Future Conditions Research Articles

Risk Assessment of Spodoptera exempta against Food Security: Estimating the Potential Global Overlapping Areas of Wheat, Maize, and Rice under Climate Change

Spodoptera exempta, known as the black armyworm, has been extensively documented as an invasive agricultural pest prevalent across various crop planting regions globally. However, the potential geographical distribution and the threat it poses to host crops of remains unknown at present. Therefore, we used an optimized MaxEnt model based on 841 occurrence records and 19 bioclimatic variables to predict the potential suitable areas of S. exempta under current and future climatic conditions, and the overlap with wheat, rice, and maize planting areas was assessed. The optimized model was highly reliable in predicting potential suitable areas for this pest. The results showed that high-risk distribution areas for S. exempta were mainly in developing countries, including Latin America, central South America, central Africa, and southern Asia. Moreover, for the three major global food crops, S. exempta posed the greatest risk to maize planting areas (510.78 × 104 km2), followed by rice and wheat planting areas. Under future climate scenarios, global warming will limit the distribution of S. exempta. Overall, S. exempta had the strongest effect on global maize production areas and the least on global wheat planting areas. Our study offers a scientific basis for global prevention of S. exempta and protection of agricultural crops.

Medium Term Climate Change Effects on Millet Yields in Gulu District, Northern Uganda

Climate change is expected to adversely affect crop yields and livelihoods of agro-dependent societies, especially in Sub-Saharan Africa. However, there remain gaps on the effects of expected regional climatic changes on key food security crops. This study assessed the projected climatic conditions and expected changes in millet yields for Paicho Sub County (S/C) in Gulu District up to the year 2033 using a cross sectional study design. To determine future climatic conditions, PRECIS (Providing Regional Climates for Impact Studies) model was used based on projected conditions at a 50 km spatial resolution while millet yields were modelled using Penman Grindley soil moisture balance model. PRECIS projected changes for 2033 reveal a strong and significant decrease in rainfall (p< 0.05). This is likely to decrease millet yields by 2.6% below the average current yields of 1.8 tons per hectare per year under business-as-usual scenario. The finding indicates a need for improved millet varieties that can survive under changed climatic conditions

Climate model selection via conformal clustering of spatial functional data

AbstractClimate model selection stands as a critical process in climate science and research. It involves choosing the most appropriate climate models to address specific research questions, simulating climate behaviour, or making projections about future climate conditions. This paper proposes a new approach, using spatial functional data analysis, to asses which of the 18 EURO CORDEX simulation models work better for predicting average temperatures in the Campania region (Italy). The method involves two key steps: first, using functional data analysis to process climate variables and select optimal models by a hierarchical clustering procedure; second, validating the chosen models by proposing a new conformal prediction approach to the anomalies associated to each cluster.

Improving Weather Forecasting Accuracy Using Machine Learning

Weather forecasting has several applications in our daily lives, ranging from agriculture to event planning. Previous weather forecasting models relied on a complex combination of mathematical instruments, which was insufficient to achieve a higher categorization rate. We offer fresh revolutionary approaches for estimating monthly rainfall using machine learning algorithms in this study. Weather forecasts are created by gathering quantitative information about the current state of the atmosphere. Machine learning algorithms may learn complicated mappings from inputs to outputs using only samples and with little effort. The dynamic nature of the atmosphere makes accurate weather prediction challenging. The fluctuation in weather conditions in previous years must be used to anticipate future weather conditions. It is extremely likely that it will match within the next two weeks of the preceding year. We proposed using linear regressions with the Random forest algorithm to forecast weather using characteristics such as temperature, humidity and wind. It will forecast weather based on prior records thus, this prediction will be accurate.

Probabilistic scenario-based assessment of national food security risks with application to Egypt and Ethiopia

Abstract This study presents a novel approach to assessing food security risks at the national level, employing a probabilistic scenario-based framework that integrates both Shared Socio-economic Pathways and Representative Concentration Pathways. This innovative method allows each scenario, encompassing socio-economic, and climate factors, to be treated as a model capable of generating diverse trajectories. This approach offers a more dynamic understanding of food security risks under varying future conditions. The paper details the methodologies employed, showcasing their applicability through a focused analysis of food security challenges in Egypt and Ethiopia, and underscores the importance of considering a spectrum of socio-economic and climatic factors in national food security assessments.

