Verification of Regional Deterministic Precipitation Analysis Products Using Snow Data Assimilation for Application in Meteorological Network Assessment in Sparsely Gauged Nordic Basins

Abstract

AbstractSparse precipitation information can result in uncertainties in hydrological modeling practices. Precipitation observation network augmentation is one way to reduce the uncertainty. Meanwhile, in basins with snowpack-dominated hydrology, in the absence of a high-density precipitation observation network, assimilation of in situ and remotely sensed measurements of snowpack state variables can also provide the possibility to reduce flow estimation uncertainty. Similarly, assimilation of existing precipitation observations into gridded numerical precipitation products can alleviate the adverse effects of missing information in poorly instrumented basins. In Canada, the Regional Deterministic Precipitation Analysis (RDPA) data from the Canadian Precipitation Analysis (CaPA) system have been increasingly applied for flow estimation in sparsely gauged Nordic basins. Moreover, CaPA-RDPA data have also been applied to establish observational priorities for augmenting precipitation observation networks. However, the accuracy of the assimilated data should be validated before being applicable in observation network assessment. The assimilation of snowpack state variables has proven to significantly improve streamflow estimates, and therefore, it can provide the benchmark against which the impact of assimilated precipitation data on streamflow simulation can be compared. Therefore, this study introduces a parsimonious framework for performing a proxy validation of the precipitation-assimilated products through the application of snow assimilation in physically based hydrologic models. This framework is demonstrated to assess the observation networks in three boreal basins in Yukon, Canada. The results indicate that in most basins, the gridded analysis products generally enjoyed the level of accuracy required for accurate flow simulation and therefore were applied in the meteorological network assessment in those cases.