1. Introduction
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]. However, gauge network is often sparse and nonexistent in many parts of the globe [
5,
6]. Moreover, it is often challenging to obtain gauge data, especially in developing countries and transboundary rivers, due to technical and administrative reasons [
7,
8,
9]. In addition, gauge observations only provide point measurements of precipitation and cannot capture the full spatial variability. Space-based precipitation estimations, therefore, have great potential application to enhance the capacity of measuring this vital water cycle component [
10,
11].
Several satellite-derived datasets have been used in previous studies, such as the Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) [
12], the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) [
13], the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) [
14], and National Oceanic and Atmospheric Administration/Climate Prediction Centre (NOAA/CPC) morphing technique (CMORPH) [
15] products. Among them, TPMA—the first space-borne product of the Earth Science Mission aimed at studying tropical and subtropical rainfall—has performed well in a wide range of applications, such as hydrological modeling [
16,
17,
18], drought monitoring [
19,
20], and agronomy [
21,
22]. TMPA products have also been evaluated as having better performance than other satellite-based rainfall products. For example, the TMPA 3B42V7 data is generally a better input in a distributed hydrological model compared to CMORPH and TMPA 3B42RT (real time) for multiple hydrological purposes, including annual water budgeting, monthly and daily streamflow simulation, and extreme flood modeling [
23]. Similarity, Tong et al. [
24] showed that 3B42V7 was a better driving force of hydrological model for both monthly and daily streamflow simulation over the Tibetan Plateau compared to CMORPH, PERSIANN, and 3B42RT. Moazami et al. used six statistical indices and contingency table to evaluate 3B42V7, concluding it was a better estimation of daily precipitation than PERSIAN and 3B42RT over Iran [
25]. Simons et al. [
26] identified that monthly TMPA 3B43 rainfall product was the most suitable satellite dataset compared to CHIRPS and CMORPH over the Red River Basin of Vietnam.
Differences between TMPA products and rain gauge observation analysis have been a cause of concern recently. Zad et al. [
27] pointed out that 3B42V7 tended to overestimate rainfall measurement by approximately 26.95% at Pahang River Basin of Malaysia and that 3B42V7 was likely to have a high accuracy of detecting rainfall events at high-altitude and mid-altitude areas compared to low-altitude regions. Kneis et al. [
28] analyzed that 3B42V7 and 3B42RT datasets were moderately correlated with their gauged-based counterpart at sub-basin level (4000 to 16,000 km
2) at the lower Mahanadi River Basin of India but that the 3B42V7 and 3B42RT data often do not reflect gauge observation at high-intensity level (>80 mm/day). The TMPA product is also likely to perform better on a monthly scale when compared to the ground data. Curtarelli et al. [
29] found that monthly 3B43 dataset had a great consistency (correlation coefficient >0.97) with ground observation data over the Itumbiara Reservoir drainage area in Central Brazil but that 3B43 tended to overestimate rainfall by 1.24%. Comparing monthly 3B43 dataset with 56 observations in Yangtze River Delta, Cao et al. [
30] also showed an inclination of 3B43 to overestimate monthly rainfall, with the bias ranging between −10% and 10% most of the study area; its correlation coefficient with observation was found to peak in March (0.96) and reach bottom in August (0.79). Although the TRMM satellite has not been operated since 2014, TMPA products are still being generated regardless [
31].
Following the highly successful TMPA, the Global Precipitation Measurement (GPM) mission was developed to continuously increase precipitation estimation over most of the globe [
32]. A range of studies in many regions have demonstrated that GPM outperforms TMPA by having a better spatial resolution, coverage area, and lower systematic bias error [
33,
34,
35]. However, GPM has only been available for a short time (since 2014), while TMPA products date back to January 1998. In addition, GPM is just a slight improvement over TMPA products [
36]. Huffman et al. [
32] aim to extend the GPM data to the same length as the longest TMPA data. Therefore, assessments on TMPA products are of paramount importance to gain insights into their performance at various regions so that their algorithms can be improved and the next generation GPMs can be developed.
While there is a clear advantage of having a high temporal and spatial resolution using TMPA products, extra work is required because bias correction needs to be performed prior to application of any TMPA products in environmental, water resources, and ecological studies [
27]. Climatology and topography are likely factors to induce errors in remote sensing retrievals [
37]. Consequently, their effects on the quality of TMPA products are inevitable. Based on the moderate inverse linear relationship between the monthly 3B43 bias and elevation, Hashemi et al. [
38] developed a linear model between 3B43 bias and elevation, especially for stations that have elevations above 1500 m above mean sea level in the U.S. The corrected monthly 3B43 product showed a significant improvement in the high elevation area. Thus, the empirical bias correction model using climatology and topography seems to be a potential investigation direction, although relatively little research has been conducted so far.
