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A new formulation of the snow routine in the HBV model is implemented in a prototype model program. The code is written in C++, and encapsulated within an object to be used within the PINE model framework. The extensions of the traditional HBV formulation was designed in order to enhance HBV's capabilities in using remotely sensed snow data. The three major changes are:
- A radiation term is included with the degree-day melt equation, in order to reduce the sense to calibration.
- The elevation zone distribution Is replaced with a full grid distribution scheme, in order to correspond better with complete raster data from satellite images.
- A new sub-grid distribution handles the snow depletion curve with one single parameter, instead of the five parameter solution in the present HBV version.
The changes are implemented and some testing is done, but the code is not yet ready for demonstrations.
Depending on the metamorphosis stage of snow, it may show all from small to large anisotropic effects. Modelling the anisotropic reflectance of snow will be important for various applications, like climatology, meteorology and water management. Accurate climatic and meteorological modelling of energy balance need to take the anisotropic effect into account. This is also the case for accurate snow-cover mapping for water management. E.g., sub-pixel snow-cover classification may lead to very wrong values for the snow cover if the effect is not taken into account.
This work cover the development of an empirical model for the anisotropic spectral reflectance of snow based on airborne DAIS-7915 spectrometer data covering the spectral range 0.4-12.6 micrometer. The data were acquired in June 1998 as part of the DAIS-LSF'98 airborne campaign and cover most of the 100 km2 Heimdalen catchment in Jotunheimen, Norway. A total length of 90 km of data with a swath with of 2.5 km wa, acquired. Flight lines were in the solar plane and orthogonal to this, and a swath angle range of +/-26 degrees was obtained. A very accurate DEM was generated from aerial photographs. Combined with the effect of terrain slopes derived from the DEM, an actual terrain incidence angle range of +/-40 degrees was obtained. Areas without snow were masked out based on another infrared high-resolution digital aerial orthophoto. Field measurements confirmed homogeneous conditions allowing a large area of data to be used. Field measurements were also applied for calibration. The derived BRDF values were compared to other works as part of the validation. The raw angle-to-reflectance data were smoothed.
Synthetic Aperture Radar (SAR) images acquired by spaceborne and airborne instruments are radiometric and geometric calibrated using corner reflectors, high precision Digital Elevation Model (DEM) and ortho-photo images. A fully automatic procedure are developed. The results show that the entire SAR data set can be calibrated to the required precision even in highly topographic areas by using the repeat coverage and a high precision DEM. The RMS geometric errors are 8m for the airborne data set and 12m for the ERS and Radarsat data set. Radar cross-section versus incidence angle is established and consistency is achieved between airborne and spaceborne data. A detailed description of procedures and results can be found in Johnsen et. al. 1998, Lauknes et. al.
An updating routine is developed and coded for the HBV model implemented within the PINE framework. The program reads the state file as written after a simulation, as web as the raster data set containing the observed snow coverage. The statistical snow redistribution function (or the snow depletion curve) in each elevation zone is then recalculated using the observed SCA value and the parameters governing snow distribution throughout the winter. At last, the state file is written and can be read by HBV for continued simulations.
The updating program is tested and verified, and can be run silently through a system command. It is thus easy to run the updating from within other applications.
An algorithm for operational mapping of wet snow cover by SAR, which is totally unsupervised. This algorithm is a combination of K-means clustering with a procedure for identifying seed points automatically, and a supervised classifier. The seed-point procedure identifies a set of pixels of snow and bare ground. A classification accuracy of more than 90% has been obtained using this method.
Snow cover area can be derived from Synthetic Aperture Radar (SAR) images acquired by spaceborne and airborne. By using fully automatic procedure entire SAR data set can be calibrated to the required precision even in highly topographic areas by using the repeat coverage and a high precision DEM.
The algorithms give a fast and very accurate estimate for the actual snow cover at the sub-pixel level. It is in particular useful for hyperspectral data. The classifier used is linear spectral unmixing, which has previously also been used experimentally for snow-cover mapping. However, the algorithm is applying local end-member spectra, which are predicted for each individual pixel. The prediction is based on a vegetation map, which with sufficient accuracy can be made from remote sensing data under snow-free conditions. The vegetation map limits the actual number of end-member classes, which are present for each pixel. Additionally, the endmember spectrum predictor takes into account the phonological development stage of the vegetation for the data acquisition time. Furthermore, a snow end-member-spectrum predictor is used to predict a spectrum which match the metamorphosis development stage of the snow. The predictors also take into account the terrain shape for. the generation of actual end-member spectra. The prior information and the predictors allow the number of possible end-members for each pixel to be very low. This will in general increase the accuracy of the spectral unmixing very much and also reduce the computation time significantly.
Provide an overview of the result which gives the reader an immediate impression of the nature of the result and its relevance and potential. Most hydrological models, including the HBV model extensively used in SnowTools, rely heavily on parameter estimation or calibration, traditionally based on observed discharge only. Usually, the information in the observed discharge data is largely insufficient to determine parameter values unambiguously. Largely different parameter values may thus produce equally good calibration results, and consequently, the state variables in a traditionally calibrated model behave arbitrarily, more reflecting the calibration than the observable situation.
In this context, updating of a state variable like snow covered area with remotely sensed (RS) information, cannot be expected to improve the simulations. A broad range of parameter sets are analysed, and with few exceptions confirm this conclusion. It is assumed that models can be adapted to RS updating, provided that such data are included in the calibration procedure, but this is not investigated.
Spaceborne microwave radiometer data are acquired at several frequencies and polarizations (channels). Information from various channels are used to estimate the snow water equivalent of dry snow using a semiempirical approach. Land-use categories and forest canopies are accounted for as they have a substantial effect on the measured values. During the snow melt season, spaceborne radar data are used to estimate the fraction of bare ground vs. wet snow using three radar images, one acquired at the time of interest, one before the melt season and one after the melt season (e.g. previous year). Both types of results help authorities and hydropower companies to follow the development of snow water equivalent and determine the amount of water available in the spring. Accounting for forest canopies is important as they tend to mask the underlying snow in radiometer results. For the first time, the effect of forest canopies has been determined over the frequency range of interest as a function of forest stem volume. In radiometer data interpretation, the effect of atmosphere is also accounted for.
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