TY - JOUR
T1 - Evaluation and comparison of satellite-derived estimates of rainfall in the diverse climate and terrain of central and northeastern ethiopia
AU - Adane, Girma Berhe
AU - Hirpa, Birtukan Abebe
AU - Lim, Chul Hee
AU - Lee, Woo Kyun
N1 - Funding Information:
The authors gratefully acknowledge the support of the OJEong Resilience Institute (OJERI) at Korea University, National Meteorological Agency of Ethiopia (NMA), NASA Earth data (https://giovanni.gsfc.nasa.gov/giovanni/ accessed on 5 March 2020), Center for Hydrometeorology and Remote Sensing (https://chrsdata.eng.uci.edu/ accessed on 15 March 2020), JAXA Global Rainfall Watch (https://sharaku.eorc.jaxa.jp/GSMaP/ accessed on 24 February 2021) for providing us with rainfall datasets.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/4/1
Y1 - 2021/4/1
N2 - Understanding rainfall processes as the main driver of the hydrological cycle is important for formulating future water management strategies; however, rainfall data availability is challenging for countries such as Ethiopia. This study aims to evaluate and compare the satellite rainfall estimates (SREs) derived from tropical rainfall measuring mission (TRMM 3B43v7), rainfall estimation from remotely sensed information using artificial neural networks.climate data record (PERSIANNCDR), merged satellitegauge rainfall estimate (IMERG), and the Global Satellite Mapping of Precipitation (GSMaP) with groundobserved data over the varied terrain of hydrologically diverse central and northeastern parts of Ethiopia.Awash River Basin (ARB). Areal comparisons were made between SREs and observed rainfall using various categorical indices and statistical evaluation criteria, and a nonparametric Mann.Kendall (MK) trend test was analyzed. The monthly weighted observed rainfall exhibited relatively comparable results with SREs, except for the annual peak rainfall shifts noted in all SREs. The PERSIANNCDR products showed a decreasing trend in rainfall at elevations greater than 2250 m above sea level in a river basin. This demonstrates that elevation and rainfall regimes may affect satellite rainfall data. On the basis of modified Kling.Gupta Efficiency, the SREs from IMERG v06, TRMM 3B43v7, and PERSIANNCDR performed well in descending order over the ARB. However, GSMaP showed poor performance except in the upland subbasin. A high frequency of bias, which led to an overestimation of SREs, was exhibited in TRMM 3B43v7 and PERSIANNCDR products in the eastern and lower basins. Furthermore, the MK test results of SREs showed that none of the subbasins exhibited a monotonic trend at 5% significance level except the GSMap rainfall in the upland subbasin. In ARB, except for the GSMaP, all SREs can be used as alternative options for rainfall frequency, flood, and droughtmonitoring studies. However, some may require bias corrections to improve the data quality.
AB - Understanding rainfall processes as the main driver of the hydrological cycle is important for formulating future water management strategies; however, rainfall data availability is challenging for countries such as Ethiopia. This study aims to evaluate and compare the satellite rainfall estimates (SREs) derived from tropical rainfall measuring mission (TRMM 3B43v7), rainfall estimation from remotely sensed information using artificial neural networks.climate data record (PERSIANNCDR), merged satellitegauge rainfall estimate (IMERG), and the Global Satellite Mapping of Precipitation (GSMaP) with groundobserved data over the varied terrain of hydrologically diverse central and northeastern parts of Ethiopia.Awash River Basin (ARB). Areal comparisons were made between SREs and observed rainfall using various categorical indices and statistical evaluation criteria, and a nonparametric Mann.Kendall (MK) trend test was analyzed. The monthly weighted observed rainfall exhibited relatively comparable results with SREs, except for the annual peak rainfall shifts noted in all SREs. The PERSIANNCDR products showed a decreasing trend in rainfall at elevations greater than 2250 m above sea level in a river basin. This demonstrates that elevation and rainfall regimes may affect satellite rainfall data. On the basis of modified Kling.Gupta Efficiency, the SREs from IMERG v06, TRMM 3B43v7, and PERSIANNCDR performed well in descending order over the ARB. However, GSMaP showed poor performance except in the upland subbasin. A high frequency of bias, which led to an overestimation of SREs, was exhibited in TRMM 3B43v7 and PERSIANNCDR products in the eastern and lower basins. Furthermore, the MK test results of SREs showed that none of the subbasins exhibited a monotonic trend at 5% significance level except the GSMap rainfall in the upland subbasin. In ARB, except for the GSMaP, all SREs can be used as alternative options for rainfall frequency, flood, and droughtmonitoring studies. However, some may require bias corrections to improve the data quality.
KW - Areal rainfall comparison
KW - Awash river basin
KW - Ethiopia
KW - Rainfall-elevation relationship
KW - Satellite-derived rainfall estimate
UR - http://www.scopus.com/inward/record.url?scp=85103376332&partnerID=8YFLogxK
U2 - 10.3390/rs13071275
DO - 10.3390/rs13071275
M3 - Article
AN - SCOPUS:85103376332
SN - 2072-4292
VL - 13
JO - Remote Sensing
JF - Remote Sensing
IS - 7
M1 - 1275
ER -
