Abstract
As the limitation of rainfall collection by ground measurement has been widely recognized, satellite-based rainfall estimate is a promising high-resolution alternative in both time and space. This study is aimed at exploring the capacity of the satellite-based rainfall product Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), including 3B42V7 research data and its real-time 3B42RT data, by comparing them against data from 29 ground observation stations over the lower part of the Red–Thai Binh River Basin from March 2000 to December 2016. Various statistical metrics were applied to evaluate the TMPA products. The results showed that both 3B42V7 and 3B42RT had weak relationships with daily observations, but 3B42V7 data had strong agreement on the monthly scale compared to 3B42RT. Seasonal analysis showed that 3B42V7 and 3B42RT underestimated rainfall during the dry season and overestimated rainfall during the wet season, with high bias observed for 3B42RT. In addition, detection metrics demonstrated that TMPA products could detect rainfall events in the wet season much better than in the dry season. When rainfall intensity was analyzed, both 3B42V7 and 3B42RT overestimated the no rainfall event during the dry season but underestimated these events during the wet season. Finally, based on the moderate correlation between climatology–topography characteristics and correction factors of linear-scaling (LS) approach, a set of multiple linear models was developed to reduce the error between TMPA products and the observations. The results showed that climatology–topography-based linear-scaling approach (CTLS) significantly reduced the percentage bias (PBIAS) score and moderately improved the Nash–Sutcliffe efficiency (NSE) score. The finding of this paper gives an overview of the capacity of TMPA products in the lower part of the Red–Thai Binh River Basin regarding water resource applications and provides a simple bias correction that can be used to improve the correctness of TMPA products.
Highlights
Precipitation is the most crucial input variable enforced in water prediction models
The results showed that daily rainfalls from both 3B42V7 and 3B42RT had very weak correlations with the ground observation data; the average of the CC and the average of Nash–Sutcliffe efficiency (NSE) were 0.387 and −0.152 for 3B42V7 data and 0.304 and −0.521 for 3B42RT data, respectively
TMPA products are recommended for wide use over the tropical and subtropical regions due to their high temporal–spatial resolution
Summary
Precipitation is the most crucial input variable enforced in water prediction models. Reliable precipitation is required for model calibration, forecast, and simulation [1,2,3]. Gauge observation is the primary collection approach to obtain precipitation information [4]. Gauge network is often sparse and nonexistent in many parts of the globe [5,6]. It is often challenging to obtain gauge data, especially in developing countries and transboundary rivers, due to technical and administrative reasons [7,8,9]. Gauge observations only provide point measurements of precipitation and cannot capture the full spatial variability. Space-based precipitation estimations, have great potential application to enhance the capacity of measuring this vital water cycle component [10,11]
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