Sources Of Uncertainty Research Articles

A comprehensive investigation of uncertainties in cavitating waterjet experiments

This paper systematically examines the sources of uncertainty in cavitating waterjet test setups and provides illustrative case studies to conduct thorough uncertainty analyses for erosion tests using cavitating waterjet method. Within this study, utilizing examples of uncertainty analysis presented by the International Towing Tank Conference (ITTC) for various testing techniques, a novel uncertainty analysis implementation suitable for cavitating waterjet tests has been conducted. Firstly, cavitation erosion experiments were carried out on samples aluminum (Al 6063) material under cavitation number conditions (σ = 0.02 to σ = 0.033 range) as part of the validation study. Subsequently, within the scope of uncertainty analysis calculations, five erosion tests were conducted for each of the two different cavitation numbers (σ = 0.03 and σ = 0.05). The study aims to examine the bias and precision uncertainty components separately for the test results of erosion rate and establish a reliable and repeatable procedure for predicting the uncertainty level of cavitating waterjet tests. For the first time in the literature, the uncertainty components of cavitating waterjet tests were investigated in detail and the uncertainty level in cavitating waterjet tests, including bias and precision errors, was predicted in accordance with specific ITTC guidelines and procedures. Based on the results, the primary bias error source was identified as the pressure inside the cavitation chamber, with the nozzle pressure being the second most significant bias error source. Consequently, it is recommended to perform the bias error prediction based on the ratio of these two, known as the cavitation number. Precision error, crucial for uncertainty, was estimated using five sets of erosion rate values, emphasizing its importance in cavitating waterjet tests. It is anticipated that the suggested methodology will serve as a benchmark for predicting uncertainty levels in cavitating waterjet tests.

A comprehensive analysis of uncertainties in warm rain parameterizations in climate models based on in situ measurements

Abstract Because of the coarse grid size of Earth system models (ESM), representing warm-rain processes in ESMs is a challenging task involving multiple sources of uncertainty. Previous studies evaluated warm-rain parameterizations mainly according to their performance in emulating collision-coalescence rates for local droplet populations over a short period of a few seconds. The representativeness of these local process rates comes into question when applied in ESMs for grid sizes on the order of 100 kilometers and time steps on the order of 20-30 minutes. We evaluate several widely used warm-rain parameterizations in ESM application scenarios. In the comparison of local and instantaneous autoconversion rates, the two parameterization schemes based on numerical fitting to stochastic collection equation (SCE) results perform best. However, because of Jessen’s inequality, their performance deteriorates when grid-mean, instead of locally-resolved, cloud properties are used in their simulations. In contrast, the effect of Jessen’s inequality partly cancels the overestimation problem of two semi-analytical schemes, leading to an improvement in the ESM-like comparison. In the assessment of uncertainty due to the large time step of ESMs, it is found that the rain-water tendency simulated by the SCE is roughly linear for time steps smaller than 10 minutes, but the nonlinearity effect becomes significant for larger time steps, leading to errors up to a factor of 4 for a time step of 20 minutes. After considering all uncertainties, the grid-mean and time-averaged rain-water tendency based on the parameterization schemes are mostly within a factor of 4 of the local benchmark results simulated by SCE.

Unravelling uncertainty in trajectory prediction using a non-parametric approach

Predicting the trajectories of road agents is fundamental for self-driving cars. Trajectory prediction contains many sources of uncertainty in data and modelling. A thorough understanding of this uncertainty is crucial in a safety-critical task like auto-piloting a vehicle. In practice, it is necessary to distinguish between the uncertainty caused by partial observability of all factors that may affect a driver’s near-future decisions, the so-called aleatoric uncertainty, and the uncertainty of deploying a model in new scenarios that are possibly not present in the training set, the so-called epistemic uncertainty. They reflect the trade-off between data collection and model improvement In this paper, we propose a new framework to systematically quantify both sources of uncertainty. Specifically, to approximate the spatial distribution of an agent’s future position, we propose a 2D histogram-based deep learning model combined with deep ensemble techniques for measuring aleatoric and epistemic uncertainty by entropy-based quantities. The proposed Uncertainty Quantification Network (UQnet) employs a causal part to enhance its generalizability so rare driving behaviours can be effectively identified. Experiments on the INTERACTION dataset show that UQnet is able to give more robust predictions in generalizability tests compared to the correlation-based models. Further analysis presents that high aleatoric uncertainty cases are mainly caused by heterogeneous driving behaviours and unknown intended directions. Based on this aleatoric uncertainty component, we estimate the lower bounds of mean-square-error and final-displacement-error as indicators for the predictability of trajectories. Furthermore, the analysis of epistemic uncertainty illustrates that domain knowledge of speed-dependent driving behaviour is essential for adapting a model from low-speed to high-speed situations. Our paper contributes to motion forecasting with a new framework, that recasts the problem of accuracy improvement in a way that focuses on differentiating between unpredictable components and rare cases for which more and different data should be collected.

