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

| openaire: EC/H2020/819202/EU//SOS.aquaterra Funding Information: We acknowledge the use of data and/or imagery from NASA's Fire Information for Resource Management System (FIRMS) ( https://earthdata.nasa.gov/firms ), part of NASA's Earth Observing System Data and Information System (EOSDIS). We warmly thank the Center for International Cooperation in Sustainable Management of Tropical Peatland (CIMTROP), University of Palangka Raya, for collaboration during our field visit and seminar in 2019. The study was funded by the Academy of Finland funded project WATVUL (grant no. 317320), European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 819202), Aalto University School of Engineering Doctoral Programme, and the Emil Aaltonen Foundation funded project "eat‐less‐water." Funding Information: We acknowledge the use of data and/or imagery from NASA's Fire Information for Resource Management System (FIRMS) (https://earthdata.nasa.gov/firms), part of NASA's Earth Observing System Data and Information System (EOSDIS). We warmly thank the Center for International Cooperation in Sustainable Management of Tropical Peatland (CIMTROP), University of Palangka Raya, for collaboration during our field visit and seminar in 2019. The study was funded by the Academy of Finland funded project WATVUL (grant no. 317320), European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 819202), Aalto University School of Engineering Doctoral Programme, and the Emil Aaltonen Foundation funded project ?eat-less-water.? Publisher Copyright: © 2021 The Authors. Earth and Space Science published by Wiley Periodicals LLC on behalf of American Geophysical Union. ; Throughout Indonesia ecological degradation, agricultural expansion, and the digging of drainage canals has compromised the integrity and functioning of peatland forests. Fragmented landscapes of scrubland, cultivation, degraded forest, and newly established plantations are then susceptible to extensive fires that recur each year. However, a comprehensive understanding of all the drivers of fire distribution and the conditions of initiation is still absent. Here we show the first analysis in the region that encompasses a wide range of driving factors within a single model that captures the inter-annual variation, as well as the spatial distribution of peatland fires. We developed a fire susceptibility model using machine learning (XGBoost random forest) that characterizes the relationships between key predictor variables and the distribution of historic fire locations. We then determined the relative importance of each predictor variable in controlling the initiation and spread of fires. The model included land-cover classifications, a forest clearance index, vegetation indices, drought indices, distances to infrastructure, topography, and peat depth, as well as the Oceanic Niño Index (ONI). The model performance consistently scores highly in both accuracy and precision across all years (>75% and >67.5% respectively), though recall metrics are much lower (>25%). Our results confirm the anthropogenic dependence of extreme fires in the region, with distance to settlements and distance to canals consistently weighted the most important driving factors within the model structure. Our results may help target the root causes of fire initiation and propagation to better construct regulation and rehabilitation efforts to mitigate future fires. ; Peer reviewed