Soil Moisture Patterns in
Amy Burke
GIS in Water Resources
Fall 2006
Introduction
In some locations across the
intermountain west it has been noted that aspens stands are being encroached
upon by conifer species. Possible
reasons for this encroachment include human alterations in disturbance regimes,
such as fire suppression and grazing, as well as climate change (Shepperd
et. al., 2006).
This is being blamed not only for a decline in the species diversity
that accompanies aspen but also a possible decline in water yield or water
quality. This project focuses on
possible differences in water yield between aspen and conifer dominated hillsides,
by concentrating on soil moisture patterns.
Importance of Soil Moisture:
Vegetation can influence hydrology
in many ways; by changing soil properties and ground cover and therefore
influencing infiltration capacity, by creating macro-pores and altering
subsurface flow-paths, by transpiring water back to the atmosphere and by
holding precipitation in the canopy or altering through-fall patterns. Previous data gathered at this study site by
Ron Ryel and others, indicates that aspen and conifer species do not transpire
significantly different amounts of water.
Rather all trees monitored used all water available in the soil profile
at the end of the spring snowmelt.
However, Ryel et al. did find significantly less snow-pack under conifer
stands. Conifer species retain their
needles year round and thus are able to accumulate more snow in their canopy,
lessening the snow-pack on the ground around them. Snow held in the canopy is more susceptible
to sublimation; a water loss to the system, or simply redistribution, possibly
to nearby aspen stands.
Whether or not vegetation can
impact water yield will depend on the waters flow paths and the timing of the
precipitation. For vegetation to be in
competition with the stream, water would need to enter the stream via
subsurface pathways, where it would be susceptible to root uptake, during times
of transpiration. Therefore detecting
subsurface flow in the hill slope is important for understanding
vegetation/water yield relations. Detecting soil moisture patterns is
one way of quantifying the amount of hill slope hydraulic connectivity, or how
much water is moving laterally in the subsurface to possibly feed a
stream. Grayson et al. report two states
in soil moisture, a dry state and a wet state.
In a dry state evapotranspiration exceeds surface water inputs and soil
moisture is controlled by local factors.
No hill slope scale pattern of soil moisture is seen while soil is in
the dry state. In a wet state, water
inputs exceed evapotranspiration and non-local factors will control soil
moisture, such as slope and hill slope scale water inputs. Grant et al. report a soil moisture content
threshold, around field capacity, at which soil switches to a wet state and the
stream becomes highly reactive to hill slope surface water inputs. Western et al. correlate patterns to
Topographic Index (TI) on a hill slope scale.
Topographic index is
ln(a/tanβ), where a is the upslope area and β is slope (Western
et al. 1999). Another function similar to TI is Wetness
Index (slope/specific catchment area) which expresses the same basic idea as TI
(Tarboton 2005). Here soil moisture will
be correlated with wetness index calculated by TauDEM.
Study Site
The study site is located in
Northern Utah in the headwaters of the
It should be noted that soil type and depth differ between
vegetation types therefore the two cannot be uncoupled. For this reason vegetation/soil types, will
be assessed rather than vegetation type alone.
Soil data was not available for this region at
the resolution needed to show specific differences in the small study
catchment. It is known that soils at
the site range from Mollisols in the uplands, to
Entisols and Inceptisol in the drainages.
It can be inferred from the pits dug during sensor installation and the
Figure 1. Study site
in Northern Utahs Ogden River Basin, within the Bear and Frost paired
watershed at Deseret Land and Livestock
Objectives
The objectives of this project are to:
Map the study area with vegetation types and a 10m digital elevation model (DEM).
Use the 10m DEM to delineate subwatersheds, streams, flow accumulation, flow direction and wetness index.
Determine if soil moisture shows an organized pattern, indicating lateral subsurface flow.
Determined if soil moisture varies between aspen and conifer dominated stands.
Determine if soil moisture correlates to wetness index.
Compare aspect and
vegetation types.
