An integrated water quality assessment model inresponse to climate change and land-use change
Haejin Han, Ph.D

Korea Environment Institute
Korea Adaptation Center for Climate Change
Korea Environment InstituteHaejin Han, PhD
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“The Climate is Changing & will Continue to Change”
Global Scale

“We are already seeing the impacts from a changing climate”
especially in Arctic, Antarctic and mountain regions

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“ What about the South Korea region ?”Observed and projected temperature change (SRES Scenario A1B)

Korea Meteorological Administration, 2009

• Temperature has risen, especially in winter and spring• Temperature will increase by ~4 ºC by the last quarter of the 21th Century,
relative to the last quarter of the 20th Century• The increases will be higher in the high latitude region

“ What about the South Korea region ?”Observed and projected precipitation change (SRES Scenario A1B)

Korea Meteorological Administration, 2009

• Annual mean precipitation has increased, especially in Aug. and Sept.
• Precipitation will increase by ~17%
• Season variations in precipitation will become bigger by the last quarter ofthe 21th Century, relative to the last quarter of the 20th Century• Extreme weather events are becoming more frequent“ Where are South Korea heading?”
Cultivation of agricultural crops
has shifted to more northern latitudein South Korea,(e.g. mandarin, apple, peach,rye, and green tea)

RyeGreen Tea

“Climate Change Impacts Will Not Occur in a Vacuum ”•Regional
Regional climate change
will change the character ofSouth Korea, especially inthe aquatic ecosystems andwater resources.
•ComplexComplex and synergistic
interactions among global
climatic drivers and regional
climatic drivers
non-climatic

ExcessnutrientloadingLand use
ClimateChangeExacerbation
Exacerbation of existing
problems
Integrated assessment is needed to comprehensively assess complex
interactions across scales, processes, and activities

Case Study sites: Kyoung-An River Basin, South Korea
Size : 560 km2
Disturbed landscape (~23% Agric,12% urban, 65% forest)
One of the upstream tributaries
draining to Lake Paldang, which is
the most important freshwater
resource for the Seoul City
Lake Paldang

KyoungAn RiverRelationships among climate change, land uses, and water quality
Human
activities
Urbanization,Cultivation,Animal husbandry.. etc

Land uses

GHG
Emissions

Climatemeteorologicalvariables:
temp., humidity, etc.

Hydrology
Pollutants

Waterquality

To provide the most consistent assessment of the impacts ofclimate change on water quality based on the SRES scenarios..
Human
activities

SRES
Emission
Scenarios

ClimateChangeHydrology

Land uses

Waterquality

Pollutants

 The climate associated with a given marker scenario should besuperimposed onto the evolving land use scenarios consistent with themaker scenario
 The narrative SRES storyline and their associated quantitativedescriptors need to be downscaled at scales appropriate for impact
assessments

Global IPCC SRES EMISSION SCENARIOS: Narrative storylines (A2, B1)

Global climate model(NCAR CCSM3)

Downscaling
Regional climate model(SNURCM)

Future regional
meteorological variables

Downscaling
Land use model(SLEUTH)

Future regional
land use pattern: temp., precip., wind speed,: urban. forest, agricultural land
relative humidity, evapotransp.
Water quality model(SWAT)
Future runoff and nutrient loadings: water discharge, SS, TN, TPSeoul National University Regional Climate model (SNURCM)
Soil moistureinitialization

MM5

NCAR CLM

SNURCM

Orography blending

Long-term region climate simulation
Extreme climate simulation
High-resolution simulation
Application in hydrology modeling

Improved lateralboundary condition(adapted from Lee et al. 2008)

SNURCM model configuration
MODEL CONFIGURATIONMODEL
SNURCM (MM5, CLM, SP)
Initial boundary
NCAR CCSM3
Governing equations
Non-hydrostatic
Vertical layers (top)21 sigma layers (70 hPa)
Horizontal grids
20km
Cumulus convection
GrellExplicit moisture
Simple icePBL
YSURadiation
CCM2
Land surface
NCAR/CLM

Regional-scale land use prediction models
A two-phase (nested scale) approach with an assessment ofaggregate quantities of land use for the entire region, followed bydownscaling procedure
Examples of downscaling- methods to estimate regional fromglobal scenario- proportional approaches s (Arnell et al. 2004)- spatial allocation procedure based on rules (Rounsevell et al. 2006)- micro-simulation with cellular automata (Solecki and Oliveri, 2004)- regional-scale economic models (Fischer and Sun, 2001)- linear programming models (Holman et al., 2005)- empirical- statistical techniques (Verburg et al., 2006)- agent-based models (Alcamo et al., 2006)

