Snow Water Equivalent Snow Cover Runoff Modeling

1. Snow Water EquivalentSnow Cover Runoff Modeling Lecture 6 By Ahmet Emre TEKELI February 19, 2009

2. Snow Cover, SWE and Runoff Modeling • Introduction • Snow Water Equivalent (SWE) • Remote Sensing in Hydrological modeling (snow dominated) • Components of Snowmelt modeling • Snowmelt Runoff Model (SRM)

3. Introduction • Microwave radiation emitted from ground, scattered in many directions by the snow grains within the snowpack. • Mw emission @ snowpack surface < ground. • Factors; snowpack depth & water equivalent, liquid water content, density, grain size and shape, temperature, stratification, snow state, land cover.

4. Introduction Mw sensitive to snow layer snow extent, snow depth snow water equivalent snow state (wet/dry) Can be derived

5. Introduction

6. Snow Water Equivalent (SWE) • A common snowpack measurement instead of depth. • Indicates the amount of water contained within the snowpack. • Thought as the depth of water that would theoretically result if the entire snowpack melted instantaneously. • SWE= Snow Depth (SD) x Snow Density

7. Snow Water Equivalent (SWE) • Same depth yields different SWE due to density. • SD= 120” density 10% • SWE=120 * 0.1= 12’’ • SD= 120” density 40% • SWE=120 * 0.4= 48’’ • The density of new snow ranges from about 5% @ Tair 14° F, to about 20% @ Tair 32° F. • After the snow falls its density increases due to gravitational settling, wind packing, melting and recrystallization.  • Typical values of snow density are 10-20% in the winter and 20-40% in the spring.

8. Snow Water Equivalent (SWE) • Same depth yields different SWE due to density.

9. Snow Water Equivalent (SWE)

10. Snow Water Equivalent (SWE) Snow pack scattering Due to scatterers within the snow pack & increases with thickness and density Deeper snow packs Result lower TB TB related to SWE

11. Snow Water Equivalent (SWE) Where, • f1 low scattering channel (commonly 18/19 GHz) • f2 high scattering channel (commonly 37 GHz) • A, B offset and slope of the regression • The coefficients should be determined for different climate and land cover conditions. • Thus, no single global algorithm can estimate snow depth and/or SWE under all snowpack and land cover conditions. TB related to SWE SWE = A+ B [ΔTB(f1,f2) ]

12. Snow Water Equivalent (SWE) • Water presence, alters the emissivity of snow, and results higher brightness temperatures • For accurate SWE, snow should be in dry conditions. Thus, prefer early morning passes (local time)

14. Snow Water Equivalent (SWE) • Depth hoar formation in bottom of the snowpack (mainly in cold regions), will increase the scattering and reduce surface emission, resulting an overestimation of snow depth and SWE.

15. Snow Water Equivalent (SWE)

16. Snow Water Equivalent (SWE) • Snow pack changes in time, seasonal aging or metamorphism changes microwave emission of snow. • To account for seasonal variability of mw emission from snow, they should be compiled for entire season, over several years.

17. Snow Water Equivalent (SWE)

18. Snow Water Equivalent (SWE) • WHY? • SWE provides important information for water resources management and is a major research topic in RS assessment of snow cover and melt.

19. RS HYDROLOGY • Runoff at the outlet of the watershed is an integrated result of the spatially varying sub-parts of the whole basin • Similarity is a serious problem in basins with pronounced topography, because of the high spatial variability of hydrometeorological parameters in these regions. • Hard/impossible to handle with classical terms • Increase in satellite platforms and improvements in the data transmission and processing algorithms, remote sensing (RS) enables to handle these spatial variations. • RS is gaining importance in distributed watershed modeling by its spatial variation handling capacity.

20. RS HYDROLOGY • The advances in RS and GIS enable new data to scientific community. New data necessitate improvements in hydrological modeling rather than using the conventional methods. • “Existing models are designed for a limited number of types of input and may need to be made more flexible to make optimum use of the range of possible inputs.” (Hydalp,2000) • New models would enable new input from RS & GIS and provide better hydrological outputs, enabling the understanding of the complex world. • Thus, there will be mutual developments between RS, GIS and hydrological modeling, leading improvements in the other ones.