Predicting regional cumulative effects of future development on coastal ecosystems to support Indigenous governance

Abstract To achieve better biodiversity outcomes and match local governance capacity, cumulative effects assessment frameworks that combine Indigenous and western knowledge to predict future development impacts on biodiversity are needed. We developed a spatial future‐focused model informed by inclusive elicitation and strategic foresight to assess the regional cumulative effects of development on ecosystem health across the land and sea. We collaborated with three First Nations on the Central Coast of British Columbia, Canada, enabling Indigenous priorities, knowledge and values to drive the process, from the choice of priority ecosystem components (including salmon, herring, seabirds and bears), to identifying future development scenarios (based on forestry, energy/mining, tourism and salmon aquaculture sectors). Bayesian networks were populated with empirical data and expert judgement elicited from knowledge holders to predict the cumulative effects of current and future pressures on species and ecosystems. Under current conditions, the lowest probability of persistence was predicted for Pacific salmon (37%), followed by Pacific herring (43%). Under future conditions, the greatest declines in species health were associated with the intense development of mining, tourism and forestry, with up to a 54% decline from the current baseline health estimates predicted for Marbled Murrelets and old‐growth forest. Future outcomes for overall ecosystem health were predicted to be worst in scenarios with high future forestry activities (>60% decline in some areas). The continuation or development of all four industries resulted in an 8% decline overall in ecosystem health across the Central Coast. In contrast, predicted ecosystem health in the tourism economy scenario increased up to 15% in some marine areas, primarily driven by the removal of salmon aquaculture and forestry activities. Synthesis and applications. Our study demonstrates an inclusive, regional approach to assessing the cumulative effects of future development on coastal species. The novel participatory tools and predictive framework draw upon and interweave multiple forms of knowledge, enabling Indigenous values to drive the process, and appropriately integrate Indigenous knowledge into regional cumulative effects assessment. Our interactive web application provides First Nations partners access to all outputs, supporting Indigenous‐led governance and in situ ecosystem‐based management of their lands and water.

A framework for climate change adaptation of port infrastructures

Adaptation of port infrastructures to climate change and sea level rise effects is highlighted as a key field among transportation systems’ lines of action for adaptation, given their highly exposed location in coastal areas and position as critical nodes in logistic chains and local, regional and national economies. The present work proposes an adaptation assessment framework that, based on a high-resolution compound climate risk assessment, identifies the main threats that climate change may pose to port performance, defines a set of optimized adaptation measures and characterizes constraints for implementation, and finally evaluates the applicability and effectiveness of these measures under diverse climate scenarios and different time frames. The framework is applied in a study case located in the northern coast of Spain. It is shown that the proposed methodology enables port managers and planners to develop tailor-fitted adaptation plans, providing tools to make them coherent with actual and future uncertain climate conditions.

A40 Brecon bypass landslide: investigation, remediation and asset management for climate resilience

During February 2020, a 300 m 3 slope failure occurred within a trunk road earthwork at Brecon, Wales, leading to the initial closure of all four lanes of the dual carriageway. As part of Welsh Government's strategy for climate resilient infrastructure and in accordance with the CD622 Managing Geotechnical Risk framework, the site investigation commenced in 2020 and proceeded through various phases to the completion of remedial works in 2022. This paper discusses the approach to management of geotechnical risk for the scheme, from desk study through implementation of an on-slope ground investigation and interim risk mitigation, to design and implementation of the remedial measures. The paper describes the nature and trigger of the landslide case study, adaptations in the design phase to provide resilience to geohazards and future climatic conditions, the constraints that were overcome during the site works and measures taken to manage residual risk and ensure ease of maintenance. Thematic collection: This article is part of the Geo-resilience and infrastructure collection available at: https://www.lyellcollection.org/topic/collections/geo-resilience-and-infrastructure

Prediction of Potential Suitable Distribution Areas for an Endangered Salamander in China.

Climate change has been considered to pose critical threats for wildlife. During the past decade, species distribution models were widely used to assess the effects of climate change on the distribution of species' suitable habitats. Among all the vertebrates, amphibians are most vulnerable to climate change. This is especially true for salamanders, which possess some specific traits such as cutaneous respiration and low vagility. The Wushan salamander (Liua shihi) is a threatened and protected salamander in China, with its wild population decreasing continuously. The main objective of this study was to predict the distribution of suitable habitat for L. shihi using the ENMeval parameter-optimized MaxEnt model under current and future climate conditions. Our results showed that precipitation, cloud density, vegetation type, and ultraviolet radiation were the main environmental factors affecting the distribution of L. shihi. Currently, the suitable habitats for L. shihi are mainly concentrated in the Daba Mountains, including northeastern Chongqing and western Hubei Provinces. Under the future climate conditions, the area of suitable habitats increased, which mainly occurred in central Guizhou Province. This study provided important information for the conservation of L. shihi. Future studies can incorporate more species distribution models to better understand the effects of climate change on the distribution of L. shihi.

