MODIS Does Not Capture the Spatial Heterogeneity of Snow Cover Induced by Solar Radiation

Estimating snowmelt in semi-arid mountain ranges is an important but challenging task, due to the large spatial variability of the snow cover and scarcity of field observations. Adding solar radiation as snowmelt predictor within empirical snow models is often done to account for topographically induced variations in melt rates. This study examines the added value of including different treatments of solar radiation within empirical snowmelt models and benchmarks their performance against MODIS snow cover area (SCA) maps over the 2003-2016 period. Three spatially distributed, enhanced temperature index models that, respectively, include the potential clear-sky direct radiation, the incoming solar radiation and net solar radiation were compared with a classical temperature-index (TI) model to simulate snowmelt, SWE and SCA within the Rheraya basin in the Moroccan High Atlas Range. Enhanced models, particularly that which includes net solar radiation, were found to better explain the observed SCA variability compared to the TI model. However, differences in model performance in simulating basin wide SWE and SCA were small. This occurs because topographically induced variations in melt rates simulated by the enhanced models tend to average out, a situation favored by the rather uniform distribution of slope aspects in the basin. While the enhanced models simulated more heterogeneous snow cover conditions, aggregating the simulated SCA from the 100 m model resolution towards the MODIS resolution (500 m) suppresses key spatial variability related to solar radiation, which attenuates the differences between the TI and the radiative models. Our findings call for caution when using MODIS for calibration and validation of spatially distributed snow models.

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A distributed temperature-index ice- and snowmelt model including potential direct solar radiation

Journal of Glaciology ◽

10.1017/s0022143000003087 ◽

1999 ◽

Vol 45 (149) ◽

pp. 101-111 ◽

Cited By ~ 231

Author(s):

Regine Hock

Keyword(s):

Solar Radiation ◽

Global Radiation ◽

Model Performance ◽

Direct Solar Radiation ◽

Balance Model ◽

Temperature Index ◽

Index Model ◽

Clear Sky ◽

Degree Day ◽

Daily Discharge

AbstractHourly melt and discharge of Storglaciären, a small glacier in Sweden, were computed for two melt seasons, applying temperature-index methods to a 30 m resolution grid for the melt component. The classical degree-day method yielded a good simulation of the seasonal patient of discharge, but the pronounced melt-induced daily discharge cycles were not captured. Modelled degree-day factors calculated for every hour and each gridcell from melt obtained from a distributed energy-balance model varied substantially, both diurnally and spatially. A new distributed temperature-index model is suggested, attempting to capture both the pronounced diurnal melt cycles and the spatial variations in melt due to the effects of surrounding topography. This is accomplished by including a radiation index in terms of potential clear-sky direct solar radiation, and thus, without the need for other data besides air temperature. This approach improved considerably the simulation of diurnal discharge fluctuations and yielded a more realistic spatial distribution of melt rates. The incorporation of measured global radiation to account for the reduction in direct solar radiation due to cloudiness did not lead to additional improvement in model performance.

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A distributed temperature-index ice- and snowmelt model including potential direct solar radiation

Journal of Glaciology ◽

10.3189/s0022143000003087 ◽

1999 ◽

Vol 45 (149) ◽

pp. 101-111 ◽

Cited By ~ 175

Author(s):

Regine Hock

Keyword(s):

Solar Radiation ◽

Global Radiation ◽

Model Performance ◽

Direct Solar Radiation ◽

Balance Model ◽

Temperature Index ◽

Index Model ◽

Clear Sky ◽

Degree Day ◽

Daily Discharge

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Information content of stream level class data for hydrological model calibration

Hydrology and Earth System Sciences ◽

10.5194/hess-21-4895-2017 ◽

2017 ◽

Vol 21 (9) ◽

pp. 4895-4905 ◽

Cited By ~ 23

Author(s):

H. J. Ilja van Meerveld ◽

Marc J. P. Vis ◽

Jan Seibert

Keyword(s):

