Remote Sensing of Precipitation: Part II

Precipitation is a well-recognized pillar in the global water and energy balances. The accurate and timely understanding of its characteristics at the global, regional and local scales is indispensable for a clearer insight on the mechanisms underlying the Earth’s atmosphere-ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises the primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne. This volume hosts original research contributions on several aspects of remote sensing of precipitation, including applications which embrace the use of remote sensing in tackling issues such as precipitation estimation, seasonal characteristics of precipitation and frequency analysis, assessment of satellite precipitation products, storm prediction, rain microphysics and microstructure, and the comparison of satellite and numerical weather prediction precipitation products

Aplicación en línea para el mapeo en Argentina de información de lluvias extremas para diseño hidrológico

Este trabajo presenta una aplicación en línea que permite visualizar de una manera directa e intuitiva, los mapas de valores de lluvias extremas requeridas para el diseño hidrológico de obras hidráulicas de pequeña y mediana envergadura en la porción continental de la República Argentina. La información que se mapea no pretende ser un reemplazo de las técnicas clásicas de análisis y procesamiento hidrológico sino un valor de referencia a nivel regional. En esta aplicación, se visualizan los valores estimados de Precipitación Máxima Diaria (PMDT) para diferentes periodos de retorno, y el Valor Límite Estimado de Precipitación (VELP) denominado habitualmente como Precipitación Máxima Probable (PMP), empleándose para la estimación de este último valor, una adaptación local generada por los autores de este trabajo a la metodología propuesta por Hershfield, (1961,1965), la cual optimiza la estimación del factor de frecuencia PMP. Para generar la información de base se emplearon registros históricos de precipitación diaria máxima anual de 1.564 estaciones, y la técnica de interpolación universal de Kriging fue utilizada para la estimación local, en el área de estudio (República Argentina Continental) la cual presentan diferentes regiones topográficas y climáticas. (Andinas y Subandinas, planicies subtropicales y pampeanas, así también como patagónicas). Los mapas de PMD y PMP, los cuales se realizaron mediante una grilla de 25 km2, muestran una clara tendencia creciente oeste-este, aunque valores de PMD de recurrencia mayor a 50 años, así como el mapa de PMP muestran una cierta uniformidad espacial. Los mapas de varianza de Kriging muestran mayores incertidumbres en regiones donde la densidad espacial de las estaciones pluviométricas no es abundante, principalmente aquellas localizaciones con elevaciones significativas sobre el nivel del mar, las cuales no fueron incluidos en los mapas.Sección: Ingeniería hidráulica, sanitaria y ambiental.Academia de la Ingeniería de la provincia de Buenos Aire

Optimizing peak gust and maximum sustained wind speed estimates from mid-latitude wave cyclones

Wind storms cause significant damage and economic loss and are a major recurring threat in many countries. Maximum sustained and peak gust weather station data from multiple historic wind storms occurring over more than three decades across Europe were analyzed to identify storm tracks, intensities, and areas of frequent high wind speeds. Wind surfaces for maximum sustained and peak gust winds were estimated based on an anisotropic (directionally-dependent) kriging interpolation methodology. Overall, wind speed magnitudes and high intensity locations were identified accurately for each storm. Directional trends and wind swaths were also consistently located in appropriate locations based on known storm tracks. Anisotropic kriging proved to be superior to isotropic (non-directional) kriging when modeling continental-scale wind storms because of the identification of strong directional correlations across space. Results suggest that coastal areas and mountainous areas experience the highest wind intensities during wind storms. These same areas also experience high variability over short distances and thus the highest error measurements associated with concurrent interpolated surfaces. For this reason, various covariates were utilized in conjunction with the cokriging interpolation technique and improved the interpolated wind surfaces for five wind storms that impacted both the mountainous and topographically-varied Alps region and the coastal regions of Europe. Land cover alone reduced station-measured standard error most significantly in a majority of the models, while aspect and elevation (singularly and collectively) also reduced station standard error in most models as compared to the original kriging models. Additional comparisons between different areal scales of kriging/cokriging models revealed that some surface wind variability is muted at the continental scale, but identifiable at the local scale. However, major patterns and trends are more difficult to ascertain for local-scale surfaces when compared to continental-scale surfaces. Large station error can be reduced through local kriging/cokriging, but additional research is needed to merge local-scale semivariograms with continental-scale models. Results showed substantial improvements in wind speed surface estimates over previous estimates and have major implications for catastrophe modeling companies, insurance needs, and construction standards. Implications of this research may be transferrable to other geographies and create an impetus for database and covariate improvement

