scholarly journals Using an Uncertainty Quantification Framework to Calibrate the Runoff Generation Scheme in E3SM Land Model V1

2021 ◽  
Author(s):  
Donghui Xu ◽  
Gautam Bisht ◽  
Khachik Sargsyan ◽  
Chang Liao ◽  
L. Ruby Leung
Keyword(s):  
Uncertainty Quantification ◽  
Water Cycle ◽  
Computational Cost ◽  
Model Performance ◽  
Parametric Uncertainty ◽  
Surrogate Models ◽  
Earth System ◽  
Runoff Generation ◽  
Parameter Values ◽  
Sensitive Parameters

Abstract. Runoff is a critical component of the terrestrial water cycle and Earth System Models (ESMs) are essential tools to study its spatio-temporal variability. Runoff schemes in ESMs typically include many parameters so model calibration is necessary to improve the accuracy of simulated runoff. However, runoff calibration at global scale is challenging because of the high computational cost and the lack of reliable observational datasets. In this study, we calibrated 11 runoff relevant parameters in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) using an uncertainty quantification framework. First, the Polynomial Chaos Expansion machinery with Bayesian Compressed Sensing is used to construct computationally inexpensive surrogate models for ELM-simulated runoff at 0.5° × 0.5° for 1991–2010. The main methodological advance in this work is the construction of surrogates for the error metric between ELM and the benchmark data, facilitating efficient calibration and avoiding the more conventional, but challenging, construction of high-dimensional surrogates for ELM itself. Second, the Sobol index sensitivity analysis is performed using the surrogate models to identify the most sensitive parameters, and our results show that in most regions ELM-simulated runoff is strongly sensitive to 3 of the 11 uncertain parameters. Third, a Bayesian method is used to infer the optimal values of the most sensitive parameters using an observation-based global runoff dataset as the benchmark. Our results show that model performance is significantly improved with the inferred parameter values. Although the parametric uncertainty of simulated runoff is reduced after the parameter inference, it remains comparable to the multi-model ensemble uncertainty represented by the global hydrological models in ISMIP2a. Additionally, the annual global runoff trend during the simulation period is not well constrained by the inferred parameter values, suggesting the importance of including parametric uncertainty in future runoff projections.

scholarly journals Inverse and forward uncertainty quantification of models for foam–assisted enhanced oil recovery

2021 ◽  
Author(s):  
◽  
Andrés Ricardo Valdez
Keyword(s):  
Uncertainty Quantification ◽  
Enhanced Oil Recovery ◽  
Oil Recovery ◽  
Computational Cost ◽  
Parametric Uncertainty ◽  
Posterior Density ◽  
Two Phase ◽  
Output Variability ◽  
Recovery Models ◽  
Laboratory Experiences

Like many other engineering applications, oil recovery and enhanced oil recovery are sensitive to the correct administration of economic resources. Pilot tests and core flood experiments are crucial elements to design an enhanced oil recovery (EOR) project. In this direction, numerical simulators are accessible alternatives for evaluating different engineering configurations at many diverse scales (pore, laboratory, and field scales). Despite the advantages that numerical simulators possess over laboratory experiences, they are not fully protected against uncertainties. In this thesis, we show advances in analyzing uncertainties in two-–phase reservoir simulations, focusing on foam–based EOR. The methods employed in this thesis analyze how experimental uncertainties affect reservoir simulator’s responses. Our framework for model calibration and uncertainty quantification uses the Markov Chain Monte Carlo method. The parametric uncertainty is tested against identifiability studies revealing situations where posterior density distributions with high variability are related to high uncertainties and practical non–identifiability issues. The model’s reliability was evaluated by adopting surrogate models based on polynomial chaos expansion when the computational cost was an issue for the analysis. Once we quantified the model’s output variability, we performed a global sensitivity analysis to map the model’s uncertainty to the input parameters distributions. Main and total Sobol indices were used to investigate the model’s uncertainty and highlight how key parameters and their interactions influence the simulation’s output. As a consequence of the results presented in this thesis, we show a technique for parameter and uncertainty estimation that can be explored to reduce the uncertainty in foam–assisted oil recovery models, which in turn can provide reliable computational simulations. Such conclusions are of utmost interest and relevance for the design of adequate techniques for enhanced oil recovery.

