Forestry and Forest Products Research Institute (FFPRI)

Promoting Forest-based Disaster Risk Reduction(F-DRR) in Developing Countries
30-31 January 2024, Japan

Integrated land use scenario to enhance
forest ecosystem services
in Nan Province, Thailand
Prof. Yongyut TRISURAT
Kasetsart University, Thailand
IPBES Bureau Member

Evolution of Forest Mgt. in Thailand

Forest Village Project(Taungya system): 3 ha/hhLogging concession
Phase 1, 1864 - 1953:
Forest exploitation

Phase 2, 1954-1980:
USFM & land conversion for agr.

Evolution of Forest Mgt. in Thailand
Phase 4, 1990-present:
Biodiversity conservation (more Pas)
People and forest co-exist & decentralization
Phase 3, 1981 - 1990:
transition to collaborative forest management
Logging banned in natural forests (1989)

Phase 2:
USFM & land conversion for agr.

Phase 1:
Forest exploitation

22 Nov 1988
Landslide @Katoon, Nakhonsri Thammarat Prov.
Dead > 700; damaged 1,500 units

Frequently Asked Questions?
July 2023

Now

What are future LU/LC and landscape patterns
driven by socio-economic development?
How will forest and the nature’s benefits be affectedby CC and land use change?
What strategies could increase the climate changeresilience of people and nature?

Advancing Co-Design of Integrated Strategies withAdaPtation to Climate Change in Thailand (ADAP-T):
2018-2021
Nan Prov.

Thai Researchers/PIs

70% steep mountain• Prof. Dr. Yongyut Trisurat
Kasetsart Uni.
• Dr. Wanchai Arunpraparut
Kasetsart Uni.
• Dr. Venus Tuankrua
Kasetsart Uni.
• Mr. Teerawach P.
MSc student
Socio-Eco Scenario Grp. at Provincial Scale
Forest Grp. at catchment (local) scale

Japanese Researchers• Prof. Dr. Kuraji Koichiro• Dr. Hiroaki Shirakawa

Tokyo Uni.
Nagoya Uni.

Counterparts

Royal Forest Department
Dept. of National Parks, Wildlife and Plant Conserv.
Ministry of Environment

Nan river basin – contribution
over 40% of the water to
Chao Phraya River

Policy Relevance

National Adaptation Plan(NAP) for Climate Change

Multistakeholder involvement topreserve and reforest Nan pristine
headwater forest without legal
barriers“Nan Sandbox Model”

Land Use Change Scenarios

Approx. 12,000 km2
PAS = 35%; slope complex 70%

Forest cover2004 – 74%;
2009 – 70%
2016 = 61%

160,000500

140,000

450

300

80,000250

60,000200
150

40,000Price (USD)

Area (ha)350

100,000(10 and 5-folds in the last decade)100

20,0000

Rubber and maize boom

400

120,00050
2007(2550)

2008(2551)

2009(2552)

2010(2553)

2011(2554)

2012(2555)

2013(2556)

2014(2557)

2015(2558)

2016(2559)

0Year (A.C./B.E.)
Rice (ha)Rubber (ha)Maize (ha)Rice (US$/ton)Rubber (US$/100 kg)

Maize (US$/ton)

Cultural and nature tourism

Land Use Scenarios 2030
100%
90%

% of the province

80%
70%

67%

60%

47%

50%
40%
30%
20%
10%
0%

Baseline (2016)(Present)

TrendsMarket-oriented
Land use scenarios 2030

Conservation

Paddy

Corn

Cash crops

Perennial treesEvergreen F.

Deciduous F.

