Kristian Persson

Climate Change Impact on Water Quality: Model Results from Southern Sweden

Starting from six regional climate change scenarios, nitrogen leaching from arable-soil, water discharge, and nitrogen retention was modeled in the Rönneå catchment. Additionally, biological response was modeled in the eutrophic Lake Ringsjön. The results are compared with similar studies on other catchments. All scenarios gave similar impact on water quality but varied in quantities. However, one scenario resulted in a different transport pattern due to less-pronounced seasonal variations in the hydrology. On average, the study shows that, in a future climate, we might expect: i) increased concentrations of nitrogen in the arable root zone (+50%) and in the river (+13%); ii) increased annual load of nitrogen from land to sea (+22%) due to more pronounced winter high flow; moreover, remote areas in the catchment may start to contribute to the outlet load; iii) radical changes in lake biochemistry with increased concentrations of total phosphorus (+50%), total nitrogen (+20%), and planktonic algae such as cyanobacteria (+80%).

Field-scale phosphorus losses from a drained clay soil in Sweden

The objective of this study was to determine and discuss field-scale phosphorus losses via subsurface tile drains. A total phosphorous (Tot-P) export, which averaged 0·29 kg ha−1 year−1, was measured over a six-year period from the 4·43 ha drainage system of a Eutric Cambisol in Central Sweden. The main part (63%) was in particulate form (PP) while the remainder was either in phosphate form (PO4-P) or in other dissolved or colloidal forms. A very small area, less than 1% of the soil surface, was demonstrated to be hydraulically active by using a staining technique in soil monoliths taken from the field. The stained macropores were few, but were continuous downward, and were relatively evenly distributed among the eight 7 dm2 areas that were investigated. The transport from the field mainly occurred in episodes during which the relationship between phosphorus concentration and discharge was characterized by hysteresis loops. On average, half of the yearly P transport occurred in 140 hours. Compared with flow-proportional and frequent sampling, manual and fortnightly sampling underestimated the transport of Tot-P and suspended solids (SS) by 59 and 42%, respectively, during the six years studied. Amounts of different phosphorus forms exported through the tile drains were very similar to those reported from other clay soils in Northern Europe and North America. Copyright

Applicability of models to predict phosphorus losses in drained fields: a review.

Most phosphorus (P) modeling studies of water quality have focused on surface runoff loses. However, a growing number of experimental studies have shown that P losses can occur in drainage water from artificially drained fields. In this review, we assess the applicability of nine models to predict this type of P loss. A model of P movement in artificially drained systems will likely need to account for the partitioning of water and P into runoff, macropore flow, and matrix flow. Within the soil profile, sorption and desorption of dissolved P and filtering of particulate P will be important. Eight models are reviewed (ADAPT, APEX, DRAINMOD, HSPF, HYDRUS, ICECREAMDB, PLEASE, and SWAT) along with P Indexes. Few of the models are designed to address P loss in drainage waters. Although the SWAT model has been used extensively for modeling P loss in runoff and includes tile drain flow, P losses are not simulated in tile drain flow. ADAPT, HSPF, and most P Indexes do not simulate flow to tiles or drains. DRAINMOD simulates drains but does not simulate P. The ICECREAMDB model from Sweden is an exception in that it is designed specifically for P losses in drainage water. This model seems to be a promising, parsimonious approach in simulating critical processes, but it needs to be tested. Field experiments using a nested, paired research design are needed to improve P models for artificially drained fields. Regardless of the model used, it is imperative that uncertainty in model predictions be assessed.