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

Water Use Efficiency in Chilean and Argentine Humid Temperate Grass-Legume Pastures

At two sites in Argentina and Chile five levels of water input were applied to four sown pastures of varying ages during spring and summer. The pastures consisted principally of C3 grasses and legumes, some of which were sown such as Lolium perenne, Trifolium repens, Dactylis glomerata. Dry matter (DM) production was measured and related to the estimated total evapotranspiration (ET): responses were both highly linear. Both responses to ET and absolute yields were higher at the Argentinian than at the Chilean site: respectively 10.7 and 15.2 kg DM/mm water evapotranspired. Nevertheless the calculated indices of sensitivity (Ky) of Doorenbos and Kassam (1979) were similar for the two sites, indicating a similar priority for irrigation in terms of expected responses

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

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

Large-scale response of the Eastern Mediterranean thermohaline circulation to African monsoon intensification during sapropel S1 formation

The formation of Eastern Mediterranean sapropels has periodically occurred during intensification of northern hemisphere monsoon precipitation over North Africa. However, the large-scale response of the Eastern Mediterranean thermohaline circulation during these monsoon-fuelled freshening episodes is poorly constrained. Here, we investigate the formation of the youngest sapropel (S1) along an across-slope transect in the Adriatic Sea. Foraminifera-based oxygen index, redox-sensitive elements and biogeochemical parameters reveal – for the first time – that the Adriatic S1 was synchronous with the deposition of south-eastern Mediterranean S1 beds. Proxies of paleo thermohaline currents indicate that the bottom-hugging North Adriatic Dense Water (NAdDW) suddenly decreased at the sapropel onset simultaneously with the maximum freshening of the Levantine Sea during the African Humid Period. We conclude that the lack of the “salty” Levantine Intermediate Water hampered the preconditioning of the northern Adriatic waters necessary for the NAdDW formation prior to the winter cooling. Consequently, a weak NAdDW limited in turn the Eastern Mediterranean Deep Water (EMDWAdriatic) formation with important consequences for the ventilation of the Ionian basin as well. Our results highlight the importance of the Adriatic for the deep water ventilation and the interdependence among the major eastern Mediterranean water masses whose destabilization exerted first-order control on S1 deposition

Large-scale response of the Eastern Mediterranean thermohaline circulation to African monsoon intensification during sapropel S1 formation

This study was supported by Shell International Exploration and Production Inc. We thank the R/V URANIA crew for at sea assistance. This is the ISMAR contribution n. 1914. We thank Dr. L. Capotondi and Dr. L. Vigliotti for their constructive comments on the first draft of the manuscript. We also thank Dr. Daria Pasqual (University of Padova, Dept. of Geosciences) for her assistance in XRF analyses. We thank two anonymous reviewers and the Editor H. Bauch for their constructive comments. We also acknowledge Prof. Gerhard Schmiedl (Universität Hamburg) and Associate Prof. Syee Weldeab (Earth Science, UC Santa Barbara) for providing published data used in this study.Peer reviewedPostprin

Intratumor Regulatory Noncytotoxic NK Cells in Patients with Hepatocellular Carcinoma

Previous studies support the role of natural killer (NK) cells in controlling hepatocellular carcinoma (HCC) progression. However, ambiguity remains about the multiplicity and the role of different NK cell subsets, as a pro-oncogenic function has been suggested. We performed phenotypic and functional characterization of NK cells infiltrating HCC, with the corresponding nontumorous tissue and liver from patients undergoing liver resection for colorectal liver metastasis used as controls. We identified a reduced number of NK cells in tumors with higher frequency of CD56(BRIGHT)CD16(-) NK cells associated with higher expression of NKG2A, NKp44, and NKp30 and downregulation of NKG2D. Liver-resident (CXCR6(+)) NK cells were reduced in the tumors where T-bet(hi)Eomes(lo) expression was predominant. HCCs showed higher expression of CD49a with particular enrichment in CD49a(+)Eomes(+) NK cells, a subset typically represented in the decidua and playing a proangiogenic function. Functional analysis showed reduced TNF-alpha production along with impaired cytotoxic capacity that was inversely related to CXCR6(-), T-bet(hi)Eomes(lo), and CD49a(+)Eomes(+) NK cells. In conclusion, we identified a subset of NK cells infiltrating HCC, including non-liver-resident cells that coexpressed CD49a and Eomes and showed reduced cytotoxic potential. This NK cell subset likely plays a regulatory role in proangiogenic function

