Precipitation in peninsular Spain: Influence of teleconnection indices and spatial regionalisation

2020 ◽  
Author(s):  
Joan Martinez‐Artigas ◽  
Marc Lemus‐Canovas ◽  
Joan Albert Lopez‐Bustins
Keyword(s):  
Teleconnection Indices ◽  
Peninsular Spain

scholarly journals The correlation between three teleconnections and leptospirosis incidence in the Kandy District, Sri Lanka, 2004–2019

2021 ◽  
Vol 49 (1) ◽  
Author(s):  
N. D. B. Ehelepola ◽  
Kusalika Ariyaratne ◽  
A. M. S. M. C. M. Aththanayake ◽  
Kamalanath Samarakoon ◽  
H. M. Arjuna Thilakarathna
Keyword(s):  
Time Series ◽  
Sri Lanka ◽  
Wavelet Analysis ◽  
Southern Oscillation ◽  
Population Data ◽  
Enso Modoki ◽  
Cross Correlation Analysis ◽  
Teleconnection Indices ◽  
Weather Parameters ◽  
Time Lagged

Abstract Background Leptospirosis is a bacterial zoonosis. Leptospirosis incidence (LI) in Sri Lanka is high. Infected animals excrete leptospires into the environment via their urine. Survival of leptospires in the environment until they enter into a person and several other factors that influence leptospirosis transmission are dependent upon local weather. Past studies show that rainfall and other weather parameters are correlated with the LI in the Kandy district, Sri Lanka. El Niño Southern Oscillation (ENSO), ENSO Modoki, and the Indian Ocean Dipole (IOD) are teleconnections known to be modulating rainfall in Sri Lanka. There is a severe dearth of published studies on the correlations between indices of these teleconnections and LI. Methods We acquired the counts of leptospirosis cases notified and midyear estimated population data of the Kandy district from 2004 to 2019, respectively, from weekly epidemiology reports of the Ministry of Health and Department of Census and Statistics of Sri Lanka. We estimated weekly and monthly LI of Kandy. We obtained weekly and monthly teleconnection indices data for the same period from the National Oceanic and Atmospheric Administration (NOAA) of the USA and Japan Agency for Marine-Earth Science and Technology (JAMSTEC). We performed wavelet time series analysis to determine correlations with lag periods between teleconnection indices and LI time series. Then, we did time-lagged detrended cross-correlation analysis (DCCA) to verify wavelet analysis results and to find the magnitudes of the correlations detected. Results Wavelet analysis displayed indices of ENSO, IOD, and ENSO Modoki were correlated with the LI of Kandy with 1.9–11.5-month lags. Indices of ENSO showed two correlation patterns with Kandy LI. Time-lagged DCCA results show all indices of the three teleconnections studied were significantly correlated with the LI of Kandy with 2–5-month lag periods. Conclusions Results of the two analysis methods generally agree indicating that ENSO and IOD modulate LI in Kandy by modulating local rainfall and probably other weather parameters. We recommend further studies about the ENSO Modoki and LI correlation in Sri Lanka. Monitoring for extreme teleconnection events and enhancing preventive measures during lag periods can blunt LI peaks that may follow.

The association between climate teleconnection indices and Upper Klamath seasonal streamflow: Trans-Niño Index

2009 ◽  
Vol 23 (7) ◽  
pp. 973-984 ◽  
Author(s):  
Adam M. Kennedy ◽  
David C. Garen ◽  
Roy W. Koch
Keyword(s):  
Teleconnection Indices ◽  
Seasonal Streamflow

scholarly journals Rhabditid species (Nematoda, Rhabditida) recorded in peninsular Spain and Balearic Islands

Graellsia ◽  
2001 ◽  
Vol 57 (1) ◽  
pp. 113-131 ◽  
Author(s):  
Reyes Peña-Santiago ◽  
Joaquín Abolafia
Keyword(s):  
Balearic Islands ◽  
Peninsular Spain