Forests high on CO2: A glimpse into how scientists study the biochemical machinery of forest ecosystems

Are forests across the planet storing more carbon as a result of rising atmospheric carbon dioxide (CO2) concentration? In this text, I propose to reflect on the work of researchers who are asking that very question. The focus is on a particular approach called free-air CO2 enrichment (FACE) experiments, in which forests in their local environment are subjected to a CO2 concentration higher than in the ambient air, to test the CO2 fertilization hypothesis. This hypothesis predicts that forests store more carbon in those conditions. FACE studies held the promise of establishing cause–effect relations and transcending the here and now. They emerged in the United States in the late 1980s, at a time when ecology scaled up to make itself relevant to the then nascent Earth System science. FACE experiments in forests are now conducted in Australia, the United Kingdom, and Brazil, and their results are closely scrutinized by a small research community. The text, first, presents the engineering feat that underpins FACE experiments in forests and discuss two problems highlighted by scientists because they impede hasty generalization from the data: the timescale needed to witness forests change and the spatial heterogeneity of forest soils. It then shows how scientists contend with their incomplete understanding of forest ecosystems as they bring together computer models and empirical data to build consensus around the sources of epistemic uncertainty. To conclude, I propose to rework the idea of “the macroscope” to conceptualize what scientists do when they seek to study forests as complex, layered, multi-scale systems. I wish to capture how researchers, who willingly endorse a planetary gaze, problematize their own predicament. This, as I suggest, has to do with the immanence of forests in the world.

Solving the waste bin location problem with uncertain waste generation rate: A bi-objective robust optimization approach.

An efficient municipal solid waste (MSW) system is critical to modern cities in order to enhance sustainability and liveability of urban life. With this aim, the planning phase of the MSW system should be carefully addressed by decision makers. However, planning success is dependent on many sources of uncertainty that can affect key parameters of the system, for example, the waste generation rate in an urban area. With this in mind, this article contributes with a robust optimization model to design the network of collection points (i.e. location and storage capacity), which are the first points of contact with the MSW system. A central feature of the model is a bi-objective function that aims at simultaneously minimizing the network costs of collection points and the required collection frequency to gather the accumulated waste (as a proxy of the collection cost). The value of the model is demonstrated by comparing its solutions with those obtained from its deterministic counterpart over a set of realistic instances considering different scenarios defined by different waste generation rates. The results show that the robust model finds competitive solutions in almost all cases investigated. An additional benefit of the model is that it allows the user to explore trade-offs between the two objectives.

Step height measurement of monoatomic silicon crystal lattice steps with a commercial atomic force microscope

Abstract Precise control of advanced materials relies on accurate dimensional metrology at the sub-nanometre scale. At this scale, the accuracy of scanning probe microscopy (SPM) has been limited by the lack of traceable transfer standard artefacts with calibration structures of suitable dimensions. With the adoption in 2019 of the silicon crystal lattice spacing as a secondary realization of the metre in the International System of Units (SI), SPM users have direct access to a realization of the SI metre at the sub-nanometre level by means of the step height of self-assembled monatomic lattice steps that can form on the surface of silicon crystals. A key challenge of successfully adopting this pathway is establishing the need for established protocols to minimize measurement errors and artifacts in routine laboratory use.
In this study, step height measurements of monoatomic lattice steps in an ordinal/staircase structure on a Si(111) crystal surface have been derived from images acquired with a commercially available, research-level atomic force microscope (AFM). Measurement results derived from AFM images using three different SPM image processing and analysis software packages are compared. Significant sources of measurement uncertainty are identified; principally the contribution from the dependence on scan direction. The calibration of the
AFM derived from this measurment was used to traceably measure the sub-nanometre lattice steps on a silicon carbide crystal surface to demonstrate the viability of this calibration pathway.

