Large Uncertainties Research Articles

Reliability ensemble averaging reduces surface wind speed projection uncertainties in the 21st century over China

Accurate prediction of future surface wind speed (SWS) changes is the basis of scientific planning for wind turbines. Most studies have projected SWS changes in the 21st century over China on the basis of the multi-model ensemble (MME) of the 6th Coupled Model Intercomparison Project (CMIP6). However, the simulation capability for SWS varies greatly in CMIP6 multi-models, so the MME results still have large uncertainties. In this study, we used the reliability ensemble averaging (REA) method to assign each model different weights according to their performances in simulating historical SWS changes and project the SWS under different shared socioeconomic pathways (SSPs) in 2015–2099. The results indicate that REA considerably improves the SWS simulation capacity of CMIP6, eliminating the overestimation of SWS by the MME and increasing the simulation capacity of spatial distribution. The spatial correlations with observations increased from 0.56 for the MME to 0.85 for REA. Generally, REA could eliminate the overestimation of the SWS by 33% in 2015–2099. Except for southeastern China, the SWS generally decreases over China in the near term (2020–2049) and later term (2070–2099), particularly under high-emission scenarios. The SWS reduction projected by REA is twice as high as that by the MME in the near term, reaching −4% to −3%. REA predicts a larger area of increased SWS in the later term, which expands from southeastern China to eastern China. This study helps to reduce the projected SWS uncertainties.

Novel techniques for in situ estimation of shear-wave velocity and damping ratio through MASW testing part II: a Monte Carlo algorithm for the joint inversion of phase velocity and phase attenuation

SUMMARY This paper deals with in situ characterization of the small-strain shear-wave velocity VS and damping ratio DS from an advanced interpretation of Multi-channel Analysis of Surface Waves (MASW) surveys. A new approach based on extracting Rayleigh wave data using the CFDBFa method has been discussed in the companion paper. This paper focuses on mapping the experimental Rayleigh wave phase velocity and attenuation into profiles of VS and DS versus depth, which is achieved through a joint inversion procedure. The joint inversion of phase velocity and attenuation data utilizes a newly developed Monte Carlo global search algorithm, which implements a smart sampling procedure. This scheme exploits the scaling properties of the solution of the Rayleigh eigenvalue problem to modify the trial earth models and improve the matching with the experimental data. Thus, a reliable result can be achieved with a limited number of trial ground models. The proposed algorithm is applied to the inversion of synthetic data and of experimental data collected at the Garner Valley Downhole Array site, as described in the companion paper. In general, inverted soil models exhibit well-defined VS profiles, whereas DS profiles are affected by larger uncertainties. Greater uncertainty in the inverted DS profiles is a direct result of higher variability in the experimental attenuation data, the limited wavelength range at which reliable values of attenuation parameters can be retrieved, and the sensitivity of attenuation data to both DS and VS. Nonetheless, the resulting inverted earth models agree well with alternative in situ estimates and geological data. The results stress the feasibility of retrieving both stiffness and attenuation parameters from active-source MASW testing and the effectiveness of extracting in situ damping ratio estimates from surface wave data.

Spectral Fingerprinting of Methane from Hyper-Spectral Sounder Measurements Using Machine Learning and Radiative Kernel-Based Inversion

Satellite-based hyper-spectral infrared (IR) sensors such as the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), and the Infrared Atmospheric Sounding Interferometer (IASI) cover many methane (CH4) spectral features, including the ν1 vibrational band near 1300 cm−1 (7.7 μm); therefore, they can be used to monitor CH4 concentrations in the atmosphere. However, retrieving CH4 remains a challenge due to the limited spectral information provided by IR sounder measurements. The information required to resolve the weak absorption lines of CH4 is often obscured by interferences from signals originating from other trace gases, clouds, and surface emissions within the overlapping spectral region. Consequently, currently available CH4 data product derived from IR sounder measurements still have large errors and uncertainties that limit their application scope for high-accuracy climate and environment monitoring applications. In this paper, we describe the retrieval of atmospheric CH4 profiles using a novel spectral fingerprinting methodology and our evaluation of performance using measurements from the CrIS sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. The spectral fingerprinting methodology uses optimized CrIS radiances to enhance the CH4 signal and a machine learning classifier to constrain the physical inversion scheme. We validated our results using the atmospheric composition reanalysis results and data from airborne in situ measurements. An inter-comparison study revealed that the spectral fingerprinting results can capture the vertical variation characteristics of CH4 profiles that operational sounder products may not provide. The latitudinal variations in CH4 concentration in these results appear more realistic than those shown in existing sounder products. The methodology presented herein could enhance the utilization of satellite data to comprehend methane’s role as a greenhouse gas and facilitate the tracking of methane sources and sinks with increased reliability.

