No abstract

Environmental time series are often affected by the "presence" of missing data, but when dealing statistically with data, the need to fill in the gaps estimating the missing values must be considered. At present, a large number of statistical techniques are available to achieve this objective; they range from very simple methods, such as using the sample mean, to very sophisticated ones, such as multiple imputation. A brand new methodology for missing data estimation is proposed, which tries to merge the obvious advantages of the simplest techniques (e.g. their vocation to be easily implemented) with the strength of the newest techniques. The proposed method consists in the application of two consecutive stages: once it has been ascertained that a specific monitoring station is affected by missing data, the "most similar" monitoring stations are identified among neighbouring stations on the basis of a suitable similarity coefficient; in the second stage, a regressive method is applied in order to estimate the missing data. In this paper, four different regressive methods are applied and compared, in order to determine which is the most reliable for filling in the gaps, using rainfall data series measured in the Candelaro River Basin located in South Italy.

The EC-funded EUROHARP project studies the harmonisation of modelling tools to quantify nutrient losses from diffuse sources. This paper describes a set of study areas used in the project from geographical conditions, to land use and land management, geological and hydro-geological perspectives. The status of data availability throughout Europe in relation to the modelling requirements is presented. The relationships between the catchment characteristics and the nutrient export are investigated, using simple data available for all the catchments. In addition, this study also analyses the hydrological representativity of the time series utilised in the EUROHARP project.