The variability of the rootzone storage capacity in Austria

Abstract

The
rootzone storage capacity (Sr) is a crucial part of the hydrological cycle.
This storage provides water access for vegetation, in order to meet the
atmospheric water demand through transpiration. The spatio-temporal variability
of Sr is not well represented in current hydrological and climate models. The
root zone storage capacity receives rapidly increasing interest from
scientists. Recent studies developed a climate-based method to determine Sr.
This method is based on the insight that ecosystems efficiently adapt their
rootzone storage capacity to survive a drought with a certain return period.
However, this method requires a vast amount of data. Long hydrological
time-series with a rather fine temporal resolution are required. These
time-series are not always available for many poorly gauged catchments.
Therefore, it is important to explore what is exactly controlling this Sr, and
if we can eventually predict it.  This
study aims to describe the spatial and temporal variability of Sr with a
combination of climate and land cover variables in Austria for the study period
1982 - 2008. To anticipate on expected snowfall, a snowfall module is included
and calibrated with the use of a MODIS satellite snow cover product. The most
important climate and land cover variables are identified, using multiple
linear regression analysis. The best performing regression models are selected
with a diverse combination of variables. This is done by comparing 21
catchments across the central and eastern part of Austria. Additionally,
a stationary time-series split method is used to explore how Sr is changing in
time. Subsequently, another multiple linear regression analysis is performed to
explore the controls of the dynamics of Sr for these catchments.  According to this study, the Run-off
coefficient describes Sr best in all studied regression models. A multiple
linear regression model compiled out of the Run-off coefficient and the
seasonality index performed best with an R2adj of 0.8. The seasonality index
seems to be specific for this study since the highest fraction of precipitation
and evaporation coincides in summer.  Land
cover seems of less importance for the estimation of Sr. However, no conclusion
could be drawn for the importance of land cover types in the regression
analysis considering the disputable applicability of the land cover data.
Furthermore, the relations of fractional cropland cover and fractional forest
cover with Sr are in contradiction with current literature.  Apart from the spatial relationships, it is
discovered that on average increased from the year 1992 onwards. However, no
indisputable explanation is encountered (R2adj 0.55). The decreasing Run-off
coefficient explains most of the increase in Sr. No conclusions could be drawn
on the influence of land cover change on Sr, caused by an irregular land cover
time-series.  Since the catchments in this study are rather
humid and have similar seasonal patterns, it would be interesting to
investigate if the discovered relationships are also valid for more arid and
seasonal varying catchments. Also, it would be useful to investigate the
unexpected relationship between land cover and Sr further.