Sub-surface modifications in silicon with ultra-short pulsed lasers above 2 µm

Nonlinear optical phenomena in silicon such as self-focusing and multi-photon absorption are strongly dependent on the wavelength, energy, and duration of the exciting pulse, especially for wavelengths >2µm. We investigate the sub-surface modification of silicon using ultra-short pulsed lasers at wavelengths in the range of 1950–2400 nm, at a pulse duration between 2 and 10 ps and pulse energy varying from 1 µJ to 1 mJ. We perform numerical simulations and experiments using fiber-based lasers built in-house that operate in this wavelength range for the surface and sub-surface processing of Si-wafers. The results are compared to the literature data at 1550 nm. Due to a dip in the nonlinear absorption spectrum and a peak in the spectrum of the third-order nonlinearity, the wavelengths between 2000 and 2200 nm prove to be more favorable for creating sub-surface modifications in silicon. This is the case even though those wavelengths do not allow as tight focusing as those at 1550 nm. This is compensated for by an increased self-focusing due to the nonlinear Kerr-effect around 2100 nm at high light intensities, characteristic for ultra-short pulses

The Late Pleistocene Po River lowstand wedge in the Adriatic Sea: Controls on architecture variability and sediment partitioning

Although facies and stratal geometries of continental margin successions can be defined in detail based on subsurface and outcrop studies, most studies lack the high-resolution age control needed to constrain the time scale of formation of such successions and infer their external forcing mechanisms. Our work on the Po River Lowstand Wedge (PRLW) indicates that deposition rates are surprisingly high with the entire 350-m-thick succession being deposited in less than 17,000 years, and with individual clinothems recording time periods ranging from 400 to 4700 years. The PRLW preserves a high-resolution record of stacked, deltaic shelf-edge clinothems deposited during the Last Glacial Maximum (31.8–14.4 ky BP) in the Adriatic basin (Mediterranean Sea). We investigated clinothem internal geometry, stacking patterns, and facies distributions to infer the main controls on their growth by integrating seismic reflection data with seismic facies attributes and paleoenvironmental proxies. The stratigraphic framework of the shelf-edge clinothems was then related to major paleoenvironmental shifts during the last glacial cycle and driven by eustatic and climatic changes. Within the PRLW, we recognized three distinctive types of 100's-m-high shelf-edge clinothems, type A, type B and type C, each with diagnostic topset geometries, shelf-edge trajectories, and associated distal basin-fill deposits. These elemental clinothem types stack into two Clinothem Sets. Clinothem Set 1, with essentially flat to slightly descending shelf-edge trajectory, is composed of stacked types A and B clinothems, and records the direct influence of river flux leading to dysoxic conditions on the bottom of the basin. In particular, clinothem accumulation rates were as much as 200 km3/ky in some of the type B clinothems. Clinothem Set 2, showing ascending shelf-edge trajectory, records an aggradational stacking coupled with a retreat of the river-entry points with benthic fauna assemblages that reflect the influence of peaks in freshwater discharge. Whereas Clinothem Set 1 developed under perturbations of river supply linked to the multi-scale waxing and waning of glaciers during an interval dominated by eustatic fall, Clinothem Set 2 reflects the main thawing of glaciers concomitant to the first phase of the eustatic rise. From a sequence stratigraphic perspective, Clinothem Set 1 is interpreted as staked high-frequency sequences, while Clinothem Set 2 represents a stack of high-frequency parasequences. The high-resolution age control from boreholes and seismic data enabled us to relate stratal character to independently constrained environmental proxies: this revealed how the evolution of a shelf-edge system intricately convolves the influences of both global (eustacy) and regional (climate-driven supply fluctuations) controls, both at sub-Milankovitch scales. Finally, the thickness, geometry, and stacking patterns of the centennial to millennial clinothems of the PRLW vary in systematic ways resulting in geometries that closely resemble those of ancient shelf-edge systems, and offering the PRLW as a sub-modern analogue. Our observations also reinforce the focus of the classic sequence-stratigraphic approach on analyzing surfaces and their geometric relations and not on time duration or formation mechanisms