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Development of an Automatic Calibration Tool Using Genetic Algorithm for the ARNO Conceptual Rainfall-Runoff Model

  • Research Article - Civil Engineering
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Abstract

Rainfall-runoff simulation is one of the key steps in hydrology. Conceptual models are frequently used in rainfall-runoff simulation. However, a major difficulty in practice remains on how to optimize the parameters of the model. This is often a time-consuming and labor-intensive task for the modeler when manual calibration is adopted together with employing the knowledge of the model structure and parameters. In this study, an automatic calibration tool was developed to calibrate the ARNO conceptual rainfall-runoff model using the simple genetic algorithm (SGA). SGA is a simple, powerful, and popular optimization method, which explores the search space for the global optimum and has been successfully employed in many optimizations problems. The ARNO model was calibrated automatically for rainfall-runoff simulation of the Pataveh basin, which is a sub-basin of Karun River basin in Iran. The simulation performance of the model was evaluated on the basis of various performance criteria. Efficiency coefficient and coefficient of determination reached values higher than 0.80 during calibration and validation. The values of the remaining performance statistics were acceptable. The results show that this model with employed automatic calibration tool can successfully be used for continuous rainfall-runoff simulation.

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Abbreviations

b :

A parameter representing spatial distribution of the soil moisture capacity

B :

Base flow

B–C:

Blaney and Criddle method

c :

Exponent used to represent drainage when saturation is not reached

C :

A small integer

CE:

Coefficient of efficiency

D :

Drainage

D max :

Maximum drainage that should be expected when the soil is completely saturated

D min :

A drainage parameter

EOPT:

An objective function

\({{\rm E\overline Q} }\) :

Percentage error of mean discharge

\({{\rm E\overline {Qp}}}\) :

Percentage error of mean annual peak discharges

ESD:

Percentage error of standard deviation

ESkew:

Percentage error of skewness

ETa :

Actual evapotranspiration

ET0:

Reference evapotranspiration

ET0H-S :

Hargreaves and Samani ET0

ET0P-M :

Penman-Monteith ET0

ETp :

Potential evapotranspiration

f′:

Scaled fitness function

GA:

Genetic algorithm

H–S:

Hargreaves and Samani method

I :

Percolation

I s :

Maximum percolation should be expected when the soil is completely saturated

K :

Number of calibration years

l :

Length of a gene

L :

Chromosome length

m :

Number of years in period of model performance evaluation

M e :

Effective meteorological input

N :

Population size

n :

Number of days in period of model performance evaluation

OBF:

An objective function

P :

Precipitation

P c :

Crossover probability

P m :

Mutation probability

P–M:

Penman-Monteith method

\({\overline {Q_{\rm obs}} }\) :

Average observed flow over the considered period

Q obs(t):

Observed flow

\({\overline {Qp_{\rm obs} } }\) :

Mean annual observed peak discharges

\({\overline {Qp_{\rm sim} } }\) :

Mean annual simulated peak discharges

\({\overline {Q_{\rm sim}} }\) :

Average simulated flow

Q sim(t):

Simulated flow

R :

Surface runoff

R 2 :

Coefficient of determination

SD (Q obd) :

Standard deviation of observed runoff

SD (Q sim):

Standard deviation of simulated runoff

SGA:

Simple genetic algorithm

S G :

Generic pervious surface area at saturation

S I :

Basin impervious area

Skew (Q obs):

Skewness of observed runoff

Skew(Q sim):

Skewness of simulated runoff

S P :

Basin pervious area

S T :

Basin surface area (excluding the surface extent of water bodies such as reservoirs or lakes)

Th:

Thornthwaite method

U max :

Upper limit of the parameter

U min :

Lower limit of the parameter

V obs :

Observed flow volume

V sim :

Simulated flow volume

w :

Elementary area soil moisture at saturation

W :

Basin average soil moisture content

W d :

Moisture content threshold value in drainage calculation

W i :

Moisture content threshold value below which the percolation is negligible

w m :

Maximum possible soil moisture in any elementary area of the basin

W m :

Basin average soil moisture content at saturation

x :

Proportion of pervious area at saturation

Δt:

Time step

μ OBF :

Average of the OBF of all the chromosomes in the population

μ OBF (90 %) :

Average of the OBF values of 90 % of best chromosomes in the population

π :

Precision of the parameter in parameter estimation

σ OBF :

Standard deviation of the OBF of all the chromosomes in the population

σ OBF (90 %) :

Standard deviation of the OBF values of 90 % of best chromosomes in the population

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Authors and Affiliations

  1. Department of Civil Engineering, Payame Noor University, PO BOX 19395-3697, Tehran, I.R of Iran

    Mohammad Reza Khazaei

  2. College of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

    Bagher Zahabiyoun

  3. Soil Conservation and Watershed Management Research Institute, Tehran, Iran

    Bahram Saghafian

  4. Department of Chemistry, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

    Shahin Ahmadi

Authors
  1. Mohammad Reza Khazaei
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  2. Bagher Zahabiyoun
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  3. Bahram Saghafian
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  4. Shahin Ahmadi
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Correspondence to Mohammad Reza Khazaei.

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Khazaei, M.R., Zahabiyoun, B., Saghafian, B. et al. Development of an Automatic Calibration Tool Using Genetic Algorithm for the ARNO Conceptual Rainfall-Runoff Model. Arab J Sci Eng 39, 2535–2549 (2014). https://doi.org/10.1007/s13369-013-0903-8

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