Download Data |
Period Mean [mm d-1] |
Bias [mm d-1] |
Bias Score [1] |
Spatial Distribution Score [1] |
Interannual Variability Score [1] |
Overall Score [1] |
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Benchmark | [-] | 0.825 | |||||||
CLMNLDAS | [-] | 0.723 | 0.137 | 0.738 | 0.918 | 0.469 | 0.708 | ||
NWPNLDAS | [-] | 0.816 | 0.230 | 0.704 | 0.921 | 0.481 | 0.702 |
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
Conventions: Please contact Prof. James Randerson (Email: jranders@uci.edu) or Dr. Mingquan Mu (mmu@uci.edu) for any question
source_file: This product is generated from yearly GRDC_Aiguo observations
title: derived GRDC Aiguo Runoff Dataset
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