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

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