Estimating degree-day factors from MODIS for snowmelt runoff modeling

He, Zhaofeng; Parajka, J.; Tian, Fuqiang; Blöschl, Günter

doi:10.5194/hess-18-4773-2014

Cited by 76 publications

(43 citation statements)

References 72 publications

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“…The values of the snowmelt coefficients are also about a half of typical literature values [11,28,46]: out of 16 sites, six yield snowmelt coefficients below 1 mm/ • C day, and only three above 2 mm/ • C day. The snowmelt coefficients are negatively and significantly correlated to the cumulative temperature (r = −0.73, p = 1.2E − 03).…”

Section: Snowmelt Coefficients and Snow Water Equivalents At Snow Depsupporting

confidence: 54%

On the Direct Calculation of Snow Water Balances Using Snow Cover Information

Pistocchi

Bagli²,

Callegari

et al. 2017

Water

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Abstract:We present a novel method for the direct determination of the snowmelt coefficient of widely used degree-day models, using only cumulated temperature and precipitation over the days of snow cover. We develop a proof of concept using (1) local measurements of precipitation, temperature and snow water equivalent (SWE) at a set of well-monitored sites in the US, and (2) available time series of snow cover from satellite and gridded daily precipitation and daily average temperature for the study region of South Tyrol, in the Italian Alps. We demonstrate how the method can reproduce the snow water balance to an acceptable extent, critically depending on the accuracy of input precipitation and temperature, highlighting the importance of a reliable representation of weather forcing if the estimate has to be robust and representative. Although not always accurate at a point, our approach yields a SWE reasonably consistent with observations, and snowmelt flows compatible with measured streamflow. At the same time, the model allows an interpretation of discrepancies between observations and simulations to detect inconsistencies between snow cover and weather forcing. This method is in principle applicable for large-scale hydrological assessments thanks to the increasing global coverage of snow cover, precipitation and temperature data. As the only other type of observation available to calibrate models is often streamflow, the direct calibration of the snow component of a model using snow cover and weather forcing reduces the number of model processes and parameters to be calibrated with streamflow, and is expected to increase model robustness.

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Section: Snowmelt Coefficients and Snow Water Equivalents At Snow Depsupporting

confidence: 54%

On the Direct Calculation of Snow Water Balances Using Snow Cover Information

Pistocchi

Bagli²,

Callegari

et al. 2017

Water

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“…The use of satellite imagery combined with groundbased snow depth data also enables to spatially estimate DDFs for simulating melt dynamics over extended areas (He et al, 2014). The improvement obtained with such combination of data sources can reduce uncertainty by taking advantage of additional data as part of the calibration procedure (Parajka and Blöschl, 2008).…”

Section: Discussionmentioning

confidence: 99%

“…Hassan (2012) used a similar approach for a 18 132 km 2 study area in northern Alberta (Canada) using moderate resolution imaging spectroradiometer (MODIS) data. Besides, other researches have used MODIS data for estimating DDF (He et al, 2014) and calibrating hydrological models (Parajka and Blöschl, 2008;Kahl, 2013). Other methods have also been used for snowpack reconstruction, including a Bayesian approach combined with remote sensing data at a similar spatial scale (Durand et al, 2008a(Durand et al, ,2008b.…”

Section: Introductionmentioning

confidence: 99%

Backward snow depth reconstruction at high spatial resolution based on time‐lapse photography

Revuelto

Jonas

López‐Moreno

2016

Hydrological Processes

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Abstract:We report a methodology for reconstructing the daily snow depth distribution at high spatial resolution in a small Pyrenean catchment using time-lapse photographs and snow depletion rates derived from an on-site measuring meteorological station. The results were compared with the observed snow depth distribution, determined on a number of separate occasions using a terrestrial laser scanner (TLS). The time-lapse photographs were projected onto a digital elevation model of the study site, and converted into snow presence/absence information. The melt-out date (MOD; first occurrence of melt out after peak snow accumulation) was obtained from the projected photograph series. Commencing the backward reconstruction for each grid cell at the MOD, the method uses simulated snow depth depletion rates using a temperature index approach, which are extrapolated to the grid cells of the domain to arrive at the snow distribution of the previous day. Two variants of the reconstruction techniques were applied (1) using a spatially constant degree day factor (DDF) for calculating the daily expected snow depth depletion rate, and (2) allowing a spatially distributed DDF calculated from two consecutive TLS acquisitions compared to the snow depth depletion rate observed at the meteorological station. Validation revealed that both methods performed well (average R 2 = 0.68; standard RMSE = 0.58), with better results obtained from the spatially distributed approach. Nevertheless, the spatially corrected DDF reconstruction, which requires TLS data, suggests that the constant DDF approach is an efficient, and for most applications sufficiently accurate and easily reproducible method. The results highlight the usefulness of time-lapse photography for not only determining the snow covered area, but also for estimating the spatial distribution of snow depth.

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“…Therefore, the use of methods applying one or more independent variables leads to applicable results as shown in our study. Promising results have been also reported using remote sensing approaches such as the use of MODIS satellite data (Duchacek, 2014;He et al, 2014;Krajčí et al, 2016;Parajka et al, 2012), aerial or terrestrial laser scanning (Grünewald et al, 2013;López-Moreno et al, 2015) and unmanned aerial systems (UAV) (De Michele et al, 2016;Lendzioch et al, 2016). We are now testing camera placed on UAV to monitor the snow depth (Lendzioch et al, 2016).…”

Section: Snow Sampling Designmentioning

confidence: 97%

Canopy structure and topography effects on snow distribution at a catchment scale: Application of multivariate approaches

Jeníček

Pevná

Matějka

2017

Journal of Hydrology and Hydromechanics

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Abstract:The knowledge of snowpack distribution at a catchment scale is important to predict the snowmelt runoff. The objective of this study is to select and quantify the most important factors governing the snowpack distribution, with special interest in the role of different canopy structure. We applied a simple distributed sampling design with measurement of snow depth and snow water equivalent (SWE) at a catchment scale. We selected eleven predictors related to character of specific localities (such as elevation, slope orientation and leaf area index) and to winter meteorological conditions (such as irradiance, sum of positive air temperature and sum of new snow depth). The forest canopy structure was described using parameters calculated from hemispherical photographs. A degree-day approach was used to calculate melt factors. Principal component analysis, cluster analysis and Spearman rank correlation were applied to reduce the number of predictors and to analyze measured data. The SWE in forest sites was by 40% lower than in open areas, but this value depended on the canopy structure. The snow ablation in large openings was on average almost two times faster compared to forest sites. The snow ablation in the forest was by 18% faster after forest defoliation (due to the bark beetle). The results from multivariate analyses showed that the leaf area index was a better predictor to explain the SWE distribution during accumulation period, while irradiance was better predictor during snowmelt period. Despite some uncertainty, parameters derived from hemispherical photographs may replace measured incoming solar radiation if this meteorological variable is not available.

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Estimating degree-day factors from MODIS for snowmelt runoff modeling

Cited by 76 publications

References 72 publications

On the Direct Calculation of Snow Water Balances Using Snow Cover Information

On the Direct Calculation of Snow Water Balances Using Snow Cover Information

Backward snow depth reconstruction at high spatial resolution based on time‐lapse photography

Canopy structure and topography effects on snow distribution at a catchment scale: Application of multivariate approaches

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