Comparing GRACE with CCI Soil Moisture data on a global scale

Abstract

Water storage and water storage changes have become a topic of increasing interest, because they can be used as an indicator of climate change impacts. Total water storage (TWS) is understood as the sum of groundwater, soil moisture (SM), surface water and snow. Changes in TWS (TWSC) can be detected using spaceborne gravimetric measurments. The Gravity Recovery and Climate Experiment (GRACE) Tellus Land dataset provides estimates of TWSC over land. Surface SM estimates can be derived from microwave remote sensing. The Climate Change Initiative Soil Moisture (CCI SM) project combines different microwave remote sensing products to one SM dataset mapping the whole world. The aim of this thesis is to evaluate the correspondence between the CCI SM and the GRACE Tellus dataset using the Pearson correlation coefficient on a global scale. Precipitation datasets (GPCC and GPCP), the ERA-Interim/Land dataset and a map of area equipped for irrigation will help to understand the results of the comparison. As expected both datasets (CCI SM and GRACE Tellus) match each other well, indicated by positive correlation coefficients. Most of the values are between 0.6 and 0.8 with no lag between the datasets. A lag of one month results in higher values (between 0.8 and 1). In the very north of the northern hemisphere and Saudi Arabia negative correlation coefficients are predominant (values between -0.6 and -0.3). For the northern hemisphere this can be explained by the fact that the CCI SM dataset only indicates liquid water. During colder periods snow and ice lead to an increase in TWS, but a decrease in SM. The decrease in SM is caused by the fact that ice and snow are masked in the CCI SM datasets. The area surrounding Saudi Arabia shows some contradictory results, considering the different datasets used. Especially the fact that TWS increases during warmer periods and decreases in colder periods needs to be reviewed critically. Concluding the CCI SM dataset can help to understand the water cycle, especially in combination with the GRACE Tellus dataset on a global scale. It is important to take into account that the mentioned areas need some further research to understand the main drivers for the resulting negative correlation coefficients.