446
E.T. Engman and G.A. Schultz
The ability to provide spatial data, rather than point data,
The potential to provide measurements of hydrological variables not available
through traditional techniques such as soil moisture and snow water content,
The ability, through satellite sensors, to provide long-term, global-wide data,
even for remote and generally inaccessible regions of the earth,
The possibility, to acquire RS data for larger areas with a high resolution in
space and time at one spot (e.g. weather radar receiving station, satellite data
center) and in real-time, which may serve as basis for water management decisions in real-time.
This chapter addresses some of the future issues related to hydrology and how
remote sensing may help fulfill these. Central to these issues are the data needs and
how new sensors and platforms may help fulfill the promise for hydrology.
20.2 Status of Hydrologic Research and Modeling
Hydrologic research has progressed rapidly in understanding the various physical
processes and developing a large number of sophisticated analysis techniques and
produce deceptively elaborate outputs. However, we apparently have not been able
to demonstrate consistently improved accuracy or reproducibility or that the complex and sophisticated process based models work any better then the older, simple
models.
For example, Naef (1981) compared the success of simple and complex models
in reproducing measured discharge. His conclusions are based on two projects the
World Meteorological Organization Intercomparison of Conceptual Models used
in operational hydrological forecasting and on a study of rainfall-runoff models
using data from small basins in Switzerland. The results show that simple models
can give satisfactory results: however, neither the simple nor the more complex
models tested were free from failure because none of them adequately describe the
rainfall process. In addition, it could not be proved that complex models give better results than simpler ones.
Another study by Loague and Freeze (1985) presented model-performance calculations for three event-based rainfall-runoff models on three data sets involving
269 events from small upland catchments. The models include a regression model,
a unit-hydro graph model, and a quasi-physically based model. The results show
surprisingly poor model efficiencies for all models on all data sets on an event-byevent basis. The poor performance of the quasi-physically based model could
probably be ascribed to a combination of model error· and input error. They
speculated that the primary barrier to the successful application of physically based
models in the field may lie in the scale problems that are associated with the unmeasurable spatial variability of rainfall and soil hydraulic properties. The simpler
less-data-intensive models provided as good or better predictions than the physically based model.
E.T. Engman and G.A. Schultz
The ability to provide spatial data, rather than point data,
The potential to provide measurements of hydrological variables not available
through traditional techniques such as soil moisture and snow water content,
The ability, through satellite sensors, to provide long-term, global-wide data,
even for remote and generally inaccessible regions of the earth,
The possibility, to acquire RS data for larger areas with a high resolution in
space and time at one spot (e.g. weather radar receiving station, satellite data
center) and in real-time, which may serve as basis for water management decisions in real-time.
This chapter addresses some of the future issues related to hydrology and how
remote sensing may help fulfill these. Central to these issues are the data needs and
how new sensors and platforms may help fulfill the promise for hydrology.
20.2 Status of Hydrologic Research and Modeling
Hydrologic research has progressed rapidly in understanding the various physical
processes and developing a large number of sophisticated analysis techniques and
produce deceptively elaborate outputs. However, we apparently have not been able
to demonstrate consistently improved accuracy or reproducibility or that the complex and sophisticated process based models work any better then the older, simple
models.
For example, Naef (1981) compared the success of simple and complex models
in reproducing measured discharge. His conclusions are based on two projects the
World Meteorological Organization Intercomparison of Conceptual Models used
in operational hydrological forecasting and on a study of rainfall-runoff models
using data from small basins in Switzerland. The results show that simple models
can give satisfactory results: however, neither the simple nor the more complex
models tested were free from failure because none of them adequately describe the
rainfall process. In addition, it could not be proved that complex models give better results than simpler ones.
Another study by Loague and Freeze (1985) presented model-performance calculations for three event-based rainfall-runoff models on three data sets involving
269 events from small upland catchments. The models include a regression model,
a unit-hydro graph model, and a quasi-physically based model. The results show
surprisingly poor model efficiencies for all models on all data sets on an event-byevent basis. The poor performance of the quasi-physically based model could
probably be ascribed to a combination of model error· and input error. They
speculated that the primary barrier to the successful application of physically based
models in the field may lie in the scale problems that are associated with the unmeasurable spatial variability of rainfall and soil hydraulic properties. The simpler
less-data-intensive models provided as good or better predictions than the physically based model.