Forest plantation

Settlement

OthersWaterShifting

1. Trends or business as usual scenario 47% forest cover
2. Market-oriented scenario : 57% forest cover (2% agr. products)
3. Conservation scenario: 67% forest coverCLUE ModelMean Species Abundance (Biodiversity):
LUC, infrastructure and fragmentation

GLOBIO Model
MSA = 0.36

MSA = 0.29

Market-based Sc
MSA = 0.35
Conserv, Sc
MSA = 0.4
Trisurat et al. (2018)

WATER YIELDTranspiration
Evaporation
Rain
Leaf type
Plant type Seasonality

SnowFogInflow

Root depth

INVEST
ModelTemp.
Current24°C
RCP 8.5 2030 26°C
Ann. rain.
Current
1,249 mm
RCP 8.5 2030 1,374 mm

Yjx
Water Availability

annualaveragewater yield
per pixel

Installation of AWS at 5 sites in Upper Nan Watershedand calibration with Metro Stations

NAN

Predicted Annual Water Yield
INVEST
ModelBaseline

4,253 mil m3

Trends5,350 mil m3

Market-based

4,436 mil m3

Conservation

4,183 mil m3

Current

RCP 8.5

Annual

1,234.5

1,423.4

Wet (%)

82.9

85.6

Dry (%)

17.1

14.4

Nash-Sutcliffe efficiency coefficient
Ens = 0.97 (excellent)Top Soil Loss (USLE Equation)
Trend

10080
6040
20012345678910Low (0-12.5 t/ha/yr)

Moderate (12.5-31.25 t/ha/yr)

High (31.25-93.75 t/ha/yr)

Very high (93.75-125 t/ha/yr)

Severe (>125 t/ha/yr)

Conservation

100
80
60
40
200

123456789Low (0-12.5 t/ha/yr)

Moderate (12.5-31.25 t/ha/yr)

High (31.25-93.75 t/ha/yr)

Very high (93.75-125 t/ha/yr)

Severe (>125 t/ha/yr)

10Nature benefits orEcosystem Services
Ecosystem Services change
index (ESCI) [Leh et al., 2013)ESCI 𝑥𝑥 =𝐸𝐸𝐸𝐸𝐶𝐶𝐶𝐶𝐶𝐶𝑥𝑥𝑗𝑗 −𝐸𝐸𝐸𝐸𝐻𝐻𝐻𝐻𝐻𝐻𝑥𝑥𝑖𝑖
𝐸𝐸𝐸𝐸𝐻𝐻𝐻𝐻𝐻𝐻𝑥𝑥𝑖𝑖

History/Baseline 2016

Water yields(floods and drought)

Sediment &nutrients

Policy Recommendation
Forest cover target(mountainous watershed)
Upper WSH (>1,000 m)•>70%
Middle WSH (800-1,000 m)•
60-70%
Lower WSH (<800 m)•
50-60%

Restoration, S&W conservation

Catchment Scale Assessment:
Na Luang Sub-watershed at Wiang Sa District, Nan
Watershed area
Na Luang sub-watershed= 12.45 sq.km.
• Forest dominant catchment = 2.51 sq.km.
• Maize dominant catchment = 4.27 sq.km.
Physical characteristic
Average elevation : 550 msl.
Average slope : 31.40%
Climate characteristic
Total rainfall : 1,237.9 mm.
Average temperature : 24 .C

Teerawach Phetcharaburanin (MSc student)Land use & climate change scenarios 2030
SC4

LU2016 +RCP8.5

Sandbox +RCP8.5

Trend +
RCP8.5(Market-oriented)

Baseline
Without S &W
Conservation
Maize 34%
Forest 54%
Rubber 1%
Others 11%

SC6SC5

With S&W
Conservation
without S&W
conservation.
Maize 41%
Forest 47%
Rubber 1%
Others 11%

Maize 22%
Forest 60%
Rubber 7%
Others 11%

Dry period

Wet period

Dry period

Water yield• CC plays more role > LUC.
• Peak flow in wet period was 2-monthsdelay• Water yield: SC1 > SC2 > SC3• Water in dry period SC2 > SC1 > SC3

Dry period

Wet period

Sediment• Sediment yield: SC1 > SC3 > SC2• Rainfall during dry period could not cause
sedimentation into the stream.

