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Introduction

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where: 

Q_obs,i    is the observed flow for time step i
Q_mod,i    is the modelled flow for time step i
N           is the number of time steps

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An alternative, but equivalent, formulation of the NSE is:

 This formulation obviates the necessity to calculate the average of the observed flows before evaluating the denominator in the traditional version.

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where:

c is a positive constant equal to the maximum of 1 ML/d (megalitre/day) (after Lerat et al, 2013) and the 10^th percentile (90% flow exceedance) of the observed non-zero flows (after Croke et al, 2006). Other terms are as defined in Equation 1.

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where:

Q_obs,i    is the observed flow for time step i
Q_mod,i    is the modelled flow for time step i
N           is the number of time steps

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• as a percent - called Volume Bias % in Source. Volume Bias % can be found in the Statistics tab in Results Manager. The bias in the modelled values expressed as a percent of the observed flow volume is defined as:

• an absolute value - called Minimise Absolute Bias in Source. Minimise Absolute Bias is available as an objective function for calibration and as one of the statistics in the Statistics tab in Results Manager. The absolute value of the relative bias is defined as:

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Bias Penalty Bias Penalty

Bias Penalty

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where B is the relative bias, as defined in Equation 4.

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Pearson's correlation coefficient measures the linear correlation between two variables and is available in Source in the Results Manager statistics tab for Bivariate Statistics. The Pearson's correlation coefficient is given by:

where:

x_i is the value of time series x at time step i
y_i is the value of time series y at time step i

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where:

R_Qobs,k is the k'th ranked observed flow of a total of N ranked flows R_Qsim,k is the k'th ranked modelled flow of a total of N ranked flows

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where c is calculated as in the NSE of Log Data

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1. the sum of errors on power transformed flow, 
2. the same sum on sorted flow values and 
3. the relative simulation bias.

The SDEB equation is:

where:

α is a weighting factor set to 0.1

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The Kling-Gupta Efficiency (KGE, after Gupta et al., 2009) is increasingly being used for model calibration and evaluation . The KGE is given by:

Equation 12

where 𝑟 is the linear correlation between observations and simulations (Pearson’s Correlation Coefficient), 𝛼 is the measure of the flow variability error and 𝛽 represents the bias.

𝛼 is given by:

where is the standard deviation and  Q_sim and Q_obs are the simulated flows and observed flows respectively. 

𝛽 is given by:

where represents the mean 

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NSE Daily - Bias Penalty NSE Daily - Bias Penalty

NSE Daily & Bias Penalty

Definition

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NSE Log Daily - Bias Penalty NSE Log Daily - Bias Penalty

NSE Log Daily & Bias Penalty

Definition

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NSE Monthly - Bias Penalty NSE Monthly - Bias Penalty

NSE Monthly & Bias Penalty

Definition

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NSE Daily - Flow Duration NSE Daily - Flow Duration

NSE Daily & Flow Duration

Definition

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NSE Daily - Log Flow Duration NSE Daily - Log Flow Duration

NSE Daily & Log Flow Duration

Definition

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The objective function Trotter (after Trotter et al., 2023) aims at solving the tendency of the NSE and KGE to preference match high flows by the inclusion of a low flow component. Therefore, Trotter is used to ensure capturing of both high-flow and low-flow aspects of hydrograph in a model as well as obtain minimal volumetric bias (Trotter et al., 2023). The objective function T is given by:

Interpretation

The first part of the model efficiency E as in the above equation is a combination of the mean of KGE of direct flows and fifth root of KGE. The use of the fifth root of flows is intended to provide greater emphasis to small flows and is a better alternative than the more common inverse or log transformations for zero-flow conditions. The second part of the equation consists of bias penalisation (after Viney et al., 2009) for reducing the efficiency value when the when the volumetric bias (B) between the observed and simulated flows deviates from zero.  The objective function  combines advantages of both KGE and Bias Penalty. 

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The objective function Split Trotter (after Fowler et al., 2018) is a time-based meta-objective function (an objective function that considers different aspects of a flow regime and combines them into a single-objective function) that explicitly considers different subperiods of the calibration period.  This function aims to rectify the tendency of objective functions with least squares to ignore dry years in the calibration process. This is achieved by calculating the objective function (Trotter) for each subperiod and averaging them over all subperiods. The Split Trotter function ST is defined as:

where, T_i is the Trotter value for the i^th subperiod,  ts_i is the number of time steps in the i^th subperiod.

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The objective function Split Trotter Weighted aims to place additional emphasis on model fitting to drier years by assigning a weight to every year. The objective function STW is defined as:

where, is the weight for the i^th subperiod, calculated as:

where, Qobs_i is the aggregate flows or the subperiod i for the observed time series and the other variables are as explained above.

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Knoben, W.J.M., J.E. Freer and R.A. Woods (2019) Technical note: Inherent benchmark or not? Comparing Nash-Sutcliffe and Kling-Gupta efficiency scores. Hydrology and Earth System Sciences, 23(10), 4323-4331, doi: https://doi.org/10.5194/hess-23-4323-2019

Lerat, J., C.A. Egan, S. Kim, M. Gooda, A. Loy, Q. Shao and C. Petheram (2013) Calibration of river models for the Flinders and Gilbert catchments. A technical report to the Australian Government from the CSIRO Flinders and Gilbert Agricultural Resource Assessment, part of the North Queensland Irrigated Agriculture Strategy. CSIRO Water for a Healthy Country and Sustainable Agriculture flagships, Australia.

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