Effects of air quality on the health of Mediterranean forests

2021 ◽  
Author(s):  
Adrián García Bruzón ◽  
Patricia Arrogante Funes ◽  
Laura Muñoz Moral
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Random Forest ◽  
Aridity Index ◽  
Plant Health ◽  
Mediterranean Forests ◽  
Random Forest Algorithm ◽  
The Mediterranean ◽  
Heterogeneous Variables ◽  
Peninsular Spain

<p>The climate change has turned out to be a determining factor in the development of forest in Spain. Production systems have emitted polluting gases and other particles into the atmosphere, for which some plants have not yet developed adaptation systems. Among the most harmful pollutants for the environment are gases such as nitrous oxides, ozone, particulate matter.</p><p>However, this condition is not the same in Peninsular Spain, and the Balearic Islands since the plant compositions differ in the territory and the bioclimatic, topographic, and anthropic characteristics. Monitoring the vegetation with sufficient spatial and temporal resolution, studying variables conditioning plant health is a challenge from the nature of the variables and the amount of data to be handled. </p><p>The Mediterranean forest is one of the most ecosystem affected by climate change because of usually experimented long periods of drought that, in combination with increased temperatures, can drastically reduce the photosynthetic activity of trees and therefore the biomass of forests.</p><p>That is why the application of environmental technologies based on Remote Sensing (which provide plant health indices from passive sensors on satellite platforms and other variables of interest), Geographic Information Systems (to integrate, process, analyze spatial and temporal data) and machine learning models (which facilitate the extraction of relationships between variables, conditioning factors and predict patterns). </p><p>In this regard, this work's objective is to evaluate the possible effect that different pollutants have on the health of the vegetation, measured from the annual values of the Normalized Difference Vegetation Index (NDVI), in the Mediterranean forests of Peninsular Spain. To achieve this, we are used machine learning techniques using the Random Forest algorithm. The study has also been done with various climatic, topographic, and anthropic variables that characterize the forest to carry it out. </p><p>The results showed that certain variables such as the aridity index had generated the NDVI values and therefore plant development, while others are limiting factors such as the concentration of certain pollutants and the direct relationship between them particulates and NOx. This study can verify how the Random Forest algorithm offers reliable results, even when working with heterogeneous variables. </p>

scholarly journals Remote sensing and ecosystem modeling to simulate terrestrial carbon fluxes

2019 ◽  
pp. 87
Author(s):  
Sergio Sánchez-Ruiz
Keyword(s):  
Remote Sensing ◽  
Carbon Cycle ◽  
Spatial Scale ◽  
Forest Ecosystems ◽  
Carbon Fluxes ◽  
Daily Basis ◽  
Ecosystem Models ◽  
Validation Process ◽  
Data Source ◽  
Peninsular Spain

<p>The main goal of this thesis is the establishment of a framework to analyze the forest ecosystems in peninsular Spain in terms of their role in the carbon cycle. In particular, the carbon fluxes that they exchange with atmosphere are modeled to evaluate their potential as carbon sinks and biomass reservoirs. The assessment of gross and net carbon fluxes is performed at 1-km spatial scale and on a daily basis using two different ecosystem models, Monteith and BIOME-BGC, respectively. These models are driven by a combination of satellite and ground data, part of the latter being also employed as a complementary data source and in the validation process.</p>

scholarly journals Preferred temperature and thermal breadth of birds wintering in peninsular Spain – the limited effect of temperature on species distribution

2016 ◽  
Author(s):  
Luis M. Carrascal ◽  
Sara Villén-Pérez ◽  
David Palomino
Keyword(s):  
Species Abundance ◽  
Bird Species ◽  
Effect Of Temperature ◽  
Response Curves ◽  
Large Variability ◽  
Preferred Temperature ◽  
Mean Temperature ◽  
The Mean ◽  
Thermal Preferences ◽  
Peninsular Spain

