Does Deforestation affect River Flows across the Mekong Basin?

Jeffrey Richey & ColleagueUniversity of Washington

SEA-BASINS

What Controls Water Flows across the Mekong Basin: (Geology), Climate, Landcover (Change), Engineering,

Mine: Evaluate the “Issue” of Mekong Flows- Measured “truth”- Modeling as analytical tool- Details of discharge regime: 1979-2001- Climate influences- Engineering- Landcover change

My friend’s sub-plot: To remind us that the integrated approach used is “more” than “just” hydrology modeling, is imminently transportable to other regions, the sheer act of building such an analysis promotes interdisciplinary cooperation, and there are very useful stakeholder/policy applications..

We have 2 OBJECTIVES:

Mae Chaem Basin, NW Thailand(as proxy for feeder to Mekong)

% croplands 0 19.918.0 19.1

050

100150200250300350400450500550

5/94 10/94 5/95 11/95 5/96 11/96 5/97 11/97 5/98 11/98 5/99 11/99 5/00 11/00

Observed discharge, m3/s

Simulated discharge, m3/sPearson r = 0.70

Yunnan

Tonle SapTonle Sap

LP

ST

Mk

Pk

Vt

PP

NP

CS

MM

BKD

BHH

MN

Ub

BCYsRS

Luang Prabang

Chiang Saen

Stung Treng

Mukdahan

Pakse

Vientiane

Phnom Penh

>4601 m4001-46003001-4000 1501-3000 1001-1500 751-1000 301-750 101-300<300 m

*How does land use intensification affect watershed functions in large-scale drainage basins (high flow, low flow)? Would switching landcover back to forest change flow regimes?

*How does total water yield depend on the distribution of rainfall and portioning between hydrologic processes, under historical and current conditions?

*How are the temporal dynamics of high and low flows of rivers influenced by spatial scale?

*How are “Far field effects” on people living downstream linked to changes in total and seasonal water yield?

Chiang Saen

0

10000

20000

30000

Luang Prabang

0

10000

20000

30000

Vientiane

0

10000

20000

30000

LONG-TERM DISCHARGE: “THE TRUTH”Mekong Mainstem

Mukdahan

0

10000

20000

30000

40000

Stung Treng

0

20000

40000

60000

80000

1910 20 30 40 50 60 70 80 90 2000

LONG-TERM DISCHARGE: “THE TRUTH”Mun-Chi (NE Thailand)

Ubon

0

2000

4000

6000

8000

10 20 30 40 50 60 70 80 90 00

Yasothon

0

500

1000

1500

2000

Rasai Salai

0

1000

2000

3000

4000

Ban Chot

0

500

1000

1500

2000

10 20 30 40 50 60 70 80 90 00

To “deconvolve” the signal, we need suitable tools…..

MEKONGVIC (Variable Infiltration Capacity)

Meso/Macroscale Landscape/Hydrologic Model+ Reservors + Irrigation

(Daily, 1-10 km)

These geospatially-explicit process-based models are an exciting new tool, as representation of space and dynamics, from tectonics to a local rainstorm. This allows exploration, but the “answer”

Should be thought of as “intelligent interpolators of diffuse data”

is grounded in the assumptions…..

Today is “Version 3,”…. not a priori

advanced as (solid) basis for discussion of detailsthe definitive answer

“Virtual Scaled Basin”The Mekong (and elsewhere) @ 10km (or 10 m) ….

Digital Elevation Model & River Network

Tonle SapTonle Sap

10-km resolution, using “upscaling”1-km GTOPO30 DEM

SOILSFrom USDA texture classes to soil parameters saturated hydraulic conductivity (Ks), porosity (qs), field capacity (qc), wilting point (qw), and parameter n in the Brooks-Corey equation for unsaturated conductivity

Top layer (0-10 cm) Deeper layer (10-100 cm)

VEGETATION – GETTING IT RIGHT

OGE UMD

GLC/SE Asia GLC/Asia

MSU MODIS/ OGE RiceOGE UMD

GLC/SE Asia GLC/Asia

MSUMSU

MODIS/ OGE Rice

VEGETATION “CLASSES”….of interest to many, for multiple purposes…

Leaf Area Index, albedoVegetation height, displacement height, roughness length, and architectural resistanceMinimum stomatal resistance, RGL, and solar radiation attenuation

Wind height and wind attenuation

Maximum rooting depth, and distribution of root mass with depth

Surface Climatology

Wind speed data: interpolated from the NCEP-NCAR Reanalysis data set; Minimum and maximum daily temperature and wind speed value, Surface Summary of Day Data (SoD) records from the National Climate Data Center (NCDC); interpolated

* Approximate from DEM

Observed vs Predicted River FlowIssues of calibration, validation

Chiang Saen

0

3000

6000

9000

12000

Luang Prabang

0

4000

8000

12000

16000

Vientiane

0

5000

10000

15000

20000

Pakse

0

10000

20000

30000

40000

0

Nakhon Phanom

0

7000

14000

21000

28000

Stung Treng

0

15000

30000

45000

60000

Phnom Penh

0

15000

30000

45000

60000

MEKONG DISCHARGE 1979 -2000 (m3/s; monthly)

