Tunisia has a long history of coping with water scarcity, and the quantification of climate change impacts on runoff is important for future water management. A major requirement for such studies is an estimation of potential evapotranspiration (PET), which is challenging as many regions often lack the observational data needed for physically based PET equations. In this study, different PET estimation approaches were used to study the impact of PET estimation on discharge projections for catchments in Northern Tunisia. Discharge was simulated for five catchments using three rainfall-runoff models (RRMs): HBV, GR4 and IHACRES. A general differential split sample test (GDSST) was used for an RRM robustness evaluation based on subperiods with contrasting climatic conditions for the 1970–2000 period. Three cases with varying PET were considered: (1) daily calculated PET, (2) long-term daily mean PET with the same values for calibration and validation periods (calculated over the calibration period) and (3) long-term daily mean PET varying between calibration and validation periods (calculated over the calibration and validation period separately). Over the historical period, the comparison between cases 1 and 3 showed little impact of reduced PET information on the RRM performance and robustness. The comparison of cases 2 and 3 indicated a limited impact of varying PET between calibration and validation on the RRM results. The impact of varying levels of PET information on hydrological projections was also analysed over two future 30-year periods: mid-term period (2040–2070) and long-term period (2070–2100), with two representative concentration pathway scenarios (RCPs 4.5 and 8.5), by comparing cases 1 and 2. The projected discharge with constant PET (case 2) was generally lower than the projected discharge with variable PET (case 1) but the difference in volume change did not exceed 9% for both the time period and the RCP scenario considered. While PET slightly increased under the different climate change scenarios, actual evapotranspiration (AET) was found to decrease. These opposite trends of PET and AET can be attributed to the projected decrease in precipitation. Overall, our results demonstrate that discharge, in semi-arid regions like Northern Tunisia, is not sensitive to PET estimates since AET is mainly controlled by the availability of soil moisture. This finding is useful for performing studies of climate change impact on hydrological cycles in arid regions, as our study shows that simple PET estimation is a valid approach for such studies.

We propose to evaluate the impact of rainfall–runoff model (RRM) structural uncertainty on climate model evaluation, performed within a process-oriented framework using the RRM. Structural uncertainty is assessed with an ensemble approach using three conceptual RRMs (HBV, IHACRES and GR4J). We evaluate daily precipitation and temperature from 11 regional climate models forced by five general circulation models (GCM–RCMs), issued from EURO-CORDEX. The assessment was performed over the reference period (1970–2000) for five catchments situated in northern Tunisia. Seventeen discharge performance indexes were used to explore the representation of hydrological processes. The three RRMs performed well over the reference period, with Nash–Sutcliffe efficiency values ranging from 0.70 to 0.90 and bias close to 0%. The ranking of GCM–RCMs according to hydrological performance indexes is more meaningful before the bias correction, which considerably reduces the differences between GCM- and RCM-driven hydrological simulations. Our results illustrate a strong similarity between the different RRMs in terms of raw GCM–RCM performances over the reference period for the majority of performance indexes, in spite of their different model structures. This proves that the structural uncertainty induced by RRMs does not affect GCM–RCM evaluation and ranking, which contributes to consolidate the RRM as a standard tool for climate model evaluation.