Time Series ◽

Citizen Science ◽

Model Calibration ◽

Hydrological Model ◽

Model Performance ◽

Added Value ◽

Distributed Data ◽

Vertical Resolution ◽

Level Data ◽

Spatially Distributed

Abstract. Citizen science can provide spatially distributed data over large areas, including hydrological data. Stream levels are easier to measure than streamflow and are likely also observed more easily by citizen scientists than streamflow. However, the challenge with crowd based stream level data is that observations are taken at irregular time intervals and with a limited vertical resolution. The latter is especially the case at sites where no staff gauge is available and relative stream levels are observed based on (in)visible features in the stream, such as rocks. In order to assess the potential value of crowd based stream level observations for model calibration, we pretended that stream level observations were available at a limited vertical resolution by transferring streamflow data to stream level classes. A bucket-type hydrological model was calibrated with these hypothetical stream level class data and subsequently evaluated on the observed streamflow records. Our results indicate that stream level data can result in good streamflow simulations, even with a reduced vertical resolution of the observations. Time series of only two stream level classes, e.g. above or below a rock in the stream, were already informative, especially when the class boundary was chosen towards the highest stream levels. There was some added value in using up to five stream level classes, but there was hardly any improvement in model performance when using more level classes. These results are encouraging for citizen science projects and provide a basis for designing observation systems that collect data that are as informative as possible for deriving model based streamflow time series for previously ungauged basins.

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Quantifying the Added Value of Snow Cover Area Observations in Passive Microwave Snow Depth Data Assimilation

Journal of Hydrometeorology ◽

10.1175/jhm-d-15-0021.1 ◽

2015 ◽

Vol 16 (4) ◽

pp. 1736-1741 ◽

Cited By ~ 30

Author(s):

Sujay V. Kumar ◽

Christa D. Peters-Lidard ◽

Kristi R. Arsenault ◽

Augusto Getirana ◽

David Mocko ◽

...

Keyword(s):

Data Assimilation ◽

Snow Cover ◽

Land Surface ◽

Snow Depth ◽

Accurate Determination ◽

Passive Microwave ◽

Added Value ◽

Surface Model ◽

Snow Cover Area ◽

The Impact

Abstract Accurate determination of snow conditions is important for several water management applications, partly because of the significant influence of snowmelt on seasonal streamflow prediction. This article examines an approach using snow cover area (SCA) observations as snow detection constraints during the assimilation of snow depth retrievals from passive microwave sensors. Two different SCA products [the Interactive Multisensor Snow and Ice Mapping System (IMS) and the Moderate Resolution Imaging Spectroradiometer (MODIS)] are employed jointly with the snow depth retrievals from a variety of sensors for data assimilation in the Noah land surface model. The results indicate that the use of MODIS data is effective in obtaining added improvements (up to 6% improvement in aggregate RMSE) in snow depth fields compared to assimilating passive microwave data alone, whereas the impact of IMS data is small. The improvements in snow depth fields are also found to translate to small yet systematic improvements in streamflow estimates, especially over the western United States, the upper Missouri River, and parts of the Northeast and upper Mississippi River. This study thus demonstrates a simple approach for exploiting the information from SCA observations in data assimilation.

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Rainfall spatial variability in the application of catchment morphing for ungauged catchments

Hydrology Research ◽

10.2166/nh.2021.053 ◽

2021 ◽

Author(s):

Jun Zhang ◽

Dawei Han ◽

Qiang Dai

Keyword(s):

Spatial Analysis ◽

Spatial Variability ◽

Spatial Information ◽

Model Performance ◽

Ungauged Catchments ◽

Distributed Information ◽

Simulation Performance ◽

Distributed Models ◽

Spatially Distributed ◽

The Uk

Abstract Catchment Morphing (CM) is a newly proposed approach to apply fully distributed models for ungauged catchments and has been experimented in several catchments in the UK. As one of the most important input datasets for hydrological models, rainfall spatial variability is influential to the stream variabilities and simulation performance. A homogenous rainfall was utilized in the previous experiments with Catchment Morphing. This study applied a spatially distributed rainfall from CEH-GEAR rainfall dataset in the morphed catchment for ungauged catchments as the follow-on study. Three catchments in the UK were used for rainfall spatial analysis and CEH-GEAR rainfall data were adopted for additional spatial analysis. The results demonstrate the influence of rainfall spatial information to the model performance with CM and illustrate the ability of morphed catchment to tackle with spatially varied information. More spatially distributed information is expected to be introduced for a wider application of CM.

