Toward Snowmelt Runoff Forecast Based on Multisensor Remote-Sensing Informnation

Baumgartner, Michael F.; Seidel, K.; Martinec, J.

doi:10.1109/tgrs.1987.289744

Cited by 26 publications

(11 citation statements)

References 4 publications

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“…Since the early 1980s, it has been shown that space-borne sensors operating in the visible and near-infrared region of the electromagnetic spectrum are very effective to map snow cover (Bowley et al, 1981;Dozier and Marks, 1987;Baumgartner et al, 1987). As of today, only low-to mid-resolution sensors such as AVHRR, PROBA-V or MODIS allow for global observations of the snow cover at a daily time step (without cloud obscuration) with a spatial resolution of 1 km to 250 m. Higher-resolution snow cover maps (30 m) are typically extracted from the Landsat program images, but they provide data at a lower frequency (16 days), which is generally inappropriate to monitor the snow cover as large snow area variations can occur within a few days during the melt season (Rango, 1993;Gómez-Landesa and Rango, 2002).…”

Section: Introductionmentioning

confidence: 99%

A snow cover climatology for the Pyrenees from MODIS snow products

Gascoin

Hagolle

Huc

et al. 2015

Hydrol. Earth Syst. Sci.

146

118

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Abstract. The seasonal snow in the Pyrenees is critical for hydropower production, crop irrigation and tourism in France, Spain and Andorra. Complementary to in situ observations, satellite remote sensing is useful to monitor the effect of climate on the snow dynamics. The MODIS daily snow products (Terra/MOD10A1 and Aqua/MYD10A1) are widely used to generate snow cover climatologies, yet it is preferable to assess their accuracies prior to their use. Here, we use both in situ snow observations and remote sensing data to evaluate the MODIS snow products in the Pyrenees. First, we compare the MODIS products to in situ snow depth (SD) and snow water equivalent (SWE) measurements. We estimate the values of the SWE and SD best detection thresholds to 40 mm water equivalent (w.e.) and 150 mm, respectively, for both MOD10A1 and MYD10A1. κ coefficients are within 0.74 and 0.92 depending on the product and the variable for these thresholds. However, we also find a seasonal trend in the optimal SWE and SD thresholds, reflecting the hysteresis in the relationship between the depth of the snowpack (or SWE) and its extent within a MODIS pixel. Then, a set of Landsat images is used to validate MOD10A1 and MYD10A1 for 157 dates between 2002 and 2010. The resulting accuracies are 97 % (κ = 0.85) for MOD10A1 and 96 % (κ = 0.81) for MYD10A1, which indicates a good agreement between both data sets. The effect of vegetation on the results is analyzed by filtering the forested areas using a land cover map. As expected, the accuracies decrease over the forests but the agreement remains acceptable (MOD10A1: 96 %, κ = 0.77; MYD10A1: 95 %, κ = 0.67). We conclude that MODIS snow products have a sufficient accuracy for hydroclimate studies at the scale of the Pyrenees range. Using a gap-filling algorithm we generate a consistent snow cover climatology, which allows us to compute the mean monthly snow cover duration per elevation band and aspect classes. There is snow on the ground at least 50 % of the time above 1600 m between December and April. We finally analyze the snow patterns for the atypical winter 2011-2012. Snow cover duration anomalies reveal a deficient snowpack on the Spanish side of the Pyrenees, which seems to have caused a drop in the national hydropower production.

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Section: Introductionmentioning

confidence: 99%

A snow cover climatology for the Pyrenees from MODIS snow products

Gascoin

Hagolle

Huc

et al. 2015

Hydrol. Earth Syst. Sci.

