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Snow Cover Forecast Model Description

Understanding the model structure


The model requires two inputs: monthly streamflow and snow cover frequency.

Basic snowmelt-runoff models assume future discharge is a function of past discharge, precipitation, and temperature (Martinec 1975; Martinec and Rango 1986).

We modified the basic snow-melt runoff model to assess the ability to predict streamflow in regions without precipitation or robust temperature measurements.

In our approach Snow Cover Frequency (SCF) serves as a proxy for snow water contributions to runoff. SCF is calculated from NASA’s Moderate Resolution Spectroradiometer daily snow cover product (MOD10A1).

SCF is a good estimator of precipitation in watersheds where snowmelt is a significant contributor to streamflow. Our basic model assumes that sublimation and evaporation are accounted for in the SCF calculations.

Our Snow Cover Forecast Model (SCFM) partitions previous years’ streamflow into seasonal and longer-term baseflow components. Recent seasonal (Qnear) and longer-term baseflow (Qlong) contributions coupled with SCF calculations predict monthly streamflow with a one month lead time based upon the equation:

Qcurrent = aSCFb + cQnear + dQlong


a, b, c,
and d are scaling coefficients specific to each model forcing.

  • a scales the influence changes in snow-covered area
  • b is an exponential scaling parameter representing the tapering effect of snowpack contributions to streamflow as it melts
  • c weights the influence of recent seasonal changes to streamflow
  • d parameter conceptually represents the contributions of baseflow from groundwater and glacial contributions

To incorporate antecedent conditions and minimize the influence of an individual runoff or melt event, we calculated a moving average of SCF, Qnear, Qlong.

The averaging period for each term represents the number of prior months data used to calculate the moving average. The optimal averaging period for each watershed was calculated using code written in MATLAB, which is available on GitHub. Alternatively the user can manually change the averaging period in each the Google Sheet tutorials.

For a complete description of the Snow Cover Forecast Model, please refer to the original paper in Water Resources Management (Sproles et al, 2016).

Next stop – A video tutorial of the cloud computing framework, how calculations of Snow Cover Frequency (SCF) are made, and how to export the cloud-based data.

 

Martinec J (1975) Snowmelt-runoff model for stream flow forecasts. Nord Hydrol 6:145–154.

Martinec J, Rango A (1986) Parameter values for snowmelt runoff modelling. J Hydrol 84:197–219.

Sproles, E. A., Orrega, N. C., Kerr, T., and Lopez, A. D. (2016). Developing a snowmelt forecast model in the absence of field data.Water Resource Management. 30: 2581. doi:10.1007/s11269-016-1271-4.

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