Systematic KMTNet Planetary Anomaly Search. X. Complete Sample of 2017 Prime-field Planets

We complete the analysis of planetary candidates found by the KMT AnomalyFinder for the 2017 prime fields that cover ∼13 deg2. We report three unambiguous planets: OGLE-2017-BLG-0640, OGLE-2017-BLG-1275, and OGLE-2017-BLG-1237. The first two of these were not previously identified, while the last was not previously published due to technical complications induced by a nearby variable. We further report that a fourth anomalous event, the previously recognized OGLE-2017-BLG-1777, is very likely to be planetary, although its light curve requires unusually complex modeling because the lens and source both have orbiting companions. One of the three unambiguous planets, OGLE-2017-BLG-1275, is the first AnomalyFinder discovery that has a Spitzer microlens parallax measurement, π E ≃ 0.045 ± 0.015, implying that this planetary system almost certainly lies in the Galactic bulge. In the order listed, the four planetary events have planet-host mass ratios q and normalized projected separations s of (logq,s)=(−2.31,0.61) , (−2.06, 0.63/1.09), (−2.10, 1.04), and (−2.86, 0.72). Combined with previously published events, the 2017 prime fields contain 11 unambiguous planets with well-measured q and one very likely candidate, of which three are AnomalyFinder discoveries. In addition to these 12, there are three other unambiguous planets with large uncertainties in q.

NAJADS: a self-contained framework for the direct determination of astrophysical J-factors

Cosmological simulations play a pivotal role in understanding the properties of the dark matter (DM) distribution in both galactic and galaxy-cluster environments. The characterization of DM structures is crucial for informing indirect DM searches, aiming at the detection of the annihilation (or decay) products of DM particles. A fundamental quantity in these analyses is the astrophysical J-factor. In the DM phenomenology community, J-factors are typically computed through the semi-analytical modelling of the DM mass distribution, which is affected by large uncertainties. With the scope of addressing and possibly reducing these uncertainties, we present NAJADS, a self-contained framework to derive the DM J-factor directly from the raw simulations data. We show how this framework can be used to compute all-sky maps of the J-factor, automatically accounting for the complex 3D structure of the simulated halos and for the boosting of the signal due to the density fluctuations along the line of sight. After validating our code, we present a proof-of-concept application of NAJADS to a realistic halo from the IllustrisTNG suite, and exploit it to make a thorough comparison between our numerical approach and traditional semi-analytical methods.

Visible and near-infrared spectral results of Chang’E-5 surficial and subsurface soils

Aims. Studies on high-resolution and high-precision laboratory reflectance spectra of the Moon have historically been restricted to the analysis of old Apollo samples (>3.0 Ga). In contrast, studies of young lunar soils have exclusively relied on the analysis of remote sensing spectra. In this study, we present the results of a laboratory spectral investigation of young lunar soils (~2.0 Ga) obtained by the Chang’E-5 (CE-5) mission. Methods. We analyzed surficial and subsurface soils collected through scooped and drilled sampling methods. The laboratory reflectance spectra of the CE-5 soils were compared with those of Apollo soils and orbital spectra. Two methods were employed for maturity inversion. The relationship between the UV-vis color and TiO2 content of young basalts was also investigated. Results. The CE-5 samples exhibit much fresher spectral features, including higher reflectance, deeper absorption depths, and a smaller visible and near-infrared continuum slope (VNCS), compared to pristine regolith. The subsurface soils sampled from a depth of approximately 10 cm exhibit a slightly fresher spectral feature compared to the surficial soils. Our comparison revealed a rapid rate of space weathering at the lunar surface compared to the vertical overturn. Compared to older iron-rich soils, the CE-5 soils have a larger reflectance but similar UV-vis ratios. The UV-vis ratio alone could not accurately predict the TiO2 content of all mare basalts. The CE-5 samples provide a new ground truth for estimating the TiO2 content of young lunar basalts, which have the largest uncertainty in TiO2 content, as estimated from spectral parameters. We find that the samples returned by the CE-5 mission represent disturbed soils and that they exhibit significantly fresher characteristics compared to pristine regolith, a fact that should be kept in mind when using samples as ground truth for remote sensing research.

