Sediment, C,

1
Sediment, C, N Dynamics in a Headwater Basin
in SE Asia Building a Foundation for
Investigation of the Impacts of Anthropogenic
Change
Alan Ziegler Chatchai Tantasirin
Kasetsart University, THAILAND Shawn
Benner, Mel Kunkel, Spencer Wood Boise State
University, USA Lu Xi Xi National University
Singapore Thomas Giambelluca, Ross Sutherland,
Mike Nullet University of Hawaii, USA Roy
Sidle Kyoto University
Southeast Asia Regional Committee for START
(SARCS) Asia Pacific Foundation (APN) NASA
2
Particulate Organic Carbon Yield
NEWS-PNU Nutrient Export from WatershedS
Particulate NUtrients Beusen et al., (GBC, 2005)
POC particulate organic carbon
HOTSPOT
20-30 nutrients for ocean primary production is
from rivers
3
RESEARCH QUESTIONS
MOTIVATION
Relationships in the NEWS-PNU model
Simply regressions based initially on TSS
Based on a handful of reports

• Ittekkot, V.S. Safiullah, B. Myche, B., et al.,
  Seasonal variability and geochemical significance
  of organic matter in the river Ganges,
  Bangladesh. Nature 317 800-803.
• Ludwig, W., and J.-L. Probst. 1996. Predicting
  the oceanic input of organic carbon by
  continental erosion. Global Biogeochemical
  Cycles 1023-41.
• Ludwig, W., and J.-L. Probst. 1998. River
  sediment discharge to the oceans present-day
  controls and global budgets. American Journal of
  Science 298 265-295.
• Meybeck, M., A. Ragu. 1995. River discharges to
  oceans an assessment of suspended solids, major
  ions and nutrients. Report to U.N. Environment
  Programme (UNEP), Nairobi.
• Syvitski, JP.M., S.D. Peckham, R. Hilberman, T.
  Mulder. 2003. Predicting the terrestrial flux
  of sediment to the global ocean a planetary
  perspective. Sed. Geol., 162 5-24.
• USGS. 1996. Data from selected U.S. Geological
  Survey National Stream Water-Quality Monitoring
  Networks (WQN), Dig, Data Ser., DDS-37, U.S.G.S.,
  Denver, CO.

4
Collaboration with NASA project THE ROLE OF
LAND-COVER CHANGE IN ALTERING REGIONAL HYDROLOGY
UNDER A CHANGING CLIMATE
Climate stations elevations in Mae Sa
Experimental Catchment (MREC)

