Spatial Downscaling of Satellite-Based Precipitation and Its Impact on Discharge Simulations in the Magdalena River Basin in Colombia

Abstract

Precipitation is one of the most important components of the water cycle and its accurate spatial and temporal representation is fundamental for hydrological modeling. In the present study, we investigated the impact of spatial resolution of various precipitation datasets on discharge estimates. First, a new precipitation spatial downscaling procedure was developed and applied to four gridded global precipitation datasets based on (i) solely satellite observations: CMORPH and PERSIANN, (ii) satellite and in situ observations: TRMM, and (iii) satellite and in situ observations and reanalysis data: MSWEP. The here presented downscaling methodology blended global precipitation datasets with data on vegetation and topography to improve the representation of precipitation spatial variability. Second, interpolated in situ, non-downscaled (25 km) and downscaled (1 km) precipitation data were used to force a grid-distributed version of the HBV-96 rainfall-runoff model for the Magdalena River basin in Colombia. Results showed that MSWEP and TRMM outperformed CMORPH and PERSIANN precipitation datasets. The downscaling procedure resulted in considerable improvements in coefficient of determination, root mean square error and bias in comparison with in situ precipitation observations. Discharge model estimates were also in better agreement with the observations when the model was forced with the downscaled precipitation. Model performance was improved with Kling Gupta efficiency increases in the order of 0.1 to 0.5. Moreover, better discharge simulations were obtained using downscaled precipitation compared to using only in situ precipitation data when using less than 100 stations.

Comparison of Different Methods for Spatial Downscaling of GPM IMERG V06B Satellite Precipitation Product Over a Typical Arid to Semi-Arid Area

“…Chen et al (2015) firstly introduced the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) to improve the downscaling results for TMPA precipitation over an arid to semi-arid area. It has also been found by Jing et al (2016) and López et al (2018) that LST and slope as well as aspect had significant influences on satellite precipitation downscaling. On the whole, the inclusion of auxiliary variables from single one to multiple ones can better describe the complicated relationships between precipitation and land surface characteristics.…”

Remote Sensing of River Discharge: A Review and a Framing for the Discipline

“…Various authors have investigated RS of each of these specific components in an effort to better constrain each one. Parr et al ( [112]; 2015) used RS ET and leaf area index products in conjunction with the VIC model, while Lopez-Lopez et al ( [113]; 2018) explored downscaling and in 2017 calibrated the PCR GLOBWB model for a basin in Morocco with RS ET and soil moisture, and both concluded that their approach is viable and improves discharge accuracy [114]. However, Mendiguren et al ( [115]; 2017) and Bowman et al ( [116]; 2016) explicitly compared RS ET energy balance models against traditionally calibrated hydrological models and found low correlation between the two products.…”

GPM-Based Multitemporal Weighted Precipitation Analysis Using GPM_IMERGDF Product and ASTER DEM in EDBF Algorithm

“…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].…”

“…Recently, the development in remote sensing and geographic information technology has given a new dimension to present precipitation observations [16][17][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][21][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][28][29], the TRMM Multi-satellite Precipitation Analysis (TMPA) [30], and the Global Precipitation Mission (GPM) [31][32][33].…”