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1.	Sobek, Sebastian, et al. (author) Predicting the volume and depth of lakes from map-derived parameters 2011 In: Inland Waters. - 2044-2041 .- 2044-205X. ; 1, s. 177-184 Journal article (peer-reviewed)abstract The volume and depth of a lake are basic properties that greatly affect a wide array of its physical, chemical, and biological properties. Nevertheless, volume and depth data are scarce in lake-rich regions of the world. We coupled the Swedish lake register to GIS-derived geographical and topographical parameters, attempting to predict the volume and depth of 6943 lakes from map-derived parameters only. Lake area and the maximum slope in a 50 m wide zone outside of the lake shoreline were the most important predictors of both lake volume and depth, explaining 92% of the variance in lake volume but <40% of the variance in both maximum and mean depth. Regression parameters of regional submodels of lake volume were similar across geographically and topographically different regions, indicating that the model probably is applicable for glacially formed lakes in general. Despite the high degree of explanation for lake volume, the uncertainty in predicted volume for a single lake is considerable (relative standard deviation, +/- 57%). However, the mean or cumulative lake volume of catchments containing several lakes (n > 15) is predictable from map-derived parameters with a greatly reduced uncertainty.

Sobek, Sebastian, et al. (author)
Predicting the volume and depth of lakes from map-derived parameters
2011
In: Inland Waters. - 2044-2041 .- 2044-205X. ; 1, s. 177-184
Journal article (peer-reviewed)abstract
- The volume and depth of a lake are basic properties that greatly affect a wide array of its physical, chemical, and biological properties. Nevertheless, volume and depth data are scarce in lake-rich regions of the world. We coupled the Swedish lake register to GIS-derived geographical and topographical parameters, attempting to predict the volume and depth of 6943 lakes from map-derived parameters only. Lake area and the maximum slope in a 50 m wide zone outside of the lake shoreline were the most important predictors of both lake volume and depth, explaining 92% of the variance in lake volume but <40% of the variance in both maximum and mean depth. Regression parameters of regional submodels of lake volume were similar across geographically and topographically different regions, indicating that the model probably is applicable for glacially formed lakes in general. Despite the high degree of explanation for lake volume, the uncertainty in predicted volume for a single lake is considerable (relative standard deviation, +/- 57%). However, the mean or cumulative lake volume of catchments containing several lakes (n > 15) is predictable from map-derived parameters with a greatly reduced uncertainty.

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