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Climate change is expected to affect hydropower generation by modifying river runoff and increasing reservoir evaporation. Anticipating the impact of climate change on hydropower generation is crucial to develop adaptation strategies and to efficiently plan a renewable energy transition. In this study, we assess the impact of climate change on hydropower generation using the Soil & Water Assessment Tool (SWAT) in Portuguese river basin with strategic importance, since it is responsible for 20% of the national hydropower generation. SWAT was calibrated against 6 reservoir flow-in and 1 river discharge, with a good agreement between simulated and observed values. Future climate projections were based on EURO-CORDEX climate simulations under RCP4.5 and 8.5 scenarios for 2031-2060 (short-term) and 2071-2100 (long-term), compared to 1976-2005. Results reveal that in the short-term, reservoir flow-in is expected to decrease up to 55% in the summer under RCP4.5, and up to 90% in the long-term under RCP8.5. Consequently, the hydropower plants may generate less 79 GWh per year in the short-term under RCP4.5, and less 272 GWh per year in the long-term under RCP8.5, which is equivalent to 11% and 38%, respectively, of the total electricity used in the study area in 2019. Our study shows that, at least in some regions, climate change can substantially reduce hydropower generation and thereby hamper the renewable energy transition. This is relevant for policymakers and water managers by allowing them to anticipate the impact of climate change on hydropower generation and better plan a renewable energy transition. The study was supported by the project CLIMALERT: Climate AlertSmart System for Sustainable Water and Agriculture, an ERA-NET initiated by JPI Climate (ERA4CS programme) co-funded by the EU commission (Grant Agreement 690462) and FCT (ERA4CS/0004/2016). Jose Pedro Ramiao was supported by Fundacao para a Ciencia e Tecnologia (FCT) (SFRH/BD/141486/2018) and the European Social Fund through the "Programa Operacional Regional do Norte" of the European Commission. Claudia Carvalho-Santos is supported by the "Contrato-Programa" UIDP/04050/2020 funded by national funds through the Fundacao para a Ciencia e Tecnologia I.P. Rute Pinto gratefully acknowledges the support of Global Water Futures as part of the Canada First Research Excellence Fund. Published online: 6 December 2022

Climate change is expected to affect hydropower generation by modifying river runoff and increasing reservoir evaporation. Anticipating the impact of climate change on hydropower generation is crucial to develop adaptation strategies and to efficiently plan a renewable energy transition. In this study, we assess the impact of climate change on hydropower generation using the Soil & Water Assessment Tool (SWAT) in Portuguese river basin with strategic importance, since it is responsible for 20% of the national hydropower generation. SWAT was calibrated against 6 reservoir flow-in and 1 river discharge, with a good agreement between simulated and observed values. Future climate projections were based on EURO-CORDEX climate simulations under RCP4.5 and 8.5 scenarios for 2031-2060 (short-term) and 2071-2100 (long-term), compared to 1976-2005. Results reveal that in the short-term, reservoir flow-in is expected to decrease up to 55% in the summer under RCP4.5, and up to 90% in the long-term under RCP8.5. Consequently, the hydropower plants may generate less 79 GWh per year in the short-term under RCP4.5, and less 272 GWh per year in the long-term under RCP8.5, which is equivalent to 11% and 38%, respectively, of the total electricity used in the study area in 2019. Our study shows that, at least in some regions, climate change can substantially reduce hydropower generation and thereby hamper the renewable energy transition. This is relevant for policymakers and water managers by allowing them to anticipate the impact of climate change on hydropower generation and better plan a renewable energy transition. The study was supported by the project CLIMALERT: Climate AlertSmart System for Sustainable Water and Agriculture, an ERA-NET initiated by JPI Climate (ERA4CS programme) co-funded by the EU commission (Grant Agreement 690462) and FCT (ERA4CS/0004/2016). Jose Pedro Ramiao was supported by Fundacao para a Ciencia e Tecnologia (FCT) (SFRH/BD/141486/2018) and the European Social Fund through the "Programa Operacional Regional do Norte" of the European Commission. Claudia Carvalho-Santos is supported by the "Contrato-Programa" UIDP/04050/2020 funded by national funds through the Fundacao para a Ciencia e Tecnologia I.P. Rute Pinto gratefully acknowledges the support of Global Water Futures as part of the Canada First Research Excellence Fund. Published online: 6 December 2022

