20 Future Perspectives

Edwin T. Engman] and Gert A. Schultz2

'Hydrological Science Branch, Code 974, Laboratory for Hydrospheric Processes­NASA/Goddard Space Flight Center, Greenbelt, MD 29771, USA

2Ruhr University Bochum, 44780 Bochum, Germany

20.1 Introduction

Except for the ftrst four chapters each chapter of this book showed, how remote sensing data may be used in the ftelds of hydrology and water management. Fur­thermore some perspectives were given on what can be expected in the foreseeable future when new sensors and platforms and new types of hydrological models will be available. It is the idea of this last chapter of the book to give a some more general perspective of future developments in the fteld of remote sensing and the expected use of remotely sensed data in the ftelds of hydrology and water man­agement. It has to be kept in mind, however, that in various ftelds the level of application ofRS data is rather different. For example, in snowmelt runoff estima­tion (Chap. 11) RS techniques have been used opemtionally already for several years, and in flood forecasting and control (Chap. 16) techniques have been devel­oped, which could be used operationally today. However, in other ftelds research is still in full development, e.g. the determination of soil moisture proftles with the aid of RS data (Chap. 9) and in others RS data can be used only as an auxiliary measure in conjunction with the use of other types of information (e.g. in the fteld of groundwater exploration (Chap. 14)). Many of the advances in using remote sensing for hydrology have come from new areas of hydrologic analysis, areas where existing methods were unsatisfactory or limiting and areas where sufficient data. were sparse or nonexistent. These areas include measurements of soil mois­ture, estimating evapotranspiration, advances in snow hydrology, and land-surface parameterizations in General Circulation Models. Equally impressive advances can be expected in the fteld of water management, particularly if RS data can be made available in real-time.

Remote Sensing, when it was introduced into hydrology in the seventies, held a great deal of promise fOr hydrology. In spite of this promise, applied or engineer­ing hydrology has been slow to embrace remote sensing as a useful source of data, presumably because existing techniques and data have been satisfactory for limited applications. Although we have seen a somewhat cool acceptance of remote sens­ing, its future impact on hy'drology and water resources is likely to be great for several reasons:

G. A. Schultz et al. (eds.), Remote Sensing in Hydrology and Water Management© Springer-Verlag Berlin Heidelberg 2000

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.

20 Future Perspectives 447

In a recent series of papers, Grayson et al (1992) question the value of distrib­uted parameter models to truly represent the processes if the fundamental algo­rithms and assumptions cannot be validated. In their conclusions they state "the misperception that model complexity is positively correlated with confidence in the results is exacerbated by the lack of full and frank discussion of a models ca­pability and limitations and the reticence to publish poor results". They go on to conclude that "the seductive attraction of the more complex models is their ability to provide information about points within the catchment, but it is concluded that the representations used in current process based models are often too crude to enable accurate, a priori application to predictive problems".

In an even more recent paper, Jakeman and Hornberger (1993) question "what limits the observed data place on the allowable complexity of rainfall-runoff mod­els". They further state "conceptual and physically based models developed and used for describing rainfall-runoff processes tend to be over parameterized. They are no more useful for prediction than are simpler models whose parameters are identifiable from available data" (Jakemanand Hornberger, 1993).

From these examples, one can conclude that the development of more complex and sophisticated models and innovative analysis techniques has not resulted in an overwhelmingly improved ability to predict runoff. Often the question is asked, why is it that more complex and more physically based models do not give us better runoff predictions? To the authors this question seems to be ill posed for the following reasons:

It is by no means logical, that a more complex and more physically based hydrological model will yield more accurate results than simpler structured models. The complex models usually contain a higher number of parameters and thus may cause more potential modeling errors and often they do not have an adequate data base which is needed for achieving accurate results,

The reason for developing models of a higher complexity which are distrib­uted in area, is usually not the desire for more accurate results, but the wish to apply the model also under changing conditions, e.g. future landuse changes, which is usually not possible with the more simple structured models,

Often the more complex structured hydrological models working on the basis of high resolution in space and time are fed with input data (e.g. precipitation, snow, radiation) with a very coarse resolution in space, and often also in time. The model complexity is not capable of making up for insufficient resolution in space and time of the input data.

