Dynamic, Integrated Model System: Sacramento-Area Application, Volume 2: Network Report

2014 ◽  
Author(s):  
◽  
◽  
◽  
Keyword(s):  
Integrated Model ◽  
Model System ◽  
Application Volume

Air Pollutant Trends over Denmark over the Last 37 Years as Simulated by the Integrated Model System THOR

2017 ◽  
pp. 49-54 ◽  
Author(s):  
Ulas Im ◽  
Jesper H. Christensen ◽  
Matthias Ketzel ◽  
Thomas Ellermann ◽  
Camilla Geels ◽  
...  
Keyword(s):  
Integrated Model ◽  
Model System ◽  
Air Pollutant

On the sustainability of an integrated model system with industrial, ecological, and macroeconomic components

2007 ◽  
Vol 50 (2) ◽  
pp. 122-129 ◽  
Author(s):  
H. Cabezas ◽  
H.W. Whitmore ◽  
C.W. Pawlowski ◽  
A.L. Mayer
Keyword(s):  
Integrated Model ◽  
Model System

scholarly journals A Global/Regional Integrated Model System‐Chemistry Climate Model: 1. Simulation Characteristics

2019 ◽  
Vol 6 (10) ◽  
pp. 2016-2030 ◽  
Author(s):  
Yong‐Cheol Jeong ◽  
Sang‐Wook Yeh ◽  
Seungun Lee ◽  
Rokjin J. Park
Keyword(s):  
Climate Model ◽  
Integrated Model ◽  
Model System ◽  
System Chemistry

Spatial and dynamic simulation for Miyun Reservoir waters in Beijing

2002 ◽  
Vol 46 (11-12) ◽  
pp. 473-479 ◽  
Author(s):  
H. Jia ◽  
S. Cheng
Keyword(s):  
Water Quality ◽  
Dynamic Simulation ◽  
Field Data ◽  
Water Quality Management ◽  
Integrated Model ◽  
Model System ◽  
Miyun Reservoir ◽  
Algae Blooms ◽  
Dynamic Simulation Model ◽  
Water Stage

In order to assist the water quality management in Miyun Reservoir, a spatial and dynamic simulation model system was built. In the model system, GIS was integrated with the WASP5 model. The integrated model system was then calibrated and verified in different sets of field data. The result showed that the integrated model system could characterize the Miyun Reservoir waters. Two scenarios were then designed and analyzed with the integrated model system. It was indicated that the water quality would improve if the cage fishery was banned, the algae blooms might occur in Miyun Reservoir if the low water stage ocurred but loads remained unchanged.

scholarly journals Long-Term Prediction of Soybean Rust Entry into the Continental United States

Plant Disease ◽  
2006 ◽  
Vol 90 (7) ◽  
pp. 840-846 ◽  
Author(s):  
Z. Pan ◽  
X. B. Yang ◽  
S. Pivonia ◽  
L. Xue ◽  
R. Pasken ◽  
...  
Keyword(s):  
United States ◽  
South America ◽  
Integrated Model ◽  
Climate Prediction ◽  
Model System ◽  
Soybean Rust ◽  
Source Distribution ◽  
Term Prediction ◽  
Long Term Prediction

This special report demonstrates the feasibility of long-term prediction of intercontinental dispersal of Phakopsora pachyrhizi spores, the causal agent of the devastating Asian soybean rust (SBR) that invaded the continental United States in 2004. The climate-dispersion integrated model system used for the prediction is the combination of the particle transport and dispersion model (HYSPLIT_4) with the regional climate prediction model (MM5). The integrated model system predicts the trajectory and concentration of P. pachyrhizi spores based on three-dimensional wind advection and turbulent transport while incorporating simple viability criteria for aerial spores. The weather input of the model system is from a seasonal global climate prediction. The spore source strength and distribution were estimated from detected SBR disease severity and spread. The model system was applied to the known P. pachyrhizi spore dispersal between and within continents while focusing on the disease entry into the United States. Prediction validation using confirmed disease activity demonstrated that the model predicted the 2004 U.S. entry months in advance and reasonably forecast disease spread from the south coast states in the 2005 growing season. The model also simulated the dispersal from Africa to South America and from southern South America to Columbia across the equator. These validations indicate that the integrated model system, when furnished with detailed source distribution, can be a useful tool for P. pachyrhizi and possibly other airborne pathogen prediction.

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