Precipitation Data from CHELSA Model
A majority of applications in environmental and ecological sciences take the benefit of high-resolution climatic datasets. These improved resolutions are mostly available at local scales; however, some advanced approaches have made it possible to generate a variety of variables at higher resolutions on a global scale. The CHELSA [Climatologies at High Resolution for The Earth’s Land Surface Areas] is a sort of reanalysis data source, which produces downscaled model outputs of climatic variables e.g. precipitation and temperature at 30 arc sec resolution (Karger et al. 2017). CHELSA applies the statistical downscaling technique on atmospheric temperature to generate high-resolution temperature data layers. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height with a subsequent bias correction (Karger & Zimmermann, 2019). CHELSAcruts consists of monthly climate datasets including mean monthly maximum temperatures, mean monthly minimum temperatures, and monthly precipitation sums for the period of 1901 to 2016. More information about the CHELSA is available in Karger & Zimmermann [2019]. In this study, the CHELSA data model was used as the high-resolution data source for precipitation.
Temperature Data from FLDAS Noah Model
FLDAS is the Famine Early Warning Systems Network [FEWS NET] Land Data Assimilation System. With the use of meteorological inputs such as temperature, rainfall, humidity and wind, FLDAS generates multi-model and multi-forcing estimations of hydro-climatic conditions such as soil moisture, evapotranspiration, and runoff (McNally et al, 2017). FLDAS offers monthly hydro-climatic variables such as rainfall, evaporation, air and soil temperature etc. modelled by two land surface models: Noah3.3 [0.1-degree resolution] and VIC4.2.1 [0.25-degree resolution]. In the current study, the FLDAS Noah3.3 data model was used as the high-resolution data source for temperature.
  1. Results and Discussion
  2. Temporal Variations of the GLDAS Based SMS and SWE
The GLDAS mission products are offered as temporal estimations of the variables of interest. Since the GRACE deliveries are in the form of anomaly estimations, the auxiliary variables SMS and SWE should be transformed into anomalies to be in harmony with GRACE TWSA measurements. GRACE gravity records are translated into anomalies of terrestrial water storage based on the mean estimations of TWS from 2004-2009. In this study, the anomalies of soil moisture storage and snow water equivalent received from GLDAS Noah model outputs were calculated according to this baseline. Figures 2 and 3 show the variations of the mean zonal values of SMS and SWE over Turkey, respectively.