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 within-day behaviour of 6 minute rainfall intensity in Australia(COPERNICUS GESELLSCHAFT MBH, 2011-01-01)Abstract. The statistical behaviour and distribution of high-resolution (6 min) rainfall intensity within the wet part of rainy days (total rainfall depth >10 mm) is investigated for 42 stations across Australia. This paper compares nine theoretical distribution functions (TDFs) in representing these data. Two goodness-of-fit statistics are reported: the Root Mean Square Error (RMSE) between the fitted and observed within-day distribution; and the coefficient of efficiency for the fit to the highest rainfall intensities (average intensity of the 5 highest intensity intervals) across all days at a site. The three-parameter Generalised Pareto distribution was clearly the best performer. Good results were also obtained from Exponential, Gamma, and two-parameter Generalized Pareto distributions, each of which are two parameter functions, which may be advantageous when predicting parameter values. Results of different fitting methods are compared for different estimation techniques. The behaviour of the statistical properties of the within-day intensity distributions was also investigated and trends with latitude, Köppen climate zone (strongly related to latitude) and daily rainfall amount were identified. The latitudinal trends are likely related to a changing mix of rainfall generation mechanisms across the Australian continent.
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ItemThe Murrumbidgee soil moisture monitoring network data set(American Geophysical Union, 2012-07-17)This paper describes a soil moisture data set from the 82,000 km2 Murrumbidgee River Catchment in southern New South Wales, Australia. Data have been archived from the Murrumbidgee Soil Moisture Monitoring Network (MSMMN) since its inception in September 2001. The Murrumbidgee Catchment represents a range of conditions typical of much of temperate Australia, with climate ranging from semiarid to humid and land use including dry land and irrigated agriculture, remnant native vegetation, and urban areas. There are a total of 38 soil moisture-monitoring sites across the Murrumbidgee Catchment, with a concentration of sites in three subareas. The data set is composed of 0–5 (or 0–8), 0–30, 30–60, and 60–90 cm average soil moisture, soil temperature, precipitation, and other land surface model forcing at all sites, together with other ancillary data. These data are available on the World Wide Web at http://www.oznet.org.au.
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ItemAnalytical methods for ecosystem resilience: A hydrological investigation(AMERICAN GEOPHYSICAL UNION, 2012-10-16)In recent years a number of papers have quantitatively explored multiple steady states and resilience within a wide range of hydrological systems. Many have identified multiple steady states by conducting simulations from different initial state variables and a few have used the more advanced technique of equilibrium or limit cycle continuation analysis to quantify how the number of steady states may change with a single model parameter. However, like resilience investigations into other natural systems, these studies often omit explanation of these fundamental resilience science techniques; rely on complex numerical methods rather than analytical methods; and overlook use of more advanced techniques from nonlinear systems mathematics. In the interests of wider adoption of advanced resilience techniques within hydrology, and advancing resilience science more broadly, this paper details fundamental methods for quantitative resilience investigations. Using a simple model of a spatially lumped unconfined aquifer, one and two parameter continuation analysis was undertaken algebraically. The shape of each steady state attractor basin was then quantified using Lyapunov stability curves derived at a range of precipitation rates, but was found to be inconsistent with the resilience behavior demonstrated by stochastic simulations. Most notably, and contrary to standard resilience concepts, the switching between steady states from wet or dry periods (and vice versa) did not occur by crossing of the threshold between the steady states. It occurred by exceedance of the two steady-state domain, producing a counterclockwise hysteresis loop. Additionally, temporary steady states were identified that could not have been detected using equilibrium continuation with a constant forcing rate. By combining these findings with the Lyapunov stability curves, new measures of resilience were developed for endogenous disturbances to the model and for the recovery from disturbances exogenous to the model.
