Evaluation of satellite-based precipitation products from GPM IMERG and GSMaP over the three-river headwaters region, China

Abstract The three-river headwaters region (TRHR) is the birthplace of the Yangtze River, the Yellow River and the Lantsang River in China. Based on the grid surface precipitation data released by China Meteorological Administration (CMA), this paper evaluated the accuracy and error components of four near-real-time satellite precipitation products (GSMaP-NRT, GSMaP-MVK, IMERG-Early and IMERG-Late) in the era of a GPM (Global Precipitation Measurement) in TRHR. The conclusions are as follows: (1) The precipitation in TRHR is concentrated in the east and south, and the precipitation in the west is very low. IMERG (Early and Late) has a good spatial distribution of precipitation, while GSMaP has an obvious spatial smoothing of precipitation distribution, and does not better highlight the local precipitation characteristics. (2) The inversion accuracy of the satellite products is the best in the source region of the Lantsang River, followed by the source region of the Yellow River. The satellite products all show the lower correlation coefficient and serious underestimation of precipitation in the west of the TRHR. In addition, the closer to the west of the TRHR, the lower hit rate and the higher false alarm rate of the satellite products, especially the NRT and MVK products. In the eastern margin of the Yellow River headwater region and the Lantsang River headwater Region, RMSE and overestimated precipitation were higher in NRT and MVK, and FAR was higher in spite of higher POD and CSI. (3) The errors of GSMaP in the source region of the Yellow River and the Lantsang River are mainly caused by misreporting precipitation and overestimating the precipitation level, while the errors of GSMaP in the west of the TRHR are mainly caused by missing measurements of precipitation events. The underestimated precipitation of IMERG mainly comes from the missed measurement of precipitation and the underestimate of precipitation level, and there is no large false precipitation. (4) In addition, we found that the satellite products in the lake distribution area of the TRHR have serious missed precipitation errors, indicating that the GPM satellite products have the poor detection ability of precipitation near plateau lakes. On the whole, the precipitation inversion accuracy of IMERG (Early and Late) products is higher, which can better detect the occurrence of precipitation events, but the estimation of precipitation level is still not accurate. The precision of precipitation of satellite products near inland lakes on the plateau is poor, so the algorithm improvement of new products needs to be further solved in the future.

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Statistical and Hydrological Evaluations of Multiple Satellite Precipitation Products in the Yellow River Source Region of China

Water ◽

10.3390/w12113082 ◽

2020 ◽

Vol 12 (11) ◽

pp. 3082

Author(s):

Chongxu Zhao ◽

Liliang Ren ◽

Fei Yuan ◽

Limin Zhang ◽

Shanhu Jiang ◽

...

Keyword(s):

Yellow River ◽

Source Region ◽

The Yellow River ◽

Climate Data ◽

Infiltration Capacity ◽

Satellite Precipitation ◽

Basin Scale ◽

Precipitation Estimation ◽

Yellow River Source ◽

Global Precipitation

Comprehensively evaluating satellite precipitation products (SPPs) for hydrological simulations on watershed scales is necessary given that the quality of different SPPs varies remarkably in different regions. The Yellow River source region (YRSR) of China was chosen as the study area. Four SPPs were statistically evaluated, namely, the Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42V7, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Integrated Multisatellite Retrievals for Global Precipitation Measurement final run (IMERG-F), and gauge-corrected Global Satellite Mapping of Precipitation (GSMaP-Gauge) products. Subsequently, the hydrological utility of these SPPs was assessed via the variable infiltration capacity hydrological model on a daily temporal scale. Results show that the four SPPs generally demonstrate similar spatial distribution pattern of precipitation to that of the ground observations. In the period of January 1998 to December 2016, 3B42V7 outperforms PERSIANN-CDR on basin scale. In the period of April 2014 to December 2016, GSMaP-Gauge demonstrates the highest precipitation monitoring capability and hydrological utility among all SPPs on grid and basin scales. In general, 3B42V7, IMERG-F, and GSMaP-Gauge show a satisfactory hydrological performance in streamflow simulations in YRSR. IMERG-F has an improved hydrological utility than 3B42V7 in YRSR.

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Evaluation of multisource precipitation input for hydrological modeling in an alpine basin: a case study from the Yellow River Source Region (China)

10.22541/au.163251564.45548736/v1 ◽

2021 ◽

Author(s):

Pengfei Gu ◽

Gaoxu WANG ◽

Guodong Liu ◽

Yongxiang Wu ◽

Hongwei Liu ◽

...

Keyword(s):

Hydrological Modeling ◽

Yellow River ◽

Swat Model ◽

Source Region ◽

Warm Season ◽

Tropical Rainfall Measuring Mission ◽

Cold Season ◽

The Yellow River ◽

Yellow River Source ◽

Precipitation Events

Alpine basins are essential to the conservation of water resources. However, they are typically poorly gauged and inaccessible, owing to the harsh prevailing environment and complex terrain. To investigate the influences of different precipitation inputs on hydrological modeling in alpine basins, two representative satellite precipitation products [Tropical Rainfall Measuring Mission (TRMM) and Integrated Multi-Satellite Retrievals for GPM (IMERG)] and two reanalysis precipitation products [China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) and Climate Forecast System Reanalysis (CFSR)] in the Yellow River Source Region (YRSR) were selected for evaluation and hydrological verification against gauge-observed data (GO). Results indicates that the accuracy of these precipitation products in the warm season is higher than that in the cold season, and IMERG has the best performance, followed by CMADS, CFSR, and TRMM. TRMM seriously overestimates high rainfall of greater than 10 mm/day. CFSR overestimates moderate precipitation events of 1–10 mm/d, while CMADS underestimates the effects of precipitation events of 1–20 mm/d. Models using the GO as input yielded satisfactory performance during 2008–2013, and precipitation products have poor simulation results. Although the model using IMERG as input yielded unsatisfactory performance during 2014–2016, this did not affect the use of IMERG as a potential data source for YRSR. After bias correction, the quality of CFSR improves significantly with R2 and NSE increasing by 0.25 and 0.31 at Tangnaihai station, respectively. Model driven by the combination of GO and CMADS precipitation performed the best in all scenarios (R2 = 0.77, NSE = 0.72 at Tangnaihai station; R2 = 0.53, NSE = 0.48 at Jimai station). These results can provide reference data, and research ideas, for improved hydrological modeling of alpine basins.

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