The idea to develop a general hydrological model for the whole water cycle, or parts of it, which functions optimal for all conditions is still utopian. At the present stage of scientific hydrology we still have to tailor the model for the purpose. In each case we have to decide, which model would be most suitable

448 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

20 Future Perspectives 449

airplanes). This infonnation allows forecasting of expected Spring and early Sum­mer flows in rivers. These forecasts lead to better hydropower scheduling provid­ing more efficiency, reliability and higher economic benefits.

The use of RS data for water management includes the application of hydrologi­cal models, the parameters of which and the input into which is, at least partially, based on remote sensing infonnation. One of the main advantages of RS for water management lies, however, in the fact, that relevant data can be gathered over large areas with high resolution in time and space at one spot, e.g. at the control center of a water authority. Furthermore this infonnation, containing large quanti­ties of spatial and temporal data can be acquired in real-time or almost real-time. Figure 20.1 is a schematic illustrating all components of a remote sensing llllor­mation system for real time water management.

This allows the use of this high quality infonnation for management decisions in real-time. This is of great value for decisions on e.g. releases from dams for water supply purposes, for irrigation scheduling, but also for water quality control and improvement. The benefits of such data are invaluable for real-time flood man­agement in river systems as well as in urban drainage systems. Here the processes are so highly dynamic that time intervals as short as 5 minutes are required for a process lasting only a few hours or less (see e.g. Chap. 16).

One problem, which is not so relevant for hydrological applications of RS data but is crucial in the field of real-time operation of water systems, is the question, what shall be done, if there is a partial or complete failure of the infonnation sys­tem used. This problem also exists for the use of conventional data. If the decision support system of a water authority were based on remote sensing data it has to be clarified in advance, how the real-time operation should be carried out in case of a failure of the RS data supply. As long as we have no answer to this problem the reluctance of water managers to use RS data will continue to exist. It should be, however, not pose too much difficulty, to solve this problem in the near future, be it by combination of RS infosystems with conventional data acquisition systems or by using RS data from different sources. Also, as usual, certain emergency rules should be designed for these cases. It is hoped, that in the near future these prob­lems will be overcome by appropriate research activities in order to enable water managers to benefit from the significant advantages remote sensing data offer as compared to conventional data.

20.4 Data Issues in Hydrology and Water Resources Management

If we accept the arguments that the current stage of model development has not given us better answers in spite of their complexity and that the new hydrological sciences are demanding even more answers from very complex systems, what is it that is needed most if we are to successfully answer the challenges of modem hydrology? We believe the single most needed item is more, better and different data. Not just numbers of measurements but the correct measurements with high

450 E.T. Engman and G.A. Schultz

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resolution in space and time. How can we truly believe a so-called physically based, distributed model that supposedly accounts for all the processes but is driven only by rainfall and validated only by a measured hydrograph?

The data issues have been addressed in Opportunities in the Hydrological Sci­ences (NRC, 1991) and base its discussions on the premise that "hydrologic sci­ence is currently data limited". The report discusses the general aspects of data needs conceding that traditionally hydrological data have been collected to address

20 Future Perspectives 451

water resources problems and not the merging hydrologic science. Dozier (1992) correctly points out that our historical data have not been able to represent the wide variety of spatial and temporal scales encountered in hydrology and the re­sulting models reflect a simple, homogeneous view of the natural world. He con­cludes that "this forced oversimplification impedes scientific understanding and management of water resources".

If the emerging Hydrological Sciences are to break away from the traditional en­gineering hydrology, a number of general and specific data needs are going to have to be addressed and solved. Again, quoting from the "Opportunities in the Hydro­logical Sciences" (NRC, 1991) some of these needs are:

"Hydrologic data are needed to measure fluxes and reservoirs in the hydro­logic cycle and to monitor hydrologic change over a variety of temporal and spatial scales.

Detection of hydrologic change requires a committed international long-term effort and requires also that the data meet rigorous standards for accuracy.

Synergism between models and data is necessary to design effective data collection efforts to answer scientific questions.

A fundamental block to progress in using most hydrologic data is our poor knowledge of how to interpolate between measurement points."

