Mean State

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Period Mean [mm d-1]
Bias [mm d-1]
Bias Score [1]
Spatial Distribution Score [1]
Interannual Variability Score [1]
Overall Score [1]
Benchmark [-] 0.825
ACCESS-ESM1-5 [-] 0.933 0.108 0.728 0.895 0.580 0.734
BCC-CSM2-MR [-] 1.03 0.201 0.705 0.813 0.513 0.677
BGCLND [-] 0.822 -0.00373 0.787 0.963 0.634 0.794
BGCLNDATM_progCO2 [-] 1.08 0.250 0.732 0.931 0.550 0.738
CanESM5 [-] 0.875 0.0491 0.676 0.786 0.506 0.656
CNRM-ESM2-1 [-] 0.991 0.166 0.704 0.855 0.535 0.698
EC-Earth3-CC [-] 0.783 -0.0422 0.801 0.902 0.599 0.767
MeanCMIP6 [-] 0.879 0.0532 0.782 0.843 0.546 0.724
MIROC-ES2L [-] 0.842 0.0164 0.696 0.793 0.552 0.680
MPI-ESM1-2-LR [-] 0.651 -0.174 0.691 0.700 0.578 0.656
MRI-ESM2-0 [-] 0.908 0.0829 0.750 0.916 0.555 0.740
NorESM2-LM [-] 1.07 0.241 0.736 0.800 0.511 0.682
UKESM1-0-LL [-] 0.996 0.171 0.763 0.917 0.564 0.748

Temporally integrated period mean

BENCHMARK MEAN
Data not available
Data not available
MODEL MEAN
Data not available
Data not available
BIAS
Data not available
Data not available

SPATIAL TAYLOR DIAGRAM
Data not available
MODEL COLORS
Data not available

Spatially integrated regional mean

MODEL COLORS
Data not available
amazon
Data not available
ob
Data not available
lena
Data not available
yenisey
Data not available
mississippi
Data not available
congo
Data not available
mackenzie
Data not available
parana
Data not available
nile
Data not available
amur
Data not available
niger
Data not available
changjiang
Data not available
yukon
Data not available
nelson
Data not available
stlawrence
Data not available
kolyma
Data not available
murray
Data not available
danube
Data not available
indus
Data not available
orange
Data not available
ganges
Data not available
columbia
Data not available
huanghe
Data not available
indigirka
Data not available
orinoco
Data not available
tocantins
Data not available
mekong
Data not available
severnayadvina
Data not available
dnepr
Data not available
colorado
Data not available
pechora
Data not available
brahmaputra
Data not available
riogrande
Data not available
coruripe
Data not available
don
Data not available
olenek
Data not available
yana
Data not available
churchill
Data not available
xijiang
Data not available
senegal
Data not available
irrawaddy
Data not available
volta
Data not available
fraser
Data not available
taz
Data not available
rhine
Data not available
vistula
Data not available
parnaiba
Data not available
huai
Data not available
liao
Data not available
kuskokwim
Data not available

All Models

Benchmark
Data not available
Data not available
ACCESS-ESM1-5
Data not available
Data not available
BCC-CSM2-MR
Data not available
Data not available
BGCLND
Data not available
Data not available
BGCLNDATM_progCO2
Data not available
Data not available
CanESM5
Data not available
Data not available
CNRM-ESM2-1
Data not available
Data not available
EC-Earth3-CC
Data not available
Data not available
MeanCMIP6
Data not available
Data not available
MIROC-ES2L
Data not available
Data not available
MPI-ESM1-2-LR
Data not available
Data not available
MRI-ESM2-0
Data not available
Data not available
NorESM2-LM
Data not available
Data not available
UKESM1-0-LL
Data not available
Data not available

Data Information

  Title:
derived GRDC Aiguo Runoff Dataset

  Source_file:
This product is generated from yearly GRDC_Aiguo observations

  Conventions:
Please contact Prof. James Randerson (Email: jranders@uci.edu) or Dr. Mingquan Mu (mmu@uci.edu) for any question

  Altitude:

  Site_name:
Amazon,Ob,Lena,Yenisey,Mississippi,Congo,Mackenzie,Parana,Nile,Amur,Niger (Issa Ber,Changjiang,Yukon,Nelson,St Lawrence,Kolyma,Murray,Danube,Indus,Orange (Senqu),Ganges (Ganga),Columbia,Huanghe,Indigirka,Orinoco,Tocantins,Mekong,Severnaya Dvina,Dnepr,Colorado-AR,Pechora,Brahmaputra,Rio Grande (Bra,Coruripe,Don,Olenek,Yana,Churchill,Xijiang,Senegal,Irrawaddy (Ayey,Volta,Fraser,Taz,Rhine,Vistula (Wisla),Parnaiba,Huai,Liao,Kuskokwim

  Creation_date:
Thu Apr 14 00:21:01 PDT 2016

  Approach:
I read the variable (FLOW) from the original data file, then pick up top 50 global largest rivers based on TRIP riverbasins. I also converted river flow from river gauge stations to river mouthes by using ratio of volume between river mouth and station (ratio_m2s), then I converted the unit from m3/s to Kg/m2/s by using drainage area at river mouth (area_mou), finally I saveed the data in NetCDF format by each month and each year.

  Temporal resolution:
monthly

  General information:
This product was derived from global 925 rivers gauge observations from Dai and Trenberth (2002).

  Spatial resolution:
river gauge observation

  Derived data code:
http://redwood.ess.uci.edu/mingquan/www/ILAMB/Download/CODES/CODES/subroutines/convert/convert-riverbasin-TRIP+runoff.ncl