Abstract
This study explored the potential for bias correction of global precipitation datasets (GPD) to support streamflow simulation for water resource management in data limited regions. Two catchments, 580 km2 and 2530 km2, in the Kilombero Valley of central Tanzania were considered as case studies to explore three GPD bias correction methods: quantile mapping (QM), daily percentages (DP) and a model based (ModB) bias correction. The GPDs considered included two satellite rainfall products, three reanalysis products and three interpolated observed data products. The rainfall-runoff model HBV was used to simulate streamflow in the two catchments using (1) observed rain gauge data; (2) the original GPDs and (3) the bias-corrected GPDs as input. Results showed that applying QM to bias correction based on limited observed data tends to aggravate streamflow simulations relative to not bias correcting GPDs. This is likely due to a potential lack of representativeness of a single rain gauge observation at the scale of a hydrological catchment for these catchments. The results also indicate that there may be potential benefits in combining streamflow and rain gauge data to bias correct GPDs during the model calibration process within a hydrological modeling framework.
Highlights
Accessible and reliable precipitation observations are limited in large parts of the world
Streamflow simulations calibrated based on model based (ModB) or Quantile Mapping (QM) + ModB bias corrected precipitation input generally increased the R2 values of the simulated streamflow compared to the simulations using non-bias corrected precipitation input
Streamflow simulations calibrated based on QM bias corrected precipitation input, on the other hand, generally lowered the R2 values of the simulated streamflow compared to the simulations using non-bias corrected precipitation input
Summary
Accessible and reliable precipitation observations are limited in large parts of the world. While central for hydroclimatological studies and development of water management schemes, precipitation observations are often based on discontinuous measurement records and have tendencies to contain discrepancies [1] This deficiency in spatial and temporal coverage has motivated several attempts to create global precipitation datasets (GPDs) which could potentially be more temporally and spatially complete and render more accurate precipitation estimates relative to the sparse data available via direct observations. Several studies have previously evaluated the quality of various GPDs in Eastern Africa both based on comparisons to precipitation estimates [4,5,6] and on their ability to produce accurate predictions from hydrological modelling [7,8,9]. With regards to the latter case, GPDs have been successfully implemented in hydrological modelling over a range of climatic zones, spatial scales
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