**4. Model simulation results and uncertainty analysis**

SWAT was calibrated against observed data for gauging station 1 ka31 for the period 1970– 1971. Calibration results yielded satisfactory results given the data scarcity. CE and R<sup>2</sup> values of 0.54 and 0.62 were achieved for the calibrated period. The P-factor (% of measured data bracketed by 95% prediction uncertainty) was 0.58 and 0.21 for the full range and behavioral simulations, respectively. The R factors for the full range and behavioral parameters were 1.91 and 0.36, respectively. These results confirm quite large uncertainty of the simulated discharge due to the large equifinality in parameters and reliability of input data (precipitation and daily evaporation data). **Table 4** shows a summary of model performance for the calibrations and a comparison between all parameter sets (full range) and behavioral parameter sets. In presenting results, the following performance measures were used;


value of 0.57 was obtained. SOL\_K (2) is the saturated soil hydraulic conductivity (mm h−1). In this study, a SOL\_K value of 1.66 mm h−1 was used. This parameter relates to water flow rate to

The **GWQMN** is the threshold water level in the shallow aquifer for return flow to occur (mm). The ground water flow to the main channel is allowed only when the depth of water in the shallow aquifer is equal to or greater than the threshold depth of water in the shallow aquifer required for the return flow to occur. An optimum value of 2071.38 (mm) was obtained. The obtained value for the mean slope steepness of the basin (**SLSUBBSN**) is 0.32, indicating that the sub-basin is influenced by low to moderate slopes and has implications for the runoff generation process. The optimum value for the maximum potential **LAI** is 4.82. The value corresponds to the MODIS data which indicates LAI for the Little Ruaha catchment ranges from low to moderate values (**Figure 5**). **CANMIX** represents the maximum canopy area, and an optimum value of 5.95 mm was obtained. This value corresponds to the leaf area index indicated in (**Figure 5**). The Manning roughness coefficient "n" for channel flow (**CH\_N (2)**) is the parameter that influences channel roughness, an optimum value of 0.06 was obtained.

SWAT was calibrated against observed data for gauging station 1 ka31 for the period 1970– 1971. Calibration results yielded satisfactory results given the data scarcity. CE and R<sup>2</sup>

of 0.54 and 0.62 were achieved for the calibrated period. The P-factor (% of measured data bracketed by 95% prediction uncertainty) was 0.58 and 0.21 for the full range and behavioral simulations, respectively. The R factors for the full range and behavioral parameters were

values

the hydraulic gradient and is a measure of the rate of water movement through the soil.

**Figure 5.** Spatial variations in leaf area index within the Little Ruaha basin.

60 Achievements and Challenges of Integrated River Basin Management

**4. Model simulation results and uncertainty analysis**


**Table 4.** Summary of performance statistics for the best simulation.

**Figure 6.** Calibration at 1 ka31-Mawande (95PPU for full range simulations).

Uncertainty analysis was implemented using the SUFI-2 algorithm. **Figures 6** and **7** show the results of the daily flow uncertainty analysis carried out in the sub-basin for the full range and behavioral parameter sets respectively. The shaded area represents the 95% predictive uncertainty (95PPU), whereas the blue lines correspond to the observed discharges and the red lines correspond to the simulated flow at the sub-basin outlet. For the full range simulations (**Figure 6**) it was found that the observations fall within the lower and upper 95% prediction uncertainty in high and moderate flow but with large uncertainty. **Figure 6** shows that the 95% prediction uncertainty of behavioral simulations (CE ≥ 45%) does not bracket the observed flow, only 15% of the data were bracketed, indicating that some processes are not well represented in the model. The prediction limits obtained with SUFI-2 are highly dependent on the threshold selected to separate behavioral from non-behavioral parameter sets. It is also important to note that in SUFI-2 parameter uncertainty is presented as a uniform distribution in the final parameter range, while parameter interactions are ignored and contribute to the large equifinality observed in these results.

show reasonable performance in the hydrologic simulations but with large uncertainties. The model performance statistics achieved in this study are like the ones achieved in other studies in Tanzania [10], but one point that should be noted is that, after calibration, parameters should have physical meaning. With the large equifinality in the parameter sets, it was not possible to get identifiable parameter sets, and it is hard to say that behavioral parameters sets are representatives of the basin's behavior. This observation highlights the challenges associated with implementing SWAT for water resources use in Tanzania and other developing countries.

