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Advanced decision support methods for solving diffuse water pollution problems

Plunge, Svajunas LU (2011) In LUMA-GIS Thesis GISM01 20102
Dept of Physical Geography and Ecosystem Science
Abstract
Dealing with water diffuse pollution is a major problem for watershed managers. This problem
raises many complicated questions, which are important to answer in order to reach water
environment protection goals. This study suggested some possible answers for the country of
Lithuania. Among them were the identification of critical source areas, the identification of
sensitive areas and the application of multi-objective spatial optimization. Those decision support
methods were not only suggested, but also examined through literature review and their application
was demonstrated practically on the Graisupis river catchment, which is located in the middle of
Lithuania. For this purpose, the SWAT (Soil and Water Assessment Tool) model... (More)
Dealing with water diffuse pollution is a major problem for watershed managers. This problem
raises many complicated questions, which are important to answer in order to reach water
environment protection goals. This study suggested some possible answers for the country of
Lithuania. Among them were the identification of critical source areas, the identification of
sensitive areas and the application of multi-objective spatial optimization. Those decision support
methods were not only suggested, but also examined through literature review and their application
was demonstrated practically on the Graisupis river catchment, which is located in the middle of
Lithuania. For this purpose, the SWAT (Soil and Water Assessment Tool) model was prepared and
successfully calibrated and validated for water flows and nitrate load simulations. The model was
calibrated for 7 years (2000-2006) and validated for 3 years period (2007-2009). The model was run
for 10 years period (2000-2009) in order to obtain results for decision support methods. Critical
source areas were defined as those areas, which have nitrate loads to surface water bodies higher by
two standard deviations from average in the catchment. Sensitivity (nutrient leaching potential) of
areas was assigned based on the response of modeled physical nature to the addition of nitrogen
fertilizers. The SWAT model was also used for the simulation of effects of best environment
practices. The results were imported into the genetic algorithm, which was used for the purposes of
multi-objective spatial optimization. Model results indicated average nitrate loading of 15.9 kg
nitrate nitrogen per hectare in the catchment. The identification of critical source areas located
12.4% of the Graisupis river catchment as risk areas. The sensitive areas identification assigned
medium or low sensitivity to 99.5% of the catchment. Only 0.4% of the catchment territory was
identified as high or very high sensitivity. Multi-objective spatial optimization increased the costeffectiveness
of diffuse pollution abatement 24 times (up to 50 times with lesser implementation
scale), if compared to the random selection of best environmental practices. Optimization with
equal weights for environmental and economic objectives resulted in 16.9 LTL for reduction of 1 kg
nitrate nitrogen to surface water bodies, while providing 62% reduction of total loads to surface
water bodies. This scenario required 24% of additional catchment territory to be converted to
grasslands and consideration of filter strips for 34% of the catchment territory. Optimization for
obtaining Pareto optimum between environmental and economic objectives provided the most cost
effective solution of 9.7 LTL for reduction of 1 kg nitrate nitrogen, while providing 25% reduction
of total loads to surface water bodies. This scenario required the application of cover crops on 2.6%,
new grasslands on 1.6% and consideration of filter strips on 11% of the Graisupis river catchment
area. Optimization for obtaining Pareto optimum between environmental and economic objectives
also provided quantifiable relationship between economic and environmental objectives in the form
of regression equation. (Less)
Please use this url to cite or link to this publication:
author
Plunge, Svajunas LU
supervisor
organization
course
GISM01 20102
year
type
H2 - Master's Degree (Two Years)
subject
keywords
diffuse pollution, non-point pollution, critical source areas, sensitive areas, multi-objective spatial optimization, best environmental practices, best management practices, SWAT model, Lithuania
publication/series
LUMA-GIS Thesis
report number
11
language
English
id
3559183
date added to LUP
2013-02-28 14:22:54
date last changed
2013-02-28 14:22:54
@misc{3559183,
  abstract     = {Dealing with water diffuse pollution is a major problem for watershed managers. This problem
raises many complicated questions, which are important to answer in order to reach water
environment protection goals. This study suggested some possible answers for the country of
Lithuania. Among them were the identification of critical source areas, the identification of
sensitive areas and the application of multi-objective spatial optimization. Those decision support
methods were not only suggested, but also examined through literature review and their application
was demonstrated practically on the Graisupis river catchment, which is located in the middle of
Lithuania. For this purpose, the SWAT (Soil and Water Assessment Tool) model was prepared and
successfully calibrated and validated for water flows and nitrate load simulations. The model was
calibrated for 7 years (2000-2006) and validated for 3 years period (2007-2009). The model was run
for 10 years period (2000-2009) in order to obtain results for decision support methods. Critical
source areas were defined as those areas, which have nitrate loads to surface water bodies higher by
two standard deviations from average in the catchment. Sensitivity (nutrient leaching potential) of
areas was assigned based on the response of modeled physical nature to the addition of nitrogen
fertilizers. The SWAT model was also used for the simulation of effects of best environment
practices. The results were imported into the genetic algorithm, which was used for the purposes of
multi-objective spatial optimization. Model results indicated average nitrate loading of 15.9 kg
nitrate nitrogen per hectare in the catchment. The identification of critical source areas located
12.4% of the Graisupis river catchment as risk areas. The sensitive areas identification assigned
medium or low sensitivity to 99.5% of the catchment. Only 0.4% of the catchment territory was
identified as high or very high sensitivity. Multi-objective spatial optimization increased the costeffectiveness
of diffuse pollution abatement 24 times (up to 50 times with lesser implementation
scale), if compared to the random selection of best environmental practices. Optimization with
equal weights for environmental and economic objectives resulted in 16.9 LTL for reduction of 1 kg
nitrate nitrogen to surface water bodies, while providing 62% reduction of total loads to surface
water bodies. This scenario required 24% of additional catchment territory to be converted to
grasslands and consideration of filter strips for 34% of the catchment territory. Optimization for
obtaining Pareto optimum between environmental and economic objectives provided the most cost
effective solution of 9.7 LTL for reduction of 1 kg nitrate nitrogen, while providing 25% reduction
of total loads to surface water bodies. This scenario required the application of cover crops on 2.6%,
new grasslands on 1.6% and consideration of filter strips on 11% of the Graisupis river catchment
area. Optimization for obtaining Pareto optimum between environmental and economic objectives
also provided quantifiable relationship between economic and environmental objectives in the form
of regression equation.},
  author       = {Plunge, Svajunas},
  keyword      = {diffuse pollution,non-point pollution,critical source areas,sensitive areas,multi-objective spatial optimization,best environmental practices,best management practices,SWAT model,Lithuania},
  language     = {eng},
  note         = {Student Paper},
  series       = {LUMA-GIS Thesis},
  title        = {Advanced decision support methods for solving diffuse water pollution problems},
  year         = {2011},
}