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Hydrological Sciences -Journal- des Sciences Hydmlogiques,tt(4) August 1996 5 6 3

Remote sensing applications in hydrological modelling

G. W. KITE & A. PIETRONIRO National Hydrology Research Institute, 11 Innovation Boulevard, Saskatoon, Saskatchewan, Canada S7N 3H5

Abstract Previous studies have suggested that remotely sensed data should provide major benefits to hydrology and water resources and yet there are few case studies that show practical benefits. One of the reasons for this is the lack of tools to convert remotely sensed data to the type of information useful to water resource systems operators. Hydro-logical models can play an important role in this translation of data to information. This paper reviews some of the techniques presently used in hydrological models to make use of remotely sensed data and provides a comprehensive reference list.

Applications de la télédétection à la modélisation hydrologique Résumé Si de précédentes études on suggéré que les données télédétectées pouvaient rendre de grands services à l'hydrologie et à l'étude des ressources en eau, il n'existe que peu d'études de cas mettant en évidence leur intérêt pratique. L'une des causes de cet état de choses est le manque d'outils permettant de traduire les données télédétectées en informations directement utilisables par les gestionnaires des ressources en eau. Les modèles hydrologiques peuvent jouer un grand rôle dans ce processus de transformation de données en informations. Le présent article passe en revue quelques unes des techniques actuellement utilisées en modélisation hydrologique permettant d'utiliser des données télédétectées et fournit une bibliographie détaillée.

INTRODUCTION

There are many excellent reports and conference proceedings which have been written on the use of remotely sensed data in hydrology and water resources. Examples include Remote Sensing and Water Resources (1AH, 1990), Hydro-logical Applications of Remote Sensing and Remote Data Transmission (Goodison, 1985) and Remote Sensing in Hydrology (Engman & Gurney, 1991). Other reports and workshops have looked more specifically at the use of remotely sensed data in hydrological modelling including: Review of Operational Remote Sensing Techniques and Streamflow Forecasting Techniques (Robinson & Associates Inc, 1986), Proceedings of the Workshop on Applications of Remote Sensing in Hydrology (Kite & Wankiewicz, 1990) and Remote Sensing in Hydrology (Kite et al., 1995).

Several studies in the USA (Castruccio et ai, 1980; Rango, 1980; Carroll, 1985) suggest possible benefit/cost ratios ranging from 75:1 to 100:1

Open for discussion until 1 February 1997

564 G. W. Kite & A. Pietroniro

for using remotely sensed data in hydrology and water resources. These estimates are based on savings from flood prevention and improved planning of irrigation and hydroelectric production. What often prevents these savings, in practice, is the lack of operational methods to derive the information needed. Hydrological models are an important tool for translating remotely sensed data into useful information.

In this review, a brief introductory description of hydrological modelling leads to a discussion of current work on physically-based models. Such models require vast numbers of spatially and temporally distributed data and this introduces the role of remote sensing as a possible source of these data. A description is given of the current uses of remotely sensed data in the various processes of a hydrological model and likely future developments are discussed.

Hydrological models

Hydrological models are attempts to represent the hydrological system from precipitation to streamflow in mathematical form. The complexity of the models varies with the user requirements and the data availability. Models vary from simple statistical techniques which use graphical methods for their solution to physically-based simulations of the complex three-dimensional nature of a watershed. Chow et al. (1988) categorize hydrological models based on three decision rules: does the model include randomness, does it include spatial variation and does it include time variation.

Runoff processes The study of storm runoff generation forms the basis from which deterministic hydrological models are derived. Historically, runoff was often simulated by a black box approach with no attempt to reproduce the detailed hydrological processes which actually generate the runoff (Chow et al. 1988). The earliest and best-known of these approaches is the unit hydrograph (Sherman, 1932). The development of the unit hydrograph method coincided with the development of the Richards equation for unsaturated flow as well as Horton's work on infiltration and the production of runoff. This era is defined by Chow (1964) as the "Period of Rationalization". In the Hortonian flow concept streamflow is generated with overland runoff occurring when the rainfall rate exceeds the infiltration capacity of the soil. Rainfall excess or "effective rainfall" is then seen as the driving parameter from which the streamflow is generated. This process can be characterized by two separate elements, a loss function for estimating effective rainfall and a time transformation of the resulting excess water (Wood et al., 1990).

Using computers and computer modelling allowed the internal processes lumped within the black box approach to be divided into a number of conceptual or empirical processes. However, at that stage in the development of models, the system states such as soil moisture were still only estimated states within the black box process (Engman, 1986). Over the last two decades research in this area has concentrated on better understanding of the streamflow generation

Remote sensing applications in hydrological modelling 565

process with much of the attention centred on infiltration and on the determination of effective rainfall. Given the diverse nature of the infiltration process, this is by no means a simple task.

A more realistic approach is to model runoff generation from both surface and subsurface sources. Kirkby (1978) discussed a more complete evaluation of runoff generation mechanisms in which processes other than the Hortonian infiltration limiting mode are examined. These mechanisms include infiltration-limited overland flow, partial area overland flow, saturation excess overland flow, subsurface stormflow and saturated wedge flow (Wood et al., 1990). The recognition that flow may be generated from many mechanisms has led to partial area hydrological models where contributing areas are primarily dependent on topographic and soil properties (Wood et al., 1990) and change during the course of a storm event.

Discretization of the watershed With the advent of powerful computers, current activity in hydrological modelling is towards more physically-based models which attempt to represent more closely the observed hydraulic phenomena. This necessitates breaking the watershed down into smaller units. Such distributed models are defined by their ability to incorporate the distributed nature of watershed parameters and inputs into a modelling framework. Fully distributed models are likely to produce the next increment of progress in streamflow modelling (Link, 1983). Many distributed models are currently under development and in use including the SHE model (Abbott et al., 1986), the Hydrotel model (Fortin et al, 1986), the USGS Precipitation Runoff Model (Leavesley & Stannard, 1990), the SLURP model (Kite, 1995a) and the WATFLOOD flood forecasting system (Kouwen, 1988).

Various rationales for basin discretization have been used. A technique proposed by Wood et al. (1988) requires discretization of the basin into representative elemental areas (REA). The REA is defined as an areal element within a basin where the hydrological properties are definable and would not be significantly different if a smaller scale of discretization were used. This technique has been implemented within the SHE model which was classified by Wood et al. (1990) as a completely physically-based distributed model. Following earlier work carried out by Freeze (1974), SHE uses a finite difference approach to solve the combinations of partial differential equations which describe surface and subsurface flow. The computational complexity of the model makes calibration difficult and it has been found (Jain et al., 1992) that model parameters have little relevance to measurements made in the field. Wood et al. (1990) also reported that runoff simulation for a period of 150 days requires a ran time of 50 hours on a Cyber supercomputer. This suggests that this type of model has research capabilities but is limited when it comes to practical applications.