Mukdahan

7000

14000

21000

28000

OBSERVED ( ) VIC-COMPUTED ( )

Inputs Tonle Sap

0

3000

6000

9000

12000

Ubon

0

2000

4000

6000

8000

Yasothon

0

600

1200

1800

2400

Rasi Salai

0

700

1400

2100

2800

Ban Chot

0

200

400

600

800 Muong Ngoy

0

700

1400

2100

2800

Ban Hin Heup

0

400

800

1200

1600

MEKONG TRIBS DISCHARGE 1979 -2000 (m3/s; monthly)OBSERVED ( ) VIC-COMPUTED( )

“Diagnostic” Landscape/Hydrology ProcessesAverage annual simulated runoff ratio

Average monthly soil moisture saturation in 1979-2000 in the calibrated simulation(farmers, flood prediction)

Functional Relations; e.g. Vientiane

Soil Moisture)

200

250

300

350

400

Discharge

0

4000

8000

12000

16000

Precipitation

0

10000

20000

30000

40000

Soil Moisture Antecedent: the 2000 Flood

Chiang Saen Ubon

DISCHARGE “ANOMALIES” (mo)

20 30 40 50 60 70 9080 00

Stueng Treng

Ubon

Vientiane

REGIONAL CLIMATE PROXIES

Pacific Decadal Oscillation

Indian Ocean Sea Surface Temperature

Southern Oscillation Index

20 30 40 50 60 70 9080 00

Composite SLP anomaly maps for 12 highest & lowest Stueng annual flow years

Jan-June (yr) Jul-Dec (yr)

Contour/shading interval = 0.5mb

Highest

Lowest

Ubol Ratana % (Σ=-14%) Nam Ngum(Σ=-15%)Input

0

40

80

120

160

0

100

200

300

400

500

Output

EFFECT OF DAMS ON FLOW

-2

400%

800%1200%

00%0%

1 2 3 4 5 6 7 8 9 10 11 12-100%

0%

200%

400%

1 2 3 4 5 6 7 8 9 10 11 12

Irrigation (esp NE Thailand)

Near Udonthani

0%

20%

40%

60%

80%

100%

120%

J F M A M J J A S O N D

Rasi Salai

YasothonUbon

Land Cover ScenariosPermanent v Swidden Agriculture (of different fallow periods)

Percentage of current forest (and classes 21 and 22) replaced by these land cover classes Scenario

Ω

f

(yr) 23

(agricul-ture)

9A (1-5

years in fallow)

9 (6-15 yearsin fallow)

9B (16-30 years in fallow)

Total

Percentage of current permanent agriculture (class 23) replaced by

forest

(forest class 5; except for China, where forest class

28 is introduced) Permanent agriculture scenarios: Sc.1 10% deforestation - - 10% - - - 10% - Sc.2 100% deforestation - - 100% - - - 100% - Sc.3 10% afforestation - - - - - - - 10% Sc.4 100% afforestation - - - - - - - 100% Swidden agriculture scenarios: Sc.5 Increased Ω 5 Ω0

=131,245km2

5 5/6 Ω0 =21,874

km2

=11.5%

25/6 Ω0 =109,371 km2

=57.4%

- - 68.9%

-

Sc.6 Increased Ω but long fallow period

5 Ω0 =131,245

km2

30 5/31 Ω0 =4,234

km2

=2.2%

25/31 Ω0 =21,169

km2

=11.1%

50/31 Ω0 =42,337

km2

=22.2%

75/31 Ω0 =63,506

km2

=33.3%

68.9%

-

Sc.7 No swidden agriculture

0 ? - - - - - 100%

Ban Hin Heup

-20%-15%-10%-5%0%5%

10%15%

10% def.100% def.10% aff.100% aff.Fallow=5 yrFallow=30 yr

No swidd. ag.

Muong Ngoy

-15%

-10%

-5%

0%

5%

10%

J F M A M J J A S O N D

Land Cover Scenarios (Tributaries)

100% Deforestation

0%

5%

10%

15%

20%100% Deforestation

J F M A M J J A S O N D J F M A M J J A S O

0%

5%

10%

15%

20%

N D

100% Afforestation 100% Afforest-20%

-15%

-10%

-5%

0%

Chiang SaenLuang PrabangVientiane

Nakhon Phanom

MukdahanPakse

Stung Treng

Rasi SalaiYasothon

Ban Hin HeupMuong Ngoy `Ban Chot Ubon

Land Cover “100%”: MEKONG BASIN

-100%

-80%-60%

-40%-20%

0%

Mekong River Commission Secretariat, Vientiane

Obj. 1. Are upstream changes detectable as “farfield” affects on water yield even in a very “big” basin?Depends –quantitatively!- on relative magnitude, in a dynamic (in)balance:

Transition forest to agriculture IrrigationDamsClimate

Obj. 2. Applications of “VSB” Construct as a Tool

•Scenario & Dynamics Analyses: Past, Present, Future•“Now-casting” – Drought and flood forecasting (especially if coupled to climate model)•Elsewhere – very much!