This shows you the differences between two versions of the page.
Both sides previous revision Previous revision Next revision | Previous revision Next revision Both sides next revision | ||
start:hype_file_reference:info.txt:criteria_equations [2016/11/04 13:50] rcapell |
start:hype_file_reference:info.txt:criteria_equations [2018/08/13 09:59] cpers [Criteria equations for a model domain (several stations)] |
||
---|---|---|---|
Line 53: | Line 53: | ||
|Median NRMSE|''MNR''|//MEDNE//| | |Median NRMSE|''MNR''|//MEDNE//| | ||
|Mean NSEW|''MNW''|//AVNSEW//| | |Mean NSEW|''MNW''|//AVNSEW//| | ||
+ | </sortable> | ||
+ | |||
+ | Equation IDs for calibration simulation assessment criteria ([[start:HYPE_file_reference:bestsims.txt|bestsims.txt]] and [[start:HYPE_file_reference:allsim.txt|allsim.txt]]): | ||
+ | |||
+ | <sortable> | ||
+ | ^ Heading ^ Description ^ Equation ID ^ | ||
+ | |''rr2''|regional Nash-Sutcliffe efficiency (data from all subbasins combined in one data series)|//REGNSE//| | ||
+ | |''sr2''|spatial Nash-Sutcliffe efficiency, calculated using annual means for all subbasins (requires at least 5 years and 5 subbasins with data) to form one data series to calculate the Nash-Sutcliffe efficiency on|//SPATNSE//| | ||
+ | |''mr2''|average of Nash-Sutcliffe efficiencies for subbasins|//AVNSE//| | ||
+ | |''rmae''|regional mean absolute error (data from all subbasins combined in one data series)|//REGMAE//| | ||
+ | |''sre''|spatial relative bias (calculated on annual means for all subbasins)|//SPATRB//| | ||
+ | |''rre''|regional relative bias (data from all subbasins combined in one data series)|//REGRB//| | ||
+ | |''mre''|average of the relative bias for all subbasins (Note: fraction, not %)|//AVRB//| | ||
+ | |''rra''|regional RA, similar to regional NSE, RA is a Nash-Sutcliffe like criterion where the square in the Nash-Sutcliffe formula is exchanged with a coefficient value|//REGRA//| | ||
+ | |''sra''|spatial RA, similar to spatial NSE, RA is a Nash-Sutcliffe like criterion where the square in the Nash-Sutcliffe formula is exchanged for a coefficient value|//SPATRA//| | ||
+ | |''mra''|average value of RA for subbasins, RA is a Nash-Sutcliffe like criterion where the square in the Nash-Sutcliffe formula is exchanged with a coefficient value|//AVRA//| | ||
+ | |''tau''|average of Kendall's Tau value for subbasins|//AVTAU//| | ||
+ | |''md2''|median of Nash-Sutcliffe efficiency for subbasins|//MEDNSE//| | ||
+ | |''mda''|median of all subbasins’ RA (Nash-Sutcliffe like criteria where the square is exchanged with a coefficient value)|//MEDRA//| | ||
+ | |''mrs''|average of error in standard deviation for subbasins|//AVRSB//| | ||
+ | |''mcc''|Pearson correlation coefficient, average of all subbasins with observations|//AVCC//| | ||
+ | |''mdkg''|median of Kling-Gupta efficiency (MKG in [[start:hype_file_reference:info.txt|info.txt]]) for subbasins|//MEDKGE//| | ||
+ | |''mare''|average of absolute relative bias for subbasins (Note: fraction. not %) (MAR in [[start:hype_file_reference:info.txt|info.txt]])|//AVARB//| | ||
+ | |''mnr''|median of normalised RMSE for subbasins|//MEDNE//| | ||
+ | |''mnw''|average of Nash-Sutcliffe efficiencies adjusted for bias for subbasins|//AVNSEW//| | ||
</sortable> | </sortable> | ||
Line 179: | Line 204: | ||
Median Nash-Sutcliffe efficiency (//MEDNSE//): | Median Nash-Sutcliffe efficiency (//MEDNSE//): | ||
- | <m> MEDNSE = {median{j=1}{mj}{NSE_{j}}} </m> | + | <m> MEDNSE = median delim{lbrace}{{NSE_{j}},{j=1..mj}}{rbrace} </m> |
Spatial Nash-Sutcliffe efficiency (//SPATNSE//): | Spatial Nash-Sutcliffe efficiency (//SPATNSE//): | ||
Line 195: | Line 220: | ||
Median efficiency with coefficient a (//MEDRA//): | Median efficiency with coefficient a (//MEDRA//): | ||
- | <m> MEDRA = {median{j=1}{mj}{RA_{j}}} </m> | + | <m> MEDRA = median delim{lbrace}{{RA_{j}},{j=1..mj}}{rbrace} </m> |
Spatial efficiency with coefficient a (//SPATRA//): | Spatial efficiency with coefficient a (//SPATRA//): | ||
Line 219: | Line 244: | ||
Median Kling-Gupta efficiency (//MEDKGE//): | Median Kling-Gupta efficiency (//MEDKGE//): | ||
- | <m> MEDKGE = {median{j=1}{mj}{KGE_{j}}} </m> | + | <m> MEDKGE = median delim{lbrace}{{KGE_{j}},{j=1..mj}}{rbrace} </m> |
Median of Normalised root mean square error (//MEDNE//): | Median of Normalised root mean square error (//MEDNE//): | ||
- | <m> MEDNE = {median{j=1}{mj}{NE_{j}}} </m> | + | <m> MEDNE = median delim{lbrace}{{NE_{j}},{j=1..mj}}{rbrace} </m> |
Average of absolute relative bias (//AVARB//): | Average of absolute relative bias (//AVARB//): |