**5. Discussions and conclusions**

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

Final calibration parameters for the Little Ruaha Drainage System with a Coefficient of Evaluation

of 0.62 for the best simulation regardless of the parameter set. The results

contribute to the large equifinality observed in these results.

62 Achievements and Challenges of Integrated River Basin Management

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

(CE) of 0.54 and R<sup>2</sup>

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 between these parameters were not considered during the sensitivity analysis.

Final calibration parameters for the Little Ruaha Drainage System are presented in **Table 4**, with a CE of 0.54 and R<sup>2</sup> of 0.62 for the best simulation regardless of the parameter set. This is 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 the model with the behavioral parameter sets shows a reduction in P-factor and R-factor values (**Table 4**). **Figure 7** shows that while the uncertainty band has been reduced, the model is undersimulating both high and low flows, and does not bracket the moderate to low flows. This could be associated with input data uncertainties, or some processes are not well represented in the model. ALPHA\_BF was the most sensitive parameter identified through the sensitivity analysis, and apart from a lack of observed ground water information, difficulties of SWAT in simulating ground water flow [12] might have contributed to the negative aspects of these results.

Developing an understanding of the hydrological processes that occur in a system is critical for the effective assessment and management of water resources. However, the lack of observational data represents a serious challenge to understanding that is difficult to resolve, especially when there are so many factors that contribute to hydrological variation and change. Scientists and practitioners within the southern African region are attempting to develop the most effective methods for water resources assessment that will contribute to effective water resources management. This study has employed the uncertainty approach for setting up the rainfall-runoff model for the Little Ruaha River basin and the assessment of uncertainties associated with simulations of naturally hydrological responses. The aim was to explore uncertainties in modeling hydrological responses and to establish a behavioral model that can be used for water resources management and future decision making. This approach has addressed a range of key issues in hydrological modeling; these include the uncertainties associated with input data, parameter equifinality and the importance of realistic uncertainty

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

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

65

representation using constraints.

Address all correspondence to: madaka.tumbo@gmail.com

Institute of Resource Assessment, University of Dar es Salaam, Tanzania

[1] Abbaspour KC, Yang J, Maximov I, Siber. Modelling hydrology and water quality in the pre-alpine Thur watershed using SWAT. Journal of Hydrology. 2007;**333**:413-430

[2] Abbaspour KC, Johnson CA, Van Genuchten MT. Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone Journal.

[3] Abbaspour KC. Calibration of hydrological models: When is the model calibrated? In: Zerger A, Argent RM, editors. International Congress on Modelling and Simulation. New Zealand: Modelling and Simulation Society of Australia and New Zealand; 2005.

[4] Abbaspour KC. SWAT-CUP, SWAT Calibration and Uncertainty Program: A User Manual. Swiss Federal Institute of Aquatic Sciences and Technology. 2007. p. 84

[5] Abbaspour KC. SWAT-CUP2: SWAT Calibration and Uncertainty Programs. A User

[6] CCKK. Water master plans for Iringa, Ruvuma and Mbeya regions. Hydrogeology. Vol. 8.

Manual. Swiss Federal Institute of Aquatic Science and Technology; 2008

Carl Bro, Cowiconsult, Kampsax, Krüger. 1982

**Author details**

**References**

Madaka Harold Tumbo

2004;**3**(4):1340-1352

pp. 2449-12455

This study assessed model uncertainty using a combined uncertainty approach that assumes all sources of uncertainty have been considered within the model. In such an approach it is hard to separate the sources of uncertainty, and therefore a follow-up analysis of uncertainty should be undertaken by determining how erroneous input data influence model results. Although not assessed within the research questions of this study, the results highlight potential uncertainties in the input rainfall and evaporation data. The use of these data was justified and used in the simulations but could potentially have influenced the overall model performance and uncertainties that cannot be explained.

The uncertainty analysis was carried out using 20 sensitive parameters, which is a large number considering the interactions between them. Therefore, some less sensitive parameters should be fixed and allow only the most sensitive parameters to vary. This will reduce the effect of parameter interactions and hence the none-uniqueness problem. Although this model has been shown to generate reasonable results, it is worthwhile to consider the challenges associated with setting up a distributed model. In this research, large-scale spatial datasets have been used, and a homogenous model was assumed because the spatial data resolution was insufficient to represent large numbers of hydrological response units. However, even when the resolution was sufficient, attribute values for most of the parameters are lacking. Because of difficulties associated with parameter representation across spatial scales, it is better to use a homogenous set up because biases and uncertainty can be added by the modeler when trying to parameterize values within the hydrological response unit at a size larger than its coverage. The overall conclusions from this assessment include;


Developing an understanding of the hydrological processes that occur in a system is critical for the effective assessment and management of water resources. However, the lack of observational data represents a serious challenge to understanding that is difficult to resolve, especially when there are so many factors that contribute to hydrological variation and change. Scientists and practitioners within the southern African region are attempting to develop the most effective methods for water resources assessment that will contribute to effective water resources management. This study has employed the uncertainty approach for setting up the rainfall-runoff model for the Little Ruaha River basin and the assessment of uncertainties associated with simulations of naturally hydrological responses. The aim was to explore uncertainties in modeling hydrological responses and to establish a behavioral model that can be used for water resources management and future decision making. This approach has addressed a range of key issues in hydrological modeling; these include the uncertainties associated with input data, parameter equifinality and the importance of realistic uncertainty representation using constraints.
