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Water 2018, 10(4), 499; https://doi.org/10.3390/w10040499

Uncertainty of Rainfall Products: Impact on Modelling Household Nutrition from Rain-Fed Agriculture in Southern Africa

1
Institute for Social-Ecological Research (ISOE), Hamburger Allee 45, 60598 Frankfurt/Main, Germany
2
Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL), 28 Robert Mugabe Avenue, 9000 Windhoek, Namibia
3
Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Senckenberganlage 25, 60325 Frankfurt/Main, Germany
4
Faculty of Engineering, Senate House, University of Bristol, Tyndall Avenue, Bristol BS8 1TH, UK
5
Institute of Hydrology and Water Resources Management (IWW), Leibniz Universität Hannover, Appelstraße 9a, 30167 Hannover, Germany
*
Author to whom correspondence should be addressed.
Received: 19 February 2018 / Revised: 9 April 2018 / Accepted: 9 April 2018 / Published: 18 April 2018
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Abstract

Good quality data on precipitation are a prerequisite for applications like short-term weather forecasts, medium-term humanitarian assistance, and long-term climate modelling. In Sub-Saharan Africa, however, the meteorological station networks are frequently insufficient, as in the Cuvelai-Basin in Namibia and Angola. This paper analyses six rainfall products (ARC2.0, CHIRPS2.0, CRU-TS3.23, GPCCv7, PERSIANN-CDR, and TAMSAT) with respect to their performance in a crop model (APSIM) to obtain nutritional scores of a household’s requirements for dietary energy and further macronutrients. All products were calibrated to an observed time series using Quantile Mapping. The crop model output was compared against official yield data. The results show that the products (i) reproduce well the Basin’s spatial patterns, and (ii) temporally agree to station records (r = 0.84). However, differences exist in absolute annual rainfall (range: 154 mm), rainfall intensities, dry spell duration, rainy day counts, and the rainy season onset. Though calibration aligns key characteristics, the remaining differences lead to varying crop model results. While the model well reproduces official yield data using the observed rainfall time series (r = 0.52), the products’ results are heterogeneous (e.g., CHIRPS: r = 0.18). Overall, 97% of a household’s dietary energy demand is met. The study emphasizes the importance of considering the differences among multiple rainfall products when ground measurements are scarce. View Full-Text
Keywords: subsistence agriculture; food security; model uncertainty; remote sensing; satellite rainfall estimates subsistence agriculture; food security; model uncertainty; remote sensing; satellite rainfall estimates
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Luetkemeier, R.; Stein, L.; Drees, L.; Müller, H.; Liehr, S. Uncertainty of Rainfall Products: Impact on Modelling Household Nutrition from Rain-Fed Agriculture in Southern Africa. Water 2018, 10, 499.

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