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

Abstract

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.

Are the Latest GSMaP Satellite Precipitation Products Feasible for Daily and Hourly Discharge Simulations in the Yellow River Source Region?

“…The results indicated that GSMaP-Gauge V7 fits well with the observed runoff in monthly simulations and presents better ability to reproduce runoff changes than 3B42V7 does. Most of previous studies demonstrated that GSMaP-Gauge presents a better hydrological ability than GSMaP-MVK at daily and sub-daily time scales whether in the rain-gauge-based benchmarked parameter scheme or the input-specific recalibration scheme [63,66]. Similarly, this study found that GSMaP-Gauge demonstrates prominently better performance in capturing daytime precipitation dynamics in YRSR than GSMaP-MVK does.…”

“…Post-real-time SPPs generally show better performance than their near-time-time versions because of the gauge-based adjustment in several regions of the world [16,[60][61][62][63]. However, the post-real-time product GSMaP-MVK significantly overestimates precipitation with higher overestimation magnitudes than the near-real-time product GSMaP-NRT-Gauge (Figures 3 and 6), and GSMaP-MVK presents the lower overall score than GSMaP-NRT-Gauge (Figure 6).…”

Assessment of PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0 Rainfall Products over a Semi-Arid Subtropical Climatic Region

“…The literature review revealed that the performance evaluation of SRPs is essential before their direct application in any region [7,[14][15][16]. In this regard, several researchers have evaluated the accuracy of different SRPs in several countries-for instance, in America [17], Brazil [18], China [19][20][21], Italy [22], Iran [23], Malaysia [24], Pakistan [14], Taiwan [25], and Saudi Arabia [26]. Huang et al [25] assessed the error characteristics of PERSIANN family products in the whole of Taiwan and concluded that all products underestimated the rainfall amount over most parts of the country.…”

Assessment of Multi-Satellite Precipitation Products over the Himalayan Mountains of Pakistan, South Asia

“…The Center for Hydrometeorology and Remote Sensing (CHRS) [11] at the University of California, Irvine (UCI), developed the [12][13][14] (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) PERSIANN system, which uses neural network function classification/approximation procedures to calculate an estimate of rainfall rate at each 0.25 • × 0.25 • pixel of an infrared brightness temperature image provided by geostationary satellites. PERSIANN-CDR [15][16][17] offers daily worldwide rainfall information at a 0.25 • grid scale. PERSIANN-CCS [18][19][20] is a cloud segmentation technique with customizable thresholds.…”