Co-evaluation of climate services. A case study for hydropower generation

Climate services are attracting growing attention and interest as instruments to promote climate change adaptation. The transparent assessment of the potential value brought by the services can play a major role. It can foster the commitment of the user towards a co-generation process increasingly central to climate services creation, can provide developers important information to better tailor the service to the user needs, and can finally increase recognition of the value of the service boosting confidence and trust in the tool. This study presents and then demonstrates the applicability of an evaluation methodology based on the Bayesian framework derived from the information value theory. The specific case study is the Smart Climate Hydropower Tool (SCHT), a climate service designed to support management decisions in hydropower generation. The service uses freely available seasonal forecasts and machine learning algorithms to predict incoming discharge to hydropower reservoirs. The user is ENEL Green Power Italy, and the testing environments are two water basins in Colombia. The study defines the expected value of perfect information, the expected value of the information currently used by the hydropower producer and the expected value of the service information. It then discusses pros and cons of the applicability of the method

Alterations in proton leak, oxidative status and uncoupling protein 3 content in skeletal muscle subsarcolemmal and intermyofibrillar mitochondria in old rats.

BACKGROUND: We considered of interest to evaluate how aging affects mitochondrial function in skeletal muscle. METHODS: We measured mitochondrial oxidative capacity and proton leak, together with lipid oxidative damage, superoxide dismutase specific activity and uncoupling protein 3 content, in subsarcolemmal and intermyofibrillar mitochondria from adult (six months) and old (two years) rats. Body composition, resting metabolic rate and plasma non esterified fatty acid levels were also assessed. RESULTS: Old rats displayed significantly higher body energy and lipids, while body proteins were significantly lower, compared to adult rats. In addition, plasma non esterified fatty acid levels were significantly higher, while resting metabolic rates were found to be significantly lower, in old rats compared to adult ones. Significantly lower oxidative capacities in whole tissue homogenates and in intermyofibrillar and subsarcolemmal mitochondria were found in old rats compared to adult ones. Subsarcolemmal and intermyofibrillar mitochondria from old rats exhibited a significantly lower proton leak rate, while oxidative damage was found to be significantly higher only in subsarcolemmal mitochondria. Mitochondrial superoxide dismutase specific activity was not significantly affected in old rats, while significantly higher content of uncoupling protein 3 was found in both mitochondrial populations from old rats compared to adult ones, although the magnitude of the increase was lower in subsarcolemmal than in intermyofibrillar mitochondria. CONCLUSIONS: The decrease in oxidative capacity and proton leak in intermyofibrillar and subsarcolemmal mitochondria could induce a decline in energy expenditure and thus contribute to the reduced resting metabolic rate found in old rats, while oxidative damage is present only in subsarcolemmal mitochondria

JNK1 ablation improves pancreatic β-cell mass and function in db/db diabetic mice without affecting insulin sensitivity and adipose tissue inflammation

The cJun N-terminal Kinases (JNK) emerged as a major link between obesity and insulin resistance, but their role in the loss of pancreatic β-cell mass and function driving the progression from insulin resistance to type-2 diabetes and in the complications of diabetes was not investigated to the same extent. Furthermore, it was shown that pan-JNK inhibition exacerbates kidney damage in the db/db model of obesity-driven diabetes. Here we investigate the role of JNK1 in the db/db model of obesity-driven type-2 diabetes. Mice with systemic ablation of JNK1 (JNK1−/−) were backcrossed for more than 10 generations in db/+ C57BL/KS mice to generate db/db-JNK1−/− mice and db/db control mice. To define the role of JNK1 in the loss of β-cell mass and function occurring during obesity-driven diabetes we performed comprehensive metabolic phenotyping, evaluated steatosis and metabolic inflammation, performed morphometric and cellular composition analysis of pancreatic islets, and evaluated kidney function in db/db-JNK1−/− mice and db/db controls. db/db-JNK1−/− mice and db/db control mice developed insulin resistance, fatty liver, and metabolic inflammation to a similar extent. However, db/db-JNK1−/− mice displayed better glucose tolerance and improved insulin levels during glucose tolerance test, higher pancreatic insulin content, and larger pancreatic islets with more β-cells than db/db mice. Finally, albuminuria, kidney histopathology, kidney inflammation and oxidative stress in db/db-JNK1−/− mice and in db/db mice were similar. Our data indicate that selective JNK1 ablation improves glucose tolerance in db/db mice by reducing the loss of functional β-cells occurring in the db/db mouse model of obesity-driven diabetes, without significantly affecting metabolic inflammation, steatosis, and insulin sensitivity. Furthermore, we have found that, differently from what previously reported for pan-JNK inhibitors, selective JNK1 ablation does not exacerbate kidney dysfunction in db/db mice. We conclude that selective JNK1 inactivation may have a superior therapeutic index than pan-JNK inhibition in obesity-driven diabetes

Mapping of stones and their deterioration forms : the Clock Tower, Venice (Italy)