Dry period

Economic value
1 THB = 4.15 ¥ (million baht)*22-years cultivation

Hydro service =Water charge +dredging costSC

HydrologicalPV income (2)*PV cost (3)*

Para

Para

benefitrubber

(1+2-3)services value (1) Maize

rubberMaizeNetNPV B/C ratio

SC140.7 321.76

9.82 263.45

4.03

23.37 13.74

1.08

SC2

6.2 384.13

9.82 314.60

4.03

69.18 15.57

1.21

SC3

9.8 202.05

79.46 165.45

32.57

73.71 40.63

1.35

SC4

3.9. 321.76

9.82 263.45

4.03

60.17 13.74

1.22

SC5

4.7 384.13

9.82 314.60

4.03

70.67 15.57

1.22

2.9 202.05

79.46
Trend165.45

32.57

82.53 40.63
Sandbox

1.40

SC6LU2016NPV = net present value
B/C = Discounted value of benefits
Discounted value of costs

Appraise theland use planning
alternatives
Limitations and criteria
Limitation score(Zhang, 1989)

Using multicriteria decision analysis (MCDA)

Hydrological services criteria
Water use (0.25)

Soil loss (0.25)(m3/person/year)a,b (tons/ha/year)c

Economic criteria
Water supply

Dredging cost

NPV of Maize

NPV of rubber

service (0.28)(0.12)

cropping (0.04) planting (0.05)(baht/year)(baht/year)(baht/month)d0> 438.0

0.0 - 12.5

> 219.0

0.0 – 1,080> 10,000

1357.7 - 438.0

12.6 - 31.3

178.9 - 219.0

1,081 – 2,700

5,001 - 10,000

3270.1 - 357.6

31.4 - 93.8

135.1 - 178.8

2,701 – 8,100

3,001 - 5,000

9182.5 - 270.0

93.9 - 125.0

91.3 - 135.0

8,101 – 10,792.5

1,500 - 3,000

Remarks:27

a is a modified <criterion
Department, RID (2001)
was 0.5 m3/person/day
in rural household,182.4from Royal Irrigation> 125.0< 91.2> 10,792.5
b is a modified criterion from Department of Water Resources, DWR (2001) was 1.2 m3/person/day in municipal district,c is a criterion from Land Development Department, LDD (2000), andd is a criterion from National Statistical Office, NSO (2015).

< 1,500

AppraiseLand use appropriate levels

the alternatives

Appropriateness (Suit)

Appropriate value range

High appropriate land use

0.0 – 9.0

Moderate appropriate land use

9.1 – 18.0

Low appropriate land use

18.1 – 27.0

AppropriatenessAppropriate score
Land use appropriate level

SC1SC2SC3SC4SC5SC6

10.20

1.22

1.25

0.47

0.47

0.12

ModerateHighHighHighHighHigh

Current LU (SC1)Not recommended due to lowest B/C ratio (1.08) and moderate suit.

Trend or BUA (SC2)
is appropriate for maize cropping only because of low investment cost. BUT soil andwater conservation is required such as terracing to reduce surface runoff andsediment in wet season.
Sandbox (SC3)
is suitable to generate household income from maize and rubber plantations (B/Cratio = 1.35). In addition, water shortage in dry season and sedimentation isminimal. 4, 5, and 6 (RCP 8.5)
ScenariosMore rainfall is expected in wet season. Water shortage in late dry season ispredicted for all scenarios (but less severe).

CONCLUSIONS

01

Changes in LU and landscape pattern driven bysocio-economic development at local and provincial
levels will affect biodiversity and nature benefits
although 35% of the province is designated asprotected areas.
Current land use generates the highest hydrologicalservices BUT it is ranked as the least overall benefits
if economic values from crop production is combined.

Appropriate forest cover target at 60-70% (Forestbased Disaster Risk Reduction (F-DRR or NBS) in
mountainous watershed is recommended in theface of LU&CC and can generate high economic
return (+ carbon credit).

Acknowledgements
SpecialSpecial thanks
thanksCounterparts & Data