Individual Sources Of Uncertainty Research Articles

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.

Uncertainty in water transit time estimation with StorAge Selection functions and tracer data interpolation

Abstract. Transit time distributions (TTDs) of streamflow are useful descriptors for understanding flow and solute transport in catchments. Catchment-scale TTDs can be modeled using tracer data (e.g. oxygen isotopes, such as δ18O) in inflow and outflows by employing StorAge Selection (SAS) functions. However, tracer data are often sparse in space and time, so they need to be interpolated to increase their spatiotemporal resolution. Moreover, SAS functions can be parameterized with different forms, but there is no general agreement on which one should be used. Both of these aspects induce uncertainty in the simulated TTDs, and the individual uncertainty sources as well as their combined effect have not been fully investigated. This study provides a comprehensive analysis of the TTD uncertainty resulting from 12 model setups obtained by combining different interpolation schemes for δ18O in precipitation and distinct SAS functions. For each model setup, we found behavioral solutions with satisfactory model performance for in-stream δ18O (KGE > 0.55, where KGE refers to the Kling–Gupta efficiency). Differences in KGE values were statistically significant, thereby showing the relevance of the chosen setup for simulating TTDs. We found a large uncertainty in the simulated TTDs, represented by a large range of variability in the 95 % confidence interval of the median transit time, varying at the most by between 259 and 1009 d across all tested setups. Uncertainty in TTDs was mainly associated with the temporal interpolation of δ18O in precipitation, the choice between time-variant and time-invariant SAS functions, flow conditions, and the use of nonspatially interpolated δ18O in precipitation. We discuss the implications of these results for the SAS framework, uncertainty characterization in TTD-based models, and the influence of the uncertainty for water quality and quantity studies.

Absolute Calibration of the Chinese HY-2B Altimetric Mission with Fiducial Reference Measurement Standards

This research and collaboration work aims at the calibration and validation (Cal/Val) of the Chinese HY-2B satellite altimeter based upon two permanent Cal/Val facilities: (1) the China Altimetry Calibration Cooperation Plan in Qingdao, Bohai Sea and the Wanshan islands, China and (2) the permanent facility for altimetry calibration established by the European Space Agency in Crete, Greece. The HY-2B satellite altimeter and its radiometer have been calibrated and monitored using uniform, standardized procedures, as well as protocols and best practices, and they also built upon trusted and indisputable reference standards at both Cal/Val infrastructures in Europe and China. The HY-2B altimeter is thus monitored in a coordinated, absolute, homogeneous, long-term and worldwide manner. Calibration of altimeters is accomplished by examining satellite observations in open seas against reference measurements. Comparisons are established through precise satellite positioning, water level observations, GPS buoys and reference models (geoid, mean dynamic topography, earth tides, troposphere and ionosphere), all defined at the Cal/Val sites. In this work, the final uncertainty for the altimeter bias will be attributed to several individual sources of uncertainty, coming from observations in water level, atmosphere, absolute positioning, reference surface models, transfer of heights from Cal/Val sites to satellite observations, etc. Through this project, the procedures, protocols and best practices, originally developed in the course of the ESA FRM4ALT project, are updated, upgraded and followed at both Cal/Val facilities in Europe and China. All in all, the HY-2B satellite altimeter observes the sea level quite well and within its specifications.

Uncertainty Analysis and Quantification in Flood Insurance Rate Maps Using Bayesian Model Averaging and Hierarchical BMA

Flood Insurance Rate Maps (FIRMs) managed by FEMA have been providing ongoing flood information to most communities in the United States over the past half-century. However, the uncertainty associated with the modeling of FIRMs, some of which are created by using a single Hydrologic Engineering Center River Analysis System (HEC-RAS) one-dimensional (1D) steady-flow model, may have adverse effects on the reliability of flood stage and inundation extent predictions. Therefore, a systematic understanding of the uncertainty in the modeling process of FIRMs is necessary. Bayesian model averaging (BMA), which is a statistical approach that can combine estimations from multiple models and produce reliable probabilistic predictions, was applied to evaluating the uncertainty associated with FIRMs. In this study, both the BMA and hierarchical BMA (HBMA) approaches were used to quantify the uncertainty within the detailed FEMA models of the Deep River and the Saint Marys River in the state of Indiana based on water stage predictions from 150 HEC-RAS 1D unsteady-flow model configurations that incorporate four uncertainty sources including bridges, channel roughness, floodplain roughness, and upstream flow input. Given the ensemble predictions and the observed water stage data in the training period, the BMA weight and the variance for each model member were obtained, and then the BMA prediction ability was validated for the observed data from the later period. The results indicate that the BMA prediction is more robust than both the original FEMA model and the ensemble mean. Furthermore, the HBMA framework explicitly shows the propagation of various uncertainty sources, and both the channel roughness and the upstream flow input have a larger impact on prediction variance than bridges. Hence, it provides insights for modelers into the relative impact of individual uncertainty sources in the flood modeling process. The results show that the probabilistic flood maps developed based on the BMA analysis could provide more reliable predictions than the deterministic FIRMs.

