An open source model for quantifying risks in bulk electric power systems from spatially and temporally correlated hydrometeorological processes

Variability (and extremes) in streamflow, wind speeds, temperatures, and solar irradiance influence supply and demand for electricity. However, previous research falls short in addressing the risks that joint uncertainties in these processes pose in power systems and wholesale electricity markets. Limiting challenges have included the large areal extents of power systems, high temporal resolutions (hourly or sub-hourly), and the data volumes and computational intensities required. This paper introduces an open source modeling framework for evaluating risks from correlated hydrometeorological processes in electricity markets at decision relevant scales. The framework is able to reproduce historical price dynamics in high profile systems, while also offering unique capabilities for stochastic simulation. Synthetic generation of weather and hydrologic variables is coupled with simulation models of relevant infrastructure (dams, power plants). Our model will allow the role of hydrometeorological uncertainty (including compound extreme events) on electricity market outcomes to be explored using publicly available models

The BEHAVE application as a tool to monitor inclusive interventions for subjects with neurodevelopmental disorders

In the last few years, many educational and therapeutic interventions for young people with neurodevelopmental disorders are based on systematic monitoring of the outcomes. These interventions are typically conducted using single-case experimental designs, (SCEDs) a set of methods aimed at testing the effect of an intervention on a single subject or a small number of subjects. In SCEDs, an effective process of decision-making needs accurate, precise, and reliable data but also that caregivers and health professionals can gather information with minimal effort. The use of Information Communication Technologies in SCEDs can support the process of data collection and analysis, facilitating the collection of accurate and reliable data, providing reports accessible also by non-experts, and promoting interactions and sharing among clinicians, educators, and caregivers. The present paper introduces the BEHAVE application, a web-based highly customizable application, designed to implement SCEDs, supporting both data collection and automatic analysis of the datasets. Moreover, the paper will describe two case studies of kindergarten children with neurodevelopmental disorders, highlighting how the BEHAVE application supported the entire process, from data collection in multiple contexts to decision-making based on the analysis provided by the system. In particular, the paper describes the case studies of Carlo and Dario, two children with severe language and communication impairments, and the inclusive education interventions carried out to maximize their participation in a typical home and school setting increasing their mand repertoire. Results revealed an increase in the mand repertoire in both children who become able to generalize the outcomes to multiple life contexts. The active participation of the caregivers played a crucial role in the ability of children to use the learned skills in settings different from the ones they were learned in

Informing Water Management by Direct Use of Snow Information as Surrogate of Medium-to-Long Range Streamflow Forecast

Medium-to-long range streamflow forecast provide a key assistance in anticipating hydro- climatic adverse events and prompting effective adaptation measures. For instance, accurate medium-long range streamflow forecasts have a great potential to improve water reservoir operation by enabling more efficient allocation of water volumes in time (e.g. via hedging). Unfortunately, these forecasts often lacks reliability and accuracy, especially when low-frequency climate forcing (e.g. ENSO) is not intense enough to improve the forecast lead time (e.g. in Europe), and might be computationally very demanding, In this work, we explore the direct use of both rough snow data (e.g. snow depth) and snow water equivalent estimates as surrogate of medium-to-long range streamflow forecast to inform the operation of a regulated lake. The underlying idea is that snow data contains key information on current and future water availability throughout the snow melting season that might significantly improve the operation's anticipation potential. We adopt a three step methodology: First, we compute the upper bound of the system performance by assuming perfect foresight and we assess the value of additional information as the difference between this ideal solution and current operation. Using input variable selection, we then select the most relevant snow information to explain the release trajectory associated to the upper bound operating policy. Finally, we derive the optimal policy conditioned upon the selected variables by Multi-Objecting Evolutionary Direct Policy Search. The methodology is demonstrated on the snow-dominated Lake Como river basin, in the Italian Alps. Lake Como is a regulated lake primarily used to supply water to a large cultivated area and snowmelt from May-July is the most important contribution to the creation of the seasonal storage. Results show that using raw data or simple SWE estimates can largely improve anticipation capability in the daily operation of the lake thus increasing the reservoir hedging potential and the overall system performance over irrigation deficit

Informing the operations of water reservoirs over multiple temporal scales by direct use of hydro-meteorological data

