Snow Hydrology

Snow is a form of precipitation; in hydrology it is treated somewhat differently because of the lag between when it falls and when it produces runoff and groundwater recharge, and is involved in other hydrologic processes. Remote sensing is a valuable tool for obtaining snow data for predicting snowmelt runoff as well as climate studies. Nearly all regions of the electromagnetic spectrum provide useful information about the snowpack. Depending on the need, one may like to know the areal extent of the snow, its water equivalent, or the "condition" or grain size, density, and presence of liquid water within the snowpack. Although no single region of the spectrum provides all these properties, techniques have been developed to provide all of the properties to some degree or other.

The water content of snow can be measured from low elevation aircraft carrying sensitive /-radiation sensors. This approach is limited to low aircraft altitudes (approximately 150 m) because the atmosphere attenuates a significant portion of the 7 radiation. Currently, this operational program covers over 1400 flight lines annually in the United States and Canada. This method is effective for measuring snow cover in open plains, but is less effective in more hilly terrain or when there is extensive forest cover. Use of satellite data for snow mapping has become operational in several regions of the world. Currently, the National Oceanic and Atmospheric Administration (NOAA) develops snow cover maps for about 3000 river basins in North America of which approximately 300 are mapped according to elevation for use in streamflow forecasting (Carroll, 1990). NOAA also produces regional and global maps of mean monthly snow cover.

Microwave remote sensing offers great promise for future applications to snow hydrology. This is because the microwave data can provide information on the snowpack properties of most interest to hydrologists, i.e., snow cover area, snow water equivalent (or depth), and the presence of liquid water in the snowpack, which signals the onset of melt. With the availability of satellite microwave data Scanning

Multichannel Microwave Radiometer (SSMR) and Special Sensor Microwave/ Imager (SSM/I), algorithms have been developed for estimating snow water equivalent for dry snow and mapping the depth and global extent of snow cover (Chang et al., 1987). The passive microwave systems are limited by their interaction with other media such as forest areas, although a method to correct for the absorption of the snow signal by forest cover has been developed (Chang et al., 1991). The spatial resolution attainable by the passive satellite systems is also a limitation but Rango et al. (1989) have shown that that reasonable snow water equivalent estimates can be made on basins smaller than 10,000 km2.

Active microwave remote sensing also has the potential to provide important information about the snowpack at very high resolution with synthetic aperture radar (SAR) (Stiles et al., 1981; Rott, 1986). Unfortunately, analysis of radar data is more complex than passive microwave data and, until very recently, there have been no orbiting SAR systems for collecting snow data. In spite of that, aircraft and shuttle SAR measurements have shown that SAR can discriminate between snow and glaciers from other targets and discriminate between wet and dry snow (Shi and Dozier, 1992, 1995).

Snowmelt runoff procedures that use remote sensing can be grouped into empirical approaches and modeling. Early use of remote sensing focused on empirical relationships between snow cover area or percent snow cover and monthly or accumulated runoff. These simple relationships work very well for some applications, particularly in data-sparse regions of the world. The snowmelt runoff model (SRM) (Martinec et al., 1983) was specifically developed for using remote sensing of snow cover by elevation zone as the primary input variable. Although SRM uses a simple degree-day melt model, it applies the model to the different elevation zones to account for the areal distribution of the snow. SRM has been extensively tested on basins of different sizes and regions of the world. Although SRM is a degree-day model that uses only snow cover as remote-sensing derived input, this model has been recently modified to include a simple snowmelt energy budget algorithm (Kustas et al., 1994). This model has been tested against lysimeter data and suggests that the radiation-based snowmelt factor may improve runoff predictions at the basin scale.


Recent advances in remote sensing have shown that soil moisture can be measured by a variety of techniques. However, only microwave technology has demonstrated a quantitative ability to measure soil moisture under a variety of topographic and vegetation cover conditions so that it could be extended to routine measurements from a satellite system.

The major factor inhibiting widespread use of remotely sensed soil moisture data in hydrology is the lack of data sets and optimal satellite systems. For the most part, scientists have been restricted to data from short-duration aircraft campaigns or analysis of the SMMR and SSM/I passive microwave satellites. Although the avail able passive systems do not have the optimum wave lengths for soil moisture, research has demonstrated that in areas of sparse vegetation a valuable estimate can be obtained (Owe et al., 1988). Historical data from the SSMR passive microwave system is more valuable than the SSM/I data because it had a C-band radiometer, which is a better instrument for soil moisture (Owe et al., 1992); however, its period of record is limited to 1982 to 1987. In both cases the footprint is rather large, varying from about 25 km for the SSM/I to about 150 km for the C-band SMMR.

The SAR systems offer perhaps the best opportunity to measure soil moisture routinely over the next few years. Currently, the European Resources Satellite (ERS-1) C-band and Japan Environmental Resources Satellite (JERS-1) L-band SARs and the Canadian RADARSAT (also C-band) are operational. Although it is believed that an L-band system would be optimum for soil moisture, the preliminary results from the ERS-1 C-band radar demonstrate its capability as a soil moisture instrument. One main drawback to the existing SAR systems is that there are no existing algorithms for the routine determination of soil moisture from single-frequency, single-polarization radars. A second limitation comes from their long period between repeat passes; for the most part it is 35 to 46 days, although the RADARSAT has 3-day capability for much of the globe in a SCANSAR (wide swath, 500 km) mode.

There continues to be speculation about the potential value for soil moisture as an input variable in hydrologic models, either to establish the initial conditions for simulating storm runoff or as a descriptor of hydrologic processes. To date there has been more promise than substance, but initial progress is beginning to appear as some of the aircraft experimental data become available.

Aircraft data taken during the Fist ISLSCP Field Experiment (FIFE) campaign were used to map the spatial pattern of soil moisture resulting from drainage and ET in a 37.7-ha watershed (Wang et al., 1989). These patterns, shown in Figure 1, were seen to match the results of a simple slab model and identified the region contributing base flow to the channel (Engman et al., 1989). Attempts to use passive microwave measurements in a small watershed showed good correlation with the ground data and may yield a reliable technique for calibrating the model (Wood et al., 1993). Even the relatively low-resolution passive data can improve the water budget calculations of a small basin (Lin et al., 1994). Goodrich et al. (1994) studied the prestorm soil moisture at various scales of basin runoff. They concluded that initial values were important but that the resolution of the final remote-sensing product was not a limitation.

The value of remotely sensed soil moisture data in a semidistributed hydrology model was demonstrated using data from the 1992 Washita microwave experiment. Initializing the surface soil moisture fields with the Electronically Steered Thinned Array Radiometer (ESTAR) L-band microwave data produced more accurate model predictions of soil moisture changes and absolute values than those produced from the model initialized with streamflow data (O'Neill and Hsu, 1997).

The feasibility of synthesizing distributed fields of remotely sensed soil moisture by the four-dimensional data assimilation applied to a hydrological model, TOPLATS, has been explored (Houser et al., 1998) with several alternative assimilation schemes. The synthetic soil moisture fields were assimilated from remote-

Model From Synthetic Soil

Figure 1 Temporal and spatial patterns of soil moisture in a small drainage basin illustrating the drying pattern (after Wang et al., 1989).

28 MAY 1987 4 JUNE 1987

Figure 1 Temporal and spatial patterns of soil moisture in a small drainage basin illustrating the drying pattern (after Wang et al., 1989).

sensing soil moisture data and the output of a soil-vegetation-atmosphere scheme. The spatially distributed hydrology model's descriptive ability was improved with the assimilation of the soil moisture data.

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