Another method of discretization is the hydrological response unit (HRU) approach. In this case a basin is subdivided into areas which represent hydro-logically homogeneous characteristics such as land cover, slope and aspect.


Kite & Kouwen (1992) note that these computational elements may be based on a grid system as in the Hydrotel system, on a sub-basin system as in the USGS model or on the basis of elevation bands as in the SRM model (Martinec et al, 1983). In these cases, the HRU will generate a distinct hydrological response but its location within the basin is only important for routing considerations (Donald, 1992). This differs from the REA approach where the element's location will influence its hydrological response.

Kouwen et al. (1993) described a grouped response unit (GRU) used in a grid square model. The GRU is a grouping of all areas with a similar land cover such that a grid square will contain a number of distinct GRUs. Runoffs generated from the different groups of GRUs are then summed together and routed to the stream and river system (Kite & Kouwen, 1992). For example, two GRUs with the same percentages of land cover types, rainfall and initial conditions will produce the same amount of runoff regardless of how these land cover classes are distributed. This stems from the well established concept in urban hydrology where runoff from small areas can be calculated by summing runoff from both pervious and impervious areas (Kouwen, 1988). Kite & Kouwen (1992) showed that a semi-distributed watershed model based on the GRU approach using land cover classifications will give better calibration and validation statistics than the lumped version of the same daily runoff model. Similar results were reported by Tao & Kouwen (1989) for an hourly flood forecasting model (WATFLOOD).

The SLURP model (Kite, 1995a) divides a watershed into a number of units known as aggregated simulation areas (ASA). An ASA is not a homogeneous area but is a group of smaller areas each of which has known properties. For example, land cover may be measured from satellite for pixels as small as 10 m but it would be impracticable for a hydrological model to operate at such a pixel dimension for a macroscale basin. Instead, the pixels are aggregated into areas which are more convenient for modelling. Such AS As do not need to be squares, rectangles or any other regularly shaped areas (although such forms are possible subsets of the ASA) and may more usually be based on stream network shapes. The basic requirements for an ASA are that the distributions of land covers and elevations for elements within the ASA are known and that the ASA contributes runoff to a definable stream channel. The latter requirement is also an operational consideration since it means that the stream system within the watershed must be at a level of detail such that each ASA contains a defined stream connected to the watershed outlet.

Remote sensing in models The development of more complex, physically realistic, distributed hydrological models has dramatically increased the demand for spatial data. At the same time, the data collection agencies are under pressure to reduce the sizes of their conventional ground-based data networks. Remote sensing technologies are often considered as innovative ways of obtaining data at a reduced cost (Koblinsky et al., 1992).

Salomonson (1983) argued that the use of remote sensing in hydrological


models can be divided into three broad categories or levels of use. The simplest of these is the use of remote sensing imagery to identify items of interest such as snow covered areas or plumes. The second level is to obtain data such as land cover, geological features, or other hydrological parameters through interpretation and classification of remotely sensed data. This interpolation of satellite data is often used in conjunction with existing hydrological models such as the SCS watershed runoff model. The third level involves the use of digital data to estimate hydrological parameters directly. This is normally achieved through correlation of known hydrometric data with remotely sensed data. Estimates of soil moisture and precipitation have been obtained in this fashion. All three categories have been used successfully in hydrological applications with the second category being particularly well suited to hydrological models.

Data types and accuracy requirements

Hydrologists are interested in remotely sensed data because of the combination of wide spatial coverage and the frequency of measurements. An improved

Table 1 Types of data required in hydroiogical modelling

Category

Meteorological/ climatological

Vegetation/ land cover

Physiography

Soils

Other

"Invariant" data

snowmelt rate lapse rate rain/snow temperature

classes of land cover roughness canopy properties

elevation slope aspect stream pattern stream properties

Time series at a point

air temperature precipitation evaporation humidity dew point wind velocity/ distance hours of sunshine radiation snow water equivalent

LAI

(dimensions, roughness) watershed boundary & area

classes of soil roughness max. infiltration rate conductivity depression storage field capacity

reservoir operating rules

albedo temperature

streamflow

Time series representing an area

air temperature precipitation evaporation cloud cover snow covered area snow water equivalent

NDVI


understanding of the hydrological cycle requires measurement of time series of data at a point, time series which vary over an area and data which do not change over the time scale of the modelling period. Table 1 lists some of the data types commonly used in hydrological models and classifies them by dimension and time-dependency.

Although no purpose-built hydrological satellite has been launched, many satellites have yielded information of use to hydrologists. Pre-eminent amongst these have been meteorological satellites, the Heat Capacity Mapping Mission and the Landsat series. These satellites have carried sensors operating in the visible and infrared portions of the electromagnetic spectrum which are re-

Table 2 Examples of parameters in hydrology and water resources currently available from satellite (after Kite, 1993)

Variable Satellite/ sensor

Wavelength or frequency

Resolution Coverage Sample reference

snow covered area NOAA

snow depth

snow water equivalent

GOES

Nimbus 7

DMSP SSM/I

0.62, 10.80mm (bands 1 and 4)

0.65 mm (visible) 37 GHz

19.3, 37.0 GHz

23, 31 GHz MOS-1 MSR

surface temperature NOAA 10.80 mm (band 4)

évapotranspiration NOAA

GOES

0.62,0.91, 10.80, 12.0 mm (bands 1, 2, 4 & 5) 0.64, 11.5 mm (visible and IR)

1 km 2 per day

2 per hour

2 per day

2 km

30 km

25 km

23-32 km

1 km

2-8 km 2 per hour

Kite, 1989 ; Carroll & Baglio 1989; Rango, 1980

Donald era/., 1990 Change/ al., 1982

Goodison, 1989 Slough & Kite, 1992 Walker et al., 1990

Dousset, 1989

Price, 1980

Bussières et al., 1990

precipitation Meteosat 0.65 mm (visible) 3 km

land cover/land use Landsat 5 MSS

vegetation NOAA

suspended sediment/ Landsat 5 algal growth MSS

water depth Landsat

0.55,0.65,0.75,0.9 80 m /im (bands 1, 2, 3 & 4)

0.62, 0.91 urn 1 km (bands 1 and 2)

0.55,0.65,0.75,0.9 80 m

(bands 1, 2, 3 & 4)

spring runoff Nimbus 5 19 GHz

temporal changes in ERS-1 snowmelt and soil moisture JERS-1

groundwater Landsat

30 km

C-band (5.3 GHz) 30 m SAR L-band (1 GHz) SAR "

0.95 jum (band 7, nearIR)

80 m

0.48, 0.56, 0.66 ^m 30 m (bands 1, 2, 3)

8-16 days

2 per day

8-16 days

2 per hour

35 days

8-16 days

Pietroniro et al., 1989

Whiting, 1990; Kite & Kouwen, 1992

Dobsone/a/., 1986 Allison et al., 1989

Ritchie et al., 1986

Wankiewicz, 1989

Wankiewicz, 1993 RSI, 1993 Wankiewicz, 1993

Bobbae/a/., 1978

Hallada, 1984


stricted from viewing the Earth by clouds and are limited in the number of hydrological parameters that can be observed. Determination of hydrological variables such as soil moisture status, snowpack water equivalent, snow wetness and flood extent is not always possible at these shorter wavelengths (Schmugge, 1984).