Methods
Soil Moisture
Transects:
Bear and Frost canyons are two small paired watersheds with a combined area of ~26.7km2, shown below in figure 2. A small catchment of ~0.7km2, within Frost canyon has been instrumented with nests of soil water content reflectometer probes, which use time-domain measurements to obtain volumetric water content. There are two transects each with 8 nests, one transect across an Aspen dominated portion of the catchment and the other across a Conifer dominated portion as mapped in figure 3. Each nest consists of 3 probes at 5, 20 and 100cm as shown below in figure 4.
Figure 2. Study area
within the Bear and Frost paired watersheds
Figure 3. Transects of
soil moisture sensors across aspen and conifer dominate stands. Light Green indicates
Figure 4. Soil
moisture probes installed at 5, 20 and 100cm.
From these soil moisture data we hope to infer whether or not lateral subsurface flow is happening on a hill slope scale. If there is lateral flow, or if upslope zones are hydraulically connected to down-slope zones, the soil profile could be feeding the stream. If soil water is feeding the stream then vegetation could be affecting stream water yield since the soil profile is also providing water for the vegetation. It is likely that lateral subsurface hill slope flow is only happening during the spring snow melt when soil water content is at its highest.
Analyses:
Terrain analyses using Arc Hydro were performed on a 10m DEM of the area. In this way flow accumulation, flow direction, slope, and stream delineation were all obtained. Wetness Index (slope/specific catchment area) was calculated by TauDEM, using the D-infinity slope grid and D-infinity specific catchment area grid. These slope and area grids were derived from a 10m digital elevation model (DEM) obtained from Ramsey 2003. The D-infinity approach assigns a flow direction on a DEM using steepest slope on a triangular facet as shown in figure 5 below (Tarboton, 2005).
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Figure 5.
Explanation of the D-infinity method for determining flow direction,
(figure obtained from TauDEM help, Tarboton, 2005).
Results
Soil Moisture Data:
Figure 8 below shows soil moisture data from Sept 3rd to Oct 14th 2006. Assigned sensor numbers increase with distance from the stream (see figure 6 for example of aspen transect). Not all sensors yielded usable data therefore not all data is shown below, but rather examples of what was typically found. Figure 7 shows the precipitation for the time period as recorded by a nearby snotel site. Though this precipitation fell as snow, temperatures were recoded around 11°C and they upper soil horizons appear to be reacting to the melt almost instantaneously. The first rise in soil water content occurs on Sept 14th, the first day of significant snowfall (of about 0.6 inches). If there was a soil moisture pattern, indicating lateral subsurface flow, we would see the graphs in figure 8 showing a clear pattern of increasing soil moisture with proximity to the stream. In other words Aspen1 through 5 would be ordered in ascending order, top to bottom in the graphs. However we only see this in briefly in the upper layers of the aspen transect. An example of this is given in figure 9. The fact that the lower depths in both transect have lower water contents that upper layers, and the fact that aspen sensors at 100cm do not react at all to the precipitation, indicate that downward flux of water is most likely not occurring. There also appears to be no difference in soil moisture between the vegetation/soil types as the Y axis scales of these graphs are very similar. There may be differences in the total amount of water in the profiles due to differences in profile depth but soil depth and percent large particles would need to be measured before this could be concluded.
Figure 6. Example of how sensors nests are numbered.
Figure 7. Precipitation from nearby snotel site from Sept 3rd to Oct 14th 2006.
Figure 8. Soil moisture data from Sept 3rd to
Oct 14th 2006
Figure 9. A brief period of organized soil moisture
between 8am and 2pm Sept 16th,
depicted graphically and spatially.
Correlation to
Wetness Index:
Wetness index as calculated by TauDEM is shown below in figure 10. A lower value, or darker color, means a greater contributing area and therefore a greater chance of moisture accumulation. The following graph in figure 11 shows all values of soil moisture data collected on Sept 16th, the day that had the highest overall soil moisture for the period measured. The figure, with an R squared value of 0.023, shows no correlation between wetness index and volumetric water content. Individual depths and transects were graphed as well and similar results were found.