Uncertainty Calculation for the Determination of Chromium Oxide in Leather

This work describes the uncertainty calculation for the determination of chromium oxide in leather. Following the various steps for the calculation of uncertainty which includes assembling a list of sources of uncertainty, with the help of basic formula used to calculate the measure and as all parameters cause an influence on the result because of the uncertainty value associated with it then the basic cause and effect diagram is made which shows the correlation between these and indicates their effect on the uncertainty of the result.All individual sources of uncertainty are evaluated by quantification. Mass, purity, and molecular mass of potassium dichromate are calculated against the volume of sodium thiosulphate following the first step in the test method is the standardization of sodium thiosulphate with the primary standard, potassium dichromate. So the overall uncertainty value depends on the uncertainties associated with the mass, purity, and molecular mass of potassium dichromate.

Measurement uncertainty evaluation of an analytical procedure for determination of terbinafine hydrochloride in creams by HPLC and optimization strategies using Analytical Quality by Design

The measurement uncertainty (MU) evaluation is important to ensure the analytical procedure performance, since the higher the uncertainty value the lower the reliability of the analytical result, leading to the risks of false decision in conformity assessment. Therefore, when an analytical procedure provides results with MU value above the recommended, it is necessary to adopt optimization strategies to improve the performance. Thus, this work aimed to evaluate the MU and the individual sources of uncertainty, as well as to propose optimization strategies using Analytical Quality by Design (AQbD) approach, considering an analytical procedure for the determination of terbinafine hydrochloride in creams by HPLC. Initially, the MU was estimated by top-down approach, based on trueness and precision data. Then, the MU was estimated by bottom-up approach, allowing the identification and quantification of the sources of uncertainty, which was used as a basis for the design of experiments, along with factors that influence the chromatographic responses. The regression models obtained were considered adequate and then were used to define the operable design region using Monte Carlo method. The optimized procedure was verified for performance, showing a significant reduction in MU (61.5%), risks of false decision (specific consumer risk by 100%, global consumer risk by 40%, global producer risk by 85%), and relative costs of decision-making in conformity assessment (27.4%). This work showed the importance of MU in the performance evaluation of analytical procedures and its impact on the risks of false decision in conformity assessment of medicines. Additionally, the proposed strategies to improve the performance proved to be effective, obtaining a robust procedure with reduced MU, ensuring results with greater reliability.

Evaluation of pressure and species concentration measurement using uncertainty propagation

This paper presents a probabilistic uncertainity evaluation method as described in the Guide to the Expression of Uncertainty in Measurements (GUM) and its application to probe measurements on pressure and fuel concentration. All sources of unceratinties are expressed as probability distributions. Consequently, the overall standard uncertainty of the quantity can be calculated using the Gaussian error propagation formula. The result of the uncertainty evaluation yields the most probable value of the measurand and describes its distribution in terms of rectangular (standard uncertainty) or gaussian (“expanded” uncertainty) distribution. A pitot-static probe and a fuel-concentration stem probe are used in order to demonstrate the principle of the probabilistic uncertainty evaluation method. The uncertainty induced by the pressure and concentration data acquisition system as well as the calibration of the fuel-concentration probe are included in the analysis. The overall “expanded” uncertainties for the measured and calculated values are presented as a function of different inlet fuel flows. In addition to this, the individual sources of uncertainty to the overall standard uncertainty are presented and discussed. Moreover, the transformation of standard uncertainty to “expanded” uncertainty will provide the deviation of the measurement in a 95% or 99% normal distributed interval instead of a 67% rectangular distributed interval.

