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.
- Results and Discussion
- 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.