A high-resolution perspective of extreme rainfall and river flow under extreme climate change in Southeast Asia

Abstract. This article provides high-resolution information on the projected changes in annual extreme rainfall and high- and low-streamflow events over Southeast Asia under extreme climate change. The analysis was performed using the bias-corrected result of the High-Resolution Model Intercomparison Project (HighResMIP) multi-model experiment for the period 1971–2050. Eleven rainfall indices were calculated, along with streamflow simulation using the PCR-GLOBWB hydrological model. The historical period 1981–2010 and the near-future period 2021–2050 were considered for this analysis. Results indicate that, over former mainland Southeast Asia, Myanmar will face more challenges in the near future. The east coast of Myanmar will experience more extreme high-rainfall conditions, while northern Myanmar will have longer dry spells. Over the Indonesian maritime continent, Sumatra and Java will suffer from an increase in dry-spell length of up to 40 %, while the increase in extreme high rainfall will occur over Borneo and mountainous areas in Papua. Based on the streamflow analysis, the impact of climate change is more prominent in a low-flow event than in a high-flow event. The majority of rivers in the central Mekong catchment, Sumatra, Peninsular Malaysia, Borneo, and Java will experience more extreme low-flow events. More extreme dry conditions in the near future are also seen from the increasing probability of future low-flow occurrences, which reaches 101 % and 90 %, on average, over Sumatra and Java, respectively. In addition, based on our results over Java and Sumatra, we found that the changes in extreme high- and low-streamflow events are more pronounced in rivers with steep hydrographs (rivers where flash floods are easily triggered), while rivers with flat hydrographs have a higher risk in terms of the probability of low-flow change.

The psychosocial needs of head and neck cancer patients: a multicenter study.

Cancer is among the most frequent pathologies and a major cause of death and disability. Scientific research and surgeons focus mainly on aspects relating to etiopathogenesis, diagnosis, and treatment, but often neglect the psychological needs of patients and related social factors. The aim of this study is to investigate the psychological and social needs of patients affected by head and neck cancer to improve patient management and achieve more empathetic care. The Need Evaluation Questionnaire (NEQ) was administered to adult patients who had to undergo surgery in 4 Italian tertiary head and neck cancer centers the day before the operation. 188 patients affected by stage I-IV head and neck tumors were enrolled. The main needs expressed by patients fall under the categories of either "information and dialogue", (64.3% more information about future conditions, 50% more information about treatments, 45% more information about the diagnosis, 44% more reassurance) or "spiritual support" (50% need to talk to someone who had the same experience as them). Common methods of explaining diagnosis, treatment, and prognosis have proven unsatisfactory to patients and most also require more psychosocial support. This highlights the need to implement interventions and activities that are increasingly geared toward supporting the psychological and relational aspects of the care journey.

Large-Scale Experimental Model Of Edge Treatments For Constructed Salt Marshes

Large stretches of Canada’s coastlines are lined with legacy dykes (or levees) to provide protection against coastal flooding and erosion. However, future sea-level rise presents a risk to these structures, which may result in the dykes being under designed when exposed to future water levels and wave conditions. Under certain conditions, these dykes can be augmented with a coastal saltmarsh to create a living dyke, which allows for the existing dyke to be more resilient against sea level rise without the need to retrofit the dyke with a higher crest elevation or larger armour stone. The coastal saltmarsh is able to attenuate storm waves, resulting in lower wave energy at the dyke, while also attracting and retaining sediment to combat coastal erosion. The Living Dyke project in Boundary Bay, Canada is a pilot project being used to test the living dyke concept by placing sediment and planting salt marsh vegetation in the foreshore combined with upgrades to the existing dyke. Kerr Wood Leidal Associates Ltd. prepared a preliminary design which incorporated four different edge treatment features that are to be installed at the offshore edge of a newly constructed salt marsh platform in order to attenuate wave energy and retain the placed sediment before the planted vegetation can be established. The proposed edge treatment features included a natural sand edge, a rounded gravel berm, an oyster-shell filled bag berm, and a brushwood dam. Large scale (full scale for all edge treatment feature tests except for the brushwood dam which was conducted at half-scale) physical model experiments were carried out by the National Research Council of Canada’s Ocean, Coastal and River Engineering Research Centre in their Large Wave-Current Flume to investigate the performance, including stability of the edge treatment features, wave attenuation ability, and stability of the salt marsh platform, of these edge treatment features under varying water level and wave conditions that are present at Boundary Bay.