scholarly journals A Fast Estimator for Binary Choice Models with Spatial, Temporal, and Spatio-Temporal Interdependence

2021 ◽  
pp. 1-7
Author(s):  
Julian Wucherpfennig ◽  
Aya Kachi ◽  
Nils-Christian Bormann ◽  
Philipp Hunziker
Keyword(s):  
Computational Cost ◽  
Choice Models ◽  
Reduced Form ◽  
Binary Choice ◽  
Monte Carlo Experiments ◽  
Binary Choice Models ◽  
Spatio Temporal ◽  
Computationally Intensive ◽  
Pseudo Maximum Likelihood ◽  
Parameter Values

Abstract Binary outcome models are frequently used in the social sciences and economics. However, such models are difficult to estimate with interdependent data structures, including spatial, temporal, and spatio-temporal autocorrelation because jointly determined error terms in the reduced-form specification are generally analytically intractable. To deal with this problem, simulation-based approaches have been proposed. However, these approaches (i) are computationally intensive and impractical for sizable datasets commonly used in contemporary research, and (ii) rarely address temporal interdependence. As a way forward, we demonstrate how to reduce the computational burden significantly by (i) introducing analytically-tractable pseudo maximum likelihood estimators for latent binary choice models that exhibit interdependence across space and time and by (ii) proposing an implementation strategy that increases computational efficiency considerably. Monte Carlo experiments show that our estimators recover the parameter values as good as commonly used estimation alternatives and require only a fraction of the computational cost.

scholarly journals Spatio-Temporal Hydrological Model Structure and Parametrization Analysis

2021 ◽  
Vol 9 (5) ◽  
pp. 467
Author(s):  
Mostafa Farrag ◽  
Gerald Corzo Perez ◽  
Dimitri Solomatine
Keyword(s):  
Hydrological Modeling ◽  
Model Building ◽  
Model Performance ◽  
Model Description ◽  
Generation Model ◽  
Model Structure ◽  
Runoff Generation ◽  
Spatial Sensitivity ◽  
Spatio Temporal ◽  
Set Up

Many grid-based spatial hydrological models suffer from the complexity of setting up a coherent spatial structure to calibrate such a complex, highly parameterized system. There are essential aspects of model-building to be taken into account: spatial resolution, the routing equation limitations, and calibration of spatial parameters, and their influence on modeling results, all are decisions that are often made without adequate analysis. In this research, an experimental analysis of grid discretization level, an analysis of processes integration, and the routing concepts are analyzed. The HBV-96 model is set up for each cell, and later on, cells are integrated into an interlinked modeling system (Hapi). The Jiboa River Basin in El Salvador is used as a case study. The first concept tested is the model structure temporal responses, which are highly linked to the runoff dynamics. By changing the runoff generation model description, we explore the responses to events. Two routing models are considered: Muskingum, which routes the runoff from each cell following the river network, and Maxbas, which routes the runoff directly to the outlet. The second concept is the spatial representation, where the model is built and tested for different spatial resolutions (500 m, 1 km, 2 km, and 4 km). The results show that the spatial sensitivity of the resolution is highly linked to the routing method, and it was found that routing sensitivity influenced the model performance more than the spatial discretization, and allowing for coarser discretization makes the model simpler and computationally faster. Slight performance improvement is gained by using different parameters’ values for each cell. It was found that the 2 km cell size corresponds to the least model error values. The proposed hydrological modeling codes have been published as open-source.

scholarly journals The Toba supervolcano eruption caused severe tropical stratospheric ozone depletion

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Sergey Osipov ◽  
Georgiy Stenchikov ◽  
Kostas Tsigaridis ◽  
Allegra N. LeGrande ◽  
Susanne E. Bauer ◽  
...  
Keyword(s):  
Ozone Depletion ◽  
Water Cycle ◽  
Stratospheric Ozone ◽  
System Model ◽  
Earth System ◽  
Sulfate Aerosols ◽  
Winter Conditions ◽  
Attenuation Mechanism ◽  
The Tropics ◽  
Sulfur Emissions

