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Rainfall records collected by gauges lead to key forcings in most hydrological studies. Depending on the type of sensor and recording systems, such data are characterised by different temporal resolutions (or temporal aggregations), ta. We present a historical analysis of the temporal evolution of ta based on a large database of operational rain gauge networks in many study areas. Globally, data were collected from 25,423 rain gauge stations in 32 geographical areas, with major contributions from Australia, USA, Italy and Spain. For very old networks, the first recordings were manual with a coarse temporal resolution, usually daily or sometimes monthly. With some exceptions, mechanical recordings on paper rolls started in the first half of the 20th century, usually with times of 1 h or 30 min. Digital records began only during the last three decades of the 20th century. This short period limits research requiring long time series of sub-daily rainfall data, e.g. analysis of the effects of climate change on short-duration (sub-hourly) intense rainfall. Furthermore, in areas with rainfall data characterised over many years by coarse temporal resolutions, the annual maximum depths of short-duration rainfall can potentially be underestimated and their use would lead to errors in the results.Currently, only 50% of the stations provide useful data at any temporal resolution, which practically means ta = 1 min. However, a significant reduction of these problems can be obtained through the information content of the present database. Finally, we suggest an integration of the database by including additional rain gauge networks to improve its usefulness, particularly in a comparative analysis of the effects of climate change on short duration extreme precipitation available at different locations., Los registros de lluvia recopilados por medidores conducen a forzamientos clave en la mayoría de los estudios hidrológicos. Dependiendo del tipo de sensor y de los sistemas de registro, dichos datos se caracterizan por diferentes resoluciones temporales (o agregaciones temporales), ta. Presentamos un análisis histórico de la evolución temporal de ta basado en una gran base de datos de redes de pluviómetros operativas en muchas áreas de estudio. A nivel mundial, se recopilaron datos de 25.423 estaciones pluviómetros en 32 áreas geográficas, con mayores contribuciones de Australia, EE. UU., Italia y España. Para las redes muy antiguas, las primeras grabaciones eran manuales con una resolución temporal aproximada, normalmente diaria o, a veces, mensual. Con algunas excepciones, los registros mecánicos en rollos de papel comenzaron en la primera mitad del siglo XX, normalmente con tiempos de 1 h o 30 min. Los registros digitales comenzaron sólo durante las últimas tres décadas del siglo XX. Este corto período limita las investigaciones que requieren largas series temporales de datos de precipitaciones subdiarias, por ejemplo, análisis de los efectos del cambio climático en precipitaciones intensas de corta duración (subhorarias). Además, en las áreas con datos de lluvia caracterizados durante muchos años por resoluciones temporales gruesas, las profundidades máximas anuales de lluvia de corta duración pueden potencialmente subestimarse y su uso produciría errores en los resultados de aplicaciones sucesivas. Actualmente, sólo el 50% de las estaciones proporcionan datos útiles en cualquier resolución temporal, lo que prácticamente significa ta = 1 min. Sin embargo, se puede obtener una reducción significativa de estos problemas a través del contenido de información de la presente base de datos. Finalmente, sugerimos una integración de la base de datos mediante la inclusión de redes de pluviómetros adicionales para mejorar su utilidad, particularmente en un análisis compar

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.

Grassland management practices and their interactions with climatic variables have significant impacts on soil greenhouse gas (GHG) emissions. Mathematical models can be used to simulate the impacts of management and potential changes in climate beyond the temporal extent of short-term field experiments. In this study, field measurements of nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4) emissions from grassland soils were used to test and validate the DNDC (DeNitrification-DeComposition) model. The model was then applied to predict changes in GHG emissions due to interactions between climate warming and grassland management in a 30-year simulation. Sensitivity analysis showed that the DNDC model was susceptible to changes in temperature, rainfall, soil carbon and N-fertiliser rate for predicting N2O and CO2 emissions, but not for net CH4 emissions. Validation of the model suggests that N2O emissions were well described by N-fertilised treatments (relative variation of 2%), while non-fertilised treatments showed higher variations between measured and simulated values (relative variation of 26%). CO2 emissions (plant and soil respiration) were well described by the model prior to hay meadow cutting but afterwards measured emissions were higher than those simulated. Emissions of CH4 were on average negative and largely negligible for both simulated and measured values. Long-term scenario projections suggest that net GHG emissions would increase over time under all treatments and interactions. Overall, this study confirms that GHG emissions from intensively managed, fertilised grasslands are at greater risk of being amplified through climate warming, and represent a greater risk of climate feedbacks.

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.