1. Estimating primary production of the Yellow Sea
Sinjae Yoo1*, Jisoo Park1, Yu-Hwan Ahn1, and Joji Ishizaka2
KORDI
Ansan, South Korea

2. The Yellow Sea is the most productive sea in NP, or is it?
(PICES NPESR, 2004)

3. Yellow Sea
UNEP LME Report,
Sherman and Hempel (2009)

4. Primary productivity of YS in-situ estimation
Some of in-situ estimates on annual production are ~150 gC m-2 y-1, which is small compared to fish landings.
Point measurements vary in the range of ∼ 3,175 ㎎ C m-2 d-1 depending on time and space.
Uncertainty is significant

5. Our previous approach using satellite (Son et al., 2005, MEPS)
We divided the YS into three areas and got averages from each.
P-I parameters (141 sets)
Vertical CHL profiles were parameterized (86 profiles).
Smith model (1936) was used for photosynthesis.
KPAR was derived from nLw555
Empirical local algorithm (Ahn, 2004) was used to retrieve chlorophyll

6. Mean Primary Production Rate
Primary production in the 3 regions of the Yellow Sea
Sub-region
Area
×103
km2
Mean Primary Production Rate
mgC m-2 d-1
×104 tonC d-1
May
Sep
CCW
58.9
590.3
589.3
3.5
MYW
147.4
946.5
722.6
13.9
10.6
KCW
28.9
734.2
553.7
2.1
1.6
mean
835.6
672.4
total
235.2
19.7
15.8
~225 gC m-2 yr-1

7. Limitations of the previous study
Estimation was possible only for two seasons (spring and autumn).
Regions were fixed geographically but not optically.
Averages of P-I parameters were used.
Averages of CHL profile parameters were used.
PP in the turbid region was not modeled accurately.
CHL algorithm needs improvements.

8. Why ‘standard’ PP estimates based on satellite data are excessively overestimated?
Major sources of error in PP estimation
Chlorophyll***
Photosynthetic parameters**
Vertical structure of chlorophyll*
Error of PP estimation when ignoring subsurface chlorophyll maximum = 17.7%~30.1% (Park, 2000)

9. 8 different combinations for PP in YS (4 CHL x 2 PP algorithms)
SeaWiFS standard
TCHL
TTCHL
GOCI

10. Table 2. Comparison of primary production in the central region of the Yellow Sea which were calculated by each chlorophyll-a algorithm. VGPM PP and AVGPM PP
† Units mgC m-2 d-1
‡ Units gC m-2 yr-1
Chl-a algorithm
VGPM
AVGPM
Daily†
Yearly‡
Daily
Yearly
SeaWiFS standard
1672
610.2
1142
416.9
TCHL
559
203.9
291
106.3
TTCHL
1222
446.1
839
306.4
GOCI
438
159.8
265
96.5

11. Comparison of SeaWiFS vs in-situ CHL in the Yellow Sea

12. Comparison of VGPM-based vs actual DIPP
in the YS
In-situ
VGPM
Min
58.1
186.2
Max
1458.8
2034.4
Mean
445.5
808.6
MNB
119.6%
RMSE
593.9 R2
0.05
slope
1.41

13. New approach New Chlorophyll-a algorithm
Yellow Sea Ocean Color Group supported by YSLME Project
New Primary Production algorithm
VGPM type
Parameterized with local data

14. Yellow Sea Ocean Color Database (Bio-optical measurements)
Siswanto et al, in prep.
China
TANG Junwu
National Satellite Ocean Application Service, SOA, Beijing
Japan
KAWAMURA Hiroshi
Tohoku University
ISHIZAKA Joji
Nagasaki University
Korea
AHN Yu-Hwan, YOO Sinjae
KORDI
KIM Sang-Woo
NFRDI

15. Results for YOC development
Two algorithms
Tchl works well in the turbid waters in the YS but underestimates in the less turbid water
TTchl works well in the less turbid water and the East China Sea
Switching of the two algorithms is employed.

16. Data Independent data sets 1998~2007
(2006 & 2007 data provided by Prof JG Park)
FEB, MAR, MAY, AUG, NOV, DEC
Time difference between in-situ and satellite observation <= ±1 day
Max CHL=12.1 mg m-3

17. Comparison of YOC vs in-situ CHL
TTCHL
TCHL
Mean
1.36
3.00
1.23
Min
0.12
0.39
0.18
Max
3.76
10.08
3.66
MNB
250.54
18.77
RMSE
2.64
0.57 R2
-0.273
0.723
Time difference=0-1 hr

18. Black: SeaWiFS standard
Red: TTCHL
Blue: TCHL
Green: GOCI

19. New PP algorithm fitted to in-situ data

20. New PP formulation In-situ Adjusted VGPM WIDR model Min 58.1 27.1 21.4
Max
1458.8
1678.4
2284.5
Mean
445.5
552.5
457.5
MNB
29.8%
0.0%
RMSE
354.1
319.9 R2
0.388
0.56
slope
1.09
0.99

21. Monthly CHL composites of 2004 based on
JAN
FEB
MAR
Monthly CHL composites of 2004 based on
Switching of
Tchl and TTchl (nLw>1.14)
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC

22. Monthly PP composites of 2004 based on
YSVGPM

23. Average daily (time series) and annual PP of the YS
Daily PP in mgC m-2 d-1
Annual PP in gC m-2 yr-1

24. Limitations A large portion of the turbid regions is excluded.
When included, PP will decrease
PP in the turbid region needs improvement
Chlorophyll algorithm needs validated
Photosynthetic
PP in the Changjiang River Plume arae needs improvements

25. Summary The upper limit of YSPP seems ~300 mgC m-2 yr-1
Improvements needed in
Chlorophyll algorithm
PP models in the Turbid region
Modeling the influence of Changjiang river discharge

26. Thank You!