Optimal exploitation of a spatially distributed fishery

2006 ◽  
pp. 1-18
Author(s):  
Frank C. Hoppensteadt
Keyword(s):  
Optimal Exploitation ◽  
Spatially Distributed

Optimal exploitation of spatially distributed trophic resources and population stability

2002 ◽  
Vol 151 (2-3) ◽  
pp. 245-260 ◽  
Author(s):  
Alberto Basset ◽  
M Fedele ◽  
D.L DeAngelis
Keyword(s):  
Population Stability ◽  
Optimal Exploitation ◽  
Trophic Resources ◽  
Spatially Distributed

Realization of Secure Ambient Wireless Network System Based on Spatially Distributed Ciphering Function

2013 ◽  
Vol E96.A (11) ◽  
pp. 2182-2184
Author(s):  
Masashi OKADA ◽  
Masahide HATANAKA ◽  
Keiichiro KAGAWA ◽  
Shinichi MIYAMOTO
Keyword(s):  
Wireless Network ◽  
Network System ◽  
Spatially Distributed

DownscaleConcept 2.3 User Manual. Downscaled, Spatially Distributed Soil Moisture Calculator

2011 ◽  
Author(s):  
M. L. Coleman ◽  
A. Q. Dozier ◽  
C. M. Fields ◽  
J. D. Niemann
Keyword(s):  
Soil Moisture ◽  
Spatially Distributed

The Importance of Rainfall Distribution in Urban Drainage Operation

1992 ◽  
Vol 23 (2) ◽  
pp. 121-136 ◽  
Author(s):  
Fons Nelen ◽  
Annemarieke Mooijman ◽  
Per Jacobsen
Keyword(s):  
Real Time ◽  
Treatment Plant ◽  
Rain Gauge ◽  
Point Of View ◽  
Rain Event ◽  
Real Time Control ◽  
Rainfall Distribution ◽  
Statistical Point ◽  
Time Control ◽  
Spatially Distributed

A control simulation model, called LOCUS, is used to investigate the effects of spatially distributed rain and the possibilities to benefit from this phenomenon by means of real time control. The study is undertaken for a catchment in Copenhagen, where rainfall is measured with a network of 8 rain gauges. Simulation of a single rain event, which is assumed to be homogeneous, i.e. using one rain gauge for the whole catchment, leads to large over- and underestimates of the systems output variables. Therefore, when analyzing a single event the highest possible degree of rainfall information may be desired. Time-series simulations are performed for both an uncontrolled and a controlled system. It is shown that from a statistical point of view, rainfall distribution is NOT significant concerning the probability of occurrence of an overflow. The main contributing factor to the potential of real time control, concerning minimizing overflows, is to be found in the system itself, i.e. the distribution of available storage and discharge capacity. When other operational objectives are involved, e.g., to minimize peak flows to the treatment plant, rainfall distribution may be an important factor.

Incorporation of spatial variability in modeling non-point source groundwater nitrate pollution

1996 ◽  
Vol 33 (4-5) ◽  
pp. 233-240 ◽  
Author(s):  
F. S. Goderya ◽  
M. F. Dahab ◽  
W. E. Woldt ◽  
I. Bogardi
Keyword(s):  
Spatial Variability ◽  
Point Source ◽  
Three Dimensional ◽  
Nitrate Contamination ◽  
Geostatistical Simulation ◽  
Chemical Measurements ◽  
Spatially Distributed ◽  
Potential Benefits ◽  
Zone Modeling ◽  
Physical And Chemical

A methodology for incorporation of spatial variability in modeling non-point source groundwater nitrate contamination is presented. The methodology combines geostatistical simulation and unsaturated zone modeling for estimating the amount of nitrate loading to groundwater. Three dimensional soil nitrogen variability and 2-dimensional crop yield variability are used in quantifying potential benefits of spatially distributed nitrogen input. This technique, in combination with physical and chemical measurements, is utilized as a means of illustrating how the spatial statistical properties of nitrate leaching can be obtained for different scenarios of fixed and variable rate nitrogen applications.

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