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Background and Aim: Obesity plays a dominant role in the etiology of atrial fibrillation (AF), and the maintenance of a normal body mass index (BMI) seems to prevent and even reduce the incidence of the arrhythmia's recurrence. We selected 270 patients (pts) to assess whether this therapeutic effect was statistically significant even in Mediterranean patients.Method and Results: In this retrospective cohort study, we analyzed every symptomatic AF relapse during a total follow-up of 657 patient-years. Clinical data, BMI variations, and pts' history were available in our clinical database. We divided the pts in four groups (Gs), according to their BMI variation during the follow-up: G1, normal weight pts, maintaining their weight; G2, overweight pts, losing weight; G3, overweight pts, maintaining their weight; G4, pts gaining weight. Their follow-up (in months) was normalized according to their AF relapses, thus obtaining a mean AF-free period for each patient. Among the overweight groups, G2 showed the best AF-free period (9.7 months). However, G3 and G4 showed a reduced AF-free interval (4.6 and 1.7 months, respectively). G1, predictably, had the longest AF-free period (10 months).Conclusion: The results of the present study confirm that simple non-invasive intervention aimed to normalize BMI and to control risk factors through appropriate lifestyle can be highly effective in reducing the AF burden, by acting on comorbidities and proarrhythmic mechanisms. Therefore, serious attempt should be made to correct risk factors before an ablation therapy is proposed. [ABSTRACT FROM AUTHOR]

Abstract: Most of the scientific community agrees that global climate change is occurring with a general increase in mean overall temperature (+0.74±0.18 °C from 1906-2005) and that the precipitation pattern in Europe is trending toward wetter conditions in the northern region and drier conditions in the southern and central-eastern regions. A much larger uncertainty concerns how the changes in precipitations will impact on the water resources, particularly on the groundwater. The goal of this paper is to investigate the variables to be considered in order to estimate the Sustainable Pumping Rate of an aquifer (SPR) in a context of climate change. For this goal the case study of the Petrignano d’Assisi porous aquifer has been considered, mainly fed by the inflow from the carbonatic ridges and by the effective infiltration; it is exploited since the 1970s through a well field (about 350 l/s). Changes in the precipitation regime could significantly affect the recharge to the aquifer and the related SPR. This study shows the key role played by the interactions of the aquifer with the surface bodies (rivers): in case of a significant decreasing in the effective infiltration, the aquifer system decreases the outflow to the rivers (base flow) leaving almost constant the sustainable pumping rate. [Copyright &y& Elsevier]

Abstract: The management of water resources always requires more and diverse approaches in which multiple skills and capacities are nested together, especially when critical situations are taken into account, such as climate change scenarios. The SimBaT software is a Decision Support Systems for water resource allocation and management. In this study, SimBaT is applied to the Montedoglio reservoir in the Tiber River Basin (Central Italy). The case study highlights how this methodology can be applied for a proactive management of critical scenarios in periods of drought due to climate change hypothesis. [Copyright &y& Elsevier]

Summary: This paper investigates the temporal stability of near-surface soil moisture at various depths at the hillslope scale. Detailed soil water content data were acquired at 0–6cm, 0–12cm and 0–20cm during three 30-day field campaigns in 2005, 2006 and 2007. Two small alpine hillslopes with relatively homogeneous soil properties and vegetation cover but contrasting morphology were chosen to assess the persistence of spatial organization of soil moisture over time and along the soil profile, to identify the representative sampling locations and to evaluate the temporal stability during wet and dry states. Results show that both study hillslopes exhibited a strong degree of time stability, as revealed by very high autocorrelation values persisting for several days. The ranking stability approach allowed the identification of sampling locations representative of the average hillslope soil water content. These locations, one for each experimental site, proved to act as good indicators of soil moisture at other depths and even on the other hillslope. The spatial structure of soil moisture fields was not affected by the occurrence of piezometric response and was well preserved at all depths during both wet and dry periods, with a slightly higher degree of temporal stability in dry conditions and for deeper layers. The remarkable persistence of soil moisture spatial patterns over time and along the soil profile on the study sites was mainly related to the macro- and micro-topographic properties of the two hillslopes but the soil wetness conditions generally skewed towards the wet state and the negligible variability of climatic forcing due to the small study scale might have contributed significantly. [ABSTRACT FROM AUTHOR]