Satellites measuring the microwave portion of the electromagnetic spectrum (longer wavelengths) have potential for monitoring several of these variables. The advantages of microwave sensing include an all weather capability, which is important for periodic observations; greater penetration depth into soil or snowpack; and sensitivity to changes in the dielectric properties of materials produced by changes in water content. Table 2 provides some examples of parameters used in hydrological modelling which have been derived from satellite data and provides sample references.

CURRENT STATUS

This section describes the current status of using remotely sensed data as inputs for hydrological modelling. A description is given of the various ways in which remotely sensed data have been used to estimate each hydrologically important parameter. As an example of the type of remotely sensed data which may easily be used in current hydrological models, Table 3 lists those parameters estimated by remote sensing data in the SLURP model (Kite, 1995a).

Table 3 Satellite data used in the SLURP watershed model

Satellite and Resolution Frequency sensor

Landsat MSS 80 m

NOAA-AVHRR 1 km

DMSP SSM/I 25km

8/16 days

2 per day

1 per day

Cost C$/imag

800

25

20

e Parameter estimated

land cover

(i) snow and cloud cover (ii) land cover classification

snow water equivalent

Precipitation

The most important input to any hydrological model is precipitation. For many watersheds, climatological data from the network of observing sites are inadequate, unreliable (Rodda, 1968) and, no matter how sophisticated a hydro-logical model is used, the results cannot be good. For example, Fig. 1 shows the differences between daily recorded and simulated streamflow for the 2000 km2 Martin watershed near Ft. Simpson in northwestern Canada using the SLURP model (Kite, 1995b). The Figure also shows the only available daily temperature and precipitation data for 1989. The shape of the simulated


faydrograph roughly follows that of the recorded flows with two major exceptions: (i) the model simulates snowmelt in mid-April while the recorded hydrograph shows none; and (ii) the model simulates a high flow in August-September while the recorded hydrograph shows none. The explanation is that there is only one climate station and this is situated 60 km outside the basin at Ft. Simpson airport. In April, the airport station shows a rise in temperature which, in the model, causes snowmelt. Since the recorded hydrograph shows no rise, the temperature record at Ft. Simpson cannot have represented conditions over the basin. Similarly, the Ft. Simpson station recorded a precipitation spike in August which generated high streamflows in the model. Since the observed streamflow shows no rise, the observed rainfall cannot have occurred over the watershed. With such unrepresentative data, a hydrological model, no matter how sophisticated, could not produce realistic hydrographs.

J in A . .. I I . » . , , ,1, ,,.1,1 l l i l L Ji 1,1 I Ii 1,1, ,il , ,ll„ 1 ill , il ill ii ii III. I. Ill I I.

94.9

Flow M3/S

'"''"''•'••"'V'lllfllfijljpi

39 Prec

nt% 26

letip O Ç

-38

Jan Feb

Rain, snow g§ Obs. Floy > | Conp. Flow > j

Mar Apr May J un Jul Aug

Precip, m 8.385E*83 Obs. Flow, m 8.119E+B3 Coop. Floy, nn 8.238E*83

Sep Oct Nov Dec

Evap, m B.821E+02 Rain/snoy tenperature 0.3 Standard Error 8.571E+88

Fig. 1 Comparison of simulated and recorded hydrographs for the Jean-Marie Basin, Ft. Simpson, NWT, for 1989 showing the inadequacy of the climate network.

Many researchers have estimated precipitation from satellite data for specific areas at specific times but, to the best of the authors' knowledge, there are no generalized procedures available to estimate daily (or more frequent) precipitation for all areas. Existing techniques have concentrated on using visible and thermal infrared channels for cloud-top reflectance and temperature (e.g. Follansbee, 1973); such techniques appear to work best in the tropics and in areas with poor raingauge networks. In the visible band, it is possible to determine the type of cloud cover based on the textural characteristics of the


cloud within the satellite image. Based on this type of information, Barrett (1970) developed the cloud indexing method of rainfall estimation. The cloud indexing method has been applied to a wide range of climatic regions and has the advantage of not being dependent on sophisticated software or computer systems. Instead, the information required for implementation of the technique can be obtained from printed images of visible band data.

Other techniques have been developed to provide real time or near real time forecasts. These methods require the interaction of a knowledgeable meteorologist to follow the life cycle of a precipitation event. Probably the most significant of these life history methods is the Scofield and Oliver technique (Scofield, 1983; Scofield & Oliver, 1977). This technique gives half-hourly or hourly rainfall estimates using GOES infrared and visible images and is designed for deep convective systems in tropical air masses (Scofield, 1983). The basis for the method is a decision tree where the estimates of convective rainfall are made by comparing the changes between consecutive infrared and visible images. Other methods, commonly referred to as bi-spectral methods, use the visible and infrared characteristics of clouds in an objective assessment of rainfall amounts. The benchmark work in this regard is that of Austin & Lovejoy (1981).

A common factor in many of the methods developed over the years is the use of either visible or infrared satellite data to distinguish cold, high cloud tops. The physical basis for this method (Barrett & Martin, 1981) is that high cloud top brightness in the visible bands means that there is a greater probability of rain due to large cloud thickness. Similarly, low cloud top temperature in the thermal band implies high cloud tops and also implies a correspondingly large cloud thickness. Therefore, precipitating clouds can be distinguished from others on the basis of their brightness or infrared temperature characteristics. This characteristic was used to estimate rainfall for a monthly water balance model developed for the Sudan-Sahel region of West Africa. This is one of the few cases where rainfall derived from remote sensing was used as input into a hydrological model (Pietroniro et al., 1989). The Sudan-Sahel region is particularly suitable for a satellite estimation approach because of the strong connection between the Inter Tropical Convergence Zone (ITCZ) and the occurrences of rainfall. The ITCZ is a zone of moist unstable air caused by the convergence of the trade winds in the vicinity of the Equator (Edwards et al., 1983). It is the most important climatic feature in West Africa and most rainfall occurs along its path. Characteristics of the ITCZ that are detectable by satellite such as cold cloud occurrences (Assad et al., 1986) and the 5-day maximum surface temperature (Cam & Lahuec, 1987) appear to be linked to rainfall amounts. A statistical model between monthly ITCZ characteristics and monthly rainfall appears sound. Pietroniro et al. (1991) developed a regression model with monthly cold cloud occurrences (Oc) and a monthly average of 5-day maximum Meteosat surface temperatures (Tmax) as the independent variables and monthly rainfall values (PPT) as the dependent variable. The regression results are given in Table 4.


