Banca de DEFESA: LUCAS DE SIQUEIRA SANTOS

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
DISCENTE: LUCAS DE SIQUEIRA SANTOS
DATA : 29/08/2025
HORA: 16:00
LOCAL: GOOGLE MEET
TÍTULO:

MACHINE LEARNING INTEGRATING CLIMATE DATA WITH GRACE MISSION DATA FOR THE RECONSTRUCTION OF TERRESTRIAL WATER STORAGE ANOMALIES IN BRAZIL.

PALAVRAS-CHAVES:

terrestrial water storage, machine learning, GRACE, climate change

PÁGINAS: 47
RESUMO:

Terrestrial water storage (TWS) is a critical component of the hydrological cycle, directly

influencing water security, energy production, and climate resilience in Brazil. Although the

country has abundant freshwater resources, their uneven spatial distribution combined with

the growing impacts of climate change exposes both the population and the economy to

hydrological risks. The Gravity Recovery and Climate Experiment (GRACE) missions have

provided valuable insights into TWS variability since 2002; however, their limited temporal

coverage constrains long-term analyses. To overcome this limitation, this research

reconstructed terrestrial water storage anomalies (TWSA) for Brazil’s 12 major river basins

from 1985 to 2002, integrating GRACE data with climatic variables (precipitation, soil

moisture, temperature, and teleconnection indices) and anthropogenic indicators derived from

land use and land cover (LULC) data. Two machine learning models—Random Forest (RF)

and Long Short-Term Memory (LSTM)—were implemented and compared to assess

performance, interpretability, and suitability for GRACE-TWSA reconstructions. Results

indicate natural seasonality throughout the year, with vegetation and climate indices emerging

as highly influential predictors of TWSA, while anthropogenic factors affect anomalies

differently across basins, particularly in areas dominated by agriculture and livestock

activities (such as cotton in the Amazon Basin and perennial crops in the São Francisco

Basin). Both RF and LSTM achieved satisfactory performance, though LSTM was able to

reconstruct the time series for only a few basins, while RF provided greater interpretability of

variable contributions. The Mann-Kendall test applied to the RF-reconstructed TWSA series

revealed significant long-term decreasing trends in the Uruguay, Parnaíba, São Francisco, and

East Atlantic basins, underscoring Brazil’s vulnerability to water stress under future climate

scenarios. By extending GRACE-derived observations, this study advances understanding of

how climate and LULC influence TWSA variability and provides evidence to support public

policies for sustainable water resource management in Brazil.

Keywords: terrestrial water storage, machine learning, GRACE, climate change

MEMBROS DA BANCA:
Interno - ***.677.059-** - HENRY DIVERTH MONTECINO CASTRO - UFPE
Externo à Instituição - PEDRO RODRIGUES MUTTI - UFRN
Presidente - 1769444 - RODRIGO MIKOSZ GONCALVES