Evaluation of Various Precipitation Products Using Ground-Based Discharge Observation at the Nujiang River Basin, China

Abstract

Precipitation observation and prediction is difficult in many high elevation regions due to the complex terrain and the lack of in situ observations for comparison. The Nujiang River (upper and middle Salween River) basin in the Tibetan Plateau is no exception. Because of this shortcoming, we propose the use of gauge-observed discharge time series at the basin outlet (e.g., Jiayuqiao hydrological station) to evaluate the performance of four different precipitation products (e.g., satellite-based products and reanalysis datasets). A physically-based distributed cryosphere hydrological model with coupled snow and frozen soil physics was adopted to transfer the basin-wide gridded precipitation into the basin-outlet discharges. First, we corrected and evaluated the four precipitation products. A correlation relationship was established between each precipitation product and the available (limited) gauge rainfall within different elevation zones, and then used to correct the four precipitation products in the study basin. Secondly, a distributed cryosphere hydrological model was used to simulate the basin-outlet runoff driven by each corrected precipitation product. The results indicated that modern-era retrospective analysis for Research and Applications, version 2 (MERRA2) precipitation has better performance in the upper Nujiang River basin relative to the other precipitation products based on comparisons of observed and simulated runoff.

Evaluation of Areal Monthly Average Precipitation Estimates from MERRA2 and ERA5 Reanalysis in a Colombian Caribbean Basin

“…MERRA2 exhibits its worst performance in the Upper Sinú subbasin, as all bias values indicate that estimations from this reanalysis product are at least twice as big as the values estimated from the IDEAM time series. This poor performance by MERRA2 in scarcely instrumented and complex areas follows findings from previous studies for other world regions, such as those by Pedreira et al [41], Quagraine et al [35], and Mao et al [75]. On the other hand, the ERA5 performance for monthly rainfall estimations in mountain areas has shown mixed results.…”

Remote Sensed and/or Global Datasets for Distributed Hydrological Modelling: A Review

“…Similarly, Singh and Saravanan (2020) [35] evaluated four rainfall products for the Wunna Riveris catchment in India and found that the Global Precipitation Climatology Project (GPCP) rainfall data, TRMM and APHRODITE to be more suitable products for the simulation of hydrological processes in India. Mao et al (2019) [36] evaluated three rainfall products, namely GLDAS, TRMM, China Meteorological Forcing Dataset (CMFD) and MERRA-2. They assessed that, for runoff simulation, MERRA-2 performed better for the Nujiang River basin, China.…”

“…In light of this, some researchers have modified global land-cover maps by incorporating additional data sources to achieve the necessary specificity for their particular study. For instance, Mao et al (2019) [36] modified the GLCC data with glacier coverage data from the International Center for Integrated Mountain Development (ICIMOD) for Nujiang River basin, China. Similarly, Soulis et al (2020) [60] updated the CORINE land cover with data from the Integrated Administration and Control System, Greece, (IACS) for the agricultural part to be used in the distributed hydrological modelling of Greece.…”

“…[106] used temperature data from ERA-Interim for the Upper Blue Nile catchment. Ha et al (2018) [71] and Mao et al (2019) [36] used air temperature datasets from GLDAS for Vietnam and the Nujiang river catchment in China, respectively. However, the lack of comment on the performance of the temperature datasets used in these studies makes it difficult to assess the accuracy and reliability of these datasets.…”

“…This information could lead to the identification of more appropriate datasets for hydrological modelling, ultimately improving the accuracy of model simulations and contributing to better water resource management. [32]; Leroux et al, (2016) [138]; Qi et al, (2016) [36]; Qi et al, (2016) [31] African Macro-scale: Pakoksung & Takagi (2021) [37]; [14]; [68]. Macro-scale: Jiang et al, (2020) [81]; Khairul et al, (2018) [17]; [106]; [37]; Siqueira et al, (2018) [42]; Schmied et al, (2021) [158]; [14]; [68]; Huang et al, (2019) [18]; [91]; Ha et al, (2018)…”