Water reservoir systems may become more adaptive and reliable to external changes by enlarging the information sets used in their operations. Models and forecasts of future hydro-climatic and socio-economic conditions are traditionally used for this purpose. Nevertheless, the identification of skillful forecasts and models might be highly critical when the system comprises several processes with inconsistent dynamics (fast and slow) and disparate levels of predictability. In these contexts, the direct use of observational data, describing the current conditions of the water system, may represent a practicable and zero-cost alternative. This paper contrasts the relative contribution of state observations and perfect forecasts of future water availability in improving multipurpose water reservoirs operation over short- and long-term temporal scales. The approach is demonstrated on the snow-dominated Lake Como system, operated for flood control and water supply. The Information Selection Assessment (ISA) framework is adopted to retrieve the most relevant information to be used for conditioning the operations. By explicitly distinguishing between observational dataset and future forecasts, we quantify the relative contribution of current water system state estimates and perfect streamflow forecasts in improving the lake regulation with respect to both flood control and water supply. Results show that using the available observational data capturing slow dynamic processes, particularly the snow melting process, produces a 10% improvement in the system performance. This latter represents the lower bound of the potential improvement, which may increase to the upper limit of 40% in case skillful (perfect) long-term streamflow forecasts are used.</p

Fostering cooperation in power asymmetrical water systems by the use of direct release rules and index-based insurance schemes

In river basin systems, power asymmetry is often responsible of inefficient and unbalanced water allocations. Climate change and anthropogenic pressure will possibly exacerbate such disparities as the dominant party controls an increasingly limited shared resource. In this context, the deployment of cooperation mechanisms giving greater consideration to a balanced distribution of the benefits, while improving system-wide efficiency, may be desirable. This often implies the intervention of a third party (e.g., the river basin water authority) imposing normative constraints (e.g., a minimum release) on the party in the dominant position. However, this imposition will be more acceptable to the dominant party if coupled with some form of compensation. For a public agency, compensation may be burdensome, especially when the allowance is triggered by natural events whose timing and magnitude are highly uncertain. In this context, index-based insurance contracts may represent a viable alternative and reduce the cost of achieving socially desirable outcomes. In this paper, we develop a hybrid cooperation mechanism composed of i) a direct normative constraint imposed by a regulator, and ii) an indirect financial tool, an index-based insurance contract, to be used as a compensation measure. The approach is developed for the Lake Como multi-purpose water system, Italy: a complex Alpine river basin, supporting several hydropower reservoirs and finally flowing into a regulated lake which supplies water to several downstream uses, mostly irrigated agriculture. The system is characterized by a manifest geographic power asymmetry: the upstream hydropower companies are free to release their stored water in time irrespective of the timing of the downstream demands. This situation can lead to financial losses by the downstream users and undesirable social outcomes. Results suggest that financial instruments may offer a reliable and relatively inexpensive alternative to other forms of compensation, and thereby favor more balanced management of multi-purpose water systems characterized by power asymmetry. This finding is especially relevant in times when granting of licenses to use/withdrawal water are often being reviewed with attention to environmental protection and equity issues

Insurance portfolio diversification through bundling for competing agents exposed to uncorrelated drought and flood risks

Reported global economic losses from climate disasters have substantially increased in the recent decades mainly due to economic growth, along with greater concentrations of people and property in threatened areas and increased weather extremes. In this context, conflict among competing water users in shared water systems can be exacerbated by a perceived increase in financial vulnerability. Risk management tools such as insurance contracts play a critical role in reducing weather related financial vulnerability and promoting risk reduction. However risk diversification is key to guarantee a functioning and sustainable insurance market. In this work we explore the potential of risk diversification strategies involving index-based insurance bundled contract solutions, to manage financial risk in a multi-purpose water system prone to both drought and flood risk. Risk diversification can allow for reduced insurance premiums in situations in which the bundled risks are entirely, or mostly, uncorrelated. Jointly covering flood and drought related risks from competing users in the same geographic area represents a novel application. The approach is demonstrated using a case study on Lake Maggiore, a regulated lake whose management is highly controversial due to numerous and competing human activities. In particular we focus on the ongoing conflict among the lakeshore population, affected by flood risk, and the downstream farmers’ districts, facing drought related losses. Results are promising and indicate that bundling uncorrelated risks from competing users is beneficial to both promoting insurance premium affordability and facilitating collaboration schemes at the catchment scale

Modeling the water-energy nexus under changing energy market and climate conditions: a case study in the Italian Alps

