Evaluating the Differences of Gridding Techniques for Digital Elevation Models Generation and Their Influence on the Modeling of Stony Debris Flows Routing: A Case Study From Rovina di Cancia Basin (North-Eastern Italian Alps)

Debris \ufb02ows are among the most hazardous phenomena in mountain areas. To cope with debris \ufb02ow hazard, it is common to delineate the risk-prone areas through routing models. The most important input to debris \ufb02ow routing models are the topographic data, usually in the form of Digital Elevation Models (DEMs). The quality of DEMs depends on the accuracy, density, and spatial distribution of the sampled points; on the characteristics of the surface; and on the applied gridding methodology. Therefore, the choice of the interpolation method affects the realistic representation of the channel and fan morphology, and thus potentially the debris \ufb02ow routing modeling outcomes. In this paper, we initially investigate the performance of common interpolation methods (i.e., linear triangulation, natural neighbor, nearest neighbor, Inverse Distance to a Power, ANUDEM, Radial Basis Functions, and ordinary kriging) in building DEMs with the complex topography of a debris \ufb02ow channel located in the Venetian Dolomites (North-eastern Italian Alps), by using small footprint full- waveform Light Detection And Ranging (LiDAR) data. The investigation is carried out through a combination of statistical analysis of vertical accuracy, algorithm robustness, and spatial clustering of vertical errors, and multi-criteria shape reliability assessment. After that, we examine the in\ufb02uence of the tested interpolation algorithms on the performance of a Geographic Information System (GIS)-based cell model for simulating stony debris \ufb02ows routing. In detail, we investigate both the correlation between the DEMs heights uncertainty resulting from the gridding procedure and that on the corresponding simulated erosion/deposition depths, both the effect of interpolation algorithms on simulated areas, erosion and deposition volumes, solid-liquid discharges, and channel morphology after the event. The comparison among the tested interpolation methods highlights that the ANUDEM and ordinary kriging algorithms are not suitable for building DEMs with complex topography. Conversely, the linear triangulation, the natural neighbor algorithm, and the thin-plate spline plus tension and completely regularized spline functions ensure the best trade-off among accuracy and shape reliability. Anyway, the evaluation of the effects of gridding techniques on debris \ufb02ow routing modeling reveals that the choice of the interpolation algorithm does not signi\ufb01cantly affect the model outcomes

Nutrition and growth in Italy, 1861-1911 what macroeconomic data hide

We investigate how nutritional status responded to economic growth in Italy during 1861-1911. By combining household-level data on food consumption with population censuses, we estimate that the incidence of undernutrition decreased by about 10-15 percent between 1881 and 1901. Consumption of calories responded elastically to income changes, although declining with the level of household income: on average, income elasticity of calories in 1901 was in the range of 0.3-0.6. Malnutrition, defined as the inadequate intake of macroand micro-nutritients, was reduced. Overall, our findings do not support the pessimists' view, ubiquitous in the Italian literature. On the contrary, the early phase of Italian industrialization was beneficial to the nutritional status of the bulk of the population, and even more so for the poorest among the poor

How Predictable are Temperature-series Undergoing Noise-controlled Dynamics in the Mediterranean

Mediterranean is thought to be sensitive to global climate change, but its future interdecadal variability is uncertain for many climate models. A study was made of the variability of the winter temperature over the Mediterranean Sub-regional Area (MSA), employing a reconstructed temperature series covering the period 1698 to 2010. This paper describes the transformed winter temperature data performed via Empirical Mode Decomposition for the purposes of noise reduction and statistical modeling. This emerging approach is discussed to account for the internal dependence structure of natural climate variability

Streamlining Energy Transition Scenarios to Key Policy Decisions

Uncertainties surrounding the energy transition often lead modelers to present large sets of scenarios that are challenging for policymakers to interpret and act upon. An alternative approach is to define a few qualitative storylines from stakeholder discussions, which can be affected by biases and infeasibilities. Leveraging decision trees, a popular machine-learning technique, we derive interpretable storylines from many quantitative scenarios and show how the key decisions in the energy transition are interlinked. Specifically, our results demonstrate that choosing a high deployment of renewables and sector coupling makes global decarbonization scenarios robust against uncertainties in climate sensitivity and demand. Also, the energy transition to a fossil-free Europe is primarily determined by choices on the roles of bioenergy, storage, and heat electrification. Our transferrable approach translates vast energy model results into a small set of critical decisions, guiding decision-makers in prioritizing the key factors that will shape the energy transition

Nutrition and growth in Italy, 1861-1911 what macroeconomic data hide.

We investigate how nutritional status responded to economic growth in Italy during 1861-1911. By combining household-level data on food consumption with population censuses, we estimate that the incidence of undernutrition decreased by about 10-15 percent between 1881 and 1901. Consumption of calories responded elastically to income changes, although declining with the level of household income: on average, income elasticity of calories in 1901 was in the range of 0.3-0.6. Malnutrition, defined as the inadequate intake of macroand micro-nutritients, was reduced. Overall, our findings do not support the pessimists' view, ubiquitous in the Italian literature. On the contrary, the early phase of Italian industrialization was beneficial to the nutritional status of the bulk of the population, and even more so for the poorest among the poor.

NUTRITION AND GROWTH IN ITALY, 1861-1911 WHAT MACROECONOMIC DATA HIDE

We investigate how nutritional status responded to economic growth in Italy during 1861-1911. By combining household-level data on food consumption with population censuses, we estimate that the incidence of undernutrition decreased by about 10-15 percent between 1881 and 1901. Consumption of calories responded elastically to income changes, although declining with the level of household income: on average, income elasticity of calories in 1901 was in the range of 0.3-0.6. Malnutrition, defined as the inadequate intake of macroand micro-nutritients, was reduced. Overall, our findings do not support the pessimists’ view, ubiquitous in the Italian literature. On the contrary, the early phase of Italian industrialization was beneficial to the nutritional status of the bulk of the population, and even more so for the poorest among the poor.