AbstractRebuilding overexploited marine populations is an important step to achieve the United Nations' Sustainable Development Goal 14—Life Below Water. Mitigating major human pressures is required to achieve rebuilding goals. Climate change is one such key pressure, impacting fish and invertebrate populations by changing their biomass and biogeography. Here, combining projection from a dynamic bioclimate envelope model with published estimates of status of exploited populations from a catch‐based analysis, we analyze the effects of different global warming and fishing levels on biomass rebuilding for the exploited species in 226 marine ecoregions of the world. Fifty three percent (121) of the marine ecoregions have significant (at 5% level) relationship between biomass and global warming level. Without climate change and under a target fishing mortality rate relative to the level required for maximum sustainable yield of 0.75, we project biomass rebuilding of 1.7–2.7 times (interquartile range) of current (average 2014–2018) levels across marine ecoregions. When global warming level is at 1.5 and 2.6°C, respectively, such biomass rebuilding drops to 1.4–2.0 and 1.1–1.5 times of current levels, with 10% and 25% of the ecoregions showing no biomass rebuilding, respectively. Marine ecoregions where biomass rebuilding is largely impacted by climate change are in West Africa, the Indo‐Pacific, the central and south Pacific, and the Eastern Tropical Pacific. Coastal communities in these ecoregions are highly dependent on fisheries for livelihoods and nutrition security. Lowering the targeted fishing level and keeping global warming below 1.5°C are projected to enable more climate‐sensitive ecoregions to rebuild biomass. However, our findings also underscore the need to resolve trade‐offs between climate‐resilient biomass rebuilding and the high near‐term demand for seafood to support the well‐being of coastal communities across the tropics.

AbstractRebuilding overexploited marine populations is an important step to achieve the United Nations' Sustainable Development Goal 14—Life Below Water. Mitigating major human pressures is required to achieve rebuilding goals. Climate change is one such key pressure, impacting fish and invertebrate populations by changing their biomass and biogeography. Here, combining projection from a dynamic bioclimate envelope model with published estimates of status of exploited populations from a catch‐based analysis, we analyze the effects of different global warming and fishing levels on biomass rebuilding for the exploited species in 226 marine ecoregions of the world. Fifty three percent (121) of the marine ecoregions have significant (at 5% level) relationship between biomass and global warming level. Without climate change and under a target fishing mortality rate relative to the level required for maximum sustainable yield of 0.75, we project biomass rebuilding of 1.7–2.7 times (interquartile range) of current (average 2014–2018) levels across marine ecoregions. When global warming level is at 1.5 and 2.6°C, respectively, such biomass rebuilding drops to 1.4–2.0 and 1.1–1.5 times of current levels, with 10% and 25% of the ecoregions showing no biomass rebuilding, respectively. Marine ecoregions where biomass rebuilding is largely impacted by climate change are in West Africa, the Indo‐Pacific, the central and south Pacific, and the Eastern Tropical Pacific. Coastal communities in these ecoregions are highly dependent on fisheries for livelihoods and nutrition security. Lowering the targeted fishing level and keeping global warming below 1.5°C are projected to enable more climate‐sensitive ecoregions to rebuild biomass. However, our findings also underscore the need to resolve trade‐offs between climate‐resilient biomass rebuilding and the high near‐term demand for seafood to support the well‐being of coastal communities across the tropics.

Recent research efforts on modeling personal thermal comfort support the integration of personalized preferences in optimal building control and further implementation in real buildings. This paper presents the development and field implementation of personal preference-based thermal control (i.e., a controller that provides thermal conditions to satisfy personal preferences) in real offices, emphasizing the role of model predictive control (MPC) and low-cost local sensing. Probabilistic thermal preference profiles were developed from experiments collected in identical private offices with controllable VAV systems. A lowcost thermal sensing network and a MPC framework were integrated into a centralized building management and control system. Customized, preference-based HVAC control was then experimentally implemented in the offices to (i) quantify the personal comfort penalty when using conventional wall thermostat versus local sensing-based operation for two distinct thermal preference profiles; (ii) evaluate the impact of personalized MPC (dynamic setpoint) on energy use and personal comfort compared with personalized simple feedback control (static setpoint), using local sensing; (iii) compare the personalized MPC performance for two distinct thermal preference profiles under different weather conditions. The results indicate the comfort benefits of monitoring local thermal conditions (vs wall thermostats) for different preference profiles and showed 28–35% energy savings with personalized MPC (vs personalized static setpoint control). The overall personalized MPC performance (and energy consumption) depends on the personal thermal preference characteristics and outdoor conditions.