Global convergence in terrestrial gross primary production response to atmospheric vapor pressure deficit.

Atmospheric vapor pressure deficit (VPD) increases with climate warming and may limit plant growth. However, gross primary production (GPP) responses to VPD remain a mystery, offering a significant source of uncertainty in the estimation of global terrestrial ecosystems carbon dynamics. In this study, in-situ measurements, satellite-derived data, and Earth System Models (ESMs) simulations were analysed to show that the GPP of most ecosystems has a similar threshold in response to VPD: first increasing and then declining. When VPD exceeds these thresholds, atmospheric drought stress reduces soil moisture and stomatal conductance, thereby decreasing the productivity of terrestrial ecosystems. Current ESMs underscore CO2 fertilization effects but predict significant GPP decline in low-latitude ecosystems when VPD exceeds the thresholds. These results emphasize the impacts of climate warming on VPD and propose limitations to future ecosystems productivity caused by increased atmospheric water demand. Incorporating VPD, soil moisture, and canopy conductance interactions into ESMs enhances the prediction of terrestrial ecosystem responses to climate change.

Vegetation Hydrodynamics To Inform Climate Mitigation And Adaptation

Coastal ecosystems, such as seagrass, provide many ecosystem services, including coastal protection and carbon sequestration, which make them an integral part of climate mitigation and adaptation (e.g., Fourqurean et al. 2012). Predicting the value of these ecosystem services requires an understanding of the interaction of fluid motion with vegetation. While seagrass meadows are recognized as global hotspots for carbon storage, the verification of seagrass carbon is complicated by significant heterogeneity. For example, Lavery et al. (2013) reported an 18-fold range in carbon stock across 17 different seagrass habitats (260 to 4800 g C m-2). This variability is a major source of uncertainty in assessing carbon stocks, motivating work to understand what drives it. Recent studies have highlighted how hydrodynamic conditions can be an important factor (e.g., Oreska et al. 2017, Novak et al, 2020). In this talk, we consider a combination of modeling and field measurement that explores the influence of wave and current conditions on carbon accretion in seagrass meadows.

Stereotactic body radiation therapy (SBRT) for prostate cancer: Improving treatment delivery efficiency and accuracy

PurposeIn stereotactic body radiation therapy (SBRT) for prostate cancer, intrafraction motion is an important source of treatment uncertainty as it could not be completely smoothed through fractionation. Herein, we compared different arrangements and beam qualities for extreme hypofractionated treatments to minimize beam delivery time and so intrafractional errors. MethodsA retrospective dataset of 11 patients was used. Three volumetric modulated arc therapy (VMAT) beam arrangements were compared for a prescription dose of 40 Gy/5 fractions: two full arcs, 6 MV flattening filter free (FFF); one full arc, 6 MV FFF; one full arc, 10 MV FFF. A plan quality index was defined to compare achievement of the planning goals. Plan complexity was evaluated with the modulation factor. Dose delivery accuracy and efficiency were measured with patient-specific quality assurance plans. ResultsAll treatment plans fulfilled all dose objectives. No statistical differences were found both in plan quality and complexity. Very accurate dose delivery was achieved with the three arrangements, with mean γ passing rates >96.5 % (2 %/2 mm criteria). Slightly but significantly higher γ passing rates were observed with single-arc 6 MV FFF. Contrariwise, statistically significant reductions of the delivery time were obtained with single-arc geometries: the average delivery times were 1.6 min (−46.1 %) and 1.3 min (−56.2 %) for 6 and 10 MV FFF respectively. ConclusionsThe high-quality, very fast and accurate dose delivery of single-arc plans confirmed the suitability of this arrangement for prostate SBRT. In particular, the significant reduction of delivery time would improve treatment robustness against intrafraction prostate motion.