A probabilistic uncertainty evaluation method for turbomachinery probe measurements

The paper presents a probabilistic uncertainty evaluation method described in the Guide to the Expression of Uncertainty in Measurement (GUM) [1] and its application to the field of Turbomachinery probe measurement techniques. All sources of uncertainties contributing to a measurement result are expressed in terms of probability distributions. Consequently, the overall standard uncertainty of the result can be calculated using the Gaussian error propagation formula. The result of the uncertainty evaluation yields the most probable value of the measurand and describes its distribution in terms of standard or extended uncertainties. A pneumatic five-hole-probe measurement technique has been chosen to show the principle of the probabilistic uncertainty evaluation method. The complete signal chain including the probe calibration, the modeling and the application in the turbine has been included in the analysis. The overall uncertainties of the measured flow angles and flow total and static pressures are presented as a function of the flow Mach number. In addition, the contribution of the individual sources of uncertainty to the overall standard uncertainty is shown. Based on this break down of uncertainties optimization options of the measurement chain are suggested.

Separation and prioritization of uncertainty sources in a raster based flood inundation model using hierarchical Bayesian model averaging

Uncertainty in a hydrodynamic model originates from input data, model structure and parameters. In order to provide the robust model predictions, the Bayesian model averaging (BMA) approach could be used as a multi-model combining method to account for the compound effects from various uncertainty sources. However, BMA cannot provide a clear picture of the impact from individual uncertainty sources. Hierarchical Bayesian model averaging (HBMA) is a recently developed approach to study the relative impact of different uncertainty sources, which explicitly considers various sources of uncertainty in the hierarchical structure (BMA tree) for analysing uncertainty propagation. In this study, HBMA is tested over the Black River watershed in Missouri and Arkansas based on water stage predictions from 243 LISFLOOD-FP model configurations that integrate five sources of uncertainty including channel shape, channel width, channel roughness, topography and flow forcing. To compare, the model perturbation approach is also applied in this study to investigate the influence of individual uncertainty sources on model prediction. Overall, the results indicate that HBMA provides an alternative way for flood modellers to deal with modelling uncertainty in data sparse region when multiple choices of uncertainty sources are considered. Findings from Black River watershed point out that without considering the model weight (model perturbation approach), channel width and topographical data resolution have the largest impact on the hydrodynamic model predictions followed by flow forcing, which has a relatively greater influence than channel cross-sectional shape and model parameter. However, when model weights are taken into account (HBMA), model input (topography and flow forcing) and model parameter have a larger impact on prediction variance than model structure (channel width and cross-sectional shape). Moreover, as results move up the hierarchy along the BMA tree, the accuracy of deterministic mean prediction also increases in general whereas the 95% confidence interval associated with the deterministic mean prediction might become larger.

Dependence of flood peaks and volumes in modeled discharge time series: Effect of different uncertainty sources

Flood estimates needed for designing efficient and cost-effective flood protection structures are usually derived using observed peak discharges. This approach neglects, firstly, that floods are characterized not only by peak discharge but also by flood volume, and, secondly, that these characteristics are subject to modifications under climate and land use changes. Bivariate flood frequency analysis based on simulated discharge time series makes it possible to consider both flood peak and flood volume in design flood estimation. Further, this approach considers changes in discharge characteristics by using discharge series generated from climate time series used as an input for a hydrological model. Such series are usually not available at an hourly resolution but at a certain aggregation level (e.g. 24 h) and might not perfectly represent observed precipitation distributions. In this study, we therefore investigate how the aggregation and distribution of precipitation series and discharge distribution affect flood peaks and volumes and their dependence. We propose a framework for assessing the uncertainty in bivariate design flood estimates that is caused by different factors in the modeling chain, which consists of precipitation-discharge modeling, flood event sampling, and bivariate flood frequency analysis. The uncertainty sources addressed are precipitation aggregation and distribution, parameter and model uncertainty, and discharge resolution. Our results show that all of these uncertainty sources are relevant for design flood estimation and that the importance of the individual uncertainty sources is catchment dependent. Our results also demonstrate that substantial uncertainty is introduced already in the first step of the model chain because commonly used calibration procedures do not take into account the reproduction of flood volumes. Researchers should be aware of such deficiencies when performing bivariate flood frequency analysis on modeled discharge time series and should aim to tailor model calibration procedures to the problem at hand.

