Abstract:
Accurate representation of precipitation over time and space is vital for hydro-climatic studies. Appropriate selection of gridded
precipitation data (GPD) is important for regions where long-term in situ records are unavailable and gauging stations are sparse.
This study was an attempt to identify the best GPD for the data-poor Amu Darya River basin, a major source of freshwater in
Central Asia. The performance of seven GPDs and 55 precipitation gauge locations was assessed. A novel algorithm, based on
the integration of a compromise programming index (CPI) and a global performance index (GPI) as part of a multi-criteria group
decision-making (MCGDM) method, was employed to evaluate the performance of the GPDs. The CPI and GPI were estimated
using six statistical indices representing the degree of similarity between in situ and GPD properties. The results indicated a great
degree of variability and inconsistency in the performance of the different GPDs. The CPI ranked the Climate Prediction Center
(CPC) precipitation as the best product for 20 out of 55 stations analysed, followed by the Princeton University Global
Meteorological Forcing (PGF) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS). Conversely, GPI
ranked the CPC product the best product for 25 of the stations, followed by PGF and CHRIPS. Integration of CPI and GPI
ranking through MCGDM revealed that the CPC was the best precipitation product for the Amu River basin. The performance of
PGF was also closely aligned with that of CPC
