Construction of design hyetographs for locations without observed data

A design hyetograph which represents the time distribution of design rainfall depth corresponding to a duration and a return period is essential in hydrologic design. However, for locations without observed data (ungauged sites), construction of design hyetographs is a difficult task because of the lack of data. Hence, an approach based on self‐organizing map (SOM) is proposed in this paper to construct design hyetographs at ungauged sites. SOM, which is a special kind of artificial neural networks (ANNs), is a powerful technique for extracting and visualizing salient features of data and for solving classification problems. The proposed approach is composed of three steps: classification, assignment and construction. First, the SOM‐based classification is performed to analyse gauged sites' design hyetographs. Second, based on the concept of indicator kriging, a method is developed to assign an ungauged site of interest to a certain cluster. Third, based on the spatial information, the clustering results, and the design hyetographs of gauged sites, the design hyetograph at the site of interest is constructed using the reciprocal‐distance‐squared method. An application is conducted to assess the advantages of the proposed approach over the conventional approaches. Moreover, cross‐validation tests are applied to evaluate the performance of the accuracy and the robustness of the proposed approach. The results confirm the improvement in performance by using the proposed approach instead of conventional approaches. The proposed approach is useful for constructing design hyetographs at ungauged sites. Copyright © 2009 John Wiley & Sons, Ltd.     

Improving the forecasts of extreme streamflow by support vector regression with the data extracted by self‐organizing map

During typhoons or storms, accurate forecasts of hourly streamflow are necessary for flood warning and mitigation. However, hourly streamflow is difficult to forecast because of the complex physical process and the high variability in time. Furthermore, under the global warming scenario, events with extreme streamflow may occur that leads to more difficulties in forecasting streamflows. Hence, to obtain more accurate hourly streamflow forecasts, an improved streamflow forecasting model is proposed in this paper. The computational kernel of the proposed model is developed on the basis of support vector machine (SVM). Additionally, self‐organizing map (SOM) is used to analyse observed data to extract data with specific properties, which are capable of providing valuable information for streamflow forecasting. After reprocessing, these extracted data and the observed data are used to construct the SVM‐based model. An application is conducted to clearly demonstrate the advantage of the proposed model. The comparison between the proposed model and the conventional SVM model, which is constructed without SOM, is performed. The results indicate that the proposed model is better performed than the conventional SVM model. Moreover, as regards the extreme events, the result shows that the proposed model reduces the forecasting error, especially the error of peak streamflow. It is confirmed that because of the use of data extracted by SOM, the improved forecasting performance is obtained. The proposed model, which can produce accurate forecasts, is expected to be useful to support flood warning systems. Copyright © 2012 John Wiley & Sons, Ltd.     

Frontogenesis and Frontal Progression of a Trapping-Generated Estuarine Convergence Front and Its Influence on Mixing and Stratification

Estuarine fronts are well known to influence transport of waterborne constituents such as phytoplankton and sediment, yet due to their ephemeral nature, capturing the physical driving mechanisms and their influence on stratification and mixing is difficult. We investigate a repetitive estuarine frontal feature in the Snohomish River Estuary that results from complex bathymetric shoal/channel interactions. In particular, we highlight a trapping mechanism by which mid-density water trapped over intertidal mudflats converges with dense water in the main channel forming a sharp front. The frontal density interface is maintained via convergent transverse circulation driven by the competition of lateral baroclinic and centrifugal forcing. The frontal presence and propagation give rise to spatial and temporal variations in stratification and vertical mixing. Importantly, this front leads to enhanced stratification and suppressed vertical mixing at the end of the large flood tide, in contrast to what is found in many estuarine systems. The observed mechanism fits within the broader context of frontogenesis mechanisms in which varying bathymetry drives lateral convergence and baroclinic forcing. We expect similar trapping-generated fronts may occur in a wide variety of estuaries with shoal/channel morphology and/or braided channels and will similarly influence stratification, mixing, and transport.     

Influence of Pore Fluid Chemistry on the Mechanical Properties of Clay-Based Materials

The producers of nuclear waste, within all countries exploring options, including Canada, have determined the long-term solution to be a deep geological repository. In the Canadian concept, within the deep geologic repository a number of clay-based barriers will separate the containers from the surrounding geosphere. Following placement the surrounding groundwater will infiltrate into the repository. In order to analyze the performance of the repository under very complex conditions, accurate material properties are required. The chemistry of the host rock is an important aspect as the behaviour of clay-based barrier materials could be affected by the saturating saline groundwater. This paper investigates the saturated mechanical behaviour of light backfill (composed of 50 % silica sand and 50 % Na-bentonite clay) and dense backfill (composed of 70 % crushed granite, 25 % glacial lake clay and 5 % Na-bentonite clay) and the quantifying the effect of pore fluid chemistry on the strength and compressibility behaviour of the materials. The results indicate that light backfill behaviour is strongly influenced by its pore fluid chemistry while dense backfill shows limited effects. The material parameters of light backfill and dense backfill are interpreted for input into numerical simulations. These results and interpretation enrich the understanding of the mechanical response of light and dense backfill, two components of the sealing system of the Canadian deep geologic repository.     

