Historical information sheds new light on the intensification of flooding in the Central Mediterranean

Hydrological disasters, such as floods, can have dire consequences for human societies. Historical information plays a key role in detecting whether particular types of hydrological disasters have increased in frequency and/or magnitude and, if so, they are more likely attributable to natural or human-induced climatic and other environmental changes. The identification of regions with similar flood conditions is essential for the analysis of regional flooding regimes. To this end, we here present the longest existing flood reconstruction for the Eastern Liguria Area (ELA) in northwestern Italy, covering 1582 to 2022 CE, which offers a case study representative of the central Mediterranean region. An Annual Flood Intensification Index was developed to transform the historical data into a continuous annual hydrological time-series contained by a homogeneous data structure for the study-area. We found two change-points (trend breaks) in the reconstructed time-series, in 1787 and 1967, with only occasional heavy floods comparable to present-day disasters occurring before the first change-point, and an increasing intensification of floods after the second change-point up to the present day. The recent intensification of flooding in the ELA, associated with changes in land use and land cover, also appears to coincide with phases in which hydrological hazards have become more changeable and extreme in disaster-affected areas. This is evidenced by river basin responses to human-induced disturbances.

Environmental drivers of dynamic soil erosion change in a Mediterranean fluvial landscape

Background

Rainfall and other climatic agents are the main triggers of soil erosion in the Mediterranean region, where they have the potential to increase discharge and sediment transport and cause long-term changes in the river system. For the Magra River Basin (MRB), located in the upper Tyrrhenian coast of Italy, we estimated changes in net erosion as a function of the geographical characteristics of the basin, the seasonal distribution of precipitation, and the vegetation cover.

Methods and findings

Based on rainfall erosivity and surface flow and transport sub-models, we developed a simplified model to assess basin-wide sediment yields on a monthly basis by upscaling the point rainfall input. Our calibration dataset of monthly data (Mg km^-2 month^-1, available for the years 1961 and 1963–1969) revealed that our model satisfactorily reproduces the net soil erosion in the study area (R² = 0.81). For the period 1950–2020, the reconstruction of an annually aggregated time-series of monthly net erosion data (297 Mg km^-2 yr^-1 on average) indicated a moderate decline in sediment yield after 1999. This is part of a long-term downward trend, which highlights the role played by land-use changes and reforestation of the mountainous areas of the basin.

Conclusion

This study shows the environmental history and dynamics of the basin, and thus the varying sensitivity of hydrological processes and their perturbations. Relying on a few climatic variables as reported from a single representative basin location, it provides an interpretation of empirically determined factors that shape active erosional landscapes. In particular, we showed that the most recent extreme storms associated with sediment yield have been characterised by lower cumulative rainfall, indicating a greater propensity for the basin to produce sediment more discontinuously over time.

Soil erosion modelling: A global review and statistical analysis

To gain a better understanding of the global application of soil erosion prediction models, we comprehensively reviewed relevant peer-reviewed research literature on soil-erosion modelling published between 1994 and 2017. We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the regions within which models are primarily applied, (iii) the regions which remain unaddressed and why, and (iv) how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To perform this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. The resulting database, named 'Global Applications of Soil Erosion Modelling Tracker (GASEMT)', includes 3030 individual modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471 articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluated and transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insights into the state-of-the-art of soil- erosion models and model applications worldwide. This database intends to support the upcoming country-based United Nations global soil-erosion assessment in addition to helping to inform soil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is an open-source database available to the entire user-community to develop research, rectify errors, and make future expansions.

Soil erosion modelling: A bibliometric analysis

Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations. Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication's CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper.

Global rainfall erosivity assessment based on high-temporal resolution rainfall records

The exposure of the Earth's surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall erosivity is challenging as it requires high temporal resolution(<30 min) and high fidelity rainfall recordings. We present the results of an extensive global data collection effort whereby we estimated rainfall erosivity for 3,625 stations covering 63 countries. This first ever Global Rainfall Erosivity Database was used to develop a global erosivity map at 30 arc-seconds(~1 km) based on a Gaussian Process Regression(GPR). Globally, the mean rainfall erosivity was estimated to be 2,190 MJ mm ha^-1 h^-1 yr^-1, with the highest values in South America and the Caribbean countries, Central east Africa and South east Asia. The lowest values are mainly found in Canada, the Russian Federation, Northern Europe, Northern Africa and the Middle East. The tropical climate zone has the highest mean rainfall erosivity followed by the temperate whereas the lowest mean was estimated in the cold climate zone.

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.