GNET Derived Mass Balance and Glacial Isostatic Adjustment Constraints for Greenland

AbstractMonitoring the Greenland mass balance (GMB) is crucial in the context of global sea level rise. Currently, three main methods are used to measure GMB, with the primary source of uncertainty arising from the glacial isostatic adjustment (GIA) contribution. Here, we propose a novel approach based on a simple methodology that uses the entire Greenland GNSS network (GNET) as an instrument to monitor the present‐day mass changes. Our method is validated against GRACE‐derived GMB, and we find a very good agreement. This leads to an independent methodology for estimating present‐day mass changes from GNSS, bridging the gap between GRACE and GRACE‐FO in GMB estimates. Through a combined analysis of GMB from GRACE and GNET, we identify a consistency relation between the gravity and uplift signature of GIA, providing a new robust constraint for GIA models.

Is the past recoverable from the data? Pseudoproxy modelling of uncertainties in palaeoecological data

Is the past recoverable from the data? Pseudoproxy modelling of uncertainties in palaeoecological data

Film dosimetry for occupant exposure monitoring within Far-UVC installations.

Far-UVC radiation between 200 and 230 nm is a promising technology for reducing airborne disease transmission. Previous work with far-UVC lamps has demonstrated the efficacy of far-UVC radiation to inactivate bacteria and viruses while presenting minimal human health hazards. While far-UVC intentionally exposes the occupied space, effectively disinfecting air between occupants, installations must still ensure that occupant eye and skin exposure is within the recommended daily limits. This study examines far-UVC-sensitive films for measuring the dose received by occupants within two real-world far-UVC installations. The film is characterized for accuracy, angular response, wavelength response, and sources of uncertainty in film response, and used to obtain individual exposure doses that account for both the non-uniform irradiance and the unique motion of individuals within the space. Dosimetry results using the films, which account for the time-weighted average exposure of an occupant, ranged from 10% to 49% of the maximum calculated stationary dose based on peak irradiance measurements. Results from this study spotlight the need to incorporate time-weighted average considerations into the design and safety assessment of far-UVC installations to ultimately operate far-UVC technology with its full potential to prevent the spread of potentially fatal infectious diseases.

Development of a multi-level factorial data assimilation approach for groundwater contaminant sources identification

The ensemble Kalman filter (EnKF) is recognized as a widely used approach for groundwater contamination sources (GCSs) parameters identification. The identification accuracy is affected by the quantification of sources of uncertainty (such as random disturbances on model inputs and measurements) in the assimilation process, but few studies have revealed the contributions of different sources of uncertainty on identification accuracy. To this end, this study developed an innovative multi-level factorial data assimilation (MFDA) approach, which integrates ensemble Kalman filtering and multi-level factorial analysis, to comprehensively reveal the impact of sources of uncertainty on the GCSs parameters identification accuracy for the first time. Not only did it explore the direct impact of individual sources of uncertainty on the GCSs parameters identification accuracy, more importantly, it also revealed the composite ones from multi-parameter interactions among these sources of uncertainty. The developed MFDA approach quantified the contributions of multiple sources of uncertainty, including model input data error (i.e. precipitation), model parameter error (i.e. hydraulic conductivity), contaminant concentration measurements error, and ensemble size. And the feasibility of the developed approach was verified by two hypothetical numerical case studies. The results indicated that: i) it was critically important to reduce the contamination concentration measurement errors to improve GCSs parameter identification accuracy. ii) model parameter and model input data were not sensitive to the identification accuracy. iii) the interaction between sources of uncertainty significantly affects the identification accuracy, which illustrated the interaction cannot be ignored in the identification process. Overall, this study highlights that the developed MFDA approach represents a promising solution for improving GCSs parameters identification accuracy.