Bayesian Hierarchical Model Uncertainty Quantification for Future Hydroclimate Projections in Southern Hills-Gulf Region, USA

The study investigates the hierarchical uncertainty of multi-ensemble hydroclimate projections for the Southern Hills-Gulf region, USA, considering emission pathways and a global climate model (GCM) as two main sources of uncertainty. Forty projections of downscaled daily air temperature and precipitation from 2010 to 2099 under four emission pathways and ten CMIP5 GCMs are adopted for hydroclimate modeling via the HELP3 hydrologic model. This study focuses on evapotranspiration (ET), surface runoff, and groundwater recharge projections in this century. Climate projection uncertainty is characterized by the hierarchical Bayesian model averaging (HBMA) method, which segregates emission pathway uncertainty and climate model uncertainty. HBMA is able to derive ensemble means and standard deviations, arising from individual uncertainty sources, for ET, runoff, and recharge. The model results show that future recharge in the Southern Hills-Gulf region is more sensitive to different climate projections and exhibits higher variability than ET and runoff. Overall, ET is likely to increase and runoff is likely to decrease in this century given the current emission path scenarios. Runoff are predicted to have an 18% to 20% decrease and ET is predicted to have around a 3% increase throughout the century. Groundwater recharge is likely to increase in this century with a decreasing trend. Recharge would increase about 13% in the early century and will have only a 3% increase in the late century. All hydrological projections have increasing uncertainty towards the end of the century. The HBMA result suggests that the GCM uncertainty dominates the overall hydrological projection uncertainty in the early century and the mid-century. The emission pathway uncertainty becomes important in the late century.

Estimating uncertainty in the volume and carbon storage of downed coarse woody debris.

Downed coarse woody debris, also known as coarse woody detritus or downed dead wood, is challenging to estimate for many reasons, including irregular shapes, multiple stages of decay, and the difficulty of identifying species. In addition, some properties are commonly not measured, such as wood density and carbon concentration. As a result, there have been few previous evaluations of uncertainty in estimates of downed coarse woody debris, which are necessary for analysis and interpretation of the data. To address this shortcoming, we quantified uncertainties in estimates of downed coarse woody debris volume and carbon storage using data collected from permanent forest inventory plots in the northeastern United States by the Forest Inventory and Analysis program of the USDA Forest Service. Quality assurance data collected from blind remeasurement audits were used to quantify error in diameter measurements, hollowness of logs, species identification, and decay class determination. Uncertainty estimates for density, collapse ratio, and carbon concentration were taken from the literature. Estimates of individual sources of uncertainty were combined using Monte Carlo methods. Volume estimates were more reliable than carbon storage, with an average 95% confidence interval of 15.9 m3/ha across the 79 plots evaluated, which was less than the mean of 31.2 m3/ha. Estimates of carbon storage (and mass) were more uncertain, due to poorly constrained estimates of the density of wood. For carbon storage, the average 95% confidence interval was 11.1 Mg C/ha, which was larger than the mean of 4.6 Mg C/ha. Accounting for the collapse of dead wood as it decomposes would improve estimates of both volume and carbon storage. On the other hand, our analyses suggest that consideration of the hollowness of downed coarse woody debris pieces could be eliminated in this region, with little effect. This study demonstrates how uncertainty analysis can be used to quantify confidence in estimates and to help identify where best to allocate resources to improve monitoring designs.

Uncertainty Assessment of Synthetic Design Hydrographs for Gauged and Ungauged Catchments

AbstractDesign hydrographs described by peak discharge, hydrograph volume, and hydrograph shape are essential for engineering tasks involving storage. Such design hydrographs are inherently uncertain as are classical flood estimates focusing on peak discharge only. Various sources of uncertainty contribute to the total uncertainty of synthetic design hydrographs for gauged and ungauged catchments. These comprise model uncertainties, sampling uncertainty, and uncertainty due to the choice of a regionalization method. A quantification of the uncertainties associated with flood estimates is essential for reliable decision making and allows for the identification of important uncertainty sources. We therefore propose an uncertainty assessment framework for the quantification of the uncertainty associated with synthetic design hydrographs. The framework is based on bootstrap simulations and consists of three levels of complexity. On the first level, we assess the uncertainty due to individual uncertainty sources. On the second level, we quantify the total uncertainty of design hydrographs for gauged catchments and the total uncertainty of regionalizing them to ungauged catchments but independently from the construction uncertainty. On the third level, we assess the coupled uncertainty of synthetic design hydrographs in ungauged catchments, jointly considering construction and regionalization uncertainty. We find that the most important sources of uncertainty in design hydrograph construction are the record length and the choice of the flood sampling strategy. The total uncertainty of design hydrographs in ungauged catchments depends on the catchment properties and is not negligible in our case.