21. RS HYDROLOGY • However, there has always been, a trade off between the scientific complexity and practical applicability • The frontiers of snowmelt modelers, Vivan 1979 , developed regression equations. These equations worked well under the circumstances similar to in which the model is developed. However, in new situations, the model may not be dependable. • On the other extreme, a fully developed model in which all the temporal and spatial variable parameters are handled may demand large data amounts. Difficult to calibrate the parameters and hard to understand the physical background by end users and difficult to get the input variables.

22. HYDROLOGIC MODELING Inputs Incoming water Hydrological Modeling nothing else than transformation Output Discharges Formulations vary from black box approaches to physically based models with different degrees of spatial and temporal variability

23. HYDROLOGIC MODELING • Formulations may depend to the type of the precipitation i.e. on rainfall (liquid) or snowfall (solid). • When precipitation occurs as snowfall, the discharge timing is not only a function of precipitation timing, but also the heat supplied to the snowpack either by temperature or by radiation.

24. HYDROLOGIC MODELING In either forms, the total runoff volume is still total precipitation minus losses; however, snowfall is stored in snowpack until warmer weather allows the phase change from snow/ice to liquid (i.e. melting).

25. SNOWMELT MODELING Snowmelt runoff modeling has four main components; 1. Extrapolation of meteorological data 2. Point melt rate calculations 3. Integration of melt water over the snow covered areas 4. Runoff Routing

26. SNOWMELT MODELING

27. Extrapolation of Meteorological Data • In snowmelt dominated basins, very hard if not impossible to find meteorological stations in adequate number and good quality with even distribution. • Existing stations mostly located in major valleys rather than the more inaccessible high portions of the basin, where most of the snow exist. • Thus, a necessity to use data from a station even though it may be a long distance away and at a much lower elevation from the snowpack.

28. Extrapolation of Meteorological Data • Air temperature, mainly used for two purposes in the snowmelt models. • Both as threshold temperature, separating precipitation as rainfall or snow and as critical temperature, used for estimating snowmelt rates. • May not to be same and both may be other than zero. • Since air temperature alters with elevation, temperature lapse rates must be used to convert the measured air temperature at the lower station to the air temperature at the snowpack location. • Although, most runoff models assume a fixed value for the lapse rate, the actual value may be a varying value depending on the present meteorological conditions. • Often the temperature lapse rate, threshold and critical temperature values are treated as calibration parameters (Hydalp, 2000) of the model used.

29. Extrapolation of Meteorological Data • Distribution of precipitation from point stations to the rest of the basin has been a problem in the hydrology. • Come up with, unrealistic and inaccurate results. • Besides, the systematic under catch of snow by most rain gauges especially under high wind speeds has long been reported in literature such as Sevruk (1983). • Precipitation amount increases with elevation.

30. Extrapolation of Meteorological Data • Exists numerous methods from simple arithmetic averaging, Theissen polygons to inverse distance relations. These methods allow the extrapolation in a horizontal plane (2D), disregarding the topography of the area under study. • Some methods such as De-trended Kriging (Garen, 2003) when distributing the meteorological variables takes the topography into consideration. • Preliminary study must be performed since some times, distributing the variables in 2D may give better results than distribution in 3D (Weibel et al., 2002).

31. Point Melt Rate Calculations • The energy flux that a surface absorbs or emits is dependent to the sum of the: • Net all wave radiation (sum of net short (net solar) and long wave (net thermal) radiation) • Sensible heat transfer to the surface by turbulent exchange from atmosphere • Latent heat of condensation or evaporation • Heat added by precipitation (if the temperature of precipitation is different than the surface temperature) • Heat conducted from ground

32. Point Melt Rate Calculations • Main discrepancy in applying the energy balance is high variability over time and over the location • Highly scientifically based equipped automated stations enable the application of the energy based snowmelt models at a point, there still remains the extrapolation of these measurements over the basin.

