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.
Highlights
Precipitation, as the immediate source of water, is the most critical input variable of the water balance
The third sub-section focuses on the results of the Agricultural Production Systems Simulator (APSIM) model
The derived nutritional scores are presented with special emphasis on the fulfilment of a household’s dietary energy demand and its range of uncertainty
Summary
Precipitation, as the immediate source of water, is the most critical input variable of the water balance. For that reason, it is used in a wide array of models estimating hydrological variables, ranging from runoff and discharge, through drought intensity, down to impacts of climate change. Water 2018, 10, 499 precipitation in modelling, any inaccuracies in the input data will have a strong impact on estimated results and, can directly compromise management decisions [2]. Rainfall estimates derived from satellite-borne sensors or radar stations, provide a promising alternative in supplying near real-time precipitation data for large areas and long time series at fine spatial and temporal resolutions [4,5]. A number of studies have evaluated a range of rainfall products, in particular their application in hydrological [7,8,9,10]
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