The interaction between incentive and opportunity in corporate tax planning: Evidence from financially constrained firms

Prior studies document that incentive factors (i.e., equity compensation, shareholder activism, financial constraints, etc.) motivate managers to avoid more taxes, which suggests that managers overlook tax planning opportunities (TPOs) in the absence of incentives. In this study, I use a prediction model to capture TPOs and find that the positive association between financial constraints (my proxy for incentive) and tax avoidance is stronger for firms with higher levels of TPOs. My results are robust to a variety of identification strategies. To shed light on a possible source of tax uncertainty, I show moderate evidence that firms with lower levels of TPOs adopt risky tax planning strategies under financial constraints to increase tax avoidance. Overall, my study contributes to the literature by demonstrating corporate tax planning as an incentive-opportunity story and identifying an important cross-section in which the connection between incentive and tax avoidance is more prominent.

Weather variability risks slow climate adaptation: An empirical analysis of forestry

The timing of climate adaptation decisions can have substantial consequences for the assessment of climate damages. Since weather variability can create risks for natural resource management that differ across adaptation choices, such variability has the potential to alter the speed of climate adaptation. This paper estimates the effect of weather variability on the timing of adaptation decisions of forest landowners in the Eastern United States. A discrete-choice econometric model of forest management is estimated and used in a bio-economic simulation that shows how variability in cold temperatures can significantly slow the rate of adapting from cold-tolerant natural hardwood forests to cold-sensitive, but highly valuable pine plantations. The range of weather variability in climate projections and across the landscape generates large differences in adaptation timing. Ignoring projected future decreases in weather variability results in a large downward bias in estimating future paths of climate adaptation. Since pine plantations produce fewer non-market ecosystem services than natural hardwood forests, an important source of future conservation uncertainty is the economic response of private forest landowners to changing weather variability.

Using NIW Observations to Assess Mixed Layer Parameterizations: A Case Study in the Tropical Atlantic

AbstractTropical sea surface temperature (SST) biases can cause atmospheric biases on global scales, hence SST needs to be represented well in climate models. A major source of uncertainties is the representation of turbulent mixing in the oceanic boundary layer, or mixed layer (ML). In the present study we focus on near‐inertial wave (NIW) induced mixing. The performance of two mixing schemes, Turbulent Kinetic Energy and K‐profile parameterization (KPP), is assessed at two sites (11.5°N, 23°W and 15°N, 38°W) in the tropical Atlantic. At 11.5°N, turbulence observations (eddy diffusivities, shear and stratification) are available for comparison. We find that the schemes differ in their representation of NIWs, but both under‐represent the observed enhanced diffusivities below the observed ML. However, we find that the models do mix below the ML at 15°N when a storm passes nearby. The near‐inertial oscillations remain below the ML for the following 10 days. Near‐inertial kinetic energy (NIKE) biases in the models are not directly correlated with the wind speed, the MLD biases, or the stratification at the ML base. Instead, NIKE biases are sensitive to the vertical mixing scheme parameterization. NIKE biases are lowest when the KPP scheme is used.

Uncertainty of scintillator-based field-output factor measurements in MR-Linacs with the two-channel chromatic stem removal technique

Current international protocols such as the IAEA/AAPM TRS-483 for small field dosimetry do not include recommendations for measurements in strong magnetic fields as found in MR-Linacs (i.e. megavoltage x-ray radiotherapy units with built-in magnetic resonance imaging). A EURAMET research project was therefore launched to support the traceability of such measurements and to contribute to the metrological basis for future recommendations. Given the high degree of water equivalence and the small size of fiber-coupled plastic scintillators, one objective of the project was to study the suitability of this detector type for dosimetry in MR-Linacs and to demonstrate their use in a clinical environment. This manuscript reports results of that work. A protocol for measuring field output factors with plastic scintillation detectors was developed. The protocol is based on calibration of the scintillators directly in a water phantom positioned in the MR-Linac rather than in a water-equivalent solid phantom as has been a common technique for conventional linacs. A detailed uncertainty budget was established involving all known sources of uncertainty including the systematic influence of light attenuation through the fiber when measuring in different field sizes. The protocol was used to measure field output factors at five MR-Linac’s (four Viewray MRIdian’s and one Elekta Unity). The protocol showed a high level of robustness leading to a less than 0.3 % deviation between field output factors calculated based on calibration data obtained before or after the field-output factor measurements. The correction for light attenuation was less than 0.4 % for all fields measured but showed a systematic offset, with larger corrections needed for small field sizes.This robustness of the protocol was further demonstrated by the closeness of agreement (agreement within uncertainty) for field-output factor measurements with two different scintillators (BCF-12 and BCF-60) for square fields (size: 0.83 cm x 0.83 cm or larger) in four nominally identical Viewray MRIdian's. To test the accuracy of the scintillator measurements, the system was used for characterization of a PTW 60019 micro diamond detector in a Viewray MRIdian. Scintillator based correction factors for output factor measurements with the micro diamond detector were found to be in agreement with independent Monte-Carlo computations. The study therefore supports that plastic scintillation detectors are an excellent option for measuring field output factors in MR-Linacs.

