Infrastructure Engineering - Research Publications
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ItemImproving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes( 2014-09-23)Abstract. Assimilation of remotely sensed soil moisture data (SM–DA) to correct soil water stores of rainfall-runoff models has shown skill in improving streamflow prediction. In the case of large and sparsely monitored catchments, SM–DA is a particularly attractive tool. Within this context, we assimilate active and passive satellite soil moisture (SSM) retrievals using an ensemble Kalman filter to improve operational flood prediction within a large semi-arid catchment in Australia (>40 000 km2). We assess the importance of accounting for channel routing and the spatial distribution of forcing data by applying SM–DA to a lumped and a semi-distributed scheme of the probability distributed model (PDM). Our scheme also accounts for model error representation and seasonal biases and errors in the satellite data. Before assimilation, the semi-distributed model provided more accurate streamflow prediction (Nash–Sutcliffe efficiency, NS = 0.77) than the lumped model (NS = 0.67) at the catchment outlet. However, this did not ensure good performance at the "ungauged" inner catchments. After SM–DA, the streamflow ensemble prediction at the outlet was improved in both the lumped and the semi-distributed schemes: the root mean square error of the ensemble was reduced by 27 and 31%, respectively; the NS of the ensemble mean increased by 7 and 38%, respectively; the false alarm ratio was reduced by 15 and 25%, respectively; and the ensemble prediction spread was reduced while its reliability was maintained. Our findings imply that even when rainfall is the main driver of flooding in semi-arid catchments, adequately processed SSM can be used to reduce errors in the model soil moisture, which in turn provides better streamflow ensemble prediction. We demonstrate that SM–DA efficacy is enhanced when the spatial distribution in forcing data and routing processes are accounted for. At ungauged locations, SM–DA is effective at improving streamflow ensemble prediction, however, the updated prediction is still poor since SM–DA does not address systematic errors in the model.
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ItemNo Preview AvailableA predictive model for spatio-temporal variability in stream water quality( 2019-07-23)Abstract. Degraded water quality in rivers and streams can have large economic, societal and ecological impacts. Stream water quality can be highly variable both over space and time. To develop effective management strategies for riverine water quality, it is critical to be able to predict these spatio-temporal variabilities. However, our current capacity to model stream water quality is limited, particularly at large spatial scales across multiple catchments. This is due to a lack of understanding of the key controls that drive spatio-temporal variabilities of stream water quality. To address this, we developed a Bayesian hierarchical statistical model to analyse the spatio-temporal variability in stream water quality across the state of Victoria, Australia. The model was developed based on monthly water quality monitoring data collected at 102 sites over 21 years. The modelling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate-nitrite (NOx), and electrical conductivity (EC). Among the six constituents, the models explained varying proportions of variation in water quality. EC was the most predictable constituent (88.6 % variability explained) and FRP had the lowest predictive performance (19.9 % variability explained). The models were validated for multiple sets of calibration/validation sites and showed robust performance. Temporal validation revealed a systematic change in the TSS model performance across most catchments since an extended drought period in the study region, highlighting potential shifts in TSS dynamics over the drought. Further improvements in model performance need to focus on: (1) alternative statistical model structures to improve fitting for the low concentration data, especially records below the detection limit; and (2) better representation of non-conservative constituents by accounting for important biogeochemical processes. We also recommend future improvements in water quality monitoring programs which can potentially enhance the model capacity, via: (1) improving the monitoring and assimilation of high-frequency water quality data; and (2) improving the availability of data to capture land use and management changes over time.
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ItemThe behavior of stratified pools in the Wimmera River, Australia(AMER GEOPHYSICAL UNION, 1996-10)Numerous inland Australian streams contain density‐stratified or saline pools, which are usually located on channel bends. Saline pools consist of a layer of saline water underlying a layer of fresh water. Saline pools generally form as a result of saline groundwater seeping into the stream and collecting in scour depressions during periods of low flow. Inflows of saline river water can also collect in scour depressions. Field and laboratory investigations of saline pool mixing by overflowing fresh water reveal that mixing depends on a balance between interfacial shear and buoyancy forces acting on a thin dense layer flowing up the downstream slope of the scour depression, and on the bend sharpness. Convection associated with surface cooling also causes mixing. A model for saline pools formed by groundwater inflows and mixed by fresh overflows is proposed and applied to several saline pools in the Wimmera River.
