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<p>Climate change is imposing sustainability challenges related to food security, energy generation and biodiversity loss all over the world. Global warming alters fluxes from hydrological compartments such as precipitation and evapotranpiration patterns, however river flow and change in watershed storage have a less understood dynamics, raising concerns about water demand for human consumption, industrial use and hydropower generation. Moreover, during the last decade droughts and floods are causing massive damage in all parts of the world and concerns for future water demand in the south of Brazil. This study addresses future discharge projections in the Miringuava river, a water resource that currently supplies water to Curitiba metropolitan region, an area with more then 2 million inhabitants. Global Climate Models (GCM) precipitation and evapotranspiration projections were used as inputs to the GR2M hydrological model for past and future streamflow projections. Historical precipitation and evapotranspiration data from 2000 to 2019 were used for model calibration via the Differential Evolution algorithm. Future projections were designed to follow IPCC’s SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. Among 56 GCMs, 23 met selection criteria, and eight were analyzed. The CMIP6 models and datasets were selected by applying standardized ESGF filters for monthly frequency, target variables (precipitation and evapotranspiration), climate scenarios, and a common variant label (r1i1p1f1), followed by the selection of models with complete scenario availability and the best agreement with observed data. Projections of monthly flows were calculated for all GCMs and SSPs from 2020 to 2100. The GCM with best representation of precipitation data for Miringuava basin between 2016 and 2019 was ACCESS-CM2, which achieved a classification of very good performance for all SSPs. Unlike the results for precipitation, the GCMs showed consistent performance across different scenarios when evapotranspiration was analyzed, the EC-Earth3 model had the best performance among the models for all scenarios, being classified as very good, while the MRI-ESM2-0 and CAS-ESM2-0 models showed the worst performance. In general, the EC-Earth3 model performed best, while KACE-1-0-G had the worst comparison with the 2016-2019 period. Future projections indicate potential increases in droughts or floods, depending on the chosen GCM. However, ensemble results suggest a rise in maximum flows as we move from SSP1-2.6 to SSP5-8.5. This research presented a unique approach of an integrated evaluation of each water balance variable (i.e. precipitation, evapotranspiration and river flow) when simulating hydrological models in future scenarios. There is a need to create more accurate versions of local models by downscaling the GCMs to understand patterns at a smaller scale. Our findings helps understanding the global warming impacts on river flow in regions with humid subtropical and hot temperate climates, supporting water management and water security.</p>

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Assessing the impacts of climate change on river flow regimes in Paraná, Brazil using GR2M and GCM projections

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月10日

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  • Mariana G. Ghirelli,
  • Felipe B. Civitate,
  • Emilio F. G. Mercuri,
  • Steffen M. Noe

摘要

Climate change is imposing sustainability challenges related to food security, energy generation and biodiversity loss all over the world. Global warming alters fluxes from hydrological compartments such as precipitation and evapotranpiration patterns, however river flow and change in watershed storage have a less understood dynamics, raising concerns about water demand for human consumption, industrial use and hydropower generation. Moreover, during the last decade droughts and floods are causing massive damage in all parts of the world and concerns for future water demand in the south of Brazil. This study addresses future discharge projections in the Miringuava river, a water resource that currently supplies water to Curitiba metropolitan region, an area with more then 2 million inhabitants. Global Climate Models (GCM) precipitation and evapotranspiration projections were used as inputs to the GR2M hydrological model for past and future streamflow projections. Historical precipitation and evapotranspiration data from 2000 to 2019 were used for model calibration via the Differential Evolution algorithm. Future projections were designed to follow IPCC’s SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. Among 56 GCMs, 23 met selection criteria, and eight were analyzed. The CMIP6 models and datasets were selected by applying standardized ESGF filters for monthly frequency, target variables (precipitation and evapotranspiration), climate scenarios, and a common variant label (r1i1p1f1), followed by the selection of models with complete scenario availability and the best agreement with observed data. Projections of monthly flows were calculated for all GCMs and SSPs from 2020 to 2100. The GCM with best representation of precipitation data for Miringuava basin between 2016 and 2019 was ACCESS-CM2, which achieved a classification of very good performance for all SSPs. Unlike the results for precipitation, the GCMs showed consistent performance across different scenarios when evapotranspiration was analyzed, the EC-Earth3 model had the best performance among the models for all scenarios, being classified as very good, while the MRI-ESM2-0 and CAS-ESM2-0 models showed the worst performance. In general, the EC-Earth3 model performed best, while KACE-1-0-G had the worst comparison with the 2016-2019 period. Future projections indicate potential increases in droughts or floods, depending on the chosen GCM. However, ensemble results suggest a rise in maximum flows as we move from SSP1-2.6 to SSP5-8.5. This research presented a unique approach of an integrated evaluation of each water balance variable (i.e. precipitation, evapotranspiration and river flow) when simulating hydrological models in future scenarios. There is a need to create more accurate versions of local models by downscaling the GCMs to understand patterns at a smaller scale. Our findings helps understanding the global warming impacts on river flow in regions with humid subtropical and hot temperate climates, supporting water management and water security.