List of Metrics of Hydrologic Skill

HydroErr.HydroErr Module

HydroErr metrics.

Functions

acc(simulated_array, observed_array[, ...])	Compute the the anomaly correlation coefficient (ACC).
d(simulated_array, observed_array[, ...])	Compute the the index of agreement (d).
d1(simulated_array, observed_array[, ...])	Compute the the index of agreement (d1).
d1_p(simulated_array, observed_array[, ...])	Compute the Legate-McCabe Index of Agreement.
dmod(simulated_array, observed_array[, j, ...])	Compute the the modified index of agreement (dmod).
dr(simulated_array, observed_array[, ...])	Compute the the refined index of agreement (dr).
drel(simulated_array, observed_array[, ...])	Compute the the relative index of agreement (drel).
ed(simulated_array, observed_array[, ...])	Compute the Euclidean distance between predicted and observed values in vector space.
g_mean_diff(simulated_array, observed_array)	Compute the geometric mean difference.
h1_mahe(simulated_array, observed_array[, ...])	Compute the H1 absolute error.
h1_mhe(simulated_array, observed_array[, ...])	Compute the H1 mean error.
h1_rmshe(simulated_array, observed_array[, ...])	Compute the H1 root mean square error.
h2_mahe(simulated_array, observed_array[, ...])	Compute the H2 mean absolute error.
h2_mhe(simulated_array, observed_array[, ...])	Compute the H2 mean error.
h2_rmshe(simulated_array, observed_array[, ...])	Compute the H2 root mean square error.
h3_mahe(simulated_array, observed_array[, ...])	Compute the H3 mean absolute error.
h3_mhe(simulated_array, observed_array[, ...])	Compute the H3 mean error.
h3_rmshe(simulated_array, observed_array[, ...])	Compute the H3 root mean square error.
h4_mahe(simulated_array, observed_array[, ...])	Compute the H4 mean absolute error.
h4_mhe(simulated_array, observed_array[, ...])	Compute the H4 mean error.
h4_rmshe(simulated_array, observed_array[, ...])	Compute the H4 mean error.
h5_mahe(simulated_array, observed_array[, ...])	Compute the H5 mean absolute error.
h5_mhe(simulated_array, observed_array[, ...])	Compute the H5 mean error.
h5_rmshe(simulated_array, observed_array[, ...])	Compute the H5 root mean square error.
h6_mahe(simulated_array, observed_array[, ...])	Compute the H6 mean absolute error.
h6_mhe(simulated_array, observed_array[, k, ...])	Compute the H6 mean error.
h6_rmshe(simulated_array, observed_array[, ...])	Compute the H6 root mean square error.
h7_mahe(simulated_array, observed_array[, ...])	Compute the H7 mean absolute error.
h7_mhe(simulated_array, observed_array[, ...])	Compute the H7 mean error.
h7_rmshe(simulated_array, observed_array[, ...])	Compute the H7 root mean square error.
h8_mahe(simulated_array, observed_array[, ...])	Compute the H8 mean absolute error.
h8_mhe(simulated_array, observed_array[, ...])	Compute the H8 mean error.
h8_rmshe(simulated_array, observed_array[, ...])	Compute the H8 root mean square error.
h10_mahe(simulated_array, observed_array[, ...])	Compute the H10 mean absolute error.
h10_mhe(simulated_array, observed_array[, ...])	Compute the H10 mean error.
h10_rmshe(simulated_array, observed_array[, ...])	Compute the H10 root mean square error.
irmse(simulated_array, observed_array[, ...])	Compute the inertial root mean square error (IRMSE) between the simulated and observed data.
kge_2009(simulated_array, observed_array[, ...])	Compute the Kling-Gupta efficiency (2009).
kge_2012(simulated_array, observed_array[, ...])	Compute the Kling-Gupta efficiency (2012).
lm_index(simulated_array, observed_array[, ...])	Compute the Legate-McCabe Efficiency Index.
maape(simulated_array, observed_array[, ...])	Compute the the Mean Arctangent Absolute Percentage Error (MAAPE).
mae(simulated_array, observed_array[, ...])	Compute the mean absolute error of the simulated and observed data.
male(simulated_array, observed_array[, ...])	Compute the mean absolute log error of the simulated and observed data.
mapd(simulated_array, observed_array[, ...])	Compute the the mean absolute percentage deviation (MAPD).
mape(simulated_array, observed_array[, ...])	Compute the the mean absolute percentage error (MAPE).
mase(simulated_array, observed_array[, m, ...])	Compute the mean absolute scaled error between the simulated and observed data.
mb_r(simulated_array, observed_array[, ...])	Compute Mielke-Berry R value (MB R).
mdae(simulated_array, observed_array[, ...])	Compute the median absolute error (MdAE) between the simulated and observed data.
mde(simulated_array, observed_array[, ...])	Compute the median error (MdE) between the simulated and observed data.
mdse(simulated_array, observed_array[, ...])	Compute the median squared error (MdSE) between the simulated and observed data.
me(simulated_array, observed_array[, ...])	Compute the mean error of the simulated and observed data.
mean_var(simulated_array, observed_array[, ...])	Compute the mean variance.
mle(simulated_array, observed_array[, ...])	Compute the mean log error of the simulated and observed data.
mse(simulated_array, observed_array[, ...])	Compute the mean squared error of the simulated and observed data.
msle(simulated_array, observed_array[, ...])	Compute the mean squared log error of the simulated and observed data.
ned(simulated_array, observed_array[, ...])	Compute the normalized Euclidian distance between the simulated and observed data in vector space.
nrmse_iqr(simulated_array, observed_array[, ...])	Compute the IQR normalized root mean square error between the simulated and observed data.
nrmse_mean(simulated_array, observed_array)	Compute the mean normalized root mean square error between the simulated and observed data.
nrmse_range(simulated_array, observed_array)	Compute the range normalized root mean square error between the simulated and observed data.
nse(simulated_array, observed_array[, ...])	Compute the Nash-Sutcliffe Efficiency.
nse_mod(simulated_array, observed_array[, ...])	Compute the modified Nash-Sutcliffe efficiency (NSE mod).
nse_rel(simulated_array, observed_array[, ...])	Compute the relative Nash-Sutcliffe efficiency (NSE rel).
pearson_r(simulated_array, observed_array[, ...])	Compute the pearson correlation coefficient.
r_squared(simulated_array, observed_array[, ...])	Compute the the Coefficient of Determination (r2).
rmse(simulated_array, observed_array[, ...])	Compute the root mean square error between the simulated and observed data.
rmsle(simulated_array, observed_array[, ...])	Compute the root mean square log error between the simulated and observed data.
sa(simulated_array, observed_array[, ...])	Compute the Spectral Angle (SA).
sc(simulated_array, observed_array[, ...])	Compute the Spectral Correlation (SC).
sga(simulated_array, observed_array[, ...])	Compute the Spectral Gradient Angle (SGA).
sid(simulated_array, observed_array[, ...])	Compute the Spectral Information Divergence (SID).
smape1(simulated_array, observed_array[, ...])	Compute the the Symmetric Mean Absolute Percentage Error (1) (SMAPE1).
smape2(simulated_array, observed_array[, ...])	Compute the the Symmetric Mean Absolute Percentage Error (2) (SMAPE2).
spearman_r(simulated_array, observed_array)	Compute the spearman rank correlation coefficient.
ve(simulated_array, observed_array[, ...])	Compute the Volumetric Efficiency (VE).
watt_m(simulated_array, observed_array[, ...])	Compute Watterson's M (M).