Climate change and growing population are expected to severely affect freshwater availability by the end of 21th century. Many river basins, especially in the Mediterranean region, are likely to become more prone to periods of reduced water supply, risking considerable impacts on the society, the environment, and the economy, thus emphasizing the need to rethink the way water resources are distributed, managed, and used at the regional and river basin scale. This paradigm shift will be essential to cope with the undergoing global change, characterized by growing water demands and by increasingly uncertain hydrologic regimes. Most of the literature traditionally focused on predicting the impacts of climate change on water resources, while our understanding of the human footprint on the hydrological cycle is limited. For example, changes in the operation of the Alpine hydropower reservoirs induced by socio-economic drivers (e.g., development of renewable energy) have been already observed over the last few years and have produced relevant impacts on multiple water uses due to the altered distribution of water volumes in time and space. Modeling human decisions as well as the links between society and environmental systems becomes key to develop reliable projections on the co-evolution of the coupled human-water systems and deliver robust adaptation strategies. This work contributes a preliminary model-based analysis of the behaviour of hydropower operators under changing energy market and climate conditions. The proposed approach is developed for the San Giacomo-Cancano reservoir system located in the Lake Como catchment. The identification of the current operating policy is supported by input variable selection methods to select the most relevant hydrological and market based drivers to explain the observed release time series. The identified model is then simulated under a set of future scenarios, accounting for both climate and socio-economic change (e.g., expansion of the electric vehicle sector, load balancing from renewable energy), to eventually estimate the impacts on the multi-sector services involved (i.e., hydropower, flood protection, irrigation supply). Preliminary results show that the magnitude of the socio-economic change impacts is comparable with the one induced by climate change

'Internal bracing' surgery in the management of solid tumor metastases of the thoracic and lumbar spine

In patients with thoracolumbar spine metastasis, surgery is aimed at patient healing only when patient has a good prognosis with long life expectancy. In patients with short life expectancy a less aggressive surgical approach of posterior decompression and stabilization could improve patient care and allow for neurological recovery. Thirty-two consecutive patients affected by symptomatic thoracolumbar spine metastases with short life expectancy and good Karnofsky index (50-70) were subjected to surgery and reviewed retrospectively. After tumor embolization, surgery consisted of posterior decompression and stabilization with laminar hooks in the dorsal spine, and laminar hooks or lumbar pedicle screws. Patient's Karnofsky Index, average survival, Frankel neurological status, and pain were recorded before and after surgery, together with surgery related complications. Primary tumors were breast carcinoma (nine patients), renal cell carcinoma (three), lung carcinoma (four), GI tract carcinoma (six), prostate carcinoma (two), carcinoma of the uterus (two), melanoma (three), and malignant tumors at other different sites (three). Average survival after surgery was 23 months, with highest survival rates in renal cancer and breast carcinoma patients, and poorest survival rates in lung and dedifferentiated carcinoma. Karnofsky index improved from average 61 to 72% posto-peratively. After surgery patients experienced significant overall improvement of Frankel score and decrease of referred pain. Hospitalization stay was on average 10 days. Results showed that operative treatment of symptomatic spinal metastases in patients with poor prognosis and good general health status improves or preserves neurological function, allows for adjuvant treatments to be performed and has a role in improving general health status in most patients

On the control of riverbed incision induced by run-of-river power plant

Water resource management (WRM) through dams or reservoirs is worldwide necessary to support key human-related activities, ranging from hydropower production to water allocation and flood risk mitigation. Designing of reservoir operations aims primarily to fulfil the main purpose (or purposes) for which the structure has been built. However, it is well known that reservoirs strongly influence river geomorphic processes, causing sediment deficits downstream, altering water and sediment fluxes, leading to river bed incision and causing infrastructure instability and ecological degradation. We propose a framework that, by combining physically based modelling, surrogate modelling techniques and Multi-Objective (MO) optimization, allows to include fluvial geomorphology into MO optimization whose main objectives is the maximization of hydropower revenue and the minimization of river bed degradation. The case study is a run-of-the-river power plant on the River Po (Italy). A 1D mobile-bed hydro-morphological model simulated the river bed evolution over a ten year horizon for alternatives operation rules of the power plant. The knowledge provided by such a physically based model is integrated into a MO optimization routine via surrogate modelling using the response surface methodology. Hence, this framework overcomes the high computational costs that so far hindered the integration of river geomorphology into WRM. We provided numerical proof that river morphologic processes and hydropower production are indeed in conflict, but that the conflict may be mitigated with appropriate control strategies.JRC.H.1-Water Resource