Abstract: Global soil moisture products retrieved from various remote sensing sensors are becoming readily available with a nearly daily temporal resolution. Active and passive microwave sensors are generally considered as the best technologies for retrieving soil moisture from space. The Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) on-board the Aqua satellite and the Advanced SCATterometer (ASCAT) on-board the MetOp (Meteorological Operational) satellite are among the sensors most widely used for soil moisture retrieval in the last years. However, due to differences in the spatial resolution, observation depths and measurement uncertainties, validation of satellite data with in situ observations and/or modelled data is not straightforward. In this study, a comprehensive assessment of the reliability of soil moisture estimations from the ASCAT and AMSR-E sensors is carried out by using observed and modelled soil moisture data over 17 sites located in 4 countries across Europe (Italy, Spain, France and Luxembourg). As regards satellite data, products generated by implementing three different algorithms with AMSR-E data are considered: (i) the Land Parameter Retrieval Model, LPRM, (ii) the standard NASA (National Aeronautics and Space Administration) algorithm, and (iii) the Polarization Ratio Index, PRI. For ASCAT the Vienna University of Technology, TUWIEN, change detection algorithm is employed. An exponential filter is applied to approach root-zone soil moisture. Moreover, two different scaling strategies, based respectively on linear regression correction and Cumulative Density Function (CDF) matching, are employed to remove systematic differences between satellite and site-specific soil moisture data. Results are shown in terms of both relative soil moisture values (i.e., between 0 and 1) and anomalies from the climatological expectation. Among the three soil moisture products derived from AMSR-E sensor data, for most sites the highest correlation with observed and modelled data is found using the LPRM algorithm. Considering relative soil moisture values for an ~5cm soil layer, the TUWIEN ASCAT product outperforms AMSR-E over all sites in France and central Italy while similar results are obtained in all other regions. Specifically, the average correlation coefficient with observed (modelled) data equals to 0.71 (0.74) and 0.62 (0.72) for ASCAT and AMSR-E-LPRM, respectively. Correlation values increase up to 0.81 (0.81) and 0.69 (0.77) for the two satellite products when exponential filtering and CDF matching approaches are applied. On the other hand, considering the anomalies, correlation values decrease but, more significantly, in this case ASCAT outperforms all the other products for all sites except the Spanish ones. Overall, the reliability of all the satellite soil moisture products was found to decrease with increasing vegetation density and to be in good accordance with previous studies. The results provide an overview of the ASCAT and AMSR-E reliability and robustness over different regions in Europe, thereby highlighting advantages and shortcomings for the effective use of these data sets for operational applications such as flood forecasting and numerical weather prediction. [Copyright &y& Elsevier]

Abstract: Reliable measurements of soil moisture at global scale might greatly improve many practical issues in hydrology, meteorology, climatology or agriculture such as water management, quantitative precipitation forecasting, irrigation scheduling, etc. Remote sensing offers the unique capability to monitor soil moisture over large areas but, nowadays, the spatio-temporal resolution and accuracy required for some hydrological applications (e.g., flood forecasting in medium to large basins) have still to be met. The Advanced SCATterometer (ASCAT) onboard the Metop satellite (VV polarization, C-band at 5.255GHz), based on a large extent on the heritage of the ERS scatterometer, provides a soil moisture product available at a coarse spatial resolution (25km and 50km) and at a nearly daily time step. This study evaluates the accuracy of the new 25km ASCAT derived saturation degree product by using in situ observations and the outcomes of a soil water balance model for three sites located in an inland region of central Italy. The comparison is carried out for a 2-year period (2007–2008) and three products derived from ASCAT: the surface saturation degree, m s, the exponentially filtered soil wetness index, SWI, and its linear transformation, SWI*, matching the range of variability of ground data. Overall, the performance of the three products is found to be quite good with correlation coefficients higher than 0.92 and 0.80 when the SWI is compared with in situ and simulated saturation degree, respectively. Considering SWI*, the root mean square error is less than 0.035m3/m3 and 0.042m3/m3 for in situ and simulated saturation degree, respectively. More notably, when the m s product is compared with modeled data at 3cm depth, this index is found able to accurately reproduce the temporal pattern of the simulated saturation degree in terms of both timing and entity of its variations also at fine temporal scale. The daily temporal resolution and the reliability obtained with the ASCAT derived saturation degree products represent the preliminary step for its effective use in operational rainfall-runoff modeling. [Copyright &y& Elsevier]

