*Article* **GPM-Based Multitemporal Weighted Precipitation Analysis Using GPM\_IMERGDF Product and ASTER DEM in EDBF Algorithm**

**Sana Ullah 1,2, Zhengkang Zuo 1, Feizhou Zhang 1, Jianghua Zheng 3, Shifeng Huang 4, Yi Lin 1, Imran Iqbal 5, Yiyuan Sun 1,6, Ming Yang <sup>1</sup> and Lei Yan 1,2,\***


Received: 13 August 2020; Accepted: 21 September 2020; Published: 26 September 2020

**Abstract:** To obtain the high-resolution multitemporal precipitation using spatial downscaling technique on a precipitation dataset may provide a better representation of the spatial variability of precipitation to be used for different purposes. In this research, a new downscaling methodology such as the global precipitation mission (GPM)-based multitemporal weighted precipitation analysis (GMWPA) at 0.05◦ resolution is developed and applied in the humid region of Mainland China by employing the GPM dataset at 0.1◦ and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m DEM-based geospatial predictors, i.e., elevation, longitude, and latitude in empirical distribution-based framework (EDBF) algorithm. The proposed methodology is a two-stepped process in which a scale-dependent regression analysis between each individual precipitation variable and the EDBF-based weighted precipitation with geospatial predictor(s), and to downscale the predicted multitemporal weighted precipitation at a refined scale is developed for the downscaling of GMWPA. While comparing results, it shows that the weighted precipitation outperformed all precipitation variables in terms of the coefficient of determination (R2) value, whereas they outperformed the annual precipitation variables and underperformed as compared to the seasonal and the monthly variables in terms of the calculated root mean square error (RMSE) value. Based on the achieved results, the weighted precipitation at the low-resolution (e.g., at 0.75◦ resolution) along-with the original resolution (e.g., at 0.1◦ resolution) is employed in the downscaling process to predict the average multitemporal precipitation, the annual total precipitation for the year 2001 and 2004, and the average annual precipitation (2001–2015) at 0.05◦ resolution, respectively. The downscaling approach resulting through proposed methodology captured the spatial patterns with greater accuracy at higher spatial resolution. This work showed that it is feasible to increase the spatial resolution of a precipitation variable(s) with greater accuracy on an annual basis or as an average from the multitemporal precipitation dataset using a geospatial predictor as the proxy of precipitation through the weighted precipitation in EDBF environment.

**Keywords:** downscaling; EDBF algorithm; GPM; geospatial predictor; spatial pattern; weighted precipitation

#### **1. Introduction**

Precipitation is the major component of the global water cycle. It is a key parameter of the ecological, hydrological, meteorological and agriculture systems [1,2]. It plays an important role in the energy exchange and material circulation of the Earth surface system [3]. It is of significant importance to understand the characteristics of precipitation, because it shows great variability both in space and time as compared to other climatic variables. Therefore, its spatial and temporal variability greatly influence vegetation distribution, soil moisture and surface runoff [4,5]. In addition, a high-quality precipitation dataset is very important in the development of different ecological and hydrological models at corresponding scales. On top of that, due to certain limiting factors, it is difficult to develop such high-quality dataset(s) from point measurements based on the traditional precipitation, which are as follows: first, the data derived from point measurements heavily depends on field observations [5,6]. Second, field observation stations are not uniformly distributed in space and limited mostly to low and medium altitude areas, with the exception of a few precipitation stations at high altitudes. Moreover, their operational capability is relative for a shorter period. Even if longer precipitation records exist from ground-based stations, they are not sufficient to provide coverage for the global/regional applications, due to deficiencies in reliability of the spatial distribution of precipitation [7], especially over ocean, desert and mountainous areas. Third, a true spatial coverage of precipitation based on the traditional rain gauge observations cannot be obtained [8], because many river basins around the world are still poorly gauged [9], or ungauged [10]. Fourth, it is difficult to effectively reflect the spatial variability of precipitation based on the observation from a finite number of rainfall stations, especially in areas where rainfall stations are sparsely distributed [11–13]. Fifth, rain gauge observations can only reflect the point rainfall within a radius around the location of instruments, and the effectiveness of such data is often under question, and adequate validation is further needed [14,15].

