Evaluation and Hydrological Validation of GPM Precipitation Products over the Nanliu River Basin, Beibu Gulf

Abstract

Adequate and high-quality precipitation estimates, from spaceborne precipitation radars, are necessary for a variety of applications in hydrology. In this study, we investigated the performance of two Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) products, against gauge observations over a small river basin, the Beibu Gulf—the Nanliu River basin, and evaluated their capability of streamflow simulation, based on a conceptual watershed model from April 2014 to December 2016. The results showed that both IMERG_Cal and IMERG_Uncal could roughly capture the spatial patterns of precipitation with slight over/underestimation (Relative Bias (RB) values of 6.5% and −5.5%, respectively) at a basin scale. At grid-cell scales, two IMERG products got an RB of −23.3% to 18.9%, Correlation Coefficient (CC) of 0.521 to 0.744, and Root Mean Square Error (RMSE) of 11.3 to 17.5 mm. There were some considerable errors in heavy precipitation events, and the IMERG significantly overestimated the amounts of these extreme events. The two IMERG products showed a higher accuracy and lower error rate, when detecting the light precipitation. IMERG-driven simulation had a better quality when the model was calibrated with satellite data rather than with rain gauge data. This analysis implied that IMERG products have potential in hydrological applications, in this region, and need further improvement in algorithms.

Validation of GPM Precipitation Products by Comparison with Ground-Based Parsivel Disdrometers over Jianghuai Region

“…A second-degree polynomial µ-Λ relationship was further derived. The relationship for winter is: Λ = 0.053µ 2 + 0.437µ + 1.540 (10) The relationship for summer is: Λ = 0.017µ 2 + 0.724µ + 1.958 (11) and the results are shown in Figure 9. Given the same Λ, we notice the parameter µ of winter is less than that of summer.…”

“…Hence, the accurate prediction of precipitation in Jianghuai region is of great importance. From ground-based gauges, disdrometers and radars to satellite-based sensors, the spatial and temporal variability of precipitation systems can be easily captured, particularly over rural areas where in situ measurements are scarce [11]. The Global Precipitation Measurement (GPM) mission, as a successor of the Tropical Rainfall Measuring Mission (TRMM), shows a significant advantage over gauge-based or radar-based estimates [12][13][14], which is expected to improve our knowledge of precipitation processes by providing greater dynamic range, more detailed information on microphysics, and better accuracy values in rainfall retrievals [15].…”

Hydrologic Validation of MERGE Precipitation Products over Anthropogenic Watersheds

“…Once the precipitation data has been prepared, they should have their accuracy assessed before having their impacts evaluated as model inputs [37]. In addition to the visual interpretation, the precipitation data were evaluated based on several extensively used validation statistical indices [3,9,12]. The following statistical indices were selected based on the recommendations Gilewski and Nawalany [10], since they allow performing a multi-aspect analysis of the results.…”

“…× 100 (12) where t is the respective daily time step; n is the total number of time steps; Q obs are the observed streamflows and Q sim are the simulated ones. NS values range from −∞ to 1, whereas NS between 0 and 1 imply some degree of model predictably [48].…”

“…According to Price et al [11] rain gauges and weather radars are the most adequate data sources for catchment modeling. This superiority may be attributed to their better accuracy, thus being conventionally used as reference for hydrological applications [12]. Yet, the punctual characteristic of rain gauges often limits their ability to represent the spatial structure of precipitation events, especially in complex terrains and tropical regions where precipitation is highly variable [13] and developing countries or remote locations where reliable data is usually scarce [14].…”