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Utilization of Global Precipitation Datasets in Data Limited Regions: A Case Study of Kilombero Valley, Tanzania
Stockholm University, Faculty of Science, Department of Physical Geography.
Stockholm University, Faculty of Science, Department of Physical Geography. The Nature Conservancy, USA.
Number of Authors: 32017 (English)In: Atmosphere, ISSN 2073-4433, E-ISSN 2073-4433, Vol. 8, no 12, article id 246Article in journal (Refereed) Published
Abstract [en]

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 km(2) and 2530 km(2), 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.

Place, publisher, year, edition, pages
2017. Vol. 8, no 12, article id 246
Keywords [en]
bias correction, quantile mapping, satellite, reanalysis, interpolated, precipitation, HBV, Kilombero, Tanzania, Eastern Africa
National Category
Earth and Related Environmental Sciences
Identifiers
URN: urn:nbn:se:su:diva-152515DOI: 10.3390/atmos8120246ISI: 000419179200016OAI: oai:DiVA.org:su-152515DiVA, id: diva2:1180923
Available from: 2018-02-07 Created: 2018-02-07 Last updated: 2018-02-07Bibliographically approved

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