Developing and testing a global-scale regression model to quantify mean annual streamflow

Barbarossa, Valerio; Huijbregts, Mark A. J.; Hendriks, A. Jan; Beusen, Arthur; Clavreul, Julie; King, Henry; Schipper, Aafke M.

doi:10.1016/j.jhydrol.2016.11.053

Cited by 20 publications

(16 citation statements)

References 66 publications

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“…Data-driven approaches have been mostly employed at a local scale 34 . Recent studies demonstrated, however, the feasibility of applying a data-driven approach at a global scale, resulting in streamflow estimates that may have greater accuracy than the output of GHMs/LSMs 31 , 32 . Despite these encouraging results, consistent high-resolution global streamflow maps are not yet available.…”

Section: Background and Summarymentioning

confidence: 99%

FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015

Barbarossa¹,

Huijbregts²,

Beusen³

et al. 2018

Sci Data

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Streamflow data is highly relevant for a variety of socio-economic as well as ecological analyses or applications, but a high-resolution global streamflow dataset is yet lacking. We created FLO1K, a consistent streamflow dataset at a resolution of 30 arc seconds (~1 km) and global coverage. FLO1K comprises mean, maximum and minimum annual flow for each year in the period 1960–2015, provided as spatially continuous gridded layers. We mapped streamflow by means of artificial neural networks (ANNs) regression. An ensemble of ANNs were fitted on monthly streamflow observations from 6600 monitoring stations worldwide, i.e., minimum and maximum annual flows represent the lowest and highest mean monthly flows for a given year. As covariates we used the upstream-catchment physiography (area, surface slope, elevation) and year-specific climatic variables (precipitation, temperature, potential evapotranspiration, aridity index and seasonality indices). Confronting the maps with independent data indicated good agreement (R2 values up to 91%). FLO1K delivers essential data for freshwater ecology and water resources analyses at a global scale and yet high spatial resolution.

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Section: Background and Summarymentioning

confidence: 99%

FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015

Barbarossa¹,

Huijbregts²,

Beusen³

et al. 2018

Sci Data

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“…We quantified species-specific thresholds for minimum and maximum weekly flow, maximum number of zero flow weeks and maximum and minimum weekly water temperature based on the present-day distribution of these characteristics within the geographic range of each species, similarly to previous studies 45 , 49 , 50 . To this end, we overlaid the species’ range maps with the weekly flow and water temperature metrics from the output of the hydrological model, calculated for each year and averaged over a 30-years historical period to conform to the standard for climate analyses 51 , 52 (1976–2005, for each GCM employed in the study). We calculated for each 5 arcminutes grid cell the long-term average minimum and maximum weekly flow ( Q min , Q max , Eqs.…”

Section: Methodsmentioning

confidence: 99%

Threats of global warming to the world’s freshwater fishes

et al. 2021

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Climate change poses a significant threat to global biodiversity, but freshwater fishes have been largely ignored in climate change assessments. Here, we assess threats of future flow and water temperature extremes to ~11,500 riverine fish species. In a 3.2 °C warmer world (no further emission cuts after current governments’ pledges for 2030), 36% of the species have over half of their present-day geographic range exposed to climatic extremes beyond current levels. Threats are largest in tropical and sub-arid regions and increases in maximum water temperature are more threatening than changes in flow extremes. In comparison, 9% of the species are projected to have more than half of their present-day geographic range threatened in a 2 °C warmer world, which further reduces to 4% of the species if warming is limited to 1.5 °C. Our results highlight the need to intensify (inter)national commitments to limit global warming if freshwater biodiversity is to be safeguarded.

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Section: Discussionmentioning

confidence: 99%

“…To the best of our knowledge, there have only recently been a few studies [48,49] focusing on mapping high-resolution global river discharge. Barbarossa et al [48] have developed a global-scale regression model to quantify the mean annual discharge based on the observations of discharge and river basin characteristics from 1885 river basins worldwide, and this model can be applied globally to estimate the mean annual discharge at any point of the river network. Moreover, Barbarossa et al [49] have created FLO1K, a consistent discharge dataset at a resolution of 30 arc seconds (~1 km) and global coverage, by means of artificial neural networks regression using the observations of discharge from 6600 monitoring stations worldwide.…”

Section: Discussionmentioning

confidence: 99%

An Efficient Method for Mapping High-Resolution Global River Discharge Based on the Algorithms of Drainage Network Extraction

Liu

et al. 2018

Water

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River discharge, which represents the accumulation of surface water flowing into rivers and ultimately into the ocean or other water bodies, may have great impacts on water quality and the living organisms in rivers. However, the global knowledge of river discharge is still poor and worth exploring. This study proposes an efficient method for mapping high-resolution global river discharge based on the algorithms of drainage network extraction. Using the existing global runoff map and digital elevation model (DEM) data as inputs, this method consists of three steps. First, the pixels of the runoff map and the DEM data are resampled into the same resolution (i.e., 0.01-degree). Second, the flow direction of each pixel of the DEM data (identified by the optimal flow path method used in drainage network extraction) is determined and then applied to the corresponding pixel of the runoff map. Third, the river discharge of each pixel of the runoff map is calculated by summing the runoffs of all the pixels in the upstream of this pixel, similar to the upslope area accumulation step in drainage network extraction. Finally, a 0.01-degree global map of the mean annual river discharge is obtained. Moreover, a 0.5-degree global map of the mean annual river discharge is produced to display the results with a more intuitive perception. Compared against the existing global river discharge databases, the 0.01-degree map is of a generally high accuracy for the selected river basins, especially for the Amazon River basin with the lowest relative error (RE) of 0.3% and the Yangtze River basin within the RE range of ±6.0%. However, it is noted that the results of the Congo and Zambezi River basins are not satisfactory, with RE values over 90%, and it is inferred that there may be some accuracy problems with the runoff map in these river basins.

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Developing and testing a global-scale regression model to quantify mean annual streamflow

Cited by 20 publications

References 66 publications

FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015

FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015

Threats of global warming to the world’s freshwater fishes

An Efficient Method for Mapping High-Resolution Global River Discharge Based on the Algorithms of Drainage Network Extraction

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