http://nova.newcastle.edu.au/vital/access/services/Feed ${session.getAttribute("locale")} 5 http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:12319 There are a number of landscape evolution models now developed to the stage where they can be routinely used for both geomorphic evaluations and quantification of soil erosion rates and processes when subject to the action of rainfall and runoff. These models have considerable advantages over traditional modelling approaches as they remove the need to manually determine slope length and angle and because they can also determine both erosion and deposition. A further advantage of digital elevation based models is that they dynamically adjust the landscape in response to erosion and deposition, thus producing a better representation of slope length and angle over the duration of the simulation. A recent advance is that these models now have the ability to employ spatially variable hydrological and erosion parameters, the spatial distribution of soil particle size at user defined soil depths as well as several different flow direction algorithms. While these options are available in these models, minimal evaluation of these hydrological and geomorphological functions has taken place to assess whether they are correct. This study evaluates the well known SIBERIA and CAESAR models for their ability to predict landscape form and erosion for a grassland catchment in South-East Australia under similar rainfall conditions. The results demonstrate that both models predict similar hillslope form as well as erosion rates over a 1000-year modelled period. They also predict erosion rates within the range of independently determined field measured data using environmental tracers at decadal time scales for the site and region demonstrating the models are reliable in the setting examined here. 2012-12-19T02:05:17.086Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:10774 Digital elevation model (DEM) data quality is paramount for accurate representation of the land surface and drainage network. This issue was investigated within a small agricultural catchment in the Upper Hunter Valley region of New South Wales, Australia for a DEM created by use of a Differential Global Positioning System (DGPS) and a 25 m DEM from the New South Wales Government's land mapping authority, Land and Property Information (LPI). A hierarchical scaling approach was used to investigate the effect of increasing DEM grid size on a number of geomorphic and hydrologic descriptors (i.e. area–slope relationship, cumulative area distribution, hypsometric curve, width function, Strahler stream order and stream network statistics), as well as addressing the issue of source data accuracy. Results of qualitative and quantitative assessments indicate that as DEM grid size increased, average slope gradients decreased and the drainage network became increasingly simplified. Geomorphic descriptors such as the width function, cumulative area distribution and hypsometric curve appear largely insensitive to DEM scale. The area–slope relationship loses definition in the diffusive region of the curve at large grid scales; however, the fluvial region appears largely insensitive to changes in DEM resolution. A comparison of long-term field soil moisture data with wetness indices derived from DEMs clearly demonstrates that high resolution DEM data are needed to model soil moisture distribution. A 5 m DEM was found to have the minimum resolution required for the current study site in order to accurately capture catchment geomorphology and hydrology and to model the spatial distribution of soil moisture. 2012-05-21T23:55:33.715Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:10394 The relationship between soil redistribution processes (i.e. soil erosion and deposition), using the caesium-137 (¹³⁷Cs) method, and the movement, storage and loss of soil organic carbon (SOC) are examined for a small catchment in south-eastern Australia. While recent studies have found strong and statistically significant relationships between ¹³⁷Cs and SOC within heavily cultivated (i.e. highly disturbed) landscapes, there has been a dearth of studies in uncultivated conditions. The site used in this study is characterized by different land use histories and soil types and therefore offers a unique opportunity to investigate the relationship between ¹³⁷Cs and SOC for both cultivated and uncultivated conditions. Depth distribution profiles and hillslope transects were sampled for ¹³⁷Cs and SOC to examine the relationship between the redistribution of soil particles and SOC at the point and hillslope scale. It was noted that the distribution of ¹³⁷Cs and SOC with depth in the soil profile differs among land use and soil types. The relationship between ¹³⁷Cs and SOC was also investigated, with results indicating that there was no relationship between ¹³⁷Cs and SOC for uncultivated hillslopes. In contrast, strong and statistically significant relationships were found for the previously cropped transects. The lack of a relationship within uncultivated hillslope areas in the current study appears to indicate that SOC and ¹³⁷Cs are not moving along the same physical pathways or by the same mechanisms, rather it is suggested that the spatial distribution of SOC is a result of biological factors (e.g. biological oxidation, mineralization). The results of this study suggest that the use of ¹³⁷Cs data to predict SOC redistribution patterns in grazing, largely undisturbed landscapes is problematic. Caution is thus required before using ¹³⁷Cs to predict the spatial distribution of SOC within uncultivated landscapes in this region of Australia, and within similar dry climatic regions. 