Infrastructure Engineering - Research Publications
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ItemToward capturing hydrologically significant connectivity in spatial patterns(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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ItemOn the computation of the quasi-dynamic wetness index with multiple-flow-direction algorithms(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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ItemCharacteristic space scales and timescales in hydrology(AMER GEOPHYSICAL UNION, 2003-10-30)We analyzed spatial and temporal variograms of precipitation, runoff, and groundwater levels in Austria to examine whether characteristic scales exist and, if so, how big they are. In time, precipitation and runoff are stationary with characteristic scales on the order of a day and a month, respectively, while groundwater levels are nonstationary. In space, precipitation is almost fractal, so no characteristic scales exist. Runoff is nonstationary but not a fractal as it exhibits a break in the variograms. An analysis of the variograms of catchment precipitation indicates that this break is due to aggregation effects imposed by the catchment area. A spatial variogram of hypothetical point runoff back calculated from runoff variograms of three catchment size classes using aggregation statistics (regularization) is almost stationary and exhibits shorter characteristic space scales than catchment runoff. Groundwater levels are nonstationary in space, exhibiting shorter‐scale variability than precipitation and runoff, but are also not fractal as there is a break in the variogram. We suggest that the decrease of spatial characteristic scales from catchment precipitation to runoff and to groundwater is the result of a superposition of small‐scale variability of catchment and aquifer properties on the rainfall forcing. For comparison, TDR soil moisture data from a comprehensive Australian data set were examined. These data suggest that in time, soil moisture is close to stationary with characteristic scales of the order of 2 weeks while in space soil moisture is nonstationary and close to fractal over the extent sampled. Space‐time traces of characteristic scales fit well into a conceptual diagram of Blöschl and Sivapalan [1995]. The scaling exponents z in T ∼ Lz (where T is time and L is space) are of the order of 0.5 for precipitation, 0.8 for runoff from small catchments, 1.2 for runoff from large catchments, 0.8 for groundwater levels, and 0.5 for soil moisture.
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ItemA rational function approach for estimating mean annual evapotranspiration(AMER GEOPHYSICAL UNION, 2004-02-05)Mean annual evapotranspiration from a catchment is determined largely by precipitation and potential evapotranspiration; characteristics of the catchment (e.g., soil, topography, etc.) play only a secondary role. It has been shown that the ratio of mean annual potential evapotranspiration to precipitation (referred as the index of dryness) can be used to estimate mean annual evapotranspiration by using one additional parameter. This study evaluates the effects of climatic and catchment characteristics on the partitioning of mean annual precipitation into evapotranspiration using a rational function approach, which was developed based on phenomenological considerations. Over 470 catchments worldwide with long‐term records of precipitation, potential evapotranspiration, and runoff were considered, and results show that model estimates of mean annual evapotranspiration agree well with observed evapotranspiration taken as the difference between precipitation and runoff. The mean absolute error between modeled and observed evapotranspiration was 54 mm, and the model was able to explain 89% of the variance with a slope of 1.00 through the origin. This indicates that the index of dryness is the most significant variable in determining mean annual evapotranspiration. Results also suggest that forested catchments tend to show higher evapotranspiration than grassed catchments and their evapotranspiration ratio (evapotranspiration divided by precipitation) is most sensitive to changes in catchment characteristics for regions with the index of dryness around 1.0. Additionally, a stepwise regression analysis was performed for over 270 Australian catchments where detailed information of vegetation cover, precipitation characteristics, catchment slopes, and plant available water capacity was available. It is shown that apart from the index of dryness, average storm depth, plant available water capacity, and storm arrival rate are also significant.
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ItemIdentifying and quantifying sources of variability in temporal and spatial soil moisture observations(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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ItemScaling from process timescales to daily time steps: A distribution function approach(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).
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ItemHydropedology: Synergistic integration of pedology and hydrology(American Geophysical Union, 2006-05-09)This paper presents a vision that advocates hydropedology as an advantageous integration of pedology and hydrology for studying the intimate relationships between soil, landscape, and hydrology. Landscape water flux is suggested as a unifying precept for hydropedology, through which pedologic and hydrologic expertise can be better integrated. Landscape water flux here encompasses the source, storage, flux, pathway, residence time, availability, and spatiotemporal distribution of water in the root and deep vadose zones within the landscape. After illustrating multiple knowledge gaps that can be addressed by the synergistic integration of pedology and hydrology, we suggest five scientific hypotheses that are critical to advancing hydropedology and enhancing the prediction of landscape water flux. We then present interlinked strategies for achieving the stated vision. It is our hope that by working together, hydrologists and pedologists, along with scientists in related disciplines, can better guide data acquisition, knowledge integration, and model-based prediction so as to advance the hydrologic sciences in the next decade and beyond.
