repeatability. Moving from the definition of accurate and detailed experimental protocols, a rainfall-runoff model was independently applied to 15 European catchments by the research groups and model results were collectively examined through a web-based discussion. We found that a detailed modelling protocol was crucial to ensure the comparability and reproducibility of the proposed experiment across groups. Our results suggest that sharing comprehensive and precise protocols and running the experiments within a controlled environment (e.g. virtual laboratory) is as fundamental as sharing data and tools for ensuring experiment repeatability and reproducibility across the broad scientific community and thus advancing hydrology in a more coherent way.

Conventional error-based statistical parameters like the Nash–Sutcliffe efficiency index are popular among hydrologists to check the accuracy of hydrological models and to compare the relative performance of alternative models in a particular modelling scenario. A major drawback of those traditional indices is that they are based on only one modelling attribute, i.e. the modelling error. This study has identified an overall model utility index as an effective error-sensitivity-uncertainty procedure which could serve as a useful quality indicator of data-based modelling. This study has also made an attempt to answer the question—should the increasing complexity of the existing model add any benefit to the model users? The study evaluates the utility of some popular and widely used data-based models in hydrological modelling such as local linear regression, artificial neural networks (ANNs), Adaptive neuro fuzzy inference system (ANFIS) and support vector machines (SVMs) along with relatively complex wavelet hybrid forms of ANN, ANFIS and SVM in the context of daily rainfall–runoff modelling. The study has used traditional error-based statistical indices to confirm capabilities of model utility index values in identifying better model for rainfall–runoff modelling. The implication of this study is that a modeller may use utility values to select the best model instead of using both calibration and validation processes in the case of data scarcity. The study comprehensively analysed the modelling capabilities of SVM and its waveform in the context of rainfall–runoff modelling.

This paper describes some of the latest developments in real-time fluvial flood-forecasting systems which utilise quantitative weather radar information. Rain-gauge, flow-measuring stations, weather radar and other sources of information are fully integrated by the real-time flood-forecasting system and provide flood warnings for civil protection. The systems have been commissioned in various regions within the UK and are undergoing continuous development in conjunction with the UK water industry and through close involvement with various European partners via the auspices of the European Union.

(2000). Using Weather Radars to Measure Rainfall in Urban Catchments. Journal of Urban Technology: Vol. 7, No. 1, pp. 85-102.

Charge Coupled Devices (CCDs) have been successfully used in several low energy X-ray astronomical satellite over the past two decades. Their high energy resolution and high spatial resolution make them an perfect tool for low energy astronomy, such as formation of galaxy clusters and environment of black holes. The Low Energy X-ray Telescope (LE) group is developing Swept Charge Device (SCD) for the Hard X-ray Modulation Telescope (HXMT) satellite. SCD is a special low energy X-ray CCD, which could be read out a thousand times faster than traditional CCDs, simultaneously keeping excellent energy resolution. A test method for measuring the charge transfer efficiency (CTE) of a prototype SCD has been set up. Studies of the charge transfer inefficiency (CTI) have been performed at a temperature range of operation, with a proton-irradiated SCD.

Mesoscale NWP model is gaining more attention in providing high-resolution rainfall forecasts at the catchment scale for real-time flood forecasting. The model accuracy is however negatively affected by the "spin-up" effect and errors in the initial and lateral boundary conditions. Synoptic studies in the meteorological area have shown that the assimilation of operational observations especially the weather radar data can improve the reliability of the rainfall forecasts from the NWP models. This study aims at investigating the potential of radar data assimilation in improving the NWP rainfall forecasts that have direct benefits for hydrological applications. The Weather Research and Forecasting (WRF) model is adopted to generate 10 km rainfall forecasts for a 24 h storm event in the Brue catchment (135.2 km2) located in Southwest England. Radar reflectivity from the lowest scan elevation of a C-band weather radar is assimilated by using the three dimensional variational (3D-Var) data assimilation technique. Considering the unsatisfactory quality of radar data compared to the rain gauges, the radar data is assimilated in both the original form and an improved form based on a real-time correction ratio developed according to the rain gauge observations. Traditional meteorological observations including the surface and upper-air measurements of pressure, temperature, humidity and wind speed are also assimilated as a bench mark to better evaluate and test the potential of radar data assimilation. Four modes of data assimilation are thus carried out on different types or combinations of observations: (1) traditional meteorological data; (2) radar reflectivity; (3) corrected radar reflectivity; (4) a combination of the original reflectivity and meteorological data; and (5) a combination of the corrected reflectivity and meteorological data. The WRF rainfall forecasts before and after different modes of data assimilation is evaluated by examining the rainfall cumulative curves and the rainfall totals which have direct impact on rainfall-runoff transformation in hydrological applications. It is found that by solely assimilating radar data, the improvement of rainfall forecasts are not as obvious as assimilating meteorological data; whereas the positive effect of radar data can be seen when combined with the traditional meteorological data, which leads to the best rainfall forecasts among the five modes. To further improve the effect of radar data assimilation, limitations of the radar correction ratio developed in this study is discussed and suggestions are made on more efficient utilisation of radar data in NWP assimilation.

