Large scale flood risk mapping in data scarce environments: An application for Romania

Large-scale flood risk assessment is essential in supporting national and global policies, emergency operations and land-use management. The present study proposes a cost-efficient method for the large-scale mapping of direct economic flood damage in data-scarce environments. The proposed framework consists of three main stages: (i) deriving a water depth map through a geomorphic method based on a supervised linear binary classification; (ii) generating an exposure land-use map developed from multi-spectral Landsat 8 satellite images using a machine-learning classification algorithm; and (iii) performing a flood damage assessment using a GIS tool, based on the vulnerability (depth-damage) curves method. The proposed integrated method was applied over the entire country of Romania (including minor order basins) for a 100-year return time at 30-m resolution. The results showed how the description of flood risk may especially benefit from the ability of the proposed cost-efficient model to carry out large-scale analyses in data-scarce environments. This approach may help in performing and updating risk assessments and management, taking into account the temporal and spatial changes in hazard, exposure, and vulnerability

Predictive Modeling of Envelope Flood Extents Using Geomorphic and Climatic-Hydrologic Catchment Characteristics

A topographic index (flood descriptor) that combines the scaling of bankfull depth with morphology was shown to describe the tendency of an area to be flooded. However, this approach depends on the quality and availability of flood maps and assumes that outcomes can be directly extrapolated and downscaled. This work attempts to relax these problems and answer two questions: (1) Can functional relationships be established between a flood descriptor and geomorphic and climatic-hydrologic catchment characteristics? (2) If so, can they be used for low-complexity predictive modeling of envelope flood extents? Linear stepwise and random forest regressions are developed based on classification outcomes of a flood descriptor, using high-resolution flood modeling results as training benchmarks, and on catchment characteristics. Elementary catchments of four river basins in Europe (Thames, Weser, Rhine, and Danube) serve as training data set, while those of the Rh\uf4ne river basin in Europe serve as testing data set. Two return periods are considered, the 10- and 10,000-year. Prediction of envelope flood extents and flood-prone areas show that both models achieve high hit rates with respect to testing benchmarks. Average values were found to be above 60% and 80% for the 10- and the 10,000-year return periods, respectively. In spite of a moderate to high false discovery rate, the critical success index value was also found to be moderate to high. It is shown that by relating classification outcomes to catchment characteristics, the prediction of envelope flood extents may be achieved for a given region, including ungauged basins

Modeling Spatio-Temporal Divergence in Land Vulnerability to Desertification with Local Regressions

Taken as a classical issue in applied economics, the notion of ‘convergence’ is based on the concept of path dependence, i.e., from the previous trajectory undertaken by the system during its recent history. Going beyond social science, a ‘convergence’ perspective has been more recently adopted in environmental studies. Spatial convergence in non-linear processes, such as desertification risk, is a meaningful notion since desertification represents a (possibly unsustainable) development trajectory of socio-ecological systems towards land degradation on a regional or local scale. In this study, we test—in line with the classical convergence approach—long-term equilibrium conditions in the evolution of desertification processes in Italy, a European country with significant socioeconomic and environmental disparities. Assuming a path-dependent development of desertification risk in Italy, we provided a diachronic analysis of the Environmental Sensitive Area Index (ESAI), estimated at a disaggregated spatial resolution at three times (1960s, 1990s, and 2010s) in the recent history of Italy, using a spatially explicit approach based on geographically weighted regressions (GWRs). The results of local regressions show a significant path dependence in the first time interval (1960–1990). A less significant evidence for path-dependence was observed for the second period (1990–2010); in both cases, the models’ goodness-of-fit (global adjusted R2) was satisfactory. A strong polarization along the latitudinal gradient characterized the first observation period: Southern Italian land experienced worse conditions (e.g., climate aridity, urbanization) and the level of land vulnerability in Northern Italy remained quite stable, alimenting the traditional divergence in desertification risk characteristic of the country. The empirical analysis delineated a more complex picture for the second period. Convergence (leading to stability, or even improvement, of desertification risk) in some areas of Southern Italy, and a more evident divergence (leading to worse environmental conditions because of urban sprawl and crop intensification) in some of the land of Northern Italy, were observed, leading to an undesired spatial homogenization toward higher vulnerability levels. Finally, this work suggests the importance of spatially explicit approaches providing relevant information to design more effective policy strategies. In the case of land vulnerability to degradation in Italy, local regression models oriented toward a ‘convergence’ perspective, may be adopted to uncover the genesis of desertification hotspots at both the regional and local scale

Neurocognitive and Psychopathological Predictors of Weight Loss After Bariatric Surgery: A 4-Year Follow-Up Study

