Hydro Tasmania

Climate change is expected to significantly affect insurers’ liabilities through an increase in claims associated with more frequent and intense meteorological hazards. These include both extreme events such as tropical cyclones and storms, and more recurrent phenomena such as floods and droughts. In this context, the mutual insurance group Covéa conducts climate-impact studies in collaboration with the specialized climate-risk Hydroclimat company.The study focuses on the projected evolution of coastal flooding risk in France by 2050. According to the HANZE database (Paprotny D., 2024), between 1950 and 2020, approximately one-third of flood events in France involved a coastal flooding component. Major historical events, such as Storm Xynthia in 2010 and the October 1987 storm, resulted in insured losses of €660 million and €1.5 billion (CCR, 2023), highlighting the significant financial exposure associated with coastal hazards.To anticipate future impacts, Hydroclimat produced coastal flood-extent maps based on CMIP6 climate projections, integrating existing coastal protection systems within a hydro-geomorphological modelling framework. Exposure and vulnerability analyses were conducted using Covéa’s national residential building database. These results provide an initial assessment of the projected increase in the number of exposed residential buildings and the associated insured losses by mid-century.This work contributes to a better understanding of future coastal flood risk under climate change and supports insurers in adapting risk assessment and portfolio management strategies to evolving coastal hazards.ReferencesPaprotny, D. (2024) - HANZE catalogue of modelled and historical floods in Europe, 1950–2020 (v1.2) https://doi.org/10.5281/zenodo.12635205Caisse Centrale de Réassurance (2023) – Risque de submersion sur la côte atlantique : l’analyse CCR – https://www.ccr.fr/submersion-marine-cote-atlantique-scenario-ccr/ [last access : 2025/12/15]

This paper examines the critical role of hydrogen as an energy carrier in achieving global decarbonization targets, analysing the key factors influencing the development of a sustainable hydrogen economy. It highlights the necessity of long-term offtake agreements for de-risking early projects and underscores the indispensable nature of proactive government support through strategic policy frameworks and financial derisking mechanisms. The analysis further emphasizes the crucial role of fostering international collaboration and knowledge sharing in establishing harmonized standards, facilitating technology transfer, and enabling the development of global supply chains. By synthesizing current research and policy approaches, this paper concludes that a concerted global effort, encompassing robust government intervention and strong international partnerships, is essential to unlock the full potential of hydrogen and accelerate the transition towards a sustainable energy future. Finally, it identifies key areas for future research, including detailed lifecycle emissions analysis, sector-specific techno-economic assessments, public perception studies, evolving international trade dynamics, and the longterm effectiveness of implemented policies.

This is a response to Littlewood IG (2025) Discussion: An updated reference table for extreme flood hydrology methods at UK dams. Dams and Reservoirs, https://doi.org/10.1680/jdare.25.00997.The contributor’s note raises the IHACRES UH approach, which has not been used for reservoir flood estimation to date. Hopefully, the four options suggested have been, or will be, considered within the Environment Agency’s Improving Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF) Estimation for Reservoir Safety study.https://www.icevirtuallibrary.com/doi/full/10.1680/jdare.2024.13