The Seoul National University AGN Monitoring Project. III. Hβ Lag Measurements of 32 Luminous Active Galactic Nuclei and the High-luminosity End of the Size–Luminosity Relation

We present the main results from a long-term reverberation mapping campaign carried out for the Seoul National University AGN Monitoring Project (SAMP). High-quality data were obtained during 2015–2021 for 32 luminous active galactic nuclei (AGNs; i.e., continuum luminosity in the range of 1044–46 erg s−1) at a regular cadence, of 20–30 days for spectroscopy and 3–5 days for photometry. We obtain time lag measurements between the variability in the Hβ emission and the continuum for 32 AGNs; 25 of those have the best lag measurements based on our quality assessment, examining correlation strength and the posterior lag distribution. Our study significantly increases the current sample of reverberation-mapped AGNs, particularly at the moderate-to-high-luminosity end. Combining our results with literature measurements, we derive an Hβ broadline region size–luminosity relation with a shallower slope than reported in the literature. For a given luminosity, most of our measured lags are shorter than the expectations, implying that single-epoch black hole mass estimators based on previous calibrations could suffer large systematic uncertainties.

Leveraging Deep Reinforcement Learning for Water Distribution Systems with Large Action Spaces and Uncertainties: DRL-EPANET for Pressure Control

Leveraging Deep Reinforcement Learning for Water Distribution Systems with Large Action Spaces and Uncertainties: DRL-EPANET for Pressure Control

Leveraging ICESat, ICESat‐2, and Landsat for Global‐Scale, Multi‐Decadal Reconstruction of Lake Water Levels

AbstractLakes provide important water resources and many essential ecosystem services. Some of Earth's largest lakes recently reached record‐low levels, suggesting increasing threats from climate change and anthropogenic activities. Yet, continuous monitoring of lake levels is challenging at a global scale due to the sparse in situ gauging network and the limited spatial or temporal coverage of satellite altimeters. A few pioneering studies used water areas and hypsometric curves to reconstruct water levels but suffered from large uncertainties due to the lack of high‐quality hypsometry data. Here, we propose a novel proxy‐based method to reconstruct multi‐decadal water levels from 1992 to 2018 for both large and small lakes using Landsat images and ICESat (2003–2009) and recently launched ICESat‐2 (2018+) laser altimeters. Using the new method, we evaluate reconstructed levels of 342 lakes worldwide, with sizes ranging from 1 to 81,844 km2. Reconstructed water levels have a median root‐mean‐square error (RMSE) of 0.66 m, equivalent to 57% of the standard deviation of monthly level variability. Compared with two recently reconstructed water level data sets, the proposed method reduces the median RMSE by 27%–32%. The improvement is attributable to the new method's robust construction of high‐quality hypsometry, with a median R2 value of 0.92. Most reconstructed water level time series have a bi‐monthly or higher frequency. Given that ICESat‐2 and Landsat can observe hundreds of thousands of water bodies, this method can be applied to conduct an improved global inventory of time‐varying lake levels and thus inform water resource management more broadly than existing methods.