In Vietnam, ground observations provide poor spatial and temporal measurement of rainfall due to the lack of a dense network for rain gauge measurement. The average rain gauge network in Vietnam is around 400 km
2 per rain gauge, which is below the World Meteorological Organization standard (area per rainfall station of 100–250 km
2 for mountainous areas; area per rainfall station of 600–900 km
2 for lowland areas) [
39]. Moreover, the rain gauge distribution in Vietnam is uneven, with insufficient gauged stations at high elevation areas. According to the Vietnam Meteorological and Hydrological Administration, most rain gauge stations (75%) are concentrated at low elevation areas (<200 m), which only cover half of Vietnam’s land [
40]. With these perspectives, satellite-based precipitation is an indispensable alternative source of precipitation data for Vietnam. Preliminary studies on satellite-based precipitation products in the country have been conducted recently. However, these studies either focused on monthly rainfall [
26,
41] or used directly satellite-derived rainfall without bias correction analysis [
42]. Therefore, further research on satellite-based precipitation products is still of fundamental importance for the country.
This study selected the Red–Thai Binh River Basin—one of the largest river systems in Vietnam—as a case study. Although it plays an essential role in Vietnam’s economic and social development, many parts of this basin do not have rainfall monitoring from ground, causing difficulties for basin rainfall estimation and water resources management. The first objective of this study was to compare the TMPA products 3B42V7 and 3B42RT with ground observation data over Red–Thai Binh River Basin in various aspects, such as calculating error statistics on a daily scale, monthly scale, dry and wet seasons, detecting rainfall events ability, and evaluating rainfall intensity. The second objective was to develop a linear-scaling bias correction model using climate–topography indices for both 3B42V7 and 3B42RT datasets. The results of the assessment and bias correction of TMPA precipitation products could help in supporting its potential application in hydrological modeling and drought monitoring in the studied region.
5. Conclusions
TMPA products are recommended for wide use over the tropical and subtropical regions due to their high temporal–spatial resolution. Therefore, this study carried out a comparison and bias correction between TMPA 3B42V7 and TMPA 3B42RT data and 29 ground observations over the lower part of the Red–Thai Binh River Basin from March 2000 to December 2016.
Based on various error metrics—CC, NSE, RMSE, and PBIAS—we compared 3B42V7 and 3B42RT against observations at different scales, including daily and monthly scales and dry and wet seasons. Our analysis showed that both products had relatively weak relationships with observations on a daily scale but this significantly improved on a monthly scale. Except for the CC score, 3B42V7 data was considerably better than 3B42RT data in the rest of the error metrics on a monthly scale. In addition, 3B42V7 data showed better performance than 3B42RT data during both dry and wet seasons, especially regarding NSE and PBIAS measurements. Both products showed overall underestimations during the dry season and overestimations during the wet season. Spatial analysis of the PBIAS score indicated significant bias of TMPA products at the lowland area of the Red–Thai Binh River Basin, while the northwestern mountainous area and the northeast coastal area had low PBIAS for both products.
The comparison between 3B42V7 and 3B42RT was also viewed from a different angle using detection metrics—POD, FAR, POFD, and CSI—against observations on daily time series, daily time series in the dry season, and daily time series in the wet season. In this case, the 3B42V7 showed a slightly better performance compared to 3B42RT for the metrics mentioned. Both products had better detection metrics in the wet season compared to the dry season. Spatial CSI score distribution showed that the lowland area of the central basin had the lowest score compared to other parts.
From the perspective of the assessment on rainfall intensity on daily time series for the dry and wet seasons, it was found out that 3B42RT performed the same as 3B42V7 data. Both products overestimated no rainfall (≤0.6 mm/day) during the dry season and underestimated rainfall intensity during the wet season. The overestimation and underestimation compromised the daily time series for the dry and wet seasons, meaning the frequency distributions of no rainfall events were almost the same for TMPA products and ground observations. On the other hand, TMPA products underestimated low rainfall intensity (0.6–6 mm/day) during the dry season and overestimated rainfall intensity during the wet season. The underestimation of low rainfall was more significant than the overestimation, resulting in a slightly lower rainfall estimation by TMPA products at the daily time series compared to observations.
In addition, we used the LS approach to do bias correction for 3B42V7 and 3B42RT products. In this approach, the correction factor is an important key to adjust satellite rainfall data closely to observations. We found that the correction factors of the LS approach were associated with climatology–topography characteristics. Therefore, a set of multiple linear regression models was developed to predict correction factors from climatology–topography characteristics for 3B42V7 and 3B42RT. After bias correction using LS and CTLS approaches, corrected TMPA products showed significant improvement compared to the results before bias correction, especially for the 3B42RT dataset with PBIAS and NSE scores. However, we found that both bias correction approaches did not improve the TMPA products significantly on other measurement scores.
In conclusion, 3B42V7 and 3B42RT data should be a good alternative source for a wide range of hydrological purposes on a monthly scale. The 3B42V7 data is also a good source for typical analysis of dry and wet seasons, although these datasets should be used with caution for daily scale purposes. The post-TMPA products after using climatology–topography characteristics are promising sources, especially for total water resource estimation.
The biggest advantage of the LS approach was to reduce PBIAS score; however, other error scores remained almost the same. Future studies may merge satellite-based and ground-based rainfall product to further improve rainfall product quality [
63]. 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 application and provides a simple bias correction that can be used to improve the correctness of TMPA products. Additionally, the study is beneficial for regions, such as Vietnam, that are seeking alternative rainfall sources. The reason for this is that approximately 60% of Vietnam’s water resources come from abroad, and hydro-climatology acquisition from upstream countries faces many challenges due to limited administration interaction [
64].