Piezoelectric materials have secured a significant place in the field of structural health monitoring (SHM) and vibration energy harvesting over the past two decades. Although these materials are available in several forms and configurations, the efficacy of curved configurations is still unexplored. This study investigates, both experimentally and numerically, the potency of the curved configuration of the piezo transducers over the straight configuration when embedded in reinforced concrete (RC) structures for vibration energy harvesting and SHM. This book, based on the M.Tech thesis of the lead author completed in the Department of Civil Engineering at IIT Delhi, covers the comparative analysis of (a) open-circuit voltage generated across the piezo transducers; (b) the power developed under the impedance matching conditions; and (c) the potential of power storage into capacitors for both configurations. The book also experimentally examines the hitherto unexplored damage detection capability of the curved piezo transducers by the electro-mechanical impedance (EMI) technique. Results clearly demonstrate that curved piezo transducers exhibit better performance in comparison to straight configurations for both SHM and vibration energy harvesting. A finite element (FE) analysis is also performed through a 3-D model of the real-life-sized RC beam with straight and curved piezo transducers embedded inside to evaluate the effect of various parameters such as the angle of bend, the thickness, the number of elements and the position of placement of the transducers for vibration energy harvesting. The FE simulations reveal an optimum range of the angle of bend as 130 to 160 degrees. There is a substantial impact of the increase in thickness of the transducers for vibration energy harvesting in terms of the open-circuit voltage. The numerical analysis also suggests that the optimum position of placement of the piezo transducers is towards the top or bottom of the beam cross-section. As a preliminary step towards the analytical analysis of curved piezo transducers for vibration energy harvesting, a 2-D skeletal analytical model of an RC beam embedded with a curved piezo transducer is developed by formulating a MATLAB code based on the direct stiffness approach. The effect of the curvature and the thickness of the transducer is also analysed and compared with the findings of the numerical investigations. Also, contrary to the numerical investigation results, the thickness of the curved transducer is found to hardly have any effect on the terminal voltage generated across it. This is because the 3-D effects of the structure and the strain variation across the thickness of the transducer are not considered in the analytical model where each element is considered as a 1-D element. Hence, it is recommended to extend the research to 3-D analytical models. The outcomes of the experimental and numerical investigations are very pivotal for the implementation of curved piezo transducers on real-life RC structures for improved vibration energy harvesting and structural health monitoring. This includes assessment of damages, either gradual during the lifespan of the structure or after some major event such as earthquakes. The sensor, being embedded inside the structure, is well protected from environmental degradation and can enable dynamic strain-based monitoring of the structure during normal operation or during an extreme event such as an earthquake which is technically simpler as compared to the traditional displacement-based monitoring and has special significance for monitoring of bridges under vehicular loads and for checking compliance to the Indian Roads Congress (IRC codes). Post-earthquake or during periodical SHM checks, the condition of the structure can be evaluated using the same sensor by employing the EMI technique.

The implications of climate change on agro-pastoral farming systems in the Peruvian Andes are not fully understood. There is already a significant impact on agricultural productivity from current climate variability and extreme weather in the region. This is exacerbated by chronic poverty in many rural areas and the need for improved government-led strategic planning. Tools to assist with policy planning for climate change adaptations that achieve environmental and social resilience are vital, and these require collaboration with rural communities to incorporate the complexities of behavioural responses to climate change, market dynamics, and policy shifts in agricultural and water management.  In this study we further develop a recent agricultural systems model (the TELLUS model; Pilditch et al., in review). The model is an agent-based simulation focussed on the behaviour of interacting populations of individual farming agents. TELLUS offers the opportunity to analyse the impact of interventions/policies in light of key scenarios and conditions of interest, with potential to uncover unforeseen emergent behaviours within farming systems (e.g., tipping points, amplifiers, system adaptations) and potential unintended consequences of scenarios and policies (e.g., increasing in equalities; increased system fragility). A difficulty in applying such models to specific case studies is in choosing valid parameter values, especially for model behaviour associated with human behaviour and decision-making. Our work over recent years includes extensive fieldwork in the Cordillera Negra and Cordillera Blanca, involving interviews and workshops with farming communities, and collaboration with regional NGOs. These interactions have been instrumental in understanding local challenges and priorities. The challenge in terms of modelling this system is turning information gained from qualitative methods (e.g. interviews) into parameter values for the model. Our novel approach is to assess the extent to which modern AI systems, specifically, Large Language Models (LLMs) can help perform this task.  We leverage the reasoning abilities of LLMs to directly estimate relevant model parameters from automated interview transcription/translations. We will discuss the extent to which this integration has aided the creation of a TELLUS model tuned specifically to the Peruvian Andes context. Our approach will hopefully serve as a novel tool, combining empirical research, community involvement, and advanced computational modelling, to explore future climate scenarios and the potential effects of policy interventions.

