Recent advances in soil moisture measurement instrumentation and the potential for online estimation...
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Recent advances in soil moisture Recent advances in soil moisture measurement instrumentation and measurement instrumentation and the potential for online the potential for online estimation of catchment status estimation of catchment status for flood and climate for flood and climate forecasting: some experience forecasting: some experience from semi-arid catchments from semi-arid catchments Garry Willgoose Garry Willgoose Earth and Biosphere Institute Earth and Biosphere Institute University of Leeds University of Leeds
Recent advances in soil moisture measurement instrumentation and the potential for online estimation of catchment status for flood and climate forecasting:
Recent advances in soil moisture measurement instrumentation
and the potential for online estimation of catchment status for
flood and climate forecasting: some experience from semi-arid
catchments Garry Willgoose Earth and Biosphere Institute University
of Leeds
Slide 2
Coworkers Walker, Rudiger, Grayson, Western: U. Melbourne
Walker, Rudiger, Grayson, Western: U. Melbourne Kalma, Hemikara,
Hancock, Saco: U. Newcastle (Aust) Kalma, Hemikara, Hancock, Saco:
U. Newcastle (Aust) Houser: NASA Hydrology Houser: NASA Hydrology
Woods: NIWA, NZ Woods: NIWA, NZ Entekhabi: MIT Entekhabi: MIT
Slide 3
The Core Hydrology Question How will emerging microwave remote
sensing techniques for soil moisture assist in estimating the
hydrology of catchments How will emerging microwave remote sensing
techniques for soil moisture assist in estimating the hydrology of
catchments ERS (early 90s) AMSR (current) Hydros (planned) Can
these techniques be integrated with new field instrumentation such
as TDR? Can these techniques be integrated with new field
instrumentation such as TDR?
Slide 4
SASMAS Objectives To ground validate AMSR-E measurements To
ground validate AMSR-E measurements To test data assimilation of SM
using AMSR- E or surrogate To test data assimilation of SM using
AMSR- E or surrogate To test data assimilation of SM using
discharge data (in heavily vegetated areas) To test data
assimilation of SM using discharge data (in heavily vegetated
areas) To understand scaling properties of SM from Ha to 100km2
scale in semi-arid To understand scaling properties of SM from Ha
to 100km2 scale in semi-arid To better understanding C, P balance
in semi- arid catchments To understand floodplain as a temp storage
for sediment from hillslope to river.
Slide 5
Time Domain Reflectometry TDR Integrated depth measurement at a
point Integrated depth measurement at a point Difficult to install
near surface Difficult to install near surface Poor in cracking
soils Poor in cracking soils
Slide 6
Microwave Remote Sensing Typical wavelengths see top few cms of
soil water and canopy water, impacted by soil surface condition
(roughness). Typical wavelengths see top few cms of soil water and
canopy water, impacted by soil surface condition (roughness).
Repeat rate at best Repeat rate at best Radiometer: twice/day @ low
space resolution (10-30 km) Radar: ~once month @ high resolution
(20- 30m) NOT measuring state of interest: whole profile soil water
at catchment scale=ET. NOT measuring state of interest: whole
profile soil water at catchment scale=ET.
Slide 7
But we can model profile soil water state Frequent measurements
of surface soil moisture and model to simulate profile. Frequent
measurements of surface soil moisture and model to simulate
profile. Potentially with sufficient soil data can remote sense
soil depth and water holding capacity. Potentially with sufficient
soil data can remote sense soil depth and water holding
capacity.
Slide 8
Assimilation Period Synthetic Simulations Surface soil moisture
drives the estimation of soil moisture down the profile
Slide 9
Field Data Dotted simulations (surface moisture DA) best track
the long-term data and the rise in May. Dotted simulations (surface
moisture DA) best track the long-term data and the rise in
May.
Slide 10
What about spatial patterns? Tarrawarra site (Grayson, Western,
Willgoose, McMahon) Tarrawarra site (Grayson, Western, Willgoose,
McMahon) Switch from arid (disorganised) to humid (organised).
Switch from arid (disorganised) to humid (organised). Is arid data
disorganised or is it deterministically linked to spatially random
soils properties? Single probe calibration. Is arid data
disorganised or is it deterministically linked to spatially random
soils properties? Single probe calibration.
Slide 11
SASMAS 01 Sampling 40 x 50km area 40 x 50km area North of
Goulburn River within unforested region North of Goulburn River
within unforested region 4 teams over 3 days 4 teams over 3 days
Sampled area about scale of AMSR pixel Sampled area about scale of
AMSR pixel 225 soil moisture samples sites (4 gravimetric, 5 TDR),
225 soil moisture samples sites (4 gravimetric, 5 TDR), 194 veg
samples 194 veg samples
The Stanley micro-site 1km x 2km for look at hillslope
organisation of soil moisture. Semi-arid => not topographic
index soils, veg? 1km x 2km for look at hillslope organisation of
soil moisture. Semi-arid => not topographic index soils, veg? 7
permanent TDR sites, 1-3 levels in the soil 7 permanent TDR sites,
1-3 levels in the soil Runoff gauging Runoff gauging
Slide 14
Sample of a at-a-point time series Strong response to rainfall
and good correlation between depths. Strong response to rainfall
and good correlation between depths.