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A probability of snow approach to removing cloud cover from MODIS Snow Cover Area products

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-13693-2012 ◽

2012 ◽

Vol 9 (12) ◽

pp. 13693-13728 ◽

Cited By ~ 2

Author(s):

V. López-Burgos ◽

H. V. Gupta ◽

M. Clark

Keyword(s):

Snow Cover ◽

Satellite Remote Sensing ◽

Cloud Cover ◽

Spatial Interpolation ◽

Similar Degree ◽

Step Method ◽

Snow Cover Area ◽

Rule Based ◽

Salt River ◽

Spatially Distributed

Abstract. Satellite remote sensing can be used to investigate spatially distributed hydrological states and fluxes for use in modeling, assessment and management. However, in the visual wavelengths, cloud cover can often obscure significant portions of the images. This study develops a rule-based, multi-step method for removing clouds from MODIS Snow Cover Area (SCA) images. The methods used include a combining images from more than one satellite, time interpolation, spatial interpolation, and estimation of the probability of snow occurrence based on topographic information. Applied over the Upper Salt River Basin in Arizona, the method reduced the degree of cloud obscuration by 93.8% while maintaining a similar degree of image accuracy to that of the original images.

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TOPMELT 1.0: a topography-based distribution function approach to snowmelt simulation for hydrological modelling at basin scale

Geoscientific Model Development ◽

10.5194/gmd-12-5251-2019 ◽

2019 ◽

Vol 12 (12) ◽

pp. 5251-5265 ◽

Cited By ~ 6

Author(s):

Mattia Zaramella ◽

Marco Borga ◽

Davide Zoccatelli ◽

Luca Carturan

Keyword(s):

Solar Radiation ◽

Snow Water Equivalent ◽

Distributed Model ◽

Temporal Aggregation ◽

Time Step ◽

Temperature Index ◽

Index Model ◽

Clear Sky ◽

Basin Scale ◽

Snow Water

Abstract. Enhanced temperature-index distributed models for snowpack simulation, incorporating air temperature and a term for clear sky potential solar radiation, are increasingly used to simulate the spatial variability of the snow water equivalent. This paper presents a new snowpack model (termed TOPMELT) which integrates an enhanced temperature-index model into the ICHYMOD semi-distributed basin-scale hydrological model by exploiting a statistical representation of the distribution of clear sky potential solar radiation. This is obtained by discretizing the full spatial distribution of clear sky potential solar radiation into a number of radiation classes. The computation required to generate a spatially distributed water equivalent reduces to a single calculation for each radiation class. This turns into a potentially significant advantage when parameter sensitivity and uncertainty estimation procedures are carried out. The radiation index may be also averaged in time over given time periods. Thus, the model resembles a classical temperature-index model when only one radiation class for each elevation band and a temporal aggregation of 1 year is used, whereas it approximates a fully distributed model by increasing the number of the radiation classes and decreasing the temporal aggregation. TOPMELT is integrated within the semi-distributed ICHYMOD model and is applied at an hourly time step over the Aurino Basin (also known as the Ahr River) at San Giorgio (San Giorgio Aurino), a 614 km2 catchment in the Upper Adige River basin (eastern Alps, Italy) to examine the sensitivity of the snowpack and runoff model results to the spatial and temporal aggregation of the radiation fluxes. It is shown that the spatial simulation of the snow water equivalent is strongly affected by the aggregation scales. However, limited degradation of the snow simulations is achieved when using 10 radiation classes and 4 weeks as spatial and temporal aggregation scales respectively. Results highlight that the effects of space–time aggregation of the solar radiation patterns on the runoff response are scale dependent. They are minimal at the scale of the whole Aurino Basin, while considerable impact is seen at a basin scale of 5 km2.

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TOPMELT 1.0: A topography-based distribution function approach to snowmelt simulation for hydrological modelling at basin scale

10.5194/gmd-2018-202 ◽

2018 ◽

Author(s):

Mattia Zaramella ◽

Marco Borga ◽

Davide Zoccatelli ◽

Luca Carturan

Keyword(s):

