Impact of Hydrological Modellers’ Decisions and Attitude on the Performance of a Calibrated Conceptual Catchment Model: Results from a ‘Modelling Contest’

Abstract

In this study, 17 hydrologists with different experience in hydrological modelling applied the same conceptual catchment model (HBV) to a Greek catchment, using identical data and model code. Calibration was performed manually. Subsequently, the modellers were asked for their experience, their calibration strategy, and whether they enjoyed the exercise. The exercise revealed that there is considerable modellers’ uncertainty even among the experienced modellers. It seemed to be equally important whether the modellers followed a good calibration strategy, and whether they enjoyed modelling. The exercise confirmed previous studies about the benefit of model ensembles: Different combinations of the simulation results (median, mean) outperformed the individual model simulations, while filtering the simulations even improved the quality of the model ensembles. Modellers’ experience, decisions, and attitude, therefore, have an impact on the hydrological model application and should be considered as part of hydrological modelling uncertainty.

Alteration of the Ecohydrological Status of the Intermittent Flow Rivers and Ephemeral Streams due to the Climate Change Impact (Case Study: Tsiknias River)

“…The Sequential Uncertainty Fitting ver.2 (SUFI2) embedded within the SWAT Calibration and Uncertainty Program (SWAT-CUP) software [44][45][46] was employed for auto calibration, using the Nash-Sutcliffe Efficiency (NSE) as the objective function criterion. Nine parameters were selected (Table 3), based on dominant processes in Tsiknias basin reported by previous studies [26,[47][48][49][50]. Previous streamflow measurements from July 2007 to July 2009 showed that the contribution of individual sub-basins, described in this work as Areas 1-5, respectively (Figure 4), were consistent with the size of the drainage area, the slope, and the existence of water springs [32].…”

Operating Small Hydropower Plants in Greece under Intermittent Flow Uncertainty: The Case of Tsiknias River (Lesvos)

“…Maximum values of air temperature, evapotranspiration and wind speed are observed in July-August and the maximum precipitation in December (~130 mm/month). In the summer period, the lowest values of precipitation and relative humidity are observed (Figure 3 The calibration of the flow resulted in a set of final parameter values (see Table 1) that gave much better model efficiency values (NSE = 0.6; KGE = 0.8) when compared with previous studies [19]. The validation performance of the model gave the KGE value of 0.5, which is mostly related to specific weakness of the observed data (observation of flow increase without rain and changing geometry of the location where the logger is installed due to severe erosion).…”

“…The validation performance of the model gave the KGE value of 0.5, which is mostly related to specific weakness of the observed data (observation of flow increase without rain and changing geometry of the location where the logger is installed due to severe erosion). Figure 4 shows the hydrograph of observed vs. simulated flow for the calibration period demonstrating the model's 'fit' and the The calibration of the flow resulted in a set of final parameter values (see Table 1) that gave much better model efficiency values (NSE = 0.6; KGE = 0.8) when compared with previous studies [19].…”

“…The meteorological station in Agia Paraskevi (95 m elevation) operates in the basin, recording precipitation, wind speed, wind direction, solar radiation, cloud cover, humidity, and barometric pressure, with an annual mean rain value of 664 mm. Water level data used in the modeling were sourced from the telemetric station (Prini bridge) installed in 2014 which records flows at 15 min intervals [19].…”