Incorporating small‐scale disturbances in models of forest structure and aboveground biomass of tropical mountains

AbstractSeveral efforts have been made to model forest structure and estimate global aboveground biomass (AGB) magnitude and distribution. However, AGB models still show large uncertainties, especially over tropical mountains that are difficult to survey and understudied. Although forest disturbance and land use greatly influence forest structure and AGB accumulation, they are rarely considered in forest structure and AGB models. Including the effect of forest disturbance and land use could improve model performance. In this study, we used Landsat time series analysis and forest inventory data to assess and model the effect of small‐scale disturbance and land use on tropical montane forest structure and AGB. We explored two approaches: abrupt forest change detection, using the algorithm Breaks For Additive Seasonal Trend (BFAST), and gradual vegetation change by calculating the variation of remote sensing vegetation indices over time. We found that tropical montane landscapes are very dynamic and heterogenous. Out of the 284 forest plots analyzed, 27% had abrupt disturbances at least once between 1993 and 2014, and some of them showed abrupt disturbances several times. Forest plots with abrupt disturbances exhibit statistically significant lower basal area, tree height, and AGB than plots without abrupt disturbances. Using linear mixed‐effects models, we found that the number of breakpoints, annual Normalized Difference Vegetation Index SD, and Normalized Difference Water Index minimum values over time were the most informative variables for predicting forest structure, particularly basal area (adjusted R2 = 0.61). These variables are easy to calculate and could add significant power to forest structure and AGB models. In conclusion, incorporating small‐scale disturbance to model forest structure and AGB at regional scales is feasible through the identification of recent abrupt and gradual forest change using remote sensing time series. Including recent forest disturbance variables as predictors in forest structure models can greatly improve biomass predictability and mapping in dynamic landscapes.

Interhemispheric Temperature Gradient and Equatorial Pacific SSTs Drive Sahel Monsoon Uncertainties under Global Warming

Abstract The Sahel is one of the most vulnerable regions to climate change. Robust estimation of future changes in the Sahel monsoon is therefore essential for effective climate change adaptation. Unfortunately, state-of-the-art climate models show large uncertainties in their projections of Sahel rainfall. In this study, we use 32 models from CMIP6 to identify the sources of this large intermodel spread of Sahel rainfall. By using maximum covariance analysis, we first highlight two new key drivers of this spread during boreal summer: the interhemispheric temperature gradient and equatorial Pacific sea surface temperature (SST) changes. This contrasts with previous studies, which have focused mainly on the Northern Hemisphere rather than the global scale, and in which the Pacific Ocean has been neglected in favor of the Atlantic. Next, we unravel the physical mechanisms behind these statistical relationships. First, the modulation of the interhemispheric temperature gradient across the models leads to varying latitudinal positions of the intertropical convergence zone and, consequently, varying Sahel rainfall intensity. Second, models that exhibit less warming than the multimodel mean in the equatorial Pacific, thereby projecting a less “El Niño–like” mean state, simulate enhanced precipitation over the central Sahel in the future through modulations of the Walker circulation, the tropical easterly jet, the meridional tropospheric temperature gradient, and hence regional zonal wind shear. Finally, we show that these two indices collectively explain 62% of Sahel rainfall change uncertainty: 40% due to the interhemispheric temperature gradient and 22% through equatorial Pacific SST.

Novel control strategy for the global model of wind turbine

This paper presents a new nonlinear control for the overall model of a three-blade horizontal axis variable speed wind turbine (VSWT) including mechanical and electrical parts, with the aim of improving its performance and making it more profitable. The proposed control is an extension of the classical sliding mode control (SMC) by converting its sliding surface into a sliding sector. The classical SMC approach is widely used for nonlinear systems due to its stability against parameter variation, it is robustness against modeling uncertainties, its good results against external disturbances, and its ease of implementation in real time. Unfortunately, the SMC has a major drawback related to the chattering phenomenon. This phenomenon is due to the utility of a higher switching gain in the case of large uncertainties, it causes high-frequency oscillations once the sliding regime is reached, and it can cause a loss of accuracy by influencing the input control variables. This is the reason that aims to develop a new control law to eliminate the chattering and to guarantee stability, which is demonstrated by the Lyapunov theory. The effectiveness of the developed control is compared with the SMC and is illustrated by numerical simulations using MATLAB toolboxes.