Basin Scale Performance of a Distributed Rainfall-Runoff Model Using Uncertainty...

http://dx.doi.org/10.5772/intechopen.78539

63

The SWAT2009 was applied to the Little Ruaha sub-basin. The model was set up using a coarse spatial dataset, interpolated rainfall data, and a single dominant HRU. Sensitivity analysis results showed that ALPHA\_BF, CN2, SURLAG, REVAPMN, CH\_K2, GWQMN, SLSUBBSN, BLAI, and CANMX are the most sensitive parameters in the basin. The Little Ruaha drainage system falls within the African land surface where the infiltration of the topsoil is good, and interflow is an important part of the total River discharge. The soils in the upper part are deeply weathered and have a good soil structure. This explains the sensitivity of the surface and subsurface parameters. The drainage is dominated by steep topography, and this explains the sensitivity of the mean slope length of the basin. Sensitivity analyses enabled the most sensitive model parameters to be identified for further calibration, but this does not mean that sensitive parameters will also be identifiable. Out of the 27 parameters, 20 were identified as sensitive, but the interactions

Final calibration parameters for the Little Ruaha Drainage System are presented in **Table 4**,

since the behavioral parameter sets are within the non-behavioral parameter sets. The results show reasonable performance in the hydrologic simulations but with large uncertainties. The model performance statistics achieved in this study are like the ones achieved in other studies in Tanzania [10], but one point that should be noted is that, after calibration, parameters should have physical meaning. With the large equifinality in the parameter sets, it was not possible to get identifiable parameter sets, and it is hard to say that behavioral parameters sets are representatives of the basin's behavior. Ref. [13] reviewed the use of the SWAT model in the Nile Basin countries, including Tanzania, and found that the model produced satisfactory or good results, but almost all the case studies reviewed gave results based on the wrong process representation. These results were problematic because when different studies in the same or similar sub-basins are compared, they give different results. In peer-reviewed papers [9, 10] some documented parameter values were not realistic, but this information was not reported in those papers [11]. This observation highlights the challenges associated with implementing SWAT for water resources use in Tanzania and other developing countries.

Even though the model gave satisfactory results based on the performance measures, a critical analysis of **Figures 6** and **7** suggests a different picture. **Figure 6** showed that there is good agreement between observed and simulated flow but associated with very large uncertainty in high to moderate flows, and the uncertainty band does not bracket the low flows. Running

of 0.62 for the best simulation regardless of the parameter set. This is

between these parameters were not considered during the sensitivity analysis.

**5. Discussions and conclusions**

with a CE of 0.54 and R<sup>2</sup>

Final calibration parameters for the Little Ruaha Drainage System with a Coefficient of Evaluation (CE) of 0.54 and R<sup>2</sup> of 0.62 for the best simulation regardless of the parameter set. The results

**Figure 7.** Calibration at 1 ka31-Mawande (95PPU for behavioral simulations).

show reasonable performance in the hydrologic simulations but with large uncertainties. The model performance statistics achieved in this study are like the ones achieved in other studies in Tanzania [10], but one point that should be noted is that, after calibration, parameters should have physical meaning. With the large equifinality in the parameter sets, it was not possible to get identifiable parameter sets, and it is hard to say that behavioral parameters sets are representatives of the basin's behavior. This observation highlights the challenges associated with implementing SWAT for water resources use in Tanzania and other developing countries.
