1. Towards Remotely-Sensed Estimation of
Alkalinity in Australian Coastal Waters
Kimberlee Baldry, Nick Hardman-Mountford, Jim Greenwood,
Francois Dufois, Bronte Tillbrook
Presented by Kimberlee Baldry
BSc Chemistry and Mathematics and Statistics (UWA)
CSIRO Vacation Scholar (2014-2016)
baldry.kimberlee@gmail.com
nick.hardman-mountford@csiro.au
3. Data: IMOS National Reference Stations
IMOS Ocean Data Portal: https://imos.aodn.org.au
Chl-a
DIC/TCO2
NO3
Temp Sal
ALK
Phytoplankton
O2
4. Background
• Look at what effects TA -> Build model
Sal Temp Chl-a
Intra-
watermass
mixing
Freshwater
inputs/outputs
Inter-
watermass
mixing
Nutrient
Changes
Primary
Productivity
Open Ocean Model
Coastal Models
?Other processes that affect TA
Lee et al. (2006)
SSS + SST + SSS^2 + SST^2 +c
5. Methods: Models
Aim
Assess predictions of TA from its proxy variables in Australian coastal
waters
Multiple Linear Regression (MLR) Analysis
(1) TA = aSal + d
(2) TA = aSal + bTemp + d
(3) TA = aSal + bTemp + cChl-a + d
(4) TA = aSal + bTemp + clog[Chl-a] + d
Coastal Models
Algorithms calculated from
ALL NRS data
Regional Models
Algorithms calculated from
INDIVIDUAL NRS data
6. Methods: Statistical Analysis
Method Pros Cons
Kolmogorov–Smirnov (K-S)
Tests
Method of comparing
observations to models
Binary
95% Confidence Level
Residual Standard Error
(RSE)
Model error in standard
units
Doesn’t consider number of
variables in model, or
number of observations
Akaike Information Criterion
(AIC)
Combined measure of
complexity, and RSE
Sensitive to number of
observations
Hard to compare
differences
Relative Probability of
Minimising Information Loss
Intuitive
In terms of probabilities
Doesn’t rely on “eyeballing”
Very sensitive
10. Results: AIC
- Combined measure of goodness of fit (RSE) and
complexity (number of parameters) of model
Model
11. Results: Minimum Model
- Relative Probability of Minimising Information Loss
- Compared in terms of probabilities, rather than just “eyeballing”
Model
15. Conclusions
• Model 4 -> Minimum model
• Chl-a influence generally small but may be important in
some areas
• Regional models are better than General Coastal or Open
Ocean Models
Further Work
• Application to ship data -> Spatially continuous model
• Investigate robustness of Earth Observation application
• Temporal robustness of algorithm
• Application to Australian-wide carbonate models
17. MLR Results: Model 1
NRS Correlation
Coefficient
Slope Intercept n RSE AIC
General 0.94 53.69 420.98 1213 10.50 9150.8
Darwin 0.96 54.58 407.94 60 9.49 444.21
Esperance 0.84 64.83 27.87 48 6.02 312.53
Kangaroo
Island
0.84 46.25 696.8 110 5.55 693.22
Maria
Island
0.85 46.61 678.44 230 3.76 1266.02
Ningaloo 0.62 36.05 1025.43 29 5.82 188.41
North
Stradbroke
Island
0.94 58.83 236.1 168 4.5 968.27
Port
Hacking
Bay
0.94 61.67 138.99 194 2.83 957.42
Rottnest
Island
0.93 58.44 252.78 167 4.68 993.41
Yongala 0.97 50.84 505.24 207 8.64 1484.12
18. NRS Correlation
Coefficient
Intercept SAL SST n RSE AIC
General 0.95 620.14 48.78 -1.28 826 8.87 5955.05
Darwin 0.96 543.2 51.32 -0.91 39 9.15 288.2
Esperance 0.87 51.17 64.75 -1.15 36 5.52 230.09
Kangaroo
Island
0.86 732 45.31 -0.12 61 5.54 386.96
Maria
Island
0.9 486.92 52.21 -0.45 142 3.44 759.17
Ningaloo 0.91 -84.86 69.29 -1.68 18 3.09 96.41
North
Stradbroke
Island
0.92 291.82 57.68 -0.66 133 4.17 762.02
Port
Hacking
Bay
0.93 190.02 60.5 -0.5 120 2.61 575.31
Rottnest
Island
0.94 90.58 63.55 -0.91 112 3.98 631.96
Yongala 0.97 447.78 51.74 1.03 165 8.24 1169.29
MLR Results: Model 2
19. NRS Correlation
Coefficient
Intercept SAL SST Chl-a n RSE AIC
General 0.95 583.85 49.68 -1.17 4.85 801 8.82 5766.72
Darwin 0.96 541.62 51.66 -1.44 6.16 39 8.79 285.98
Esperance 0.87 20.01 65.61 -1.25 6.01 36 5.51 230.85
Kangaroo
Island
0.86 764.92 44.52 -0.3 -5.58 56 5.7 359.62
Maria
Island
0.91 290.05 57.91 -0.86 2.28 132 3.37 701.47
