TY - JOUR
T1 - Evaluation and comparison of precipitation estimates and hydrologic utility of CHIRPS, TRMM 3B42 V7 and PERSIANN-CDR products in various climate regimes
AU - Zhang, Yuefen
AU - Wu, Chuanhao
AU - Yeh, Pat J.-F.
AU - Li, Jianzhu
AU - Hu, Bill X.
AU - Feng, Ping
AU - Jun, Changhyun
N1 - Funding Information:
This research was supported by funding from the National Natural Science Foundation of China (Grant Nos. 51909106 , 51879108 ), the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2020A1515011038 ), the Natural Science Foundation of Guangdong Province, China (Grant No. 2018A030310653 ), The high-level talent project for the “Pearl River Talent Plan” of Guangdong Province (Grant No. 2017GC010397 ), and the Youth Innovative Talents Project for Guangdong Colleges and Universities (Grant No. 2017KQNCX010 ).
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2022/1
Y1 - 2022/1
N2 - Evaluation of satellite-based quantitative precipitation estimates (QPEs) with reliable and independent ground-based measurements is important for both product developers and users. Here, we present a comprehensive evaluation on 3 high-resolution QPEs, namely, the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), the latest non-real-time post-processing version of Tropical Rainfall Measuring Mission (TRMM 3B42 V7), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), in 3 basins with different climates in China. The accuracy of 3 QPEs in reproducing the spatial extent of daily and monthly precipitation (PR) as well as extreme PR indices was evaluated. Two simulation scenarios were utilized to evaluate the efficiency of hydrologic events forecasting of these 3 QPEs quantitatively. The results indicated that the 3 QPEs generally show high accuracy in estimating monthly PR in 3 basins, among which TRMM 3B42 V7 performs best (coefficient of determination R2 < 0.94) followed by CHIRPS (R2 < 0.91). However, all QPEs tend to overestimate daily PR of 3 basins, resulting in low accuracy at the daily scale (R2 < 0.35). For estimation of the extreme PR indices, the 3 QPEs show large differences in the spatio-temporal accuracy, but all with better performance in humid (R2 < 0.86) than arid (R2 < 0.7) basins. Similarly, all 3 QPEs show better performance in simulating streamflow in humid than arid basins. TRMM 3B42 V7 (Nash-Sutcliffe coefficient of efficiency, NSCE < 0.96) and CHIRPS (NSCE < 0.9) perform better in simulating streamflow in humid basins than PERSIANN-CDR (NSCE < 0.88), while PERSIANN-CDR performs best in arid basins (NSCE < 0.67). However, 3 QPEs mostly underestimate peak flow and overestimate soil moisture in all basins, suggesting that the necessity of improving hydrologic efficiency for all of them.
AB - Evaluation of satellite-based quantitative precipitation estimates (QPEs) with reliable and independent ground-based measurements is important for both product developers and users. Here, we present a comprehensive evaluation on 3 high-resolution QPEs, namely, the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), the latest non-real-time post-processing version of Tropical Rainfall Measuring Mission (TRMM 3B42 V7), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), in 3 basins with different climates in China. The accuracy of 3 QPEs in reproducing the spatial extent of daily and monthly precipitation (PR) as well as extreme PR indices was evaluated. Two simulation scenarios were utilized to evaluate the efficiency of hydrologic events forecasting of these 3 QPEs quantitatively. The results indicated that the 3 QPEs generally show high accuracy in estimating monthly PR in 3 basins, among which TRMM 3B42 V7 performs best (coefficient of determination R2 < 0.94) followed by CHIRPS (R2 < 0.91). However, all QPEs tend to overestimate daily PR of 3 basins, resulting in low accuracy at the daily scale (R2 < 0.35). For estimation of the extreme PR indices, the 3 QPEs show large differences in the spatio-temporal accuracy, but all with better performance in humid (R2 < 0.86) than arid (R2 < 0.7) basins. Similarly, all 3 QPEs show better performance in simulating streamflow in humid than arid basins. TRMM 3B42 V7 (Nash-Sutcliffe coefficient of efficiency, NSCE < 0.96) and CHIRPS (NSCE < 0.9) perform better in simulating streamflow in humid basins than PERSIANN-CDR (NSCE < 0.88), while PERSIANN-CDR performs best in arid basins (NSCE < 0.67). However, 3 QPEs mostly underestimate peak flow and overestimate soil moisture in all basins, suggesting that the necessity of improving hydrologic efficiency for all of them.
KW - Accuracy assessment
KW - CREST model
KW - Extreme precipitation indices
KW - Hydrological utility
KW - Satellite QPEs
UR - http://www.scopus.com/inward/record.url?scp=85117227928&partnerID=8YFLogxK
U2 - 10.1016/j.atmosres.2021.105881
DO - 10.1016/j.atmosres.2021.105881
M3 - Article
AN - SCOPUS:85117227928
SN - 0169-8095
VL - 265
JO - Atmospheric Research
JF - Atmospheric Research
M1 - 105881
ER -