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ItemSeasonal and event dynamics of spatial soil moisture patterns at the small catchment scale(AMERICAN GEOPHYSICAL UNION, 2012-10-27)Our understanding of short- and long-term dynamics of spatial soil moisture patterns is limited due to measurement constraints. Using new highly detailed data, this research aims to examine seasonal and event-scale spatial soil moisture dynamics in the topsoil and subsoil of the small spruce-covered Ẅstebach catchment, Germany. To accomplish this, univariate and geo-statistical analyses were performed for a 1 year long 4-D data set obtained with the wireless sensor network SoilNet. We found large variations in spatial soil moisture patterns in the topsoil, mostly related to meteorological forcing. In the subsoil, temporal dynamics were diminished due to soil water redistribution processes and root water uptake. Topsoil range generally increased with decreasing soil moisture. The relationship between the spatial standard deviation of the topsoil soil moisture (SDθ) and mean water content (θ) showed a convex shape, as has often been found in humid temperate climate conditions. Observed scatter in topsoil SD θ(θ) was explained by seasonal and event-scale SD θ(θ) dynamics, possibly involving hysteresis at both time scales. Clockwise hysteretic SDθ(θ) dynamics at the event scale were generated under moderate soil moisture conditions only for intense precipitation that rapidly wetted the topsoil and increased soil moisture variability controlled by spruce throughfall patterns. This hysteretic effect increased with increasing precipitation, reduced root water uptake, and high groundwater level. Intense precipitation on dry topsoil abruptly increased SDθ but only marginally increased mean soil moisture. This was due to different soil rewetting behavior in drier upslope areas (hydrophobicity and preferential flow caused minor topsoil recharge) compared with the moderately wet valley bottom (topsoil water storage), which led to a more spatially organized pattern. This study showed that spatial soil moisture patterns monitored by a wireless sensor network varied with depth, soil moisture content, seasonally, and within single wetting and drying episodes. This was controlled by multiple factors including soil properties, topography, meteorological forcing, vegetation, and groundwater.
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ItemAssimilation of stream discharge for flood forecasting: The benefits of accounting for routing time lags(AMERICAN GEOPHYSICAL UNION, 2013-04-01)General filtering approaches in hydrologic data assimilation, such as the ensemble Kalman filter (EnKF), are based on the assumption that uncertainty of the current background prediction can be reduced by correcting errors in the state variables at the same time step. However, this assumption may not be valid when assimilating stream discharge into hydrological models to correct soil moisture storage due to the time lag between the soil moisture and the discharge. In this paper, we explore the utility of an ensemble Kalman smoother (EnKS) for addressing this time-lag issue. The EnKF and the EnKS are compared for two different updating schemes with the probability distributed model (PDM) via synthetic experiments: (i) updating soil moisture only and (ii) updating soil moisture and routing states simultaneously. The results show that the EnKS is superior to the EnKF when only soil moisture is updated, while the EnKS and the EnKF exhibit similar results when both soil moisture and routing storages are updated. This suggests that the EnKS can better improve the stream flow forecasting for models that do not adopt storage-based routing schemes (e.g., unit-hydrograph-based routing). For models with dynamic routing stores, errors in soil moisture are transferred to the routing stores, which can be corrected effectively by real-time filters. The EnKS-based soil moisture updating scheme is also tested with the GR4H model, for which unit-hydrograph-based routing is used. The result confirms that the EnKS is superior to the EnKF in improving both soil moisture and stream flow forecasting.
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ItemDe-noising of passive and active microwave satellite soil moisture time series(AMERICAN GEOPHYSICAL UNION, 2013-07-28)Satellite microwave retrievals and in situ measurements of surface soil moisture are usually compared in the time domain. This paper examines their differences in the conjugate frequency domain to develop a spectral description of the satellite data, suggesting the presence of stochastic random and systematic periodic errors. Based on a semiempirical model of the observed power spectral density, we describe systematic designs of causal and noncausal filters to remove these erroneous signals. The filters are applied to the retrievals from active and passive satellite sensors and evaluated against field data from the Murrumbidgee Basin, southeast Australia, to show substantive increase in linear correlations.
