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Annual mean spring discharge (ASD) is an important water supply source, essential for ecological systems and societies dependent on groundwater resources. Influenced by both regional and local climate fluctuations, the inter-annual variability of ASD represents a climate memory signal, significantly affected when the drought pattern manifests itself in changing climatic regimes. Gaining a better historical perspective on ASD changes requires extended time-series of discharge data and relevant climate drivers. Here, using a parsimonious model, we present a continuous (modelled) time-series of annual ASD for the karst spring of Caposele, in the Cervialto Massif of southern Italy, which is hitherto the longest (1601–2020 CE) such time-series for the entire Mediterranean region. The model was designed to capture the importance of large-scale seasonal (spring, autumn and winter) precipitation (hydro-meteorological factor), and flood and drought indices (climatological factor), and to be consistent with a sample (1920–2020 CE) of actual data. We show a limited overall sensitivity of ASD to climate variability, with a mean of 4.21 m3 s−1 and a drop from ∼1759 CE. With a mean value of ∼3.60 m3 s−1 after ∼1987 CE, ASD has revealed a substantial descending trend—possibly a fingerprint of recent warming—with a depletion of regional water reservoirs. These results highlight the need to strengthen the capacity of groundwater resources in the face of changing, and possibly enhanced, drought patterns in the Mediterranean region.

Annual mean spring discharge (ASD) is an important water supply source, essential for ecological systems and societies dependent on groundwater resources. Influenced by both regional and local climate fluctuations, the inter-annual variability of ASD represents a climate memory signal, significantly affected when the drought pattern manifests itself in changing climatic regimes. Gaining a better historical perspective on ASD changes requires extended time-series of discharge data and relevant climate drivers. Here, using a parsimonious model, we present a continuous (modelled) time-series of annual ASD for the karst spring of Caposele, in the Cervialto Massif of southern Italy, which is hitherto the longest (1601–2020 CE) such time-series for the entire Mediterranean region. The model was designed to capture the importance of large-scale seasonal (spring, autumn and winter) precipitation (hydro-meteorological factor), and flood and drought indices (climatological factor), and to be consistent with a sample (1920–2020 CE) of actual data. We show a limited overall sensitivity of ASD to climate variability, with a mean of 4.21 m3 s−1 and a drop from ∼1759 CE. With a mean value of ∼3.60 m3 s−1 after ∼1987 CE, ASD has revealed a substantial descending trend—possibly a fingerprint of recent warming—with a depletion of regional water reservoirs. These results highlight the need to strengthen the capacity of groundwater resources in the face of changing, and possibly enhanced, drought patterns in the Mediterranean region.

Soil loss is a challenge for sustainable river management, not the least in the Mediterranean region, with climate extremes altering erosion and overland sediment transport. The dynamics of these processes, and their ecological consequences, are poorly documented in the Mediterranen region. For the Arno River Basin (ARB), located by the Tyrrhenian coast of Italy, we estimated changes in annual sediment discharge (SD) as a function of the landscape characteristics within the basin, the seasonal distribution of precipitation and flooding, and the vegetation cover. Owing to a semi-empirical modelling framework, we identified critical erosion-/deposition-prone periods from 1500 to 2019 CE. The long-term estimated mean gross soil erosion in the ARB was ∼900 Mg km−2 yr−1, whereas mean net erosion was ∼200 Mg km−2 yr−1. The results revealed that during the Little Ice Age (ending ∼1850 CE), increased storms and SD had a large impact on the landscape and agricultural activity of the ARB. The downward trend in SD observed after ∼1903 CE occurred with high soil movement, along with environmental change and basin reforestation. The basin area thus experienced, on average, only slight soil losses, with ∼76% of the eroded soil being trapped along the stream reaches, on gently sloping areas as well as in the foot slopes and the valley terrains which divide the basin area. Relying on credible information regarding SD and its hydrological drivers, we showed how historical climatology and parsimonious modelling can be implemented to obtain basin-wide estimates of soil loss, and are appropriate tools for sustainable river basin management.

Rainfall erosivity causes considerable environmental damage by driving soil loss. However, the long-term evolution of erosive forcing (over centennial to millennial time-scales) remains essentially unknown. Using a rainfall erosivity model (REMARB), this study simulates the variability of rainfall erosivity in Arno River Basin (ARB), Italy, a Mediterranean fluvial basin, for the period 1000–2019 CE resulting in the world’s longest time-series of erosivity. The annual estimates show a noticeable and increasing variability of rainfall erosivity during the Little Ice Age (∼1250–1849), especially after c. 1490, until the end of 18th century. During this cold period, erosive forcing reached ∼1600 MJ mm hm−2 h−1 yr−1 once every four years, and ∼3000 MJ mm hm−2 h−1 yr−1 once every 20 years. The extremes of rainfall erosivity (the 98th percentile) followed a similar increasing trend, with an acceleration of the hydrological hazard (erosivity per unit of rainfall) during the 20th century. The comparison of REMARB output with the sediment yield of the basin (1951–2010) confirmed the model’s ability to predict geomorphological effects in the ARB. Thus, our methodology could be applied to simulate erosivity in environmentally similar basins. A relationship has been identified between the Atlantic Multidecadal Variation and erosivity patterns, suggesting a role of North Atlantic circulation dynamics on the hydrology of central Italy’s fluvial basins.

Rainfall erosivity causes considerable environmental damage by driving soil loss. However, the long-term evolution of erosive forcing (over centennial to millennial time-scales) remains essentially unknown. Using a rainfall erosivity model (REM (ARB)), this study simulates the variability of rainfall erosivity in Arno River Basin (ARB), Italy, a Mediterranean fluvial basin, for the period 1000-2019 CE resulting in the world's longest time-series of erosivity. The annual estimates show a noticeable and increasing variability of rainfall erosivity during the Little Ice Age (similar to 1250-1849), especially after c. 1490, until the end of 18th century. During this cold period, erosive forcing reached similar to 1600 MJ mm hm(-2) h(-1) yr(-1) once every four years, and similar to 3000 MJ mm hm(-2) h(-1) yr(-1) once every 20 years. The extremes of rainfall erosivity (the 98th percentile) followed a similar increasing trend, with an acceleration of the hydrological hazard (erosivity per unit of rainfall) during the 20th century. The comparison of REM (ARB) output with the sediment yield of the basin (1951-2010) confirmed the model's ability to predict geomorphological effects in the ARB. Thus, our methodology could be applied to simulate erosivity in environmentally similar basins. A relationship has been identified between the Atlantic Multidecadal Variation and erosivity patterns, suggesting a role of North Atlantic circulation dynamics on the hydrology of central Italy's fluvial basins.

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 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