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E.T. Engman and G.A. Schultz
for which process. The model complexity has to be adapted to the model purpose and available data and it should not be a goal by itself.
Remote sensing can provide new types of data that can help make the complex
models easier to use as well as improve their performance. For example, remote
sensing may be the only viable approach to incorporate spatial variability of watershed properties and - equally important - the spatial variability of the required
model input data (precipitation, snowmelt, radiation etc). This is based on the
uniqueness of remote sensing to obtain spatially distributed information as well as
some entirely new forms of measurement.
Looking back at the history of the development of hydrological models it becomes quite clear that the structure of the models was chosen such that existing
hydrological data (particularly precipitation, runoff, radiation) are used for model
calibration and validation instead of structuring a model such, that the hydrological
process under consideration is represented in an optimum way. A typical example
for this deficiency of existing models lies in the fact, that the central hydrological
variable, i.e. the soil water storage or the soil moisture profile, distributed in space
and time, is considered as a residual in these models. It would be logical to structure these models such that observed soil moisture (or soil water storage content)
would be the basic model variable, since it decides, how much water will flow
from an area element in vertical direction (evapotranspiration, infiltration, percolation) or laterally (surface runoff, interflow, groundwater movement). A primary
reason for the nonexistence of such models lies in the non-availability of spatial
soil water data almost everywhere in the world. If the new RS techniques of soil
moisture measurements with the aid of microwave data from space become operational, then such models could be developed and there can be no doubt, that such -
obviously complex - models would also provide a much better accuracy than present complex models can achieve.
20.3 Water Management
In the field of scientific hydrology, the application of remote sensing techniques
has led to methods which are frequently used in practice. Unfortunately, in the
field of water resources management, we observe a certain reluctance to apply
these techniques operationally, although the potential benefits to be gained from
the use ofRS seems rather obvious. The reason for this reluctance lies certainly in
the conservatism of many water agencies, which not only have to operate their
water management systems technically efficiently and economically, but they also
have to guarantee reliability of their systems. The introduction of completely new
techniques leads to a dependence on such new methodologies including unknown
uncertainties and deficiencies that are always inherent in new technologies. Although this reluctance of water managers was understandable in the early phases of
RS time seems to have come now that the obvious benefits of RS should be exploited. Hydropower companies in Norway, e.g. use RS techniques for the exploration of snow covered areas ( satellite data) and snow water equivalents (data from
E.T. Engman and G.A. Schultz
for which process. The model complexity has to be adapted to the model purpose and available data and it should not be a goal by itself.
Remote sensing can provide new types of data that can help make the complex
models easier to use as well as improve their performance. For example, remote
sensing may be the only viable approach to incorporate spatial variability of watershed properties and - equally important - the spatial variability of the required
model input data (precipitation, snowmelt, radiation etc). This is based on the
uniqueness of remote sensing to obtain spatially distributed information as well as
some entirely new forms of measurement.
Looking back at the history of the development of hydrological models it becomes quite clear that the structure of the models was chosen such that existing
hydrological data (particularly precipitation, runoff, radiation) are used for model
calibration and validation instead of structuring a model such, that the hydrological
process under consideration is represented in an optimum way. A typical example
for this deficiency of existing models lies in the fact, that the central hydrological
variable, i.e. the soil water storage or the soil moisture profile, distributed in space
and time, is considered as a residual in these models. It would be logical to structure these models such that observed soil moisture (or soil water storage content)
would be the basic model variable, since it decides, how much water will flow
from an area element in vertical direction (evapotranspiration, infiltration, percolation) or laterally (surface runoff, interflow, groundwater movement). A primary
reason for the nonexistence of such models lies in the non-availability of spatial
soil water data almost everywhere in the world. If the new RS techniques of soil
moisture measurements with the aid of microwave data from space become operational, then such models could be developed and there can be no doubt, that such -
obviously complex - models would also provide a much better accuracy than present complex models can achieve.
20.3 Water Management
In the field of scientific hydrology, the application of remote sensing techniques
has led to methods which are frequently used in practice. Unfortunately, in the
field of water resources management, we observe a certain reluctance to apply
these techniques operationally, although the potential benefits to be gained from
the use ofRS seems rather obvious. The reason for this reluctance lies certainly in
the conservatism of many water agencies, which not only have to operate their
water management systems technically efficiently and economically, but they also
have to guarantee reliability of their systems. The introduction of completely new
techniques leads to a dependence on such new methodologies including unknown
uncertainties and deficiencies that are always inherent in new technologies. Although this reluctance of water managers was understandable in the early phases of
RS time seems to have come now that the obvious benefits of RS should be exploited. Hydropower companies in Norway, e.g. use RS techniques for the exploration of snow covered areas ( satellite data) and snow water equivalents (data from