A physical model study on the hydraulic performances of vertical breakwaters with retreated wave walls

This paper describes a 2D physical model study on the hydraulic performances of composite vertical breakwaters with retreated wave walls. The research is an expansion of a previous experimental study by the same Authors: a very large number of experiments, in the order of 2000, have been carried out by exploring and varying a wide range of wave and geometrical parameters of the structure, to investigate their effect and importance. In order to make feasible the execution of this large number of tests, a small-scale wave flume was used and regular wave conditions were reproduced. The influence of the wave wall retreat has been investigated in terms of wave-induced forces, reflection coefficients and wave overtopping discharges, comparing the hydraulic performances of structures with retreated walls with those of a flushed wall configuration under the same wave conditions. The large number of experiments allowed to formulate a detailed description of the complex phenomena at hand, providing statistical indicators that can be used as guidelines for preliminary design purposes of such structures, quantifying the relevant sources of uncertainty. The analysis confirmed and extended the previous findings and indicates that, on average, the hydraulic performances of structures with retreated crown wall vary significantly from those of flushed wall configurations. Specifically: (I) the forces acting on the wave wall increase of a factor up to 1.5, due to the occurrence of impulsive loads; (II) the forces acting on the caisson trunk decrease of a factor up to 0.91; (III) the global forces can decrease reaching a minimum reduction factor of 0.87, although some dangerous exceptions, in which equal or larger loads, than those occurring for standard flushed wall configuration, have been registered; (IV) the reflection coefficients decrease of a factor up to 0.83; (V) the wave overtopping discharges increase up to 2.55 times those with flushed walls.

Study of boundary layer parameterization simulation uncertainties of sand-dust storm windfield using high-resolution three-dimensional Doppler wind lidar data

The processes of dust emission, the development, and dissipation of sand and dust storms all exhibit dynamic and rapidly changing characteristics, posing significant challenges for us to accurately represent their complex behaviors in numerical simulations. In the numerical weather prediction model, the selection and optimization of planetary boundary layer (PBL) parameterization schemes have a direct impact on the simulation results of PBL wind fields, primarily due to the significant uncertainty in the fine-grained simulation of PBL wind field structure. To gain a deeper understanding of the sources of uncertainty in simulating sand-dust storms through vertical wind field structure simulations of the PBL, this study utilizes high-resolution 3D Doppler wind lidar (DWL) data. Focusing on sand-dust storm wind fields with distinctive regional characteristics in Inner Mongolia, China, it examines the turbulent momentum equation and assumptions of the PBL. A comprehensive and systematic evaluation was conducted on the characteristics of horizontal and vertical wind fields, wind shear, as well as the dynamic and thermodynamic differences that arise when simulating typical sand-dust storm wind fields using the Weather Research and Forecasting (WRF) Model with various PBL schemes. This evaluation focused on simulating the typical wind field of sand-dust storms during various stages, including dust generation, occurrence, development, and dissipation. The findings indicate that non-adiabatic heating plays a significant role in the sustainability and evolution of sand-dust storms. The wind shear index holds particular importance as it embodies the dynamic factor in the model. The PBL schemes such as Mellor-Yamada Nakanishi and Niino Level 2.5 (MYNN2), A new version of asymmetric convective model (ACM2), and Grenier-Bretherton-McCaa (GBM), which factor in buoyancy effects, demonstrate more accurate wind shear simulations in sand-dust storms. Furthermore, the ACM2 scheme effectively replicates the fundamental variation characteristics of meteorological elements throughout the sand-dust storm lifecycle.

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.