Relationship of weather types on the seasonal and spatial variability of rainfall, runoff, and sediment yield in the western Mediterranean basin

Rainfall is the key factor to understand soil erosion processes, mechanisms, and rates. Most research was conducted to determine rainfall characteristics and their relationship with soil erosion (erosivity) but there is little information about how atmospheric patterns control soil losses, and this is important to enable sustainable environmental planning and risk prevention. We investigated the temporal and spatial variability of the relationships of rainfall, runoff, and sediment yield with atmospheric patterns (weather types, WTs) in the western Mediterranean basin. For this purpose, we analyzed a large database of rainfall events collected between 1985 and 2015 in 46 experimental plots and catchments with the aim to: (i) evaluate seasonal differences in the contribution of rainfall, runoff, and sediment yield produced by the WTs; and (ii) to analyze the seasonal efficiency of the different WTs (relation frequency and magnitude) related to rainfall, runoff, and sediment yield. The results indicate two different temporal patterns: the first weather type exhibits (during the cold period: autumn and winter) westerly flows that produce the highest rainfall, runoff, and sediment yield values throughout the territory; the second weather type exhibits easterly flows that predominate during the warm period (spring and summer) and it is located on the Mediterranean coast of the Iberian Peninsula. However, the cyclonic situations present high frequency throughout the whole year with a large influence extended around the western Mediterranean basin. Contrary, the anticyclonic situations, despite of its high frequency, do not contribute significantly to the total rainfall, runoff, and sediment (showing the lowest efficiency) because of atmospheric stability that currently characterize this atmospheric pattern. Our approach helps to better understand the relationship of WTs on the seasonal and spatial variability of rainfall, runoff and sediment yield with a regional scale based on the large dataset and number of soil erosion experimental stations

Relationship of Weather Types on the Seasonal and Spatial Variability of Rainfall, Runoff, and Sediment Yield in the Western Mediterranean Basin

Rainfall is the key factor to understand soil erosion processes, mechanisms, and rates. Most research was conducted to determine rainfall characteristics and their relationship with soil erosion (erosivity) but there is little information about how atmospheric patterns control soil losses, and this is important to enable sustainable environmental planning and risk prevention. We investigated the temporal and spatial variability of the relationships of rainfall, runoff, and sediment yield with atmospheric patterns (weather types, WTs) in the western Mediterranean basin. For this purpose, we analyzed a large database of rainfall events collected between 1985 and 2015 in 46 experimental plots and catchments with the aim to: (i) evaluate seasonal differences in the contribution of rainfall, runoff, and sediment yield produced by the WTs; and (ii) to analyze the seasonal efficiency of the different WTs (relation frequency and magnitude) related to rainfall, runoff, and sediment yield. The results indicate two different temporal patterns: the first weather type exhibits (during the cold period: autumn and winter) westerly flows that produce the highest rainfall, runoff, and sediment yield values throughout the territory; the second weather type exhibits easterly flows that predominate during the warm period (spring and summer) and it is located on the Mediterranean coast of the Iberian Peninsula. However, the cyclonic situations present high frequency throughout the whole year with a large influence extended around the western Mediterranean basin. Contrary, the anticyclonic situations, despite of its high frequency, do not contribute significantly to the total rainfall, runoff, and sediment (showing the lowest efficiency) because of atmospheric stability that currently characterize this atmospheric pattern. Our approach helps to better understand the relationship of WTs on the seasonal and spatial variability of rainfall, runoff and sediment yield with a regional scale based on the large dataset and number of soil erosion experimental stations.Spanish Government (Ministry of Economy and Competitiveness, MINECO) and FEDER Projects: CGL2014 52135-C3-3-R, ESP2017-89463-C3-3-R, CGL2014-59946-R, CGL2015-65569-R, CGL2015-64284-C2-2-R, CGL2015-64284-C2-1-R, CGL2016-78075-P, GL2008-02879/BTE, LEDDRA 243857, RECARE-FP7, CGL2017-83866-C3-1-R, and PCIN-2017-061/AEI. Dhais Peña-Angulo received a “Juan de la Cierva” postdoctoral contract (FJCI-2017-33652 Spanish Ministry of Economy and Competitiveness, MEC). Ana Lucia acknowledge the "Brigitte-Schlieben-Lange-Programm". The “Geoenvironmental Processes and Global Change” (E02_17R) was financed by the Aragón Government and the European Social Fund. José Andrés López-Tarazón acknowledges the Secretariat for Universities and Research of the Department of the Economy and Knowledge of the Autonomous Government of Catalonia for supporting the Consolidated Research Group 2014 SGR 645 (RIUS- Fluvial Dynamics Research Group). Artemi Cerdà thank the funding of the OCDE TAD/CRP JA00088807. José Martínez-Fernandez acknowledges the project Unidad de Excelencia CLU-2018-04 co-funded by FEDER and Castilla y León Government. Ane Zabaleta is supported by the Hydro-Environmental Processes consolidated research group (IT1029-16, Basque Government). This paper has the benefit of the Lab and Field Data Pool created within the framework of the COST action CONNECTEUR (ES1306)

