Use of remote sensing to test and update simulated snow cover in hydrological models

Turpin, Owen; Ferguson, Rob; Johansson, Barbro

doi:10.1002/(sici)1099-1085(199909)13:12/13<2067::aid-hyp886>3.0.co;2-x

Cited by 19 publications

(5 citation statements)

References 18 publications

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“…HBV allows for variable SWE within elevation zones (Lindstrom and others, 1997; Turpin and others, 1999) by separating SWE into a number of equal-area subzones whose SWE values form an arithmetic series centred on the zone mean (Fig. 2).…”

Section: Conceptual Snowpack Modelling Within Hbvmentioning

confidence: 99%

“…The timing and volume of runoff from predominantly snow-covered mountainous basins is determined by the interplay between snow accumulation and seasonal heat energy input to the snowpack. Conceptual snowmelt-runoff models must either assume a mean snow-water equivalent (SWE) and distribution at the beginning of the modelled melt season or accumulate snowfall throughout the preceding winter (Turpin and others, 1999). The latter approach is taken by many general-purpose conceptual hydrological models with a snow routine.…”

Section: Introductionmentioning

confidence: 99%

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Verification of simulated snow cover in an Arctic basin using satellite-derived snow-cover maps

et al. 2000

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Time series of Earth observation (EO) data (Landsat Thematic Mapper (TM), U.S. National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) and European Remote-sensing Satellite synthetic-aperture radar (ERS SAR)) were obtained for a 2250 km2 mountainous basin in northern Sweden to validate snow-cover area (SCA) estimates produced by a conceptual model (HBV) during three melt seasons. SCA depletion curves derived for each image type, arid coincident images, reveal that the SCA estimate varies with the sensor. Discrepancies betweenTM and AVHRR appear to be an effect of spatial resolution. However, differences betweenTM and SAR are not simply related. Since more AVHRR thanTM data were available, a TM-equivalent SCA was derived from AVHRR by relating TM SCA to AVHRR channel 1 reflectance. The TM-equivalent SCA was used to test SCA simulated by HBV for the 1992 melt season. Although the modelled and TM-equivalent SCA were in reasonable agreement, the modelled SCA declined faster than the TM-equivalent SCA. Partial recalibration of model parameters controlling snowpack accumulation improved the match between the modelled and EO-derived SCA decline. The recalibrated parameters were verified using SCA maps generated for the 1996 and 1998 melt seasons. The adjusted parameter sets had little effect on the Nash-Sutcliffe R2 runoff fit but improved the volume fit in all three years.

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Section: Conceptual Snowpack Modelling Within Hbvmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Verification of simulated snow cover in an Arctic basin using satellite-derived snow-cover maps

et al. 2000

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“…Given the time‐static drivers of snow accumulation and snowmelt discussed above, many studies have found interannually‐persistent relationships between a region's 1) snow cover, and 2) the evolution of snow volume and variability (Liston, 2004; Luce & Tarboton, 2004; Pflug & Lundquist, 2020; Shamir & Georgakakos, 2007). This relationship has even been used as the basis for model assimilation strategies, which employ remotely sensed observations of snow cover to update and parametrize snow states (Clark et al, 2006; Gómez‐Landesa & Rango, 2002; McGuire et al, 2006; Turpin et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

Inferring watershed‐scale mean snowfall magnitude and distribution using multidecadal snow reanalysis patterns and snow pillow observations

Pflug

Margulis

Lundquist

2022

Hydrological Processes

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The magnitude and spatial heterogeneity of snow are difficult to model in mountainous terrain. We investigated how historic snow patterns from a 32-year (1985-2016) snow reanalysis, which uses the timing and rate of remotely-sensed snow disappearance to reconstruct snow water equivalent in past years, could be used to improve current year simulations of snowfall in the Upper Tuolumne, Upper Kings, and Sagehen Creek watersheds in California's Sierra Nevada. Building on past research linking observations of fractional snow-covered area (fSCA) to spatial patterns of

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“…Turpin et al () have pointed out that the calibration of snowmelt runoff and glacier runoff models using flow data alone can lead to maladjustment of parameters so that errors in the estimation of different parameters cancel out each other. This means that snowmelt runoff and glacier runoff models may produce erroneous evaluations of the melting, even if the total discharge is well reproduced, when the melting process is not well simulated.…”

Section: Introductionmentioning

confidence: 99%

Melted snow volume control in the snowmelt runoff model using a snow water equivalent statistically based model

Bavera

Michele

Pepe³

et al. 2012

Hydrological Processes

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Abstract:Snowmelt is an important component of the river discharge in mountain environments. In the past 40 years, the snowmelt dynamics has been mostly evaluated using degree-day-based models like the snowmelt runoff model (SRM). This model has no control on the volume of the melting snow, even if SRM includes as data input the snow-covered area. This lack explains why the application of SRM may lead to inaccurate snowmelt volume estimations, even if the discharge volumes are accurately reproduced. Here we introduce in SRM the control on the melted snow volume and consider it in the determination of SRM parameters. The total snow volume, accumulated at the end of winter season, is evaluated by a snow water equivalent statistically based model, SWE-SEM, and used as an estimate of the melting snow during the summer season. The benefit derived from the introduction of the control on the melting snow volume was investigated in the Mallero basin (northern Italy) for the 2003 and 2004 snow melting seasons. The analysis compares the model's results adopting different parameter sets, both considering and ignoring the control on the melting snow volume.

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Use of remote sensing to test and update simulated snow cover in hydrological models

Cited by 19 publications

References 18 publications

Verification of simulated snow cover in an Arctic basin using satellite-derived snow-cover maps

Verification of simulated snow cover in an Arctic basin using satellite-derived snow-cover maps

Inferring watershed‐scale mean snowfall magnitude and distribution using multidecadal snow reanalysis patterns and snow pillow observations

Melted snow volume control in the snowmelt runoff model using a snow water equivalent statistically based model

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