Solar Wind Current Sheets: MVA Inaccuracy and Recommended Single‐Spacecraft Methodology

AbstractWe present the analysis of 1,831 current sheets (CS) observed aboard four Cluster spacecraft in a pristine solar wind. Four‐spacecraft estimates of the CS normal and propagation velocity are compared with different single‐spacecraft estimates. The Minimum Variance Analysis (MVA) of the magnetic field is shown to be highly inaccurate in estimating the normal. The MVA normal often differs by more than 60° from the normal obtained by multi‐spacecraft timing method, likely due to ambient turbulent fluctuations. In contrast, the cross‐product of magnetic fields at the CS boundaries delivers the normal with an uncertainty of less than 15° at the confidence level of 90%. The CSs are essentially frozen into plasma flow, since their propagation velocity is consistent with local ion flow velocity within 20% at the confidence level of 90%. The single‐spacecraft methodology based on the cross‐product method and frozen‐in assumption delivers the CS thickness and current density amplitude within 20% of their actual values at the confidence level of 90%. The CSs are kinetic‐scale structures with half‐thickness λ from a few tenths to tens of local proton inertial length λp and scale‐dependent shear angle and current density amplitude, and . The classification of the CSs in terms of tangential and rotational discontinuities remains a challenge, because even the four‐spacecraft normal has too large uncertainties to reveal the actual normal magnetic field component. The presented results will be valuable for the analysis of solar wind CSs, when only single‐spacecraft measurements are available.

Maternal pertussis immunization and the blunting of routine vaccine effectiveness: a meta-analysis and modeling study

A key goal of pertussis control is to protect infants too young to be vaccinated, the age group most vulnerable to this highly contagious respiratory infection. In the last decade, maternal immunization has been deployed in many countries, successfully reducing pertussis in this age group. Because of immunological blunting, however, this strategy may erode the effectiveness of primary vaccination at later ages. Here, we systematically reviewed the literature on the relative risk (RR) of pertussis after primary immunization of infants born to vaccinated vs. unvaccinated mothers. The four studies identified had ≤6 years of follow-up and large statistical uncertainty (meta-analysis weighted mean RR: 0.71, 95% CI: 0.38–1.32). To interpret this evidence, we designed a new mathematical model with explicit blunting mechanisms and evaluated maternal immunization’s short- and long-term impact on pertussis transmission dynamics. We show that transient dynamics can mask blunting for at least a decade after rolling out maternal immunization. Hence, the current epidemiological evidence may be insufficient to rule out modest reductions in the effectiveness of primary vaccination. Irrespective of this potential collateral cost, we predict that maternal immunization will remain effective at protecting unvaccinated newborns, supporting current public health recommendations.

Data-driven estimates for the geostatistical characterization of subsurface hydraulic properties

Abstract. The geostatistical characterization of the subsurface is confronted with the double challenge of large uncertainties and high exploration costs. Making use of all available data sources is consequently very important. Bayesian inference is able to mitigate uncertainties in such a data-scarce context by drawing on available background information in the form of a prior distribution. To make such a prior distribution transparent and objective, it should be calibrated against a data set containing estimates of the target variable from available sites. In this study, we provide a collection of covariance and/or variogram functions of the subsurface hydraulic parameters from a large number of sites. We analyze this data set by fitting a number of widely used variogram model functions and show how they can be used to derive prior distributions of the parameters of said functions. In addition, we discuss a number of conclusions that can be drawn for our analysis and possible uses for the data set.

