5 Remote Sensing in Hydrological Modeling
89
data can be used to derive plant parameters for estimating ET in a GIS based
model.
Soil Moisture. There continues to be speculation about the potential value for soil
moisture data as in input variable in hydrologic models, either to establish the
initial conditions for simulating storm runoff, or as a descriptor of hydrologic
processes and much progress is beginning to appear as some of the aircraft experimental data become available (see also Chap. 9).
Aircraft data taken during the FIFE campaign were used to map the spatial pattern of soil moisture resulting from drainage and ET in a 37.7 ha watershed (Wang
et aI., 1989). These patterns were seen to map the results of a simple slab model
and identified the region contributing base flow to the channel (Engman, et al.,
1989). Attempts to use passive microwave measurements in a small watershed
showed good correlation with the ground data and may yield a reliable technique
for calibrating the model (Wood et. al., 1993). Also, even the relatively lowresolution passive data can improve the water budget calculations of a small basin
(Lin, et. al., 1994). Goodrich et al., (1994) studied the pre-storm soil moisture at
various scales of basin runoff. They concluded that initial values were important
but that the resolution of the fmal remote sensing product was not a limitation.
Soil water storage capacity. All hydrological catchment models, rainfall-runoff
models as well as water balance models contain a component dealing with the soil
water storage process. As long as there are difficulties to measure soil water storage by remote sensing directly a substitute is often used, which uses remote sensing information coupled with other information for the determination of the soil
water storage capacity. If this is known, the soil water storage process in time and
space can be simulated. Here an example will be briefly given, how this can be
done.
It is assumed, that the soil water storage in the upper soil zone is determined by
soil type and vegetation type. As can be seen in Color Plate 5A (bottom left) a soil
map giving information on soil type is used in order to determine the effective soil
porosity as can be seen in Color Plate 5.A (bottom center). Furthermore Landsat
imagery is used (Color Plate 5.A, top left) in order to determine the landuse by a
suitable landuse classification technique (Color Plate 5.A, top center). The knowledge of the landuse allows to determine the root depth over the catchment area
(Color Plate 5.A, top right). Under the assumption that root depth and soil porosity
determine the maximum soil storage capacity in a catchment area it is possible to
merge the digital information contained in the maps of soil porosity and root depth
in Color Plate 5.A in order to determine the soil storage capacity as shown in
Color Plate 5.A, bottom right. As can be seen from the soil storage capacity map in
Color Plate 5.A the storage capacity varies considerably over the catchment area.
This information can be used in hydrological modeling in different ways. It is
possible, e.g. to base the vertical and lateral flow modules of the hydrological
model on a pixel by pixel simulation of evapotranspiration, infiltration and lateral
flows, or it is possible to generate distribution functions of maximum soil storage
capacity for the total catchment area or sub-areas in the form ofHRU's or GRU's
as mentioned above. Figure 5.1 shows such maximum soil water storage capacity
89
data can be used to derive plant parameters for estimating ET in a GIS based
model.
Soil Moisture. There continues to be speculation about the potential value for soil
moisture data as in input variable in hydrologic models, either to establish the
initial conditions for simulating storm runoff, or as a descriptor of hydrologic
processes and much progress is beginning to appear as some of the aircraft experimental data become available (see also Chap. 9).
Aircraft data taken during the FIFE campaign were used to map the spatial pattern of soil moisture resulting from drainage and ET in a 37.7 ha watershed (Wang
et aI., 1989). These patterns were seen to map the results of a simple slab model
and identified the region contributing base flow to the channel (Engman, et al.,
1989). Attempts to use passive microwave measurements in a small watershed
showed good correlation with the ground data and may yield a reliable technique
for calibrating the model (Wood et. al., 1993). Also, even the relatively lowresolution passive data can improve the water budget calculations of a small basin
(Lin, et. al., 1994). Goodrich et al., (1994) studied the pre-storm soil moisture at
various scales of basin runoff. They concluded that initial values were important
but that the resolution of the fmal remote sensing product was not a limitation.
Soil water storage capacity. All hydrological catchment models, rainfall-runoff
models as well as water balance models contain a component dealing with the soil
water storage process. As long as there are difficulties to measure soil water storage by remote sensing directly a substitute is often used, which uses remote sensing information coupled with other information for the determination of the soil
water storage capacity. If this is known, the soil water storage process in time and
space can be simulated. Here an example will be briefly given, how this can be
done.
It is assumed, that the soil water storage in the upper soil zone is determined by
soil type and vegetation type. As can be seen in Color Plate 5A (bottom left) a soil
map giving information on soil type is used in order to determine the effective soil
porosity as can be seen in Color Plate 5.A (bottom center). Furthermore Landsat
imagery is used (Color Plate 5.A, top left) in order to determine the landuse by a
suitable landuse classification technique (Color Plate 5.A, top center). The knowledge of the landuse allows to determine the root depth over the catchment area
(Color Plate 5.A, top right). Under the assumption that root depth and soil porosity
determine the maximum soil storage capacity in a catchment area it is possible to
merge the digital information contained in the maps of soil porosity and root depth
in Color Plate 5.A in order to determine the soil storage capacity as shown in
Color Plate 5.A, bottom right. As can be seen from the soil storage capacity map in
Color Plate 5.A the storage capacity varies considerably over the catchment area.
This information can be used in hydrological modeling in different ways. It is
possible, e.g. to base the vertical and lateral flow modules of the hydrological
model on a pixel by pixel simulation of evapotranspiration, infiltration and lateral
flows, or it is possible to generate distribution functions of maximum soil storage
capacity for the total catchment area or sub-areas in the form ofHRU's or GRU's
as mentioned above. Figure 5.1 shows such maximum soil water storage capacity