Partitioning multi-source uncertainties in simulating nitrogen loading in stream water using a coherent, stochastic framework: Application to a rice agricultural watershed in subtropical China

Uncertainty is recognized as a critical consideration for accurately predicting stream water nitrogen (N) loading, but identifying the relative contribution of individual uncertainty sources within the total uncertainty remains unclear. In this study, a powerful method, referred to as the Bayesian inference combined with analysis of variance (BayeANOVA) was adopted to detect the timing and magnitude of multiple uncertainty sources and their relative contributions to total uncertainty in simulating daily loadings of three stream water N species (ammonium-N: NH4+-N, nitrate-N: NO3−-N and total N: TN) in a rice agricultural watershed (the Tuojia watershed) as influenced by non-point source N pollution. Five sources of uncertainty have been analyzed in this study, which arise from model structure, parameters, inputs, interaction effects between parameters and inputs, and internal variability (induced by random errors of model or environment). The results show that uncertainty in parameters relating to the processes of both N and hydrologic cycles contributed the largest fractions of total uncertainty in N loading simulations (58.83%, 63.48% and 61.64% for NH4+-N, NO3−-N and TN loading, respectively). Additionally, three of the largest uncertainties (i.e. parameters, inputs and interaction effects) in all three simulated N loadings were on average significantly greater in the rice-growing season relative to the fallow season, primarily due to the excess fertilization application during the rice-growing season. The predicted TN uncertainty was mainly attributed to the inaccuracy of NO3−-N simulation, which contributed to 75.48% of predicted TN uncertainty. It is concluded that reducing the parameter uncertainty of NO3−-N loading simulation during the rice-growing season is the key factor to improving stream water N modeling precision in rice agricultural watersheds.

Uncertainty determination in high-temperature spectral emissivity measurement method of coatings

The paper deals with uncertainty analyses of the new laboratory method for the measurement of spectral emissivity of high-temperature coatings. These coatings are intended to increase heat transfer in various industrial applications. The experimental set-up of the method is shortly introduced. The method is innovative in the application of scanning laser heating and in the coating surface temperature measurement using an infrared camera with a reference coating. Methods for total and partial uncertainty evaluation are described. As the uncertainty is always related to individual sample being measured, the DupliColor 800°C paint (MOTIP Dupli Ltd.) is used as an example to introduce the results. Except the absorption bands the uncertainty is below 4% with coverage factor k=2. Uncertainty spectral and temperature dependences are analyzed. Contribution of individual uncertainty sources as measured sample signal, measured laboratory blackbody signal, sample surface temperature, laboratory blackbody temperature, surroundings temperature, blackbody effective emissivity and surroundings emissivity and their sub-components are discussed.

Calibration and testing of wide‐field UV instruments

AbstractAs with all optical systems the calibration of wide‐field ultraviolet (UV) systems includes three main areas: sensitivity, imaging quality, and imaging capability. The one thing that makes UV calibrations difficult is the need for working in vacuum substantially extending the required time and effort compared to visible systems. In theory a ray tracing and characterization of each individual component of the optical system (mirrors, windows, and grating) should provide the transmission efficiency of the combined system. However, potentially unknown effects (contamination, misalignment, and measurement errors) can make the final error too large and unacceptable for most applications. Therefore, it is desirable to test and measure the optical properties of the whole system in vacuum and compare the overall response to the response of a calibrated photon detector. A proper comparison then allows the quantification of individual sources of uncertainty and ensures that the whole instrument performance is within acceptable tolerances or pinpoints which parts fail to meet requirements. Based on the experience with the IMAGE Spectrographic Imager, the Wide‐band Imaging Camera, and the ICON Far Ultraviolet instruments, we discuss the steps and procedures for the proper radiometric sensitivity and passband calibration, spot size, imaging distortions, flatfield, and field of view determination.