Abstract: Soil moisture controls the partitioning of rainfall into runoff and infiltration and, consequently, the runoff generation. On the catchment scale its routine monitoring can be performed through remote sensing technologies. Within this framework, the purpose of this study is to investigate the potential of the Advanced Microwave Sounding Unit (AMSU), radiometer on board the NOAA (National Oceanic and Atmospheric Administration) satellites and operating since 1998, for the assessment of soil wetness conditions by comparing soil moisture data with both those measured in situ and provided by a continuous rainfall–runoff model applied to four catchments located in the Upper Tiber River (Central Italy). In particular, in order to perform a robust analysis an extensive and long-term period (nine years) of data was investigated. In detail, the Soil Wetness Variation Index, derived from the AMSU data modified in order to take account of the difference between the soil layer investigated by the satellite sensor and that used as a benchmark, was found to be correlated both with the in-situ and modeled soil moisture variations showing correlation coefficients in the range of 0.42–0.49 and 0.33–0.48, respectively. As far as the soil moisture temporal pattern is concerned, higher correlations were obtained (0.59–0.84 for the in-situ data and 0.82–0.87 for the modeled data set) partly due to the soil moisture seasonal pattern that enhances the correlation. Overall, the root mean square error was found to be less than 0.05m3/m3 for both the comparisons, thus assessing the potential of the AMSU sensor to quantitatively retrieve soil moisture temporal patterns. Moreover, the AMSU sensor can be considered as a useful tool to provide a reliable and frequently updated global soil moisture data set, considering its higher temporal resolution now available (about 4 passes per day) thanks to the presence of the sensor aboard different satellites. [Copyright &y& Elsevier]

Summary: Rainfall scenarios are of considerable interest for design flood and flood risk analysis. To this end, the stochastic generation of continuous rainfall sequences is often coupled with the continuous hydrological modelling. In this context, the spatial and the temporal rainfall variability represents a significant issue, especially for basins in which the rainfall field cannot be approximated through the use of a single station. Therefore, methodologies for the spatially and temporally correlated rainfall generation are welcome. An example of such a methodology is the well-established Spatial–Temporal Neyman-Scott Rectangular Pulse (STNSRP), a modification of the single-site Neyman-Scott Rectangular Pulse (NSRP) approach, designed to incorporate specific features to reproduce the rainfall spatial cross-correlation. In order to provide a simple alternative to the STNSRP, a new method of generating synthetic rainfall time series with pre-set spatial–temporal correlation is proposed herein. This approach relies on the single-site NSRP model, which is used to generate synthetic hourly independent rainfall time series at each rain gauge station with the required temporal autocorrelation (and several other appropriately selected statistics). The rank correlation method of Iman and Conover (IC) is then applied to these synthetic rainfall time series in order to introduce the same spatial cross-correlation that exists between the observed time series. This combination of the NSRP model with the IC method consents the reproduction of the observed spatial–temporal variability of a rainfall field. In order to verify the proposed procedure, four sub-basins of the Upper Tiber River basin are investigated whose basin areas range from 165km2 to 2040km2. Results show that the procedure is able to preserve both the rainfall temporal autocorrelation at single site and the rainfall spatial cross-correlation at basin scale, and its performance is comparable with that of the STNSRP model for rainfall field generation. Given its simple formal structure (based on well established methods: i.e. NSRP and IC), we believe that the proposed approach can be conveniently utilized to generate spatially and temporally correlated rainfall scenarios. [Copyright &y& Elsevier]

