Small-scale surface heterogeneity and potential inhomogeneities in temperature time series: a case study

Measurements in seven sites in the Campus of the University of the Balearic Islands (UIB; Mallorca, Spain) during an experimental campaign to study the contribution of local surface heterogeneities on the surface energy budget at one point have been used to characterize the differences in extreme daily temperatures between the sites during a summer month. Absolute temperature differences in this month reached up to 1.92 (with a median of 0.73) and 2.02 (median of 1.21)°C for daily maximum and minimum, respectively. Higher differences in the minimum temperature can be attributed to the stably stratified and weak turbulent conditions at night that enhance local differences in the surface energy fluxes, especially in an area with strong variability of the surface characteristics like the UIB Campus. Instead, during daytime, maximum temperature differences are smoothed due to the convection and the horizontal advection due to the sea-breeze. Two sites with longer records allowed to study the seasonal variations of these differences, which were substantially lower in the colder months. These results suggest that relocation of observatories, even at distances as short as 200 m, may introduce important inhomogeneities in the temperature series. Therefore, raw values of series from nearby stations should not be used to infill missing data in other series without adequate statistical adjustments.This work was part of of the research project grants CGL2015-65627-C3-1-R and RTI2018- 098693-B-C31 funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe

Analysis and prediction of COVID-19 for EU-EFTA-UK and other countries

The present report aims to provide a comprehensive picture of the pandemic situation of COVID-19 in the EU countries, and to be able to foresee the situation in the next coming days. We employ an empirical model, verified with the evolution of the number of confirmed cases in previous countries where the epidemic is close to conclude, including all provinces of China. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of tendencies. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 5-7 days later. The model and predictions are based on two parameters that are daily fitted to available data: a: the velocity at which spreading specific rate slows down; the higher the value, the better the control. K: the final number of expected cumulated cases, which cannot be evaluated at the initial stages because growth is still exponential. Next, we show a report with 8 graphs and a table with the short-term predictions for (1) European Union and its countries, (2) other countries, (3) Spain and its autonomous communities. We are currently adjusting the model to countries and regions with at least 4 days with more than 100 confirmed cases and a current load over 200 cases. The predicted period of a country depends on the number of datapoints over this 100 cases threshold: Group A: countries that have reported more than 100 cumulated cases for 10 consecutive days or more ¿ 3-5 days prediction; Group B: countries that have reported more than 100 cumulated cases for 7 to 9 consecutive days ¿ 2 days prediction; Group C: countries that have reported more than 100 cumulated cases for 4 to 6 days ¿ 1 d ay prediction. We have introduced a change in fittings, that are now weighted at some points. The whole methodology employed in the inform is explained in the last pages of this document.These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00Postprint (published version

Analysis and prediction of COVID-19 for EU-EFTA-UK and other countries

The present report aims to provide a comprehensive picture of the pandemic situation of COVID-19 in the EU countries, and to be able to foresee the situation in the next coming days. We employ an empirical model, verified with the evolution of the number of confirmed cases in previous countries where the epidemic is close to conclude, including all provinces of China. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of tendencies. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 5-7 days later. The model and predictions are based on two parameters that are daily fitted to available data: a: the velocity at which spreading specific rate slows down; the higher the value, the better the control. K: the final number of expected cumulated cases, which cannot be evaluated at the initial stages because growth is still exponential. Next, we show a report with 8 graphs and a table with the short-term predictions for (1) European Union and its countries, (2) other countries, (3) Spain and its autonomous communities. We are currently adjusting the model to countries and regions with at least 4 days with more than 100 confirmed cases and a current load over 200 cases. The predicted period of a country depends on the number of datapoints over this 100 cases threshold: Group A: countries that have reported more than 100 cumulated cases for 10 consecutive days or more ¿ 3-5 days prediction; Group B: countries that have reported more than 100 cumulated cases for 7 to 9 consecutive days ¿ 2 days prediction; Group C: countries that have reported more than 100 cumulated cases for 4 to 6 days ¿ 1 d ay prediction. We have introduced a change in fittings, that are now weighted at some points. The whole methodology employed in the inform is explained in the last pages of this document.These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00Postprint (published version

Empiric model for short-time prediction of COVID-19 spreading

Covid-19 appearance and fast spreading took by surprise the international community. Collaboration between researchers, public health workers and politicians has been established to deal with the epidemic. One important contribution from researchers in epidemiology is the analysis of trends so that both current state and short-term future trends can be carefully evaluated. Gompertz model has shown to correctly describe the dynamics of cumulative confirmed cases, since it is characterized by a decrease in growth rate that is able to show the effect of control measures. Thus, it provides a way to systematically quantify the Covid-19 spreading velocity. Moreover, it allows to carry out short-term predictions and long-term estimations that may facilitate policy decisions and the revision of in-place confinement measures and the development of new protocols. This model has been employed to fit the cumulative cases of Covid-19 from several Chinese provinces and from other countries with a successful containment of the disease. Results show that there are systematic differences in spreading velocity between countries. In countries that are in the initial stages of the Covid-19 outbreak, model predictions provide a reliable picture of its short-term evolution and may permit to unveil some characteristics of the long-term evolution. These predictions can also be generalized to short-term hospital and Intensive Care Units (ICU) requirements, which together with the equivalent predictions on mortality provide key information for health officials.CP, PJC and MC received funding from La Caixa Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; PJC received funding from Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR), Grup Unitat de Tuberculosi Experimental, 2017-SGR-500; CP, DL, SA, MC received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00. This work has been also partially funded by the European Comission - DG Communications Networks, Content and Technology through the contract LC-01485746.Preprin