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ItemPreferred states in spatial soil moisture patterns: Local and nonlocal controls(AMER GEOPHYSICAL UNION, 1997-12)In this paper we develop a conceptual and observational case in which soil water patterns in temperate regions of Australia switch between two preferred states. The wet state is dominated by lateral water movement through both surface and subsurface paths, with catchment terrain leading to organization of wet areas along drainage lines. We denote this as nonlocal control. The dry state is dominated by vertical fluxes, with soil properties and only local terrain (areas of high convergence) influencing spatial patterns. We denote this as local control. The switch is described in terms of the dominance of lateral over vertical water fluxes and vice versa. When evapotranspiration exceeds rainfall, the soil dries to the point where hydraulic conductivity is low and any rainfall that occurs essentially wets up the soil uniformly and is evapotranspired before any significant lateral redistribution takes place. As evapotranspiration decreases and/or rainfall increases, areas of high local convergence become wet, and runoff that is generated moves downslope, rapidly wetting up the drainage lines. In the wet to dry transitional period a rapid increase in potential evapotranspiration (and possibly a decrease in rainfall) causes drying of the soil and “shutting down” of lateral flow. Vertical fluxes dominate and the “dry” pattern is established. Three data sets from two catchments are presented to support the notion of preferred states in soil moisture, and the results of a modeling exercise on catchments from a range of climatic conditions illustrate that the conclusions from the field studies may apply to other areas. The implications for hydrological modeling are discussed in relation to methods for establishing antecedent moisture conditions for event models, for distribution models, and for spatially distributing bulk estimates of catchment soil moisture using indices.
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ItemThe Tarrawarra data set: Soil moisture patterns, soil characteristics, and hydrological flux measurements(AMER GEOPHYSICAL UNION, 1998-10)Experiments investigating the spatial variability of soil moisture conducted in the 10.5 ha Tarrawarra catchment, southeastern Australia, are described. The resulting data include high‐resolution soil moisture maps (over 10,000 point measurements at up to 2060 sites), information from 125 soil cores, over 1000 soil moisture profiles from 20 sites, 2500 water level measurements from 74 piezometers, surface roughness and vegetation measurements, meteorological and hydrological flux measurements, and topographic survey data. These experiments required a major commitment of resources including 250 person days in the field, with a further 100 person days in the laboratory preparing for field trips and checking and collating data. These data are available on the World Wide Web (http://www.civag.unimelb.edu.au/data/).
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ItemObserved spatial organization of soil moisture and its relation to terrain indices(AMER GEOPHYSICAL UNION, 1999-03)We analyze the degree of spatial organization of soil moisture and the ability of terrain attributes to predict that organization. By organization we mean systematic spatial variation or consistent spatial patterns. We use 13 observed spatial patterns of soil moisture, each based on over 500 point measurements, from the 10.5 ha Tarrawarra experimental catchment in Australia. The measured soil moisture patterns exhibit a high degree of organization during wet periods owing to surface and subsurface lateral redistribution of water. During dry periods there is little spatial organization. The shape of the distribution function of soil moisture changes seasonally and is influenced by the presence of spatial organization. Generally, it is quite different from the shape of the distribution functions of various topographic indices. A correlation analysis found that ln(a), where a is the specific upslope area, was the best univariate spatial predictor of soil moisture for wet conditions and that the potential radiation index was best during dry periods. Combinations of ln(a) or ln(a/tan(β)), where β is the surface slope, and the potential solar radiation index explain up to 61% of the spatial variation of soil moisture during wet periods and up to 22% during dry periods. These combinations explained the majority of the topographically organized component of the spatial variability of soil moisture a posteriori. A scale analysis indicated that indices that represent terrain convergence (such as ln(a) or ln(a/tan(β))) explain variability at all scales from 10 m up to the catchment scale and indices that represent the aspect of different hillslopes (such as the potential solar radiation index) explain variability at scales from 80 m to the catchment scale. The implications of these results are discussed in terms of the organizing processes and in terms of the use of terrain attributes in hydrologic modeling and scale studies. A major limitation on the predictive power of terrain indices is the degree of spatial organization present in the soil moisture pattern at the time for which the prediction is made.
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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 -: art. no. 1304(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.