Estimation of Hydropower Potential Using Bayesian and Stochastic Approaches for Streamflow Simulation and Accounting for the Intermediate Storage Retention

Hydropower is the most widely used renewable power source worldwide. The current work presents a methodological tool to determine the hydropower potential of a reservoir based on available hydrological information. A Bayesian analysis of the river flow process and of the reservoir water volume is applied, and the estimated probability density function parameters are integrated for a stochastic analysis and long-term simulation of the river flow process, which is then used as input for the water balance in the reservoir, and thus, for the estimation of the hydropower energy potential. The stochastic approach is employed in terms of the Monte Carlo ensemble technique in order to additionally account for the effect of the intermediate storage retention due to the thresholds of the reservoir. A synthetic river flow timeseries is simulated by preserving the marginal probability distribution function properties of the observed timeseries and also by explicitly preserving the second-order dependence structure of the river flow in the scale domain. The synthetic ensemble is used for the simulation of the reservoir water balance, and the estimation of the hydropower potential is used for covering residential energy needs. For the second-order dependence structure of the river flow, the climacogram metric is used. The proposed methodology has been implemented to assess different reservoir volume scenarios offering the associated hydropower potential for a case study at the island of Crete in Greece. The tool also provides information on the probability of occurrence of the specific volumes based on available hydrological data. Therefore, it constitutes a useful and integrated framework for evaluating the hydropower potential of any given reservoir. The effects of the intermediate storage retention of the reservoir, the marginal and dependence structures of the parent distribution of inflow and the final energy output are also discussed

Adherence to CPAP therapy improves quality of life and reduces symptoms among obstructive sleep apnea syndrome patients

Purpose: The aim of the study was to asses quality of life and symptoms of obstructive sleep apnea syndrome (OSAS) patients after adhering to 6 months of continuous positive airway pressure (CPAP) treatment. Methods: A group of 50 patients (41 men and 9 women) were diagnosed by polysomnography and treated with CPAP therapy for 6 months. Their symptoms and healthrelated quality of life were assessed by administering a validated and translated version of the sleep apnea quality of life index (SAQLI). Sleepiness was measured using the Epworth Sleepiness Scale (ESS) and through electronic monitoring of CPAP usage per night of sleep. Results Mean CPAP usage was 4.5±0.5 h per night. Comparisons between quality of life indexes before and after CPAP treatment showed an improvement in the total SAQLI score (3.8±0.9 vs. 5.8±0.8 after CPAP, p<0.01), in daily functioning (4.2±1.4 vs. 6.0±0.9, p<0.01), social interactions (4.8±1.3 vs.6.3±0.7, p<0.01), emotional functioning (4.4±1.4 vs. 5.7±1.0, p<0.01), symptoms (1.6±0.8 vs. 5.8±1.2, p<0.01), and in the ESS (13.7±6.5 vs. 3.9±3.8, p<0.01). Regarding the patients' symptoms, improvement was noticed for "sleepiness while watching a spectacle" (96%), "reading" (95%), "carrying on a conversation"(95%), "driving" (92.9%), "restless sleep" (87.8%), and "urinating more than once per night" (84.8%). Smaller improvements were observed for the reported "dry mouth-throat upon awakening" (36.1%),"excessive fatigue"(54.5%), and "decreased energy" (55.3%). Conclusion We conclude that OSAS patients who adhere to nighttime CPAP therapy show significant improvement of their quality of life, daytime sleepiness, and other symptoms after 6 months of treatment with CPAP. © Springer-Verlag 2011