Abstract Droughts often evolve gradually and cover large areas, and therefore, affect many people and activities. This motivates developing techniques to integrate different satellite observations, to cover large areas, and understand spatial and temporal variability of droughts. In this study, we apply probabilistic techniques to generate satellite derived meteorological, hydrological, and hydro-meteorological drought indices for the world's 156 major river basins covering 2003–2016. The data includes Terrestrial Water Storage (TWS) estimates from the Gravity Recovery And Climate Experiment (GRACE) mission, along with soil moisture, precipitation, and evapotranspiration reanalysis. Different drought characteristics of trends, occurrences, areal-extent, and frequencies corresponding to 3-, 6-, 12-, and 24-month timescales are extracted from these indices. Drought evolution within selected basins of Africa, America, and Asia is interpreted. Canonical Correlation Analysis (CCA) is then applied to find the relationship between global hydro-meteorological droughts and satellite derived Sea Surface Temperature (SST) changes. This relationship is then used to extract regions, where droughts and teleconnections are strongly interrelated. Our numerical results indicate that the 3- to 6-month hydrological droughts occur more frequently than the other timescales. Longer memory of water storage changes (than water fluxes) has found to be the reason of detecting extended hydrological droughts in regions such as the Middle East and Northern Africa. Through CCA, we show that the El Niño Southern Oscillation (ENSO) has major impact on the magnitude and evolution of hydrological droughts in regions such as the northern parts of Asia and most parts of the Australian continent between 2006 and 2011, as well as droughts in the Amazon basin, South Asia, and North Africa between 2010 and 2012. The Indian ocean Dipole (IOD) and North Atlantic Oscillation (NAO) are found to have regional influence on the evolution of hydrological droughts. Graphical Abstract Highlights • Using GRACE TWS results in more intense drought indices than soil-moisture reanalysis. • The areal extent of the 2003–2016 hydrological droughts is generally increasing. • Droughts of the Middle East, America, and South Asia are intense and being worsened. • SST and CCA are efficient to explore teleconnections and droughts hot spots. • The 2006 and 2011 droughts in Asia and Australia are largely correlated with ENSO. [ABSTRACT FROM AUTHOR]

In this study, an integration of microwave data obtained from the SMAP and AMSR2 satellite radiometers has been attempted, to achieve an accurate estimation of the Soil Moisture Content (SMC). This research aimed to overcome the failure of radar sensor in SMAP satellite as well as the failure to generate the radar/radiometer combined SMC product at a spatial resolution of 9 km × 9 km. A disaggregation technique, based on the Smoothing Filter based Intensity Modulation (SFIM), enabled us to obtain co-located SMAP and AMSR2 brightness measurements at L, C, X, Ku and Ka bands at approximately 10 km × 10 km on the selected test area, which corresponds to the entire Italian territory. These disaggregated microwave data were used as inputs of the “HydroAlgo” retrieval algorithm based on Artificial Neural Networks (ANN), which were able to exploit the synergy between radiometric acquisitions from these two sensors. The algorithm was defined, implemented and tested using all the overlapping orbits of SMAP and AMSR2 over Italy throughout the 9_month period between April and December 2015. Distributed SMC reference values for implementing and validating the algorithm were obtained from the Soil Water Balance hydrological model, SWBM. Through HydroAlgo, an SMC product at a resolution of approximately 10 km × 10 km was obtained. This result is close to the original Radar/Radiometer SMC product from SMAP, with an average correlation coefficient R > 0.75 and RMSE ≅ 0.03 m 3 /m 3 , in both ascending and descending orbits. [ABSTRACT FROM AUTHOR]

Satellite rainfall products have been available for many years (since '90) with an increasing spatial/temporal resolution and accuracy. Their global scale coverage and near real-time products perfectly fit the need of an early warning landslide system. Notwithstanding these characteristics, the number of studies employing satellite rainfall estimates for predicting landslide events is quite limited. In this study, we propose a procedure that allows us to evaluate the capability of different rainfall products to forecast the spatial-temporal occurrence of rainfall-induced landslides using rainfall thresholds. Specifically, the assessment is carried out in terms of skill scores, and receiver operating characteristic (ROC) analysis. The procedure is applied to ground observations and four different satellite rainfall estimates: 1) the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis, TMPA, real time product (3B42-RT), 2) the SM2RASC product obtained from the application of SM2RAIN algorithm to the Advanced SCATterometer (ASCAT) derived satellite soil moisture (SM) data, 3) the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), and 4) the Climate Prediction Center (CPC) Morphing Technique (CMORPH). As case study, we consider the Italian territory for which a catalogue listing 1414 rainfall-induced landslides in the period 2008–2014 is available. Results show that satellite products underestimate rainfall with respect to ground observations. However, by adjusting the rainfall thresholds, satellite products are able to identify landslide occurrence, even though with less accuracy than ground-based rainfall observations. Among the four satellite rainfall products, CMORPH and SM2RASC are performing the best, even though differences are small. This result is to be attributed to the high spatial/temporal resolution of CMORPH, and the good accuracy of SM2RSC. Overall, we believe that satellite rainfall estimates might be an important additional data source for developing continental or global landslide warning systems. [ABSTRACT FROM AUTHOR]

