Multiscale Hydrologic Applications of the Latest Satellite Precipitation Products in the Yangtze River Basin using a Distributed Hydrologic Model

Abstract

The present study aims to evaluate three global satellite precipitation products [TMPA 3B42, version 7 (3B42 V7); TMPA 3B42 real time (3B42 RT); and Climate Prediction Center morphing technique (CMORPH)] during 2003–12 for multiscale hydrologic applications—including annual water budgeting, monthly and daily streamflow simulation, and extreme flood modeling—via a distributed hydrological model in the Yangtze River basin. The comparison shows that the 3B42 V7 data generally have a better performance in annual water budgeting and monthly streamflow simulation, but this superiority is not guaranteed for daily simulation, especially for flood monitoring. It is also found that, for annual water budgeting, the positive (negative) bias of the 3B42 RT (CMORPH) estimate is mainly propagated into the simulated runoff, and simulated evapotranspiration tends to be more sensitive to negative bias. Regarding streamflow simulation, both near-real-time products show a region-dependent bias: 3B42 RT tends to overestimate streamflow in the upper Yangtze River, and, in contrast, CMORPH shows serious underestimation in those downstream subbasins while it is able to effectively monitor streamflow into the Three Gorges Reservoir. Using 394 selected flood events, the results indicate that 3B42 RT and CMORPH have competitive performances for near-real-time flood monitoring in the upper Yangtze, but for those downstream subbasins, 3B42 RT seems to perform better than CMORPH. Furthermore, the inability of all satellite products to capture some key features of the July 2012 extreme floods reveals the deficiencies associated with them, which will limit their hydrologic utility in local flood monitoring.

Calibration of Spatially Distributed Hydrological Processes and Model Parameters in SWAT Using Remote Sensing Data and an Auto-Calibration Procedure: A Case Study in a Vietnamese River Basin

“…Satellite precipitation data offers an attractive alternative to supplement in situ precipitation measurements in hydrological modelling, particularly in poorly gauged basins [29,63] [64]. In a similar study for Australia and Southeast Asia, TRMM and CMORPH outperformed, and an ensemble precipitation product was suggested as a reduction of system-specific and random errors [65].…”

Evaluation of Satellite-Derived Rainfall Estimates for an Extreme Rainfall Event over Uttarakhand, Western Himalayas

“…To compare three SBRE rainfall products over these districts with the benchmark data IMD, 82 random points were selected, and the distributions of these points have been shown in Figure 3a. The scatterplots of precipitation during peak storm over four days (15)(16)(17)(18) June) with respect to IMD gauge-based data ( Figure 3) exhibited overestimation of precipitation by GSMaP, while underestimation by TMPA and CMORPH irrespective of lower or higher surface elevations. In all three cases (Figure 3b-d), these comparisons were statistically significant at p value < 0.001 (df = 80) with Pearson's coefficient of determination (R 2 ) in the range from 0.42 to 0.44.…”

“…In this week (13)(14)(15)(16)(17)(18)(19), Rudraprayag district of Uttarakhand state has received 366.3 mm of intense unprecedented rainfall, which was 580% more than the normal rainfall (54 mm). The recurrence of this multi-day cloud Numerous applications were undertaken comprehensively using the aforementioned products.…”

“…The recurrence of this multi-day cloud Numerous applications were undertaken comprehensively using the aforementioned products. Some of these applications include extreme rainfall events, annual water budgeting, flood modelling, and drought monitoring [13][14][15][16][17]. While foolproof precipitation estimates are preferred, studies have pointed out various rainfall estimate errors owing to the instrument, algorithm, sampling method, and georeferencing [18][19][20].…”

Using a Kalman Filter to Assimilate TRMM-Based Real-Time Satellite Precipitation Estimates over Jinghe Basin, China

“…Intercomparison of rainfall estimates from satellite retrievals and ground observations is an important means to assess the confidence in satellite algorithms, which provides a benchmark for their future development and improvement. In many prior studies, the data errors of those TRMM-based multi-satellite precipitation estimates have been extensively investigated by using ground-based gauges and radars to serve as the reference over different regions [26][27][28][29][30].…”