33. Point Melt Rate Calculations “It is rare for such instrumentation to be available at even a single point in a basin, and even then a problem remains in extrapolating the measurements to other parts of the basin” (Hydalp, 2000).

34. Point Melt Rate Calculations • Instead of measuring all components some approximations are provided, called “parametric energy balance” methods. • In here, some energy components are derived from available data. • Such as; Incident radiation, F(latitude, time of year, shading effects, cloud cover and snow albedo). Sensible heat= wind speed * air temperature Precipitation heat supply = Rainfall rate * Rainfall temperature • But still, extrapolating over the other parts of the basin ?. • And additional assumptions about the seasonal variations of these terms should be made.

35. Point Melt Rate Calculations • Air temperature, common factor in all energy balance equations except the net radiation. • However, there is generally a good correlation between them (Hydalp, 2000). • Temperature can be considered as the driving factor for the day to day variations of the heat supply to snowpack.

36. Integration of melt water over the snow covered areas • Depletion of snow cover takes place over a period called “melting season” during both incident solar radiance and air temperatures increase. • SC doesn’t disappear at the same time everywhere in the basin. • Even uniform melting, differences in initial snow distribution, results variations. (longer SC @ higher elevations)

37. Integration of melt water over the snow covered areas • Wrong runoff predictions even correct melt rates are applied to faulty SC. • High SC leads over estimation, low underestimation. • Dealing with SC there are two main approaches, snow pack formation is modeled or observed.

38. Integration of melt water over the snow covered areas Formation observed; • Snow on the surface is monitored. • May start with initialization of the melting • RS may be helpful • Actually one of the practical uses of RS in hydrology since 1970’s • Basis of Snowmelt Runoff Model (SRM)

39. Integration of melt water over the snow covered areas Formation modeled; • Simulation starts at autumn before the melt season. • T and P, used to model the snow pack growth and SWE instead of depth used due to compaction of snow • SC is given for areas where SWE>0 • Ground data useful for cal./val.

40. infiltration percolation Runoff Routing

41. Runoff Routing • Simplified in conceptual terms, upper and lower stores for fast and slow flow.

42. Runoff Routing • Stores can provide linear or nonlinear flow rates Q = A.S A not constant F(S or Q)

43. Runoff Routing • Stores can be in series, parallel or both. • Two or even single may provide good approximations for daily rainfall runoff predictions • An extra snowpack model may be needed.

44. Quality Assessment Model Accuracy • Mainly by comparing observed and simulated discharges • May also compare SWE and SCA but mostly the hyrographs are compared

45. Quality Assessment Model Accuracy • Goodness of fit (R2) Nash & Sutcliffe • Volume Difference

46. Not Sleepy yet? BREAK?

47. Snowmelt Runoff Model (SRM) • Developped by Martinec in 1975 in Swiss Snow and Avalanche Research Institute. • Changed & developed with collaboration of Albert Rango (US ARS), Ralph Roberts (US ARS), Michael Baumgartner (University of Bern) Klaus Seidel (University of Zürich) • Various versions exist. http: // hydrolab.arsusda.gov/cgibin/srmhome

48. Snowmelt Runoff Model (SRM) • Contrary to general purpose hydrological models with extra snow modules, • SRM, initially developed to predict the snowmelt runoff • Year around simulation capability was also shown by Martinec et al., (1998). • Applied over 100 basins in 25 countries @ altitudes 32-60 oN & 33-54oS with basin sizes varying from <1 to 120 000 km2 documented in about 80 scientific references (Seidel and Martinec, 2004)

49. SnowMelt Runoff Model Physically based Semi Distributed Deterministic Same input same output

50. SnowMelt Runoff Model • SRM , based on degree day method, can be used to simulate/forecast the snowmelt runoff SRM Simulate daily flows in snowmelt season or year around Provide short term and long term forecasts Analyze the effect of climate change