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ItemMultiple hydrological attractors under stochastic daily forcing: 1. Can multiple attractors exist?(AMERICAN GEOPHYSICAL UNION, 2014-04-01)Including positive feedbacks in hydrological models has recently been shown to result in complex behavior with multiple steady states. When a large disturbance, say a major drought, is simulated within such models the hydrology changes. Once the disturbance ends the hydrology does not return to that prior to the disturbance, but rather, persists within an alternate state. These multiple steady states (henceforth attractors) exist for a single model parameterization and cause the system to have a finite resilience to such transient disturbances. A limitation of past hydrological resilience studies is that multiple attractors have been identified using mean annual or mean monthly forcing. Considering that most hydrological fluxes are subject to significant forcing stochasticity and do not operate at such large timescales, it remains an open question whether multiple hydrological attractors can exist when a catchment is subject to stochastic daily forcing. This question is the focus of this paper and it needs to be addressed prior to searching for multiple hydrological attractors in the field. To investigate this, a previously developed semidistributed hillslope ecohydrological model was adopted which exhibited multiple steady states under average monthly climate forcing. In this paper, the ecohydrological model was used to explore if feedbacks between the vegetation and a saline water table result in two attractors existing under daily stochastic forcing. The attractors and the threshold between them (henceforth repellor) were quantified using a new limit cycle continuation technique that upscaled climate forcing from daily to monthly (model and limit cycle code is freely available). The method was used to determine the values of saturated lateral hydraulic conductivity at which multiple attractors exist. These estimates were then assessed against time-integration estimates, which they agreed with. Overall, multiple attractors were found to exist under stochastic daily forcing. However, changing the climate forcing from monthly to daily did significantly reduce the parameter range over which two attractors existed. This suggests fewer catchments may have multiple attractors than previously considered.
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ItemMultiple hydrological attractors under stochastic daily forcing: 2. Can multiple attractors emerge?(AMERICAN GEOPHYSICAL UNION, 2014-04-01)The companion paper showed that multiple steady state groundwater levels can exist within a hill-slope Boussinesq-vegetation model under daily stochastic forcing. Using a numerical limit-cycle continuation algorithm, the steady states (henceforth attractors) and the threshold between them (henceforth repellor) were quantified at a range of saturated lateral conductivity values, ksmax. This paper investigates if stochastic daily forcing can switch the catchment between both of the attractors. That is, an attractor may exist under average forcing conditions but can stochastic forcing switch the catchment into and out of each of the attractor basins-; i.e., making the attractor emerge. This was undertaken using the model of the companion paper and by completing daily time-integration simulations at six values of the saturated lateral hydraulic conductivity, ksmax; three having two attractors and three having only a deep water table attractor. By graphically analyzing the simulations, and comparing against simulations from a model modified to have only one attractor, multiple attractors were found to emerge under stochastic daily forcing. However, the emergence of attractors was significantly more subtle and complex than that suggested by the companion paper. That is, an attractor may exist but never emerge; both attractors may exist and both may emerge but identifying the switching between attractors was often ambiguous; and only one attractor may exist and but a second temporary attractor may exist and emerge during periods of high precipitation. This subtle and complex emergence of attractors was explained using continuation analysis of the climate forcing rate, and not a model parameter such as ksmax. It showed that the temporary attractor existed over a large range of ksmax values and this suggests that more catchments may have multiple attractors than suggested by the companion paper. By combining this continuation analysis with the time-integration simulations, hydrological signatures indicative of a switch of multiple attractors were proposed. These signatures may provide a means for identifying actual catchments that have switched between multiple attractors. Key Points Stochastic daily forcing can switch a catchment to both attractors Emergence of attractors differs significantly from the existence of attractors Switching between attractor basins can be subtle and difficult to identify