Spatial downscaling of TRMM-based precipitation data using vegetative response in Xinjiang, China

2016 ◽  
Vol 37 (10) ◽  
pp. 3895-3909 ◽  
Author(s):  
Qiang Zhang ◽  
Peijun Shi ◽  
Vijay P. Singh ◽  
Keke Fan ◽  
Jiajun Huang
Keyword(s):  
Precipitation Data ◽  
Spatial Downscaling

Evaluation and spatial downscaling of CRU TS precipitation data in the Philippines

2018 ◽  
Vol 4 (3) ◽  
pp. 891-898 ◽  
Author(s):  
Arnold R. Salvacion ◽  
Damasa B. Magcale-Macandog ◽  
Pompe C. Sta. Cruz ◽  
Ronaldo B. Saludes ◽  
Ireneo B. Pangga ◽  
...  
Keyword(s):  
The Philippines ◽  
Precipitation Data ◽  
Spatial Downscaling

scholarly journals Spatial Downscaling of Remote Sensing Precipitation Data in the Beijing-Tianjin-Hebei Region

2021 ◽  
Vol 09 (06) ◽  
pp. 191-202
Author(s):  
Nuan Wang ◽  
Jie Yu ◽  
Lin Zhu ◽  
Yanbing Wang ◽  
Zhengyang He
Keyword(s):  
Remote Sensing ◽  
Precipitation Data ◽  
Spatial Downscaling

scholarly journals Spatial Downscaling of Satellite Precipitation Data in Humid Tropics Using a Site-Specific Seasonal Coefficient

Water ◽  
2018 ◽  
Vol 10 (4) ◽  
pp. 409 ◽  
Author(s):  
Mohd. Mahmud ◽  
Mazlan Hashim ◽  
Hiroshi Matsuyama ◽  
Shinya Numata ◽  
Tetsuro Hosaka
Keyword(s):  
Humid Tropics ◽  
Precipitation Data ◽  
Site Specific ◽  
Satellite Precipitation ◽  
A Site ◽  
Spatial Downscaling

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

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
Hicham Ezzine ◽  
Ahmed Bouziane ◽  
Driss Ouazar ◽  
Moulay Driss Hasnaoui
Keyword(s):  
Statistical Analysis ◽  
Coastal Area ◽  
Mediterranean Area ◽  
The Other ◽  
Precipitation Data ◽  
Best Approximations ◽  
Correlation Models ◽  
Variable Distance ◽  
Coarse Precipitation ◽  
Spatial Downscaling

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 R2, 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.

Tenderfoot Creek Experimental Forest Annual Precipitation Data: 1993-2010

2011 ◽  
Author(s):  
David K. Wright ◽  
Lance S. Glasgow ◽  
Ward W. McCaughey ◽  
Elaine K. Sutherland
Keyword(s):  
Annual Precipitation ◽  
Precipitation Data
Sign in / Sign up

Export Citation Format

Share Document