Research papersImpact of mesoscale spatial variability of climatic inputs and parameters on the hydrological response

- A distributed RR model is applied over a French mesoscale catchment.
- A spatial split-sample test is applied to assess two types of spatial variability.
- The impact of the spatial variability is assessed over 4 hydrological signatures.
- The major improvement arises from the spatial variability of the climatic inputs.
- The spatial variability of the model parameters are of secondary importance.

The spatial variability of the hydrological response is controlled by the interaction of two spatial variabilities: climatic forcing and physiographic characteristics. This paper investigates their impact on streamflow modelling throughout a large catchment. They are assessed by successively refining the spatial information of climatic inputs and model parameters, going from the lumped to the sub-catchment or mesh scale. The analysis is conducted according to a spatial split-sample test. For each resolution, the distributed model first calibrated on calibration station(s) is then evaluated in light of this(these) station(s) in addition to validation stations previously treated as ungauged. For that purpose, simulated and observed streamflows are compared focusing on four hydrological signatures: daily runoff, daily regime, flood and low flow.The above framework is applied at the daily time step over a French basin, the Loire catchment at Gien covering 35,707 km2, whose streamflow data are available at the catchment outlet and 105 interior points. The results show that spatially distributed climatic forcing greatly enhances the four signatures as the resolution increases, until the right rainfall volume is provided for each simulation point. By taking advantage of the interior calibration stations to spatialise the parameters, the model's performance progresses again but to a lesser extent. Finally, the catchment's hydrological behaviour is mainly driven by the spatial variability of the climatic inputs (around 90% over the validation stations), while that of the parameters seems to be of second order.