Mathematical modeling applied to the uncertainty analysis of a tank prover calibration: Understanding the influence of calibration conditions on the GUM validation using the Monte Carlo method

This paper presents the calibration of the volume of a tank prover using the gravimetric method. A mathematical model of this calibration has been developed and it is non-linear. An evaluation of measurement uncertainty of the tank prover calibration has been carried out using the “Guide to the Expression of Uncertainty in Measurement” (GUM) and the Monte Carlo method. The measurement results have been compared. The influence of calibration conditions on the validation of GUM method by means of the Monte Carlo method (MCM) has been investigated computationally, as well as the behavior of the expanded uncertainty (GUM method) as a function of these calibration conditions. Seven case studies have been analyzed in this research work. In reference case, the tank prover calibration is carried out in the normal laboratory condition. In other case studies, the tank prover calibration is modeled in various hypothetical situations, such as change in tank prover scale quality, variation of calibration water density during calibration, large temperature gradient of water inside the tank prover and combination of the above conditions. In general, GUM method has not been validated by MCM due to one or more of the following conditions: a) the dominant source of uncertainty is a rectangular distribution; b) the uncertainty budget contains large uncertainty and the measurand model is non-linear; c) the measurand probability distribution is not normal.

Potential Locations of Strong Earthquakes in Bulgaria and the Neighbouring Regions

Information about potential earthquake sources is a key issue for seismic hazard assessment. This study presents the application of a phenomenological approach based on pattern recognition to determine the possible locations of strong earthquakes in the Bulgarian region. The technique assumes the origin of strong earthquakes in morphostructural nodes formed around the intersections of morphostructural lineaments identified by morphostructural zoning. For the territory of the Bulgaria and neighbouring regions, 178 nodes were defined in this work. The CORA-3 pattern recognition algorithm identified 59 seismogenic nodes analysing a set of geophysical and geological node’s characteristics. The identified seismogenic nodes are capable to generate earthquakes with magnitude equal to or greater than 6 and are located at the boundaries between the largest tectonic domains: Rila, Pirin, and Rhodope orogens; the Serbian-Macedonian massif; and in the Stara Planina belt. The set of characteristic features of seismogenic nodes indicates that the vicinity of potential nodes is characterized by a high contrast of neotectonic movements of the Earth’s crust and the presence of deep heterogeneities in the Earth’s crust. About 40% of the recognized nodes are not associated with any earthquakes, while the rest of the recognized seismogenic nodes are characterized by an area with a radius of 25 km where earthquakes are known to occur. Part of these “non active” seismogenic nodes are close to the historical events with magnitudes higher than 5.5 since the magnitude and location of historical events have large uncertainties. Another part of the seismogenic nodes may slightly change the location due to the uncertainties in morphostructural zonation. Other nodes may indicate unknown historical seismicity or paleoearthquakes. Defined M6+ seismogenic nodes can fill the potential gaps in the recorded seismicity on the territory of Bulgaria, thus to improve the seismic hazard assessment of the studied region.

Identifying transient storage model parameters in karst conduits using the normal-score ensemble smoother with multiple data assimilation

Transient storage model (TSM) is widely used to describe solute transport processes in karst areas. Accurately identifying model parameters is essential for predicting and mitigating groundwater contamination. This paper employs the normal-score ensemble smoother with multiple data assimilation (NS-ES-MDA) algorithm to automatically identify model parameters (cross-sectional area of main channel A, dispersion coefficient D, cross-sectional area of storage zone As, and exchange coefficient α), while evaluating their uncertainty across four cases: identification of only model parameters (case 1), model parameters alongside tracer parameters (case 2), model parameters with conduit length (case 3), and all three –model parameters, tracer parameters, and conduit length (case 4). We examine the variations in uncertainty of model parameters when tracer parameters and/or conduit length are known or unknown. We further investigate the effects of concentration observation limits and observation error variance on the identification performance. The results demonstrate that the proposed method can successfully identify model parameters, even in cases where tracer parameters and/or conduit length are unknown, although with a certain level of uncertainty. However, the calibration of tracer parameters leads to an increase in uncertainty for A and D, and a slight rise in uncertainty for α. The calibration of conduit length significantly elevates the uncertainties of A and As and causes an increase in uncertainty for D. Compared with tracer parameters, conduit length causes larger uncertainty in A and As. The identification performance significantly deteriorates when the tail of observed breakthrough curve (BTC) is truncated, and reaches its peak at an observation error variance of 1 × 10-4. This implies that it is crucial to monitor the tail in BTC and to assign a reasonable observation error when using this method to identify model parameters. Although these findings are derived from a synthetic karst conduit, they offer valuable insights for identifying model parameters as tracer data and/or conduit length are uncertain in field conditions.

