Plavšić, Jasna

Todorović, Andrijana

GraFar - Repository of the Faculty of Civil Engineering

Geophysical Research AbstractsVol. 17, EGU2015-968, 2015EGU General Assembly 2015© Author(s) 2014. CC Attribution 3.0 License.Impact of the calibration period on the conceptual rainfall-runoff modelparameter estimatesAndrijana Todorovic and Jasna PlavsicUniversity of Belgrade, Institute for Hydraulic and Environmental Engineering, Belgrade, Serbia(andrijana@hikom.grf.bg.ac.rs, 381113218530)A conceptual rainfall-runoff model is defined by its structure and parameters, which are commonly inferredthrough model calibration. Parameter estimates depend on objective function(s), optimisation method, andcalibration period. Model calibration over different periods may result in dissimilar parameter estimates, whilemodel efficiency decreases outside calibration period. Problem of model (parameter) transferability, whichconditions reliability of hydrologic simulations, has been investigated for decades.In this paper, dependence of the parameter estimates and model performance on calibration period is anal-ysed. The main question that is addressed is: are there any changes in optimised parameters and model efficiencythat can be linked to the changes in hydrologic or meteorological variables (flow, precipitation and temperature)?Conceptual, semi-distributed HBV-light model is calibrated over five-year periods shifted by a year (sliding timewindows). Length of the calibration periods is selected to enable identification of all parameters. One water yearof model warm-up precedes every simulation, which starts with the beginning of a water year. The model iscalibrated using the built-in GAP optimisation algorithm. The objective function used for calibration is composedof Nash-Sutcliffe coefficient for flows and logarithms of flows, and volumetric error, all of which participate inthe composite objective function with approximately equal weights. Same prior parameter ranges are used in allsimulations. The model is calibrated against flows observed at the Slovac stream gauge on the Kolubara Riverin Serbia (records from 1954 to 2013). There are no trends in precipitation nor in flows, however, there is astatistically significant increasing trend in temperatures at this catchment.Parameter variability across the calibration periods is quantified in terms of standard deviations of nor-malised parameters, enabling detection of the most variable parameters. Correlation coefficients among optimisedmodel parameters and total precipitation P, mean temperature T and mean flow Q are calculated to give an insightinto parameter dependence on the hydrometeorological drivers.The results reveal high sensitivity of almost all model parameters towards calibration period. The highestvariability is displayed by the refreezing coefficient, water holding capacity, and temperature gradient. The onlystatistically significant (decreasing) trend is detected in the evapotranspiration reduction threshold. Statisticallysignificant correlation is detected between the precipitation gradient and precipitation depth, and between thetime-area histogram base and flows. All other correlations are not statistically significant, implying that changesin optimised parameters cannot generally be linked to the changes in P, T or Q. As for the model performance,the model reproduces the observed runoff satisfactorily, though the runoff is slightly overestimated in wet periods.The Nash-Sutcliffe efficiency coefficient (NSE) ranges from 0.44 to 0.79. Higher NSE values are obtained overwetter periods, what is supported by statistically significant correlation between NSE and flows.Overall, no systematic variations in parameters or in model performance are detected. Parameter variability maytherefore rather be attributed to errors in data or inadequacies in the model structure. Further research is required toexamine the impact of the calibration strategy or model structure on the variability in optimised parameters in time.

Impact of the calibration period on the conceptual rainfall-runoff model parameter estimates

 GraFar - Repository of the Faculty of Civil Engineering

Geophysical Research Abstracts
Vol. 17, EGU2015-968, 2015
EGU General Assembly 2015
© Author(s) 2014. CC Attribution 3.0 License.
Impact of the calibration period on the conceptual rainfall-runoff model
parameter estimates
Andrijana Todorovic and Jasna Plavsic
University of Belgrade, Institute for Hydraulic and Environmental Engineering, Belgrade, Serbia
(andrijana@hikom.grf.bg.ac.rs, 381113218530)
A conceptual rainfall-runoff model is defined by its structure and parameters, which are commonly inferred
through model calibration. Parameter estimates depend on objective function(s), optimisation method, and
calibration period. Model calibration over different periods may result in dissimilar parameter estimates, while
model efficiency decreases outside calibration period. Problem of model (parameter) transferability, which
conditions reliability of hydrologic simulations, has been investigated for decades.
In this paper, dependence of the parameter estimates and model performance on calibration period is anal-
ysed. The main question that is addressed is: are there any changes in optimised parameters and model efficiency
that can be linked to the changes in hydrologic or meteorological variables (flow, precipitation and temperature)?
Conceptual, semi-distributed HBV-light model is calibrated over five-year periods shifted by a year (sliding time
windows). Length of the calibration periods is selected to enable identification of all parameters. One water year
of model warm-up precedes every simulation, which starts with the beginning of a water year. The model is
calibrated using the built-in GAP optimisation algorithm. The objective function used for calibration is composed
of Nash-Sutcliffe coefficient for flows and logarithms of flows, and volumetric error, all of which participate in
the composite objective function with approximately equal weights. Same prior parameter ranges are used in all
simulations. The model is calibrated against flows observed at the Slovac stream gauge on the Kolubara River
in Serbia (records from 1954 to 2013). There are no trends in precipitation nor in flows, however, there is a
statistically significant increasing trend in temperatures at this catchment.
Parameter variability across the calibration periods is quantified in terms of standard deviations of nor-
malised parameters, enabling detection of the most variable parameters. Correlation coefficients among optimised
model parameters and total precipitation P, mean temperature T and mean flow Q are calculated to give an insight
into parameter dependence on the hydrometeorological drivers.
The results reveal high sensitivity of almost all model parameters towards calibration period. The highest
variability is displayed by the refreezing coefficient, water holding capacity, and temperature gradient. The only
statistically significant (decreasing) trend is detected in the evapotranspiration reduction threshold. Statistically
significant correlation is detected between the precipitation gradient and precipitation depth, and between the
time-area histogram base and flows. All other correlations are not statistically significant, implying that changes
in optimised parameters cannot generally be linked to the changes in P, T or Q. As for the model performance,
the model reproduces the observed runoff satisfactorily, though the runoff is slightly overestimated in wet periods.
The Nash-Sutcliffe efficiency coefficient (NSE) ranges from 0.44 to 0.79. Higher NSE values are obtained over
wetter periods, what is supported by statistically significant correlation between NSE and flows.
Overall, no systematic variations in parameters or in model performance are detected. Parameter variability may
therefore rather be attributed to errors in data or inadequacies in the model structure. Further research is required to
examine the impact of the calibration strategy or model structure on the variability in optimised parameters in time.


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http://grafar.grf.bg.ac.rs//bitstream/id/3484/1349.pdf

Impact of the calibration period on the conceptual rainfall-runoff model parameter estimates

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