Modelling the effects of motion on primary production in the mixed layer of lakes

A model of the time dependent relationship between productivity and light intensity following changes in light intensity is briefly described. The model incorporates two response timescales simulating initial response and photoinhibition, although additional timescales could easily be incorporated. The model is calibrated against one set of time dependent data, and applied to two simple models of motion in the upper mixed layer of a lake. The two models are: organised motion simulating Langmuir cells, and disorganised motion simulating the turbulent velocity field associated with surface wind stirring. The depth and therefore light histories for a number of photosynthesising particles are calculated by these models, and used by the productivity model to calculate mean productivities. The results show that the influence of the time dependent nature of the productivity relationship depends on the ratio of the mixed layer depth to the euphotic depth, and to a less extent, on the rate at which the particles circulate in the mixed layer.     

Isofactorial models for granulodensimetric data

Existing isofactorial models developed for disjunctive kriging using a cutoff grade on one variable are extended to the bivariate case which arises when dealing with granulo-densimetric data, such as are obtained from coal washing or mineral processing.     

A comparison of two avalanche-models with exemplary avalanches of Tyrol and Switzerland and the effects to hazard zoning

For the purposes of a thesis at the Institute of avalanche and torrent control at the university of agriculture in Vienna a comparison of two avalanche-models, a hydraulic one from Voellmy/Salm/Gubler and a statistical one from Laatsch/Zenke/Dankerl with exemplary exactly known avalanches of Tyrol and Switzerland was started in 1993. The result of this work was that both models failed by the calculation of avalanches with high recurrence intervals (over 300 years). For the calculation of avalanches with recurrence intervals under 300 years the results of both models are regular. The conclusion is that a combination of two models i.e. a hydraulic one improved by statistical calculations will be the best.     

Grand canyon—a quantitative approach to the erosion and weathering of a stratified bedrock

A new method is proposed for solving a differential equation arising from weathering-limited development of a valley. Allowance is made for horizontal stratification and overhang can be allowed to develop. The model is applied to the Grand Canyon and comments are included on its relevance.     

Experimental superensemble forecasts of tropical cyclones over the Bay of Bengal

This study entails the implementation of an experimental real time forecast capability for tropical cyclones over the Bay of Bengal basin of North Indian Ocean. This work is being built on the experience gained from a number of recent studies using the concept of superensemble developed at the Florida State University (FSU). Real time hurricane forecasts are one of the major components of superensemble modeling at FSU. The superensemble approach of training followed by real time forecasts produces the best forecasts for tracks and intensity (up to 5 days) of Atlantic hurricanes and Pacific typhoons. Improvements in track forecasts of about 25–35% compared to current operational forecast models has been noted over the Atlantic Ocean basin. The intensity forecasts for hurricanes are only marginally better than the best models. In this paper, we address tropical cyclone forecasts over the Bay of Bengal for the years 1996–2000. The main result from this study is that the position and intensity errors for tropical cyclone forecasts over the Bay of Bengal from the multimodel superensemble are generally less than those of all of the participating models during 1- to 3-day forecasts. Some of the major tropical cyclones, such as the November 1996 Andhra Pradesh cyclone and October 1999 Orissa super cyclone were well handled by this superensemble approach. A conclusion from this study is that the proposed approach may be a viable way to construct improved forecasts of Bay of Bengal tropical cyclone positions and intensity.     

A data-based regional scale autoregressive rainfall-runoff model: a study from the Odra River