Twenty to thirty percent of patients experience weight regain at mid and long-term follow- up. Impaired cognitive functions are prevalent in people suffering from obesity and in those with binge eating disorder, thereby, affecting the weight-loss outcomes. The aim of our study was to investigate neurocognitive and psychopathological predictors of surgical efficacy in terms of percentage of excess weight loss (%EWL) at follow-up intervals of one year and 4-year. Psychosocial evaluation was completed in a sample of 78 bariatric surgery candidates and included psychometric instruments and a cognitive battery of neuropsychological tests. A schedule of 1-year and 4-year follow-ups was implemented. Wisconsin Sorting Card Test total correct responses, scores on the Raven’s Progressive Matrices Test, and age predicted %EWL at, both, early and long-term periods after surgery while the severity of pre-operative binge eating (BED) symptoms were associated with lower %EWL only four years after the operation. Due to the role of pre-operative BED in weight loss maintenance, the affected patients are at risk of suboptimal response requiring ongoing clinical monitoring, and psychological and pharmacological interventions when needed. As a result of our findings and in keeping with the latest guidelines we encourage neuropsychological assessment of bariatric surgery candidates. This data substantiated the rationale of providing rehabilitative interventions tailored to cognitive domains and time specific to the goal of supporting patients in their post-surgical course

Safer_RAIN: A DEM-based hierarchical filling-&-spilling algorithm for pluvial flood hazard assessment and mapping across large urban areas

The increase in frequency and intensity of extreme precipitation events caused by the changing climate (e.g., cloudbursts, rainstorms, heavy rainfall, hail, heavy snow), combined with the high population density and concentration of assets, makes urban areas particularly vulnerable to pluvial flooding. Hence, assessing their vulnerability under current and future climate scenarios is of paramount importance. Detailed hydrologic-hydraulic numerical modeling is resource intensive and therefore scarcely suitable for performing consistent hazard assessments across large urban settlements. Given the steadily increasing availability of LiDAR (Light Detection And Ranging) high-resolution DEMs (Digital Elevation Models), several studies highlighted the potential of fast-processing DEM-based methods, such as the Hierarchical Filling-&-Spilling or Puddle-to-Puddle Dynamic Filling-&-Spilling Algorithms (abbreviated herein as HFSAs). We develop a fast-processing HFSA, named Safer_RAIN, that enables mapping of pluvial flooding in large urban areas by accounting for spatially distributed rainfall input and infiltration processes through a pixel-based Green-Ampt model. We present the first applications of the algorithm to two case studies in Northern Italy. Safer_RAIN output is compared against ground evidence and detailed output from a two-dimensional (2D) hydrologic and hydraulic numerical model (overall index of agreement between Safer_RAIN and 2D benchmark model: sensitivity and specificity up to 71% and 99%, respectively), highlighting potential and limitations of the proposed algorithm for identifying pluvial flood-hazard hotspots across large urban environments

Similar levels of efficacy of two different maintenance doses of adalimumab on clinical severity and quality of life of patients with hidradenitis suppurativa

Adalimumab is the only biologic agent approved for the treatment of moderate-to-severe hidradenitis suppurativa (HS) patients (i.e., with Hurley II or III), which is recommended in two different maintenance doses (i.e., 40 mg weekly or 80 mg every two weeks). We conducted a prospective multicentric study to measure outcomes related to the severity of disease and quality of life (QoL) of patients affected by moderate-to-severe HS, treated with adalimumab at a maintenance dosing of 40 mg or 80 mg. Assessments were performed at baseline (T0) and after 32 weeks of treatment (T32). We enrolled 85 moderate-to-severe HS Italian patients, 43 men (50.6%) and 42 women, aged between 16 and 62 years (median 31 years, interquartile range 24.4-43.8). Statistically significant improvements were observed for clinical status (with a mean reduction of 7.1 points for the International Hidradenitis Suppurativa Severity Score System (IHS4)), pain levels (3.1 mean decrease in VAS), and QoL (3.4 mean improvement in DLQI score). Patients with no comorbidities, and those with higher levels of perceived pain showed significantly greater improvement in QoL than their counterpart from T0 to T32. As for the proportion of patients who at follow-up reached the minimal clinical important difference (MCID) in QoL, significantly higher proportions of success were observed for age (patients in the 29-39 category), pain (patients with higher reported pain), and Hurley stage III. While both treatment regimen groups (i.e., 40 vs. 80 mg) improved significantly, no statistical differences were observed when comparing the two treatment dosages

Molecular dynamics simulations of non-equilibrium systems

Peer reviewe

100-year flood susceptibility maps for the continental U.S. derived with a geomorphic method

Binary raster dataset (.txt format) containing flood susceptibility maps related to 100-year river floods occurring in the continental U.S. These mapping products were derived through terrain analysis and a technique of pattern classification performed on DEMs obtained from HydroSHEDS (http://hydrosheds.cr.usgs.gov/overview.php) with a 3 arc-second resolution (0.00083333 degree, approximatively 90 m at the equator). Specifically, the flood-prone areas were identified by applying a linear binary classifier based upon the Geomorphic Flood Index (Manfreda et al., 2015; Samela et al., 2015; Samela et al., 2016 ). The raster maps have a 90 m resolution and are geo-referenced. The coordinate system of the maps is UTM (Universal Transverse Mercator) Zone 17N, the projection is Transverse Mercator, and the geodetic system is NAD (North American Datum) 1983. To simplify the management and the use of the data, the continental U.S. was divided into eighteen major water resources regions according to the hydrologic units identified by the United States Geological Survey