Water storage variability across Brazil

Abstract

Brazil hosts a large amount of freshwater. Knowing how this stored water is partitioned in space and time between surface and subsurface components is a crucial step towards a more correct depiction of the country’s water cycle, which has major implications for decision making related to water resources management. Here, we extracted monthly water storage (WS) variability, from 2003 to 2020, based on multiple state-of-the-art datasets representing different WS components – groundwater (GW), soil moisture (SM), surface waters (SW), and artificial reservoirs (RS) – in all Brazilian Hydrographic Regions (BHRs), and computed each component’s contribution to the total variability. Most of the variability can be attributed to SM (40-68%), followed by GW (18-40%). SW has great influence in the north-western BHRs (humid monsoon influenced) with 18-40% and the southern BHRs (subtropical system influenced) with 5-10%. RS has important contributions in the Paraná with 12.1%, São Francisco with 3.5%, and Tocantins-Araguaia with 2.1%. In terms of long-term variability, water storages have been generally decreasing in the eastern and increasing in north-western and southern BHRs, with GW and RS being the most affected, although it can also be observed in SW peaks. Comparisons made with previous studies show that the approach and datasets used can have a considerable impact in the results. Such analysis can have broad implications in identifying the nature of amplitude and phase variability across regions in order to better characterize them and to obtain better evaluations of hydrological trends under a changing environment.

Drought Propagation in Brazilian Biomes Revealed by Remote Sensing

“…Regarding the classification of 'hydrological drought', this study focused on TWSA, which refers to all water storage and does not focus on groundwater. This was due to the occurrence of substantial uncertainty in soil moisture data from remote sensing [83], leading to higher uncertainties in estimates of groundwater from TWSA. In addition, some limitations are associated with the analysis of variables with different spatial resolutions.…”