Uncertainty Assessment for Measurement Processes in the Aerospace Manufacturing Industry

Measurement processes are critical to the aerospace industry, which products must follow strict regulations and customer requirements. Additionally, measurement of uncertainty is fast becoming a requirement from both certification bodies and customers. An uncertainty assessment must be carried out for all processes that need to add an uncertainty statement to the measurement result. In order to maintain defined quality standards, aerospace manufacturing companies need to identify all measurement disciplines that benefit from stating the level of uncertainty and define a methodology to calculate it for complex measurement processes.An extensive research has been conducted in order to define the most appropriate methodology to assess uncertainty on complex aerospace components and a case study has been applied to assess the strain gauge calibration test uncertainty of different aerospace components.This study develops a generic framework, which helps the assessment of all individual sources of uncertainty and completes the one established by the Guide to the Expression of Uncertainty in Measurement. Conclusions have been extracted from the outcome of the case study.The conducted research contributes to a better understanding of measurement processes and good practices that lead to lower uncertainty. The outcome will help manufacturing companies to be aware of the contributors of uncertainty to the tests, how to reduce this uncertainty and the reliability of the measurements taken during the process.

An analysis of uncertainties and skill in forecasts of winter storm losses

Abstract. This paper describes an approach to derive probabilistic predictions of local winter storm damage occurrences from a global medium-range ensemble prediction system (EPS). Predictions of storm damage occurrences are subject to large uncertainty due to meteorological forecast uncertainty (typically addressed by means of ensemble predictions) and uncertainties in modelling weather impacts. The latter uncertainty arises from the fact that local vulnerabilities are not known in sufficient detail to allow for a deterministic prediction of damages, even if the forecasted gust wind speed contains no uncertainty. Thus, to estimate the damage model uncertainty, a statistical model based on logistic regression analysis is employed, relating meteorological analyses to historical damage records. A quantification of the two individual contributions (meteorological and damage model uncertainty) to the total forecast uncertainty is achieved by neglecting individual uncertainty sources and analysing resulting predictions. Results show an increase in forecast skill measured by means of a reduced Brier score if both meteorological and damage model uncertainties are taken into account. It is demonstrated that skilful predictions on district level (dividing the area of Germany into 439 administrative districts) are possible on lead times of several days. Skill is increased through the application of a proper ensemble calibration method, extending the range of lead times for which skilful damage predictions can be made.

Using statistical model to simulate the impact of climate change on maize yield with climate and crop uncertainties

Assessment of the impact of climate change on crop productions with considering uncertainties is essential for properly identifying and decision-making agricultural practices that are sustainable. In this study, we employed 24 climate projections consisting of the combinations of eight GCMs and three emission scenarios representing the climate projections uncertainty, and two crop statistical models with 100 sets of parameters in each model representing parameter uncertainty within the crop models. The goal of this study was to evaluate the impact of climate change on maize (Zea mays L.) yield at three locations (Benxi, Changling, and Hailun) across Northeast China (NEC) in periods 2010–2039 and 2040–2069, taking 1976–2005 as the baseline period. The multi-models ensembles method is an effective way to deal with the uncertainties. The results of ensemble simulations showed that maize yield reductions were less than 5 % in both future periods relative to the baseline. To further understand the contributions of individual sources of uncertainty, such as climate projections and crop model parameters, in ensemble yield simulations, variance decomposition was performed. The results indicated that the uncertainty from climate projections was much larger than that contributed by crop model parameters. Increased ensemble yield variance revealed the increasing uncertainty in the yield simulation in the future periods.

Climate model uncertainty versus conceptual geological uncertainty in hydrological modeling

Abstract. Projections of climate change impact are associated with a cascade of uncertainties including in CO2 emission scenarios, climate models, downscaling and impact models. The relative importance of the individual uncertainty sources is expected to depend on several factors including the quantity that is projected. In the present study the impacts of climate model uncertainty and geological model uncertainty on hydraulic head, stream flow, travel time and capture zones are evaluated. Six versions of a physically based and distributed hydrological model, each containing a unique interpretation of the geological structure of the model area, are forced by 11 climate model projections. Each projection of future climate is a result of a GCM–RCM model combination (from the ENSEMBLES project) forced by the same CO2 scenario (A1B). The changes from the reference period (1991–2010) to the future period (2081–2100) in projected hydrological variables are evaluated and the effects of geological model and climate model uncertainties are quantified. The results show that uncertainty propagation is context-dependent. While the geological conceptualization is the dominating uncertainty source for projection of travel time and capture zones, the uncertainty due to the climate models is more important for groundwater hydraulic heads and stream flow.