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Summary The application of simple empirical equations for estimating reference evapotranspiration (ET o ) is the only alternative in many cases to robust approaches with high input requirements, especially at the local scale. In particular, temperature-based approaches present a high potential applicability, among others, because temperature might explain a high amount of ET o variability, and also because it can be measured easily and is one of the most available climatic inputs. One of the most well-known temperature-based approaches, the Hargreaves (HG) equation, requires a preliminary local calibration that is usually performed through an adjustment of the HG coefficient (AHC). Nevertheless, these calibrations are site-specific, and cannot be extrapolated to other locations. So, they become useless in many situations, because they are derived from already available benchmarks based on more robust methods, which will be applied in practice. Therefore, the development of accurate equations for estimating AHC at local scale becomes a relevant task. This paper analyses the performance of calibrated and non-calibrated HG equations at 30 stations in Eastern Spain at daily, weekly, fortnightly and monthly scales. Moreover, multiple linear regression was applied for estimating AHC based on different inputs, and the resulting equations yielded higher performance accuracy than the non-calibrated HG estimates. The approach relying on the ratio mean temperature to temperature range did not provide suitable AHC estimations, and was highly improved by splitting it into two independent predictors. Temperature-based equations were improved by incorporating geographical inputs. Finally, the model relying on temperature and geographic inputs was further improved by incorporating wind speed, even just with simple qualitative information about wind category (e.g. poorly vs. highly windy). The accuracy of the calibrated and non-calibrated HG estimates increased for longer time steps (daily < weekly < fortnightly < monthly), although with a decreasing accuracy improvement rate. The variability of goodness-of-fit between AHC models was translated into lower variability of accuracy between the corresponding HG calibrated ET o estimates, because a single AHC was applied per station. The AHC fluctuations throughout the year suggest the convenience of using monthly or, at least, seasonal models. [ABSTRACT FROM AUTHOR]

At present there is increasing interest in modelling biomass to estimate carbon sequestration or the availability of forest products for use as bioenergy. The biomass of different tree components can be estimated to provide more detailed information. However, the different components have not been clearly defined. Moreover, the greater the number of components considered, the more difficult it is to fit the system of equations with any guarantee of statistical robustness. To overcome these limitations, we developed a continuous function that predicts cumulative biomass from the stump until any top diameter (including the biomass of branches). We also used two different methods to predict bole biomass: a cumulative continuous biomass function and conversion from volume to biomass by use of a taper equation and average wood density. We used a mixed-effects modelling framework to account for correlated errors in developing the taper equation. We developed a separate equation to estimate the foliar biomass for use in estimating total aboveground tree biomass. The cumulative aboveground woody biomass equation is implicitly additive, and no heteroscedasticity was observed, thereby resolving two of the main modelling goals in the development of biomass equations. For predicting cumulative bole biomass, estimation from volume generated less error, after bias correction, than direct estimation. Moreover, the indirect method also yields useful variables such as volume and height limits. Other variables such as carbon and nutrient contents, calorific power, ash content, etc. can be estimated by multiplying the mean contents by the predicted biomass or, for more accurate predictions, by using equations based on the relative diameter. [ABSTRACT FROM AUTHOR]

Objective To compare the effectiveness and costs associated with first-line medical treatments for chronic heavy menstrual bleeding (HMB) in Spain. Study design A cost-effectiveness analysis was conducted comparing the levonorgestrel-releasing intrauterine system (LNG-IUS) with the estradiol valerate/dienogest multiphase oral contraceptive (E2V/DNG), combined oral contraceptives (COC) and progestins (PROG). Study patients were fertile women diagnosed with HMB who initially wished to remain fertile. A Markov model based on reported clinical data and the opinion of a panel of experts was used. The time horizon of the analysis was 5 years. The analysis was conducted from the perspective of the Spanish National Health System (NHS), discounting both costs (€ 2013) and future effects at an annual rate of 3%. One-way sensitivity analyses and probabilistic sensitivity analysis were performed to test the robustness of the results. Results In the analysis at 5 years, the LNG-IUS was associated with a gain of 0.67, 2.22, and 3.53 symptoms free months (SFM) compared with E2V/DNG, COC and PROG, respectively. LNG-IUS contributed more quality-adjusted life months (QALM) than the other treatment alternatives (+1.74 vs. E2V/DNG, +3.33 vs. COC +3.53 vs. PROG). First-line LNG-IUS treatment resulted in savings of € 583, € 988, and € 1891 vs. E2V/DNG, COC and PROG, respectively. These cost benefits, coupled with the greater clinical benefits in terms of SFM and QALM, show that LNG-IUS is the dominant option (less costly and more effective). Conclusion LNG-IUS is the medical treatment of choice and cost-saving option for the control of HMB in Spain. [ABSTRACT FROM AUTHOR]

Highlights: [•] A new methodology of the functional depth has detected outliers in the Miño river basin (NW Spain). [•] Detection of atypical observations is an important concern in water quality control. [•] The effectiveness and robustness of the proposed method are evaluated. [•] The method is able to identify outliers in multivariate data with success. [ABSTRACT FROM AUTHOR]

Abstract: Complex networks are considered robust against random failures but sensitive to failures in target. Under this perspective we analyze the robustness of the Spanish telecommunication networks for digital transmission, the Synchronous Digital Hierarchy (SDH), considering failures in the equipments that control the flow of information. Our results suggest that the robustness depends not only on the connectivity of equipments removed but also on the capacity and types of links associated with that removal. We have also observed that the robustness of SDH networks depends on the particular initial design and evolution of each Spanish province. [Copyright &y& Elsevier]

Abstract: The need to obtain automatic filters to treat wind data is clear due to the huge amount of data available in any wind farm analysis. Automatic detection of performance deviations of wind turbines is required for maintenance purposes. In spite of some alarms being registered by the wind farm’s SCADA system, many times automatic use of real data use is not valid due to its low quality. In this paper, the problems to carry out the characterization of the turbines in a wind farm are stated and a new technique to filter the data is presented. The robust statistical technique of the Least Median of Squares (LMedS) combined with a random search is adapted to deal with this problem with promising results. A mathematical model representing the dependency of the wind turbine behaviour in function not only of the wind speed but also of other variables such as the direction of the wind is also proposed. The results achieved are tested with long term real data from wind farms in Spain. [Copyright &y& Elsevier]