Robust Estimation of Mean and Dispersion Functions in Extended Generalized Additive Models

Generalized Linear Models are a widely used method to obtain parametric es- timates for the mean function. They have been further extended to allow the re- lationship between the mean function and the covariates to be more flexible via Generalized Additive Models. However the fixed variance structure can in many cases be too restrictive. The Extended Quasi-Likelihood (EQL) framework allows for estimation of both the mean and the dispersion/variance as functions of covari- ates. As for other maximum likelihood methods though, EQL estimates are not resistant to outliers: we need methods to obtain robust estimates for both the mean and the dispersion function. In this paper we obtain functional estimates for the mean and the dispersion that are both robust and smooth. The performance of the proposed method is illustrated via a simulation study and some real data examples.dispersion;generalized additive modelling;mean regression function;quasilikelihood;M-estimation;P-splines;robust estimation

A bivariate extension of the Hosking and Wallis goodness-of-fit measure for regional distributions

This study presents a bivariate extension of the goodness-of-fit measure for regional frequency distributions developed by Hosking and Wallis (1993) for use with the method of L-moments. Utilizing the approximate joint normal distribution of the regional L-skewness and L-kurtosis, a graphical representation of the confidence region on the L-moment diagram can be constructed as an ellipsoid. Candidate distributions can then be accepted where the corresponding theoretical relationship between the L-skewness and L-kurtosis intersects the confidence region, and the chosen distribution would be the one that minimizes the Mahalanobis distance measure. Based on a set of Monte Carlo simulations, it is demonstrated that the new bivariate measure generally selects the true population distribution more frequently than the original method. Results are presented to show that the new measure remains robust when applied to regions where the level of intersite correlation is at a level found in real world regions. Finally the method is applied to two different case studies involving annual maximum peak flow data from Italian and British catchments to identify suitable regional frequency distributions.This study presents a bivariate extension of the goodness-of-fit measure for regional frequency distributions developed by Hosking and Wallis (1993) for use with the method of L-moments. Utilizing the approximate joint normal distribution of the regional L-skewness and L-kurtosis, a graphical representation of the confidence region on the L-moment diagram can be constructed as an ellipsoid. Candidate distributions can then be accepted where the corresponding theoretical relationship between the L-skewness and L-kurtosis intersects the confidence region, and the chosen distribution would be the one that minimizes the Mahalanobis distance measure. Based on a set of Monte Carlo simulations, it is demonstrated that the new bivariate measure generally selects the true population distribution more frequently than the original method. Results are presented to show that the new measure remains robust when applied to regions where the level of intersite correlation is at a level found in real world regions. Finally the method is applied to two different case studies involving annual maximum peak flow data from Italian and British catchments to identify suitable regional frequency distributions. Key Points: A new bivariate GOF measure for regional frequency distributions using L-moments New measure performs better than existing Hosking and Wallis measure New measure performs well in homogeneous but moderately correlated region

Detection and attribution of urbanization effect on flood extremes using nonstationary flood-frequency models

This study investigates whether long-term changes in observed series of high flows can be attributed to changes in land use via nonstationary flood-frequency analyses. A point process characterization of threshold exceedances is used, which allows for direct inclusion of covariates in the model; as well as a nonstationary model for block maxima series. In particular, changes in annual, winter, and summer block maxima and peaks over threshold extracted from gauged instantaneous flows records in two hydrologically similar catchments located in proximity to one another in northern England are investigated. The study catchment is characterized by large increases in urbanization levels in recent decades, while the paired control catchment has remained undeveloped during the study period (1970–2010). To avoid the potential confounding effect of natural variability, a covariate which summarizes key climatological properties is included in the flood-frequency model. A significant effect of the increasing urbanization levels on high flows is detected, in particular in the summer season. Point process models appear to be superior to block maxima models in their ability to detect the effect of the increase in urbanization levels on high flows

