Infrastructure Engineering - Research Publications

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    Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes
    Alvarez-Garreton, C ; Ryu, D ; Western, AW ; Su, C-H ; Crow, WT ; Robertson, DE ; Leahy, C ( 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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    A predictive model for spatio-temporal variability in stream water quality
    Guo, D ; Lintern, A ; Webb, JA ; Ryu, D ; Bende-Michl, U ; Liu, S ; Western, AW ( 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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    Assimilation of stream discharge for flood forecasting: The benefits of accounting for routing time lags
    Li, Y ; Ryu, D ; Western, AW ; Wang, QJ (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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    De-noising of passive and active microwave satellite soil moisture time series
    Su, C-H ; Ryu, D ; Western, AW ; Wagner, W (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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    Beyond triple collocation: Applications to soil moisture monitoring
    Su, C-H ; Ryu, D ; Crow, WT ; Western, AW (AMER GEOPHYSICAL UNION, 2014-06-16)
    Abstract Triple collocation (TC) is routinely used to resolve approximated linear relationships between different measurements (or representations) of a geophysical variable that are subject to errors. It has been utilized in the context of calibration, validation, bias correction, and error characterization to allow comparisons of diverse data records from various direct and indirect measurement techniques including in situ remote sensing and model‐based approaches. However, successful applications of TC require sufficiently large numbers of coincident data points from three independent time series and, within the analysis period, homogeneity of their linear relationships and error structures. These conditions are difficult to realize in practice due to infrequent spatiotemporal sampling of satellite and ground‐based sensors. TC can, however, be generalized within the framework of instrumental variable (IV) regression theory to address some of the conceptual constraints of TC. We review the theoretics of IV and consider one possible strategy to circumvent the three‐data constraint by use of lagged variables (LV) as instruments. This particular implementation of IV is suitable for circumstances where multiple data records are limited and the geophysical variable of interest is sampled at time intervals shorter than its temporal correlation length. As a demonstration of utility, the LV method is applied to microwave satellite soil moisture data sets to recover their errors over Australia and to estimate temporal properties of their relationships with in situ and model data. These results are compared against standard two‐data linear estimators and the TC estimator as benchmark.
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    Key factors influencing differences in stream water quality across space
    Lintern, A ; Webb, JA ; Ryu, D ; Liu, S ; Bende-Michl, U ; Waters, D ; Leahy, P ; Wilson, P ; Western, AW (WILEY, 2018-01-01)
    Globally, many rivers are experiencing declining water quality, for example, with altered levels of sediments, salts, and nutrients. Effective water quality management requires a sound understanding of how and why water quality differs across space, both within and between river catchments. Land cover, land use, land management, atmospheric deposition, geology and soil type, climate, topography, and catchment hydrology are the key features of a catchment that affect: (1) the amount of suspended sediment, nutrient, and salt concentrations in catchments (i.e., the source), (2) the mobilization ,and (3) the delivery of these constituents to receiving waters. There are, however, complexities in the relationship between landscape characteristics and stream water quality. The strength of this relationship can be influenced by the distance and spatial arrangement of constituent sources within the catchment, cross correlations between landscape characteristics, and seasonality. A knowledge gap that should be addressed in future studies is that of interactions and cross correlations between landscape characteristics. There is currently limited understanding of how the relationships between landscape characteristics and water quality responses can shift based on the other characteristics of the catchment. Understanding the many forces driving stream water quality and the complexities and interactions in these forces is necessary for the development of successful water quality management strategies. This knowledge could be used to develop predictive models, which would aid in forecasting of riverine water quality. WIREs Water 2018, 5:e1260. doi: 10.1002/wat2.1260 This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water Quality
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    An evaluation of a methodology for seasonal soil water forecasting for Australian dry land cropping systems
