This study modified the BTOPMC (Block-wise TOPMODEL with the Muskingum-Cunge routing method) distributed hydrological model to make it applicable to semi-arid regions by introducing an adjustment coefficient for infiltration capacity of the soil surface, and then applied it to two catchments above the dams in the Karun River basin, located in semi-arid mountain ranges in Iran. The application results indicated that the introduced modification improved the model performance for simulating flood peaks generated by infiltration excess overland runoff at a daily time scale. The modified BTOPMC was found to fulfil the need to reproduce important signatures of basin hydrology for water resource development, such as annual runoff, seasonal runoff, low flows and flood flows. However, it was also very clear that effective model use was significantly constrained by the scarcity of ground-gauged precipitation data. Considerable efforts to improve the precipitation data acquisition should precede water resource development planning.Key words BTOPMC; water resource development; infiltration excess overland runoff; snowpack and snowmelt; data scarcity; semi-arid; Karun River basin; Iran Défis de l'analyse hydrologique pour le développement des ressources en eau dans les régions montagneuses semi-arides : étude de cas en Iran Résumé Dans cette étude, nous avons modifié le modèle hydrologique distribué BTOPMC (Block-wise TOPMODEL with the Muskingum-Cunge routing method -TOPMODEL par bloc avec méthode de routage Muskingum-Cunge) pour le rendre applicable à des régions semi-arides, en introduisant un coefficient d'ajustement de la capacité d'infiltration de la surface du sol. Nous l'avons ensuite appliqué à deux bassins versants situés en amont de barrages sur le bassin de la rivière Karun, dans des zones montagneuses semiarides d'Iran. Les résultats de cette application ont indiqué que la modification introduite a amélioré la performance du modèle pour simuler les pointes de crue générées par ruissellement de surface par dépassement de la capacité d'infiltration, au pas de temps journalier. Nous avons trouvé que le modèle BTOPMC modifié répond au besoin de reproduction des signatures importantes de l'hydrologie du bassin pour le développement des ressources en eau, telles que les écoulements annuels ou saisonniers, les débits d'étiage ou de crue. Cependant, il est apparu également très clairement que l'utilisation concrète du modèle a été considérablement contrainte par la rareté des données de précipitations issues de postes au sol. Des efforts considérables devraient être faits pour améliorer l'acquisition de données de précipitations préalablement à la planification du développement de la ressource en eau.Mots clefs BTOPMC ; développement des ressources en eau ; ruissellement de surface par dépassement de la capacité d'infiltration ; manteau neigeux et fonte nivale ; rareté des données ; semi-aride ; bassin de la rivière Karun ; Iran

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Abstract:This study assesses future changes in low precipitation patterns over land around the globe under the Special Report on Emissions Scenarios A1B scenario. We use global precipitation data sets derived by the super-high-resolution Atmospheric General Circulation Model of the Japan Meteorological Agency and Meteorological Research Institute (MRI-AGCM3.1S) and Atmospheric Ocean coupled General Circulation Models compiled in the phase 3 of the Coupled Model Intercomparison Project (CMIP3 AOGCMs). The low precipitation patterns refer to the temporal and spatial distribution of annual minimum of accumulated precipitation over a given time interval using occurrence probability and observation windows in unfixed seasons. As for the low precipitation patterns, this study assesses low precipitation quantiles in six probability levels approximated by the Weibull distribution using annual minima of monthly accumulated precipitation over 1-, 3-, 6-and 12-month time intervals. Also, low precipitation occurrence season were assessed from the centroid of the frequency distribution of the ending months with annual minima of the low precipitation over the four time intervals. The model capability of reproducing current low precipitation patterns was examined in reference to the global precipitation observation data set of VASClimO, from which MRI-AGCM3.1S showed the best performance among all models. The MRI-AGCM3.1S projections, as well as the multi-model ensemble mean calculated over 16 GCMs, indicate that 0.1 probability low precipitation quantiles in 3-and 6-month intervals would decrease by 10 to 50% in Mexico, southern Brazil, southern Argentina, Mediterranean area and southern Africa with high model consistency. The projected shifts in low precipitation occurrence seasons were of concern; however, they show a considerable degree of uncertainty with low model consistency.

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