Event-scale nutrient transport revealed by integrated runoff monitoring and time-lapse imagery

Diffuse nutrient pollution during rainfall–runoff events is a major pressure on lake water quality, particularly in agricultural catchments. Short-lived runoff events can deliver disproportionally large nutrient loads, yet event-scale runoff observations are rare due to monitoring limitations. This restricts the design and evaluation of nature-based solutions (NBS) and catchment restoration measures.

Within the Horizon Europe EUROLakes project, Lake Vico (Central Italy) is used as a pilot site to test innovative approaches for monitoring diffuse pollution. Lake Vico is a volcanic lake affected by agricultural nutrient inputs. To better capture event-driven nutrient transport, an experimental surface runoff monitoring setup was established in the Cerreto sub-catchment.

A representative sub-basin was identified based on land use and topography, and an automatic runoff sampling system was installed to trigger autonomously during surface flow events. The system is integrated with a low-cost time-lapse camera acquiring images every 30 minutes, providing continuous visual information on soil moisture conditions and the occurrence of overland flow. Runoff samples are analyzed for key water-quality parameters, including nitrite, ammonium, reactive phosphorus, total nitrogen, and total phosphorus, allowing nutrient dynamics to be quantified at the event scale.

To detect runoff directly from the image time series, a Python-based automated classification workflow is being developed. The method uses two regions of interest per image and simple grayscale features to distinguish dry soil from active runoff, accounting for day-night conditions. The workflow processes the full image archive and produces runoff flags and diagnostic indicators for further analysis.

We present preliminary results from the first monitoring period and assess the performance of the image-based runoff detection. Key uncertainties related to illumination changes, vegetation dynamics, and night-time conditions are discussed. Ongoing work focuses on linking runoff occurrence to rainfall intensity and duration. Beyond site-specific insights, the resulting event-scale runoff and water-quality dataset provides a critical empirical basis for calibrating process-based hydrological models (e.g. SWAT) and supports the evaluation of NBS in data-limited lake catchments.