Climate Change Responses of High-Elevation Polylepis Forests

The high mountain forests on Earth are characterized by sharp environmental heterogeneity, high species endemism, and unique phenotypic adaptations. Yet, global warming is jeopardizing this ecosystem as no other as some forests may have nowhere to go beyond the mountain’s summits. One of the most human-endangered high-elevation ecosystems is the Polylepis forest. Despite its vulnerability, forecasting climate reactions in this distinctive high mountain forest type remains a formidable challenge. Therefore, in this study, we modeled climate change responses of high-elevation allopatric Polylepis sericea and P. quadrijuga (Rosaceae) in the northern Andes. The analysis took into account VIF-prioritized bioclimatic variables for near-to-present and future (2081–2100 MPI-ESM1-2-HR) conditions. Model selection was carried out following the AUC scores of 12 MaxEnt and six machine learning algorithms. Predictive power reached 0.97 for MaxEnt’s model fc.H_rm.1 (H-1). Results indicate a moderate vulnerability of P. sericea, with a 29% loss of area in the trailing edge, due to climate change for the period 2081–2100. On the other hand, P. quadrijuga is likely to experience even larger distribution losses, up to 99%, for the same period. The findings of this study suggest that P. quadrijuga, as compared to P. sericea, exhibits a restricted ability to maintain the corresponding habitat requirements in the face of climatic change. Higher niche specialization of P. quadrijuga in the environmentally heterogeneous Eastern Cordillera contrasts with the more generalist nature of P. sericea in the topographically less complex Central Cordillera. In addition to climate change, this trend may be exacerbated by the detrimental effects of agriculture, mining, and an expanding rural population, which represent significant human-driven pressures yet to be modeled in the northern Andean highlands. Yet, based on previous studies, the historical population dynamics during the past glacial cycles suggests that range shifts could play a more significant role at deeper time scales than previously forecasted in the species’ reaction to climate change. Additionally, Polylepis forests may be able to endure at the current locations via local adaptation and plasticity, major drivers of the phenotypic variation in long-lived trees, counteracting the vulnerability scenario under a niche conservatism hypothesis. Ultimately, the modeling procedure employed in this study provides insights into the potential effects of climate change on Polylepis forests 70 years from now. Oncoming studies may consider alternative responses inherent to the gene pool of the species and the interaction with edaphic and biotic agents. We advocate for the application of comparable estimations in other high-elevation tree communities found at the tree line.

Experimental and numerical approach for the evaluation of PV-system performance on energy and environmental behavior of nearly zero energy buildings: Case study in Mediterranean climate

Nowadays, the performance of photovoltaic (PV) systems has strategic role for satisfying the standard of net or nearly zero energy building with in-situ production. In this frame, the novelty of the paper is the proposal of a combined experimental and numerical approach for evaluating the mismatch between PV production and consumptions during design, operational and future performance under expected emissions scenarios. First of all, it proposes a comparison of production estimation by means of several tools for the evaluation of the energy balance in the design phase; then the expected and real behavior is compared with also the evaluation of the performance ratio (PR) evolution as function of cell temperature. Finally, a sensitivity analysis is proposed in the short and medium scenarios by varying the size of installed system and considering the degradation rate under future weather conditions. The approach is applied to a case study in a typical Mediterranean climate, starting from the monitoring data available for the building energy request and the renewable production (2016–2022). The results of the case study indicate that the PR greatly influences the performance, mainly under expected climate changes, since the operational emissions could increase of more than 15 % compared to actual performance also if the zero energy balance is satisfied. Furthermore, through a sensitivity analysis on the PV-system size, the study demonstrates that with a maximum linear degradation rate of 1.51 %/year, the building could be configured as nearly zero energy in the medium term (20 years) also with lower peak power. The analysis also suggests that the maximization of installed power is a prerequisite to follow the implementation of positive energy goal because, under all considered climatic evolutions it can balance the penalties both due to technical aspects that to modification of consumption and production profiles.

Predicting the current and future suitable distribution range of Trilocha varians (Walker, 1855) (Lepidoptera: Bombycidae) in China.

