Global - Land

	Download Data	Period Mean [mm d-1]		Bias [mm d-1]		Bias Score [1]	Spatial Distribution Score [1]	Interannual Variability Score [1]
Benchmark	[-]	0.825
ELM_Control	[-]	1.29		0.462		0.632	0.852	0.259
ELM_ShallowSnow	[-]	1.28		0.459		0.632	0.851	0.258

Global - Land Temporally integrated period mean

  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