A National-Scale Nutrient Loading Model for Finnish Watersheds—VEMALA

VEMALA is an operational, national-scale nutrient loading model for Finnish watersheds. It simulates hydrology; nutrient processes; leaching; and transport on land, rivers, and lakes. The model simulates nutrient gross load, retention, and net load from Finnish watersheds to the Baltic Sea. It was d...

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Veröffentlicht in:Environmental modeling & assessment 2016-01, Vol.21 (1), p.83-109
Hauptverfasser: Huttunen, Inese, Huttunen, Markus, Piirainen, Vanamo, Korppoo, Marie, Lepistö, Ahti, Räike, Antti, Tattari, Sirkka, Vehviläinen, Bertel
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Sprache:eng
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Zusammenfassung:VEMALA is an operational, national-scale nutrient loading model for Finnish watersheds. It simulates hydrology; nutrient processes; leaching; and transport on land, rivers, and lakes. The model simulates nutrient gross load, retention, and net load from Finnish watersheds to the Baltic Sea. It was developed over a period of many years and three versions are currently operational, simulating different nutrients and processes. The first version of VEMALA (vs. 1.1) is based on a regression model between nutrient concentration and runoff. Since the first version, the model has been developed towards a more process-based nutrient loading model, by developing a catchment scale, semi-process-based model of total nitrogen loading, VEMALA-N, and by incorporating and developing a field-scale process-based model, ICECREAM, for total phosphorus loading simulations (VEMALA-ICECREAM). The model performance was tested in two ways: (1) by comparison of simulated net nitrogen and phosphorus loads with loads calculated from monitoring data for all major watersheds in Finland and (2) by comparing simulated and observed daily nutrient concentrations for the river Aurajoki by both old and new, process-based model approaches. Comparison of the results shows that the model is suitable for nutrient load simulation at a watershed scale and at a national scale; the new versions of the model are also suitable for applications at a smaller scale.
ISSN:1420-2026
1573-2967
DOI:10.1007/s10666-015-9470-6