Abstract
The new IMERG and GSMaP-v6 satellite rainfall estimation (SRE) products from the Global Precipitation Monitoring (GPM) mission have been available since January 2015. With a finer grid box of 0.1°, these products should provide more detailed information than their latest widely-adapted (relatively coarser spatial scale, 0.25°) counterpart. Integrated Multi-satellitE Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation version 6 (GSMaP-v6) assessment is done by comparing their rainfall estimations with 247 rainfall gauges from 2014 to 2016 in Bolivia. The comparisons were done on annual, monthly and daily temporal scales over the three main national watersheds (Amazon, La Plata and TDPS), for both wet and dry seasons to assess the seasonal variability and according to different slope classes to assess the topographic influence on SREs. To observe the potential enhancement in rainfall estimates brought by these two recently released products, the widely-used TRMM Multi-satellite Precipitation Analysis (TMPA) product is also considered in the analysis. The performances of all the products increase during the wet season. Slightly less accurate than TMPA, IMERG can almost achieve its main objective, which is to ensure TMPA rainfall measurements, while enhancing the discretization of rainy and non-rainy days. It also provides the most accurate estimates among all products over the Altiplano arid region. GSMaP-v6 is the least accurate product over the region and tends to underestimate rainfall over the Amazon and La Plata regions. Over the Amazon and La Plata region, SRE potentiality is related to topographic features with the highest bias observed over high slope regions. Over the TDPS watershed, the high rainfall spatial variability with marked wet and arid regions is the main factor influencing SREs.
Highlights
Accurate rainfall estimation is crucial to monitor long- and short-term hydro-climatologic variations
TRMM Multi-satellite Precipitation Analysis (TMPA) and Integrated Multi-satellitE Retrievals for GPM (IMERG) seem more sensitive to extreme rainfall than GSMaP-v6 with higher rainfall amount observed which are closer to the maximum observed from gauge measurements (Figure 2)
Similar Coefficient Correlation (CC), Root Mean Square Error (RMSE) and Bias value from both IMERG and TMPA were obtained while GSMaP-v6 considerably underestimated rainfall amount by 30% (Table 1)
Summary
Accurate rainfall estimation is crucial to monitor long- and short-term hydro-climatologic variations. Drought and flood scenarios can be predicted or understood from the rainfall analysis, Remote Sens. 2017, 9, 369 protecting the local environment and increasing population security. Few meteorological stations are available and are unevenly distributed due to difficulties of access for installation and maintenance. Over the last few decades, numerous Satellite Rainfall Estimates (SREs) were made available from different organizations allowing a high quality of rainfall monitoring over the same periods. A new generation of SREs is being made available to ensure continuity in rainfall monitoring, addressing previous SREs’ deficiencies related to aging sensors. Two SRE groups, which are derived from the Global Precipitation Monitoring (GPM) mission launched on 27 February
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