Downscaling of Open Coarse Precipitation Data through Spatial and Statistical Analysis, Integrating NDVI, NDWI, Elevation, and Distance from Sea

Abstract

This study aims to improve the statistical spatial downscaling of coarse precipitation (TRMM 3B43 product) and also to explore its limitations in the Mediterranean area. It was carried out in Morocco and was based on an open dataset including four predictors (NDVI, NDWI, DEM, and distance from sea) that explain TRMM 3B43 product. For this purpose, four groups of models were established based on different combinations of the four predictors, in order to compare from one side NDVI and NDWI based models and the other side stepwise with multiple regression. The models that have given rise to the best approximations and best fits were used to downscale TRMM 3B43 product. The resulting downscaled and calibrated precipitations were validated by independent RGS. Aside from that, the limitations of the proposed approach were assessed in five bioclimatic stages. Furthermore, the influence of the sea was analyzed in five classes of distance. The findings showed that the models built using NDVI and NDWI have a high correlation and therefore can be used to downscale precipitation. The integration of elevation and distance improved the correlation models. According to 2 , RMSE, bias, and MAE, the study revealed that there is a great agreement between downscaled precipitations and RGS measurements. In addition, the analysis showed that the contribution of the variable (distance from sea) is evident around the coastal area and decreases progressively. Likewise, the study demonstrated that the approach performs well in humid and arid bioclimatic stages compared to others.

Solar Radiation Models and Gridded Databases to Fill Gaps in Weather Series and to Project Climate Change in Brazil

“…Regarding rainfall data, the major limitation for their spatial interpolation based on satellite data, as done by AgCFSR, AgMERRA, and NASA/POWER, is the low or inadequate resolution of the images which is not good enough to capture extreme events [56,57] and local spatial variability associated with the topography [58,59]. Similarly, the poor performance of all databases to estimate WS 2m is related to two main aspects: the small magnitude of this variable, which leads to large errors even with small deviations, and its high spatial variability associated with the topography and land cover [60].…”

Simulation and construction of the glacier mass balance in the Manas River Basin, Tianshan, China from 2000 to 2016

“…Therefore, remote sensing meteorological data, such as MOD11C3 and TRMM 3B43, can provide a new way to obtain meteorological data in alpine glacier regions and overcome the limitations of the observed meteorological data. However, the lower spatial resolution of the remote sensing data cannot meet the input requirements of the degree-day model, therefore, it is necessary to improve the resolution of MOD11C3 and TRMM 3B43 data via downscaling methods (Ezzine et al, 2017). To reflect the particular changes of the GMB, glacier meltwater is often introduced and regarded as a product of the GMB such that it has a profound impact on regional water resources.…”

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

“…), assuming a spatial stationarity of the relationship between precipitation and the proxy variables (Duan and Bastiaanssen, 2013;Fang et al, 2013;Park, 2013;Hunink et al, 2014). In addition, most studies limited their analyses to satellite-based precipitation datasets and did not take full advantage of all available data sources, combining remotely sensed and in situ observations (Ceccherini et al, 2015;Xu et al, 2015;Ezzine et al, 2017). In this study, a new downscaling methodology based on earlier work of Duan and Bastiaanssen (2013), Hunink et al (2014) and Ceccherini et al (2015) was developed using four proxies (EVI, elevation, slope, and aspect) in a Geographically Weighted Regression (GWR) algorithm, in which regression parameters varied with location.…”