Fluvial nutrient dynamics in a humanized landscape. Insights from a hierarchical perspective

Enviem correu als editors per informar-nos sobre la política de drets d'autorGlobal change driven by human activity is overimposed on the hierarchical structure of fluvial ecosystems, causing a myriad of effects on their physical template and hydrology as well as on the quantity and quality of the resources for stream biota. Global change operates at all scales within this hierarchy, but its effects on the ecology of fluvial ecosystems at any particular scale may be exacerbated or overridden by concomitant effects occurring at other scales. The resulting effects can have major ecological implications on both ecosystem services (namely, biogeochemical processes associated to energy and matter flow) and biodiversity (namely, community structure), which currently are issues of central concern in environmental management. In this paper we focus on a particular ecological attribute of fluvial ecosystems, the capacity to process and retain nutrients, and examine how physical and chemical alterations caused by human activities, occurring at different scales, may interact to affect this capacity. We illustrate these effects based on existing knowledge and highlight the key changes at different scales which can be susceptible of major effects.El cambio global derivado de la actividad humana se sobrepone a la estructura jerárquica de los ecosistemas fluviales, causando múltiples efectos sobre la estructura física, la hidrología, y la cantidad y calidad de los recursos para los organismos de los ríos. El cambio global opera sobre todos los niveles de esta jerarquía estructural, pero sus efectos sobre la ecología de los ecosistemas fluviales en cada nivel pueden ser exacerbados o anulados por efectos que ocurren a otros niveles. Los efectos resultantes tienen implicaciones ecológicas tanto en relación con los servicios de los ecosistemas (por ejemplo, los procesos biogeoquímicos asociados al flujo de energía y materia) y la biodiversidad (por ejemplo, la estructura biótica de las comunidades). Actualmente, estos temas son una preocupación central en la gestión ambiental. En este artículo nos centramos en un atributo ecológico concreto de los ecosistemas fluviales, la capacidad de procesar y retener nutrientes, y examinamos cómo alteraciones físicas y químicas causadas por la actividad humana, que tienen lugar a diferentes niveles espaciales, pueden incidir en esta capacidad biogeoquímica. Estos efectos son ilustrados en base al conocimiento existente y enfatizan los cambios clave a diferentes niveles que pueden ser susceptibles de estos efectos

Analysis and prediction of COVID-19 for EU-EFTA-UK and other countries

The present report aims to provide a comprehensive picture of the pandemic situation of COVID‐19 in the EU countries, and to be able to foresee the situation in the next coming days. We employ an empirical model, verified with the evolution of the number of confirmed cases in previous countries where the epidemic is close to conclude, including all provinces of China. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of trends. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 7-10 days later. The model and predictions are based on two parameters that are daily fitted to available data: a: the velocity at which spreading specific rate slows down; the higher the value, the better the control. K: the final number of expected cumulated cases, which cannot be evaluated at the initial stages because growth is still exponential. We show an individual report with 8 graphs and a table with the short-term predictions for different countries and regions. We are adjusting the model to countries and regions with at least 4 days with more than 100 confirmed cases and a current load over 200 cases. The predicted period of a country depends on the number of datapoints over this 100 cases threshold, and is of 5 days for those that have reported more than 100 cumulated cases for 10 consecutive days or more. For short-term predictions, we assign higher weight to last 3 points in the fittings, so that changes are rapidly captured by the model. The whole methodology employed in the inform is explained in the last pages of this document. In addition to the individual reports, the reader will find an initial dashboard with a brief analysis of the situation in EU-EFTA-UK countries, some summary figures and tables as well as long-term predictions for some of them, when possible. These long-term predictions are evaluated without different weights to datapoints. We also discuss a specific issue every day.These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00Postprint (author's final draft

Analysis and prediction of COVID-19 for EU-EFTA-UK and other countries

The present report aims to provide a comprehensive picture of the pandemic situation of COVID-19 in the EU countries, and to be able to foresee the situation in the next coming days. We employ an empirical model, verified with the evolution of the number of confirmed cases in previous countries where the epidemic is close to conclude, including all provinces of China. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of trends. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 7-10 days later. The model and predictions are based on two parameters that are daily fitted to available data: a: the velocity at which spreading specific rate slows down; the higher the value, the better the control. K: the final number of expected cumulated cases, which cannot be evaluated at the initial stages because growth is still exponential. We show an individual report with 8 graphs and a table with the short-term predictions for different countries and regions. We are adjusting the model to countries and regions with at least 4 days with more than 100 confirmed cases and a current load over 200 cases. The predicted period of a country depends on the number of datapoints over this 100 cases threshold, and is of 5 days for those that have reported more than 100 cumulated cases for 10 consecutive days or more. For short-term predictions, we assign higher weight to last 3 points in the fittings, so that changes are rapidly captured by the model. The whole methodology employed in the inform is explained in the last pages of this document. In addition to the individual reports, the reader will find an initial dashboard with a brief analysis of the situation in EU-EFTA-UK countries, some summary figures and tables as well as long-term predictions for some of them, when possible. These long-term predictions are evaluated without different weights to datapoints. We also discuss a specific issue every day.These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00Postprint (published version