Recently, the development in remote sensing and geographic information technology has given a new dimension to present precipitation observations [16–18], almost at the global scale over a long period, which also reflects the spatial patterns and temporal variability of precipitation [19]. In this regard, various research institutions and government organizations have developed a series of gridded global precipitation datasets, including Earth observations, in situ datasets and models at both regional and global scales, i.e., the Global Precipitation Climatology Project (GPCP) [2,20–22], the Global Satellite Mapping of Precipitation (GSMaP) project [23], the Multi-Source Weighted-Ensemble Precipitation (MSWEP) [24], the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) [25], the Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Networks-Climate Record (PERSIANN-CDR) [26], the Tropical Rainfall Measuring Mission (TRMM) [27–29], the TRMM Multi-satellite Precipitation Analysis (TMPA) [30], and the Global Precipitation Mission (GPM) [31–33].

Spatial downscaling is a recently developed approach to obtain the high spatial resolution of a variable based on conjugation between the variable at a coarse scale and geospatial predictor(s) at the low resolution [34,35]. In this regard, using spatial downscaling techniques on a precipitation dataset may provide a better representation of the spatial variability of precipitation to be used for different purposes. Several authors have used downscaling methodologies to increase the spatial resolution of satellite-based precipitation, often in combination with Earth observations data available on hydro-meteorological variables related to precipitation, including normalized difference vegetation index (NDVI) [30,35–42], digital elevation model [30,38,43,44], land surface temperature [30], soil moisture [37], in situ rain gauged precipitation [37,38], slope [38], aspect [38], and wind [31]. Moreover, few authors have used different satellite-based precipitation datasets for

TRMM products [30,42]. Additionally, some studies used regression analysis with model parameters spatially constant (multiple linear, polynomial, exponential, regression kriging, etc.), assuming a spatial stationarity of the relationship between precipitation and the predicting variables [34,35,38,41,44–48]. On top of that, some studies limited their analysis only to satellite-based precipitation datasets and did not take full advantage of all available data sources, combining remotely sensed and in situ observations [42,49,50].

In this research work, a new downscaling methodology (Figure 1), based on the earlier work of [3,38,39], such as the GMWPA is developed using DEM (Figure 2a) to delineate into three geospatial predictors, i.e., elevation, longitude, and latitude [44], in EDBF algorithm. Two different satellite-based precipitation datasets, such as the GPM-based multitemporal precipitation data (Figure 2b–i) for the prediction of high-resolution downscaled weighted precipitation from 0.1◦ to 0.05◦ resolution, and the GPM (Figure 2j,k) and the TRMM (Figure 2l–o) datasets for the verification of proposed methodology is used over the humid (the Southern) region of Mainland China. During the execution, certain objectives are set to achieve the required results, which are as follows [51]: to evaluate the multitemporal precipitation (2001–2015) dataset through regression analysis, i.e., polynomial regression at different upscaled resolutions, e.g., 0.25◦, 0.5◦, 0.75◦, 1.0◦, 1.25◦, 1.50◦; (2) based on the regression output, EDBF algorithm is run to evaluate the multitemporal precipitation at each upscaled resolution by assigning weight to each temporal component; (3) to verify the output of EDBF algorithm through the TRMM and the GPM datasets; and (4) to generate the high-resolution downscaled weighted precipitation at 0.05◦ resolution based on the best performing upscaled resolution. This research can have practical implications, particularly for climate change, drought assessment, and water resources planning, which require long-term precipitation estimates at finer resolution.

**Figure 1.** Proposed methodology to predict the high-resolution downscaled weighted precipitation.

**Figure 2.** The dataset required for downscaling of the multitemporal precipitation: (**a**) the DEM of study area; (**b**) the GPM-based average winter precipitation; (**c**) the GPM-based average spring precipitation; (**d**) the GPM-based average summer precipitation; (**e**) the GPM-based average autumn precipitation; (**f**) the GPM-based average monthly precipitation; (**g**) the GPM-based average annual (2001–2015) precipitation; (**h**) the GPM-based wet year (2004) precipitation; (**i**) the GPM-based dry year (2001) precipitation, (**j**) the GPM-based annual (2006) precipitation; (**k**) the GPM-based annual (2012) precipitation; (**l**) the TRMM-based annual (2001) precipitation; (**m**) the TRMM-based annual (2006) precipitation; (**n**) the TRMM-based annual (2012) precipitation; (**o**) the TRMM-based average annual (2001–2015) precipitation, respectively.