SLUETH model (Clarke, 1998)
SLUETH is one kind of model in the family of Cellular
Automata
Cellular Automata models are dynamic simulation
models, where cell transitions are based on the state of
the current cell and the states of neighboring cells
SLUETH is an acronym for the input layers that themodel uses in gridded map form: Slope, Land use,Exclusion, Urban extent, Transportation, and Hillshade
Growth parameters: Dispersion, Breed, Spread, Slope,Road gravity
Growth rules: Spontaneous growth, New spreading
centers, Edge growth, Road-influenced growthInput dataset used in this study
1980

1990

2004

2007

Land use

Urban extent

Exclusion

Transportation

Slope

HillshadeDownscaling SRES scenarios into the SLUETH
model (Solecki and Oliveri, 2004)
Multi step process was developed to translate the SRES Scenarios
into SLUETH defined modeling experiments
Step 1. Elaborate the broad narrative of each scenario within thecontext of urban growth and change in the target country settings
Step 2. Define specific growth parameters from the narratives
Step 3. Translate the scenario growth parameters into specific
SLUETH program applications

Example: A2 scenario (Pessimistic future)• Population: Continuously increased

Broadnarrative ofA2Specificgrowthparametersof A2
SLEUTHmodeling
adjustments• Governance: local-based, individualistic• Energy: Higher reliance on fossil fuel• Transportation: greater reliance on automobile  Per-capita
automobile vehicle miles ↑• New growth center ↑• New limited access highway loop road through the ex urban part of theregion• Road corridor growth and growth associated with new suburban and periurban employment centers↑• Infilling in existing urban centers↓• Breed and spread coefficients ↑• New transportation layer with ring road added future• Highways given increased weighting• Dynamic exclusion layer increase percent exclusion around existing urbancenters

Example: B2 scenario (optimistic future)• Population: lower population growth than A2

Broad
narrative ofA2

• Governance: local-based, individualistic

Specificgrowthparametersof A2• Growth along public transportation corridors ↑• Spontaneous, leaf-frog sprawl growth ↓• Infilling, compact growth, and edge growth ↑• Protection of environmental resources ↑• Active re-greening and afforestation ↑• Energy: Conversion to alternative source of energy• Transportation: less reliance on automobile  Per-capita
automobile vehicle miles ↓• Highways given increased weighting

SLEUTHmodelingadjustments• Breed and dispersion coefficients ↓• Dynamic exclusion layer increase percent exclusion around nonurbanized areas• Percent exclusion of Protected areas in exclusion layer ↑SWAT model
SWAT is an acronym for Soil and Water Assessment
ToolLong-term, continuous watershed simulation model (Arnold etal,1998)

Assesses impacts of climate and management on yieldof water, sediment, and agricultural chemicals
Physically based including hydrology, soil temperature,
plant growth, nutrients, pesticides and land
management
Inputs: Elevation map, Soil, Land use, Agricultural
practice, Metrological data

SRES
Scenarios

GCM

MM5

SLEUTH
ModelRCM

Futureland use

Future weather data

100

Observed

90

Simulated

80100

Simulated
Water discharge

7060
50403020

Simulated
Water quality

80
Total P (Mg)

Discharge (m3/s)

SWAT604020

100805
cDe05
yMa
4
t-0
Oc
4r -0
Ma03
pSe
03
bFe2
l- 0
Ju01
cDe01
yMa00
vNo0
r-0
Ap99
pSe99
b-l- 9

Fe98
n-Ju

Ja0

Observation

Future water quality

Observation

Conclusions

Studies of the impact of climate change on water quality need toaccount for future changes in land uses and land coverThis is because land use has a large influence on regional patternsof human activities and associated problems of aquatic
environmental degradation and pollution
There have been a small number of impact assessments that haveused the climate changes resulting from the SRES emission
scenarios, but very few that have also used the corresponding landuse- change scenarios
To provide the most consistent water quality impact estimates , theclimate associated with a given marker scenario should besuperimposed onto the corresponding future land use change as well
as socio-economic and demographic characteristics of the storylinethat drives that maker scenario
Coupling GCM, RCM, SWAT, and SLEUTH models can provide an
opportunity to provide a better integrated assessment of water quality inresponse to changes in climate and land usesThanks!!
Questions ??