Abstract
Hydrological variables are among the most influential when analyzing or modeling stream ecosystems. However, available hydrological data are often limited in their spatiotemporal scale and resolution for use in ecological applications such as predictive modeling of species distributions. To overcome this limitation, a regression model was applied to a 1 km gridded stream network of Germany to obtain estimated daily stream flow data (m3 s−1) spanning 64 years (1950–2013). The data are used as input to calculate hydrological indices characterizing stream flow regimes. Both temporal and spatial validations were performed. In addition, GLMs using both the calculated and observed hydrological indices were compared, suggesting that the predicted flow data are adequate for use in predictive ecological models. Accordingly, we provide estimated stream flow as well as a set of 53 hydrological metrics at 1 km grid for the stream network of Germany. In addition, we provide an R script where the presented methodology is implemented, that uses globally available data and can be directly applied to any other geographical region.
Highlights
Background & SummaryNatural flow regimes have a large influence in shaping biological communities[1] and regulate numerous ecological processes in stream ecosystems[2,3,4]
Information about flow regimes is often not available or sufficiently diverse for detailed modeling analyses such as species distribution models (SDMs)[7,8], which are a common tool used in ecological analysis
The metrics can provide essential information on freshwater ecosystems in general and on the impact of human activities and may support river management and conservation. They are well suited to be applied in predictive modeling (i.e. SDMs) and can be used under future hydrological scenarios to assess the effects of climate change on species distribution
Summary
Natural flow regimes have a large influence in shaping biological communities[1] and regulate numerous ecological processes in stream ecosystems[2,3,4]. The metrics can provide essential information on freshwater ecosystems in general and on the impact of human activities and may support river management and conservation They are well suited to be applied in predictive modeling (i.e. SDMs) and can be used under future hydrological scenarios to assess the effects of climate change on species distribution. Depending on the complexity of hydrological model (such as SWAT13, WaSIiM-ETH14) and the large amount of input data required, it can become tedious to simulate on these spatial scales Given these limitations, in order to fill the much-needed data gap for ecological analyses, linear regression models are simple and fast methods that can be applied for the spatiotemporal extrapolation of stream flow[15,16]. We provide R scripts that allow users to apply the model to other geographical regions or calculate the hydrological metrics for different time periods
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