2012-03-13T01:20:03.667Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:7150 Soil erosion rates are quantified using the fallout radionuclide(caesium-137) approach and models (empirical RUSLE and physically based SIBERIA) for a small catchment in south-eastern Australia. Two hillslope transects (under native grass) were sampled for ¹³⁷Cs activity and soil redistribution rates were determined using empirical and theoretical conversion methods. These soil redistribution rates were compared with RUSLE predictions for the two transects and SIBERIA model predictions for the entire catchment. The net soil loss rates established in this study were also compared with the results of other studies in the region obtained with a range of different methods. Estimates based on ¹³⁷Cs using an empirical conversion method compared well with published regional rates derived using rainfall-runoff plots, sediment yields and ¹³⁷Cs, whereas theoretical ¹³⁷Cs conversion models were found to over-estimate soil redistribution rates. Similarly, the RUSLE model significantly overestimated soil erosion rates in this study as was the case in other studies in the region. The agreement between SIBERIA and ¹³⁷Cs, and erosion rates obtained elsewhere in the region, provides confidence in SIBERIA for catchment scale erosion assessments. The results of this study demonstrate the limitations associated with using theoretical ¹³⁷Cs conversion models in environments for which they are not suited. This study also highlights the need for caution when quantifying soil erosion using both field methods and modelling approaches. The results demonstrate that DEM based erosion models are reliable tools for the prediction of soil erosion on the hillslope and catchment scale. 2011-02-02T23:10:31.296Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:3269 Characterising the development of evapotranspiration through time is a difficult task, particularly when utilising remote sensing data, because retrieved information is often spatially dense, but temporally sparse. Techniques to expand these essentially instantaneous measures are not only limited, they are restricted by the general paucity of information describing the spatial distribution and temporal evolution of evaporative patterns. In a novel approach, temporal changes in land surface temperatures, derived from NOAA-AVHRR imagery and a generalised split-window algorithm, are used as a calibration variable in a simple land surface scheme (TOPUP) and combined within the Generalised Likelihood Uncertainty Estimation (GLUE) methodology to provide estimates of areal evapotranspiration at the pixel scale. Such an approach offers an innovative means of transcending the patch or landscape scale of SVAT type models, to spatially distributed estimates of model output. The resulting spatial and temporal patterns of land surface fluxes and surface resistance are used to more fully understand the hydro-ecological trends observed across a study catchment in eastern Australia. The modelling approach is assessed by comparing predicted cumulative evapotranspiration values with surface fluxes determined from Bowen ratio systems and using auxiliary information such as in-situ soil moisture measurements and depth to groundwater to corroborate observed responses. 2010-04-27T05:25:57.770Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:3204 In the absence of direct erosion measurements, the soil activity of the fallout radionuclide caesium–137 (¹³⁳Cs) offers an attractive tool for the estimation of long–term (approximately 45 years) net surface and minor rill soil erosion rates for hillslopes. A transect–based soil sampling technique was applied to one woodland and five grazed pasture hillslopes in the Williams River water–supply catchment in the Hunter Valley, New South Wales. An Australian regression model (SOILOSS) relating net soil loss from runoff–erosion plots to ¹³⁳Cs deficit in soils was used to calculate a weighted net surface and minor rill erosion rate for the six hillslopes. The net median surface erosion rates ranged between 0.00 and 0.64 t ha⁻¹yr⁻¹with the average median soil erosion rate of 0.19 t ha⁻¹ yr⁻¹(std. dev. = 0.23), indicating that these hillslopes were unlikely to be major sources of sediment to the catchment's waterways. Net soil loss rates were also shown to be low in comparison to Australia–wide data and comparable to hillslope data obtained elsewhere in the same region. Minimum and maximum error bounds were provided with each erosion rate to account for radionuclide detector count error. For one hillslope the estimated error due to detection was 1.34 t ha⁻¹yr⁻¹, while the remaining five hillslopes exhibited error of up to 0.41 t ha⁻¹yr⁻¹. Correlation analyses between the net soil loss rates and physical hillslope characteristics were non–significant. 2010-04-27T05:24:41.018Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:3403 This paper reports on a new soil moisture disaggregation scheme based on topography and soil depth information. It is designed for low resolution remote sensing data assimilation into hydrological modelling. The scheme makes use of a simple Soil Vegetation Atmosphere Transfer model coupled to the TOPMODEL formalism. Water and energy balance are computed at the catchment scale, taking lateral flows due to topography into account. Lumped values of near-surface and deep soil water content are then disaggregated at local scale using simple relationship between mean quantities, local topography and soil depth information. Results for a small water catchment in South-eastern Australia show satisfactory reproduction of the local soil moisture patterns using a combination of topography and soil depth information. Due to subgrid variability and differences between the simulation and observation scale (the Digital Elevation Model pixel versus the point measurement), the point-to-point comparison between observations and simulations shows a poor correlation. Rescaling shows that a good correlation is obtained when averaging the simulated and observed soil moisture over a length of 100 m. 2010-04-27T05:01:10.621Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:3401 The determination of relative contributions of potential sediment sources is an important step in the development of management strategies to combat soil erosion. In a 1.2 km² gullied catchment in southeastern New South Wales, multi-parameter fingerprinting of sediment deposited in successive downstream pools has identified gully walls as the dominant sediment source when the grazed pasture surface was the only other potential source. The median fractional contributions remained relatively steady in the successive downstream pools, with the gully walls responsible for between 90% and 98% of the pool sediment. This result was achieved despite the ratio of the source areas varying considerably between successive nested subareas. Reliability bounds on the predictions, accounting for limited sampling of sources, were well constrained and varied between 5.4% and 13.8%. Downstream of an unsealed road crossing, sediment from the road source dominated the pool sediments such that contributions from the pasture surface and gully sources could not be determined. 