The 18th AIM international Workshop

Climate change impact on the TN
generation of Lake Rotokakahi
catchment, New Zealand
Wei Ye1
Wang Yao2
David Hamilton1
1University of Waikato, New Zealand
2Hohai University, China

The 18th AIM international Workshop

Background• The quality of most New Zealand lakes has been
threatened by the dramatic land use change in theirlake catchment;• The hydrological process is the main driving force intransporting the land based pollutant to become
pollutant load in a lake;• Climate change will have add-on effects on thisdynamic process due to its impact on regionalhydrology;• This research presents the climate change impact on
extreme rainfall and subsequently its effects on lakecatchment TN generation.

The 18th AIM international Workshop

Method• Integration of hydrological model and climatechange impact assessment model
Hydrological model selection:• Including required
information as input;• Including required
information as output;• Applying for ungagged
catchment modeling.

Climate change impact
assessment model selection:• Efficient scenario generation
for multiple model ensemble• Including all uncertainty
sources in climate change
scenarios.

The 18th AIM international WorkshopMethod• Hydrological model: SWAT - Soil and Water Assessment Tool
– Process based distributed model;– Besides climate factors, also includes detailed land and soil
information that affect the hydrological process;– Including all required major hydrological/bio-chemical as outputs– Can be used for ungagged catchment modelling.
• Climate change scenario model: CLIMPACTS– A pattern scaling based GCM (RCM) model ensemble method ofclimate scenario generation;– Including the uncertainty sources of GHG emission; climatesensitivity; as well as uncertainties among GCMs (RCMs);– Including extreme event change scenario generation.

The 18th AIM international Workshop

Schematic of pathways for available water movement in SWAT (Neitsch et al., 2005)

The 18th AIM international Workshop

CLIMPACTS Model Structure•Scenario selections

Global -Mean
Temperature and Sea
Level Projections•Spatial climatologies•Time-series climate data

Local Climate

User -•Model parametervalues model-Means, variability, extremes

Sectoral Impact Models
Coast
Agriculture

Effects

WaterHealth•GCM patterns• GCM patte rns•SyntheticSynthetic changes•Land data•Other spatial data

The 18th AIM international Workshop

Case study area
Lake Rotokakihi

The water surface area of the lake is 4.6 km2. The mean
depth is just 17.5 metres. The Lake catchment has a total
land area 15 km2

The 18th AIM international Workshop

SWAT model Set-up
Lake Rotokakihi:• Ungagged catchment;• Small in size and mostly fed by surface flow and lateral flow
• Minimum human interference
Model parameterization:• Calibration and validation was carried in a nearby catchment about
8 km away, with the same meteorological station data;• Two catchments are characterised by similar geographic and land
use features;• Five most sensitive parameters was adopted from the nearby
catchment validation;• Soil attributes are obtained or estimated from observation;• SWAT default settings were used for other model parameters

The 18th AIM international Workshop

SWAT model Result
(2)

(1)
(3)

SWAT simulated annual average TN load for each subcatchment.
Sub-catchment
Area (ha)(4)(5)

SWAT model sub-catchment
delineation for Lake
Rotokakahi (25x25 m DEM)

Simulated annualaverage TN load(kg/ha/Year)

123

45386140129162670

8.90 11.67

8.90

7.94 16.10

The 18th AIM international Workshop

SWAT model Result

Observed monthly normal rainfall and SWAT simulated monthly normal TN for theperiod of 1993 to 2007

The 18th AIM international Workshop

SWAT Model Validation

Observed Lake Rotokakahi TN over 2006 to 2007 (from Butterworth, 2008)

The 18th AIM international Workshop

SWAT Model Validation

SWAT simulated monthly TN for Lake Rotokakahi catchment over 2006 to 2007

The 18th AIM international Workshop

Climate Change Scenario

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Climate Change Scenario

1Consecutive 2 day rainfall2Projection based the median value of 12 GCM ensemble with IPCC SRES A1Bemission scenario and Mid Climate Sensitivity for the future year of 2100

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Impact Assessment
SWAT 2 day annual maximum rainfall event results for the simulation period
Date

AnnualReturn period (years)
Maximum 2 day
Baseline
2100
rainfall (mm)projection

AnnualEvent tototal rainfall annual ratio(mm)(%)

1-2 May 1999

225

6824

1397

16%

23-24 Dec. 1995

163

95.5

1823

9%

25-26 Jan. 2006

162

95.5

1469

11%

17-18 Jul. 2004

157

7.551504

10%The 18th AIM international Workshop

Impact Assessment
SWAT simulated TN corresponding to the extreme rainfall events during thesimulation period
DateTN caused by extreme rainfall event foreach sub-catchment (kg/ha)
12

3

4

Event
Annualtotal (kg) total (kg)5

Event toannual ratio(%)

1-2 May 1999

3.01

3.11

2.99

3.18

6.64

6946

20431

34%

23-24 Dec. 1995

0.95

1.35

0.95

1.07

2.43

2440

18992

13%

25-26 Jan. 2006

1.77

2.36

1.77

2.38

3.073685

24693

15%

17-18 Jul. 2004

3.77

4.03

3.77

4.07

8.57

8905

2904931%The 18th AIM international Workshop

Impact Assessment• The total rainfall over the simulation period is20229 mm and the TN load simulated for thewhole period is 273668 kg, which indicates along term average TN per unit rainfall
generation of 13.5 kg/mm• The TN from these four events is account for
about 8% of the TN simulated for the wholeperiod. The TN per unit rainfall generation ofthese four extreme events is 31 kg/mm,

The 18th AIM international Workshop

Impact Assessment𝑛1∆𝑇𝑇 = 𝑁 �𝑖=1

𝑇𝑇𝑖 𝐴𝐴𝐴𝑖𝑏𝐴𝐴𝐴𝑖𝑏𝐴𝐴𝐴𝑖𝑏𝑏× ( 𝑓 − 1) − 𝑇𝑇 × 𝑃𝑖 × ( 𝑓 − 1)
𝐴𝐴𝐴𝑖
𝐴𝐴𝐴𝑖

∆𝑇𝑇 is annual average increase of TN due to climate change impact on extreme rainfall event;
N is the number of simulation years; n is number of extreme rainfall events in the simulation
period;𝐴𝐴𝐴𝑖𝑏 is the baseline annual return year of the ith extreme event;𝑓𝐴𝐴𝐴𝑖 is the annual return year of the ith extreme event in the future year f;TNi (APIib) is the TN load generated by the extreme event of i;
Pib is the total rainfall of the ith extreme event.
∑𝑁𝑦=1 𝑇𝑇 𝑦

𝑇𝑇 is the long term average TN load produced by rainfall: 𝑇𝑇 = ∑𝑁
y is the simulation year; TN(y) is the TN generated in year y; andP(y) is the total annual rainfall of year y.

𝑦=1 𝑃 𝑦The 18th AIM international WorkshopConclusion• Based on a middle range climate change scenario, the Lake
Rotokakihi catchment annual TN load generation will likely
increase by 4% by 2100 from present;• The middle range climate change scenario is based on abusiness as usual GHG emission scenario (SRES A1B) and amedian value of extreme rainfall projection from 12 GCM
model ensemble;• Given the pastoral farming as the biggest land based TN
contributor, it is critical to optimise farmland management orconverse some of pastoral land to forest in order to maintainand restore the water quality for Lake Rotokakihi.

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Limitation and Future Work
• Limited observation data
– For model calibration and validation– For establish reliable statistical relationshipbetween extreme rainfall and TN generation• Transient scenario simulation• Integration of a dynamic lake model for lake
quality modelling