Performance of bias-correction schemes for CMORPH rainfall estimates in the Zambezi River basin

Webster Gumindoga, Tom H.M. Rientjes, Alemseged Tamiru Haile, Hodson Makurira, Paolo Reggiani

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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 crossvalidation 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 t tests showed that there is no significant difference (p>0:05) 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 (>2:5mmd1), for which most effective bias reduction is shown, in particular for the wet season. Similar findings are shown through quantile quantile (q q) plots. The spatial cross-validation approach revealed that most bias-correction schemes removed bias by <28 %. 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 <10 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.

Original languageEnglish
Pages (from-to)2915-2938
Number of pages24
JournalHydrology and earth system sciences
Volume23
Issue number7
DOIs
Publication statusPublished - 12 Jul 2019

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river basin
rainfall
gauge
open water
time series
climate prediction
basin
wet season
seasonality
transform
diagram
station

Keywords

  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-GOLD

Cite this

Gumindoga, Webster ; Rientjes, Tom H.M. ; Tamiru Haile, Alemseged ; Makurira, Hodson ; Reggiani, Paolo. / Performance of bias-correction schemes for CMORPH rainfall estimates in the Zambezi River basin. In: Hydrology and earth system sciences. 2019 ; Vol. 23, No. 7. pp. 2915-2938.
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abstract = "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 crossvalidation 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 t tests showed that there is no significant difference (p>0:05) 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 (>2:5mmd1), for which most effective bias reduction is shown, in particular for the wet season. Similar findings are shown through quantile quantile (q q) plots. The spatial cross-validation approach revealed that most bias-correction schemes removed bias by <28 {\%}. 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 <10 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.",
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Performance of bias-correction schemes for CMORPH rainfall estimates in the Zambezi River basin. / Gumindoga, Webster; Rientjes, Tom H.M.; Tamiru Haile, Alemseged; Makurira, Hodson; Reggiani, Paolo.

In: Hydrology and earth system sciences, Vol. 23, No. 7, 12.07.2019, p. 2915-2938.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Performance of bias-correction schemes for CMORPH rainfall estimates in the Zambezi River basin

AU - Gumindoga, Webster

AU - Rientjes, Tom H.M.

AU - Tamiru Haile, Alemseged

AU - Makurira, Hodson

AU - Reggiani, Paolo

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N2 - 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 crossvalidation 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 t tests showed that there is no significant difference (p>0:05) 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 (>2:5mmd1), for which most effective bias reduction is shown, in particular for the wet season. Similar findings are shown through quantile quantile (q q) plots. The spatial cross-validation approach revealed that most bias-correction schemes removed bias by <28 %. 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 <10 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.

AB - 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 crossvalidation 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 t tests showed that there is no significant difference (p>0:05) 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 (>2:5mmd1), for which most effective bias reduction is shown, in particular for the wet season. Similar findings are shown through quantile quantile (q q) plots. The spatial cross-validation approach revealed that most bias-correction schemes removed bias by <28 %. 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 <10 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.

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