859 research outputs found

    The MuTHRE Model for High Quality Sub-seasonal Streamflow Forecasts

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    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

    Evaluating post-processing approaches for monthly and seasonal streamflow forecasts

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    Streamflow forecasting is prone to substantial uncertainty due to errors in meteorological forecasts, hydrological model structure, and parameterization, as well as in the observed rainfall and streamflow data used to calibrate the models. Statistical streamflow post-processing is an important technique available to improve the probabilistic properties of the forecasts. This study evaluates post-processing approaches based on three transformations – logarithmic (Log), log-sinh (Log-Sinh), and Box–Cox with λ=0.2 (BC0.2) – and identifies the best-performing scheme for post-processing monthly and seasonal (3-months-ahead) streamflow forecasts, such as those produced by the Australian Bureau of Meteorology. Using the Bureau's operational dynamic streamflow forecasting system, we carry out comprehensive analysis of the three post-processing schemes across 300 Australian catchments with a wide range of hydro-climatic conditions. Forecast verification is assessed using reliability and sharpness metrics, as well as the Continuous Ranked Probability Skill Score (CRPSS). Results show that the uncorrected forecasts (i.e. without post-processing) are unreliable at half of the catchments. Post-processing of forecasts substantially improves reliability, with more than 90 % of forecasts classified as reliable. In terms of sharpness, the BC0.2 scheme substantially outperforms the Log and Log-Sinh schemes. Overall, the BC0.2 scheme achieves reliable and sharper-than-climatology forecasts at a larger number of catchments than the Log and Log-Sinh schemes. The improvements in forecast reliability and sharpness achieved using the BC0.2 post-processing scheme will help water managers and users of the forecasting service make better-informed decisions in planning and management of water resources.Fitsum Woldemeskel, David McInerney, Julien Lerat, Mark Thyer, Dmitri Kavetski, Daehyok Shin, Narendra Tuteja and George Kuczer

    Particles at oil–air surfaces : powdered oil, liquid oil marbles, and oil foam

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    The type of material stabilized by four kinds of fluorinated particles (sericite and bentonite platelet clays and spherical zinc oxide) in air–oil mixtures has been investigated. It depends on the particle wettability and the degree of shear. Upon vigorous agitation, oil dispersions are formed in all the oils containing relatively large bentonite particles and in oils of relatively low surface tension (γla < 26 mN m⁻¹) like dodecane, 20 cS silicone, and cyclomethicone containing the other fluorinated particles. Particle-stabilized oil foams were obtained in oils having γla > 26 mN m⁻¹ where the advancing air–oil–solid contact angle θ lies between ca. 90° and 120°. Gentle shaking, however, gives oil-in-air liquid marbles with all the oil–particle systems except for cases where θ is <60°. For oils of tension >24 mN m⁻¹ with omniphobic zinc oxide and sericite particles for which advancing θ ≥ 90°, dry oil powders consisting of oil drops in air which do not leak oil could be made upon gentle agitation up to a critical oil:particle ratio (COPR). Above the COPR, catastrophic phase inversion of the dry oil powders to air-in-oil foams was observed. When sheared on a substrate, the dry oil powders containing at least 60 wt % of oil release the encapsulated oil, making these materials attractive formulations in the cosmetic and food industries

    Smart Skin Patterns Protect Springtails

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    Springtails, arthropods who live in soil, in decaying material, and on plants, have adapted to demanding conditions by evolving extremely effective and robust anti-adhesive skin patterns. However, details of these unique properties and their structural basis are still unknown. Here we demonstrate that collembolan skin can resist wetting by many organic liquids and at elevated pressures. We show that the combination of bristles and a comb-like hexagonal or rhombic mesh of interconnected nanoscopic granules distinguish the skin of springtails from anti-adhesive plant surfaces. Furthermore, the negative overhang in the profile of the ridges and granules were revealed to be a highly effective, but as yet neglected, design principle of collembolan skin. We suggest an explanation for the non-wetting characteristics of surfaces consisting of such profiles irrespective of the chemical composition. Many valuable opportunities arise from the translation of the described comb-like patterns and overhanging profiles of collembolan skin into man-made surfaces that combine stability against wear and friction with superior non-wetting and anti-adhesive characteristics

    Preservation of York Minster historic limestone by hydrophobic surface coatings

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    Magnesian limestone is a key construction component of many historic buildings that is under constant attack from environmental pollutants notably by oxides of sulfur via acid rain, particulate matter sulfate and gaseous SO 2 emissions. Hydrophobic surface coatings offer a potential route to protect existing stonework in cultural heritage sites, however, many available coatings act by blocking the stone microstructure, preventing it from 'breathing' and promoting mould growth and salt efflorescence. Here we report on a conformal surface modification method using self-assembled monolayers of naturally sourced free fatty acids combined with sub-monolayer fluorinated alkyl silanes to generate hydrophobic (HP) and super hydrophobic (SHP) coatings on calcite. We demonstrate the efficacy of these HP and SHP surface coatings for increasing limestone resistance to sulfation, and thus retarding gypsum formation under SO/H O and model acid rain environments. SHP treatment of 19th century stone from York Minster suppresses sulfuric acid permeation

    Fractal Nanotechnology

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    Self-similar patterns are frequently observed in Nature. Their reproduction is possible on a length scale 102–105 nm with lithographic methods, but seems impossible on the nanometer length scale. It is shown that this goal may be achieved via a multiplicative variant of the multi-spacer patterning technology, in this way permitting the controlled preparation of fractal surfaces
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