Abstract
Increasing stress on the world’s fresh water resources demands
improvements to be made in increasing water productivity and encouraging
efficient use of this limited resource. Global scale land surface models help
us understand and predict the behaviour of terrestrial, atmospheric, climatic
and hydrological processes that govern the ability to continuously update
environmental policies. Irrigated agriculture is the largest consumer of the
world’s fresh water resources and is a key component of the terrestrial
water cycle. Global food security, which greatly depends upon irrigated
agriculture, is facing serious threats due to limited fresh water availability.
One of the most important factors for sustainability of irrigation in the
future, under water stress circumstances, is the effort towards developing a
better understanding of the land-atmosphere coupling processes that
govern the hydrological principles and lead to effective use of water with
better model predictions. Quantification of evapotranspiration is one of the
vital components for water budgeting, efficient irrigation scheduling,
cropping practices and water regulation in an irrigation system. Many
remote-sensing algorithms have been developed over the years to model
spatial actual evapotranspiration for larger areas to help improve water
balances. More recently, efforts to derive surface soil moisture information
to improve agricultural monitoring quality through remote sensing have also
increased with the advancements in microwave sensing and retrieval
models.
Continuous efforts over the past few years to model soil moisture
and relate it to point- and remote-sensed observations have led to somewhat improved availability and quality of surface soil moisture
datasets. As a result of increased availability of microwave soil moisture
datasets, it is now recommendable to test the potential of estimating rootzone
soil moisture from remote sensing derived surface soil moisture to
understand its spatial distribution. This study aims to explore the coupling
of microwave derived soil moisture with surface energy balance
components, and investigate the potential of estimating root-zone soil
moisture by using a practicable, simplified assimilation technique in the
Murrumbidgee catchment.
The study can be categorised into three stages. In the first stage, a
Large Aperture Scintillometer (LAS) was installed over a horticultural farm
near Leeton, NSW, to provide ground calibration for remote sensing energy
balance modelling. LAS scintillation data was used to calculate sensible heat
flux for the entire half-hourly time-series data. The latent heat flux was then
estimated by solving an energy balance of fluxes measured by net
radiometer and soil heat flux, and H calculated using LAS. The LAS
performed very well for the purpose of heat flux estimation for energy
balance closure, provided the data was filtered for bad or missing values
generated by various meteorological conditions or sensor errors. A network
of meteorological stations enabled the testing of sensitivity of micrometeorological variations occurring within the path-length of the LAS on
sensible heat flux calculations. One-way between groups ANOVA analysis
and Tukey’s HSD analysis suggested that moving the AWS further along the
stretched path length will generate statistically significant differences in
sensible heat flux, and eventually influence the whole energy balance
closure i.e. meteorological conditions differed enough towards the centre of the LAS beam to produce a mean difference of ~14 W/m2 in the sensible
heat flux. The difference in the instantaneous estimate of H reached up to
100 W/m2 in some instances. A 50–100 W/m2 difference in sensible heat
flux can result in a 1.25–2.5 mm.d-1 error in daily evapotranspiration flux
and affect the entire water balance for large regions. Further, energy
balance modelling over the Murrumbidgee catchment was performed using
Terra/MODIS data for year 2010/11. The results revealed that SEBS
overestimated soil heat flux for higher values while it underestimated net
radiation for higher values.
Later, root-zone soil moisture dynamics were modelled using an
exponential filter (Wagner et al., 1999) using an AMSR-E surface soil
moisture dataset over six ground calibration sites. Time-step-based
statistical analysis between SEBS-derived actual evapotranspiration was
analysed with in-situ observations of surface soil moisture. Weak negative
correlation was observed between moisture and actual evapotranspiration,
which was not seen while relating surface fluxes to AMSR-E-derived
moisture at these sites. Finally, cross-correlation analysis was carried out
between measured surface and measured root-zone moisture time-series
with a time lag of 1 day to match Aqua/AMSR-E temporal resolution. A
strong positive correlation was found between the observed surface
moisture (SMSL(t)) and observed root-zone moisture of next (SMRZ(t+1)). An
exponential filter was applied on the AMSR-E soil moisture time-series to
calculate sub-surface moisture. The model performed poorly in estimating
root-zone moisture from AMSR-E data. A maximum correlation of 0.4681 with a low Nash-Sutcliffe coefficient value of -1.23 was observed. The applied exponential filter model-DA showed potential for root-zone moisture extraction, but the accuracy observation serves as a pre-requisite to base our understanding of spatial distribution of moisture and its coupling with actual evapotranspiration on it. Improvements in remotely sensed soil moisture observations will act as the cornerstone in enhancing understandings in land-atmosphere coupling by facilitating an operational assimilation scheme for estimating spatial root-zone soil moisture that is representative of the actual moisture state.
improvements to be made in increasing water productivity and encouraging
efficient use of this limited resource. Global scale land surface models help
us understand and predict the behaviour of terrestrial, atmospheric, climatic
and hydrological processes that govern the ability to continuously update
environmental policies. Irrigated agriculture is the largest consumer of the
world’s fresh water resources and is a key component of the terrestrial
water cycle. Global food security, which greatly depends upon irrigated
agriculture, is facing serious threats due to limited fresh water availability.
