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

Rainfall Erosivity in Soil Erosion Processes

This book gathers recent international research on the association between aggressive rainfall and soil loss and landscape degradation. Different contributions explore these complex relationships and highlight the importance of the spatial patterns of precipitation intensity on land flow under erosive storms, with the support of observational and modelling data. This is a large and multifaceted area of research of growing importance that outlines the challenge of protecting land from natural hazards. The increase in the number of high temporal resolution rainfall records together with the development of new modelling capabilities has opened up new opportunities for the use of large-scale planning and risk prevention methods. These new perspectives should no longer be considered as an independent research topic, but should, above all, support comprehensive land use planning, which is at the core of environmental decision-making and operations. Textbooks such as this one demonstrate the significance of how hydrological science can enable tangible progress in understanding the complexity of water management and its current and future challenges

Geospatial and visual modeling for exploring sediment source areas across the Sele river landscape, Italy

This study uses the revised universal soil loss equation (RUSLE) and Geographic Information System technology to map erosion-prone areas in the Sele basin (Campania-Basilicata regions, southern Italy). Current land use/cover, soil erodibility and climate factors were evaluated to determine their effects on average annual soil loss. Geospatial technologies were applied to generate RUSLE factors and erosion map. Long-term soil losses were 53 Mg ha-1 per year averaged over an area of 2500 km2 and more than 30% of the Sele basin was subjected to soil losses higher than 20 Mg ha-1 per year. Data available in the study area allowed to estimate soil losses, but the absence of direct sediment measurements prevents an accurate evaluation of the model performance. Nevertheless, the results are similar to the ones from other studies, and provide useful preliminary information for landscape management and restoration

CliFEM - Climate Forcing and Erosion Modelling in the Sele River Basin (Southern Italy)

Abstract. This study presents a revised and scale-adapted Foster-Meyer-Onstad model (Foster et al., 1977) for the transport of soil erosion sediments under scarce input data, with the acronym CliFEM (Climate Forcing and Erosion Modelling). This new idea was addressed to develop a monthly time scale invariant Net Erosion model (NER), with the aim to consider the different erosion processes operating at different time scales in the Sele River Basin (South Italy), during 1973–2007 period. The sediment delivery ratio approach was applied to obtain an indirect estimate of the gross erosion too. The examined period was affected by a changeable weather regime, where extreme events may have contributed to exacerbate soil losses, although only the 19% of eroded sediment was delivered at outlet of the basin. The long-term average soil erosion was very high (73 Mg ha−1 per year ± 58 Mg ha−1). The estimate of monthly erosion showed catastrophic soil losses during the soil tillage season (August–October), with consequent land degradation of the hilly areas of the Sele River Basin

A millennium-long reconstruction of damaging hydrological events across Italy.

Damaging hydrological events are extreme phenomena with potentially severe impacts on human societies. Here, we present the hitherto longest reconstruction of damaging hydrological events for Italy, and for the whole Mediterranean region, revealing 674 such events over the period 800-2017. For any given year, we established a severity index based on information in historical documentary records, facilitating the transformation of the data into a continuous time-series. Episodes of hydrological extremes disrupted ecosystems during the more severe events by changing landforms. The frequency and severity of damaging hydrological events across Italy were likely influenced by the mode of the Atlantic Multidecadal Variability (AMV), with relatively few events during the warm Medieval Climate Anomaly dominated by a positive phase of the AMV. More frequent and heavier storms prevailed during the cold Little Ice Age, dominated by a more negative phase of the AMV. Since the mid-19th century, a decreasing occurrence of exceptional hydrological events is evident, especially during the most recent decades, but this decrease is not yet unprecedented in the context of the past twelve centuries

Author Correction: A millennium-long reconstruction of damaging hydrological events across Italy

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Mapping monthly rainfall erosivity in Europe

Rainfall erosivity as a dynamic factor of soil loss by water erosion is modelled intra-annually for the first time at European scale. The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall erosivity both spatially and temporally. During winter months, significant rainfall erosivity is present only in part of the Mediterranean countries. A sudden increase of erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall erosivity in summer is almost 4 times higher (315MJmmha-1h-1) compared to winter (87MJmmha-1h-1). The Cubist model has been selected among various statistical models to perform the spatial interpolation due to its excellent performance, ability to model non-linearity and interpretability. The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sum of all months has to be close to annual erosivity. The performance of the Cubist models proved to be generally high, resulting in R2 values between 0.40 and 0.64 in cross-validation. The obtained months show an increasing trend of erosivity occurring from winter to summer starting from western to Eastern Europe. The maps also show a clear delineation of areas with different erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year

Using historical precipitation patterns to forecast daily zxtremes of rainfall for the coming secades in naples (Italy)

The coasts of the Italian peninsula have been recently affected by frequent damaging hydrological events driven by intense rainfall and deluges. The internal climatic mechanisms driving rainfall variability that generate these hydrological events in the Mediterranean are not fully understood. We investigated the simulation skill of a soft-computing approach to forecast extreme rainfalls in Naples (Italy). An annual series of daily maximum rainfall spanning the period between 1866 and 2016 was used for the design of ensemble projections in order to understand and quantify the uncertainty associated with interannual to interdecadal predictability. A predictable structure was first provided, and then elaborated by exponential smoothing for the purposes of training, validation, and forecast. For the time horizon between 2017 and 2066, the projections indicate a weak increase of daily maximum rainfalls, followed by almost the same pace as it was in the previous three decades, presenting remarkable wavelike variations with durations of more than one year. The forecasted pattern is coupled with variations attributed to internal climate modes, such as the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO)