Suchergebnisse

| 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

Abstract. Water infrastructure development is considered necessary to drive economic
growth in the Mekong region of mainland Southeast Asia. Yet the current
understanding of hydrological and flood pattern changes associated with
infrastructural development still contains several knowledge gaps, such as
the interactions between multiple drivers, which may have serious
implications for water management, agricultural production, and ecosystem
services. This research attempts to conduct a cumulative assessment
of basin-wide hydropower dam construction and irrigation expansion, as well
as climate change, implications on discharge, and flood changes in the
Cambodian Mekong floodplain. These floodplains offer important
livelihoods for a considerable part of the 6.4 million people living on
them, as they are among the most productive ecosystems in the world –
driven by the annual flood pulse. To assess the potential future impacts, we
used an innovative combination of three models: Mekong basin-wide
distributed hydrological model IWRM-VMod, with the Mekong delta 1D flood
propagation model MIKE-11 and 2D flood duration and extent model IWRM-Sub
enabling detail floodplain modelling. We then ran scenarios to approximate
possible conditions expected by around 2050. Our results show that the
monthly and seasonal hydrological regimes (discharges, water levels, and
flood dynamics) will be subject to substantial alterations under future
development scenarios. Projected climate change impacts are expected to
decrease dry season flows and increase wet season flows, which is in
opposition to the expected alterations under development scenarios that
consider both hydropower and irrigation. The likely impact of decreasing
water discharge in the early wet season (up to −30 %) will pose a
critical challenge to rice production, whereas the likely increase in water
discharge in the mid-dry season (up to +140 %) indicates improved water
availability for coping with drought stresses and sustaining environmental
flows. At the same time, these changes would have drastic impacts on total
flood extent, which is projected to decline by around 20 %, having
potentially negative impacts on floodplain productivity and aquaculture,
whilst reducing the flood risk to more densely populated areas. Our findings
demonstrate the substantial changes that planned infrastructural development
will have on the area, potentially impacting important ecosystems and
people's livelihoods, calling for actions to mitigate these changes as well
as planning potential adaptation strategies.