Solar Radiation ◽

Snow Water Equivalent ◽

Eastern Alps ◽

Distributed Model ◽

Temperature Index ◽

Distributed Models ◽

Index Model ◽

Clear Sky ◽

Radiation Fluxes ◽

Basin Scale

Abstract. Enhanced temperature-index distributed models for snowpack simulation, incorporating air temperature and a term for clear sky potential solar radiation, are increasingly used to simulate the spatial variability of the snow water equivalent. This paper presents a new snowpack model (termed TOPMELT) which integrates an enhanced temperature index model into a lumped basin scale hydrological model by exploiting a statistical representation of the distribution of clear sky potential solar radiation. This is obtained by discretising the full spatial distribution of clear sky potential solar radiation into a number of radiation classes. The computation required to generate a spatially distributed water equivalent reduces to a single calculation for each radiation class. This turn into a potentially significant advantage when parameter sensitivity and uncertainty estimation procedures are carried out. The model includes a routine, which accounts for the variability of clear sky radiation distributions with time, ensuring a consistent temporal simulation of the snow mass balance. Thus, the model resembles a classical temperature-index model when only one radiation class for each elevation band is used, whereas it approximates a fully distributed model with increasing the number of the radiation classes (and correspondingly decreasing the area corresponding to each class). TOPMELT is applied over the Aurino basin at S. Giorgio, a 614 km\\textsuperscript{2} catchment in the Upper Adige river basin (Eastern Alps, Italy) to examine the sensitivity of the snowpack model results to the temporal and spatial aggregation of the radiation fluxes.

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Comparison of Modeled Water Input and Measured Discharge Prior to a Release Event: Unteraargletscher, Bernese Alps, Switzerland

Hydrology Research ◽

10.2166/nh.2002.0003 ◽

2002 ◽

Vol 33 (1) ◽

pp. 27-46 ◽

Cited By ~ 15

Author(s):

Thomas Schuler ◽

Urs H. Fischer ◽

Regina Sterr ◽

Regine Hock ◽

G. Hilmar Gudmundsson

Keyword(s):

Solar Radiation ◽

Water Pressure ◽

Drainage System ◽

Model Parameters ◽

Outburst Flood ◽

Temperature Index ◽

Clear Sky ◽

Input And Output ◽

Hydrological System

A distributed temperature index melt model including potential clear-sky solar radiation was applied to Unteraargletscher, Bernese Alps, Switzerland, to quantify water input to the glacial hydrological system during the ablation season 1999. Model parameters were determined by calibrating calculated melt with ablation measurements. Discharge was measured in the proglacial stream for 18 days until the station was destroyed by an outburst flood. Comparison of modeled melt and measured discharge reveals that routing processes of water through the glacier vary with time as the water is transferred through a dynamic drainage system. Furthermore, an imbalance of water input and output suggests that water was stored in or beneath the glacier during this period. The culminating outburst flood presumably released this en– or subglacially stored water and may be related to a change in the configuration of the glacial drainage system as inferred from measurements of subglacial water pressure.

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Snow-cover reconstruction methodology for mountainous regions based on historic in situ observations and recent remote sensing data

The Cryosphere ◽

10.5194/tc-9-451-2015 ◽

2015 ◽

Vol 9 (2) ◽

pp. 451-463 ◽

Cited By ~ 16

Author(s):

A. Gafurov ◽

S. Vorogushyn ◽

D. Farinotti ◽

D. Duethmann ◽

A. Merkushkin ◽

...

Keyword(s):

Remote Sensing ◽

Snow Cover ◽

Remote Sensing Data ◽

Snow Cover Area ◽

Sensing Data ◽

Mountainous Regions ◽

Snow Cover Extent ◽

Spatially Distributed ◽

Data Gap ◽

Availability Assessment

Abstract. Spatially distributed snow-cover extent can be derived from remote sensing data with good accuracy. However, such data are available for recent decades only, after satellite missions with proper snow detection capabilities were launched. Yet, longer time series of snow-cover area are usually required, e.g., for hydrological model calibration or water availability assessment in the past. We present a methodology to reconstruct historical snow coverage using recently available remote sensing data and long-term point observations of snow depth from existing meteorological stations. The methodology is mainly based on correlations between station records and spatial snow-cover patterns. Additionally, topography and temporal persistence of snow patterns are taken into account. The methodology was applied to the Zerafshan River basin in Central Asia – a very data-sparse region. Reconstructed snow cover was cross validated against independent remote sensing data and shows an accuracy of about 85%. The methodology can be used in mountainous regions to overcome the data gap for earlier decades when the availability of remote sensing snow-cover data was strongly limited.

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