AbstractSupervolcano eruptions have occurred throughout Earth’s history and have major environmental impacts. These impacts are mostly associated with the attenuation of visible sunlight by stratospheric sulfate aerosols, which causes cooling and deceleration of the water cycle. Supereruptions have been assumed to cause so-called volcanic winters that act as primary evolutionary factors through ecosystem disruption and famine, however, winter conditions alone may not be sufficient to cause such disruption. Here we use Earth system model simulations to show that stratospheric sulfur emissions from the Toba supereruption 74,000 years ago caused severe stratospheric ozone loss through a radiation attenuation mechanism that only moderately depends on the emission magnitude. The Toba plume strongly inhibited oxygen photolysis, suppressing ozone formation in the tropics, where exceptionally depleted ozone conditions persisted for over a year. This effect, when combined with volcanic winter in the extra-tropics, can account for the impacts of supereruptions on ecosystems and humanity.

scholarly journals Analyzing catchment behavior through catchment modeling in the Gilgel Abay, Upper Blue Nile River Basin, Ethiopia

2010 ◽  
Vol 14 (10) ◽  
pp. 2153-2165 ◽  
Author(s):  
S. Uhlenbrook ◽  
Y. Mohamed ◽  
A. S. Gragne
Keyword(s):  
Water Resources ◽  
Model Performance ◽  
Computation Time ◽  
Hydrological Processes ◽  
Runoff Generation ◽  
Time Step ◽  
Blue Nile ◽  
Model Transferability ◽  
Catchment Modeling ◽  
Data Limitations

Abstract. Understanding catchment hydrological processes is essential for water resources management, in particular in data scarce regions. The Gilgel Abay catchment (a major tributary into Lake Tana, source of the Blue Nile) is undergoing intensive plans for water management, which is part of larger development plans in the Blue Nile basin in Ethiopia. To obtain a better understanding of the water balance dynamics and runoff generation mechanisms and to evaluate model transferability, catchment modeling has been conducted using the conceptual hydrological model HBV. Accordingly, the catchment of the Gilgel Abay has been divided into two gauged sub-catchments (Upper Gilgel Abay and Koga) and the un-gauged part of the catchment. All available data sets were tested for stationarity, consistency and homogeneity and the data limitations (quality and quantity) are discussed. Manual calibration of the daily models for three different catchment representations, i.e. (i) lumped, (ii) lumped with multiple vegetation zones, and (iii) semi-distributed with multiple vegetation and elevation zones, showed good to satisfactory model performances with Nash-Sutcliffe efficiencies Reff > 0.75 and > 0.6 for the Upper Gilgel Abay and Koga sub-catchments, respectively. Better model results could not be obtained with manual calibration, very likely due to the limited data quality and model insufficiencies. Increasing the computation time step to 15 and 30 days improved the model performance in both sub-catchments to Reff > 0.8. Model parameter transferability tests have been conducted by interchanging parameters sets between the two gauged sub-catchments. Results showed poor performances for the daily models (0.30 < Reff < 0.67), but better performances for the 15 and 30 days models, Reff > 0.80. The transferability tests together with a sensitivity analysis using Monte Carlo simulations (more than 1 million model runs per catchment representation) explained the different hydrologic responses of the two sub-catchments, which seems to be mainly caused by the presence of dambos in Koga sub-catchment. It is concluded that daily model transferability is not feasible, while it can produce acceptable results for the 15 and 30 days models. This is very useful for water resources planning and management, but not sufficient to capture detailed hydrological processes in an ungauged area.

Review of Hybrid Emissions Prediction Tools and Uncertainty Quantification Methods for Gas Turbine Combustion Systems

2017 ◽  
Author(s):  
Sajjad Yousefian ◽  
Gilles Bourque ◽  
Rory F. D. Monaghan
Keyword(s):  
Uncertainty Quantification ◽  
Gas Turbine ◽  
Kinetic Modelling ◽  
Computational Cost ◽  
Chemical Kinetic ◽  
Design Development ◽  
Deterministic Models ◽  
Prediction Tools ◽  
Gas Turbine Combustion ◽  
Combustion Systems

