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For the baseline scenario a parameter-uncertainty analysis is conducted, while for the future scenarios additionally effects of climate change scenario uncertainty have been considered.

5.7.1 Uncertainty in Baseline Scenario

In this study, uncertainties of the conceptual model were diminished, by developing SWAT to SWAT-MAROC, reflecting process understanding of a semi-arid mountainous hydrological system. Uncertainties of the model structure have been addressed in section 5.1 and uncertainties of the input data have been addressed in section 5.3 and during the validation (section 5.5). Both subjects have been addressed rather qualitatively, but some uncertainties can be converted into parameter values. For example a wide array of recharge rates of the deep aquifer will be assumed to account for a lack of process studies and consequently weak knowledge of water distribution in complex aquifer systems. On the other hand the standard deviation of the soil depth distribution (input data) can be easily converted into parameter ranges and can therefore be considered in a parameter uncertainty analysis.

Therefore the parameter uncertainty analysis conducted in the following section partly accounts for other sources of uncertainty. These uncertainties are quantified using the SUFI2-algorithm (Abbaspour et al. 2004) as introduced in section 4.1.6.3. As this study aims to provide dependable estimates of surface water availability on a monthly to annual basis, for time periods until 2049, the focus of the uncertainty analysis is set on the runoff/rainfall-ratio and the irrigation module.

Consequently the effects of seven parameters have been addressed in this study (see Table 5-28).

The Curve Number (CN2), as an empirical parameter used for approximating the amount of direct runoff from a rainfall event, is always open to question (Hawkins et al. 2009). CN2 has been calibrated, but many intermediate- or short-term effects on land use and soils, such as drought and changing stocking densities as well as effects of varying storm intensities etc. can be covered by moderate variations of the Curve Number only.

The revaporation coefficient (GW_REVAP) is the conceptual representation of the complex process of evapotranspiration from groundwater. As pointed out earlier (section 5.3.5) plausible ranges can be estimated, but exact values can neither be measured, nor be assumed to be constant.

GW_REVAP has been calibrated, but allowing variations within the bounds outlined earlier is imperative.

Groundwater is represented via conceptual storage modules within SWAT-MAROC. The distribution of percolating water among the two aquifers (RCHRG_DA) is done according to literature values (section 5.3.5). The distribution has a minor effect on total water availability, but

due to different recession coefficients effects on seasonal water availability, hence irrigation volumes can be assumed. Plausible ranges have been discussed earlier.

Soil depth (SOL_Z) has the most pronounced effects on discharge composition and discharge volume among all soil parameters. Though soil depth can be assumed to be constant, even over time horizons covered by the scenarios, it can be measured only locally and regionalization is difficult, hence it is the most uncertain parameter from the soil database (Klose 2008a). Therefore possible deviations have to be considered in the uncertainty analysis. The depth up to which

Table 5-28: Parameters and parameter ranges considered in the Uncertainty Analysis (see Appendix 3 for satisfied depends strongly on the conceptual ESCO-factor. This factor cannot be measured and has been adapted during calibration. Within the irrigation module two factors qualify for uncertainty analysis. The fraction of streamflow that is available for irrigation (IRR_FRAC) and the irrigation efficiency (IRR_EFF). IRR_FRAC has been adapted during calibration; the irrigation efficiency has been set according to literature values (section 5.3.3.2).

Both parameters are held constant by the model (due to a lack of data), but given the variability of irrigated areas and crops on the one hand and the different management strategies dependent on water availability on the other hand, parameter variations have to be accounted for by the uncertainty analysis.

Three runs of the SUFI2-algorythm have been performed, with 500 model parameterizations each.

In the final run (parameter ranges given in Table 5-28) a P-value of 0.56 (i.e. modeled data brackets 56% of measured data) and an R-Value of 0.26 (i.e. average uncertainty range is 26% of the standard deviation of measured data) have been obtained (for details of the algorithm see section 4.1.6.3). Given the uncertainties not covered by SUFI2 (e.g. conceptual model, precipitation, discharge; see previous sections), these results are satisfying. Given our inability to represent exactly how the system works in a hydrological model, there will always be different models and equifinal parameter sets that represent equally well an observed variable (e.g. discharge). Such equally acceptable models are called behavioral (Beven 2008). Within the final parameter range a set of 20 behavioral models has been chosen according to their Nash-Sutcliffe Efficiency (NSE). Since the 20 behavioral models on average reproduce well the measured annual discharge (deviation of the average model discharge to the measured discharge:

1.7%), no further selection criterion has been used. Mean annual discharge of the selected models

ranges from 272 Mm³ to 424 Mm³ and NSE ranges from 0.82 to 0.89 (see Appendix 11). The results from all these models are evaluated and the spread in results is assumed to reflect the uncertainty in the hydrological system, i.e. the “real” systems response is assumed to be bracketed by the spread of results from the behavioral models.

5.7.2 Uncertainty in Future Scenarios

Uncertainties in scenario analysis are considered in this study as follows: Two emission scenarios have been used (IPCC SRES A1b and B2). These scenarios envelope an array of possible future developments based on low to medium emissions (see section 4.1.5). Three ensemble members with varying initial conditions for each model were generated. These ensembles are considered to reflect model uncertainties of the Climate Model. Furthermore three downscaling approaches for the climate data have been used (see section 5.6.1) and 21 behavioral parameterizations of the hydrological model have been used (see section 5.7.1).

This results in 378 model realizations for the period 2000-2029 and 2020-2049:

2 Different sources of uncertainty can now be quantified by averaging all model results of e.g.

each downscaling approach. The remaining spread in results can then be attributed to the downscaling approach. For the SNRm (section 4.2.2.1), the average scenario result compared to the baseline scenario result is the signal, while the spread in scenario results is the noise.

Nevertheless some sources of uncertainty have not been covered by this approach. The recent growth rate of atmospheric carbon dioxide is larger than assumed in all IPCC scenarios (Canadell et al. 2007), but this shortcoming can only be dealt with after revision of the official emission scenarios in upcoming IPCC reports (Moss et al. 2010). Both scenarios have been calculated with the model REMO (nested in the ECHAM5/MPI-OM, see section 5.6.2). The comparison of different RCM/GCM-data is desirable, but the warming and decrease in precipitation in Northern Africa has been identified as a robust trend within 15 different GCMs (Bates et al. 2008). Climate simulations with REMO are so far the only ones available for Western Africa that have a spatial resolution of 0.5° and that include land cover changes as a driving force. These features are available in regional models of Europe or Northern America, but in the study area no equivalent models exist. Therefore the effect of structural uncertainties within the climate model had not been assessed.

For the reservoirs water balance model three time series of annual inflows (1^st tercile, median and 3^rd tercile) have been used as input. Since these time series feature the exact variability as

in the baseline scenario each has been converted into five time series that start in the 1, 7, 13, 19 and 25 year and goes back to year one, when the end of the record is reached. By this approach a reasonable interannual variability is assured, while the impression of projecting future dry or wet periods can be avoided, as those cannot be derived from the climate change scenarios.