Abstract [en]

In PLC5, the type concentrations for nitrogen (N) and phosphorus (P) in discharge from forest land in northern Sweden were estimated from their relations to altitude, while the inorganic fractions were handled as constants. For southern Sweden, the type concentrations were based on median values obtained from measurements in a number of streams with forests and wetlands as dominating land cover in the catchment. The only forest operation taken into account was clear-felling. In southern Sweden, the N type concentration from clear-felling was based on the relation between N deposition and the N concentration in soil water. In northern Sweden the N type concentration was obtained by multiplying the forest type concentration with a factor of 2. For P, the type concentration from clearfelling was obtained in a similar way by multiplying the forest type concentration with a factor.

In earlier SMED projects (SMED report no 52:2011, SMED report no 100:2011, SMED report no 109:2012), nitrogen and phosphorus concentrations in runoff from forest land and wetlands have been studied by measurements in stream water in about 200 randomly selected small forest and wetland dominated catchments. In these earlier projects forest status (increment, biomass, tree species etc. was classified by the Probabilistic Classifier method and data from satellite images and the national forest inventory (NFI). Thereafter, models have been created to estimate the N and P type concentrations. The explanatory power of these models was much higher compared with those used for PLC5. Additionally, the data from the randomly selected streams showed that the PLC5 type concentrations tangibly underestimated the N and P concentrations in southwest Sweden. For PLC6, there is a need of improved N and P type concentrations from forest land and wetland, especially in southern Sweden.

Forest data based on the Probilistic Classifier method do not exist from large areas of Sweden, which implies that improved PLC6 type concentrations cannot be developed for these regions. In this project we have therefore used nationally covering geographic information from kNNSverige and Lantmäteriet to describe forest status. kNN-Sverige is another method for characterizing forest status and it is also based on satellite and NFI data.

These publically available forest status data were used to model nitrogen and phosphorus concentrations in runoff from forest land and wetland in southern Sweden south of Lake Siljan during four seasons. The models were validated against independent data from 22 streams in southern Sweden. This data originate from national and regional monitoring and the Swedish Forest Agency forest soil liming project (SKOKAL).

It was found that forest status based on kNN-data did not add any significant information to the models. The most important explanatory variables were instead geographic coordinates (longitude, latitude, altitude) and the proportion of forests and wetlands in the catchment.

Based on models without kNN-data as explanatory variables, the modeling was relatively successful for total N with R2 between 0.22 and 0.46 for the randomly selected streams and R2 between 0.11 and 0.51 for the 17 streams with data on total-N in the test dataset. For total P modeling was less successful with R2 between 0.04 and 0.27 for the calibration dataset and R2 close to zero for the test dataset. For the strams in the test dataset, the new models overestimated the N and P concentrations in many cases. This may be explained by longer water residence time in these systems, which creates prerequisites for larger N and retention.

Based on models without kNN-data as explanatory variables, calculations of new N and P type concentrations were made for the PLC5 sub-catchments. On average, the new estimates of concentrations are higher than those used in PLC5, especially in low elevation areas in southernmost Sweden. The new N concentrations are better than those used in PLC5 and can be used for southern Sweden (south of lake Siljan) in PLC6. In southeast, the modeled N concentrations are less certain due to lack of water chemical data from randomly selected streams. Such studies should be initiated in order to validate the model estimates. The modeled P concentrations should not be used for PLC6 due to low accuracy in the estimates.