This paper aims to compare the performances of multivariate autoregressive (MAR) techniques and univariate autoregressive (AR) methods applied to regional scale rainfall-runoff modelling. We focus on the case study from the upper and middle reaches of the Odra River with its main tributaries in SW Poland. The rivers drain both the mountains (the Sudetes) and the lowland (Nizina Śląska). The region is exposed to extreme hydrologic and meteorological events, especially rain-induced and snow-melt floods. For the analysis, four hydrologic and meteorological variables are chosen, i.e., discharge (17 locations), precipitation (7 locations), thickness of snow cover (7 locations) and groundwater level (1 location). The time period is November 1971–December 1981 and the temporal resolution of the time series is of 1 day. Both MAR and AR models of the same orders are fitted to various subsets of the data and subsequently forecasts of discharge are derived. In order to evaluate the predictions the stepwise procedure is applied to make the validation independent of the specific sample path of the stochastic process. It is shown that the model forecasts peak discharges even 2–4 days in advance in the case of both rain-induced and snow-melt peak flows. Furthermore, the accuracy of discharge predictions increases if one analyses the combined data on discharge, precipitation, snow cover, and groundwater level instead of the pure discharge multivariate time series. MAR-based discharge forecasts based on multivariate data on discharges are more accurate than AR-based univariate predictions for a year with a flood, however, this relation is reverse in the case of the free-of-flooding year. In contrast, independently of the occurrence of floods within a year, MAR-based discharge forecasts based on discharges, precipitation, snow cover, and groundwater level are more precise than AR-based predictions.     

Mapping and initial dilution estimation of an ocean outfall plume using an autonomous underwater vehicle

A monitoring mission to study the shape and estimate initial dilution of the S. Jacinto outfall plume using an autonomous underwater vehicle (AUV) was performed on July 30, 2002. In order to reduce the uncertainty about plume location and to concentrate the vehicle mission only in the hydrodynamic mixing zone, outputs of a near-field prediction model, based on effective real-time in situ measurements of current speed and direction and density stratification, were opportunistically used to specify in real time the mission transects. The surface characteristics of the outfall plume were found to be influenced strongly by the relatively weak stratification and low current velocities. Dilution was estimated using a temperature–salinity (TS-) diagram with initial mixing lines between wastewater and ambient waters. Effluent dilutions were at least 30:1 in this study. In order to efficiently map the plume dispersion we applied the least-squares collocation method technique. Our results demonstrate that AUVs can provide high-quality measurements of physical properties of effluent plumes in a quite effective manner and valuable considerations about the initial mixing processes under real oceanic conditions can be further investigated.     

Deriving rules from activity diary data: A learning algorithm and results of computer experiments

 Activity-based models consider travel as a derived demand from the activities households need to conduct in space and time. Over the last 15 years, computational or rule-based models of activity scheduling have gained increasing interest in time-geography and transportation research. This paper argues that a lack of techniques for deriving rules from empirical data hinders the further development of rule-based systems in this area. To overcome this problem, this paper develops and tests an algorithm for inductively deriving rules from activity-diary data. The decision table formalism is used to exhaustively represent the theoretically possible decision rules that individuals may use in sequencing a given set of activities. Actual activity patterns of individuals are supplied to the system as examples. In an incremental learning process, the system progressively improves on the selection of rules used for reproducing the examples. Computer experiments based on simulated data are performed to fine-tune rule selection and rule value update functions. The results suggest that the system is effective and fairly robust for parameter settings. It is concluded, therefore, that the proposed approach opens up possibilities to derive empirically tested rule-based models of activity scheduling. Follow-up research will be concerned with testing the system on empirical data. Received: 31 January 2001 / Accepted: 13 September 2001     

A Eulerian air pollution model for Europe with nonlinear chemistry

A long-range transport model with nonlinear chemical reactions is described. The model contains 35 pollutants and 70 chemical reactions. This is a Eulerian model defined on a space domain containing the whole of Europe. The spherical space domain (corresponding to the Earth's surface covered by the model) is mapped into a square plane domain and discretized by using a 32×32 grid. The grid increments are equidistant (both along the Ox axis and along the Oy axis). The choice of values of the physical parameters involved in the model and the numerical treatment of the model are shortly discussed. The model is tested with meteorological data for 1985 and 1989. The numerical results are compared with measurements at stations located in different European countries. Extensive comparisons of ozone concentrations for July 1985 with measurements taken at 24 European stations are also carried out. Results concerning three episodes in July 1985 as well as results obtained in the study of the sensitivity of the ozone concentrations to variations of NO _x and/or anthropogenic VOC emissions are presented. The advantages and the limitations of such a model are discussed. The model is continuously improved by adding new modules to it. The plans for improvements in the near future are outlined.