2010-04-27T05:01:01.766Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:3356 In this paper, a disaggregation approach is suggested for the task of modelling hydrological responses within a spatially and temporally variable environment. With such an approach, large-scale environmental characteristics are tested for their ability to provide insight into the dominant physical mechanisms responsible for observed catchment responses. Using a regional-scale catchment in eastern Australia as a case study, the approach is firstly used to determine the utility of physical catchment data, and its organization in space, to provide insight into the compartmentalization of soil water storage within the catchment. In a second application, temporal disaggregation of the rainfall-runoff record into the cold-wet and warm-dry phases of the El Niño/Southern Oscillation (ENSO) phenomenon is utilized to provide an objective comparison between alternative model structures, based on the 'consistency' of model parameters in describing the effect of ENSO phase on water yield. Finally, combining the improved spatial representation of hydrological response with the model structure identified by temporal analysis is shown to result in a predictive framework whose level of uncertainty is lower than either of the individual strategies, and whose responses are consistent with the available evidence. It is noted that such modelling insight is unlikely to have been gained with traditional modelling strategies that seek to force a predetermined model structure to 'fit' the observed data. 2010-04-27T04:54:50.859Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:3358 Important catchment-scale water and energy balance parameters are derived for a small catchment in southeastern Australia by assimilation in a catchment-scale soil-vegetation-atmosphere transfer (SVAT) model of subcatchment- scale soil water content observations and land surface temperature measurements. In order to incorporate the subcatchment-scale soil moisture variability and its time evolution in a data assimilation scheme, an extended Kalman filter (EK.F) method is used in combination with a cost function minimization approach to derive effective parameters for the catchment as a whole. These parameters are the minimum surface resistance to evaporation and the soil hydrodynamic parameters. This method provides a balanced assessment of all the uncertainties regarding the description of the catchment hydrological behaviour. Moreover, these uncertainties are propagated forward in time in a single framework, which combines soil moisture correction with effective parameter estimation. Two issues are addressed in this paper: (a) the applicability of the method for scaling purposes (effective parameterization) and (b) the relationship between effective parameters obtained by this method and parameters obtained by a classical minimization routine that ignores soil moisture correction and observation uncertainty. Effective parameters are found to be consistent with the available local parameter measurements. Derived parameter sets with and without EKF have been found to be very similar and this has been explained in a number of ways. 2010-04-27T04:54:45.908Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:5258 Soil moisture is highly variable both spatially and temporally. It is widely recognized that improving the knowledge and understanding of soil moisture and the processes underpinning its spatial and temporal distribution is critical. This paper addresses the relationship between near-surface and root zone soil moisture, the way in which they vary spatially and temporally, and the effect of sampling design for determining catchment scale soil moisture dynamics. In this study, catchment scale nearsurface (0–50 mm) and root zone (0–300 mm) soil moisture were monitored over a four-week period. Measurements of near-surface soil moisture were recorded at various resolutions, and near-surface and root zone soil moisture data were also monitored continuously within a network of recording sensors. Catchment average near-surface soil moisture derived from detailed spatial measurements and continuous observations at fixed points were found to be significantly correlated (r² = 0.96; P = 0.0063; n = 4). Root zone soil moisture was also found to be highly correlated with catchment average near-surface, continuously monitored (r² = 0.81; P < 0.0001; n = 26) and with detailed spatial measurements of near-surface soil moisture (r² = 0.84). The weaker relationship observed between near-surface and root zone soil moisture is considered to be caused by the different responses to rainfall and the different factors controlling soil moisture for the soil depths of 0–50 mm and 0–300 mm. Aspect is considered to be the main factor influencing the spatial and temporal distribution of near-surface soil moisture, while topography and soil type are considered important for root zone soil moisture. The ability of a limited number of monitoring stations to provide accurate estimates of catchment scale average soil moisture for both near-surface and root zone is thus demonstrated, as opposed to high resolution spatial measurements. Similarly, the use of near-surface soil moisture measurements to obtain a reliable estimate of deeper soil moisture levels at the small catchment scale was demonstrated. 2010-04-27T04:33:43.197Z ]]>