Developing drought impact functions for drought risk management

Drought management frameworks are dependent on methods for monitoring and prediction, but quantifying the hazard alone is arguably not sufficient; the negative consequences that may arise from a lack of precipitation must also be predicted if droughts are to be better managed. However, the link between drought intensity, expressed by some hydrometeorological indicator, and the occurrence of drought impacts has only recently begun to be addressed. One challenge is the paucity of information on ecological and socioeconomic consequences of drought. This study tests the potential for developing empirical “drought impact functions” based on drought indicators (Standardized Precipitation and Standardized Precipitation Evaporation Index) as predictors and text-based reports on drought impacts as a surrogate variable for drought damage. While there have been studies exploiting textual evidence of drought impacts, a systematic assessment of the effect of impact quantification method and different functional relationships for modeling drought impacts is missing. Using Southeast England as a case study we tested the potential of three different data-driven models for predicting drought impacts quantified from text-based reports: logistic regression, zero-altered negative binomial regression (“hurdle model”), and an ensemble regression tree approach (“random forest”). The logistic regression model can only be applied to a binary impact/no impact time series, whereas the other two models can additionally predict the full counts of impact occurrence at each time point. While modeling binary data results in the lowest prediction uncertainty, modeling the full counts has the advantage of also providing a measure of impact severity, and the counts were found to be reasonably predictable. However, there were noticeable differences in skill between modeling methodologies. For binary data the logistic regression and the random forest model performed similarly well based on leave-one-out cross validation. For count data the random forest outperformed the hurdle model. The between-model differences occurred for total drought impacts and for two subsets of impact categories (water supply and freshwater ecosystem impacts). In addition, different ways of defining the impact counts were investigated and were found to have little influence on the prediction skill. For all models we found a positive effect of including impact information of the preceding month as a predictor in addition to the hydrometeorological indicators. We conclude that, although having some limitations, text-based reports on drought impacts can provide useful information for drought risk management, and our study showcases different methodological approaches to developing drought impact functions based on text-based data

Tortugas marinas en aguas argentinas

Con la ayuda de los sensores remotos y los sistemas de posicionamiento geográfico, los científicos están descubriendo varias facetas sobre las vida de las tortugas marinas. Se describe el método para realizar el siguimiento satelital, sus migraciones, y se dan las características de las especies de tortugas marinas encontradas en aguas costeras de la Argentina. Este trabajo de divulgación científica hace hincapié en la importancia de su conservación, según la Unión Internacional para la Conservación de la Naturaleza, éstas se encuentran en peligro o en peligro crítico de extinción en todo el mundo. Se incluyen al final otras lecturas sugeridas sobre el tema

CEH-GEAR: 1 km resolution daily and monthly areal rainfall estimates for the UK for hydrological and other applications

The Centre for Ecology & Hydrology – Gridded Estimates of Areal Rainfall (CEH-GEAR) data set was developed to provide reliable 1 km gridded estimates of daily and monthly rainfall for Great Britain (GB) and Northern Ireland (NI) (together with approximately 3500 km2 of catchment in the Republic of Ireland) from 1890 onwards. The data set was primarily required to support hydrological modelling. The rainfall estimates are derived from the Met Office collated historical weather observations for the UK which include a national database of rain gauge observations. The natural neighbour interpolation methodology, including a normalisation step based on average annual rainfall (AAR), was used to generate the daily and monthly rainfall grids. To derive the monthly estimates, rainfall totals from monthly and daily (when complete month available) rain gauges were used in order to obtain maximum information from the rain gauge network. The daily grids were adjusted so that the monthly grids are fully consistent with the daily grids. The CEH-GEAR data set was developed according to the guidance provided by the British Standards Institution. The CEH-GEAR data set contains 1 km grids of daily and monthly rainfall estimates for GB and NI for the period 1890–2012. For each day and month, CEH-GEAR includes a secondary grid of distance to the nearest operational rain gauge. This may be used as an indicator of the quality of the estimates. When this distance is greater than 100 km, the estimates are not calculated due to high uncertainty

A bivariate trend analysis to investigate the effect of increasing urbanisation on flood characteristics

