RESUMEN
Climate change has diversified negative implications on environmental sustainability and water availability. Assessing the impacts of climate change is crucial to enhance resilience and future preparedness particularly at a watershed scale. Therefore, the goal of this study is to evaluate the impact of climate change on the water balance components and extreme events in Piabanha watershed in the Brazilian Atlantic Forest. In this study, extreme climate change scenarios were developed using a wide array of global climate models acquired from the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Reports (AR6). Two extreme climate change scenarios, DryHot and WetCool, were integrated into the Soil and Water Assessment Tools (SWAT) hydrological model to evaluate their impacts on the hydrological dynamics in the watershed. The baseline SWAT model was first developed and evaluated using different model performance evaluation metrics such as coefficient of determination (R2), Nash-Sutcliffe (NSC), and Kling-Gupta efficiency coefficient (KGE). The model results illustrated an excellent model performance with metric values reaching 0.89 and 0.64 for monthly and daily time steps respectively in the calibration (2008 to 2017) and validation (2018 to 2023) periods. The findings of future climate change impacts assessment underscored an increase in temperature and shifts in precipitation patterns. In terms of streamflow, high-flow events may experience a 47.3 % increase, while low-flows could see an 76.6 % reduction. In the DryHot scenario, annual precipitation declines from 1657 to 1420 mm, with evapotranspiration reaching 54 % of precipitation, marking a 9 % rise compared to the baseline. Such changes could induce water stress in plants and lead to modifications on structural attributes of the ecosystem recognized as the Atlantic rainforest. This study established boundaries concerning the effects of climate change and highlighted the need for proactive adaptation strategies and mitigation measures to minimize the potential adverse impacts in the study watershed.
RESUMEN
Rainfall data is a vital input for many ecosystem service modeling in general and hydrological modeling in particular. However, accurate rainfall data with sufficient spatiotemporal distribution is inadequate in the Blue Nile Basin (Ribb watershed) due to uneven distribution of rain gauge networks. Advances in remote sensing science have provided alternative sources of rainfall data with high spatiotemporal resolution. But the accuracies of different satellite rainfall datasets are not uniform across space and time that need to be checked. The overarching objective of this study is to evaluate the performance of four satellite-based rainfall products [Tropical Applications of Meteorology using Satellite and ground-based observations (TAMSAT-v2.0 and v3.0), Climate Hazards Group InfraRed Precipitation with Station data version two (CHIRPS-v2.0), and Tropical Rainfall Measuring Mission version seven (TRMM-3B43 v7.0)] in the data-scarce region of the Blue Nile Basin in Ethiopia. The evaluation was carried out through direct comparison with the observed rainfall and through simulation of annual water yield using InVEST model for monthly, seasonal, and annual time scales. In general, the results show that the performance of satellite rainfall differs in time scale, topography, and method of evaluation. CHIRPS v2.0 rainfall product shows good performance both at monthly (R2 = 0.83) and annual (r = 0.85) time scales regardless of elevation. TRMM-3B43 v7.0 well performed over the mountainous area, which makes it the best rainfall data than other products at seasonal time scale (r = 0.86). CHIRPS v2.0 and TAMSAT v3.0 are equally applicable to that of gauged rainfall data for annual water yield simulation (Bias = 1.01 and 1.08 respectively). The findings of this study indicated the best performance of CHIRPS v2.0 and TAMSAT v3.0 satellite rainfall products, and hence, these products can be used for water management and decision-making process, particularly in the data-scarce watersheds.