Multidimensional Forecasting of Precipitation and Potential Evapotranspiration in the Paranapanema River Basin Using Neural Network Time Series

Abstract

Spatial and temporal forecasts of the hydrological cycle compartments aiming projections of extreme drought scenarios represent a challenge for the planning, management and monitoring of water resources in order to mitigate potential impacts on the natural environment, civil society and wildlife under climate change. Machine Learning (ML) methods can help in this task, combining constant updating of model information and further scenarios evaluation. This study investigated the application of multidimensional forecast of precipitation and potential evapotranspiration at the Paranapanema River Basin (PRB) for the years 2023 to 2025. PRB is a region that provides hydrological, energy and agricultural resources, located in the southeast of Brazil that has suffered several problems related to water deficit and stress as well as droughts in the last 10 years. For these reasons, geospatial technologies such as remote sensing and Geographic Information Systems (GIS) were applied to generate time series between 2001 and early 2023 for a total of 22 Hydrological Planning Units (HPUs) in the PRB. Subsequently, a Neural Network Auto Regression (NNAR) was used to forecast precipitation and potential evapotranspiration of the HPUs in the period 2023–2025, finding for the months of May, June, July and August of 2024 and later in 2025 possible periods of water deficit in the central and northern regions. Finally, a comparative analysis of possible impacts on the agricultural, energy and social sectors based on the ETA regional climate model and the forecast developed by the NNAR network is presented, showing possible scenarios for short and mid-term water planning in the PRB.