A SEMI-DISTRIBUTED ADAPTIVE MODEL FOR REAL-TIME FLOOD FORECASTING1

ABSTRACT: A semi-distributed deterministic model for real-time flood forecasting in large basins is proposed. Variability of rainfall and losses in space is preserved and the effective rainfall-direct runoff model segment based on the Clark procedure is incorporated. The distribution of losses in space is assumed proportional to rainfall intensity and their evolution in time is represented by the φ-index; furthermore, an initial period without production of effective rainfall is considered. The first estimation of losses and the associated forecasts of flow are performed at the time corresponding to the first rise observed in the hydrograph. Then the forecasts of flow are corrected at each subsequent time step through the updating of the φ-index. The model was tested by using rainfall-runoff events observed on two Italian basins and the predictions of flow for lead times up to six hours agree reasonably well with the observations in each event. For example, for the coefficient of persistence, which compares the model forecasts with those generated by the no-model assumption, appreciable positive values were computed. In particular, for the larger basin with an area of 4,147 km², the mean values were 0.4, 0.4 and 0.5 for forecast lead times of two hours, four hours and six hours, respectively. Good performance of the model is also shown by a comparison of its flow predictions with those derived from a unit hydrograph based model     

EFFECTS OF SIMULATED CLOUD SEEDING ON STREAMFLOW OF SELECTED WATERSHEDS IN PENNSYLVANIA1

A mathematical model was developed to simulate the hydrologic behavior of five small watersheds in central Pennsylvania. Continuous hydrographs for the 6-month period, April to September 1964, were simulated. Synthesized rainfall cycles consisting of increasing rainfall by 10, 20, and 30 percent to simulate the effects of cloud seeding were processed through the watershed model to determine the effects on low flow augmentation. Other rainfall cycles used consisted of increasing every third storm by 30 percent and of developing a rainfall cycle by processing daily radiosonde data through a mathematical cumulus cloud model to obtain a prediction of rainfall following seeding. A comparison of actual and predicted hydrographs indicated that simulated cloud seeding resulted in significant monthly and seasonal water yields. In general, the results of the study appear to indicate that on a theoretical basis cloud seeding would be a feasible method of augmenting low stream-flow during the summer months on watersheds in the northern Appalachian region.     

Lactation patterns of pinnipeds are not explained by optimization of maternal energy delivery rates

Within pinnipeds, phocids and otariids show differing maternal care strategies. Phocids rear young out of body stores in a yearly cycle with a single stay ashore when the mother fasts while lactating, whereas otariids provision their young by repeated foraging trips to sea alternating with brief stays ashore where they suckle their young. In a previous optimality model, these differences have been interpreted as adaptations based on differing energy requirements of large (phocid) and smaller (otariid) species, and the time budget of the large elephant and the much smaller Antarctic fur seal were correctly predicted. Our refined model—extended to pinniped species of all sizes—predicts lactation strategies to shift from attendance cycles to 1-year cycles with increasing body mass and provides an explanation for the finding that phocid pups are weaned at lower relative mass than otariid pups. However, other predictions do not correspond to empirical findings. In particular, the model does not explain the behavior of large otariids and small phocids. Thus, maternal metabolic requirements alone appear insufficient to explain observed lactation patterns. In the light of our results, we discuss more generally the scope and limitations of optimality models when applied in a comparative framework to a group of related species.     

QSAR计算中的分子连接性指数法

本文介绍了分子连接性指数法和原理及各阶指数的计算方法，以及在ＱＳＡＲ计算中的应用实例，并对特殊连接性指数的用法作了分析。     

有机化合物厌氧生物降解性的测定和预测

测定有机物厌氧生物降解性的方法包括非特性参数和特性参数测定法。本文着重介绍有机物厌氧生物降解性的筛选测定法，以基团贡献法为基础，不外加其它理化参数的有机物结构与生物降解性关系的预测已经由简单的线性模型发展至专家系统和人工神经网络模型，并显示出极好的应用前景。     

应用模式识别法研究芳腈化合物结构与活性的关系

基于２３个芳腈衍生物对发光菌的毒性疚和一批结构参数，用模式识别法研究了结构与活性的关系。在化合物的生物活性类别与模式特征之间建立的３个类别判别函数对化合物活性类别的判别正确率达９１．３％，并为进一步的研究工作合理地挑选了４个代表性化合物。     