An Approach to Developing a Spatio-Temporal Composite Measure of Climate Change-Related Human Health Impacts in Urban Environments

Introduction: Rapid population growth along with an increase in the frequency and intensity of climate change-related impacts in costal urban environments emphasize the need for the development of new tools to help disaster planners and policy makers select and prioritize mitigation and adaptation measures. Using the concept of the resilience of a community, which is a measure of how rapidly the community can recover to its previous level of functionality following a disruptive event is still a relatively new concept for many engineers, planners and policy makers, but is becoming recognized as an increasingly important and some would argue, essential component for the development and subsequent assessment of adaptation plans being considered for communities at risk of climate change-related events. The holistic approach which is the cornerstone of resilience is designed to integrate physical, economic, health, social and organizational impacts of climate change in urban environments. This research presents a methodology for the development of a quantitative spatial and temporal composite measure for assessing climate change-related health impacts in urban environments. Methods: The proposed method is capable of considering spatial and temporal data from multiple inputs, relating to both physical and social parameters. This approach uses inputs such as the total population density and densities of various demographics, burden of diseases conditions, flood inundation mapping, and land use change for both historical and current conditions. The research has demonstrated that the methodology presented generates sufficiently accurate information to be useful for planning adaptive strategies. To assemble all inputs into a single measure of health impacts, a weighting system was assigned to apply various priorities to the spatio-temporal data sources. Weights may be varied to assess how they impact the final results. Finally, using spatio-temporal extrapolation methods the future behavior of the same key spatial variables can be projected. Although this method was developed for application to any coastal mega-city, this thesis demonstrates the results obtained for Metro Vancouver, British Columbia, Canada. The data was collected for the years 1981, 1986, 1991, 1996, 2001, 2006 and 2011, as information was readily available for these years. Fine resolution spatial data for these years was used in order to give a dynamic simulation of possible health impacts for future projections. Linear and auto-regressive spatio-temporal extrapolations were used for projecting a 2050’s Metro Vancouver health impact map (HIM). Conclusion: Results of this work show that the approach provides a more fully integrated view of the resilience of the city which incorporates aspects of population health. The approach would be useful in the development of more targeted adaptation and risk reduction strategies at a local level. In addition, this methodology can be used to generate inputs for further resilience simulations. The overall value of this approach is that it allows for a more integrated assessment of the city vulnerability and could lead to more effective adaptive strategies

Emerging Hydro-Climatic Patterns, Teleconnections and Extreme Events in Changing World at Different Timescales

This Special Issue is expected to advance our understanding of these emerging patterns, teleconnections, and extreme events in a changing world for more accurate prediction or projection of their changes especially on different spatial–time scales

