Agricultural and Forest Meteorology 104 2000 273–287
Long-term snow depth simulations using a modified atmosphere–land exchange model
C.E. Kongoli, W.L. Bland
∗
Department of Soil Science, 1525 Observatory Drive, University of Wisconsin-Madison, Madison, WI 53706, USA Received 20 October 1999; received in revised form 15 March 2000; accepted 16 June 2000
Abstract
Significant areas of agricultural lands are subject to seasonal, relatively thin snow covers. This cover affects temperature and moisture in the soil beneath, watershed hydrology, and energy budgets. The depth of snow impacts soil freezing with
implications for soil hydraulic properties and over-winter survival of certain crops. The objective of this study was to incor- porate a sophisticated snow cover routine into the atmosphere–land exchange ALEX model to simulate snow depths and
dynamics of the relatively thin snowpacks of the US Upper Midwest. The ALEX model is used in several agricultural modeling projects, and as the land-surface parameterization in a mesoscale forecast model, but with only crude snow cover treatment.
We combined parameterizations and empiricisms from the literature with the ALEX structure. Only three parameters were adjusted to find a set that worked well for 48 station years from three sites in Wisconsin. These were a correction for gauge
catch deficiency, the air temperature that differentiates rain from snow, and a parameter related to drainage of liquid water from a melting snowpack. A further independent test included 13 years from one site in Minnesota. The air temperature
differentiating rain from snow was also determined by analysis of weather observations, independently of the snow model. Both this analysis and the model revealed 0
◦
C to be the best choice for this temperature in our region. Results showed that with a minimum of calibration the model gives good predictions of continuous snow depth, capturing critical processes of
accumulation, ablation and melt in a wide variety of situations. Discrepancies between model and measurements generally originated from a single event and were mainly attributed to processes of blowing snow, misclassification of precipitation type,
and anomalous new snow densities. Our results demonstrated the robustness of existing parameterizations and empiricisms for translating environmental observations into snow depth in dynamic simulation models. © 2000 Elsevier Science B.V. All
rights reserved.
Keywords: Snow cover; Snow depth; New snow density; Rain–snow transition temperature; Precipitation; Blowing snow
1. Introduction
Significant areas of agricultural lands are subject to seasonal snow covers. This cover affects temperature
and moisture in the soil beneath, watershed hydrology,
∗
Corresponding author. Tel.: +1-608-262-0221; fax: +1-608-265-2595.
E-mail address: wlblandfacstaff.wisc.edu W.L. Bland.
and energy budgets. Snow is an appreciable fraction of soil water recharge in some areas, representing an im-
portant source of moisture for agricultural crops e.g. western Canadian prairies; Granger and Male, 1978.
The depth of snow regulates soil freezing Flerchinger, 1991 and influences soil hydraulic properties and the
over-winter survival of certain crops. Decreased hy- draulic conductivity of frozen soils increases the po-
tential for high snowmelt runoff losses, while freezing
0168-192300 – see front matter © 2000 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 8 - 1 9 2 3 0 0 0 0 1 6 9 - 6
274 C.E. Kongoli, W.L. Bland Agricultural and Forest Meteorology 104 2000 273–287
injury can cause yield loss to over-wintering crops, e.g. alfalfa Kanneganti et al., 1998.
Motivated by water supply and safety concerns, the snow literature is dominated by studies of relatively
deep snowpacks of mountainous or forested regions. Agricultural environments in the US Midwest more
commonly have relatively thin snowpack, and issues of practical importance can be significantly different
from those surrounding deeper snow. Compared to deep snowpacks, greater attention may be given to
dates of complete disappearance in agricultural set- tings. Early ablation, for instance, can have important
implications for alfalfa survival. Responses of envi- ronmental factors to thinner snow cover are more rapid
than in deep snow, e.g. to the diurnal cycles of surface energy fluxes Granger and Male, 1978.
Snow cover models vary in complexity. An early, essentially complete physically based model of snow
cover was that of Anderson 1976. This numerical model included physical descriptions of snowpack
accumulation, change of albedo, settling and com- paction, snowmelt, and meltwater retention and per-
colation as well as the snowpack energy balance. The major advantage of models of this type is that
they allow mechanistic understanding of snow cover processes and so should be transportable. However,
their use is limited by data needs and computational burden. Extensions to Anderson 1976 model ex-
panded the physical system to include the soil be- neath SNTHERM; Jordan, 1991, and vegetation and
residue SHAW; Flerchinger and Saxton, 1989.
The objective of this study is to test the validity of Anderson’s parameterizations for the snow depths
and dynamics of the relatively thin snowpacks of Wisconsin. Additionally, we needed to incorporate a
sophisticated snow routine into the atmosphere–land exchange ALEX model, which we use in several
agricultural modeling products Anderson et al., 1998; Bland et al., 1998; Diak et al., 1998. The speed and
simplicity of ALEX is suitable for landscape-to-global scale applications where calculations must be made
at thousands of locations. One such application is our project to assess the impact of landscape position and
time of year on optimizing wintertime disposal of an- imal manure. Additionally, ALEX is the land-surface
parameterization in the CRASS mesoscale forecast model personal communication; Diak, 1999, but
with only crude snow cover treatment. Finally, restruc- turing of the US National Weather Service during the
past decade eliminated many sites where professional observers recorded snow cover changes. The future
prospect is for fewer observations, so simulation will play an increasing role in real-time management
problems.
This study is unique in the extent of record used to demonstrate the validity of the model: four stations
totaling 61 site years of continuous hourly and daily weather observations. Detailed snow cover models
such as SNTHERM and SHAW have so far been ap- plied to only shorter records for verification purposes
typically extending 1–3 years. In contrast, snow cover routines subjected to longer-term verifications are
generally less sophisticated Motoyama, 1990; Yang et al., 1997. Additionally, the model created here
is both complete in process as defined by Anderson 1976 and numerically efficient enough to integrate
into a model suitable for mesoscale modeling projects. Because of computational limitations, snow submod-
els used in regional or climate studies so far exhibit low to intermediate complexity Slater et al., 1998,
typically lacking internal processes e.g. melt water retention and percolation Yang et al., 1997. In our
work, the snow cover parameters were successfully applied to the entire data set, covering a wide variety
of snow cover conditions. Results demonstrate that available parameterizations as implemented in ALEX
are robust.
2. The ALEX model and modifications for snow