A very convincing case is made for the need to not make hydrologic data collec­tion an after thought. In addition the issue of data accessibility and management is addressed as part of the general data issue. This is a particularly important issue not only because all the data in the world are useless unless one can access them but also technology for storage, transmitting, displaying and analyzing data is changing very rapidly. Other than to encourage hydrologists to keep abreast of these rapidly changing developments, further discussions of this topic is beyond the scope of this book.

The question must now be raised if one accepts the premise that "hydrologic sci­ence is data limited", what do we do about it? Two issues that traditional hydro­logic instrumentation cannot address is the spatial variability of hydrologic proc­esses and wide disparity of space and time scale that scientific hydrology must address. Remote sensing does meet these needs. Remote sensing can address the spatial heterogeneity and the scale disparity problems. For example, remote sens­ing may be the only viable approach to handle spatial variability of drainage basin properties and hydrologic process because the basic data are spatial in nature. Space borne instruments also have the ability to make measurements that span the many scales from near point processes to global. However, to realize the potential of remote sensing a number of carefully planned and executed studies must be carried out that demonstrate the relationship between traditional point data and remote sensing data.

452 E.T. Engman and G.A. Schultz

20.5 Intensive Field Campaigns

In many ways the conduct of several of the existing and planned intensive field campaigns may provide the best combinations of remote sensing and traditional hydrologic data for model development and validation. The intensive field cam­paigns provide an opportunity to plan and carry out a data collection program that specifically addresses the needs of hydrologic modelers. The schematic shown in Fig. 20.2 illustrates the full range and scale of a well planned field campaign.

Unfortunately this is not generally the case with applications of satellite data to hydrology. In the latter case, all too frequently one has to make concessions and use data that are not designed for a specific hydrologic application in the -time interval between measurements, the spatial resolution, or the sensor wavelength.

The intensive field campaigns have the advantage of providing an opportunity to collect data where and when it is needed from a purely hydrologic need. In addi­tion the opportunity exists to use some of the newly developed sensors that have not yet progressed to a satellite platform. A good example of this is the measure­ment of soil moisture with the PBMR in the MONSOON 90 and the EST AR in the MONSOON 91 and the WASHITA 92 campaigns (Jackson, et al., 1995). The data would not be possible from satellite because no long wave microwave instruments with a short revisit time are currently flying. These data sets were possible only with aircraft mounted instruments.

The value of such data sets cannot be overlooked. Although they eclipse only a few days over a limited area, they do provide a preview of the types of data that

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20 Future Perspectives 453

may be readily available in the future. These data sets, together with the concomi­tant detailed hydrologic data create a foundation for evaluating the hydrologic value of these new measurements and for developing hydrologic models that can maximize the information content in the remote sensing data. Successfully demon­strating the value of these new instruments in hydrology is one of the necessary steps that leads to a satellite borne sensor.

There are a number of international and regional field campaigns currently un­derway or being planned. Table 20.1 lists some of the better recognized large scale hydrologic field campaigns that also are designed around the use of remote sensing measurements. Even more information is available from an American Geophysical Union web page: http://www.agu.org/eos3Iec/97035e.html

Table 20.1 Descriptive infonnation on large scale hydrologic field campaigns

Pro.iect Summary Date

GCIP Mississippi River Basin: Study the terrestrial-atmospheric coupling 1995 - 2000 at the regional to continental scale to improve quantitative predic-tions of coupled atmospheric and hydrological phenomena

BALTEX Baltic Sea Region: Study coupled hydrological processes between 1997 - 2001 land, sea and ice and the atmospheric circulation to detennine the energy and water balances of the Baltic Sea and related river basins

NOPEX Scandinavian Boreal Forest: Study the hydro-meteorological ex- 1995-change processes between the patchy forests, agricultural fields and lakes

BOREAS Boreal Forest of Canada: Improve process models of energy, water, 1994 - 1997 carbon and trace constituents between the boreal forest and the atmosphere and to develop methods to apply these over large areas

MAGS MacKenzie River Basin: Coordinated hydrological modeling and 1994 - 1997 process studies of water and energy balances of the Canadian Arctic

LBA Amazonia: To study coupling of energy, moisture and carbon 1997 - 1998 budget with atmospheric circulation of region and will link with intensive eco-c1imate studies

GAME Asian Monsoon Region: Study of the total atmospheric and land 1998 - 1999 surface water and energy balances in four diverse climate regions in the eastern Eurasian continent

