Infrastructure Engineering - Research Publications
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ItemMulti-scale analysis of bias correction of soil moisture( 2014-07-29)Abstract. Remote sensing, in situ networks and models are now providing unprecedented information for environmental monitoring. To conjunctively use multi-source data nominally representing an identical variable, one must resolve biases existing between these disparate sources, and the characteristics of the biases can be non-trivial due to spatiotemporal variability of the target variable, inter-sensor differences with variable measurement supports. One such example is of soil moisture (SM) monitoring. Triple collocation (TC) based bias correction is a powerful statistical method that increasingly being used to address this issue but is only applicable to the linear regime, whereas nonlinear method of statistical moment matching is susceptible to unintended biases originating from measurement error. Since different physical processes that influence SM dynamics may be distinguishable by their characteristic spatiotemporal scales, we propose a multi-time-scale linear bias model in the framework of a wavelet-based multi-resolution analysis (MRA). The joint MRA-TC analysis was applied to demonstrate scale-dependent biases between in situ, remotely-sensed and modelled SM, the influence of various prospective bias correction schemes on these biases, and lastly to enable multi-scale bias correction and data adaptive, nonlinear de-noising via wavelet thresholding.
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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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ItemVery early onset of amiodarone-induced pulmonary toxicity.(The Korean Society of Cardiology, 2013-10)Amiodarone is a widely used antiarrhythmic agent. Among its various adverse effects, amiodarone-induced pulmonary toxicity (APT) is the most life threatening complication, which has been described mostly in patients who have been in treatment with high accumulative doses for a long duration of time. However, amiodarone therapy in short-term duration induced APT was rarely reported. We describe a case of a 54-year-old man who is presented with symptoms of APT after a few days of therapy for post-myocardial infarction ventricular tachycardia. For early diagnosis and successful treatment, awareness and high suspicion of this rare type of early onset APT is crucial in patients with amiodarone therapy.
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ItemLoss of Sirt1 function improves intestinal anti-bacterial defense and protects from colitis-induced colorectal cancer.(Public Library of Science (PLoS), 2014)Dysfunction of Paneth and goblet cells in the intestine contributes to inflammatory bowel disease (IBD) and colitis-associated colorectal cancer (CAC). Here, we report a role for the NAD+-dependent histone deacetylase SIRT1 in the control of anti-bacterial defense. Mice with an intestinal specific Sirt1 deficiency (Sirt1int-/-) have more Paneth and goblet cells with a consequent rearrangement of the gut microbiota. From a mechanistic point of view, the effects on mouse intestinal cell maturation are mediated by SIRT1-dependent changes in the acetylation status of SPDEF, a master regulator of Paneth and goblet cells. Our results suggest that targeting SIRT1 may be of interest in the management of IBD and CAC.
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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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ItemBeyond triple collocation: Applications to soil moisture monitoring(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 ofinstrumental 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.