This study investigates the spatial and temporal dynamics of DOC concentration in a Mediterranean headwater catchment (Turbolo River catchment, southern Italy) equipped with two multi-parameter sondes providing more than two-year (May 2019 to November 2021) continuous high-frequency measurements of several DOC-related parameters. The sondes were installed in two nested sections, a quasi-pristine upstream sub-catchment and a downstream outlet with some anthropogenic disturbances on water quality. DOC estimates were achieved by correcting the fluorescent dissolved organic matter - fDOM - values through an original procedure not requiring extensive laboratory measurements. Then, DOC dynamics at the seasonal and storm event scales were analyzed. At the seasonal scale, results confirmed the climate control on DOC production, with increasing background concentrations in hot and dry summer months. The hydrological regulation proved crucial for DOC mobilization and export, with the top 10th percentile of discharge associated with up to 79% of the total DOC yield. The analysis at the storm scale using flushing and hysteresis indices highlighted substantial differences between the two catchments. In the steeper upstream catchment, the limited capability of preserving hydraulic connection in time with DOC sources determined the prevalence of transport as the limiting factor to DOC export. Downstream, transport- and source-limited processes were observed almost equally. The correlation between the hysteretic behaviour and antecedent precipitation was not linear since the process reverted to transport-limited for high accumulated rainfall values. The study demonstrated the importance of high-resolution measurements to explain DOC dynamics at multiple time scales using a quantitative approach.

Enhancing an understanding of expansion/contraction dynamics of active drainage networks is fundamental for both scientific purposes and environmental planning and management. This study analyzes for the first time the network shrinking and dry down in two seasonally dry Mediterranean catchments (overall area 1.15 km2) using a comprehensive approach based on monitoring and modeling of the active network. A seasonal field campaign consisting of 19 subweekly visual surveys was carried out at the beginning of the summer of 2019. Observations were then used to calibrate and validate an integrated model aimed at estimating the time evolution of the total active drainage network length based on meteorological drivers and defining the position of the active stretches based on topographic and geological information. Statistical modeling of the active length showed that weather can successfully describe the observed variability of network dynamics during the summer recession. In particular, the study emphasizes the role of evapotranspiration in the seasonal contraction of the stream network. The modeling of the spatial patterns of the active network achieved good performance when topographic data were used as explanatory variables. Nevertheless, the model performance further increased when site-specific geological information was integrated into the model, with accuracies higher than 90% in cell-by-cell comparisons. The proposed methodology, which combines meteorological, topographic and geological information in a sequential manner, was able to accurately represent the space/time dynamics of the active drainage network in the study area, proving to be an effective and flexible tool for the study of network dynamics.

Magnitude and frequency are prominent features of river floods informing design of engineering structures, insurance premiums and adaptation strategies. Recent advances yielding a formal characterization of these variables from a joint description of soil moisture and daily runoff dynamics in river basins are here systematized to highlight their chief outcome: the PHysically-based Extreme Value (PHEV) distribution of river flows. This is a physically-based alternative to empirical estimates and purely statistical methods hitherto used to characterize extremes of hydro-meteorological variables. Capabilities of PHEV for predicting flood magnitude and frequency are benchmarked against a standard distribution and the latest statistical approach for extreme estimation in two ways. The methods are first applied to an extensive dataset to compare their skills for predicting observed flood quantiles in a wide range of case studies. Synthetic time series of streamflow, generated for select river basins from contrasting hydro-climatic regions, are later used to assess performances for rare events. Both analyses reveal fairly unbiased capabilities of PHEV to estimate flood magnitudes corresponding to return periods much longer than the sample size used for calibration. The results also emphasize reduced prediction uncertainty of PHEV for rare floods when the mechanistic hypotheses postulated by the method are fulfilled, notably if the flood magnitude-frequency curve displays an inflection point. These features, arising from the mechanistic understanding embedded in the novel distribution of the largest river flows, are key for a reliable assessment of the actual flooding hazard associated to poorly sampled rare events, especially when lacking long observational records.