One of the most important factors for sustainability of irrigation in the
future, under water stress circumstances, is the effort towards developing a
better understanding of the land-atmosphere coupling processes that
govern the hydrological principles and lead to effective use of water with
better model predictions. Quantification of evapotranspiration is one of the
vital components for water budgeting, efficient irrigation scheduling,
cropping practices and water regulation in an irrigation system. Many
remote-sensing algorithms have been developed over the years to model
spatial actual evapotranspiration for larger areas to help improve water
balances. More recently, efforts to derive surface soil moisture information
to improve agricultural monitoring quality through remote sensing have also
increased with the advancements in microwave sensing and retrieval
models.
Continuous efforts over the past few years to model soil moisture
and relate it to point- and remote-sensed observations have led to somewhat improved availability and quality of surface soil moisture
datasets. As a result of increased availability of microwave soil moisture
datasets, it is now recommendable to test the potential of estimating rootzone
soil moisture from remote sensing derived surface soil moisture to
understand its spatial distribution. This study aims to explore the coupling
of microwave derived soil moisture with surface energy balance
components, and investigate the potential of estimating root-zone soil
moisture by using a practicable, simplified assimilation technique in the
Murrumbidgee catchment.
The study can be categorised into three stages. In the first stage, a
Large Aperture Scintillometer (LAS) was installed over a horticultural farm
near Leeton, NSW, to provide ground calibration for remote sensing energy
balance modelling. LAS scintillation data was used to calculate sensible heat
flux for the entire half-hourly time-series data. The latent heat flux was then
estimated by solving an energy balance of fluxes measured by net
radiometer and soil heat flux, and H calculated using LAS. The LAS
performed very well for the purpose of heat flux estimation for energy
balance closure, provided the data was filtered for bad or missing values
generated by various meteorological conditions or sensor errors. A network
of meteorological stations enabled the testing of sensitivity of micrometeorological variations occurring within the path-length of the LAS on
sensible heat flux calculations. One-way between groups ANOVA analysis
and Tukey’s HSD analysis suggested that moving the AWS further along the
stretched path length will generate statistically significant differences in
sensible heat flux, and eventually influence the whole energy balance
closure i.e. meteorological conditions differed enough towards the centre of the LAS beam to produce a mean difference of ~14 W/m2 in the sensible
heat flux. The difference in the instantaneous estimate of H reached up to
100 W/m2 in some instances. A 50–100 W/m2 difference in sensible heat
flux can result in a 1.25–2.5 mm.d-1 error in daily evapotranspiration flux
and affect the entire water balance for large regions. Further, energy
balance modelling over the Murrumbidgee catchment was performed using
Terra/MODIS data for year 2010/11. The results revealed that SEBS
overestimated soil heat flux for higher values while it underestimated net
radiation for higher values.
Later, root-zone soil moisture dynamics were modelled using an
exponential filter (Wagner et al., 1999) using an AMSR-E surface soil
moisture dataset over six ground calibration sites. Time-step-based
statistical analysis between SEBS-derived actual evapotranspiration was
analysed with in-situ observations of surface soil moisture. Weak negative
correlation was observed between moisture and actual evapotranspiration,
which was not seen while relating surface fluxes to AMSR-E-derived
moisture at these sites. Finally, cross-correlation analysis was carried out
between measured surface and measured root-zone moisture time-series
with a time lag of 1 day to match Aqua/AMSR-E temporal resolution. A
strong positive correlation was found between the observed surface
moisture (SMSL(t)) and observed root-zone moisture of next (SMRZ(t+1)). An
exponential filter was applied on the AMSR-E soil moisture time-series to
calculate sub-surface moisture. The model performed poorly in estimating
root-zone moisture from AMSR-E data. A maximum correlation of 0.4681 with a low Nash-Sutcliffe coefficient value of -1.23 was observed. The applied exponential filter model-DA showed potential for root-zone moisture extraction, but the accuracy observation serves as a pre-requisite to base our understanding of spatial distribution of moisture and its coupling with actual evapotranspiration on it. Improvements in remotely sensed soil moisture observations will act as the cornerstone in enhancing understandings in land-atmosphere coupling by facilitating an operational assimilation scheme for estimating spatial root-zone soil moisture that is representative of the actual moisture state.
| Original language | English |
|---|---|
| Qualification | Doctor of Philosophy |
| Awarding Institution |
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| Supervisors/Advisors |
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| Award date | 18 Nov 2014 |
| Place of Publication | Australia |
| Publisher | |
| Publication status | Published - 2014 |