Explainable machine learning in soil mapping: Peeking into the black box

Während des Anthropozäns und insbesondere in den letzten Jahrzehnten hat sich die Umwelt der Erde stark verändert. Die planetarischen Grenzen stehen zunehmend unter Druck. Da der Boden als wichtiger Teil der Kohlenstoff- und Stickstoffkreisläufe das Klima beeinflusst, ist er eine wichtige Ressource bei der Bewältigung dieser Umweltprobleme. Folglich spielt das Wissen über den Boden, Bodenprozesse und Bodenfunktionen eine wesentliche Rolle bei der Erforschung und Lösung dieser schwerwiegenden ökologischen und sozioökonomischen Herausforderungen. Die Kartierung und Modellierung des Bodens liefert räumliche Kenntnis über den Zustand des Bodens und seine Veränderungen im Laufe der Zeit. Dies ermöglicht es, Methoden der Bodenbewirtschaftung und Lösungsansätze für Umweltprobleme zu beurteilen und zu bewerten. Methoden des maschinellen Lernens haben sich für die räumliche Kartierung und Modellierung des Bodens als geeignet erwiesen. Oft handelt es sich dabei aber um Black Boxes und die Modellentscheidungen und -ergebnisse werden nicht erklärt. Allerdings würden erklärbare Bodenmodelle auf der Grundlage des maschinellen Lernens die Erkennung von Umweltveränderungen erleichtern, zur Entscheidungsfindung für den Umweltschutz beitragen und die Akzeptanz von Wissenschaft, Politik in Gesellschaft fördern. Daher sind die jüngsten Bemühungen im Bereich des maschinellen Lernens darauf ausgerichtet, den konventionellen Rahmen des maschinellen Lernens auf er¬klärbares maschinelles Lernen zu erweitern, um 1) Entscheidungen zu begründen, 2) die Modelle besser zu steuern und 3) zu verbessern und 4) neues Wissen zu generieren. Die Kernelemente für erklärbares maschinelles Lernen sind Transparenz, Interpretierbarkeit und Erklärbarkeit. Darüber hinaus sind domain knowledge und wissenschaftliche Konsistenz entscheidend. Bei der Bodenmodellierung spielten die Konzepte des erklärbaren maschinellen Lernens jedoch bisher eine geringe Rolle. Ziel dieser Arbeit war es, zu untersuchen und zu beschreiben, wie Transparenz, Interpretierbarkeit und Erklärbarkeit im Rahmen der Bodenmodellierung erreicht werden können. Die Fallbeispiele zeigten, wie Konsistenz mit Modellvergleichen bewertet werden kann und domain knowledge in die Modelle einfließt. Ebenso zeigten die Studien, wie Transparenz mit reproduzierbarer Proben- und Variablenauswahl erreicht werden kann und wie die Interpretation der Modelle mit domain knowledge verknüpft werden kann, um die Modellergebnisse besser zu erklären und in Bezug zu bodenkundlichem Wissen zu setzen sind.During the Anthropocene and especially in the past decades earth’s environment has undergone major changes. The planetary boundaries are increasingly under pressure. Since soil affects climate as compartment of the carbon and nitrogen cycles, it is an important resource in approaching these environmental problems. Consequently, knowledge about soil, soil processes and soil functions plays an essential role in research on and solutions for these severe environmental and socio-economic challenges. The mapping and modelling of soil provides spatial knowledge of soil status and changes over time, which allows to assess and evaluate soil management practices and attempts to solve to environmental problems. Machine learning methods have proven to be suitable for spatial mapping and modelling of soil, but often are black boxes and the model decisions and prediction results remain unexplained. However, explainable soil models based on machine learning would facilitate detection of environmental changes, contribute to decision making for environmental protection and foster acceptance in science, politics, and society. Therefore, latest efforts in machine learning were to expand the conventional machine learning framework to explainable machine learning to 1) justify decisions, 2) control, and 3) improve models and 4) to discover new knowledge. The core elements for explainable machine learning are transparency, interpretability and explainability. Additionally, domain knowledge and scientific consistency are crucial. However, to date the concepts of explainable machine learning played a marginal role in soil modelling and mapping. Objective of this thesis was to explore and describe how transparency, interpretability and explainability can be achieved in the soil mapping framework. The example studies showed how scientific consistency can be evaluated with model comparison and domain knowledge was and incorporated in DSM models. The studies showed how transparency can be accomplished with reproducible sample and covariate selection, and how interpretation of the models can be linked with domain knowledge about soil formation and processes to explain the model results

Remote Sensing of Hydro-Meteorology

Flood/drought, risk management, and policy: decision-making under uncertainty. Hydrometeorological extremes and their impact on human–environment systems. Regional and nonstationary frequency analysis of extreme events. Detection and prediction of hydrometeorological extremes with observational and model-based approaches. Vulnerability and impact assessment for adaptation to climate change

European Atlas of Natural Radiation

Natural ionizing radiation is considered as the largest contributor to the collective effective dose received by the world population. The human population is continuously exposed to ionizing radiation from several natural sources that can be classified into two broad categories: high-energy cosmic rays incident on the Earth’s atmosphere and releasing secondary radiation (cosmic contribution); and radioactive nuclides generated during the formation of the Earth and still present in the Earth’s crust (terrestrial contribution). Terrestrial radioactivity is mostly produced by the uranium and thorium radioactive families together with potassium. In most circumstances, radon, a noble gas produced in the radioactive decay of uranium, is the most important contributor to the total dose. This Atlas aims to present the current state of knowledge of natural radioactivity, by giving general background information, and describing its various sources. This reference material is complemented by a collection of maps of Europe displaying the levels of natural radioactivity caused by different sources. It is a compilation of contributions and reviews received from more than 80 experts in their field: they come from universities, research centres, national and European authorities and international organizations. This Atlas provides reference material and makes harmonized datasets available to the scientific community and national competent authorities. In parallel, this Atlas may serve as a tool for the public to: • familiarize itself with natural radioactivity; • be informed about the levels of natural radioactivity caused by different sources; • have a more balanced view of the annual dose received by the world population, to which natural radioactivity is the largest contributor; • and make direct comparisons between doses from natural sources of ionizing radiation and those from man-made (artificial) ones, hence to better understand the latter.JRC.G.10-Knowledge for Nuclear Security and Safet