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Two streamflow gauging station datasets were used as the source of training and cross-validation data for study models — the World Meteorological Organization Global Runoff Data Centre (GRDC) database (n ≈ 10,000) and a complementary subset of the Global Streamflow Indices and Metadata archive (GSIM, n ≈ 25,000), a compilation of twelve free-to-access national and international streamflow gauging station databases.
Whereas the GRDC offers daily water discharge values for most stations, GSIM only contains time series summary indices computed at the yearly, seasonal and monthly resolution (calculated from daily records whose open-access release is restricted for some of the compiled data sources). Therefore, we used the GRDC database as the core of our training/testing set and complemented it with a subset of streamflow gauging stations from GSIM.
A GSIM station was included only if:
1. it was not already part of the GRDC database,
2. it included auxiliary information on the drainage area of the monitored reach (for reliably associating it to RiverATLAS), and if it was located either
3. on an IRES or
4. in a river basin which did not already contain a GRDC station (assessed based on level 5 sub-basins of the global BasinATLAS database52, average sub-basin area = 2.9 x 104 km2).
We applied the described GSIM selection criteria to balance the relative amount of non-perennial vs. perennial records and the spatial distribution of stations in the model training dataset.
Each station in the combined dataset was geographically associated with a reach in the RiverATLAS stream network and every discharge time series was quality-checked through statistical and manual outlier detection (see Supplementary Information B1 for details on these procedures).
Non-perennial gauging stations were only included in the dataset if they were free of anomalous zero-flow values (e.g. from instrument malfunction, gauge freezing, tidal flow reversal). We also excluded stations whose streamflow was potentially dominated by reservoir outflow regulation (i.e. with a degree of regulation > 50% or whose discharge time series exhibited an obvious alteration from natural flow permanence, see Supplementary Information B1), as flow regulating structures may change the flow class of a river either from perennial to non-perennial or vice-versa depending on their mode and rules of operation. We further narrowed our selection by adding only gauging stations with streamflow time series spanning at least 10 years — excluding years with more than 20 days of missing records for the calculation of this criterion and in subsequent analysis. Finally, we classified stations as non-perennial if their recorded discharge dropped to zero at least one day per year on average over the years of record, and as perennial otherwise. Stations with at least one zero-flow day per year on average (i.e. non-perennial) but without a zero-flow day during 20 consecutive valid years of data (those with ≤ 20 missing days), anywhere in their record, were deemed either to have experienced a shift in flow intermittence class (regardless of the direction of the shift) or to have ceased to flow due to exceptional conditions of drought and were also excluded.
Based on these selection criteria, the training dataset contained data for 3,967 perennial river reaches and for 1,388 non-perennial reaches, with 45 and 34 years of daily streamflow data on average, respectively, across all continents (except Antarctica).
On this map are shown all global gauging stations that were reliably matched to the RiverATLAS global digital river network.
Blue-colored points show perennial stations (< 1 zero-flow day per year on average across the record)
Red-colored points show non-perennial stations (>= 1 zero-flow day per year on average across the record)
Semi-transparent points show stations which were kept and included for the training and testing of predictive models.
Opaque points show stations which were excluded from further analysis due to one of the various reasons explained in the previous sections.
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4.6 rstudioapi_0.11 whisker_0.4 knitr_1.29
[5] magrittr_1.5 R6_2.4.1 rlang_0.4.7 highr_0.8
[9] stringr_1.4.0 tools_4.0.2 xfun_0.17 git2r_0.27.1
[13] htmltools_0.5.0 ellipsis_0.3.0 rprojroot_1.3-2 yaml_2.2.1
[17] digest_0.6.25 tibble_3.0.1 lifecycle_0.2.0 crayon_1.3.4
[21] later_1.0.0 vctrs_0.3.4 promises_1.1.0 fs_1.5.0
[25] glue_1.4.0 evaluate_0.14 rmarkdown_2.3 stringi_1.4.6
[29] compiler_4.0.2 pillar_1.4.4 backports_1.1.10 httpuv_1.5.4
[33] renv_0.9.3 pkgconfig_2.0.3