Flood frequency analyses are usually based on the assumption of stationarity, which might be unrealistic if changes in climate, land uses or urbanisation impact the study catchment. Moreover, most non-stationarity studies only focus on peak flows, ignoring other flood characteristics. In this study, the potential effect of increasing urbanisation on the bivariate relationship of peak flows and volumes is investigated in a case study in the northwest of England, consisting of an increasingly urbanised catchment and a nearby hydrologically and climatologically similar unchanged rural (control) catchment. The study is performed via Kendall's tau and copulas. Temporal trends are studied visually and by formal tests, considering variables individually and jointly. Bivariate joint return period curves associated with consecutive time periods are compared to understand the joint implications of such bivariate trends. Although no significant bivariate trends were detected, hydrologically relevant trends were found in the urbanised catchment

Making better use of local data in flood frequency estimation

Flood frequency estimates are an essential part of flood risk management. They are an important ingredient of many important decisions, informing the cost-effectiveness, design and operation of flood defences, flood mapping and planning decisions in flood risk areas. They also inform the National Flood Risk Assessment, the setting of insurance premiums and long-term investment planning. Methods described in the Flood Estimation Handbook (FEH) published in 1999, and many subsequent updates, are considered the industry standard for flood estimation in the UK. They are used extensively by hydrologists from both the public and private sectors. Flood frequency estimates – also known as design flood estimates – are associated with many sources of uncertainty. These hydrological uncertainties often constitute the most uncertain component in any flood study. Uncertainty can lead to difficulty in having confidence in the outputs of studies, whether these are for investment planning, insurance, asset design, development planning or other purposes. As a result, there is considerable benefit to be gained from any reduction in the uncertainty of flood frequency estimation. There are many supplementary sources of information that can help to refine estimates of design floods and potentially reduce uncertainty. Examples include long-term flood history, river level records, photographs of floods and information obtained from field visits. These and similar types of information are defined as ‘local data’. The FEH Local research project aimed to: quantify the uncertainty of design floods estimated from FEH methods develop procedures and guidance for incorporating local and historical data into flood estimation to reduce uncertainties The primary objective of this report is to describe the reviews and research carried out during the FEH Local project. Another output from the project was a document giving guidance to practitioners on how to estimate uncertainty in flood frequency and how to find and incorporate local data. The practitioner guidance, ‘Using Local Data to Reduce Uncertainty in Flood Frequency Estimation’, will be disseminated early in 2017. This report aims to avoid duplication with the practitioner guidance and so is intended mainly for those with an interest in the background to the methods presented in the guidance

Winter Greenhouse Tomato Cultivation : Matching Leaf Pruning and Supplementary Lighting for Improved Yield and Precocity

Unidad de excelencia María de Maeztu CEX2019-000940-MSolar radiation entering a high-wire tomato greenhouse is mostly intercepted by the top of the crop canopy, while the role of lower leaves diminishes with age, turning them into sink organs rather than sources. Accordingly, the defoliation of basal leaves is a widely applied agronomic practice in high-wire greenhouse cultivation management. However, the recent increase in the application of supplemental light emitting diode (LED) lighting for high-density tomato production may affect the role of basal leaves, promoting their source role for fruit development and growth. The present research aims to explore the application of supplementary LED lighting on Solanum lycopersicum cv. Siranzo in the Mediterranean area during the cold season in combination with two regimes of basal defoliation. The defoliation factors consisted of the early removal of the leaves (R) right under the developing truss before the fruit turning stage and a non-removal (NR) during the entire cultivation cycle. The lighting factors consisted of an artificial LED lighting treatment with red and blue diodes for 16 h d−1 (h 8-00) with an intensity of 180 µmol s−1 m−2 (RB) and a control cultivated under natural light only (CK). The results demonstrated a great effect of the supplemental LED light, which increased the total yield (+118%), favoring fruit setting (+46%) and faster ripening (+60%) regardless of defoliation regimes, although the increased energy prices hinder the economic viability of the technology. Concerning fruit quality, defoliation significantly reduced the soluble solid content, while it increased the acidity when combined with natural light