Abstract
Gridded precipitation products from remote sensing are currently available and could potentially enhance the use of precipitation data in regions with sparse network of ground rain gauges. Thus, this study aimed to evaluate the performance and propose correction models for Global Satellite Mapping of Precipitation (GSMaP) use in the Upper Tocantins River basin, a key area for food and electricity production in central Brazil, but with sparse network of ground rain gauges, challenging water resources and agriculture decision makers. GSMaP data were compared with data from a rain gauge network between 2000 and 2019. Evaluations were made at daily and monthly temporal scales. In general, GSMaP products show an overestimate bias for drizzle (0.1 ~ 1 mm day−1) and underestimate for rainfalls above 10 mm day−1. The use of monthly scale data significantly reduces the bias observed in the daily scale, but with an underestimation trend of -28.3% and -39.7% for the dry and rainy periods, respectively. Categorical indices showed that the GSMaP system had better hit rates for rain detection in the rainy season (October–April) than in the dry season (May–September). For the studied region, the use of GSMaP data on daily and monthly scales should be preceded by a bias analysis as a function of rain gauge network data. The use of bias coefficient corrected observed rainfall data underestimation on daily and monthly scales, improved correlation between GSMaP and observed rainfall data and reduced errors associated with rainfall network data within the basin influence area. The findings of this study indicate how decision makers could adjust and apply GSMaP products to estimate rainfall for water resources, agriculture and drought management challenges in the Upper Tocantins River basin.
Highlights
IntroductionRainfall plays a crucial role in the global energy balance and hydrological cycle, interacting with the hydrosphere, atmosphere, lithosphere, and biosphere (Yuan et al 2017)
This study aimed to evaluate the performance of GSMaP (Global Satellite Mapping of Precipitation) in estimating rainfall in central Brazil, using the Upper Tocantins River sub-basin as a specific area of analysis
Since the launch of the first atmospheric satellites in the 1960s, many sensors have been developed for monitoring rainfall from space (Sun et al 2018). They all focus on two main spectral categories: visible and infrared (VIS/IR) onboard geostationary and orbital satellites and low-orbit passive microwave (PMW) sensors (Levizzani et al 2002)
Summary
Rainfall plays a crucial role in the global energy balance and hydrological cycle, interacting with the hydrosphere, atmosphere, lithosphere, and biosphere (Yuan et al 2017). Obtaining reliable rainfall information at regional scales is still a challenge due to its high spatiotemporal distribution heterogeneity (Chen et al 2019). Remote sensing data can provide information at varied spatiotemporal resolutions on a regional scale, which can be used for systematic mapping of rainfall distribution on the surface of the Earth (Sharifi et al 2019). Since the launch of the first atmospheric satellites in the 1960s, many sensors have been developed for monitoring rainfall from space (Sun et al 2018). They all focus on two main spectral categories: visible and infrared (VIS/IR) onboard geostationary and orbital satellites and low-orbit passive microwave (PMW) sensors (Levizzani et al 2002). The Global Precipitation Measurement mission (GPM) is a result of a collaboration between the American (NASA) and Japanese (JAXA) space agencies to unify and promote advances in rainfall measurements from an operational constellation of microwave sensors, which have systematically provided global rainfall data on an hourly scale and at different correction levels (Hou et al 2014)
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