Influence of hydro-meteorological data spatial aggregation on streamflow modelling

Data availability is important for virtually any purpose in hydrology. While some parts of the world continue to be under-monitored, other areas are experiencing an increased availability of high-resolution data. The use of the highest available resolution has always been preferred and many efforts have been made to maximize the information content of data and thus improve its predictive power and reduce the costs of maintenance of hydrometric sensor networks. In the light of ever-increasing data resolution, however, it is important to assess the added value of using the highest resolution available. In this study we present an assessment of the relative importance of hydro-meteorological data resolution for hydrological modelling. We used a case study with high-resolution data availability to investigate the influence of using models calibrated with different levels of spatially aggregated meteorological input data to estimate streamflow for different periods and at different locations. We found site specific variations, but model parameterizations calibrated using sub-catchment specific meteorological input data tended to produce better streamflow estimates, with model efficiency values being up to 0.35 efficiency units higher than those calibrated with catchment averaged meteorological data. We also found that basin characteristics other than catchment area have little effect on the performance of model parameterizations applied in different locations than the calibration site. Finally, we found that using an increased number of discharge data locations has a larger impact on model calibration efficiency than using spatially specific meteorological data. The results of this study contribute to improve the knowledge on assessing data needs for water management in terms of adequate data type and level of spatial aggregation