Pitfalls and improvements in the joint inference of heteroscedasticity and autocorrelation in hydrological model calibration

Residual errors of hydrological models are usually both heteroscedastic and autocorrelated. However, only a few studies have attempted to explicitly include these two statistical properties into the residual error model and jointly infer them with the hydrological model parameters. This technical note shows that applying autoregressive error models to raw heteroscedastic residuals, as done in some recent studies, can lead to unstable error models with poor predictive performance. This instability can be avoided by applying the autoregressive process to standardized residuals. The theoretical analysis is supported by empirical findings in three hydrologically distinct catchments. The case studies also highlight strong interactions between the parameters of autoregressive residual error models and the water balance parameters of the hydrological model. ©2013. American Geophysical Union. All Rights Reserved.Guillaume Evin, Dmitri Kavetski, Mark Thyer, and George Kuczer

State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application

Monthly to seasonal streamflow forecasts provide useful information for a range of water resource management and planning applications. This work focuses on improving such forecasts by considering the following two aspects: (1) state updating to force the models to match observations from the start of the forecast period, and (2) selection of a shorter calibration period that is more representative of the forecast period, compared to a longer calibration period traditionally used. The analysis is undertaken in the context of using streamflow forecasts for environmental flow water management of an open channel drainage network in southern Australia. Forecasts of monthly streamflow are obtained using a conceptual rainfall–runoff model combined with a post-processor error model for uncertainty analysis. This model set-up is applied to two catchments, one with stronger evidence of non-stationarity than the other. A range of metrics are used to assess different aspects of predictive performance, including reliability, sharpness, bias and accuracy. The results indicate that, for most scenarios and metrics, state updating improves predictive performance for both observed rainfall and forecast rainfall sources. Using the shorter calibration period also improves predictive performance, particularly for the catchment with stronger evidence of non-stationarity. The results highlight that a traditional approach of using a long calibration period can degrade predictive performance when there is evidence of non-stationarity. The techniques presented can form the basis for operational monthly streamflow forecasting systems and provide support for environmental decision-making.Matthew S. Gibbs, David McInerney, Greer Humphrey, Mark A. Thyer, Holger R. Maier, Graeme C. Dandy and Dmitri Kavetsk

An efficient causative event-based approach for deriving the annual flood frequency distribution

In ungauged catchments or catchments without sufficient streamflow data, derived flood frequency methods are often applied to provide the basis for flood risk assessment. The most commonly used event-based methods, such as design storm and joint probability approaches are able to give fast estimation, but can also lead to prediction bias and uncertainties due to the limitations of inherent assumptions and difficulties in obtaining input information (rainfall and catchment wetness) related to events that cause extreme floods. An alternative method is a long continuous simulation which produces more accurate predictions, but at the cost of massive computational time. In this study a hybrid method was developed to make the best use of both event-based and continuous approaches. The method uses a short continuous simulation to provide inputs for a rainfall-runoff model running in an event-based fashion. The total probability theorem is then combined with the peak over threshold method to estimate annual flood distribution. A synthetic case study demonstrates the efficacy of this procedure compared with existing methods of estimating annual flood distribution. The main advantage of the hybrid method is that it provides estimates of the flood frequency distribution with an accuracy similar to the continuous simulation approach, but with dramatically reduced computation time. This paper presents the method at the proof-of-concept stage of development and future work is required to extend the method to more realistic catchments. © 2014 Elsevier B.V.Jing Li, Mark Thyer, Martin Lambert, George Kuczera, Andrew Metcalf

Probabilistic streamflow prediction and uncertainty estimation in ephemeral catchments

