Published April 14, 2023 | Version v1
Dataset Open

Streamflow drought hazard indicators for monitoring drought hazard for human water supply and river ecosystems at the global scale (WaterGAP 2.2d, WFDEI-GPCC)

  • 1. Institute of Physical Geography, Goethe University Frankfurt, Frankfurt am Main, 60438, Germany
  • 2. Institute of Physical Geography, Goethe University Frankfurt, Frankfurt am Main, 60438, Germany; Senckenberg Leibniz Biodiversity and Climate Research Centre Frankfurt (SBiK-F), Frankfurt am Main, 60325, Germany

Description

1) Streamflow drought hazard indicators (SDHIs) as computed by WaterGAP 2.2d (climate data WFDEI-GPCC) for the whole globe except Antarctica, spatial resolution: 0.5°, monthly data for the reference period 1986-2015:

Indicators of drought magnitude: SSI1, SSI12, EP1, RDQI1

Indicators of drought severity:

CDQI1-Q50, CDQI1-Q50_f, CDQI1-Q80, CDQI1-Q80_f, CDQI1-Q80-HS, CDQI6-Q80, CDQI6-Q80_f,

CDQI1-WUs, CDQI1-WUs-EFR, CDQI1-WUs-EFR_f,

CEP1(20%), CEP1(20%)_f, CRDQI1(-50%), CRDQI1(-50%)_f

2) WaterGAP grid cell IDs ("arcid") with longitude and latitude:

(WaterGAP_ArcID_lon_lat.txt)

3) Streamflow observations at 220 GRDC gauging stations and list of the 220 GRDC station numbers with related WaterGAP grid cell ID ("arcid"):

Observed_monthly_streamflow_km3month_220_calstations_1986_2015.txt

GRDC_number_WaterGAP_ArcID_220_stations.txt

4) SDHIs for four GRDC gauging stations:

time_series_danube_river.txt, time_series_angara_river.txt, time_series_white_river.txt, time_series_orange_river.txt

5) WaterGAP output: Mean monthly surface water abstractions in km3 per month: Mean_monthly_WUs_km3_per_month_WFDEI_GPCC_ant_22d_1986_2015.txt

 

Files

CDQI1_Q50_f__simulated_Q_1986_2015.zip

Files (1.4 GB)

Name Size Download all
md5:aad7bbe016d944e271ce0cbea6003116
100.2 MB Preview Download
md5:55b5202b2198f28f300e4e8bcf2a2713
103.9 MB Preview Download
md5:b199558697170ac42d111e08b4471e1f
38.5 MB Preview Download
md5:f1ea2b6b1f491530f150db105666a0d9
42.7 MB Preview Download
md5:8630a0803adcbb5afecbfb50b4e424cd
42.6 MB Preview Download
md5:d57209f4b300e274b62059b8f72dde1d
89.5 MB Preview Download
md5:199b0970e1ababf4a239072165829e95
119.1 MB Preview Download
md5:d3ac0893f97e5812871106654d0140cc
25.7 MB Preview Download
md5:e515c2a0befaa8beb487abe355a97332
46.4 MB Preview Download
md5:b31fffe5970b86d3ee1669406b568554
45.0 MB Preview Download
md5:7eb46e80f721cedc3c4ef25d5628ef6c
37.3 MB Preview Download
md5:4aba2409f13ae44f202cb88f64e9fb51
15.2 MB Preview Download
md5:9864c7d8b2ab990bc8f12dd2a3237b6e
54.4 MB Preview Download
md5:90dd270a530edc1d3e902c4fcbb28939
51.8 MB Preview Download
md5:a58973722292966fa4db34bb1590981b
34.1 MB Preview Download
md5:ff8209ab21667484148718392ab8393e
3.3 kB Preview Download
md5:9d86653cbcbb9f259304c64a21704213
8.0 MB Preview Download
md5:3ec9d41e27b8dc157ab211229661a5f9
966.9 kB Preview Download
md5:8c7b53c73a71baebb1c26cc97fe1e63f
200.2 MB Preview Download
md5:caa984216b6eaa99b2ff8ccddb9acd3a
194.8 MB Preview Download
md5:e867bd3b30192949d68084c83dd57807
173.4 MB Preview Download
md5:81bb769e4d6c7b018d8ca67efff44612
42.2 kB Preview Download
md5:ef44279f76780714364c3a67c56d78d5
42.5 kB Preview Download
md5:d9a7b48c1dcdd665fce542dfdbf8f26e
44.4 kB Preview Download
md5:d740babc34d3e0eaf4d6337469b6c978
44.1 kB Preview Download
md5:a4c102de6d4475a6a93c8cd4b7f90ce9
1.3 MB Preview Download

Additional details

Related works

Is referenced by
10.5194/nhess-2022-174 (DOI)