Combination of geostatistics and self-organizing maps for the spatial analysis of groundwater level variations in complex hydrogeological systems

Successful modelling of the groundwater level variations in hydrogeological systems in complex formations considerably depends on spatial and temporal data availability and knowledge of the boundary conditions. Geostatistics plays an important role in model-related data analysis and preparation, but has specific limitations when the aquifer system is inhomogeneous. This study combines geostatistics with machine learning approaches to solve problems in complex aquifer systems. Herein, the emphasis is given to cases where the available dataset is large and randomly distributed in the different aquifer types of the hydrogeological system. Self-Organizing Maps can be applied to identify locally similar input data, to substitute the usually uncertain correlation length of the variogram model that estimates the correlated neighborhood, and then by means of Transgaussian Kriging to estimate the bias corrected spatial distribution of groundwater level. The proposed methodology was tested on a large dataset of groundwater level data in a complex hydrogeological area. The obtained results have shown a significant improvement compared to the ones obtained by classical geostatistical approaches

Cytokines and pathological sleep

Cytokines are proteins produced by leukocytes and other cells that function as intercellular mediators acting on several target tissues, resulting in multiple biologic actions. Over the last decade, medical research has explored the interaction between cytokines and sleep disorders. The aim of this review is to illustrate recent advances in knowledge about the relationship between cytokines and disorders of excessive sleepiness. Cytokines may have an important role in mediating excessive daytime sleepiness in sleep loss or insomnia. Alterations of the immune system have also been associated with narcolepsy. The relationship between cytokines and hormonal regulatory mechanisms may explain symptoms like fatigue and sleepiness in chronic inflammatory diseases. Cytokines may play an important role in the pathogenesis of obstructive sleep apnea and cardiovascular consequences of this condition. New biologic treatments targeting cytokines have been investigated in conditions characterized by sleep disturbance. © 2007 Elsevier B.V. All rights reserved

An integrated method to study and plan aquifer recharge

This study presents a simple framework methodology for selecting the most appropriate locations for managed aquifer recharge (MAR). The proposed approach is applicable to aquifers that are located in coastal or mountainous areas and are used either for agricultural or industrial (e.g., mining) activities. A characteristic case study for the identification of the areas that are the most suitable for aquifer recharge using a GIS multi-criteria decision analysis (GIS-MCDA) method by means of MAR type spreading methods is the Geropotamos basin, Crete, Greece. Criteria combining a high relevance and high data availability, and providing unique information, were selected to assess the suitability of aquifer recharge in the basin. The criteria applied to evaluate the sites’ suitability for MAR spreading methods are hydrogeology, slope, land use, rainfall, groundwater level, soil texture and distance to source water. This study uses the ‘Pairwise comparison’ to assign criteria weights, as part of the Analytic Hierarchy Process (AHP), and examines four different scenarios. In all four scenarios, downstream areas, and close to the river Geropotamos, coincide as the most appropriate for aquifer recharge

Large-scale exploratory analysis of the spatiotemporal distribution of climate projections: applying the STRIVIng toolbox

Extreme hydrological events (EHEs), such as droughts and floods, vary spatially and temporally in nature. The increase in the number of events in the last few decades has motivated the research of the spatiotemporal variability of the future extreme precipitation and temperature. To study the consequences on the EHEs due to the uncertainty of projected climate changes, the analysis in more detail of precipitation and temperature, in space and time, is vital. In addition, for proper planning and decision-making process to address EHEs, understanding such climate changes requires more information. In this chapter we present a summarized assessment of the spatiotemporal variations of climate projections. A simplified way to aggregate global data is used for the spatiotemporal analysis of precipitation and temperature. To carry out this analysis, the Spatio-TempoRal distribution and Interannual VarIability of projections (STRIVIng) toolbox is proposed for statistical exploratory analysis of climate projections. Three large-scale applications were carried out for illustration: Dominican Republic (48,670 km2), Mexico (1,972,550 km2), and Amazon basin (6,171,148.7 km2). The methodology and toolbox presented here allow regions to be identified where the changes are expected to be more severe on precipitation and temperature, as well as months in which those changes are likely to occur. The STRIVIng toolbox is open source and helps to provide basic information to increase the interpretations and research in the space–time analysis of extremes