Slide 15
Stanley Deep Soil Moisture Good correlation over 2km Good
correlation over 2km Appears likely to be able to calibrate a
single probe (i.e. difference between sites due to permanent
effects) Appears likely to be able to calibrate a single probe
(i.e. difference between sites due to permanent effects) Soil
moisture correlations are parallel => soil moisture process is
vertical rather than a lateral topographic index type process Soil
moisture correlations are parallel => soil moisture process is
vertical rather than a lateral topographic index type process
Slide 16
Stanley Surface Soil Moisture Correlation of surface soil
moistures not as good Correlation of surface soil moistures not as
good Cross correlation with deeper soil moistures also not as good
Cross correlation with deeper soil moistures also not as good Is
+/- 10% accuracy good enough? Is +/- 10% accuracy good enough?
Implications for remote sensing Implications for remote sensing
Soil moisture correlations definitely parallel Soil moisture
correlations definitely parallel
Slide 17
Short distance (sample scale) correlation Significant
correlation scale of 0.2- 0.5m. None up to 10m. Apparently
unrelated to vegetation patterns. Also unrelated to SM status.
Soils? Significant correlation scale of 0.2- 0.5m. None up to 10m.
Apparently unrelated to vegetation patterns. Also unrelated to SM
status. Soils? Implication: Hand held sampling is unrepeatable at
the hillslope scale, though fixed sites indicate significant
spatial correlation at this scale. Implication: Hand held sampling
is unrepeatable at the hillslope scale, though fixed sites indicate
significant spatial correlation at this scale. More handheld
sampling planned in March for the 10-1000m scale. More handheld
sampling planned in March for the 10-1000m scale. If SM correlation
can be used as surrogate for soil variability what drives the soil
variability? Implications for hydrology? If SM correlation can be
used as surrogate for soil variability what drives the soil
variability? Implications for hydrology?
Slide 18
A tentative Conclusion from field data There appears to be a
nontrivial spatial correlation 1-3 km (from surface soil moisture
maps). Still processing recent SASMAS field campaigns. There
appears to be a nontrivial spatial correlation 1-3 km (from surface
soil moisture maps). Still processing recent SASMAS field
campaigns. This correlation appears to be consistent through time
(from correlation between permanent stations) This correlation
appears to be consistent through time (from correlation between
permanent stations) We can assimilate profile soil moisture from
surface measurements (whether radar or TDR ) We can assimilate
profile soil moisture from surface measurements (whether radar or
TDR ) Conclusion: The spatial correlation is a function of
permanent properties of the catchment (e.g. soil, vegetation)
rather than temporally uncorrelated fns such as rainfall.
Conclusion: The spatial correlation is a function of permanent
properties of the catchment (e.g. soil, vegetation) rather than
temporally uncorrelated fns such as rainfall. Implications: We can
(in principle) predict catchment scale soil moisture from single
site TDR measurements (but short correlation scale => permanent
sites required not hand held) Implications: We can (in principle)
predict catchment scale soil moisture from single site TDR
measurements (but short correlation scale => permanent sites
required not hand held)
Slide 19
Results from a synthetic data assimilation study using stream
runoff (for heavy veg sites) Root zone soil moisture well
assimilated Root zone soil moisture well assimilated Surface soil
moisture also well simulated but more sensitive to noise Surface
soil moisture also well simulated but more sensitive to noise
Slide 20
Climate Model Initialisation
Slide 21
Soil moisture and climate Koster (NASA) showed that global
climate dynamics/forecasts (months-years) sensitive to soil
moisture (through energy partitioning ET) Koster (NASA) showed that
global climate dynamics/forecasts (months-years) sensitive to soil
moisture (through energy partitioning ET) Entekhabi (MIT) showed
bimodal continental climates as a result of rainfall feedback
Entekhabi (MIT) showed bimodal continental climates as a result of
rainfall feedback Eltahir (MIT) showed Sahel had three stable
climate/vegetation states due to feedbacks. Eltahir (MIT) showed
Sahel had three stable climate/vegetation states due to
feedbacks.
Slide 22
Continental feedbacks Relative strength of ET to ocean moisture
determines the local feedback Relative strength of ET to ocean
moisture determines the local feedback Ocean moisture ET
Rainfall
Slide 23
How much latent heat transfer from vegetation? From Choudhury
(NASA)
Slide 24
Potential role of TDR and RS Vegetation extracts from deeper
layers so raw remote sensing will not capture full behaviour
profile modelling necessary. Vegetation extracts from deeper layers
so raw remote sensing will not capture full behaviour profile
modelling necessary. TDR ground truth soil moisture potentially
calibratable to regional averages. TDR ground truth soil moisture
potentially calibratable to regional averages. Potential for a
network attached to meteorology stations. Potential for a network
attached to meteorology stations.
Slide 25
Conclusions Point monitoring and telemetering of soil moisture
now possible and economic. Point monitoring and telemetering of
soil moisture now possible and economic. Not easy to use upcoming
RS data (concentrated on surface response). Not easy to use
upcoming RS data (concentrated on surface response). TDR point
scale data appears to be regionalisable. Profile data would
complement surface imaging. TDR point scale data appears to be
regionalisable. Profile data would complement surface imaging.