Bayesian model-based cluster analysis for predicting macrofaunal communities

To predict macrofaunal community composition from environmental data a two-step approach is often followed: (1) the water samples are clustered into groups on the basis of the macrofauna data and (2) the groups are related to the environmental data, e.g. by discriminant analysis. For the cluster analysis in step 1 many hard, seemingly arbitrary choices have to be made that nevertheless influence the solution (similarity measure, clustering strategy, number of clusters). The stability of the solution is often of concern, e.g. in clustering by the program. In the discriminant analysis of step 2 it can occur that a water sample is misclassified on the basis of the environmental data but on further inspection happens to be a borderline case in the cluster analysis. One would then rather reclassify such a sample and iterate the two steps. Bayesian latent class analysis is a flexible, extendable model-based cluster analysis approach that recently has gained popularity in the statistical literature and that has the potential to address these problems. It allows the macrofauna and environmental data to be modelled and analyzed in a single integrated analysis. An exciting extension is to incorporate in the analysis prior information on the habitat preferences of the macrofauna taxa such as is available in lists of indicator values. The output of the analysis is not a hard assignment of water samples to clusters but a probabilistic (fuzzy) assignment. The number of clusters is determined on the basis of the Bayes factor. A standard feature of the Bayesian method is to make predictions and to assess their uncertainty. We applied this approach to a data set consisting of 70 water samples, 484 macrofauna taxa and four environmental variables for which previously a five cluster solution had been proposed. The standard for Bayesian estimation, the Gibbs sampler, worked fine on a subset with only 12 selected taxa but did not converge on the full set with 484 taxa. This is due to many configurations in which the assignment probabilities are all very close to either 0 or 1. This convergence problem is comparable with the local optima problem in classical cluster optimization algorithms, including the EM algorithm used in Latent Gold, a Windows program for latent class analysis. The convergence problem needs to be solved before the benefits of Bayesian latent class analysis can come to fruition in this application. We discuss possible solutions.     

Review and comparison of methods to study the contribution of variables in artificial neural network models

Convinced by the predictive quality of artificial neural network (ANN) models in ecology, we have turned our interests to their explanatory capacities. Seven methods which can give the relative contribution and/or the contribution profile of the input factors were compared: (i) the ‘PaD’ (for Partial Derivatives) method consists in a calculation of the partial derivatives of the output according to the input variables; (ii) the ‘Weights’ method is a computation using the connection weights; (iii) the ‘Perturb’ method corresponds to a perturbation of the input variables; (iv) the ‘Profile’ method is a successive variation of one input variable while the others are kept constant at a fixed value; (v) the ‘classical stepwise’ method is an observation of the change in the error value when an adding (forward) or an elimination (backward) step of the input variables is operated; (vi) ‘Improved stepwise a’ uses the same principle as the classical stepwise, but the elimination of the input occurs when the network is trained, the connection weights corresponding to the input variable studied is also eliminated; (vii) ‘Improved stepwise b’ involves the network being trained and fixed step by step, one input variable at its mean value to note the consequences on the error. The data tested in this study concerns the prediction of the density of brown trout spawning redds using habitat characteristics. The PaD method was found to be the most useful as it gave the most complete results, followed by the Profile method that gave the contribution profile of the input variables. The Perturb method allowed a good classification of the input parameters as well as the Weights method that has been simplified but these two methods lack stability. Next came the two improved stepwise methods (a and b) that both gave exactly the same result but the contributions were not sufficiently expressed. Finally, the classical stepwise methods gave the poorest results.     

CHLOROPHYLL-BIOMASS-NUTRIENT RELATIONSHIPS FOR NATURAL ASSEMBLAGES OF FLORIDA PHYTOPLANKTON1

Parallel determination of phytoplankton biomass and chlorophyll a concentration were made on spring and summer phytoplankton samples collected from 165 Florida lakes. There was a significant correlation between chlorophyll a concentration and phytoplankton biomass (r=0.80; P < 0.01). Chlorophyll content per unit phytoplankton biomass ranged over two orders of magnitude. Nitrogen seemed to be a major factor influencing the chlorophyll content of Florida algae. Multiple regression analyses indicated that phytoplankton biomass was dependent on both the total phosphorus and total nitrogen concentration. Nutrient-phytoplankton and Secchi-phytoplankton relationships for the Florida lakes had higher coefficients of determination if chlorophyll a concentrations rather than phytoplankton biomass data were used in regression analyses.     

Changes in interacting species with disturbance

Human-influenced changes in the diversity and abundance of native wildlife in a southern boreal forest area, which became a national park in 1975, are used to develop working hypotheses for predicting and subsequently measuring the effects of disturbance or restoration programs on groups of interacting species. Changes from presettlement conditions began with early 1900 hunting, which eliminated woodland caribou (Rangifer tarandus) and elk (Cervus elaphus), and reduced moose (Alces alces) to the low numbers which still persist. Increases in white-tailed deer (Odocoileus virginianus), as these other cervid species became less abundant or absent, provided enough alternative food to sustain the system's carnivores until plant succession on previously burned or logged areas also caused deer to decline. With increased competition for reduced food, carnivore species also became less abundant or absent and overexploited some prey populations. The abilities of interacting species to maintain dynamically stable populations or persist varied with their different capacities to compensate for increased exploitation or competition. These relationships suggested a possible solution to the problem of predicting the stability of populations in disturbed systems. For the 1976–1985 period, a hypothesis that the increased protection of wildlife from exploitation in a national park would restore a more diverse, abundant, and productive fauna had to be rejected.