Figure 10. The wetness index
distribution inside the study catchment.
Figure 11. Wetness index vs. soil moisture for Sept 16,
the day with highest all around soil water contents.
Comparison of aspect and vegetation types:
Below
there are two maps overlain to give a qualitative analysis of how vegetation
and aspect correlate. The two maps are
shown in figure 12. On the vegetation
map green represents conifers, yellow aspen and white everything else, which
mostly consists of sagebrush/grassland.
The field season it was noted that conifers appeared to dominate steep
north facing slopes further down in the catchments, while aspen occupied mid
and upslope areas. This is important
because if vegetation type is determined by aspect, then we cannot separate the
two factors. An effect interpreted to be
caused by vegetation could really be caused by aspect. Aspect here is expressed as one of 8 cardinal
directions in the flow direction grid.
Conifers do appear to be on more north facing slopes than other
directions, but again this in only a qualitative analysis.
Figure 12. Vegetation and flow
direction. On the left, green is conifer
yellow is aspen, white is sagebrush/grassland.
On right, flow directions breaks slope into one of 8 cardinal directions.
Figure13. The combination of vegetation data and aspect
(expressed as flow direction). Green
indicates north facing conifers in the map on the left. Purple indicates south facing aspen in the
map on the left.
Preliminary
Conclusions
Organization of soil moisture appears to be very minimal in the small precipitation events recorded. It would be a stretch to conclude that lateral subsurface flow is occurring. It is obvious that lower soil horizons, especially those in the aspen stand, are not affected by this level of precipitation.
There appears to be no difference in volumetric water content between the vegetation/soil types but further measurements should be taken before this can be confidently concluded and quantified. There was also no correlation between wetness index and soil moisture for this precipitation event. Only time will tell if larger soil water inputs in the spring snowmelt will show more correlation.
Qualitatively vegetation does appear to correlate to some degree with aspect, in particular conifers to north facing slopes. Again a quantitative analysis would be needed to make stronger conclusions.
Limitations and
Future Plans:
The most limiting factor in this project is the fact that soil moisture is only measured in transects and therefore spatial patterns cannot be detected. Measurements this coming field season will expand out, perpendicular to each transect and the subsequent data will be able to be analyzed spatially. It is also hoped that a greater time scale, including more significant water inputs will show greater reaction in the soil moisture and perhaps some correlation to terrain indices. Some of the GIS work is also resolution limited as the catchment is quite small. This also calls for more ground surveys, especially of vegetation and snow pack. It is also important to realize that the analyses done so far are all topographic but subsurface water may not follow surface topography. Seismic response or a similar application would be needed to fully understand the subsurface, depth to bedrock and bedrock type. Stream flow needs to be quantified continuously with soil moisture so that stream reaction to the hillside can be monitored. Until this is done, no connectivity between the hill slope and stream can be detected, only connectivity within the hill slope itself.
References and Data Sources
David
Tarboton, 2005 TauDEM Software http://hydrology.neng.usu.edu/taudem/
Grant L, Seyfried M, McNamara J. 2004. Spatial variation and temporal stability of soil water in a snow-dominated, mountain catchment. Hydrological Processes 18: 3493-3511.
Grayson et al, (Preferred states is spatial soil moisture patterns:
local and non-local controls, Water Resources Research Vol 33 No 12
Pages 2897-2908
December 1997)
NRCS snotel. http://www.wcc.nrcs.usda.gov/snotel/snotel.pl?sitenum=533&state=ut
Pineview Reservoir Lake Reports. http://www.waterquality.utah.gov/watersheds/lakes/PINEVIEW.pdf
Ryel Ron, LaMalfa E. 2006. Presentation: Water relations and water yield in aspen and conifer
forests presentation . Department of Wildland
Resources,
Soil Survey of Rich Country,