A merging procedure is applied to five daily rainfall estimates achieved via SM2RAIN applied to the soil moisture products obtained by the Advanced SCATterometer, the Advanced Microwave Scanning Radiometer 2, the Soil Moisture Active and Passive mission, the Soil Moisture and Ocean Salinity mission and backscattering observations of RapidScat. The precipitation estimates are evaluated against dense ground networks in the period ranging from April to December, 2015, in India and in Italy, at 0.25°/daily spatial/temporal resolution. The merged product derived by combining the different SM2RAIN rainfall products shows better results in term of statistical and categorical metrics with respect to the use of the single products. A good agreement with reference to ground observations is obtained, with median correlations equal to 0.65 and 0.77 in India and in Italy, respectively. The merged dataset is found to slightly outperform those of the IMERG product of the Global Precipitation Measurement mission underlying the large potential of the proposed approach. [ABSTRACT FROM AUTHOR]

In this study we evaluate the skill of a new, merged soil moisture product (ECV_SM) that has been developed in the framework of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative projects. The product combines in a synergistic way the soil moisture retrievals from four passive (SMMR, SSM/I, TMI, and AMSR-E) and two active (ERS AMI and ASCAT) coarse resolution microwave sensors into a global data set spanning the period 1979–2010. The evaluation uses ground-based soil moisture observations of 596 sites from 28 historical and active monitoring networks worldwide. Besides providing conventional measures of agreement, we use the triple collocation technique to assess random errors in the data set. The average Spearman correlation coefficient between ECV_SM and all in-situ observations is 0.46 for the absolute values and 0.36 for the soil moisture anomalies, but differences between networks and time periods are very large. Unbiased root-mean-square differences and triple collocation errors show less variation between networks, with average values around 0.05 and 0.04 m 3 m − 3 , respectively. The ECV_SM quality shows an upward trend over time, but a consistent decrease of all performance metrics is observed for the period 2007–2010. Comparing the skill of the merged product with the skill of the individual input products shows that the merged product has a similar or better performance than the individual input products, except with regard to the ASCAT product, compared to which the performance of ECV_SM is inferior. The cause of the latter is most likely a combination of the mismatch in sampling time between the satellite observations and in-situ measurements, and the resampling and scaling strategy used to integrate the ASCAT product into ECV_SM on the other. The results of this study will be used to further improve the scaling and merging algorithms for future product updates. [ABSTRACT FROM AUTHOR]

Soil moisture from ERA-Land, a revised version of the land surface components of the European Centre for Medium-Range Weather Forecasts Interim reanalysis (ERA-Interim), is used to monitor at a global scale the consistency of a new microwave based multi-satellite surface soil moisture date set (SM-MW) over multi-decadal time period (1980–2010). ERA-Land results from Land Surface Model simulations forced by high quality atmospheric forcing data. It was shown to adequately capture the temporal dynamic of soil moisture. ERA-Land's large scale nature, frozen configuration, global availability and ability to accurately represent soil moisture variability make it suitable to complement typical validation approaches of soil moisture from remote sensing based on ground measurements. Considering locations that have significant correlations for each 3-year sub periods within 1980–2010, averaged soil moisture correlations of SM-MW with ERA-Land (at 95% Confidence Interval) are increasing steadily from 1986 to 2010 (from 0.52±0.10, to 0.66±0.04). The lower correlations mirror the periods where only passive microwave from the Special Sensor Microwave/Image (SSM/I, Ku band at 19.3GHz) sensor was used, highlighting the importance of multi-sensor capabilities. Overall SM-MW is relatively stable over time with respect to ERA-Land. Good agreement is obtained in semi-arid areas, whilst the tropics and high latitudes (and altitudes) present lower correlations values. [ABSTRACT FROM AUTHOR]

This paper presents a community effort to develop good practice guidelines for the validation of global coarse-scale satellite soil moisture products. We provide theoretical background, a review of state-of-the-art methodologies for estimating errors in soil moisture data sets, practical recommendations on data pre-processing and presentation of statistical results, and a recommended validation protocol that is supplemented with an example validation exercise focused on microwave-based surface soil moisture products. We conclude by identifying research gaps that should be addressed in the near future. • Satellite soil moisture validation methods are reviewed • Community-agreed validation good practice guidelines are presented • A standardized satellite soil moisture validation protocol is provided [ABSTRACT FROM AUTHOR]