Table 4 Regression results for monthly rainfall estimates, Sudan/Sahel

Statistic May June My August September October Season _ _ _ _ _ _ _ _ _ _ r 0.74 0.69 0.79 0.79 0.76 0.83 0.91 Error (% of mean) 60% 31% 42% 41% 43% 52% 26%

In all basins the remotely sensed basin rainfall estimates yielded better model results, with respect to the standard error calculated from known runoff values. The standard errors of validation of runoff for study basins are given in Table 5. The standard errors of the monthly hydrographs demonstrate a path for improved rainfall estimation (Pietroniro et al., 1991). This is of particular interest for areas such as the Sahel where there is only a sparse network of rainfall gauges. Also demonstrated was the possibility of assessing monthly flows for small basins in the region where no hydrometric data exist.

Table 5 Standard errors of runoff from conventional and remotely sensed rainfalls

Basin Name

Douni Kobani Degou Doundian Ouaireba

Station name

Point398 Nimbrini Diolala Wahire Wahire

Conventional runoff error (mm)

6.39 31.07

8.34 16.92 11.96

Remote sensing runoff error (mm)

5.76 28.53

7.13 9,89

11.75

Other hydrological models using remotely sensed rainfall have been applied in Africa. In the aftermath of a severe flood in August 1988 at Khartoum, the World Bank funded Delft Hydraulics to develop a flood early warning system (FEWS) for the Sudan Ministry of Irrigation and Water Resources (El Amin El Nur et al., 1992). The system covers the entire Blue Nile and Atbara Basins, the White Nile north of Malakal and the combined Nile north to Dongola and provides six days lead time for floods at Khartoum. Meteosat TIR data in a cold cloud duration technique are used to estimate daily rainfall over the Blue Nile and Atbara Basins using a version of the Sacramento hydrological model.

In Cairo, the US NO A A has prepared a Nile Forecast System (NFS) for the Egyptian Ministry of Public Works. The NFS structure consists of a continuous distributed hydrological model using gridded rainfall inputs (NOAA, 1993). A conceptual water balance component is linked to physically-based hillslope and channel routing components. The model uses the Meteosat pixel size of 5.5 km and routes runoff from one grid square to the next. The precipitation input to the model is derived every 30 min from the geostationary Meteosat satellite infrared sensor using the same cold cloud duration technique.


Microwave data from space would appear to offer promise for rainfall estimation given that ground-based radar are used operationally to estimate precipitation for flood forecasting systems but, so far, no operational techniques have emerged.

Soil moisture

Moisture in the upper layers of the soil profile is an important component of the total water balance of the Earth-atmosphere system and is a vital state variable in any hydrological model. The soil layer that is usually considered is that which can interact with the atmosphere through évapotranspiration. The depth of this layer depends on the type and stage of maturity of the plant cover, but is typically 1-2 m. Estimating the amount of water stored in a soil profile is essential in most water management projects and the implementation of appropriate techniques for water management and conservation practices requires the quantitative assessment of the soil water status. This is particularly true in agricultural water management projects and operational hydrological modelling such as flood forecasting. In many cases, particularly at the watershed scale of monitoring or modelling, soil moisture is inferred from more easily obtainable hydrological variables such as rainfall, runoff and temperature. Conventional in situ measurements of soil moisture are costly and provide information at only a few selected points.

Although extensive work has been carried out over the last few years in soil moisture monitoring using remote sensing, the application of the work has only rarely been coupled to hydrological runoff modelling. In most hydro-logical models, the soil moisture component is an intermediary component within the water balance equation and is not assessed using measured soil moisture data. Engman (1990) states that: "In these models, soil moisture is a system state that must be initiated and then, timewise, recomputed by increasing it when precipitation is added or decreased by drainage and évapotranspiration. In general, existing models' representation of soil moisture is simply a step to make the model work and not a physical representation of soil moisture."

In order to improve this picture, modellers need to obtain spatial and temporal estimates of soil moisture on a watershed scale which will require the use of non-traditional approaches.

The soil moisture models developed over the last two decades are usually divided into four main groups categorized by the specific bandwidth of the electromagnetic spectrum that is used. The wavelength regions are: the reflected visible and infrared, the thermal infrared, active microwave and passive microwave. Colwell (1983) has tabulated the advantages, disadvantages and major sources of error. The paragraphs below discuss the uses of each of the wavebands for estimating soil moisture.

Salomonson (1983) noted that predicting hydrological state variables such


as rainfall, évapotranspiration and soil moisture for hydrological modelling is an important means of making full use of remotely sensed data. However, few researchers have applied remotely sensed soil moisture data within the framework of a hydrological model. Most existing models do not lend themselves well to these types of inputs, because the data are incompatible with the model structure. Engman (1990) integrated soil moisture time series into a new form of hydrological modelling. His research examined the recession response of a small basin through conceptual modelling of the subsurface flow component using the Sloan & Moore (1983) sloping slab model. Using time series of soil moisture and évapotranspiration, estimates of infiltration to the water table could be made. The changing water table was then the main driving force to the interflow component. Other studies have proposed conceptual frameworks for incorporating remotely sensed soil moisture but these frameworks have largely remained only concepts. Nonetheless, a noteworthy example is provided by Van de Griend & Engman (1985) who examined the link between the characteristics of contributing areas and remote sensing. They introduced the possibility of characterizing the spatial and temporal variation of contributing areas through monitoring soil moisture conditions with a variety of remote sensing methods. Although the paper was descriptive, it provided a framework for the development of new ideas in the characterization of hydro-logically significant state variables.

Preliminary work in the detection of saturated areas using active radar remote sensing with a C band scatterometer is reported by Brun et al. (1990). They report that for areas exceeding 45% soil moisture by weight, the expressions developed by Ulaby et al. (1982) and Bernard et al. (1986) are not applicable and conclude that it is possible to map saturated areas by simply using a threshold of —5 dB or less. Essentially, they found that for high moisture levels near or at ponding conditions, the normalized radar cross section begins to behave specularly. With the radar set for optimum configuration for soil moisture detection (HH polarization and incidence angle of 15°) a — 5 dB threshold of radar backscattering corresponds to a mean surface soil gravimetric soil moisture higher than 45 %.

