Assessment of Three Long-Term Gridded Climate Products for Hydro-Climatic Simulations in Tropical River Basins

Abstract

Gridded climate products (GCPs) provide a potential source for representing weather in remote, poor quality or short-term observation regions. The accuracy of three long-term GCPs (Asian Precipitation-Highly-Resolved Observational Data Integration towards Evaluation of Water Resources: APHRODITE, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record: PERSIANN-CDR and National Centers for Environmental Prediction Climate Forecast System Reanalysis: NCEP-CFSR) was analyzed for the Kelantan River Basin (KRB) and Johor River Basin (JRB) in Malaysia from 1983 to 2007. Then, these GCPs were used as inputs into calibrated Soil and Water Assessment Tool (SWAT) models, to assess their capability in simulating streamflow. The results show that the APHRODITE data performed the best in precipitation estimation, followed by the PERSIANN-CDR and NCEP-CFSR datasets. The NCEP-CFSR daily maximum temperature data exhibited a better correlation than the minimum temperature data. For streamflow simulations, the APHRODITE data resulted in strong results for both basins, while the NCEP-CFSR data showed unsatisfactory performance. In contrast, the PERSIANN-CDR data showed acceptable representation of observed streamflow in the KRB, but failed to track the JRB observed streamflow. The combination of the APHRODITE precipitation and NCEP-CFSR temperature data resulted in accurate streamflow simulations. The APHRODITE and PERSIANN-CDR data often underestimated the extreme precipitation and streamflow, while the NCEP-CFSR data produced dramatic overestimations. Therefore, a direct application of NCEP-CFSR data should be avoided in this region. We recommend the use of APHRODITE precipitation and NCEP-CFSR temperature data in modeling of Malaysian water resources.

Monitoring Rainfall Patterns in the Southern Amazon with PERSIANN-CDR Data: Long-Term Characteristics and Trends

“…Nonetheless, the probability density functions and cumulative distribution functions show that the estimates are well centered (density peak around 0 and cumulative distribution functions characterized by a well-marked logarithmic shape). Finally, our results are consistent with those of other studies on the validation of PERSIANN-CDR data in other regions of the world [52,54,56] and confirm the interest of testing this dataset to explore potential indices of climate change in the southern Amazon. …”

“…Again in China, Yang et al [53] compared four satellite precipitation products and concluded that the performance of PERSIANN-CDR data is stable over the year, especially because of monthly gauge adjustment. In Malaysia, Tanet al [54] judged the performance of PERSIANN-CDR to be moderate as it appeared to slightly underestimate extreme precipitation events. Concerning the Amazon, comparisons of PERSIANN (which is a different product to PERSIANN-CDR) and rain gauge data are available [23].…”

“…We then conducted a quantitative analysis focused on the southern Amazon and based on the comparison of the rainfall measured at rain gauges with the corresponding rainfall estimates based on PERSIANN-DCR data. This comparison was based on continuous and categorical statistical indices as proposed by Tan et al [54], Miao et al [52] and Ringard et al [56].…”

A Guideline for Successful Calibration and Uncertainty Analysis for Soil and Water Assessment: A Review of Papers from the 2016 International SWAT Conference

“…24-Tan et al [46] investigated the accuracy of three long-term gridded data records: APHRODITE, PERSIANN-CDR, and NCEP-CFSR. They concluded that the APHRODITE and PERSIANN-CDR data often underestimated extreme precipitation and streamflow, while the NCEP-CFSR data produced dramatic overestimations.…”

Applications of Satellite-Based Rainfall Estimates in Flood Inundation Modeling—A Case Study in Mundeni Aru River Basin, Sri Lanka

“…In addition, the RRI model has been utilized with GSMaP to simulate the 2008 flood event in Pakistan [20]. In the meantime, SRE products have been compared with each other and their characteristics and accuracy have also been evaluated [21][22][23]. However, evaluation among multiple SREs in terms of flood inundation reproducibility has not been done yet.…”