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ItemMultiple stable states in hydrological models: An ecohydrological investigation(American Geophysical Union, 2009-03-07)Many physical-based models of surface and groundwater hydrology are constructed without the possibility of multiple stable states for the same parameter set. For such a conceptualization, at the cessation of a transient hydrological disturbance of any magnitude the model will return to the same stable state and thus show an infinite resilience. To highlight and falsify this assumption, a numerical distributed ecohydrological model (coupled hillslope Boussinesq-vertically lumped vadose zone) is presented, in which qualitatively different steady state water table elevations exist for the same parameter set. The multiple steady states are shown to emerge from a positive feedback arising from a reduction in leaf area index (LAI) and thus transpiration, as a saline water table approaches the surface. Limit cycle continuation is also undertaken to quantify the state-space location of the threshold (repellor) between the steady states (attractors) and quantify the resilience. While the model is biophysically simple, it is sufficiently complex to challenge this potentially significant assumption within water resource planning.
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ItemOptimization of a similarity measure for estimating ungauged streamflow(AMER GEOPHYSICAL UNION, 2009-10-17)One approach to predicting streamflow in an ungauged catchment is to select an ensemble of hydrological models previously identified for similar gauged catchments, where the similarity is based on some combination of important physical catchment attributes. The focus of this paper is the identification of catchment attributes and optimization of a similarity measure to produce the best possible ungauged streamflow predictions given a data set and a conceptual model structure. As a case study, the SimHyd rainfall‐runoff model is applied to simulate monthly streamflow in 184 Australian catchments. Initial results show that none of 27 catchment attributes can be safely said to consistently give a better ensemble of models than random selection when used independently of other attributes. This is contrary to prior expectations and indicates the sparseness of information within our database of catchments, the importance in this case of prior knowledge for defining important attributes, and the potential importance of combining multiple attributes in order to usefully gauge similarity. Seven relatively independent attributes are then selected on the basis of prior knowledge. The weight with which each of these attributes contributes to the similarity measure is optimized to maximize streamflow prediction performance across a set of 95 catchments. The other 89 catchments are used to independently test the accuracy of streamflow predictions. Using the optimal set of weights led to marked improvement in the accuracy of predictions, showing that the method, while inferior to local calibration, is superior to alternative methods of model regionalization based on regression and spatial proximity. However, there is evidence of nonuniqueness in the optimal solution and the possibility that the attribute weights are somewhat dependent on the catchments used.
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ItemStochastic modelling of annual rainfall data(Conference Design, 2006-01-01)Rainfall data are generally required in computer simulations of rainfall-runoff processes, crop growth and water supply systems. The length of historical climate data is usually not long enough to describe the complete range of variability that might be experienced during the life of a water resources or agricultural project. Using the statistical characteristics of historical data, it is possible to generate many sequences of data that better represent the climatic variability. In developing the stochastic models, the data are generally assumed stationary in the broad sense and any long-term fluctuations in the data are ignored. Typically, only in monthly, daily and sub-daily models, is the seasonal variation within a year considered explicitly in stochastic models. However, there is a growing interest and concern about the role of interdecadal variability in climate and its influence on rainfall. One approach is to identify any long-term fluctuations in the observed rainfall and model them explicitly. Empirical Mode Decomposition (EMD) was used to identify any low frequency fluctuations in annual rainfall data from 44 sites in Australia. The results did not allow easy identification of low frequency fluctuations in the data. As a means of aiding interpretation of the EMD results, the following ploy was adopted. The AR1 model, the most widely used model for the generation of annual rainfall data, was used to generate stochastic data based on the statistics of the observed sequences and the EMD analysis was performed on the stochastic data sets. The results of the analysis comparing both the historical and generated data showed that, in general, both the data sets have similar low frequency characteristics except for Perth.