Extreme Eutrophication in Shallow Estuaries and Lagoons of California Is Driven by a Unique Combination of Local Watershed Modifications That Trump Variability Associated with Wet and Dry Seasons

Rapidly growing human populations have caused heavy modifications to the watersheds of many Mediterranean climate estuaries, subjecting them to excessive nutrient enrichment and harmful macroalgal blooms. Despite these impacts, comprehensive studies in these systems are rare and comparisons between systems are lacking. We surveyed five southern California estuaries that ranged in size from 93 to 1,000 ha and incorporated differing land usages and watershed sizes. We sampled environmental variables (sediment redox potential, organic content, total nitrogen and total phosphorus, water column nitrate, ammonium, and salinity) and macroalgal cover and biomass quarterly at three locations within each estuary over 15 months to compare spatial and wet vs. dry season patterns. Maximum mean water column nitrate concentration across all estuaries ranged from 47 to 1,700 μM, showing that all estuaries were highly enriched with nitrogen, at least at some times. Mean macroalgal biomass ranged from 0 to 1,500 g wet wt m^?2. However, neither nutrient concentrations nor algal biomass showed consistent seasonal patterns as maximum values occurred in different seasons in different estuaries. Three-dimensional principal components analysis followed by regression analyses confirmed that macroalgal abundance was not directly related to water or sediment N concentrations. Rather each of these southern California estuaries showed individual patterns in all measured variables, which were most likely induced by a suite of physical modifications unique to each system and its watershed.     

Quantifying uncertainty of the semivariogram of transmissivity of an existing groundwater monitoring network

A method is presented for quantifying the uncertainty of the semivariogram of transmissivity and determining the required number of measurements. In this method, the estimated semivariogram and its 95% confidence limits are first determined from a finite number of measurements. The uncertainty of the estimated semivariogram is then quantified using the random field simulation technique. For a given value of the quantitative index of uncertainty, the required number of measured data can finally be obtained. Actual transmissivity data of an existing groundwater monitoring network are used in the application of the proposed method. The required numbers of measurements of transmissivity for four different values of the quantitative index of uncertainty are provided, from which reliable semivariograms of the transmissivity can be obtained. Copyright © 2004 John Wiley & Sons, Ltd.     

Optimal tonal detectors based on the power spectrum

The detection of tonals embedded in noise is an important sonar function and the traditional power spectrum analysis method has been widely used for this purpose. Wagstaff et al. (1997) proposed the WISPR (Wagstaff's Integration Silencing PRocessor) family processors, which perform a nonlinear integration or combination of the power spectrum observations. In this paper, we analyze the statistical property of the power spectrum observations and develop novel tonal detectors by optimally integrating the spectrum observations. The optimal detectors are derived by using the method of maximum likelihood hypothesis test. The results from simulations and real sea trial data have shown that the proposed detectors are promising in detecting tonals     

An RBF‐based model with an information processor for forecasting hourly reservoir inflow during typhoons

Inflow forecasting is essential for decision making on reservoir operation during typhoons. In this paper, a radial basis function (RBF)‐based model with an information processor is proposed for more accurate forecasts of hourly reservoir inflow. Firstly, based on the multilayer perceptron neural (MLP) network, an information processor is developed to pre‐process the typhoon information (namely, typhoon characteristics and rainfall) and to produce forecasts of rainfall. The forecasted rainfall and the observed inflow are then used as input to the RBF‐based model, which is a nonlinear function approximator, to produce forecasts of hourly inflow. For parameter estimation of the RBF‐based model, the fully‐supervised learning algorithm is used. Actual applications of the proposed model are performed to yield 1‐ to 6‐h ahead forecasts of inflow. To assess the improvement due to the use of the typhoon information processor, models without the typhoon information processor are constructed and compared with the proposed model. The results show that the proposed model performs the best and is capable of providing improved forecasts of hourly inflow, especially for long lead‐time. In conclusion, the proposed model with a typhoon information processor can extract useful information from typhoon characteristics and rainfall, and consequently improve the forecasting performance. Copyright © 2009 John Wiley & Sons, Ltd.