Nopu headwater catchment

KLEINHANS [2003] has successfully undertaken a simulation of the Nopu catchment (2.3 km²) with WASIM-ETH for the period 01.11.2001-19.02.2003 with an achieved model efficiency of R²=0.85. A daily temporal resolution and spatial grid of 30m*30m was applied for the model construction. The catchment is equipped with three gauging stations, two climate stations and a total of six rain gauges. Within the model construction of the Gumbasa River catchment this well observed Nopu headwater catchment was defined as a subcatchment, but the available period where discharge data is available for both models (01.09.2002-19.02.2003) was not included into the calibration process of the Gumbasa River model. Therefore the comparison of observed versus simulated discharge allows to evaluate the performance of the constructed model without the calibration influence of this particular gauging site.

Though in comparison to the Gumbasa catchment model the constructed model by KLEINHANS [2004] was equipped with a far smaller grid size and a denser net of meteorological stations, the impact of grid size and station density could be analysed.

Figure 6.16 shows the observed versus the simulated discharge with the simulated baseflow for the Nopu headwater catchment for the period 01.09.2002-19.02.2003.

MODEL APPLICATION: GUMBASA RIVER CASE STUDY 94

Figure 6. 16: Observed versus simulated discharge with simulated baseflow for the Nopu subbasin (daily resolution, 500m*500m grid) for the period (01.09.2002-19.02.2003); model efficiency R²=0.84.

The achieved model performance for the considered time period with a model efficiency of R²=0.84 is surprisingly very good and can be compared to the model performance achieved by KLEINHANS [2004]. A grid size of 30m*30m results for a 2.4 km² catchment in about 2667 grid cells, whereas a grid size of 500m*500m results in a total of about 9.6 grid cells. The comparison of different grid sizes shows that apparently for the simulation of specific discharge of the total headwater catchment the loss of spatial pattern by a factor of 277 does not influence the achieved model efficiency. The Nopu catchment was taken as a leitcatena for the whole Gumbasa River catchment, which means that the actual soil physical properties are best represented at the Nopu catchment itself. Therefore with respect to the model efficiency of other sub-basins, for example the Takkelemo subbasin, good model efficiencies could be expected for the Nopu catchment. Surprisingly the calibration of the adjustable parameter k_rec for the PHAs with discharge data of other sub-basins lead to satisfactory results for the uncalibrated Nopu catchment. The results of the Nopu catchment demonstrate that the classification and parameterisation PHA’s as soil units are valid for the entire catchment. Moreover the derived optimum parameter set, which is defined by the minimum objective function is also valid for ungauged sub-catchments.

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6.5.4 Validation

Similar to the Takkelemo test catchment the same validation method was also applied for the Gumbasa River catchment. Again the validation was split into the pure validation period (01.09.2003-31.08.2004), and the total period of simulation (01.09.2002-31.08.2004). The Gumbasa River gauging site was excluded from the validation process, because due to technical problems no continuous hydrograph was available. Table 6.12-6.14 list the complete calibration and validation results for all considered statistical measures (coefficient of efficiency R², index of agreement d, ratio root mean square error, mean square error ∆ RMSE / MSE) of the Danau Lindu, Sopu and Takkelemo gauging sites simulation results on a daily and weekly resolution. Compared to the calibration period, the daily and weekly coefficients of efficiency describe a moderate decline for all gauging sites for the split sample validation period. For the whole validation period R² reaches acceptable results.

Simultaneously the degree of outliers increases on a daily resolution, which is indicated by the rise of the ∆ RMSE / MSE ratio. Since it has been already discussed that for the adaptation of the factors that control the calculation of areal precipitation one year of calibration period is not adequate the instability of areal precipitation is reflected by the decline of model efficiency due to the existence of outliers. Here the weekly resolution does not seem to be an appropriate measure to analyse the overall model performance, the existence of outliers is obviously smoothed in some cases by a weekly mean value. The index of agreement d shows stable results for the considered validation periods, indicating an overall satisfying performance of the hydrological model.

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Table 6. 12: List of the coefficient of efficiency R² for Danau Lindu, Sopu and Takkelemo sub-catchment on a daily and weekly resolution for the calibration, split sample and validation-whole period.

Table 6.12: List of the index of agreement d for Danau Lindu, Sopu and Takkelemo sub-catchment on a daily and weekly resolution for the calibration, validation-split sample and validation-whole period.

Table 6. 13: List of ∆ RMSE / MSE for Danau Lindu, Sopu and Takkelemo sub-catchment on a daily and weekly resolution for the calibration, validation-split sample and validation-whole period.

Danau Lindu Sopu Takkelemo

R²

daily weekly daily weekly daily weekly

calibration period 0.83 0.86 0.79 0.77 0.58 0.77

validation period - split sample 0.61 0.62 0.58 0.44 0.41 0.50 validation period - whole 0.74 0.76 0.69 0.68 0.45 0.61

Danau Lindu Sopu Takkelemo

d

daily weekly daily weekly daily weekly

calibration period 0.96 0.96 0.94 0.93 0.85 0.93

validation period - split sample 0.87 0.93 0.80 0.93 0.76 0.89 validation period - whole 0.93 0.94 0.87 0.91 0.82 0.89

Danau Lindu Sopu Takkelemo

∆ RMSE / MSE

daily weekly daily weekly daily weekly

calibration period 0.10 0.57 0.15 0.88 0.23 0.70

validation period - split sample 0.14 0.12 0.21 1.2 0.28 0.14 validation period - whole 0.13 0.8 0.17 0.91 0.25 0.93

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