75-km2
16 STATIONS Water energy balance estimates and
modeling.. 11 RF stations (1 per 7 km2)
5
Landscape
6
Filtering (0.7 ?m)
Depth-integrated TSS sampling for turbidity
calibration
5,000 mg/L
STREAM SAMPLING
Ultimate Goal Prediction of TSS Nutrient Loads
Goal Continuous signals of Q and Turbidity
Filtering 20-L samples lt 63 ?m, 63 ?m - 2 mm, gt
2 mm
Turbidity probe deployment
7
STORM-BASED SAMPLING
also weekly baseflow
2-BASEFLOW 7-RISING LIMB 2-PEAK
12-FALLING LIMB
Stage (cm)
2-BASEFLOW 10-RISING LIMB 1-PEAK
7-FALLING LIMB
Turbidity (NTU)
2006-07 12 storms 230 samples
8
RESULTS Total Suspended Solids
8 monitored events
Big stuff 1
Fine sand 20-40
Silt 40-70
Clay 4-11
Common events (weekly recurrence) 50-100 Mg
Suspended sediment 20-200 Mg Bedload
(coarse sand)
9
RESULTS C N Concentrations
8 monitored events (ranked by peak Q)
gt2000 µm
0.7-63 µm
63-2000 µm
lt 0.7 µm
CN 14-19
CN 7-13
CN 25-50
10
RESULTS C N loads
8 monitored events
63-2000 µm
gt2000 µm
lt 0.7 µm
0.7-63 µm
synthesis
The larger the event the larger the load PN gt N
N POC 10DOC POC 12PN
POC
one Mg
PN
11
RESULTS C N Transported
8 monitored events
PERCENTAGE OF LOAD TRANSPORTED BY EACH FRACTION
12
Mae Sa vs. major rivers in the world
At the low end Scale-related issue ?
Artifact of high sediment yields ?
13
Mae Sa vs major rivers in the world
DOC (Mt km-3)
wet
dry
POC (Mt km-3)
Mae Sa
TSS (Mt km-3)
Argument for the uniqueness of headwater streams
??
14
What is the best predictor of TSS?
Nephelometric turbidity units
TURBIDITY BASED DISCHARGE BASED
Neither one method is good always
Suggests a combined approach might be best
15
Comparison of prediction methods
Example one week
Most uncertainty in falling limbs
Implication Particulate C N yields off 20-50
Total TSS SUMMARY
18500 Mg ? 20 too high 7800 Mg ? 50 too low
15100 Mg ? getting better ???
16
Unique sediment sources have unique
turbidity/TSS relationships
Local Nong Hoi Met 425 upper W. basin Entire
basin Misc. baseflow Flush from upper
basin Mostly upper basin Nong Hoi Mixed
sources Lower basin sources Lower basin
source Upper basin material Large local
flush Beyond calibration of turbidity Nong Hoi /
other Ag. Small local flush Spurious flush bank
failure
out of range
Turbidity (NTU)
Nephelometric turbidity units
IMPLICATION knowing source improves TSS
prediction EXPLOIT Rainfall location is
proxy for the source
17
50 Agriculture 45 Forest 5 village
35 Agriculture 45 Forest 20 village
(a)
(b)
Rainfall 060808
Rainfall 060908
060808
060908
060808
060808
060908
060908
060908
060808
060908
060808
(c)
(d)
different source different signature
different regression fuzzy
18
FUZZY MODELING TWIST
MULTIPLE REGRESSION
E 24
FUZZY
E 20
Data from six 2006 events only Relative error
improves from 24 to 20 only
Still seems the way to proceed for TSS, C, N
19

TURBIDITY BASED DISCHARGE BASED
Data pending
Silt clay (mg/l)
Silt clay (mg/l)
Two steps farther do for C and N
One step farther partitioning (fractions
limbs)
63-1000 ?m (mg/l)
63-1000 ?m (mg/l)
gt1000 ?m (mg/l)
gt1000 ?m (mg/l)
20
Mae Sa Watershed, Thailand 75 square km
Expose Sediment Sources
Sept 16-17, 2004 57 shallow slumps 1 debris
flow
21
Rainfall and Soil Moisture Changes
prior week
wetting to near saturation
Depth 225 mm mean 22.5 mm/d Max RF rate lt
60 mm/h (for 10 min)
4 cm
100 cm
constant wetting
200 cm
TDR probes
events occurred
22
Comparison with Other Events

Mae Sa 16-17 Sept 2004
QUESTION Why did these low rainfall depths
generate 58 mass wasting events?
23
Mae Sa Mass Wasting Events (16-17 Sept 2004)
24
KEY
Ramification 7500 Mg Coarse Material to Stream
(bedload)
Sept. 16-17, 2004 57 shallow slumps 1 debris
flow (1000
6500 7500 Mg)
25
2 INFLUENCING FACTORS

• Roads
• over-steepened hillslope
• overload the fillslopes
• concentrate drainage water

Management (a) Compaction ? HOF
(telephone lines) (b) Broken water main ?
saturation on a terrace hillslope (debris
flow) (c) PVC irrigation pipe breaking
(a)
(b)
(c)
26
Bridge to NASA objectives
thans
27
Original Swidden (Laos)
Progression of landscape change

Various consequences
28
Relationship to NASA
Take home points
Preliminary Results

• NASA OUTPUTS
• Land-cover / Land-use in Mae Sa over next 50-100
  years
• Future Climate (RF, Temp, etc)
• Water Balance Simulations in Mae Sa (Surface RO)
• BECOME INPUTS
• Prediction of ? Erosion (surface and mass
  wasting)
• ? Nutrient Export from Basin
• Status of the OUTPUTS