Evaluation of Multiple Climate Data Sources for Managing Environmental Resources in East Africa

Gebrechorkos, Solomon H., Hülsmann, Stephan and Bernhofer, Christian, (2018). Evaluation of Multiple Climate Data Sources for Managing Environmental Resources in East Africa. Hydrology and Earth System Sciences, 22(8), 4547-4564

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  • Sub-type Journal article
    Author Gebrechorkos, Solomon H.
    Hülsmann, Stephan
    Bernhofer, Christian
    Title Evaluation of Multiple Climate Data Sources for Managing Environmental Resources in East Africa
    Appearing in Hydrology and Earth System Sciences
    Volume 22
    Issue No. 8
    Publication Date 2018-08-28
    Place of Publication Göttingen
    Publisher Copernicus Publications on behalf of the European Geosciences Union
    Start page 4547
    End page 4564
    Language eng
    Abstract Managing environmental resources under conditions of climate change and extreme climate events remains among the most challenging research tasks in the field of sustainable development. A particular challenge in many regions such as East Africa is often the lack of sufficiently long-term and spatially representative observed climate data. To overcome this data challenge we used a combination of accessible data sources based on station data, earth observations by remote sensing, and regional climate models. The accuracy of the Africa Rainfall Climatology version 2.0 (ARC2), Climate Hazards Group InfraRed Precipitation (CHIRP), CHIRP with Station data (CHIRPS), Observational-Reanalysis Hybrid (ORH), and regional climate models (RCMs) are evaluated against station data obtained from the respective national weather services and international databases. We did so by performing a comparison in three ways: point to pixel, point to area grid cell average, and stations' average to area grid cell average over 21 regions of East Africa: 17 in Ethiopia, 2 in Kenya, and 2 in Tanzania. We found that the latter method provides better correlation and significantly reduces biases and errors. The correlations were analysed at daily, dekadal (10 days), and monthly resolution for rainfall and maximum and minimum temperature (Tmax and Tmin) covering the period of 1983–2005. At a daily timescale, CHIRPS, followed by ARC2 and CHIRP, is the best performing rainfall product compared to ORH, individual RCMs (I-RCM), and RCMs' mean (RCMs). CHIRPS captures the daily rainfall characteristics well, such as average daily rainfall, amount of wet periods, and total rainfall. Compared to CHIRPS, ARC2 showed higher underestimation of the total (−30%) and daily (−14%) rainfall. CHIRP, on the other hand, showed higher underestimation of the average daily rainfall (−53%) and duration of dry periods (−29%). Overall, the evaluation revealed that in terms of multiple statistical measures used on daily, dekadal, and monthly timescales, CHIRPS, CHIRP, and ARC2 are the best performing rainfall products, while ORH, I-RCM, and RCMs are the worst performing products. For Tmax and Tmin, ORH was identified as the most suitable product compared to I-RCM and RCMs. Our results indicate that CHIRPS (rainfall) and ORH (Tmax and Tmin), with higher spatial resolution, should be the preferential data sources to be used for climate change and hydrological studies in areas of East Africa where station data are not accessible.
    UNBIS Thesaurus REMOTE SENSING
    RAINWATER
    Climate Change
    Copyright Holder The Authors
    Copyright Year 2018
    Copyright type Creative commons
    DOI 10.5194/hess-22-4547-2018
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    Created: Thu, 04 Oct 2018, 20:54:57 JST by Claudia Matthias on behalf of UNU FLORES