In tidyhydro v0.1.1.9000, 7 metrics are implemented.
| Name | Abbr. | Function calls | Reference |
|---|---|---|---|
| Kling-Gupta Efficiency | \(KGE\) |
kge, kge_vec
|
Gupta, H.V.; Kling, H.; Yilmaz, K.K.; Martinez, G.F. (2009). Journal of Hydrology, 377(1–2), 80–91. |
| Modified Kling-Gupta Efficiency | \(KGE'\) |
kge2012, kge_vec
|
Kling, H., Fuchs, M., & Paulin, M. (2012). Journal of Hydrology, 424–425, 264–277. |
| Nash-Sutcliffe Efficiency | \(NSE\) |
nse, nse_vec
|
Nash, J. E., & Sutcliffe, J. V. (1970). Journal of Hydrology, 10(3), 282–290. |
| Mean Squared Error | \(MSE\) |
mse, mse_vec
|
Clark, M. P., Vogel, R. M., Lamontagne, J. R., Mizukami, N., Knoben, W. J. M., Tang, G., Gharari, S., Freer, J. E., Whitfield, P. H., Shook, K. R., & Papalexiou, S. M. (2021). The Abuse of Popular Performance Metrics in Hydrologic Modeling. Water Resources Research, 57(9), e2020WR029001. |
| Percent BIAS | \(pBIAS\) |
pbias, pbias_vec
|
Gupta, H. V., S. Sorooshian, and P. O. Yapo. (1999). Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. J. Hydrologic Eng. 4(2): 135-143 |
| PRediction Error Sum of Squares | \(PRESS\) |
press, press_vec
|
Rasmussen, P. P., Gray, J. R., Glysson, G. D. & Ziegler, A. C. Guidelines and procedures for computing time-series suspended-sediment concentrations and loads from in-stream turbidity-sensor and streamflow data. in U.S. Geological Survey Techniques and Methods book 3, chap. C4 53 (2009) |
| Standard Factorial Error | \(SFE\) |
sfe, sfe_vec
|
Herschy, R.W. 1978: Accuracy. Chapter 10 In: Herschy, R.W. (ed.) Hydrometry - principles and practices. John Wiley and Sons, Chichester, 511 p. |
avacha dataset
The package includes the mean daily water discharge values (obs in m³/s) measured at the state gauging station Avacha River — Elizovo City (site No. 2090). Alongside the measured water discharge, the mean water discharge in the last 24 hours derived from the GloFAS-ERA5 v4.0 reanalysis is provided (sim).
library(ggplot2)
library(tidyhydro)
data(avacha)
avacha |>
ggplot(aes(x = date)) +
geom_line(aes(y = obs, colour = "Measured")) +
geom_line(aes(y = sim, colour = "Predicted")) +
scale_colour_brewer(name = "", palette = "Set1") +
labs(x = "", y = "Water Discharge, m³/s")
One can estimate the desired metrics using the tidyverse syntax. For example, to get the Nash-Sutcliffe Efficiency (\(NSE\)) or Modified Kling-Gupta Efficiency (\(KGE'\)) for the avacha dataset, one can run:
Or using the yardstick helper functions, one can create a metric set, combining it with other yardstick metrics, such as \(R^2\):
library(yardstick)
hydro_metrics <- metric_set(kge, pbias, rsq)
hydro_metrics(avacha, obs, sim)
#> # A tibble: 3 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 kge standard 0.947
#> 2 pbias standard 0.0540
#> 3 rsq standard 0.898Such syntax is particularly useful when running a group analysis, for example, estimating model performance for different months:
library(lubridate)
library(dplyr)
avacha |>
mutate(month = month(date)) |>
group_by(month) |>
nse(obs, sim)
#> # A tibble: 12 × 4
#> month .metric .estimator .estimate
#> <dbl> <chr> <chr> <dbl>
#> 1 1 nse standard -2.78
#> 2 2 nse standard 0.180
#> 3 3 nse standard 0.0335
#> 4 4 nse standard -0.00438
#> 5 5 nse standard 0.815
#> 6 6 nse standard -2.78
#> 7 7 nse standard -0.244
#> 8 8 nse standard -0.228
#> 9 9 nse standard 0.359
#> 10 10 nse standard 0.466
#> 11 11 nse standard 0.439
#> 12 12 nse standard 0.455Alternatively, one can still use the vectorised versions of the metrics, ending with the *_vec suffix:
nse_vec(truth = avacha$obs, estimate = avacha$sim)
#> [1] 0.895008