Introducing Hysteresis in Snow Depletion Curves to Improve the Water Budget of a Land Surface Model in an Alpine Catchment

Magand, Claire; Ducharne, Agnès; Moine, Nicolás Le; Gascoin, Simon

doi:10.1175/jhm-d-13-091.1

Cited by 44 publications

(51 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…5 for the case of the SD). This result is interesting because it reflects the hysteresis in the relationship between the amount of snow on the ground and its extent, which was often observed in alpine catchments (e.g., Magand et al, 2014). Small snow depths can cover large areas during the accumulation period.…”

Section: In Situ Data Vs Modis Productsmentioning

confidence: 58%

A snow cover climatology for the Pyrenees from MODIS snow products

Gascoin

Hagolle

Huc

et al. 2015

Hydrol. Earth Syst. Sci.

146

118

View full text Add to dashboard Cite

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.

show abstract

Section: In Situ Data Vs Modis Productsmentioning

confidence: 58%

A snow cover climatology for the Pyrenees from MODIS snow products

Gascoin

Hagolle

Huc

et al. 2015

Hydrol. Earth Syst. Sci.

146

118

View full text Add to dashboard Cite

show abstract

“…In spring, the SWE is systematically overestimated except over the Lena, whose seasonal cycle is well reproduced by ORCHIDEE-MICT. This corresponds to an excessive persistence of the snow cover, which may be explained by the absence of hysteresis in the snow depletion curve relating the snow cover extent and the SWE (e.g., Magand et al, 2014). In the Yenisei and Mackenzie basins, the SWE in winter is closer to observations with the GSWP3 forcing, with the exception of springtime values, which are better under CRUNCEP forcing.…”

Section: Snow-related Processes Controlling Land Water Storage In Thementioning

confidence: 90%

ORCHIDEE-MICT (v8.4.1), a land surface model for the high latitudes: model description and validation

et al. 2018

Self Cite

View full text Add to dashboard Cite

Abstract. The high-latitude regions of the Northern Hemisphere are a nexus for the interaction between land surface physical properties and their exchange of carbon and energy with the atmosphere. At these latitudes, two carbon pools of planetary significance -those of the permanently frozen soils (permafrost), and of the great expanse of boreal forestare vulnerable to destabilization in the face of currently observed climatic warming, the speed and intensity of which are expected to increase with time. Improved projections of future Arctic and boreal ecosystem transformation require improved land surface models that integrate processes specific to these cold biomes. To this end, this study lays out relevant new parameterizations in the ORCHIDEE-MICT land surface model. These describe the interactions between soil carbon, soil temperature and hydrology, and their resulting feedbacks on water and CO 2 fluxes, in addition to a recently developed fire module. Outputs from ORCHIDEE-MICT, when forced by two climate input datasets, are extensively evaluated against (i) temperature gradients between the atmosphere and deep soils, (ii) the hydrological components comprising the water balance of the largest highlatitude basins, and (iii) CO 2 flux and carbon stock observations. The model performance is good with respect to empirical data, despite a simulated excessive plant water stress andPublished by Copernicus Publications on behalf of the European Geosciences Union. 122M. Guimberteau et al.: ORCHIDEE-MICT, a LSM for the high latitudes a positive land surface temperature bias. In addition, acute model sensitivity to the choice of input forcing data suggests that the calibration of model parameters is strongly forcingdependent. Overall, we suggest that this new model design is at the forefront of current efforts to reliably estimate future perturbations to the high-latitude terrestrial environment.

show abstract

“…Snow density generally increases with the increasing of snow age. We know that there is in fact a hysteresis in the relationship between snow water equivalent and snow depth (Magand et al, 2014): during accumulation days, snowfall occurs all over the catchment, and the mean snow depth tends to increase quickly and uniformly over the catchment. By contrast, during the ablation days, snowmelt tends to occur in preferential locations due to variability of topography and vegetation in the catchment.…”

Section: Discussionmentioning

confidence: 99%

“…Singh and Kumar (1996) and Singh et al (2000) demonstrated a seasonal decrease of DDF S with increasing albedo due to seasonal changes of land surface characteristics. Spatial variations of basin topography, such as elevation, terrain slope, aspect and terrain shading change the spatial energy conditions for snowmelt and lead to significant variations of DDF S (Marsh et al, 2012;Bormann et al, 2014). Generally, regions with a large contribution of sensible heat flux to the heat balance tend to have low degree-day factors (Hock, 2003).…”

Section: Z H He Et Al: Estimating Degree-day Factors From Modis Fomentioning

confidence: 99%

Estimating degree-day factors from MODIS for snowmelt runoff modeling

Parajka

Tian

et al. 2014

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Degree-day factors are widely used to estimate snowmelt runoff in operational hydrological models. Usually, they are calibrated on observed runoff, and sometimes on satellite snow cover data. In this paper, we propose a new method for estimating the snowmelt degree-day factor (DDF S ) directly from MODIS snow covered area (SCA) and ground-based snow depth data without calibration. Subcatchment snow volume is estimated by combining SCA and snow depths. Snow density is estimated to be the ratio between observed precipitation and changes in the snow volume for days with snow accumulation. Finally, DDF S values are estimated to be the ratio between changes in the snow water equivalent and difference between the daily temperature and the melt threshold value for days with snow melt. We compare simulations of basin runoff and snow cover patterns using spatially variable DDF S estimated from snow data with those using spatially uniform DDF S calibrated on runoff. The runoff performances using estimated DDF S are slightly improved, and the simulated snow cover patterns are significantly more plausible. The new method may help reduce some of the runoff model parameter uncertainty by reducing the total number of calibration parameters. This method is applied to the Lienz catchment in East Tyrol, Austria, which covers an area of 1198 km 2 . Approximately 70 % of the basin is covered by snow in the early spring season.

show abstract

Introducing Hysteresis in Snow Depletion Curves to Improve the Water Budget of a Land Surface Model in an Alpine Catchment

Cited by 44 publications

References 49 publications

A snow cover climatology for the Pyrenees from MODIS snow products

A snow cover climatology for the Pyrenees from MODIS snow products

ORCHIDEE-MICT (v8.4.1), a land surface model for the high latitudes: model description and validation

Estimating degree-day factors from MODIS for snowmelt runoff modeling

Contact Info

Product

Resources

About