Integrating satellite rainfall estimates with hydrologicalwater balance model: Rainfall-runoff modeling in awash river basin, ethiopia

Girma Berhe Adane; Birtukan Abebe Hirpa; Belay Manjur Gebru; Cholho Song; Woo Kyun Lee

doi:10.3390/w13060800

Integrating satellite rainfall estimates with hydrologicalwater balance model: Rainfall-runoff modeling in awash river basin, ethiopia

Girma Berhe Adane, Birtukan Abebe Hirpa, Belay Manjur Gebru, Cholho Song, Woo Kyun Lee

Research output: Contribution to journal › Article › peer-review

12 Citations (Scopus)

Abstract

Hydrologic models play an indispensable role in managing the scarce water resources of a region, and in developing countries, the availability and distribution of data are challenging. This research aimed to integrate and compare the satellite rainfall products, namely, Tropical Rainfall Measuring Mission (TRMM 3B43v7) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), with a GR2M hydrological water balance model over a diversified terrain of the Awash River Basin in Ethiopia. Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), coefficient of determination (R2), and root mean square error (RMSE) and Pearson correlation coefficient (PCC) were used to evaluate the satellite rainfall products and hydrologic model performances of the basin. The satellite rainfall estimations of both products showed a higher PCC (above 0.86) with areal observed rainfall in the Uplands, the Western highlands, and the Lower sub-basins. However, it was weakly associated in the Upper valley and the Eastern catchments of the basin ranging from 0.45 to 0.65. The findings of the assimilated satellite rainfall products with the GR2M model exhibited that 80% of the calibrated and 60% of the validated watersheds in a basin had lower magnitude of PBIAS (<±10), which resulted in better accuracy in flow simulation. The poor performance with higher PBIAS (≥±25) of the GR2M model was observed only in the Melka Kuntire (TRMM 3B43v7 and PERSIANN-CDR), Mojo (PERSIANN-CDR), Metehara (in all rainfall data sets), and Kessem (TRMM 3B43v7) watersheds. Therefore, integrating these satellite rainfall data, particularly in the data-scarce basin, with hydrological data, generally appeared to be useful. However, validation with the ground observed data is required for effective water resources planning and management in a basin. Furthermore, it is recommended to make bias corrections for watersheds with poorlyww performing satellite rainfall products of higher PBIAS before assimilating with the hydrologic model.

Original language	English
Article number	800
Journal	Water (Switzerland)
Volume	13
Issue number	6
DOIs	https://doi.org/10.3390/w13060800
Publication status	Published - 2021 Mar 2

Bibliographical note

Funding Information:
Acknowledgments: The authors gratefully acknowledge the support of the OJEong Resilience Institute (OJERI) at Korea University, the National Meteorological Agency of Ethiopia, Ministry of Water, Irrigation and Electricity (MoWIE) of Ethiopia, the Global Runoff Data Center (GRDC), National Meteorological Agency of Ethiopia (NMA), NASA Earth data (https://giovanni.gsfc.nasa. gov/giovanni/ accessed on 5 March 2020), and Center for Hydrometeorology and Remote Sensing ( https://chrsdata.eng.uci.edu/ accessed on 15 March 2020). This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Publisher Copyright:
© 2021 by the authors.

Keywords

Awash River Basin
GR2M Hydrologic Model
PERSIANN-CDR
TRMM 3B43v7

ASJC Scopus subject areas

Biochemistry
Geography, Planning and Development
Aquatic Science
Water Science and Technology

Access to Document

10.3390/w13060800

Cite this

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title = "Integrating satellite rainfall estimates with hydrologicalwater balance model: Rainfall-runoff modeling in awash river basin, ethiopia",

abstract = "Hydrologic models play an indispensable role in managing the scarce water resources of a region, and in developing countries, the availability and distribution of data are challenging. This research aimed to integrate and compare the satellite rainfall products, namely, Tropical Rainfall Measuring Mission (TRMM 3B43v7) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), with a GR2M hydrological water balance model over a diversified terrain of the Awash River Basin in Ethiopia. Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), coefficient of determination (R2), and root mean square error (RMSE) and Pearson correlation coefficient (PCC) were used to evaluate the satellite rainfall products and hydrologic model performances of the basin. The satellite rainfall estimations of both products showed a higher PCC (above 0.86) with areal observed rainfall in the Uplands, the Western highlands, and the Lower sub-basins. However, it was weakly associated in the Upper valley and the Eastern catchments of the basin ranging from 0.45 to 0.65. The findings of the assimilated satellite rainfall products with the GR2M model exhibited that 80% of the calibrated and 60% of the validated watersheds in a basin had lower magnitude of PBIAS (<±10), which resulted in better accuracy in flow simulation. The poor performance with higher PBIAS (≥±25) of the GR2M model was observed only in the Melka Kuntire (TRMM 3B43v7 and PERSIANN-CDR), Mojo (PERSIANN-CDR), Metehara (in all rainfall data sets), and Kessem (TRMM 3B43v7) watersheds. Therefore, integrating these satellite rainfall data, particularly in the data-scarce basin, with hydrological data, generally appeared to be useful. However, validation with the ground observed data is required for effective water resources planning and management in a basin. Furthermore, it is recommended to make bias corrections for watersheds with poorlyww performing satellite rainfall products of higher PBIAS before assimilating with the hydrologic model.",

