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Correction published on 16 April 2018, see Atmosphere 2018, 9(4), 148.
Open AccessArticle

Utilization of Global Precipitation Datasets in Data Limited Regions: A Case Study of Kilombero Valley, Tanzania

1
Department of Physical Geography, Stockholm University, 10691 Stockholm, Sweden
2
Department of Geography, Hydrology and Climate, University of Zurich, 8006 Zurich, Switzerland
3
The Nature Conservancy, Delmont, NJ 08314, USA
*
Author to whom correspondence should be addressed.
Atmosphere 2017, 8(12), 246; https://doi.org/10.3390/atmos8120246
Received: 3 October 2017 / Revised: 16 November 2017 / Accepted: 27 November 2017 / Published: 7 December 2017
(This article belongs to the Special Issue Precipitation Variability and Change in Africa)
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. View Full-Text
Keywords: bias correction; quantile mapping; satellite; reanalysis; interpolated; precipitation; HBV; Kilombero; Tanzania; Eastern Africa bias correction; quantile mapping; satellite; reanalysis; interpolated; precipitation; HBV; Kilombero; Tanzania; Eastern Africa
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MDPI and ACS Style

Koutsouris, A.J.; Seibert, J.; Lyon, S.W. Utilization of Global Precipitation Datasets in Data Limited Regions: A Case Study of Kilombero Valley, Tanzania. Atmosphere 2017, 8, 246.

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