Analysis and prediction of COVID-19 for EU-EFTA-UK and other countries

The present report aims to provide a comprehensive picture of the pandemic situation of COVID-19 in the EU countries, and to be able to foresee the situation in the next coming days. We employ an empirical model, verified with the evolution of the number of confirmed cases in previous countries where the epidemic is close to conclude, including all provinces of China. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of trends. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 7-10 days later. The model and predictions are based on two parameters that are daily fitted to available data: ¿ a: the velocity at which spreading specific rate slows down; the higher the value, the better the control. ¿ K: the final number of expected cumulated cases, which cannot be evaluated at the initial stages because growth is still exponential. We show an individual report with 8 graphs and a table with the short-term predictions for different countries and regions. We are adjusting the model to countries and regions with at least 4 days with more than 100 confirmed cases and a current load over 200 cases. The predicted period of a country depends on the number of datapoints over this 100 cases threshold, and is of 5 days for those that have reported more than 100 cumulated cases for 10 consecutive days or more. For short-term predictions, we assign higher weight to last 3 points in the fittings, so that changes are rapidly captured by the model. The whole methodology employed in the inform is explained in the last pages of this document. In addition to the individual reports, the reader will find an initial dashboard with a brief analysis of the situation in EU-EFTA-UK countries, some summary figures and tables as well as long-term predictions for some of them, when possible. These long-term predictions are evaluated without different weights to datapoints. We also discuss a specific issue every day.These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00Postprint (published version

Analysis and prediction of COVID-19 for EU-EFTA-UK and other countries

The present report aims to provide a comprehensive picture of the pandemic situation of COVID-19 in the EU countries, and to be able to foresee the situation in the next coming days. We employ an empirical model, verified with the evolution of the number of confirmed cases in previous countries where the epidemic is close to conclude, including all provinces of China. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of trends. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 7-10 days later. The model and predictions are based on two parameters that are daily fitted to available data: ¿ a: the velocity at which spreading specific rate slows down; the higher the value, the better the control. ¿ K: the final number of expected cumulated cases, which cannot be evaluated at the initial stages because growth is still exponential. We show an individual report with 8 graphs and a table with the short-term predictions for different countries and regions. We are adjusting the model to countries and regions with at least 4 days with more than 100 confirmed cases and a current load over 200 cases. The predicted period of a country depends on the number of datapoints over this 100 cases threshold, and is of 5 days for those that have reported more than 100 cumulated cases for 10 consecutive days or more. For short-term predictions, we assign higher weight to last 3 points in the fittings, so that changes are rapidly captured by the model. The whole methodology employed in the inform is explained in the last pages of this document. In addition to the individual reports, the reader will find an initial dashboard with a brief analysis of the situation in EU-EFTA-UK countries, some summary figures and tables as well as long-term predictions for some of them, when possible. These long-term predictions are evaluated without different weights to datapoints. We also discuss a specific issue every day.These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00Postprint (published version

Analysis and prediction of COVID-19 for EU-EFTA-UK and other countries

The present report aims to provide a comprehensive picture of the pandemic situation of COVID-19 in the EU countries, and to be able to foresee the situation in the next coming days. We employ an empirical model, verified with the evolution of the number of confirmed cases in previous countries where the epidemic is close to conclude, including all provinces of China. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of trends. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 7-10 days later. The model and predictions are based on two parameters that are daily fitted to available data: ¿ a: the velocity at which spreading specific rate slows down; the higher the value, the better the control. ¿ K: the final number of expected cumulated cases, which cannot be evaluated at the initial stages because growth is still exponential. We show an individual report with 8 graphs and a table with the short-term predictions for different countries and regions. We are adjusting the model to countries and regions with at least 4 days with more than 100 confirmed cases and a current load over 200 cases. The predicted period of a country depends on the number of datapoints over this 100 cases threshold, and is of 5 days for those that have reported more than 100 cumulated cases for 10 consecutive days or more. For short-term predictions, we assign higher weight to last 3 points in the fittings, so that changes are rapidly captured by the model. The whole methodology employed in the inform is explained in the last pages of this document. In addition to the individual reports, the reader will find an initial dashboard with a brief analysis of the situation in EU-EFTA-UK countries, some summary figures and tables as well as long-term predictions for some of them, when possible. These long-term predictions are evaluated without different weights to datapoints. We also discuss a specific issue every day.These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00Postprint (published version