Trilocha varians is one of the major pests of Ficus spp. Based on 19 bioclimatic variables provided by the Worldclim, our study analysed the suitable distribution areas of T. varians under current and future climate changes (SSP1-2.6, SSP2-4.5, SSP5-8.5) for two periods (the 2050s and 2090s) using the maximum entropy algorithm (MaxEnt) model. Key environmental variables affecting the geographic distribution of T. varians were also identified, and the changes in the area of suitable range under current and future climate changes were compared. The results showed that the key environmental variables affecting the distribution of T. varians were temperature and precipitation, comprising annual mean temperature (bio1), temperature seasonality (standard deviation × 100) (bio4), precipitation of driest month (bio14), and precipitation of driest quarter (bio17). Under the current climatic conditions, the suitable distribution area of T. varians is within the range of 92°13'E-122°08'E, 18°17'N-31°55'N. The current high, medium, and low suitable areas for T. varians predicted by the MaxEnt model are 14.00 × 104, 21.50 × 104, and 71.95 × 104 km2, of which the high suitable areas are mainly distributed in southern Guangdong, southwestern Guangxi, western Taiwan, Hong Kong, and Hainan. Under different future climatic conditions, some of the high, medium, and low suitability zones for T. varians increased and some decreased, but the mass centre did not migrate significantly. The Pearl River Basin is predicted to remain the main distribution area of T. varians.

Variation in prevalence and intensity of macroparasites in moose and their interactions with winter tick load in eastern Canada

Wild animals are infected with a large diversity and abundance of parasites that can affect their behavior, growth, body condition, and ultimately their survival. Although the adverse effects of parasites and the mechanisms involved in the interactions between a host and its parasites are generally well studied, much less is known about the additive or synergistic effects of multiple parasite species on a host. Moose populations in eastern Canada are infected by several species of endoparasites. In the last decades, the intensity of infestations by winter ticks, an ectoparasite, on moose have increased as a result of increased moose densities and favorable weather conditions that benefit winter tick survival. We aimed to document the diversity, intensity, prevalence, and distribution of different parasite species of moose in southern Quebec, Canada. We then evaluated the potential interaction between winter tick and endoparasites of moose, and we evaluated the effect of the simultaneous presence of ticks and endoparasites on moose body condition. To do so, we collected organs to identify and count endoparasite species, estimate winter tick abundance, and measure subcutaneous fat thickness from 174 hunted moose in fall 2019 in 8 regions of Quebec. Our results showed that the prevalence and intensity of winter tick and gastrointestinal parasites differed among regions, as well as the prevalence of the heart parasite Taenia krabbei and the intensity of lung parasite Echinoccocus granulosus. Moose body condition, however, was not influenced by the simultaneous presence of winter tick and endoparasites. The documentation of the interactive effects of multiple parasite species on a host is fundamental given that future environmental conditions in temperate climate will favor the reproduction, development, and survival of several parasite species, which could affect parasite diversity and abundance in the environment and modify host–parasite dynamics.

The hot sex: is there evidence for climate‐driven sex allocation in mammals?

Abstract Climate change predictions indicate that future conditions will be hotter and drier, and rainfall will be more variable. Climatic conditions can affect individual condition and shape offspring sex ratios. The implications that climate change have for mammalian sex allocation are vast but are currently poorly understood. There are several scenarios under which climate‐driven sex allocation responses might emerge. Here, I make predictions on the direction of offspring sex ratio biases under hypothetical situations of restricted resource availability and altered social/spatial structures that may result from hotter, drier conditions. I searched the literature for existing evidence of climate‐driven offspring sex ratio biases among individuals studied in wild populations. There was no strong, directional support for the production of sons or daughters under ‘good’ or ‘poor’ climatic conditions among the 30 studies had reported on climatic variables and offspring sex ratio. I also searched the literature for studies that assessed maternal condition and offspring sex ratio in natural populations (i.e. there is a link between climate, resource availability and maternal condition). Of the 27 investigations, half reported a bias in favour of sons born to mothers in ‘good’ condition (56%). I discuss how this outcome could be due to a publication bias, or that it may indicate that low resource availability has the potential to lead to mothers in poor condition and the production of fewer sons. I consider the conservation implications of climate‐driven offspring sex ratios skews being carried through to the population level. I discuss the challenge of being able to account for the multiple factors that influence offspring sex ratios in natural populations and emphasise the growing need for more research in this important area of mammalian evolutionary ecology.

The functional diversity-productivity relationship of woody plants is climatically sensitive.