146

118

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“…To control classification accuracy, a parallel snow classification was carried out at two selected test sites, using high-resolution Landsat-MSS data (56 m * 79 m instead of 1.1 km * 1.1 km when using NOAA/AVHRR). Earlier studies have shown that such a control can help to prevent systematic classification errors when using coarse resolution satellite systems (Baumgartner et al, 1987;Baumgartner & Seidel, 1988). The control classifications showed that the classification accuracy with NOAA/AVHRR data was very reliable and no corrections had to be made.…”

Section: Methods and Datamentioning

confidence: 99%

Monitoring the regional and temporal variability of winter snowfall in the arid Andes using digital NOAA/AVHRR data

Vuille¹,

Baumgartner²

1998

Geocarto International

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This paper deals with the spatial distribution and the temporal variability of snowfall in the most arid part of the Andes (18°-28°S) during southern hemisphere winter (May-September). As the official precipitation data is of poor quality, analyses were carried out by means of digital image processing techniques, using NOAA/AVHRR satellitedata. Through analysis of 24 different snowfall events from six winters, a previously unknown spatial and temporal precipitation pattern in this remote and unexplored area was revealed. Snowfall is most abundant in the southernmost part of the research area and on the western side of the Andes, indicating the Pacific origin of the snowfall.Nevertheless, the typical snowfall pattern is modified during different periods of the winter. Three typical time periods could be defined and distinguished from one another. Each of these three periods is characterized by typical weather conditions (cold fronts and "cut-offs ") leading to a distinct snowfall pattern.As this study is part of a broader paleoclimatic project, the results will serve as a basis for paleoclimatic reconstruction of past climate. Only by knowing the modern circulation and precipitation patterns is it possible to interpret paleoclimatic signals and archives found in the study area (e.g. paleosol, moraines) correctly.

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“…The utilization of snow-cover information as an important source of data for runoff prediction started in 1930s with the use of aerial photographs (Potts, 1937). Many daily regional-scale satellite-derived estimates of snow-covered area (SCA) have become available since 1972 with the advent of National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) (Rango, 1986(Rango, , 1996 and have been serving as input into snowmelt runoff models or weather prediction models around the world (Rango, 1980;Dey et al, 1983;Baumgartner et al, 1987;Richard and Gratton, 2001;Landesa and Rango, 2002). Especially in data-sparse regions such as the Himalayan or Andean mountains, satellitederived SCA information is the best routinely available SCA input for snowmelt runoff estimates (Rango, 1985;Compagnucci and Vargas, 1998).…”

Section: Introductionmentioning

confidence: 99%

A comparison of MODIS and NOHRSC snow‐cover products for simulating streamflow using the Snowmelt Runoff Model

Lee

Klein

Over

2005

Hydrological Processes

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Abstract:Remote sensing is an important source of snow-cover extent for input into the Snowmelt Runoff Model (SRM) and other snowmelt models. Since February 2000, daily global snow-cover maps have been produced from data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS). The usefulness of this snow-cover product for streamflow prediction is assessed by comparing SRM simulated streamflow using the MODIS snow-cover product with streamflow simulated using snow maps from the National Operational Hydrologic Remote Sensing Center (NOHRSC). Simulations were conducted for two tributary watersheds of the Upper Rio Grande basin during the 2001 snowmelt season using representative SRM parameter values. Snow depletion curves developed from MODIS and NOHRSC snow maps were generally comparable in both watersheds: satisfactory streamflow simulations were obtained using both snow-cover products in larger watershed (volume difference: MODIS, 2Ð6%; NOHRSC, 14Ð0%) and less satisfactory streamflow simulations in smaller watershed (volume difference: MODIS, 33Ð1%; NOHRSC, 18Ð6%). The snow water equivalent (SWE) on 1 April in the third zone of each basin was computed using the modified depletion curve produced by the SRM and was compared with in situ SWE measured at Snowpack Telemetry sites located in the third zone of each basin. The SRM-calculated SWEs using both snow products agree with the measured SWEs in both watersheds. Based on these results, the MODIS snow-cover product appears to be of sufficient quality for streamflow prediction using the SRM in the snowmelt-dominated basins.

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Toward Snowmelt Runoff Forecast Based on Multisensor Remote-Sensing Informnation

Cited by 26 publications

References 4 publications

A snow cover climatology for the Pyrenees from MODIS snow products

A snow cover climatology for the Pyrenees from MODIS snow products

Monitoring the regional and temporal variability of winter snowfall in the arid Andes using digital NOAA/AVHRR data

A comparison of MODIS and NOHRSC snow‐cover products for simulating streamflow using the Snowmelt Runoff Model

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