keywords = "Awash River Basin, GR2M Hydrologic Model, PERSIANN-CDR, TRMM 3B43v7",

author = "Adane, {Girma Berhe} and Hirpa, {Birtukan Abebe} and Gebru, {Belay Manjur} and Cholho Song and Lee, {Woo Kyun}",

note = "Funding Information: Acknowledgments: The authors gratefully acknowledge the support of the OJEong Resilience Institute (OJERI) at Korea University, the National Meteorological Agency of Ethiopia, Ministry of Water, Irrigation and Electricity (MoWIE) of Ethiopia, the Global Runoff Data Center (GRDC), National Meteorological Agency of Ethiopia (NMA), NASA Earth data (https://giovanni.gsfc.nasa. gov/giovanni/ accessed on 5 March 2020), and Center for Hydrometeorology and Remote Sensing ( https://chrsdata.eng.uci.edu/ accessed on 15 March 2020). This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Publisher Copyright: {\textcopyright} 2021 by the authors.",

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AU - Adane, Girma Berhe

AU - Hirpa, Birtukan Abebe

AU - Gebru, Belay Manjur

AU - Song, Cholho

AU - Lee, Woo Kyun

N1 - Funding Information: Acknowledgments: The authors gratefully acknowledge the support of the OJEong Resilience Institute (OJERI) at Korea University, the National Meteorological Agency of Ethiopia, Ministry of Water, Irrigation and Electricity (MoWIE) of Ethiopia, the Global Runoff Data Center (GRDC), National Meteorological Agency of Ethiopia (NMA), NASA Earth data (https://giovanni.gsfc.nasa. gov/giovanni/ accessed on 5 March 2020), and Center for Hydrometeorology and Remote Sensing ( https://chrsdata.eng.uci.edu/ accessed on 15 March 2020). This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Publisher Copyright: © 2021 by the authors.

PY - 2021/3/2

Y1 - 2021/3/2

N2 - Hydrologic models play an indispensable role in managing the scarce water resources of a region, and in developing countries, the availability and distribution of data are challenging. This research aimed to integrate and compare the satellite rainfall products, namely, Tropical Rainfall Measuring Mission (TRMM 3B43v7) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), with a GR2M hydrological water balance model over a diversified terrain of the Awash River Basin in Ethiopia. Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), coefficient of determination (R2), and root mean square error (RMSE) and Pearson correlation coefficient (PCC) were used to evaluate the satellite rainfall products and hydrologic model performances of the basin. The satellite rainfall estimations of both products showed a higher PCC (above 0.86) with areal observed rainfall in the Uplands, the Western highlands, and the Lower sub-basins. However, it was weakly associated in the Upper valley and the Eastern catchments of the basin ranging from 0.45 to 0.65. The findings of the assimilated satellite rainfall products with the GR2M model exhibited that 80% of the calibrated and 60% of the validated watersheds in a basin had lower magnitude of PBIAS (<±10), which resulted in better accuracy in flow simulation. The poor performance with higher PBIAS (≥±25) of the GR2M model was observed only in the Melka Kuntire (TRMM 3B43v7 and PERSIANN-CDR), Mojo (PERSIANN-CDR), Metehara (in all rainfall data sets), and Kessem (TRMM 3B43v7) watersheds. Therefore, integrating these satellite rainfall data, particularly in the data-scarce basin, with hydrological data, generally appeared to be useful. However, validation with the ground observed data is required for effective water resources planning and management in a basin. Furthermore, it is recommended to make bias corrections for watersheds with poorlyww performing satellite rainfall products of higher PBIAS before assimilating with the hydrologic model.

AB - Hydrologic models play an indispensable role in managing the scarce water resources of a region, and in developing countries, the availability and distribution of data are challenging. This research aimed to integrate and compare the satellite rainfall products, namely, Tropical Rainfall Measuring Mission (TRMM 3B43v7) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), with a GR2M hydrological water balance model over a diversified terrain of the Awash River Basin in Ethiopia. Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), coefficient of determination (R2), and root mean square error (RMSE) and Pearson correlation coefficient (PCC) were used to evaluate the satellite rainfall products and hydrologic model performances of the basin. The satellite rainfall estimations of both products showed a higher PCC (above 0.86) with areal observed rainfall in the Uplands, the Western highlands, and the Lower sub-basins. However, it was weakly associated in the Upper valley and the Eastern catchments of the basin ranging from 0.45 to 0.65. The findings of the assimilated satellite rainfall products with the GR2M model exhibited that 80% of the calibrated and 60% of the validated watersheds in a basin had lower magnitude of PBIAS (<±10), which resulted in better accuracy in flow simulation. The poor performance with higher PBIAS (≥±25) of the GR2M model was observed only in the Melka Kuntire (TRMM 3B43v7 and PERSIANN-CDR), Mojo (PERSIANN-CDR), Metehara (in all rainfall data sets), and Kessem (TRMM 3B43v7) watersheds. Therefore, integrating these satellite rainfall data, particularly in the data-scarce basin, with hydrological data, generally appeared to be useful. However, validation with the ground observed data is required for effective water resources planning and management in a basin. Furthermore, it is recommended to make bias corrections for watersheds with poorlyww performing satellite rainfall products of higher PBIAS before assimilating with the hydrologic model.

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Integrating satellite rainfall estimates with hydrologicalwater balance model: Rainfall-runoff modeling in awash river basin, ethiopia

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Keywords

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