More recent attempts to incorporate remotely sensed moisture directly in a hydrological model are reported by Pietroniro et al. (1993). A regression model was applied on a medium sized watershed in Southern Ontario, Canada. Using a land cover map and a GIS, bare fields were selected as index areas. A two-parameter model with both volumetric soil moisture and incidence angle was derived from selected ground truth fields with known average moisture. The equation was then applied on all bare fields within the watershed to derive a soil moisture map. The regression results for the ground truth study site are given in Table 6 (Pietroniro et al., 1994).

Applying the regression to index fields not included in the original data set produced soil moisture maps for the basin, which then provided antecedent conditions for model validation. Differences in runoff hydrographs using both the API generated soil moisture fields and the SAR generated soil moisture


Table 6 Regression results for soil moisture estimates at Oxford County, Canada, 1990

Type

Volumetric

Gravimetric

Depth

0-5 cm 5-10 cm 0-5 cm 5-10 cm

Coefficient of determination

0.54 0.82 0.62 0.72

Significant at 1 %

Y Y Y Y

Runoff (CMS) Rainfall (mm)

~ 4 API Runoff

SAR Runoff - 3

Rainfall (mm)

- 2

Hi

o 0 50 100 150 200 250 300

Time (hrs) Fig. 2 Distributed soil moisture and simulation results for Big Otter Creek, Ontario, Canada.

fields are shown in the simulation (Fig. 2) for the downstream gauge in the basin. Additional research is required for the operational extraction of soil moisture from SAR data. Efforts need to be directed toward determining the relative contribution to the radar backscatter from soil moisture, vegetation and surface roughness. However, the results to date indicate that the estimation of soil moisture in the upper soil profile could become a reality using the RADARSAT type of satellite (Hirose, 1992).

One approach to extracting soil moisture information would make use of a time series of SAR images. This will require that the data be radiometrically stable so that the image intensities on successive days will be comparable. Utilization of a change detection approach will require relative radiometric calibration which means that the SAR must exhibit good stability over a period of days, weeks or perhaps seasons. To compare moisture content from season to season, geographic location relative to other locations and platform to platform will require that radiometric calibration be valid over the lifetime of the sensor. The discrimination of five discrete levels of soil moisture from wet to saturated (20% variation in each level) corresponds to about 2 dB per level

1H

12

10

8

6

4

Runoff from Calton Gauge

; /'x

-

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/' x

/ / " '̂ • / \ x

i , x

. / \ \ II 'O-• 1 ' x

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' : , : i , I , 1 . 1


for vegetated areas; these distinctions necessitate a radiometric calibration with a fidelity of less than 1 dB. A relative instrument calibration and stability of ± 1 dB (and preferably ±0.5 dB) is therefore required (NASA, 1989).

The study of spatial variability and scale within a hydrological basin will also require that the SAR exhibit cross-track and along-track radiometric fidelity on the order of +0.25 dB. This is to ensure that the image intensity at the centre of a scene can be compared to the image intensity at the edge of a scene. Therefore it is desired that the SAR images exhibit cross- and along-track radiometric fidelity to a fraction of a dB. This is based upon the need to establish comparisons to within a few percent of soil moisture levels and surface roughness expressions between various zones across the SAR swath (Brown etal., 1993).

Snow coyer mapping

In many areas of the world the majority of freshwater available for consumption and irrigation results from snowpack runoff. In order to make efficient use of runoff, water resources agencies must be able to make early predictions of the total flow. Areal extent and snow water equivalent (SWE) are the most important parameters to be determined in order to model this process (Rott, 1986).

Snow extent may be mapped from most satellites with sensors in the visible band including the NOAA series, Landsat, SPOT and GOES. The US National Weather Service distributes snow cover maps operationally using NOAA images (Carroll & Baglio, 1989). The scale of the sensor used is of great importance. Using snow covered area estimated from Landsat TM (30 m resolution) as baseline data, Holroyd et al. (1989) showed that, for the San Juan Mountains in Colorado, NOAA-AVHRR (1.1 km resolution) measurements of snow covered area varied from this baseline by up to + 30 % while measurements from the GOES satellite (25 km resolution) varied by more than 50% from the baseline estimates. Such differences are not due solely to resolution but also to variations in cloud cover, slope and aspect between the various satellite paths and the times of observation.

There are many examples of the use of snow cover extent in hydrological models. The Snowmelt Runoff Model (Rango & van Katwijk, 1990) makes use of satellite data for snow extent to develop snow cover depletion curves by elevation zone. The model then simulates streamflow from each elevation zone. SRM has been used in many areas including India (Kumar et al., 1991). The SLURP model (Kite, 1989) uses NOAA-AVHRR bands 1 (visible, 0.58-0.68 (ira) and 4 (infrared, 10.3-11.3 fim) to measure snow covered area in a classification scheme. The addition of snow cover data from satellite has been shown to result in improved performance for the SSARR hydrological model (US Army Corps of Engineers, 1972) in the Pacific Northwest area (Dillard & Orwig, 1979). Snow covered area has also been used directly in simple


regression equations to derive seasonal runoff in areas such as Pakistan (Rango et al., 1977), India (Ramamoorthi, 1987) and Norway (0strem et al., 1981). Similar work was done by Makhdoom & Solomon (1986) for the Indus River in Pakistan. More recently, Donald et al. (1992) used airborne SAR data to detect snow cover extent in a discontinuous ripe snow pack for an area in southern Ontario, Canada. In this case, snow contained in low lying areas of high accumulation during the winter redistribution and along linear features such as fences and ditches could be detected because of the high resolution and sensitivity to wet snow of the radar.

Snow water equivalent

The earlier discussion noted that it is not sufficient to know the areal extent of the snow; one must also know its water content. Visible and infrared wavelengths are not suitable for estimating the water equivalent of a snowpack; instead attention has focused on the use of passive and active microwave data. Algorithms have been developed to infer snow water equivalent from passive microwave data using the Defense Military Satellite Program (DMSP) special sensor microwave instrument (SSM/I) (Rango etal., 1979). These data are well suited for monitoring the SWE at a watershed basis (Goodison, 1989), but their performance in forested environment is still to be assessed. Some work has been done on the use of such data in mountain environments in Colorado, USA, (Rango et al., 1989) and in British Columbia, Canada, (Slough & Kite, 1992) but no universal algorithms are available.

The potential of estimating SWE from active microwave sensors has been explored by Matzler & Schanda (1984) and Ulaby & Stiles (1980) using data from ground scatterometers. Leconte & Pultz (1990), using airborne C-HH SAR data, observed that dry snow did not seem to be transparent. They suggested that the signal could be affected by snow and ice layers in the snowpack, and that changes in underlying ground roughness could result in either a decrease or an increase of radar backscatter. Bernier & Fortin (1991) suggested that whether the soil is frozen or not has a significant effect on the total backscatter of the snowpack and on the ability to extract SWE. Mapping the extent of wet snow appears to be a viable approach (Rott et al., 1988; Donald et al., 1992) as wet snow produces low radar return in contrast to the high return under dry conditions.