Background. Availability of environmental energy, as measured by temperature, is expected to limit the abundance and distribution of endotherms wintering at temperate latitudes. A prediction of this hypothesis is that birds should attain their highest abundances in warmer areas. However, there may be a spatial mismatch between species preferred habitats and species preferred temperatures, so some species might end-up wintering in sub-optimal thermal environments. Methods. We model the influence of minimum winter temperature on the relative abundance of 106 terrestrial bird species wintering in peninsular Spain, at 10x10 Km2 resolution, using 95%-quantile regressions. We analyze general trends across species on the shape of the response curves, the environmental preferred temperature (at which the species abundance is maximized), the mean temperature in the area of distribution and the thermal breadth (area under the abundance-temperature curve). Results. There is a large interspecific variability on the thermal preferences and specialization of species. Despite this large variability, there is a preponderance of positive relationships between species abundance and temperature, and on average species attain their maximum abundances in areas 1.9 ºC warmer than the average temperature available in peninsular Spain. The mean temperature in the area of distribution is lower than the thermal preferences of the species, although both parameters are highly correlated. Discussion. Most species prefer the warmest environments to overwinter, which suggests that temperature imposes important restrictions to birds wintering in the Iberian Peninsula. However, most individuals overwinter in locations colder than the species thermal preferences, probably reflecting a limitation of environments combining habitat and thermal preferences. Beyond these general trends, there is a high inter-specific variation in the versatility of species using the available thermal space .

scholarly journals Evolutionary history predicts the response of tree species to forest loss: A case study in peninsular Spain

PLoS ONE ◽  
2018 ◽  
Vol 13 (9) ◽  
pp. e0204365 ◽  
Author(s):  
Rafael Molina-Venegas ◽  
Sonia Llorente-Culebras ◽  
Paloma Ruiz-Benito ◽  
Miguel A. Rodríguez
Keyword(s):  
Tree Species ◽  
Evolutionary History ◽  
Forest Loss ◽  
Peninsular Spain

Enhancement of seasonal hydrological forecasting with “pattern-based” large-scale climatology 

2021 ◽  
Author(s):  
Wei Yang ◽  
Kean Foster ◽  
Ilias G. Pechlivanidis
Keyword(s):  
Large Scale ◽  
Fuzzy Rule ◽  
River System ◽  
Reanalysis Data ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Local Climate ◽  
Weather Regimes ◽  
Hydrological Forecasting ◽  
Teleconnection Indices

&lt;p&gt;The hydrological forecasting on seasonal (up to 7 months ahead) timescales is needed for decision-making in the hydropower sector. Being one of the vital influencing factors on hydro-production, a lot of development in dynamical forecasting at seasonal timescales has been done recently. However, the forecast bias still remains in different variables and consequently the skill of corresponding streamflow forecasts varies from month to month.&lt;/p&gt;&lt;p&gt;This study aims to explore the potential for &amp;#8220;pattern-based&amp;#8221; seasonal hydrological forecasts that make use of hydrological weather regimes and teleconnection indices to improve forecast skill. The work is built on the hypothesis that hydrological weather regimes and teleconnection indices can be used to select analogue years (setting an ensemble) from a record of historical precipitation and temperature data with which to force a hydrological model to generate tailored seasonal forecasts of reservoir inflows. The hydrological weather regimes have been classified based on the concept of fuzzy sets using the anomalies of daily mean sea level pressure from reanalysis data (i.e., ERA-Interim). Precipitation records, measured in the Ume&amp;#228;lven river basin during 1981-2016 are used as local observations to optimize each fuzzy rule that describes a type of &amp;#8220;average&amp;#8221; variability of local climate in terms of the frequency and magnitude of precipitation events. The teleconnection indices are compiled from the Climate Prediction Center, which describe global atmospheric variability. The methodology has been applied to 84 sub-catchments across seven of the most important hydropower producing river systems in Northern Sweden. However, the performance for the Ume&amp;#228;lven river system is of particular interest here.&lt;/p&gt;&lt;p&gt;Comparing to the traditional Ensemble Streamflow Prediction (ESP) method, the &amp;#8220;pattern-based&amp;#8221; seasonal hydrological forecasting shows a marked improvement, which is likely due to the weighted analogue-ESP approach as well as the selected analogues using the large-scale climate information described by hydrological weather regimes and teleconnection indices. The general performance of the two different approaches for selecting the analogues are similar; however, occasionally there are large differences in both the best analysis lead times and the spread of skill across the sub-catchments suggesting that those results are achieved using analogues based on different physical processes.&lt;/p&gt;

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