Impact of rain gauge density on hydrological simulation in a small mountain catchment in southern Ecuador

Abstract

In places with high spatio-temporal rainfall variability, as mountain regions, input data could be a large source of uncertainty in hydrological modelling. Here we evaluate the impact of rainfall estimation in runoff modelling in a small páramo catchment located in the Zhurucay Ecohydrological Observatory (7.53 km2) in the Ecuadorian Andes, using a network of 12 rain gauges. First, 8 structures of the HBV-light semi-distributed model were analyzed in order to select the best model structure to represent the observed runoff and its sub-flow components. Then, we used five rainfall scenarios to evaluate the impact of spatial rainfall estimation in model performance and parameters. Finally, we explored how a model calibrated with far-from-perfect rainfall estimation would perform using new improved rainfall data. Results show that while all model structures were able to represent the overall runoff, the standard model structure outperforms the others for simulating sub-flow components. Model performance improved by increasing the quality of spatial rainfall estimation. Three rain gauges distributed in the upper, middle and lower catchment were able to represent properly the mean areal rainfall and this was translated to a good runoff simulation. Finally, improved rainfall data enhanced the runoff simulation from a model calibrated with rainfall far-from-perfect. These results confirm that in mountain regions model uncertainty is much related to spatial rainfall and, therefore, to the number and location of rain gauges.