20.6 Existing Sensors and Platforms

Continuing high spatial resolution data from the Landsat and SPOT satellites, passive microwave data from the Special SensorlMicrowave Imager (SSM/I) and continuing meterological satellite coverage from the NOAA, GOES, GMS and Meteosat series all mean that the remotely sensed techniques can continue to be employed and expanded upon. However, new sensors, particularly in the micro­wave region, promise great potential for hydrologic applications. There are several satellites, such as ERS-l/2 launched by the European Space Agency, the JERS-1

454 E.T. Engman and G.A. Schultz

launched by the Japanese, and RADARSAT launched by the Canadians that will provide useful data for hydrologists. All carry single polarization, single-frequency SARs. An additional satellite that was recently launched will have considerable hydrologic interest is the Tropical Rainfall Measurement Mission (TRMM) (Simp­son et aI., 1988).

While, space borne sensors are ideal for long term, large area and global obser­vations, there are a number of cases in which aircraft sensors can be extremely useful for hydrologic applications. Situations in which airborne sensors may be optimal are for extremely high resolution sensing, detailed topographic mapping, and flying of new sensors that are not currently on spacecraft. A good example of the later is the A VERIS hyperspectral instrument. Additional uses for airborne instruments are intensive, but short duration field campaigns discussed above or one time detailed mapping of an area or special situation such as flooding or an environmental spill.

In the field of ground-based remote sensing the use of weather radar data for measuring precipitation with a high resolution in space and time is most important. This information is particularly useful as input to hydrological rainfall-runoff models for real-time flood forecasting. The implementation of ground-based weather radar stations is in progress on all continents. Very advanced systems covering the whole country are already operational in the UK and in Japan. In western Europe and in North America such integrated weather radar networks are under construction and partially operational. It can be expected, that in many countries and even continents, these integrated weather radar networks will be accessible to flood forecasting centers. Use of these radar data will be available in real-time to make online flood forecasts for all relevant points in the river network, for small catchments as well as large river basins.

As far as the satellite borne sensor systems are concerned, not all existing and approved sensors are ideal for hydrology. The currently available instruments do provide, however, very useful data for hydrological applications even though none of the currently available satellite sensors have been designed with hydrological applications as the primary goal. The result is that we often have to use less than ideal data for a given application, but in spite of this they have been very useful under limited circumstances. The listing of currently available satellite systems and sensors characteristics has been given in the Appendices 20.1 and 20.2 at the end of this chapter.

20.7 Planned and Proposed Sensors and Platforms

The currently available satellite sensors have not been used optimally in hydrol­ogy. However, it is possible that with the future satellite sensors we will really see an advancement in the hydrologic sciences. This will be accomplished by im­proved spatial resolution, narrower and more specific spectral bands, but most importantly more sensors operating in the microwave region of the spectrum. The microwave region of the spectrum provides the opportunity to make unique meas­urements of system states under all weather conditions.

20 Future Perspectives 455

There are a number of existing or soon-to-be launched sensors that may provide useful hydrologic measurements. These include sensors that in principle at least can measure just about all hydrologic variables. Unfortunately being able to make a measurement is only part of the story with satellite remote sensing. One also has to consider the time frequency of measurement as well as the spatial resolution. For example, the JERS-1 SAR at 1.4 GHz should be a useful instrument for meas­uring soil moisture, but its 46 day repeat cycle renders it essentially useless for obtaining useful information on soil moisture. Similarly, the 19 GHz channel on the SSMII platform should provide useful information on snow depth, but its large footprint greatly reduces its capabilities for measuring snow in alpine regions.

NASA's Earth Observational System (EOS) (Butler et aI., 1988), and its Inter­national partners from Europe and Japan will lead to considerable advances in the understanding of all the earth sciences, including hydrology. The EOS instruments of most interest to hydrologists would include the MODIS and AMSR, the latter is a Japanese microwave instrument with a C-band radiometer which should provide interesting measurements of the land surface soil moisture conditions and deep snow conditions. EOS also includes the organization of a data information and archiving system which is extremely important. This data system will allow many types of data to be used simultaneously to calibrate or be assimilated into numeri­cal models. Figure 20.3 shows conceptually the complexity of the Earth Observing System.