A comparison of point SWE from snowcourses and areally-averaged snow water equivalent data from satellite-measured passive microwave radiation was made using the SLURP model for the 7 100 km2 Kootenay Basin (Slough & Kite, 1992). The model was first calibrated and then applied in two simulation runs. In the first simulation ran the model generated streamflow by updating its internally-computed SWE at the beginning of each month with SWE measured at snowcourses. There are twelve snowcourses in the basin, an average of one snowcourse per 600 km2. In the second simulation the model updated the internally-computed SWE with SWE from DMSP SSM/I passive


microwave data which has a footprint of about 625 km2. The algorithm used passive microwave radiation data at 19, 22 and 37 GHz, air temperature and land cover. Air temperature is used to stop the algorithm as soon as snowmelt starts and land cover is included because the large footprint of the DMSP satellites covers a mix of land covers. Table 7 compares the average S WE over the watershed derived internally by SLURP (without any updating) with the average of the SWEs measured at the twelve snowcourses for the period January to June, 1988. Table 8 compares five measures of error for streamflow generated from the two sources of data. The criteria are either in metres cubed per second (m3 s"1) or dimensionless. The final column of Table 8 lists the optimum values for each of the error criteria. All five criteria are closer to their optima when the hydrological model updates its SWE from passive microwave data than when using snowcourse data. The reason is that the model SWEs are areal averages and the satellite data are also areal averages. On the other hand, the snowcourse data are point values which may not be representative of larger areas.

Table 7 Comparison of watershed-average snow water equivalents computed internally by SLURP with those measured at snowcourses, Kootenay Basin, Canada, 1988

Date Average SWE from SLURP model (mm)

Average SWE Range in SWE from from snowcourses snowcourses (%) (mm)

Difference bet-ween SWE from snowcourses and from SLURP (%)

Jan. 1 Feb. 1 Mar.l Apr.l May 1 May 15 Jun. 1

98 135 135 174 165 110

0

100 123 147 224 157 140

0

0 to -100 51 to 179 56 to 188 57 to 170 21 to 190

+ 3 - 9 +9

+29 -5

only one station with snow data +27 no snow 0

Table 8 Comparison of errors when updating SWE from snowcourses and from DMSP SSM/I passive microwave data, SLURP model, Kootenay Basin, Canada, 1986-1990

Criterion

Mean error (m3 s"1) Standard error (m3 s"1) Nash/Sutcliffe criterion Garrick criterion Previous-day criterion

Snowcourse

5.72 59.7 0.77

-0 .19 -8 .65

Passive microwave

1.89 29.7

0.94 0.71

-1 .39

Optimum

0.0 0.0 1.0 1.0 1.0

Land cover

Land cover is an intrinsic part of the terrestrial ecosystem and, as such, it forms an important parameter in hydrological modelling. The distributions of


land cover may be determined by classifying data from any of the satellites with visible band data. Satellites commonly used include Landsat and NOAA, depending on the resolution required (Kite & Kouwen, 1992). Land cover classification requires good knowledge of ground conditions and is made more difficult by varying view angles. Satellite measurements of land cover over long time periods have the potential of identifying indicators of global change.

As land cover classification was one of the earliest products of satellite data it has often been used to provide data for conventional hydrological techniques. For example, Rango et al. (1983) describe the use of land cover from Landsat MSS data as input to flood frequency models for urban planning. The cost of obtaining the land cover information from Landsat was estimated at about one third that of conventional techniques for basins exceeding 25 km2.

Land cover may also be used as a classifier for parameters of a hydro-logical model (Kite, 1989). For example, each type of land cover will have a distinct roughness coefficient and a distinct infiltration rate. This parameterization is included implicitly in the early Soil Conservation Service (SCS) hydrological model (USDA, 1972) in which runoff curve numbers (RCN) based on land type and soil group are used to parameterize interception, depression storage and infiltration and then to derive runoff volumes. The land types were conventionally derived from observation or aerial photography. Blanchard (1973), Ragan & Jackson (1980) and Harvey & Solomon (1984) have all used remotely sensed land cover data to estimate runoff curve numbers for further input into the SCS model. In the first two studies Landsat data were used for the estimation of the land cover and subsequently, the runoff curve numbers, time to peak and peak flow; the third study used GOES data and restricted the classification to three land types. This use was extended by Duchon et al. (1992) using Landsat MSS data to divide a watershed into land cover types before applying the USDA SWRRB model (a variation on the SCS RCN concept) to each land cover separately. In all cases the results obtained from the remote sensing land covers were acceptable. Tao & Kouwen (1989) also showed that using remotely sensed data for land cover meant that information was no longer lumped, thus allowing modellers to estimate rainfall excess and runoff separately for each land cover class.

The Upper Columbia Basin was used as an example to compare two different resolutions of land cover data and their appropriateness for macroscale hydrological modelling (Kite, 1995b). Landsat data for the Upper Columbia Basin were classified by the University of Waterloo (Kouwen & Soulis, 1994) and NOAA data were classified by the Manitoba Remote Sensing Centre (Pokrant & Palko, 1991) using National Atlas of Canada land classes. Table 9 compares the percentages of each land cover estimated from the two satellites. All the land classes have similar percentages except for "perennial snow/ice". This class shows a significant difference because perennial snow and ice occurs in relatively small patches on mountain tops which are not always distinguishable at the 1.1 km AVHRR resolution. This study showed that, for watersheds the size of the Upper Columbia (36 000 km2) or larger, the coarser


Table 9 Comparison of land cover percentages from Landsat MSS and NOAA AVHRR, Upper Columbia Basin, Canada

Land class

coniferous forest deciduous/mixed forest water barren land perennial snow/ice

Landsat MSS (80 m)

36.53 33.45 3.48

19.64 6.90

NOAA-AVHRR (1 km)

40.84 36.23 3.06 17.38 2.49

resolution NOAA data contain as much information for macroscale modelling as do the Landsat MSS data. The usefulness of this conclusion is that classification of NOAA data is much less costly than that of Landsat. For the Upper Columbia, one near-zero cost NOAA image with 30 000 pixels covers the same area as eight Landsat images costing about US$850 each. In addition, NOAA images are available more frequently (daily vs every 16 days) and are easier to register and to classify.

This conclusion is not valid for smaller watersheds, however. Pietroniro et al. (1995) have shown that for three small ( < 2 000 km2) basins near Ft. Simpson, Landsat data provide considerably more information than the NOAA-AVHRR when used in a macroscale model.