Although the nonlinear power form model structure is widely accepted by practitioners in the flood regionalization modelling, there is a lack of studies on whether there is a room for further improvement, and if the answer is yes, what should be done to explore alternative model structures. A framework is proposed in this study towards investigating this issue by the following steps: (i) a universal data-driven model is utilized to see if there is a room for improvement compared with the conventional model, and (ii) if improvement is achieved, this means that there should exist more effective model structures than the current form. However, because the universal data-driven models are usually opaque, more explicit model structures should be explored, which are convenient for practical usage. In this study, the proposed framework is applied in a case study using the catchment characteristics from the Flood Estimation Handbook in conjunction with the gamma test, support vector machine (SVM) and genetic programming (GP). First, the gamma test is used for the purpose of input variables selection where no model structure needs to be defined as a priori, and therefore, the result can be applied to any model structures for model building. Second, an SVM, which is a powerful data-driven nonlinear model capable of modelling a variety of nonlinear systems, is applied to the index flood model for the first time. Once the best model is determined using those two data-driven tools, GP is employed to find an alternative model structure. As the SVM is not formulated for producing explicit model functional form, the GP offers an advantage at this point where it can infer an explicit mathematical model functional form. Copyright © 2012 John Wiley & Sons, Ltd.

Small electric-heating reactors are widely used in chemical experiments, fine and special chemical production processes. Because of their large time-delay, strong nonlinearity and time-varying properties, they are difficult to control accurately using the ordinary PID controller with fixed PID parameters. By summarizing the PID tuning experience and knowledge of human operators into fuzzy tuning rules, a fuzzy auto-tuning PID (FA-PID) control algorithm was developed for the temperature control of a small electric-heating reactor using the fuzzy tuning rules to tune PID parameters real-time on-line. The FA-PID control algorithm was successfully implemented in Matlab software using its M programming language, and integrated tightly with configuration and monitoring software KINGVIEW through their dynamic data exchange (DDE) channel. The FA-PID control system was applied to the temperature control experiments of a small electric-heating reactor with strong nonlinear and time-varying properties, and it achieved much better control performance than the ordinary PID controller. The experimental results demonstrated that FA-PID had good adaptability to process changes.

[1] Flood estimation for ungauged catchments is a challenging task for hydrologists. A modern geographical information system is able to extract a large number of catchment characteristics as input variables for regionalization analysis. Effective and efficient selection of the best input variables is urgently needed in this field. This paper explores a new methodology for selecting the best input variable combination on the basis of the gamma test and leave-one-out cross validation (LOOCV) to estimate the median annual maximum flow (as an index flood). Since the gamma test is capable of efficiently calculating the output variance on the basis of the input without the need to select a model structure type, more effective regionalization models could be developed because there is no need to define an a priori model structure. A case study from 20 catchments in southwest England has been used to illustrate and validate the proposed scheme. It has been found that the gamma test is able to narrow down the search options to be further explored by the LOOCV. The best formula from this approach outperforms the conventional approaches based on cross validation, data filtering with Spearman's rank correlation matrix, and corrected Akaike information criterion. In addition, the developed formula is significantly more accurate than the existing equation used in the Flood Estimation Handbook.

Human activities could have important impacts on weather and climate at local, regional and global scales. However, it is also important to realise that climate has always been naturally changing with time. Modelling global climate change is very difficult and subjective. The techniques used and results obtained in climate modelling provoke controversy, therefore there are huge uncertainties with the ’predictions‘ provided from all the existing climate modelling systems.

Floods can be characterised by peak flow, flood volume with a defined duration, or other attributes. They are considered as independent and identically distributed. Empirical flood frequency provides the basis for analytical flood frequency estimation. Computer software helps to speed up the frequency analysis process.

Regionalisation is a useful approach in dealing with data poor catchments. A set of catchment attributes are used to select hydrologically similar catchments in order to overcome the data shortage problem.

Flood forecasting is a non-structural measure in flood risk management. Real-time flood forecasting provides valuable information for flood warning services so that the general public and emergency agencies would have sufficient time to prepare counter-measures against the impending floods.

In statistics, the longer the records are, the more accurate the statistical estimation would be. Historical and palaeo floods are important complementary data sources to instrumental floods. They should be integrated together to provide improved flood frequency estimations.

There are structural and non-structural flood mitigation measures. Risk and uncertainty analysis is the foundation for designing and choosing appropriate flood mitigation options. The socioeconomic analysis is becoming increasingly important in the future.