The trend for future Earth observing missions is to smaller, cheaper and more reliable satellite systems. The large, complex and expensive multi-sensor platforms such as the EOS-AM and PM are no longer viable. This trend is supported by the NASA Earth System Science Pathfmder (ESSP) and ESA Living Planet Explorer programs. The hydrological science community is also becoming involved in the

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Fig. 20.3. Schematic demonstrating the complexity of the Earth Observing System

456 E.T.Engman and G.A. Schultz

long range planning of future missions. Appendices 20.1 and 20.2 list those future satellite/sensors that can be expected

in the near future and hoped for in the more distant future. Some of these are well along in the planning and approval states whereas others are only in the proposal stage.

20.8 Remote Sensing and Future Needs in Hydrology

There are potentially many new and exciting observations of the hydrologic cycle that are going to be available from new satellite systems. For example, HYDRO STAR (a NASA ESSP) and SMOS (an ESA Living Planet Explorer pro­posal) are planned small satellites that would carry a L-band radiometer that uses aperture synthesis to achieve frequent global soil moisture measurements at about 30-50 km spatial resolution. If one of these missions is chosen for flight, it will be the first satellite designed purely for hydrologic applications.

New models to allow the data to be analyzed and address previously intractable problems will have to be developed specifically to use the new data types such as soil moisture or snow wetness. Remote sensing can provide many of the necessary data to supplement conventional data to expand hydrology in new directions and also provide entirely new data types and forms that will help hydrologists tackle previously unsolvable questions.

The future progress in the hydrological sciences will depend a great deal upon the availability of adequate data for model development and validation. Remote sensing can and should playa pivotal role in this progress. Without it, it is very possible that future progress in the hydrological sciences will be severely retarded if not completely stopped. With it hydrological sciences should be able to advance rapidly and to successfully address some of the previously intractable problems in hydrology and water management at all relevant scales, i.e. from microscale to global.

References Chahine M.T. (1992) The hydrologic cycle and its influence on climate, Nature, Vol. 359, pp

373-380 Dozier J. (1992) Opportunities to improve hydrologic data, Reviews of Geophysics, 30, 4, pp

315-331 Federal Council for Science and Technology (1962) Scientific Hydrology, Washington, D.C. Grayson R.B.; Moore I.D. and McMahon T.A. (1992) Physically Based Hydrologic Modeling. 1.

A Terrain-Based ModelJor Investigative Purposes, Water Resources Research, Vol. 28, No. 10, pp 2639-2658

Grayson R.B.; Moore I.D. and McMahon T.A. (1992) Physically Based Hydrologic Modeling. 2. Is the Concept Realistic?, Water Resources Research, Vol. 26, No. 10, pp. 2659-2666

Hornberger G.M. (1994) Data and analysis note: A new type of article for Water Resources Research, Water Resources Research, Vol. 30, No. 12, pp 3241-3242

20 Future Perspectives 457

Hornberger G.M. (1992) Hydrologic Science: Keeping Pace with Changing Values and Percep­tions, Proceedings: Sustaining Our Water Resources, Water Science and Technology Board, Tenth Anniversary Symposium

Jackson TJ.; LeVine D.M.; Swift C.T.; Schmugge T.J. and F.R. Schiebe (1995) Large Area Mapping of Soil Moisture Using the ESTAR Passive Microwave Radiometer in Washita '92 (accepted by) Remote Sensing of Environment

Jakeman AJ. and Hornberger G.M. (1993) How Much Complexity Is Warranted in a Rainfall­Runoff Model? Water Resources Research, Vol. 29, No.8, pp 2637-2649

Loague K.M. and Freeze R.A. (1985) A comparison of rainfall-runoff-modeling techniques on small upland catchments, Water Resources Res., Vol. 21, No.2, pp 229-248

NaefF. (1981) Can we model the rainfall-runoff process today? Hydrologic Sci. Bull., Vol. 26, No.3, pp 281-289

NRC (National Research Council) (1991) Opportunities in the Hydrological Sciences, Wash­ington, D.C., National Academy Press

Schultz, G.A., 1993. Hydrological modeling based on remote sensing information. Adv. Space res., Vol 13, No.5, pp 149-166