Leaf area Index and vegetation Index

Leaf area index (LAI), the area of leaf above a given area of ground, has been identified by Running et al. (1986) as the single most important variable for quantifying energy and mass exchange by plant canopies over landscapes. Gholz et al. ( 1976) showed that knowledge of leaf area index and its spatial distribution is essential for estimating photosynthesis, transpiration, respiration, interception and energy transmission to the ground. Denisenko & Lozinskaya (1994), in computing evaporation and transpiration for the KUREX88 field experiment in Russia, computed actual transpiration, Ea, as a function of maximum possible (potential) evaporation under a closed canopy by introducing LAI.

The importance of these results for hydrological modelling is that LAI is land cover specific and fits well with models which use land cover information as the basis for parameter definition (Kite & Kouwen, 1992). If a relationship can be defined relating LAI to Ea, a much more dispersed representation of Ea can be used in the hydrological model. In the SLURP model (Kite, 1995a) daily values of LAI are used in transpiration algorithms for each land cover type.

Leaf area index has traditionally been calculated using species-specific allometric equations relating stem diameter and foliage biomass. This is very laborious and imprecise (Gower & Norman, 1991) and so Running et al.


(1986) suggested the use of satellite data to estimate LAI. The visible and infrared bands of the AVHRR sensor can be used to derive indices of stomatal activity (Running & Nemani, 1988). The Normalized Difference Vegetation Index (NDVI) is the most commonly used. However, AVHRR channels 1 and 2 are subject to differential atmospheric scattering and absorption and so other, less susceptible, indices have also been proposed (Pinty & Verstraete, 1992).

Kite & Spence (1995) used monthly NDVI values for the Upper Columbia Basin from two sources. NDVI directly from NOAA-AVHRR 1.1 km data were compared to the 10-minute (about 20 km) Global Ecosystem Database (NOAA-EPA, 1992) in the SLURP hydrological model (Kite, 1995a). Figure 3 compares the two sets of data for October 1988. The comparison concluded that for this size of watershed the 10-minute data are not detailed enough for macroscale hydrological modelling.

Fig. 3 Comparison of 1 km and 10-minute NDVI data for the Upper Columbia Basin, Canada, October 1988.


Evapotranspiration

Remotely sensed techniques have been developed to estimate évapotranspiration. However, the validation of the techniques is difficult due to the imperfect knowledge of soil moisture distribution in a basin (Price, 1980). Several of the parameters needed for computing évapotranspiration may be estimated from remotely sensed data. For example, albedo or global radiation may be estimated by measuring reflectances from bare soil and surface temperature may be measured using thermal infrared wavelengths. Duchon et al. (1992) showed that daily insolation from GOES data compared well with pyranometer observations and was suitable for use in the SWRRB model to estimate evaporation. However, current évapotranspiration models invariably require the humidity of the air as an input and, at the moment, it is not possible to derive this parameter from remotely sensed data.

To overcome this difficulty, Granger (1995) used the feedback relationship between surface temperature and vapour pressure deficit to estimate areal évapotranspiration from a variety of land cover types on the Canadian prairies. This feedback mechanism shows that the relationship between potential évapotranspiration and actual évapotranspiration is an inverse one. That is, the higher the actual evaporation, the lower the potential and vice versa. Morton (1983) used this mechanism as the basis for his complementary relationship areal evaporation (CRAE) model used in the SLURP hydrological model (Kite, 1989).

Granger (1995) showed that the relationship between surface temperature and vapour pressure deficit was well defined, an increase in surface temperature being accompanied by an increase in vapour pressure deficit. Since satellite data is, at best, obtainable only a few times each day, relationships were developed between mean daily surface temperature and observed mid-day temperature and between mean daily vapour pressure deficit and saturation vapour pressure deficit at the mean daily air temperature.

The mid-day surface temperature was derived from the two NOAA-AVHRR infrared channels using a split-window technique for atmospheric corrections. Albedo from AVHRR channel 1 reflectivity was used with observed global radiation to estimate net radiation. The net radiation and vapour pressure deficit were then used to compute évapotranspiration at each 1.1 km pixel for an area of central Saskatchewan for five consecutive days in July 1991. The regional évapotranspiration compared very well with estimates from the CRAE model and from an inverted energy balance model using GOES data (Bussières & Louie, 1989).

Bussières (1995) used a similar technique to estimate 24-hour total évapotranspiration from 24-hour net radiation and the difference in canopy and air temperatures near mid-day. Brightnesses from AVHRR channels 1 (visible) and 4 (thermal infrared) were used to distinguish between cloudy and clear pixels for a series of eleven images of the Mackenzie Basin in northwestern Canada in summer 1994. Maximum brightness from channel 4 non-cloudy pixels were used as surface temperatures and albedo was estimated from channel 1.


Stuttard et al. (1994) used reflectance values from AVHRR channels 1 and 2 to estimate albedo for pixels classified as bare soil in the Kenyan Rift Valley area using a linear additive model in which the coefficients take into account the solar zenith angle and the bi-directional reflectance function. The computed albedos were then used in the computation of potential évapotranspiration from the Penman equation.

Glacier runoff

In many parts of the world, glaciers can have a very significant effect on the hydrological regime. Both the hydrology and ecology of glacier-fed rivers require detailed understanding of snow and ice phenomena on the glacier to address these concerns adequately and yet glaciers are often treated as just large snowpacks in many conceptual hydrological models. Moore (1993) notes that conceptual models in glacierized catchments need to account for the generation of meltwater from firn and ice surfaces as well as from the retreating snow-pack. Various modelling studies are summarized by Moore (1993) including those by Tangborn (1984), Pipes & Quick (1987) and Braun & Allen (1990).

The mapping of glacier "faciès" or cover types using Landsat TM data was introduced by Williams et al., 1991 and is one approach which shows promise for improved runoff modelling. Recent investigation by Pietroniro & Brugman (1995) has shown the possibility of using digital terrain information in conjunction with Landsat TM imagery to improve glacier facies classification. This allowed mapping of snow, ice, firn and other features which are considered important to the modelling effort. This classification was strongly affected by the amount of direct light incident on the surface, and caused slightly shadowed regions in the upper portion of the glacier to incorrectly map as firn. Since the albedo of firn (0.3-0.8) is much lower than that of snow (0.7-1.0) this could cause a very large error (20-70%) in the computed energy available for glacier melt. This error produced by the unsupervised classification scheme is especially important for modelling runoff during the time of glacier runoff from July to September. In order to correct this classification problem with a minimum of assumptions, a simple topographic correction term was introduced to the classification scheme. A cosine correction term was applied to the regions of the image that were illuminated and no correction term was applied to shadowed regions. Several bright areas were identified in the accumulation area that were caused by locations where all wavelength bands were over-saturated. These were automatically classified correctly as snow.

