Suchergebnisse

| openaire: EC/H2020/819202/EU//SOS.aquaterra Funding Information: Marko Kallio and Vili Virkki were funded by the Aalto University School of Engineering Doctoral Programme. Marko Kallio additionally received funding from Maa-ja Vesitekniikan Tuki ry. Matti Kummu received funding from the Academy of Finland project WATVUL (grant no. 317320), Emil Aaltonen Foundation project eat-less-water, and European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant no. 819202) from which Vili Virkki received additional funding. Joseph H. A. Guillaume received funding from an Australian Research Council Discovery Early Career Researcher Award (project no. DE190100317) Funding Information: Financial support. Marko Kallio and Vili Virkki were funded by the Aalto University School of Engineering Doctoral Programme. Marko Kallio additionally received funding from Maa-ja Vesitekni-ikan Tuki ry. Matti Kummu received funding from the Academy of Finland project WATVUL (grant no. 317320), Emil Aaltonen Foundation project "eat-less-water", and European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant no. 819202) from which Vili Virkki received additional funding. Joseph H. A. Guillaume received funding from an Australian Research Council Discovery Early Career Researcher Award (project no. DE190100317). Publisher Copyright: © 2021 Marko Kallio et al. ; An increasing number of different types of hydrological, land surface, and rainfall-runoff models exist to estimate streamflow in river networks. Results from various model runs from global to local scales are readily available online. However, the usability of these products is often limited, as they often come aggregated in spatial units which are not compatible with the desired analysis purpose. We present here an R package, a software library Hydrostreamer v1.0, which aims to improve the usability of existing runoff products by addressing the modifiable area unit problem and allows non-experts with little knowledge of hydrology-specific modelling issues and methods to use them for their analyses. Hydrostreamer workflow includes (1) interpolation from source zones to target zones, (2) river routing, and (3) data assimilation via model averaging, given multiple input runoff and observation data. The software implements advanced areal interpolation methods and area-to-line interpolation not available in other products and is the first R package to provide vector-based routing. Hydrostreamer is kept as simple as possible - intuitive with minimal data requirements - and minimises the need for calibration. We tested the performance of Hydrostreamer by downscaling freely available coarse-resolution global runoff products from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) in an application in 3S Basin in Southeast Asia. Results are compared to observed discharges as well as two benchmark streamflow data products, finding comparable or improved performance. Hydrostreamer v1.0 is open source and is available from http://github.com/mkkallio/hydrostreamer/ (last access: 5 May 2021) under the MIT licence. ; Peer reviewed

Our digital age is characterized by both a generalized access to data and an increased call for participation of the public and other stakeholders and communities in policy design and decision-making. This context raises new challenges for political decision-makers and analysts in providing these actors with new means and moral duties for decision support, including in the area of environmental policy. The concept of "policy analytics" was introduced in 2013 as an attempt to develop a framework, tools, and methods to address these challenges. This conceptual initiative prompted numerous research teams to develop empirical applications of this framework and to reflect on their own decision-support practice at the science-policy interface in various environmental domains around the world. During a workshop in Paris in 2018, participants shared and discussed their experiences of these applications and practices. In this paper, we present and analyze a set of applications to identify a series of key properties that underpin a policy analytics approach, in order to provide the conceptual foundation for policy analytics to address current policy design and decision-making challenges. The induced properties are demand-orientedness, performativity, normative transparency, and data meaningfulness. We show how these properties materialized through these six case studies, and we explain why we consider them key to effective policy analytics applications, particularly in environmental policy design and decision-making on environmental issues. This clarification of the policy analytics concept eventually enables us to highlight research frontiers to further improve the concept.