Performance of bias-correction schemes for CMORPH rainfall estimates in the Zambezi River basin
<p>Satellite rainfall estimates (SREs) are prone to bias as they are indirect derivatives of the visible, infrared, and/or microwave cloud properties, and hence SREs need correction. We evaluate the influence of elevation and distance from large-scale open water bodies on bias for Climate Pred...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2019-07-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://www.hydrol-earth-syst-sci.net/23/2915/2019/hess-23-2915-2019.pdf |
| Summary: | <p>Satellite rainfall estimates (SREs) are prone to bias as they are indirect
derivatives of the visible, infrared, and/or microwave cloud properties,
and hence SREs need correction. We evaluate the influence of elevation and
distance from large-scale open water bodies on bias for Climate Prediction
Center-MORPHing (CMORPH) rainfall estimates in the Zambezi basin. The
effectiveness of five linear/non-linear and time–space-variant/-invariant
bias-correction schemes was evaluated for daily rainfall estimates and
climatic seasonality. The schemes used are spatio-temporal bias (STB),
elevation zone bias (EZ), power transform (PT), distribution transformation
(DT), and quantile mapping based on an empirical distribution (QME). We
used daily time series (1998–2013) from 60 gauge stations and CMORPH SREs
for the Zambezi basin. To evaluate the effectiveness of the bias-correction
schemes spatial and temporal cross-validation was applied based on eight
stations and on the 1998–1999 CMORPH time series, respectively. For
correction, STB and EZ schemes proved to be more effective in removing bias.
STB improved the correlation coefficient and Nash–Sutcliffe efficiency by 50 % and 53 %, respectively, and reduced the root mean squared difference
and relative bias by 25 % and 33 %, respectively. Paired <span class="inline-formula"><i>t</i></span> tests showed
that there is no significant difference (<span class="inline-formula"><i>p</i> <i><</i> 0.05</span>) in the daily
means of CMORPH against gauge rainfall after bias correction. ANOVA post hoc
tests revealed that the STB and EZ bias-correction schemes are preferable.
Bias is highest for very light rainfall (<span class="inline-formula"><i><</i> 2.5</span> mm d<span class="inline-formula"><sup>−1</sup></span>), for
which most effective bias reduction is shown, in particular for the wet
season. Similar findings are shown through quantile–quantile (<span class="inline-formula"><i>q</i></span>–<span class="inline-formula"><i>q</i></span>) plots.
The spatial cross-validation approach revealed that most bias-correction schemes removed bias by <span class="inline-formula"><i>></i> 28</span> %. The temporal
cross-validation approach showed effectiveness of the bias-correction
schemes. Taylor diagrams show that station elevation has an influence on
CMORPH performance. Effects of distance <span class="inline-formula"><i>></i> 10</span> km from large-scale
open water bodies are minimal, whereas effects at shorter distances are
indicated but are not conclusive for a lack of rain gauges. Findings of this study
show the importance of applying bias correction to SREs.</p> |
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| ISSN: | 1027-5606 1607-7938 |