Plot-scale experiments indicate that functional diversity (FD) plays a pivotal role in sustaining ecosystem functions such as net primary productivity (NPP). However, the relationships between functional diversity and NPP across larger scale under varying climatic conditions are sparsely studied, despite its significance for understanding forest-atmosphere interactions and informing policy development. Hence, we examine the relationships of community-weighted mean (CWM) and functional dispersion (FDis) of woody plant traits on NPP across China and if such relationships are modulated by climatic conditions at the national scale. Using comprehensive datasets of distribution, functional traits, and productivity for 9120 Chinese woody plant species, we evaluated the distribution pattern of community-weighted mean and functional dispersion (including three orthogonal trait indicators: plant size, leaf morphology, and flower duration) and its relationships with NPP. Finally, we tested the effects of climatic conditions on community-weighted mean/functional dispersion-NPP relationships. We first found overall functional diversity-NPP relationships, but also that the magnitude of these relationships was sensitive to climate, with plant size community-weighted mean promoting NPP in warm regions and plant size functional dispersion promoting NPP in wet regions. Second, warm and wet conditions indirectly increased NPP by its positive effects on community-weighted mean or functional dispersion, particularly through mean plant size and leaf morphology. Our study provides comprehensive evidence for the relationships between functional diversity and NPP under varying climates at a large scale. Importantly, our results indicate a broadening significance of multidimensional plant functional traits for woody vegetation NPP in response to rising temperatures and wetter climates. Restoration, reforestation actions and natural capital accounting need to carefully consider not only community-weighted mean and functional dispersion but also their interactions with climate, to predict how functional diversity may promote ecosystem functioning under future climatic conditions.

Application of GIS and spatiotemporal analyses in viral infection modelling using multiple datasets – A case study on the SARS-CoV-2 epidemic

Introduction/objectiveViral and infectious diseases such as COVID-19 continue to pose a significant public health threat. In order to create an early warning system for new pandemics or emerging versions of the virus, it is imperative to study its epidemiology. In this study, we created a geospatial model to predict the weekly contagion and lethality rates of COVID-19 in Ireland. MethodsMore than forty parameters including atmospheric pollutants, metrological variables, sociodemographic factors, and lockdown phases were introduced as input variables to the model. The significant parameters in predicting the number of new cases and the death toll were identified. QGIS software was employed to process input data, and a principal component regression (PCR) model was developed using the statistical add-on XLSTAT. Results and conclusionsThe developed models were able to predict more than half of the variations in contagion and lethality rates. This indicates that the proposed model can serve to help prediction systems for the identification of future high-risk conditions. Nevertheless, there are additional parameters that could be included in future models, such as the number of deaths in care homes, the percentage of contagion and mortality among health workers, and the degree of compliance with social distancing.

Dynamic quantitative assessment of multiple uncertainty sources in future hydropower generation prediction of cascade reservoirs with hydrological variations

Hydropower is the dominant renewable energy source, which can significantly reduce carbon dioxide emissions while providing electric energy. It is a crucial way to achieve the dual-carbon goal of carbon peak and carbon neutrality. However, climate change inevitably threatens hydropower, either directly or indirectly. Global climate models (GCMs) and hydrological models (HMs) under different representative concentration pathways (RCPs) have been combined to investigate future hydropower generation responses to climate change. The contribution of multiple uncertainty sources to multi-scale hydropower generation uncertainties with hydrological variations remains unclear. Therefore, to address the above challenge, a three-step framework was proposed in this paper: (1) predicting future inflows to reservoirs using three HMs (HBV, SWAT and XAJ) with different structures driven by six GCMs (BNU-ESM, GFDL-ESM2G, GFDL-ESM2M, CanESM2, MIROC-ESM-CHEM and CSIRO-MK3-6-0) under three RCPs (i.e., RCP2.6, RCP4.5 and RCP8.5); (2) diagnosing future hydrological conditions using the Comprehensive Differential Split-sample Test; (3) predicting future hydropower generation of cascade reservoirs by developing a cascade hydropower generation model and dynamically quantifying the contribution of multiple uncertainty sources to multi-scale hydropower generation uncertainties with hydrological conditions based on the variance decomposition method through subsampling. The results indicate that GCMs, HMs, climate emission scenarios and their interactions significantly impacted the uncertainty of hydropower generation with hydrological variations. In the flood season, the relative contributions of GCMs and HMs were the largest, accounting for about 40%. The interaction effects of multiple uncertainty sources gradually increased before, after and during the non-flood season, with its relative contribution accounting for about 36%. These findings can facilitate the active response to the new challenges of strategic hydropower energy development under climate change, reducing the losses caused by climate change.