    Western, AW ; Dassanayake, KB ; Perera, KC ; Argent, RM ; Alves, O ; Young, G ; Ryu, D (ELSEVIER, 2018-05-01)
    Soil water is a critical resource in many rain-fed agricultural systems. Climate variability represents a significant risk in these systems, which has been addressed in the past through seasonal weather outlooks. This study undertakes a pilot assessment of the potential to extend seasonal weather outlooks to plant available soil water (PASW). We analyse 20 sites in the southeast Australian wheat belt using seasonal weather outlooks from the Predictive Ocean-Atmosphere Model for Australia (POAMA; (the operational seasonal model of the Australian Bureau of Meteorology), which were downscaled and used in conjunction with the Agricultural Production Simulator (APSIM). Hindcast rainfall, potential evapotranspiration (PET) and PASW outlooks were produced on a monthly basis for 33 years at a point scale. The outlooks were assessed using a range of ensemble verification tools. The results showed hit rates that outperformed climatology for rainfall and PET in the short-term (0–2 months), and for PASW with longer lead times (2–5 months). Continuous rank probability skill scores (CRPSS) were generally statistically worse than climatology for rainfall and PET and statistically better than climatology for PASW over 1–3 months. The influence of initial soil water is seasonally dependent, with longer dependence in low evapotranspiration periods. Improved weather model downscaling approaches would transition to climatology and could improve both weather and PASW outlooks. PASW outlooks were strongly reliant on initial conditions, indicating the importance of understanding current soil water status, which needs to be interpreted in a seasonal context as its influence varies over the year. Expanded operational soil water monitoring would be important if PASW outlooks are to become routine.
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    Dual assimilation of satellite soil moisture to improve streamflow prediction in data-scarce catchments
    Alvarez-Garreton, C ; Ryu, D ; Western, AW ; Crow, WT ; Su, C-H ; Robertson, DR (AMER GEOPHYSICAL UNION, 2016-07)
    Abstract This paper explores the use of active and passive microwave satellite soil moisture products for improving streamflow prediction within four large (>5000km2) semiarid catchments in Australia. We use the probability distributed model (PDM) under a data‐scarce scenario and aim at correcting two key controlling factors in the streamflow generation: the rainfall forcing data and the catchment wetness condition. The soil moisture analysis rainfall tool (SMART) is used to correct a near real‐time satellite rainfall product (forcing correction scheme) and an ensemble Kalman filter is used to correct the PDM soil moisture state (state correction scheme). These two schemes are combined in a dual correction scheme and we assess the relative improvements of each. Our results demonstrate that the quality of the satellite rainfall product is improved by SMART during moderate‐to‐high daily rainfall events, which in turn leads to improved streamflow prediction during high flows. When employed individually, the soil moisture state correction scheme generally outperforms the rainfall correction scheme, especially for low flows. Overall, the combined dual correction scheme further improves the streamflow predictions (reduction in root mean square error and false alarm ratio, and increase in correlation coefficient and Nash‐Sutcliffe efficiency). Our results provide new evidence of the value of satellite soil moisture observations within data‐scarce regions. We also identify a number of challenges and limitations within the schemes.
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    Supplementary material to: "A predictive model for spatio-temporal variability in stream water quality"
    Guo, D ; Lintern, A ; Webb, JA ; Ryu, D ; Bende-Michl, U ; Liu, S ; Western, AW (Copernicus Publications, 2019)
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    Towards estimating root-zone soil moisture using surface multispectral and thermal sensing: A spectral and hydrometeorological dataset from the Dookie experiment site, Victoria, Australia
    Akuraju, VR ; Ryu, D ; Western, AW ; Young, RI (John Wiley & Sons Ltd., 2019-07-01)
    This paper describes surface hydrometeorological and spectral datasets collected from two tower sites located in the University of Melbourne's Dookie experimental farm, Victoria, Australia. The datasets were collected from different vegetation types including wheat, canola, and grazed pasture during the 2012, 2013, and 2014 cropping seasons. The dataset includes 30‐min frequency latent and sensible heat flux measurements and layer‐average soil moisture data at profile depths of 0–5, 0–30, 30–60, 60–90, and 90–120 cm. Air temperature, wind speed, wind direction, relative humidity, precipitation, and incoming and outgoing longwave and shortwave radiation data were also collected from two locations in the study area. The dataset described in this paper is available online.