hfcourse snow

MODELING OF COLD SEASON PROCESSES: 

MODELING OF COLD SEASON PROCESSES Snow Ablation and Accumulation Frozen Ground Processes

Snow Ablation and Accumulation: 

Snow Ablation and Accumulation Snow physics Commonly Used Parameterizations How well do we model snow cover NOAh and SNOW-17 Models Input Data Uncertainties Model Structure/Parameter Uncertainties Effect of Weather Conditions Summary Where to go in Snow Modeling?

Snow Physics Sources of Energy Exchange: 

Snow Physics Sources of Energy Exchange Net Radiation, Qn Solar Incoming – Reflected Incoming from Atmosphere – Outgoing from Snow Turbulent Exchange Sensible Heat: Based on the Temperature Gradient, Qh Latent Heat: Based on the Moisture Gradient, Qe Mass Transfer: Heat from Precipitation, Qm Heat Exchange with the Underlying Soil, Qg Qn + Qh + Qe + Qm + Qg = ΔQ

Snow Physics Problem Complexities: 

Snow Physics Problem Complexities Net Solar Radiation is not easy observable and it varies significantly with weather and snow conditions (sky, snow reflectance (albedo), forest, topography)

Snow Physics: Problem Complexities (cont.): 

Snow Physics: Problem Complexities (cont.) Turbulent Transfer is a very non-linear Function of the Wind Speed. A Turbulent Transfer Coefficient of this Function is defined under a Number of Assumptions. Large Uncertainties in Internal Snow Cover Processes: Density Changes in Time and Depth, Liquid Water Refreezing, Liquid Water Retention and Transmission, Rain Effect. Spatial Variability and Redistribution due to Wind

Commonly Used Parameterizations:Energy Based vs. Temperature Index: 

Commonly Used Parameterizations: Energy Based vs. Temperature Index Energy Based Snowpack Layers Albedo Definition Snowpack Property Dynamics Wind Function Approximation Temperature Index Fully Index Based or Combination Snow Melt Rate Dynamics Wind Function Approximation Snowpack Property Dynamics

How well do We Model Snow Cover : 

How well do We Model Snow Cover

How well do We Model Snow Cover: 

How well do We Model Snow Cover

NOAh Energy Based Model: 

NOAh Energy Based Model One Layer snowpack Variable Snow Properties Multi-Layer Soil Profile

NOAh Energy Based Model: 

NOAh Energy Based Model

SNOW-17: Temperature Index Model: 

SNOW-17: Temperature Index Model Conceptual model Simplified Heat Balance During Rainfall Events Degree-Day Melt Factor During Non-Rain Events Input Variables: Air Temperature & Precipitation Output: Melt, WE, Depth, Areal Extend Snow Cover Processes Snow accumulation Surface Energy Exchange Snow Compaction Liquid Water Transmission Watershed /Areal Application Areal Extent of Snow Cover

SNOW-17: Surface Energy Exchange: 

SNOW-17: Surface Energy Exchange Ta > 0oC – Snowmelt If Rainfall > 0.25 mm/hr - Simplified Heat Balance Equation: - no Solar Radiation, - Atmosphere & Snow is a Black Body, - Relative Humidity = 90%, - Rainwater at an Air Temperature Else - Degree-day Melt Factor (Seasonal Variable) Ta < 0oC – No Snowmelt - Estimate Change in Snow Heat Deficit

SNOW-17: Model Parameters: 

SNOW-17: Model Parameters Major Minor Accumulation SCF (1-1.2) PXTEMP (0.6-0.2) Surface Melt MFMAX (1.7-2.0) MBASE (0.0) MFMIN (0.2-0.6) UADJ (0.002*U) Heat Storage & NMF (0.15) Water Retention TIPM (0.05) PLWHC (0.02-0.05) Areal Coverage SI ( > Wmax ) Depletion Curve Ground Melt DAYGM

SWE (Plot 3) and Depth (Plot 4) from NOAh (red lines) & SNOW-17 (white lines), Swiss Alps Site, 1992-1993: 

SWE (Plot 3) and Depth (Plot 4) from NOAh (red lines) & SNOW-17 (white lines), Swiss Alps Site, 1992-1993

Input Data UncertaintiesRequired Data for Energy Budget Models : 

Input Data Uncertainties Required Data for Energy Budget Models Incoming Solar Radiation Reflected Solar Radiation or Albedo Incoming Long-Wave Radiation Snow Surface Temperature Wind Speed Air Temperature Dew-Point Precipitation and Phase Wet Bulb Temperature Snow Cover Profile (Density, Temperature, etc.) Soil Temperature

Input Data Uncertainties: 

Input Data Uncertainties Swiss Alps Site, 1992-1993 Goose Bay Site, Canada, 1981-1982

Input Data UncertaintiesEffect of Net Radiation : 

Input Data Uncertainties Effect of Net Radiation Sleepers River Site, USA, 1996-1997 Swiss Alps Site, 1992-1993 Observed fluxes (solid lines), Incoming solar radiation with a 20 Wm-2 error added (dotted lines), and the constant albedo of 0.7 (dashed lines).

Input Data UncertaintiesEffect of Wind Speed: V=1m/s (white), V=15m/s (colored) : 

Input Data Uncertainties Effect of Wind Speed: V=1m/s (white), V=15m/s (colored) Goose Bay Site, Canada

Input Data UncertaintiesEffect of Precipitation Phase: with use (red), w/o use (blue): 

Input Data Uncertainties Effect of Precipitation Phase: with use (red), w/o use (blue) Danville, Vermont, USA

Model Structure/Parameter Uncertainties: 

Model Structure/Parameter Uncertainties

Effect of Weather Conditions: 

Effect of Weather Conditions SWE and Depth from NOAh (red) & SNOW-17 (white), Col de Porte Site, France

Effect of Weather Conditions: 

Effect of Weather Conditions Simulated snowmelt variables from NOAH-LSM (dashed lines) & SNOW-17 (solid lines) Col de Porte, France February 10-22, 1998. Weather: Very low wind; High diurnal amplitude of air temperature Effect: (a) Much faster snowmelt from SNOW-17; (b) Significant effect of net radiation and liquid water refreezing

Effect of Weather Conditions: 

Effect of Weather Conditions Weissfluhjoch Site Switzerland, July 11-16, 1993 Weather: Negative Tair High wind speed Effect: Significant snowmelt from NOAH, Accumulation from SNOW-17

Summary: 

Summary Model Complexity does not Guaranty Accuracy Temperature Index Models Provide Practically Reasonable Results, however They are Sensitive to Weather Conditions, Specifically Wind and Solar Radiation Conditions Energy Based Models are Sensitive to Input Data Errors, Specifically Wind, Solar Radiation and Albedo Treatment Some Calibration/Tunning is Needed to get Better Results from Both Simple and Complex Models

Where to go in Snow Modeling?: 

Where to go in Snow Modeling? Energy and Temperature Based Models may Coexist for a Long Time, Specifically in River Runoff Prediction Improvements to Temperature Based Models can be Achieved by Incorporating Wind and Humidity Data Regionalization of the Most Critical Parameters to run in a Distributed Mode Energy Based Model Improvements Define the Most Reliable Data Sources Using Sensitivity Tests Better Parameterizations of Albedo and Wind Function Improvement in Prediction of Weather Variables

Slide30: 

Appendix 1

Slide31: 

Appendix 2

Slide32: 

Appendix 3

Slide33: 

Appendix 4