From the origins of hydrology, the time of concentration, t c , has conventionally been tackled as a constant quantity. However, theoretical proof and empirical evidence imply that t c exhibits significant variability against rainfall, making its definition and estimation a hydrological paradox. Adopting the assumptions of the Rational method and the kinematic approach, an effective procedure in a GIS environment for estimating the travel time across a catchment's longest flow path is provided. By application in 30 Mediterranean basins, it is illustrated that t c is a negative power function of excess rainfall intensity. Regional formulas are established to infer its multiplier (unit time of concentration) and exponent from abstract geomorphological information, which are validated against observed data and theoretical literature outcomes. Besides offering a fast and easy solution to the paradox, we highlight the necessity of implementing the varying t c concept within hydrological modelling, signalling a major shift from current engineering practices.

A high-quality daily runoff time series of the Lake Como inflow and outflow, the longest for Italian Alps, was reconstructed for the 1845-2016 period in the Adda river basin. It was compared with contemporary monthly precipitation and temperature observations and estimated potential evapotranspiration losses. Trend analyses were conducted for daily flow maxima and 7-day duration minima of inflows into the lake showing a non-significant decrease and a significant increase, respectively. Although the annual precipitation time series exhibits a non-significant decrease, annual runoff volumes decrease with a rate of −136 mmÁcentury −1 , with a significance level of 5%. Possible causes of variability of rainfall and runoff as North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation and Western Mediterranean Oscillation indexes and sunspot activity were also explored. Wavelet spectra analyses of monthly precipitation and runoff show some changes in the energy both at small and large scales and are effective in pointing out phenomena as droughts and the effects of dams' regulation. Conversely, wavelet coherence spectra indicate a weak correlation of NAO and sunspots with precipitation. In addition, the analysis of temperature and potential evapotranspiration tendencies suggests that the decrease of runoff has to be ascribed mostly to anthropogenic factors, including water abstraction for irrigation and increased evapotranspiration losses due to natural afforestation and, only in part, to climatic variability.

Terraced agroecosystems (TAS)—apart from being an important cultural heritage element—are considered vital for sustainable water resource management and climate change adaptation measures. However, this traditional form of agriculture, with direct implications in food security at a local scale, has been suffering from abandonment or degradation worldwide. In light of this, the need to fully comprehend the complex linkage of their abandonment with different driving forces is essential. The identification of these dynamics makes possible an appropriate intervention with local initiatives and policies on a larger scale. Therefore, the main aim of this paper is to introduce a comprehensive multidisciplinary framework that maps the dynamics of the investigated TAS’s abandonment, by defining cause–effect relationships on a hydrogeological, ecological and social level, through tools from System Dynamics studies. This methodology is implemented in the case of Assaragh TAS, a traditional oasis agroecosystem in the Moroccan Anti-Atlas, characterized by data scarcity. Through field studies, interviews, questionnaires and freely accessible databases, the TAS’s abandonment, leading to a loss in agrobiodiversity, is linked to social rather than climatic drives. Additionally, measures that can counteract the phenomenon and strengthen the awareness of the risks associated with climate change and food security are proposed.

Abstract. In recent years, copula multivariate functions were used to model, probabilistically, the most important variables of flood events: discharge peak, flood volume and duration. However, in most of the cases, the sampling uncertainty, from which small-sized samples suffer, is neglected. In this paper, considering a real reservoir controlled by a dam as a case study, we apply a structure-based approach to estimate the probability of reaching specific reservoir levels, taking into account the key components of an event (flood peak, volume, hydrograph shape) and of the reservoir (rating curve, volume-water depth relation). Additionally, we improve information about the peaks from historical data and reports through a Bayesian framework, allowing the incorporation of supplementary knowledge from different sources and its associated error. As it is seen here, the extra information can result in a very different inferred parameter set and consequently this is reflected as a strong variability of the reservoir level, associated with a given return period. Most importantly, the sampling uncertainty is accounted for in both cases (single-site and multi-site with historical information scenarios), and Monte Carlo confidence intervals for the maximum water level are calculated. It is shown that water levels of specific return periods in a lot of cases overlap, thus making risk assessment, without providing confidence intervals, deceiving.

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