Infrastructure Engineering - Research Publications

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    Toward capturing hydrologically significant connectivity in spatial patterns
    Western, AW ; Blöschl, G ; Grayson, RB (AMER GEOPHYSICAL UNION, 2001-01)
    Many spatial fields exhibit connectivity features that have an important influence on hydrologic behavior. Examples include high‐conductivity preferred flow paths in aquifers and saturated source areas in drainage lines. Connected features can be considered as arbitrarily shaped bands or pathways of connected pixels having similar (e.g., high) values. Connectivity is a property that is not captured by standard geostatistical approaches, which assume that spatial variation occurs in the most random possible way that is consistent with the spatial correlation, nor is it captured by indicator geostatistics. An alternative approach is to use connectivity functions. In this paper we apply connectivity functions to 13 observed soil moisture patterns from the Tarrawarra catchment and two synthetic aquifer conductivity patterns. It is shown that the connectivity functions are able to distinguish between connected and disconnected patterns. The importance of the connectivity in determining hydrologic behavior is explored using rainfall‐runoff simulations and groundwater transport simulations. We propose the integral connectivity scale as a measure of the presence of hydrologic connectivity. Links between the connectivity functions and integral connectivity scale and simulated hydrologic behavior are demonstrated and explained from a hydrologic process perspective. Connectivity functions and the integral connectivity scale provide promising means for characterizing features that exist in observed spatial fields and that have an important influence on hydrologic behavior. Previously, this has not been possible within a statistical framework.
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    On the computation of the quasi-dynamic wetness index with multiple-flow-direction algorithms
    Chirico, GB ; Grayson, RB ; Western, AW (AMER GEOPHYSICAL UNION, 2003-05-06)
    The quasi‐dynamic wetness index, in its original development, was computed by calculating the travel time along all the possible upslope flow paths on a contour‐based terrain network. In more recent applications the same approach has been extended to gridded digital elevation models with single‐flow‐direction algorithms. Multiple‐flow‐direction algorithms, although more effective in representing flow paths, have not been used because they are not practicable with the established methodology. We propose an alternative method for computing the quasi‐dynamic wetness index based on the numerical integration of the linear‐kinematic wave equation. This method can be applied to any of the terrain‐based flow‐direction algorithms currently published. The method is robust and efficient.
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    Identifying and quantifying sources of variability in temporal and spatial soil moisture observations
    Wilson, DJ ; Western, AW ; Grayson, RB (American Geophysical Union, 2004-02-20)
    Soil moisture is an important component of the hydrological cycle. It is a control in the partitioning of energy and water related to evapotranspiration and runoff and thereby influences the hydrological response of an area. Characterizing the temporal and spatial distribution of soil moisture has important hydrologic applications, yet soil moisture varies in response to many processes acting over a variety of scales; the relative importance of different temporal and spatial controls on soil moisture is still poorly understood. In this paper we analyze both temporal and spatial soil moisture data empirically for two catchments in Australia and a further three in New Zealand. Hydrological conditions at these field sites covered a wide range over a 2 year period. The ground-based soil moisture data set is unique in its temporal and, in particular, its spatial coverage. Analyses attempt to isolate and quantify different deterministic sources of variability, measurement error, and a remaining unexplained component of variability. Because of limited data (especially relating to soils) we take a pragmatic approach of removing patterns that we can define in time and space (namely, seasonality and terrain) and then analyzing the unexplained variation. We then look for consistent patterns in this unexplained variability and argue that these are related to meteorological conditions, especially precipitation events, in the temporal case, and a combination of soils and vegetation in the spatial case. We were able to explain most of the observed variance in time and space, and the temporal variance was typically 5 times larger than spatial variance. Seasonality is the dominant source of temporal variability at our sites, although this conclusion obviously depends on climate and does not hold where soil water storage is limited. Most importantly, in controlling the distribution of soil moisture in space, the spatial distribution of soils and vegetation seems to be of similar importance to that of topography, a fact often ignored in hydrological modeling, or else surrogate soils patterns are used, but these are often not well correlated to the actual patterns [Grayson and Blöschl, 2000]. Better methods for defining the spatial properties of soils and vegetation as they affect soil moisture patterns are a key challenge.
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    Scaling from process timescales to daily time steps: A distribution function approach
    Kandel, DD ; Western, AW ; Grayson, RB (American Geophysical Union, 2005-02)
    A new temporal scaling method applicable to many rainfall-runoff-erosion models is presented. The method is based on the probability distribution approach used in a number of spatial hydrological models, and it uses statistical distributions of rainfall intensity to represent subdaily intensity variations in a daily time step model. This allows the effect of short timescale nonlinear processes to be captured while modeling at a daily time step, which is often attractive due to the wide availability of total daily rainfall data. The approach relies on characterizing the rainfall intensity variation within a day using a probability distribution function (pdf). This pdf is then modified by various linear and nonlinear processes typically represented in hydrological and erosion models. The statistical description of subdaily variability is thus propagated through the model, allowing the effects of variability to be captured in the simulations. This results in pdfs of various fluxes, the integration of which over a day gives respective daily totals. The method is tested using 42 plot years of daily runoff and erosion plot data from field studies in different environments from Australia and Nepal. Significant improvements in the simulation of surface runoff and erosion are achieved, compared with a similar model using average daily rainfall intensities. The probability-based model compares well with a subhourly (2 and 6 min) model using similar process descriptions. This suggests that the probability-based approach captures the important effects of sub–time step variability while utilizing commonly available information. It is also found that the model parameters are more robustly defined using the probability-based approach compared with the daily effective parameter model. This suggests that the probability-based approach may offer improved model transferability spatially (to other areas) and temporally (to other periods).