The HYPERION EU project aims to develop a Decision Support System to improve resilience and sustainable reconstruction of historic areas faced with climate change and extreme events. In this context, Venice presents an outstanding example of urban and architectural complexity and richness. The mapping of the ornamental stones of the facade of the Venice Clock Tower (Torre dell'Orologio) and their deterioration patterns acts as a milestone on which to build the knowledge-acquisition process of the system as regards stone artefacts and their decay products. The Clock Tower is an early Renaissance building (1499) in Lombardesque style and stands over the entrance to the Mercerie on the northern side of St. Mark's Square. Detailed surveys and mapping of both building materials (mainly stones) and deterioration patterns were carried out, the latter following the glossary of weathering forms, coupled with an easy-to-use scale of evaluation of their intensity. The data output consists of several monothematic maps which can be handled separately, each one focusing on precise lithological or specific deterioration aspects. This study also proposes a simple approach to summarizing the total state of deterioration of the building in the form of a Total Deterioration Rank (TDR) and its representation. The stones used in the facade are regional (Ammonitico Rosso and Scaglia Rossa) and extra-regional limestones (Istrian Stone), as well as Mediterranean white and coloured marbles and stones already used in antiquity (i.e., Fior di Pesco or marmor chalcidicum, lapis porphyrites, a volcanic rock from the Egyptian Eastern Desert, Proconnesian marble from the Island of Marmara, Pavonazzetto toscano and white Carrara marble from the Italian Apuan Alps). The most frequent forms of deterioration detected are black crusts, patinas, discoloration and patterns linked to erosion processes. The interrelation of different mappings led to a number of useful considerations concerning differences in the effectiveness of maintenance procedures between public and private management of the monument

Prediction of Elevated Temperature Flexural Strength of Lightweight Foamed Concrete Strengthened with Polypropylene Fibre and Fly Ash

This paper focuses on an experimental investigation and statistical analysis of elevated temperature flexural strengths of lightweight foamed concrete (LFC) strengthened with polypropylene fiber (PF) and fly ash (FA) up to 600°C. Five mixes of LFC with 600, 800, 1000, 1200 and 1400 kg/m³ densities were made and tested in current exploration. Two mixes were casted by substituting 15% and 30% of cement content with FA and in other two series; PF was added to LFC mix, correspondingly by 0.2% and 0.4% of binder volume, one controlled mixture without additives was also fabricated. From the experimental results, it can be concluded that the lessening of LFC flexural strength exposed to elevated temperature may be mainly due to the formation of micro cracks at temperature exceeding 93°C since the flexural strength is unfavourably influenced by formation of cracks so that a rigorous strength loss was experiential at 600°C and the flexural strength was only about 40% of its original value. In order to predict the flexural strength of LFC at high temperatures, some existing models applied for normal strength concrete have been considered. The most consistent model for predicting flexural strength of LFC strengthened with PF and FA and also LFC made by ordinary Portland Cement CEM1 at elevated temperature is Li and Guo prediction model. Keywords: foamed concrete, flexural strength, bending strength, elevated temperature, polypropylene fiber, fly as

The economic value of a climate service for water irrigation. A case study for Castiglione District, Emilia-Romagna, Italy

The use of climate services to support decision makers in incorporating climate change adaptation in their practices is well established and widely recognized. Their role is particularly relevant in a climate sensitive sector like agriculture where they can provide evidence for the adoption of transformative solutions from seasonal to multi-decadal time scales. Adaptation solutions are often expensive and irreversible in the short/medium run. Accordingly, end users should have a reliable reference to make decisions. Here, we propose and apply a methodology, co-developed with service developers and a representative potential user, to assess the value of the IRRICLIME climate service, whose information is used to support decisions on climate smart irrigation investment by water planners in a sub-irrigation district in Italy. We quantify the value of the information provided by the climate service, that we consider the intrinsic value of the service, or the value of adaptation. We demonstrate that under three different climate change scenarios, the maximum potential value of IRRICLIME could range between 2,985 €/ha and 7,480 €/ha

Smart Climate Hydropower Tool: A Machine-Learning Seasonal Forecasting Climate Service to Support Cost–Benefit Analysis of Reservoir Management

This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. SCHT is technically designed to make use of information from state-of-art seasonal forecasts provided by the Copernicus Climate Data Store (CDS) combined with a range of different machine learning algorithms to perform the seasonal forecast of the accumulated inflow discharges to the reservoir of hydropower plants. The machine learning algorithms considered include support vector regression, Gaussian processes, long short-term memory, non-linear autoregressive neural networks with exogenous inputs, and a deep-learning neural networks model. Each machine learning model is trained over past decades datasets of recorded data, and forecast performances are validated and evaluated using separate test sets with reference to the historical average of discharge values and simpler multiparametric regressions. Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. Methods are tested for forecasting the accumulated seasonal river discharges up to six months in advance for two catchments in Colombia, South America. Results indicate that the machine learning algorithms that make use of a complex and/or recurrent architecture can better simulate the temporal dynamic behaviour of the accumulated river discharge inflow to both case study reservoirs, thus rendering SCHT a useful tool in providing information for water resource managers in better planning the allocation of water resources for different users and for hydropower plant managers when negotiating power purchase contracts in competitive energy markets