Conference theme 'Digital Water.'Probabilistic streamflow predictions at the daily scale are of major practical interest for environmental management and planning, including risk assessment as part of reservoir management operations. Ephemeral catchments, where streamflow is frequently zero or negligible, pose particularly stark challenges in this context, due to asymmetry of the error distribution and the discrete (rather than continuous) nature of zero flows. In this work, our focus is on two practical error modelling approaches where predictive uncertainty is approximated by a (transformed) Gaussian error model. The first approach, termed "pragmatic", does not distinguish between zero and positive flows during calibration, but sets negative flows to zero when making predictions. The second approach, termed "explicit", applies a "censored" Gaussian assumption in both calibration and prediction. We report a comparison of these two approaches over 74 Australian catchments with diverse hydroclimatology, using multiple performance metrics. The performance of the approaches depended on the catchment type as follows: (1) "mid-ephemeral" catchments, where 5-50% of days have zero flows, are best modelled using the "explicit" approach in combination with the Box-Cox streamflow transformation with a power parameter of 0.2; (2) "low-ephemeral" catchments, with fewer than 5% zero flow days, can be modelled using the pragmatic approach with (relatively) little loss of predictive performance; (3) "high-ephemeral" catchments, with more than 50% zero flow days, prove challenging to both approaches, and require more specialised techniques. The findings provide practical guidance towards improving probabilistic streamflow predictions in ephemeral catchments. Previous chapter Next chapterDmitri Kavetski, David McInerney, Mark Thyer, Julien Lerat and George Kuczer

The MuTHRE Model for High Quality Sub-seasonal Streamflow Forecasts

Conference theme 'Digital Water.'Sub-seasonal streamflow forecasts, with lead times up to 30 days, can provide valuable information for water management, including reservoir operation to meet environmental flow, irrigation demands, and managing flood protection storage. A key aim is to produce “seamless” probabilistic forecasts, with high quality performance across the full range of lead times (1-30 days) and time scales (daily to monthly). This paper demonstrates that the Multi-Temporal Hydrological Residual Error (MuTHRE) model can address the challenge of “seamless” sub-seasonal forecasting. The MuTHRE model is designed to capture key features of hydrological errors, namely seasonality, dynamic biases due to hydrological non-stationarity, and extreme errors poorly represented by the common Gaussian distribution. The MuTHRE model is evaluated comprehensively over 11 catchments in the MurrayDarling Basin using multiple performance metrics, across a range of lead times, months and years, and at daily and monthly time scales. It is shown to provide “high” improvements, in terms of reliability for short lead times (up to 10 days), in dry months, and dry years. Forecast performance also improved in terms of sharpness. Importantly, improvements are consistent across multiple time scales (daily and monthly). This study highlights the benefits of modelling multiple temporal characteristics of hydrological errors, and demonstrates the power of the MuTHRE model for producing seamless sub-seasonal streamflow forecasts that can be utilized for a wide range of applications.David McInerney, Mark Thyer, Dmitri Kavetski, Richard Laugesen, Narendra Tuteja, and George Kuczer

A novel role for a major component of the vitamin D axis: vitamin D binding protein-derived macrophage activating factor induces human breast cancer cell apoptosis through stimulation of macrophages.

The role of vitamin D in maintaining health appears greater than originally thought, and the concept of the vitamin D axis underlines the complexity of the biological events controlled by biologically active vitamin D (1,25(OH)(2)D3), its two binding proteins that are the vitamin D receptor (VDR) and the vitamin D-binding protein-derived macrophage activating factor (GcMAF). In this study we demonstrate that GcMAF stimulates macrophages, which in turn attack human breast cancer cells, induce their apoptosis and eventually phagocytize them. These results are consistent with the observation that macrophages infiltrated implanted tumors in mice after GcMAF injections. In addition, we hypothesize that the last 23 hydrophobic amino acids of VDR, located at the inner part of the plasma membrane, interact with the first 23 hydrophobic amino acids of the GcMAF located at the external part of the plasma membrane. This al1ows 1,25(OH)(2)D3 and oleic acid to become sandwiched between the two vitamin D-binding proteins, thus postulating a novel molecular mode of interaction between GcMAF and VDR. Taken together, these results support and reinforce the hypothesis that GcMAF has multiple biological activities that could be responsible for its anti-cancer effects, possibly through molecular interaction with the VDR that in turn is responsible for a multitude of non-genomic as well as genomic effects

Diagnostic and gender differences in the expressed fears of anxious patients

Fear Survey Schedule data are presented for a sample of 141 psychiatric patients who met the DSM-III criteria for an anxiety disorder. Diagnostic and gender differences in expressed fears are presented and the results are discussed in light of previous research.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/25643/1/0000195.pd