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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)[email protected]
Motivation
Data: IMOS National Reference Stations
IMOS Ocean Data Portal: https://imos.aodn.org.au
Chl-a
DIC/TCO2
NO3
Temp Sal
ALK
Phytoplankton
O2
Background• Look at what effects TA -> Build model
Sal Temp Chl-aIntra-watermass mixing
Freshwater inputs/outputs
Inter-watermass mixing
Nutrient Changes
Primary Productivity
AUSTRALIAN WATERSOpen Ocean Model
Coastal Models
?Other processes that affect TA
Lee et al. (2006)SSS + SST + SSS^2 + SST^2 +c
Methods: ModelsAim 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 ModelsAlgorithms calculated from ALL NRS dataRegional ModelsAlgorithms calculated from INDIVIDUAL NRS data
Methods: Statistical AnalysisMethod Pros Cons
Kolmogorov–Smirnov (K-S) Tests
Method of comparing observations to models
Binary95% 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 observationsHard to compare differences
Relative Probability of Minimising Information Loss
IntuitiveIn terms of probabilitiesDoesn’t rely on “eyeballing”
Very sensitive
Results: Lee et al. (2006) Open Ocean Model
NRS Model 1Sal
Model 2Sal-Temp
Model 3Sal-Temp-Chl-a
Model 4Sal-Temp-log(Chl-a)
Open OceanLee et al.
(2006)Regional Coastal Regional Coastal Regional Coastal Regional Coastal
1.Darwin ü û ü û ü û ü û û2.Esperance ü ü ü ü ü ü ü ü û3.Kangaroo Island ü û ü û ü û ü û ü4.Maria Island ü û ü ü ü ü ü ü û5.Ningaloo ü û ü ü ü ü ü ü ü6.North Stradbroke Island û ü ü û ü û ü û û7.Port Hacking Bay ü û ü ü ü ü ü ü û8.Rottnest Island ü ü ü û ü û ü û û9.Yongala û û ü û ü û û û û
Results: K-S Tests- ü Drawn from the same distribution- û Not drawn from the same distribution
Results: 95% Confidence Error* - Model Error
Model
* 1.95 x RSE
Results: AIC- Combined measure of goodness of fit (RSE) and complexity (number of parameters) of model
Model
Results: Minimum Model- Relative Probability of Minimising Information Loss- Compared in terms of probabilities, rather than just “eyeballing”
Model
Implications: Modelling TA Sal-TempSal-Temp-log(Chl-a)
Sal
Implications: The Bigger Picture
Model 2 vs Model 4
Model 1 vs Model 2
% difference
% difference
Implications: Modelling pHRegional
Sal-Temp-log(Chl-a)
Coastal
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
Thankyou and Acknowledgements
MLR Results: Model 1NRS 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
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
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
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
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
modelRelative 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
K-S Tests - pvalues
NRSSSS SSS-SST SSS-SST-Chl-a SSS-SST-log(Chl-a) Open Ocean
Regional Coastal Regional Coastal Regional Coastal Regional Coastal Lee et al.
(2006)
Darwin 0.9757 0 0.1345 0.0003 0.0799 0.0001 0.1366 0.0003 0.0002Esperance 0.0858 0.0885 0.1081 0.0578 0.6632 0.0654 0.1945 0.0636 0.0337Kangaroo Island 0.6219 0 0.9082 0 0.2536 0 0.748 0 0.9078Maria Island 0.3863 0 0.659 0.0843 0.2409 0.0617 0.5181 0.0658 0Ningaloo 0.7166 0 0.9416 0.4407 0.939 0.232 0.9451 0.2328 0.1105North Stradbroke Island 0.0131 0.0791 0.7328 0.0009 0.1682 0.0007 0.6348 0.0006 0Port Hacking Bay 0.6665 0.0242 0.3613 0.2925 0.1639 0.9949 0.612 0.5038 0Rottnest Island 0.4865 0.2648 0.3284 0.0148 0.6301 0.007 0.8487 0.0146 0.002Yongala 0.0209 0 0.0559 0 0.2176 0.0002 0.0331 0.0007 0
Cross et al. 2013