Tikhonov Regularization for the Fully Coupled Integral Method of Incremental Hole-Drilling

BackgroundThe unit pulse integral method is used extensively with the incremental hole-drilling residual stress measurement technique. The ASTM E837 standard, which applies only to isotropic materials, recommends the use of Tikhonov regularization to reduce instability when many depth increments are used. In its current formulation, Tikhonov regularization requires the decoupling of stress, as is possible for isotropic materials. The fully coupled integral method is needed for residual stress determination in layered composite laminates and is currently employed without Tikhonov regularization. This causes greater sensitivity to measurement errors and consequently large stress uncertainties. An approximate method of applying Tikhonov regularization exists for biaxial composites, but is not applicable to more complex laminates.ObjectiveExtend Tikhonov regularization to the fully coupled integral method to improve residual stress determination in composite laminates.MethodsThis work investigates the use of the approximate and fully coupled regularization approaches in an angle ply composite laminate of [+45/-45/0/90]s construction. Experimental validation in a [0/+45/90/-45]s laminate is also presented where the regularized fully coupled integral method is compared to the series expansion method that includes all in-plane stress and strain directions simultaneously in a least-squares solution.ResultsThe regularized integral method produces comparable results to those of series expansion while requiring twelve times less FE computation to calculate the compliances. The optimal degree of regularization is also more convenient to determine than the optimal combination of series order required by series expansion.ConclusionsThe new method is easily applied and should find wide application in the measurement of residual stresses in composite laminates.

Response of runoff and its components to climate change in the Manas River of the Tian Shan Mountains

A warming–wetting climate trend has led to increased runoff in most watersheds in the Tian Shan Mountains over the past few decades. However, it remains unclear how runoff components, that is, rainfall runoff (Rrain), snowmelt runoff (Rsnow), and glacier meltwater (Rglacier), responded to historical climate change and how they will evolve under future climate change scenarios. Here, we used a modified Hydrologiska Byråns Vattenbalansavdelning (HBV) model and a detrending method to quantify the impact of precipitation and temperature changes on runoff components in the largest river (Manas River) on the northern slope of the Tian Shan Mountains from 1982 to 2015. A multivariate calibration strategy, including snow cover, glacier area, and runoff was implemented to constrain model parameters associated with runoff components. The downscaled outputs of 12 general circulation models (GCMs) from the Sixth Coupled Model Intercomparison Project (CMIP6) were also used to force the modified HBV model to project the response of runoff and its components to future (2016–2100) climate change under three common socio-economic pathways (SSP126, SSP245, and SSP585). The results indicate that Rrain dominates mean annual runoff with a proportion of 42%, followed by Rsnow (37%) and Rglacier (21%). In terms of inter-annual variation, Rrain and Rsnow show increasing trends (0.93 (p < 0.05) and 0.31 (p > 0.05) mm per year), while Rglacier exhibits an insignificant (p > 0.05) decreasing trend (−0.12 mm per year), leading to an increasing trend in total runoff (1.12 mm per year, p > 0.05). The attribution analysis indicates that changes in precipitation and temperature contribute 8.16 and 10.37 mm, respectively, to the increase in runoff at the mean annual scale. Climate wetting (increased precipitation) increases Rrain (5.03 mm) and Rsnow (3.19 mm) but has a limited effect on Rglacier (−0.06 mm), while warming increases Rrain (10.69 mm) and Rglacier (5.79 mm) but decreases Rsnow (−6.12 mm). The negative effect of glacier shrinkage on Rglacier has outweighed the positive effect of warming on Rglacier, resulting in the tipping point (peak water) for Rglacier having passed. Runoff projections indicate that future decreases in Rglacier and Rsnow could be offset by increases in Rrain due to increased precipitation projections, reducing the risk of shortages of available water resources. However, management authorities still need to develop adequate adaptation strategies to cope with the continuing decline in Rglacier in the future, considering the large inter-annual fluctuations and high uncertainty in precipitation projection.