Many water management agencies rely on stochastic inflow scenarios to plan water operations. For example, Hydro Tasmania, Australia’s largest hydropower generator and water manager, relies on 20+ year inflow scenarios to assess the long-range sustainability of their power generation system. A variety of methods are available for stochastic data generation, but many assume a stationary climate. In locations where inflow has long-term trends, assuming a stationary climate in stochastic data generation is likely to underestimate future wet or dry extremes, in particular for sequences of dry or wet months or years.To address this issue, we have developed the Trend and Uncertainty in Long Inflow Predictions (TULIP) model. TULIP is a Bayesian model that generates long-range predictions of inflows at the monthly time step. TULIP accounts for:Heteroscedasticity and skew in inflow data by using data transformation with the sinh-arcsinh transformation, and zero values with censoring Spatial correlation between inflow sites Autocorrelation using a first-order autoregressive model Linear trend in inflow Seasonal variation in properties (1)-(4), using Fourier series to control the parameters TULIP is being implemented operationally by Hydro Tasmania to replace its existing method of generating stochastic scenarios, which assumes a stationary climate. At sites with long-term trends in historical inflow, we show that TULIP produces more reliable long-range predictions than is possible if a stationary climate is assumed. This allows TULIP to produce sharper ensembles and more realistic projections of future drought, allowing Hydro Tasmania to better plan for the long-range sustainability of its system. In this presentation we describe the TULIP model and its performance. We also discuss future plans to incorporate information on inflow trends from global and regional climate models into TULIP.

Le dihydrogène, communément appelé « hydrogène », figure au premier rang des solutions identifiées pour atteindre la neutralité carbone. À ce jour, c’est toute une palette de couleurs, aussi riche qu’un arc-en-ciel, qui est utilisée pour le catégoriser selon son empreinte carbone et sa valeur environnementale (vert, bleu, etc.). Une couleur dont on ne parle que trop peu, c’est l’hydrogène blanc, aussi appelée « gold hydrogen », ou encore « hydrogène naturel », voire « hydrogène natif ». L’hydrogène naturel est une nouvelle source d’énergie primaire renouvelable, qui permet d’accélérer l’atteinte des objectifs climatiques mondiaux. À l’origine de cette découverte, faite au Mali, Hydroma Inc., la société canadienne de l’entrepreneur malien, Aliou Diallo, qui a su faire preuve de résilience et d’innovation dans un contexte sécuritaire et sous-régional complexe. Dans cet article, nous retraçons le chemin parcouru, de la découverte de l’hydrogène naturel jusqu’à son usage dans l’unité pilote de Bourakebougou. Nous présentons également les enjeux et les perspectives de cette découverte pour le continent africain et la transition énergétique.

An array of rigid sails installed on a large powered ship could provide a viable means to reduce fuel oil consumption (FOC) and emissions by using the power of the wind as a source of supplementary propulsion. This paper describes the study of airflow around a concept ship design fitted with 14 segment rigid sails (SRS) using a virtual wind tunnel software application and also investigates the propulsive force that a fixed sail array could provide using computational fluid dynamics (CFD) analysis.

<p>Quantification of long-term hydrologic change in groundwater often requires the comparison of states pre- and post- change. The assessment of these changes in ungauged catchments is particularly difficult from a conceptual point of view and due to parameter non-uniqueness and associated uncertainty of quantitative frameworks. In these contexts, the use of data assimilation, sensitivity analysis and uncertainty quantification techniques are critical to maximise the use of available data both in terms of conceptualisation and quantification. This paper summarises findings of a study undertaken in the Lake Muir-Unicup Natural Diversity Recovery Catchment (MUNDRC), where a number of techniques were applied to inform both conceptual and numerical models. The MUNDRC is and small-scale endorheic basin located in southwestern Australia listed under the Ramsar Convention as a Wetland of International Importance and have been subject to a systematic decline in rainfall rates since 1970. Conceptual and numerical frameworks have been development to understand and quantify impacts of rainfall decline on the catchment using a variety of metrics involving groundwater and lake levels, as well as fluxes between these compartments and mass balance components. Conceptualisation was facilitated with the use a novel data-driven method relating rainfall and groundwater response running backwards in time, allowing the establishment of baseline conditions prior to rainfall decline, estimation of net recharge rates and providing initial heads for the forward numerical modelling. Parameter and predictive uncertainties associated with data gaps have been minimised and quantified utilising an Iterative Ensemble Smoother (White, 2018), while further refinement of conceptual model was undertaken following results from sensitivity analysis, where major parameter controls groundwater levels and other predictions of interest were quantified.</p>