In this chapter, the authors propose a novel statistical model with a residual correction of downscaling coarse precipitation TRMM 3B43 product. The presented study was carried out over Morocco, and the objective is to improve statistical downscaling for TRMM 3B43 products using a machine learning algorithm. Indeed, the statistical model is based on the Transformed Soil Adjusted Vegetation Index (TSAVI), elevation, and distance from the sea. TSAVI was retrieved using the quantile regression method. Stepwise regression was implemented with the minimization of the Akaike information criterion and Mallows' Cp indicator. The model validation is performed using ten in-situ measurements from rain gauge stations (the most available data). The result shows that the model presents the best fit of the TRMM 3B43 product and good accuracy on estimating precipitation at 1km according to 𝑅2, RMSE, bias, and MAE. In addition, TSAVI improved the model accuracy in the humid bioclimatic stage and in the Saharan region to some extent due to its capacity to reduce soil brightness.
TopIntroduction
Precipitation is the most significant component in the hydrologic cycle (Elhassnaoui et al., 2019). Indeed, precipitation data is a fundamental requirement for full features of meteorology, hydrology, groundwater, streamflow, flood, drought, agriculture, and economics (Maidment, 1993). In a context of climate change, providing high precise precipitation data as a vital variable that describe a variety of features and phenomenon is very significant (Chen & li, 2016; l. Tang et al., 2015; Zhao et al., 2017).
From ancient times, rain gauge stations have been an essential tool for precipitation observation in a hydro-meteorological perspective(Schneider et al., 2014; Schwaller & Morris, 2011). However, due to the topographical characteristic of the catchments, the rain gauge network suffers from sparse spatial distribution (Guofeng et al., 2016; Maggioni et al., 2016). The sparse rain gauge network cannot provide a significant statistical distribution rainfall using interpolation (Kro & Law, 2005). Furthermore, rain gauge stations face many impediments to record and monitor precipitation data namely the weather modification, wind speed, type of precipitation, which lead to significant errors in rainfall observation (Essery & Wblcock, 1991; peck, 1974; Rodda & Smith, 1986; John Rodda & Dixon, 2011; Spring & Peck, 1980).
To overcome rain gauge errors, and provide a quantitative spatial measurement of precipitation, remotes sensing techniques have been developed (Gregg & Casey, 2004; Karaska et al., 2004). Besides, passive satellite sensors can provide accurate global coverage of precipitation data without data interpolation (Duan & Bastiaanssen, 2013; Kidd & Levizzani, 2011; Zhang & Li, 2018). Indeed, satellite sensors with a set of algorithms that embody microwaves radiation have a high potential for measuring precipitation, because, microwaves are directly related to the raindrop through emission, absorption and scattering techniques (Ezzine et al., 2017; Liu et al., 2018; Maidment, 1993). The temperature threshold method is the standard method for estimating rainfall by remote sensing (Arkin et al., 1980.; Arkin et al., 1979).
Satellite sensors programs have become an advanced and efficient tool for providing accurate precipitation data (Silva & Lopes, 2017), covering a broad spatial distribution (Ezzine et al., 2017; Irvem & Ozbuldu, 2019; Kidd, 2001).
Straight away, many derived data from satellite sensors are freely available to the public, namely the Climate Precipitation Center morphing technique (COMRPH), the Tropical Rainfall Measure Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), and the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) mission, ERA-Interim (ERAI), Global Precipitation Climatology Centre (GPCC) and Multi-Source Weighted Ensemble Precipitation (MSWEP) (Belay et al., 2020; Ezine et al., 2017; Mondal et al., 2014; Nichol & Abbas, 2015; Xue et al., 2015; Zheng, 2015).
Among these satellite products, the Tropical Rainfall Measure Mission (TRMM) is a passive microwave sensor that provides precipitation data (Najja et al., 2018). It is considered as a convenient sensor to estimate precipitation at a spatial resolution of 0.25°×0.25°(Chen & Li, 2016; Milewski et al., 2015). TRMM product has been used TRMM was developed jointly by the National Aeronautics and Space Administration (NASA) and the Japanese Aerospace Exploration Agency (JAXA) in 1997. Moreover, TRMM was retired on April 8, 2015 after more than 17 years of rich data collection (Zhou et al., 2020).