Ningaloo 0.95 -392.98 78.5 -2.43 21.37 18 2.44 88.62
North
Stradbroke
Island
0.92 294.77 57.57 -0.64 1.87 133 4.17 762.89
Port
Hacking
Bay
0.94 184.22 60.58 -0.4 1.1 110 2.59 527.61
Rottnest
Island
0.94 83.07 63.74 -0.9 2.29 112 3.99 633.44
Yongala 0.97 448.94 51.74 1.00 -2.08 165 8.23 1169.71
MLR Results: Model 3
20. NRS Correlation
Coefficient
Intercept SAL SST logChl-a n RSE AIC
General 0.95 570.95 50.16 -1.08 3.21 801 8.75 5753.43
Darwin 0.96 566.45 51.19 -1.5 6.92 39 8.81 286.15
Esperance 0.87 25.14 65.62 -1.26 2.95 36 5.48 230.35
Kangaroo
Island
0.86 763.08 44.44 -0.28 -2.02 56 5.67 359.05
Maria
Island
0.91 284.14 58.19 -0.94 1.91 132 3.32 697.17
Ningaloo 0.94 -342.81 77.49 -2.43 6.86 18 2.56 90.32
North
Stradbroke
Island
0.92 294.29 57.62 -0.62 0.94 133 4.15 761.94
Port
Hacking
Bay
0.94 190.09 60.44 -0.37 0.84 110 2.58 526.96
Rottnest
Island
0.94 86.86 63.67 -0.9 0.42 112 3.99 633.75
Yongala 0.97 460.53 51.21 1.04 -3.44 165 7.95 1158.29
MLR Results: Model 4
21. Methods: Statistical Analysis
Kolmogorov–Smirnov (K-S) Test
- H0: Two sets of data are drawn from the same distribution
- Two parameter test that tests mean and spread
- Bootstrapped
Akaike’s information criterion (AIC)
- Measures relative quality of statistical models
- Combined measure of goodness of fit (RSE) and complexity (number of parameters) of
model
Relative Probability of Minimising Information Loss
- Application of AIC values
- exp( (AICj – AICmin)/2 )
- Allows differences in AIC to be quantified and compared in terms of probabilities,
rather than just “eyeballing”
Residual Standard Error (RSE)
- Measure of the error of a model
- Is in absolute units
- Multiply by 1.645 to get an error corresponding to a 95% confidence level
- Hello! Name, CSIRO Vac scholarships, Nick and Jim
-Motivation- marine biodiversity
-36000 km coast
Potential vulnerability to OA
Coral reefs – barrier- GBR – Fringing – Ningaloo – lie in coastal waters
Large tuna populations in great southern ocean
East Australian Current and Leuwinn Current – larval dispersion
Quote from finding Nemo
9 NRS quarterly-monthly Sal, Temp, Alk, Chl-a.
Other measurements are available at ODP- website
only extensive collection of TA, some minimal cruise data
Leaving TA sparse temporally and spatially
Laborious and costly
RS cost effective
3 proxy variables
Should capture …. However no model is perfect
Recent RS advances
For aus waters – open ocean models have been calculated, most well used is lee… this was studied in order to draw comparisons and determine if holds because lit has shown…..
No direct coastal models have been calculated or investigation done into the distribution of TA in Aus coastal waters
Aim also to attempt to build more refined models
MLR
3/4 comparison –chl-a log normal dist in ocean
Coastal vs regional models
Reconstruction
Assess robustness, compare models and find minimum model
Go through table
-Before results – NRS key and low numbers in MLR assumption
Open ocean bias confirmed
Go through table
- KS tests- open ocean
- SSS also poor
- Location dependency – coastal results
Inclusion of temp
Yongala
Conclusions
Which is the better model?
Does it matter which one you choose?
Pretty good models
Yongalla, Darwin high model error
Location dependency
Not clear which is “best”
Temp seems like significantly improves model
Hard to distinguish “significant” differences
Interesting results about minimum model
logchla- min model
?
- Seems like doesn’t make a difference
Might in future as TA depends on non conservative processes
Chl/sst coefficients may be larger
(Cross, Mathis et al. 2013) Conservative and Non –conservative variability of TA on the S/E bearing shelf
- Rough comparison
With regional models we can get a pretty good error for modelling pH with TA
Plot also shows the location dependency/benefit of using regional models
