Natural Hazards and Earth System Sciences (Dec 2013)

Validation study of TMPA 3B42V6 in a typical alpine and gorge region: Jinsha River basin, China

  • Y. C. Yang,
  • G. W. Cheng,
  • J. H. Fan,
  • W. P. Li,
  • J. Sun,
  • Y. K. Sha

DOI
https://doi.org/10.5194/nhess-13-3479-2013
Journal volume & issue
Vol. 13, no. 12
pp. 3479 – 3492

Abstract

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Because of density and distribution flaws inherent with in situ rainfall measurements, satellite-based rainfall products, especially the Tropical Rainfall Measuring Mission (TRMM), were expected to offer an alternative or complement for modeling of hydrological processes and water balance analysis. This study aims at evaluating the validity of a standard product, the TRMM Multi-satellite Precipitation Analysis (TMPA) 3B42V6, by comparing it with in situ ground gauge datasets on a typical alpine and gorge region in China, the Jinsha River basin. The validation study involved the performance of the 3B42V6 product on 3 h, daily and monthly temporal scales. Statistical analysis methods were used for rainfall and rain event estimation. The results affirmed that the 3B42V6 product demonstrated increasing accuracy when the temporal scales were increased from 3 h to daily to monthly. The mean correlation coefficient of rainfall time series between the 3B42V6 product and the gauge over the Jinsha River basin reached 0.34 on the 3 h scale, 0.59 on the daily scale, and 0.90 on the monthly scale. The mean probability of detection (POD) of the 3B42V6 product reached 0.34 on the 3 h scale and 0.63 on the daily scale. The 3B42V6 product of 80.4% of stations obtained an acceptable bias (± 25%) over the investigation area. A threshold of nearly 5.0 mm d−1 in daily rainfall intensity split the 3B42V6 product into overestimates (−1) and underestimates (> 5.0 mm d−1). The terrain elements of altitude, longitude, and latitude were the major influencing factors for 3B42V6 performance. In brief, the 3B42V6 dataset has great potential for research on hydrologic processes, especially daily or large temporal scale. As for fine temporal scale applications, such as flood predictions based on a 3 h scale dataset, it is necessary to conduct adjustments or to combine the 3B42V6 product with gauges to be more accurate regarding the issues in the study area or in analogous regions with complicated terrains.