A weather dependent approach to estimate the annual course of vegetation parameters for water balance simulations on the meso- and macroscale

摘要

In order to simulate long-term water balances hydrologic models have to beparameterized for several types of vegetation. Furthermore, a seasonaldependence of vegetation parameters has to be accomplished for a successfulapplication. Many approaches neglect inter-annual variability and shifts dueto climate change. In this paper a more comprehensive approach fromliterature was evaluated and applied to long-term water balance simulations,which incorporates temperature, humidity and maximum bright sunshine hoursper day to calculate a growing season index (GSI). A validation of thisthreshold-related approach is carried out by comparisons with normalizeddifference vegetation index (NDVI) data and observations from thephenological network in the state of Lower Saxony. The annual courses of GSIand NDVI show a good agreement for numerous sites. A comparison withlong-term observations of leaf onset and offset taken from the phenologicalnetwork also revealed a good model performance. The observed trendsindicating a shift toward an earlier leaf onset of 3 days per decade in thelowlands were reproduced very well. The GSI approach was implemented in thehydrologic model Panta Rhei. For the common vegetation parameters like leafarea index, vegetated fraction, albedo and the vegetation height a minimumvalue and a maximum value were defined for each land surface class. Theseparameters were scaled with the computed GSI for every time step to obtain aseasonal course for each parameter. Two simulations were carried out each forthe current climate and for future climate scenarios. The first run wasparameterized with a static annual course of vegetation parameters. Thesecond run incorporates the new GSI approach. For the current climate bothmodels produced comparable results regarding the water balance. Althoughthere are no significant changes in modeled mean annual evapotranspirationand runoff depth in climate change scenarios, mean monthly values of thesewater balance components are shifted toward a lower runoff in spring andhigher values during the winter months.