THE IMPLEMENTATION OF VARIABLE SOIL DEPTH INTO CLM

THE IMPLEMENTATION OF
VARIABLE SOIL DEPTH INTO
CLM4.5
MICHAEL A. BRUNKE, PATRICK BROXTON, PIETER
HAZENBERG, JON PELLETIER, XUBIN ZENG, and coauthors
Photo courtesy of Axel Kristinsson
INTRODUCTION
2
HOW CAN CLM BE MADE MORE REALISTIC?
In the past, climate models have used constant soil depth.
Lack of knowledge of how deep bedrock is globally.
We know that soil depth is variable.
Uplands have shallow soil, lowlands have deeper soil.
Weathering rates vary from slope to slope (Pelletier and
Rasmussen 2009).
Inclusion of variable soil depth is important to land
modeling.
Gochis et al. (2010) showed that surface fluxes were improved
when actual soil depth was used in Noah at a couple of locations in a
semi-arid region.
MODEL ADAPTATION
GLOBAL DEPTH TO
BEDROCK ESTIMATES
▸Estimate depth to bedrock (DTB)
globally (based on global
topographic, vegetation/climate,
and geologic data).
3
Input Spatial Datasets
Topography
Topographic
Roughness
Index (m)
200
100
50
10
0
Vegetation + Climate
Maximum
Green
Vegetation
Fraction (%)
100
▸Differentiate between soil depths
in uplands vs. lowlands because:
75
50
25
0
▹Difference in soil depths.
▹Different data representative
of different areas.
Geology
Sediments
Quaternary
Neogene
Paleogene
Undivided
Cenezoic
MODEL ADAPTATION
4
GLOBAL DTB ESTIMATES
Upland DTB Model
DTB (m) as depicted in CONUS polygons
DTB < 0.5 m
0.5 – 0.75 m
0.75 – 1.0 m
1.0 – 1.25 m
1.25 – 1.5 m
DTB > 1.5 m
Calibrated with the
CONUS soil dataset
Calibrated with well
data from state well
databases and national
USGS database
60
Depth to Bedrock (m)
Lowland DTB Model
Locations of wells in IN, KY, PA, and NY
Kentucky Cenozoic
50
40
Indiana Glacial
30
Indiana Non-glacial
Pennsylvania
20
New York
10
Kentucky older bedrock
0
0
Combine using fraction of upland/lowland
Based on geologic
data (including US
surficial deposits
mapping) and 30 m
topographic data
Green: Thick unconsolidated sediments (non-glacial)
Blue: Thick unconsolidated sediments (glacial)
Red: Other alluvial sediments
20
40
Topographic Roughness (Index (m)
60
Red: Grid Cells modeled as having >95% lowland)
MODEL ADAPTATION
5
2 – 10 10 – 20 20 – 30 30 +
0.5
0.75
Overall Depth to Bedrock (1 km resolution)
0 - 2 meters
2 - 10 meters
10 – 20 meters
20 – 30 meters
30 + meters
1.0
1.25
1.5
Fraction Lowland (-)
0–2
Upland DTB (m)
Lowland DTB (m)
PRELIMINARY GLOBAL MAP OF DTB ESTIMATES
0
0.25
0.5
0.75
1
MODEL ADAPTATION
USING THE DTB DATA IN THE MODEL
0.9°×1.25°
▸The DTB map is used to determine how many CLM soil layers is needed for each grid
cell.
▸A minimum of 5 (0.3 m bottom) and a maximum of 14 layers (28 m bottom).
6
MODEL ADAPTATION
WHEN THE WATER TABLE WITHIN THE SOIL
COLUMN T
T
Ev
Ev
P
CONTROL
CLM Tech Note
Fig. 7.1 qliq,0
Eg
Es
qmelt
qinfl
Es
qover
qliq,0
Eg
qmelt
qinfl
VAR SOIL
qover
qrecharge
qrecharge
Saturated
soil
Aquifer
P
Saturated
soil
qdrai
qbottom = 0
/////////////////////////
qdrai
(as for
CLM4.5
saturated
soil)
7
MODEL ADAPTATION
8
WHEN THE SOIL COLUMN IS UNSATURATED
T
T
Ev
Ev
P
CONTROL
CLM Tech Note
Fig. 7.1 qliq,0
Eg
Es
qmelt
qinfl
Es
qover
qmelt
qliq,0
P
Eg
qinfl
VAR SOIL
qover
qperch
qdrai = 0
qrecharge
Aquifer
qdrai
qbottom = 0
/////////////////////////
qxs
(as for
saturation
excess in
CLM4.5)
9
RESULTS
CONTROL
θ
VAR SOIL
θ/θsat
CONTROL
VAR SOIL
Northern
Saskatchewan
Yakutsk, N.W.
Siberia
Tucson, Ariz.
Tapajos Nat’l
Forest, Brazil
Hindu Kush
Year
Year
Year
Year
10
RESULTS
Rainfall
(mm/day)
Sfc. runoff
Topo drainage
0-3.5
-0.2-+1.0
-0.02-+0.08
0-2.8
-0.1-+0.7
-9-+9x10-5
Northern
Saskatchewan
Yakutsk, N.W.
Siberia
0-2.8
Qian
CONTROL
-0.02-+0.12
-9-+9x10-5
Tucson, Ariz.
VAR SOIL
0-15
0-+2.4
-2.0-+8.0
Tapajos Nat’l
Forest, Brazil
0-4
0-+0.60
-0.003-+0.018
Hindu Kush
Year
Year
Year
11
RESULTS
CHANGES IN THE
SIMULATION OF SOIL
TEMPERATURE
▸Soil temperature affected:
▹Decreased seasonality at
deeper layers except in the
Hindu Kush.
▹Increased Hindu Kush
seasonality tied to drier and
wetter tongues.
▹Possibly due to soil water
changes or due to change
to soil parameters.
CONTROL
VAR SOILCONTROL
Year
Year
Northern
Saskatchewan
Yakutsk, N.W.
Siberia
Tucson, Ariz.
Tapajos Nat’l
Forest, Brazil
Hindu Kush
12
RESULTS
Small changes in latent heat flux except
in arid regions and in some parts of
mid-latitudes in winter.
Sensible heat fluxes generally small in JJA
but can be large in mid- and high latitudes in
other seasons.
CONCLUSIONS
▸Inclusion of variable soil depth changes:
▹Dry tongues to be drier, wet tongues to be wetter.
▹Surface runoff and topographic drainage.
▹The seasonality in deeper soil temperature.
▸Surface quantities differences can be large.
▹Latent heat flux in arid regions and mid-latitudes.
▹Sensible heat flux in mid- and high latitudes.
▸Are these results realistic?  More analysis
▸First step towards hybrid 3-D hydrological modeling.
▹Separately represent hillslope and valley bottom hydrology.
▹Hydrological interaction between gridcells.
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