Floods, defined as water that temporarily submerges land for over 72 hours or longer, are the largest natural hazard in terms of life loss and economic damage. Effective and immediate disaster response management can reduce the impact of floods but it requires near real-time information on flood occurrence, typically derived based on Earth Observation data.
Water stress is a global concern as a changing climate leads to variations in weather patterns and agricultural and urban areas continue to use water-intensive practices. Understanding spatial and temporal factors of surface water dynamics is key to better managing our resources and limiting the effects of water stress
By influencing water tables of saline aquifers, multiyear dry or wet periods can significantly delay or accelerate dryland salinity, but this effect remains poorly quantified at the large river basin scale.
The animations provided here are part of the publication,[Tulbure, M.G. and M. Broich (2018)]. The method is described in [Tulbure et al. (2016)]. The animations are based on statistically validated surface water and flooding extent dynamics data …
The layers provided here are part of the publication, [Tulbure, M.G. and M. Broich (2018)]. The method is described in [Tulbure et al. (2016)]. Data are provided in GeoTIFF format per season per year. File naming convention is …
Spatiotemporal distribution and systematic quantification of surface water and their drivers of change are critical. However, quantifying this distribution is challenging due to a lack of spatially explicit and temporally dynamic empirical data of both surface water and its drivers of change at large spatial scales.
Vegetation response to flooding across large dryland areas such as Australia's Murray Darling Basin (MDB) is not understood synoptically and with locally relevant detail.
Recent studies have developed novel long-term records of surface water (SW) maps on continental and global scales but due to the spatial and temporal resolution constraints of available satellite sensors, they are either of high spatial and low temporal resolution or vice versa.
Despite calls for landscape connectivity research to account for spatiotemporal dynamics, studies have overwhelmingly evaluated the importance of habitats for connectivity at single or limited moments in time. Remote sensing time series represent a promising resource for studying connectivity within dynamic ecosystems. However, there is a critical need to assess how static and dynamic landscape connectivity modelling approaches compare for prioritising habitats for conservation within dynamic environments.
In dynamic dryland regions, accounting for spatiotemporal landscape dynamics is essential to understanding how ecological habitat networks are affected by hydroclimatic variability at regional or sub-continental scales. Here we assess how changes in the distribution and availability of surface water influence potential landscape connectivity for water-dependent organisms by combining graph theory network analysis with a Landsat-derived, seasonally continuous 25-year surface-water time-series.