Hydro-climatic forcing of dissolved organic carbon in two boreal lakes of Canada

The boreal forest of the northern hemisphere represents one of the world's largest ecozones and contains nearly one third of the world's intact forests and terrestrially stored carbon. Long-term variations in temperature and precipitation have been implied in altering carbon cycling in forest soils, including increased fluxes to receiving waters. In this study, we use a simple hydrologic model and a 40-year dataset (1971-2010) of dissolved organic carbon (DOC) from two pristine boreal lakes (ELA, Canada) to examine the interactions between precipitation and landscape-scale controls of DOC production and export from forest catchments to surface waters. Our results indicate that a simplified hydrologically-based conceptual model can enable the long-term temporal patterns of DOC fluxes to be captured within boreal landscapes. Reconstructed DOC exports from forested catchments in the period 1901-2012 follow largely a sinusoidal pattern, with a period of about 37years and are tightly linked to multi-decadal patterns of precipitation. By combining our model with long-term precipitation estimates, we found no evidence of increasing DOC transport or in-lake concentrations through the 20th century.

Storminess and geo-hydrological events affecting small coastal basins in a terraced Mediterranean environment

This study was prompted by the occurrence of an extreme Damaging geo-Hydrological Event (DHE) which occurred on October 25th 2011 and which affected a wide area of the northern Mediterranean region. After analysing the storm by means of the precipitation time series, the study attempts to relate the October 25th 2011 DHE with a series of other DHEs that occurred in the period 1954-2012, assessed via the use of historical data and classified according to severity, with a Storm Erosivity Indicator (Ra). The annual mean of the Ra value (2582 MJ mm ha(-1) h(-1) y(-1)) confirmed that the study area is one of the European regions with the highest rainfall erosivity level. A shift in storminess during 1991-2012 with respect to 1954-1990 was observed. A return period of 1000 years was calculated for the single storm erosivity of October 25th, which contributed to 84% of the total annual storm erosivity of 2011 A quite good agreement was found comparing DHE distribution and severity with Ra anomalies over time. As a matter of fact, most of the low severity DHEs (62.5%) occurred in years in which the Ra was below the average value. Moreover, almost all DHEs (93%) ranging from medium- to very high-severity occurred in years for which the Ra exceeded the average value. With regard to the occurrence of the most severe DHE classes, a threshold of the Ra and a recurrence time of approximately 3300 MJ mm ha(-1) h(-1) y(-1) and 12 years, respectively, were identified. Finally, some evidences suggest that an increasing frequency of DHEs is expected in the forthcoming years. It is argued that understanding these issues is a major priority for future research in order to improve land and urban planning strategies for preserving people and the environment, leading ultimately to an effective risk reduction.

Modelling vegetation greenness responses to climate variability in a Mediterranean terrestrial ecosystem

This work presents a modelling study where monthly-based climate data are used to estimate the Normalized Difference Vegetation Index (NDVI). The latter is a measure of vegetation greenness, usually derived from satellite-driven information. A model was developed to link NDVI data to rainfall and temperature measures. The test area was a 3 x 3 km grid centred to the top of Monte Pino hill (Southern Italy), for which multi-year (from 1996 to 2004) climate and satellite-derived NDVI data were available. The simulated NDVI data compared well with the remote-sensed measurements (e.g. modelling efficiency approximately 0.80), thus showing a strong linking between vegetation greenness and climate patterns in spite of the many disturbances exerted from farming. The model was used to reconstruct an extended series of monthly NDVI values for a period antecedent 1996 (1972-1995). The analysis of long-term anomalies indicated a positive trend of NDVI over time, consistent with the air temperature increase registered in the same period.

Geostatistical uncertainty modelling for the environmental hazard assessment during single erosive rainstorm events

This paper presents an environmental hazard assessment to account the impacts of single rainstorm variability on river-torrential landscape identified as potentially vulnerable mainly to erosional soil degradation processes. An algorithm for the characterisation of this impact, called Erosive Hazard Index (EHI), is developed with a less expensive methodology. In EHI modelling, we assume that the river-torrential system has adapted to the natural hydrological regime, and a sudden fluctuation in this regime, especially those exceeding thresholds for an acceptable range of flexibility, may have disastrous consequences for the mountain environment. The hazard analysis links key rainstorm energy variables expressed as a single-storm erosion index (EISTO), with impact thresholds identified using an intensity pattern model. Afterwards, the conditional probabilities of exceeding these thresholds are spatially assessed using non-parametric geostatistical techinques, known as indicator kriging. The approach was applied to a test site in river-torrential landscape of the Southern Italy (Benevento province) for 13 November 1997 rainstorm event.