Combined use of VEGETATION and RADARSAT data for updating areal distribution and water equivalent of snow cover , in the HYDROTEL hydrological forecasting model by (original) (raw)
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Determination of snow water equivalent using RADARSAT SAR data in eastern Canada
Hydrological Processes, 1999
In the 1998-1999 winter, the operational feasibility of using RADARSAT SAR data to estimate the spatial distribution of snow water equivalent (SWE) in a large hydroelectric complex managed by Hydro-Que bec (La Grande River watershed) has been successfully demonstrated. This watershed is located in the subarctic climatic region in the north-west of the Que bec province. The vegetation consists of moderately dense to open Black Spruce forests, open lands, burned lands and peat bogs. In the last few years, an original approach well adapted for this region has been developed to estimate the SWE from SAR data (ERS-1, RADARSAT). This approach is based on the fact that the snow cover characteristics in¯uence the underlying soil temperature which in¯uences the dielectric properties of the soil and then the recorded backscattering signal. Then, a linear relationship between the backscattering ratios of a winter image and a snow-free ( fall) image, and the snowpack thermal resistance (thermal insulation properties) has been established. Consequently, the algorithm infers the SWE from the estimated thermal resistance and the measured mean density of the snowpack. This algorithm has been implemented within a MapInfo 2 application that has been named EQeau. It allows mapping of the spatial distribution of the estimated SWE at the desired level ( pixel, square grid, sub-watershed). During the 1998-1999 winter, EQeau has been used successfully in a pre-operational mode using calibrated Wide beam images (W1) from RADARSAT. The algorithm has given mean estimated SWE values similar to the SWE values derived from Hydro-Quebec snow transects (relative dierence between 1% and 13%). Also, the SWE increase measured from January to March 1999 is clearly detected on the maps covering almost 77 000 km 2 . The next steps will be the evaluation of the ScanSAR images and the demonstration of the economical advantages of using RADARSAT data in a hydrological forecasting system.
Snow Cover Area Estimation Using Radar and Optical Satellite Information
Atmospheric and Climate Sciences, 2014
Obtaining the seasonal variation of snow cover in areas of the Argentinian Andes is important for hydrological studies and can facilitate proper planning of water resources, with regard to irrigation, supply, flood attenuation and hydroelectricity. Remote sensors that work in the visible and infrared wavelength range are operational tools for monitoring the snow in clear skies. However, microwave satellites are able to obtain data regardless of atmospheric conditions. The advantage of using radar images is that they are very useful to obtain highly accurate parameters such as snow moisture depth, density and water equivalent resulting in improved forecasting models. In this paper, we analyze an ERS-2 image of the Andes mountain range in the northern region of the Neuquén province, Patagonia, Argentina. The objective was to obtain the spatial distribution of wet and dry snow and to compare these results with data from optical sensors (LANDSAT) in order to understand the topographic variables that influence the spatial distribution of wet snow. Optical information from sensors like LANDSAT TM 5 was analyzed to obtain fractional and binary snow indexes during a passage simultaneously with radar data. Surface temperature is used to study the association between the different types of snow altitudinal ranges and surface temperature. In this paper, we selected a scene on October 8th 2005. The entire methodology was systematized in a code implemented in IDL language.
2006 IEEE International Symposium on Geoscience and Remote Sensing, 2006
Information on physical snow cover characteristics, such as snow water equivalent (SWE) and the areal coverage fraction of snow covered area (SCA), can be obtained from spaceborne remote sensing data. The feasible instruments include optical spectrometers and microwave radars (SCA mapping), and microwave radiometers (SWE mapping). As data assimilation techniques are applied, the EO data-derived information can improve the performance of river discharge forecasting models and the knowledge on snow climatology. The results discussed here indicate that the assimilation of EO databased SCA estimates to hydrological modeling significantly improves the accuracy of operational river discharge forecasts. The results also indicate that the employment of space-borne microwave radiometer data using the data assimilation technique improves the SWE or snow depth mapping accuracy when compared with the use of values interpolated from synoptic observations.
Operative estimation of snow covered area for the needs of hydrological modelling
… for European-wide …, 2003
In hydrological cycle, snow acts as a seasonal water storage, from where water is released during the melting period. Predicting this process is important for flood prevention and hydropower industry. It is accomplished by hydrological models, where the areal extent and the water equivalence of the snow pack are often included as state variables. The problem with the models is the low spatial and/or temporal resolution of input data. This defect can be alleviated by employing remote sensing techniques to produce spatially (and temporally) well-distributed information on snow cover and taking this information into the model. Optical and near-infrared sensors are useful for monitoring the extent of snow cover. We have developed a feasible remote sensing method for operative monitoring of snow covered area (SCA), with the aim of using the results in hydrological forecasting. The implementation of the method was carried out for NOAA/AVHRR data, starting from melting period 2001. SCA estimates are calculated whenever cloudiness permitting and published also as thematic snow maps in WWW-pages of the Finnish Environment Institute. The operative Finnish national hydrological modelling system is currently under modification in order to optimally utilize the remote sensing based information on SCA.
Hydrology and Earth System Sciences, 2010
A technique of using satellite-derived data for constructing continuous snow characteristics fields for distributed snowmelt runoff simulation is presented. The satellite-derived data and the available ground-based meteorological measurements are incorporated in a physically based snowpack model. The snowpack model describes temporal changes of the snow depth, density and water equivalent (SWE), accounting for snow melt, sublimation, refreezing melt water and snow metamorphism processes with a special focus on forest cover effects. The remote sensing data used in the model consist of products include the daily maps of snow covered area (SCA) and SWE derived from observations of MODIS and AMSR-E instruments onboard Terra and Aqua satellites as well as available maps of land surface temperature, surface albedo, land cover classes and tree cover fraction. The model was first calibrated against available ground-based snow measurements and then applied to calculate the spatial distribution of snow characteristics using satellite data and interpolated ground-based meteorological data. The satellite-derived SWE data were used for assigning initial conditions and the SCA data were used for control of snow cover simulation. The simulated spatial distributions of snow characteristics were incorporated in a distributed physically based model of runoff generation to calculate snowmelt runoff hydrographs. The presented technique was applied to a study area of approximately 200 000 km 2 including the Vyatka River basin with catchment area of 124 000 km 2 . The Correspondence to: A. Gelfan (hydrowpi@aqua.laser.ru) correspondence of simulated and observed hydrographs in the Vyatka River are considered as an indicator of the accuracy of constructed fields of snow characteristics and as a measure of effectiveness of utilizing satellite-derived SWE data for runoff simulation.
Hydrology and Earth System Sciences Discussions, 2009
A new technique for constructing spatial fields of snow characteristics for runoff simulation and forecasting is presented. The technique incorporates satellite land surface monitoring data and available ground-based hydrometeorological measurements in a physical based snowpack model. The snowpack model provides simulation of tem-5 poral changes of the snow depth, density and water equivalent (SWE), accounting for snow melt, sublimation, refreezing melt water and snow metamorphism processes with a special focus on forest cover effects. The model was first calibrated against available ground-based snow measurements and then was applied to calculate the spatial distribution of snow characteristics using satellite data and interpolated ground-based 10 25 5506 HESSD 6, 5505-5536Abstract Introduction Conclusions References Tables Figures Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion 25 mapping due to snow interception and ability to mask snow cover on the forest floor. A possible way to improve characterization of the snow cover spatial distribution and temporal variability consists in coupling satellite data with ground-based hydromete-5507 HESSD 6, 5505-5536Abstract 20 5511 HESSD 6, 5505-5536
Experiences from Real Time Runoff Forecasts by Snow Cover Remote Sensing
Seasonal and short-term runoff forecasts for two hydroelectric stations in the upper Rhine basin are carried out in real time based on snow cover monitoring by Landsat and SPOT satellites. Evaluation of snow reserves on 1 April 1993 from satellite data reveals uncertainties in estimates using point measurements on the ground as index. Runoff is computed by the SRM model with snow covered areas as well as temperature and precipitation forecasts as input variables. These runoff forecasts can be exploited, among other purposes, for optimizing the hydropower production and for timely decisions on the electricity market.
Hydrological Processes, 2012
The ecological situation of the Tarim River basin in China seriously declined since the early 1950s, mainly due to a strong increase in water abstraction for irrigation purposes. To restore the ecological system and support sustainable development of the Tarim River basin region in China, more hydrological studies are demanded to properly understand the processes of the watershed and efficiently manage the water resources. Such studies are, however, complicated due to the limited data availability, especially in the mountainous headwater regions of the Tarim River basin. This study investigated the usefulness of remote sensing (RS) data to overcome that lack of data in the spatially distributed hydrological modelling of the basin. Complementary to the conventional station-based (SB) data, the RS products that are directly used in this study include precipitation, evapotranspiration and leaf area index. They are derived from raw image data of the Chinese Fengyun meteorological satellite and from the Moderate Resolution Imaging Spectroradiometer (MODIS). The MODIS land surface temperature was used to calculate the atmospheric temperature lapse rate to describe the temperature dependency on topographical variations. Moreover, MODIS-based snow cover images were used to obtain model initial conditions and as validation reference for the snow model component. Comparison of model results based on RS input versus conventional SB input exhibited similar results in terms of high and low river runoff extremes, cumulative runoff volumes in both runoff and snow melting seasons and spatial and temporal variability of snow cover. During summer time, when the snow cover shrinks in the permanent glacier region, it was found that the model resolution influences the model results dramatically, hence, showing the importance of detailed (RS based) spatially distributed input data.
Potential and limitations of RADARSAT SAR data for wet snow monitoring
IEEE Transactions on Geoscience and Remote Sensing, 2000
Based on Canadian Satellite (RADARSAT) synthetic aperture radar (SAR) images and simulations from a radar-backscattering model, we determined that conventional wet snow-mapping algorithms should perform optimally for a snowpack with a liquid-water content 3%, at low incidence angle ( = 20-30 ) and for a rather smooth surface (rms height ≤ 2.1 mm).
Comparison of Satellite-Based Data with Modeled Snow–Water Equivalent for Open and Forested Areas
Interpreting microwave-frequency data acquired from the Special Sensor Microwave Imager (SSM/I) for snow–water equivalent is difficult because of the large number of factors that affect the microwave signal. So far, there has been limited success in the interpretation of the SSM/I data for the forested northern areas of Ontario. In this study, the effect of the different channels of the microwave signal, precipitation, air temperature and time of satellite overpass was investigated. Brightness temperature difference (BTD), multivariable linear regression (MVLR) and principle component analyses (PCA) were performed over the winter period of 1998–1999. Interpretation of the SSM/I data was improved when analyzed over separate snow periods (accumulation, transition and melt) rather than over the full winter data set. The MVLR and PCA methods show that the use of additional SSM/I channels can improve the satellite data interpretation as the R 2 value improved from 0.467 to 0.931 and from...