Soluble phosphorus content of Lake Balaton sediments

Figures & data

Figure 1. The previous phosphorus map was produced in the GIS laboratory of the Hungarian Academy of Sciences. (Csermák & Máté, 2004).

The figure presents an old map that was created by using the Thyssen polygon interpolation technique, it cannot reflect the spatially continuous nature of the phosphorus concentration of bed sediments.

Figure 2. Scatter plot of the observed and estimated phosphorus content (mg/kg) of the lake sediment.

The figure shows the result of the validation, values of the test data and the estimated data are demonstrated on the plot, a strong correlation can be observed between measured and estimated phosphorus content.

Table 1. Characteristics of Lake Balaton sediment mapping.

Measured parameters	Units	Number of records	Measurement principle	References
pHKCl	–	3 840	Potentiometer	Buzás et al., 1988
Hygroscopic moisture	–	2 838	Sík’s hygroscopic	Buzás et al., 1993
Organic carbon	%	4 046	Tyurin method	Buzás et al., 1988
Total nitrogen	mg kg^−1	4 079	Kjeldahl method
AL-P2O5	mg kg^−1	4 054	MSZ–08 0455–80 Hungarian standard	Baranyai et al., 1987
K2O	mg kg^−1	3 849	MSZ–08 0454–80 Hungarian standard
CaCO3	%	3 851	Scheibler’s calcimeter	Buzás et al., 1988
Mg^2+	mg kg^−1	3 811	MSZ–08 0450–80 Hungarian standard	Baranyai et al., 1987
Na^+	mg kg^−1	3 444	MSZ–08 0451–80 Hungarian standard
Zn^2+	mg kg^−1	3 830
Cu^2+	mg kg^−1	3 830
Mn^2+	mg kg^−1	3 828
SO4^2-	mg kg^−1	3 529

Table 2. Auxiliary variables used in modelling and mapping phosphorus content

	Resolution	Data source	Derived variables
Morphology	100 m	Lake Balaton Hydrological Survey maps	elevation, hillshade, slope, aspect, LS factor, channel network base level, vertical distance to channel network, valley depth, relative slope position
Hydrodynamics	25 m	Monte Carlo simulation with hydrodynamic models	exceedance probability of critical shear stresses (0.05 and 0.2 Pa), exceedance probability of wave heights (30 and 50 cm)
Vegetation	8 cm	Data of aerial photographic survey that was integrated into the General National Habitat Classification System (Á-NÉR) and General Plant Coenology Habitat Classification System (C-NÉR)	vegetation (category) map

Table 3. Summary of PCA in the order of decreasing explained variance

Auxiliary variables	Explained variance	Explained cumulative variance
elevation	15.47	15.47
analytical hillshading	11.34	26.81
slope	9.18	35.99
LS factor	8.37	44.36
channel network base level	7.70	52.07
vertical distance to channel network	7.07	59.14
valley depth	7.03	66.17
relative slope position	6.66	72.83
wave height probability (30 cm)	6.65	79.47
wave height probability (50 cm)	6.75	86.22
critical shear stress (0.05 Pa)	6.93	93.15
critical shear stress (0.2 Pa)	6.85	100.00

Table 4. Model summary

Variogram model	0.46+(1600)×(1–exp((−0.45×X) /1800))
Residual standard error (mg/kg) – regression model	37.86
Multiple R-squared (R^2, regression model)	0.23
RMSE (mg/kg) – validation	19.92
Correlation coefficient (R^2) -validation	0.81

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Data availability statement

The data supporting the findings of this study are not public but are available from the Hungarian Academy of Science (www.mta.hu).