Load Estimation Method Using Distributions with Covariates: A Comparison with Commonly Used Estimation Methods

Load estimates obtained using an approach based on statistical distributions with parameters expressed as a function of covariates (e.g., streamflow) (distribution with covariates hereafter called DC method) were compared to four load estimation methods: (1) flow‐weighted mean concentration; (2) integral regression; (3) segmented regression (the last two with Ferguson's correction factor); and (4) hydrograph separation methods. A total of 25 datasets (from 19 stations) of daily concentrations of total dissolved solids, nutrients, or suspended particulate matter were used. The selected stations represented a wide range of hydrological conditions. Annual flux errors were determined by randomly generating 50 monthly sample series from daily series. Annual and interannual biases and dispersions were evaluated and compared. The impact of sampling frequency was investigated through the generation of bimonthly and weekly surveys. Interannual uncertainty analysis showed that the performance of the DC method was comparable with those of the other methods, except for stations showing high hydrological variability. In this case, the DC method performed better, with annual biases lower than those characterizing the other methods. Results show that the DC method generated the smallest pollutant load errors when considering a monthly sampling frequency for rivers showing high variability in hydrological conditions and contaminant concentrations.     

The Analysis of Sustainability Indicators as Socially Constructed Policy Instruments: benefits and challenges of 'interactive research'

Sustainability indicator sets are increasingly being discussed on the policy level as fruitful contributions to the improvement of political decision- making and to the implementation of programs oriented towards the achievement of strategic goals of sustainable development. The vast number of different indicator type tools, their varying contexts of use and their differing objectives indicate that there is no simple answer to what sustainable indicator type tools should look like or could be used for. Instead, more than the final products (e.g. a specific indicators set), the analyses of the discourse on this topic reveal a lot of information. Thus, an innovative research approach is recommended focusing on understanding the production of social meaning and processes of social interaction within political-administrative systems. Firstly, there is a need to identify the development, purpose and use of sustainability indicator sets, which depend on the different interests of policy actors, their relationships and existing governance structures. Secondly, one should identify any reasons for the ineffective use of indicator sets where the goals of sustainability are concerned. The approach of 'interactive research' understood as a research process, in which 'researchers' and 'practitioners' develop knowledge for solving problems in a communicative, reflexive and collaborative way, facilitates this challenging research task. This paper critically examines the approach of interactive research and sheds some light on benefits as well as challenges of it via extracting the lessons learnt in an EU-funded project called 'Promoting Action for Sustainability through Indicators at the Local Level in Europe' (PASTILLE), which applied an interactive research approach.