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Glyphosate [N-(phosphonomethyl) glycine] and glufosinate ammonium [ammonium dl-homoalanin-4-(methyl) phosphinate] are broad-spectrum, nonselective, post-emergence herbicides extensively used in various applications for weed control in both agricultural and non-crop areas. Aminomethylphosphonic acid (AMPA) is the major degradation product of glyphosate found in plants, water, and soil. Due to glyphosate's presumed low mobility, its monitoring in European water was limited. Recently both glyphosate and AMPA have been detected in several groundwater samples in Europe, U.S, Canada, Argentina, and China. Understanding the sources of these substances in water, especially in groundwater used for drinking, becomes a priority. In the present work the occurrences and the main drives of glyphosate, AMPA, and glufosinate ammonium in the groundwater of hilly vineyards located in the North-West of Italy were evaluated. Groundwater monitoring results showed frequent detection and concentrations above EQSGW for glyphosate and AMPA, while glufosinate ammonium was never detected. More frequent occurrences and higher concentrations were detected in the samples collected from wells located in the farmyards, most of them being used for irrigation and/or preparation of PPPs mixtures. Indeed, AMPA was the only compound detected in one groundwater well used for drinking, at values bellow EQSGw/DWQS. Such monitoring results were not expected as the modelling estimations under local pedoclimatic conditions indicated no risk of leaching to groundwater. However, the modelling performance and output may have been influenced by the non-consideration of important specific processes. Integrating monitoring and modelling results with information concerning the agricultural practices adopted and the wells use and location, possible contamination drivers were identified. These include the non-agricultural use of glyphosate in the farmyard, the point source contamination of wells and the possible tran

Evaluating a three-dimensional lake model requires large datasets of many variables, including velocity fields, that are seldom available. Here we discuss how to assess the performance of a model at multiple scales (in time and space) with data from standard monitoring systems, i.e., mostly limited to water temperature. The modeling chain consists of a lake hydrodynamic model (Delft3D-Flow) forced by an atmospheric model (WRF, Weather Research and Forecasting). The two models are tested on the case study of Lake Garda (Italy), where a comprehensive dataset of atmospheric and water temperature observations is available. Results show that a consistent picture of the inherent dynamics can be reproduced from a heterogeneous set of water temperature data, by distilling information across diverse spatial and temporal scales. The choice of the performance metrics and their limitations are discussed, with a focus on the procedures adopted to manage and homogenize data, visualize results and identify sources of error.