Recent research efforts on modeling personal thermal comfort support the integration of personalized preferences in optimal building control and further implementation in real buildings. This paper presents the development and field implementation of personal preference-based thermal control (i.e., a controller that provides thermal conditions to satisfy personal preferences) in real offices, emphasizing the role of model predictive control (MPC) and low-cost local sensing. Probabilistic thermal preference profiles were developed from experiments collected in identical private offices with controllable VAV systems. A lowcost thermal sensing network and a MPC framework were integrated into a centralized building management and control system. Customized, preference-based HVAC control was then experimentally implemented in the offices to (i) quantify the personal comfort penalty when using conventional wall thermostat versus local sensing-based operation for two distinct thermal preference profiles; (ii) evaluate the impact of personalized MPC (dynamic setpoint) on energy use and personal comfort compared with personalized simple feedback control (static setpoint), using local sensing; (iii) compare the personalized MPC performance for two distinct thermal preference profiles under different weather conditions. The results indicate the comfort benefits of monitoring local thermal conditions (vs wall thermostats) for different preference profiles and showed 28–35% energy savings with personalized MPC (vs personalized static setpoint control). The overall personalized MPC performance (and energy consumption) depends on the personal thermal preference characteristics and outdoor conditions.

Indoor thermal comfort represents a key aspect of building design. The reference standards do not consider the thermal adaptability of the human body, and HVAC system control strategies are based on a steady-state assumption that returns an incorrect estimate of occupants' thermal demand with a consequential misleading of the building energy consumption and of system sizing. To overcome these issues, a physiological thermal comfort model for the human body thermal behaviour evaluation is developed in MatLab environment for assessing the dynamic variation of the physiological parameters and for characterizing the occupants’ thermal sensation. Finally, the developed human body multi-node model is implemented in a building energy simulation tool (called DETECt 2.4) to perform three proposed strategies for the dynamic control of the building thermo-hygrometric parameters of the building and of the corresponding heating and cooling demands. These strategies provide an hourly regulation of relative humidity and air temperature by means of a two-step optimization that maximizes the thermal comfort of the occupants and minimizes energy consumption. To show the potentiality of the developed model, a suitable case study consisting of an office space is considered. Here, space heating and cooling demands obtained by applying the novel developed model are compared to those obtained through standard set-point values of air temperature and relative humidity (20 °C, 45% for heating needs, and 26 °C, 50% for cooling ones). By the comparison, between the proposed model – which considers the optimal hourly set point assessing the occupant in thermal evolution – and the reference ones, interesting results are obtained. Generally, a higher consumption is achieved by considering the proposed comfort strategies (from 2 to 16%), representing the price to be paid to maximize the comfort of the occupants and to annul the 3650 h of discomfort of the reference case.

Indoor thermal comfort represents a key aspect of building design. The reference standards do not consider the thermal adaptability of the human body, and HVAC system control strategies are based on a steady-state assumption that returns an incorrect estimate of occupants' thermal demand with a consequential misleading of the building energy consumption and of system sizing. To overcome these issues, a physiological thermal comfort model for the human body thermal behaviour evaluation is developed in MatLab environment for assessing the dynamic variation of the physiological parameters and for characterizing the occupants’ thermal sensation. Finally, the developed human body multi-node model is implemented in a building energy simulation tool (called DETECt 2.4) to perform three proposed strategies for the dynamic control of the building thermo-hygrometric parameters of the building and of the corresponding heating and cooling demands. These strategies provide an hourly regulation of relative humidity and air temperature by means of a two-step optimization that maximizes the thermal comfort of the occupants and minimizes energy consumption. To show the potentiality of the developed model, a suitable case study consisting of an office space is considered. Here, space heating and cooling demands obtained by applying the novel developed model are compared to those obtained through standard set-point values of air temperature and relative humidity (20 °C, 45% for heating needs, and 26 °C, 50% for cooling ones). By the comparison, between the proposed model – which considers the optimal hourly set point assessing the occupant in thermal evolution – and the reference ones, interesting results are obtained. Generally, a higher consumption is achieved by considering the proposed comfort strategies (from 2 to 16%), representing the price to be paid to maximize the comfort of the occupants and to annul the 3650 h of discomfort of the reference case.