The seasonal movement of snow on a glacier has dramatic affects on the overall glacier albedo. Investigations into tracking this snowline movement with satellite SAR are on-going (Adam et al., 1995) and show some promise. By mapping each glacier unit, facies effects can be taken into account for improved runoff predictions by utilizing remote sensing data (e.g. SAR and Landsat TM). As more data on the wavelength and angle of incidence dependence of surface


albedo become available then the facies approach to albedo measurement and snowline movement tracking with SAR will provide the most flexible approach to runoff modelling.

Streamflow

Streamflow is not used directly in hydrological model development but may be used to verify results. Remote sensing techniques cannot estimate streamflow directly but may be of use indirectly. At a simple level, river flows at specific return periods may be related to geomorphological characteristics. For example, Inglis (1947) suggested that the 100-year flood is a linear function of the river's meander length; if the meander length can be measured from a satellite image then the flood flow can be estimated. Engman & Gurney (1991) recommend the use of the visible red band (MSS band 5, TM band 3) for discerning stream channel networks.

If a river is wide enough and has a stable stage-discharge relationship, then the actual discharge on a particular day may be estimated from measurements of river level using a radar altimeter. For example, Cudlip et al. (1992) used Seasat data to produce a river surface elevation profile for the Amazon River from 32 altimeter crossings of the river over a 17 day period in July 1978. The accuracy of the river elevations is estimated at ± 10 cm to ±20 cm. If a rating curve exists for a location along this profile, then the river discharge could be computed.

Similarly Cudlip et al. (1992) have derived a 20 m interval contour map for the Sudd swamp in southern Sudan from Seasat radar altimeter data. This swamp covers an area of 11 000 km2 of seasonally inundated floodplain for which it is very difficult to obtain ground-based data. Such a contour map could be used to estimate slopes along the swamp for use in a hydrological model of this important component of the River Nile.

Lake levels

Lake levels are used in hydrological modelling for evaporation estimates and flow routing. The advent of satellites with onboard radar altimeters such as Geosat, Seasat, ERS-1 and the recent USA/France TOPEX/Poseidon has allowed hydrologists to measure lake and river levels to centimetre accuracy with frequent repeat cycles (Birkett, 1994). Altimeters measure the distances between the surface and the satellite orbital position; surface heights may be obtained from a reference ellipsoid. The TOPEX/Poseidon satellite has two altimeter systems, the NASA TOPEX dual-frequency Ku/C band (13.6/5.3 GHz) altimeter and the CNES Poseidon single frequency Ku band solid state altimeter (Benada, 1993). Figure 4 plots water level vs time for a position in Lake Victoria about 100 km west of Kisumu for a series of TOPEX


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Fig. 4 Radar altimeter water level profiles for the period from June 1992 to December 1993 for a point in Lake Victoria, Canada.

satellite passes from June 1992 to December 1993 (Dalton & Kite, 1995). The data show a plausible pattern of lake level changes but no surface-based measurements are currently available for comparison.

Stuttard et al. (1994) used data from the ERS-1 radar altimeter to estimate levels of Lake Nakuru, Kenya, for April and May 1993. None of the altimeter tracks crossed the lake and so an off-ranging correction was derived by modelling the altimeter response within the footprint using an ocean-like backscatter with a polar response of six degrees half-power width. The error estimate of ±80 cm was dominated by the error in the off-ranging correction. Results of other work (Cudlip et al., 1994) showed that for lakes directly crossed by the altimeter track RMS lake level errors of ± 15 cm are obtainable. Of this error, ±10 cm is orbit error. Despite the results obtained, Stuttard et al. (1994) conclude that, at the moment, the application of radar altimetry for routine lake level monitoring of a specific lake is not appropriate on the grounds of cost, complexity and accuracy.

CONCLUSIONS

Studies have suggested that the use of remotely sensed data in hydrology and water resources could yield benefit/cost ratios in the order of 100:1 from savings in flood damage and in improved planning of irrigation and hydroelectric production. Such applications are well suited for hydrological modelling. However, the current use of remotely sensed data in modelling is low. The main reason for this is that there are few universally applicable operational methods of deriving the hydrologically important variables from the remotely sensed data. It is really no use having vast quantities of data available if there are no suitable techniques to use them.

The variables for which remotely sensed data (particularly from satellites with sensors in the visible bands) have been used most in hydrological models


are land cover and snow extent. Apart from these two variables the algorithms for using satellite data' in hydrological modelling are limited in the areas and times to which they can be applied. For example, passive microwave data are used routinely to estimate snow water equivalent but the algorithms are presently restricted to specific regions and land covers. Knowledge of soil moisture is vital for hydrological models but existing algorithms to derive this variable from remotely sensed data are often simplistic and apply only to specific locations and times.

Similarly, most hydrological models in operational use are not designed to use spatially distributed data which is a prerequisite to the sensible use of remotely sensed data.

Further uses of visible data from satellite are limited to relatively cloud free conditions. Even for relatively simple tasks such as land classification, clouds (and, more particularly, cloud shadows) complicate the analysis. Microwave satellites offer the potential of (almost) all-weather application but the algorithms to use the data in hydrology and water resources are not yet available. Microwave satellites also have the possibility of providing moisture data for modelling (both as soil moisture and as snow water equivalent) but, again, the necessary algorithms are not universally applicable.

Research is needed into the development of generalized algorithms and into the design of hydrological models more suited to the routine use of remotely sensed data. Such research should lead to operational uses of hydrological modelling. The benefit will be an increased use of satellite data in applications other than research.

Satellite remote sensing can also be an appropriate tool to help alleviate some of the hydrometric data collection and management problems facing many of the developing nations.

A second important reason for the low usage of remotely sensed data in hydrological modelling is the lack of appropriate education and training. Operational agencies and consultants predominantly use traditional techniques. Efforts need to be made by the data suppliers to educate potential customers in the advantages of remotely sensed data.

Historic satellite data are often difficult to obtain and there is a need for archiving more of the data than is currently being done. In order to achieve this it is necessary that potential users are made aware of the usefulness of these data. Before the advent of satellites, remotely sensed data came mainly from aircraft sensors flown on specific missions. Now, remotely sensed data for hydrological modelling generally means data from non-mission specific satellites because of the need for large spatial coverage and frequent measurements. Such satellites have disadvantages for hydrology: the sensors are not ideal for hydrological purposes, the resolutions are often inadequate and the return periods are often too long. In the near future, however, these problems may be addressed by once again using aircraft, this time using solar-powered machines. These aircraft will be able to fly at altitudes up to 20 km in any desired flight path at minimal cost for virtually unlimited time periods. Sensors on such


aircraft could achieve higher resolutions than those on satellites and could be replaced easily and cheaply. For hydrological modelling, such platforms could obtain multisensor data continuously over any area of interest.

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