There is a need for fast and reliable emissions prediction tools in the design, development and performance analysis of gas turbine combustion systems to predict emissions such as NOx, CO. Hybrid emissions prediction tools are defined as modelling approaches that (1) use computational fluid dynamics (CFD) or component modelling methods to generate flow field information, and (2) integrate them with detailed chemical kinetic modelling of emissions using chemical reactor network (CRN) techniques. This paper presents a review and comparison of hybrid emissions prediction tools and uncertainty quantification (UQ) methods for gas turbine combustion systems. In the first part of this study, CRN solvers are compared on the bases of some selected attributes which facilitate flexibility of network modelling, implementation of large chemical kinetic mechanisms and automatic construction of CRN. The second part of this study deals with UQ, which is becoming an important aspect of the development and use of computational tools in gas turbine combustion chamber design and analysis. Therefore, the use of UQ technique as part of the generalized modelling approach is important to develop a UQ-enabled hybrid emissions prediction tool. UQ techniques are compared on the bases of the number of evaluations and corresponding computational cost to achieve desired accuracy levels and their ability to treat deterministic models for emissions prediction as black boxes that do not require modifications. Recommendations for the development of UQ-enabled emissions prediction tools are made.

scholarly journals Inconsistent strategies to spin up models in CMIP5: implications for ocean biogeochemical model performance assessment

2016 ◽  
Vol 9 (5) ◽  
pp. 1827-1851 ◽  
Author(s):  
Roland Séférian ◽  
Marion Gehlen ◽  
Laurent Bopp ◽  
Laure Resplandy ◽  
James C. Orr ◽  
...  
Keyword(s):  
Performance Assessment ◽  
Initial Conditions ◽  
Model Performance ◽  
Cmip5 Models ◽  
Biogeochemical Model ◽  
Earth System ◽  
Model Intercomparison ◽  
System Models ◽  
Marine Biogeochemistry ◽  
Earth System Models

Abstract. During the fifth phase of the Coupled Model Intercomparison Project (CMIP5) substantial efforts were made to systematically assess the skill of Earth system models. One goal was to check how realistically representative marine biogeochemical tracer distributions could be reproduced by models. In routine assessments model historical hindcasts were compared with available modern biogeochemical observations. However, these assessments considered neither how close modeled biogeochemical reservoirs were to equilibrium nor the sensitivity of model performance to initial conditions or to the spin-up protocols. Here, we explore how the large diversity in spin-up protocols used for marine biogeochemistry in CMIP5 Earth system models (ESMs) contributes to model-to-model differences in the simulated fields. We take advantage of a 500-year spin-up simulation of IPSL-CM5A-LR to quantify the influence of the spin-up protocol on model ability to reproduce relevant data fields. Amplification of biases in selected biogeochemical fields (O2, NO3, Alk-DIC) is assessed as a function of spin-up duration. We demonstrate that a relationship between spin-up duration and assessment metrics emerges from our model results and holds when confronted with a larger ensemble of CMIP5 models. This shows that drift has implications for performance assessment in addition to possibly aliasing estimates of climate change impact. Our study suggests that differences in spin-up protocols could explain a substantial part of model disparities, constituting a source of model-to-model uncertainty. This requires more attention in future model intercomparison exercises in order to provide quantitatively more correct ESM results on marine biogeochemistry and carbon cycle feedbacks.

scholarly journals Benchmarking and Process Diagnostics of Land Models

2018 ◽  
Vol 19 (11) ◽  
pp. 1835-1852 ◽  
Author(s):  
Grey S. Nearing ◽  
Benjamin L. Ruddell ◽  
Martyn P. Clark ◽  
Bart Nijssen ◽  
Christa Peters-Lidard
Keyword(s):  
Land Surface ◽  
Model Performance ◽  
Land Surface Model ◽  
Surface Model ◽  
Process Diagnostics ◽  
Surface Energy Fluxes ◽  
Structural Error ◽  
Intercomparison Project ◽  
Parameter Values ◽  
Process Level

Abstract We propose a conceptual and theoretical foundation for information-based model benchmarking and process diagnostics that provides diagnostic insight into model performance and model realism. We benchmark against a bounded estimate of the information contained in model inputs to obtain a bounded estimate of information lost due to model error, and we perform process-level diagnostics by taking differences between modeled versus observed transfer entropy networks. We use this methodology to reanalyze the recent Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) land model intercomparison project that includes the following models: CABLE, CH-TESSEL, COLA-SSiB, ISBA-SURFEX, JULES, Mosaic, Noah, and ORCHIDEE. We report that these models (i) use only roughly half of the information available from meteorological inputs about observed surface energy fluxes, (ii) do not use all information from meteorological inputs about long-term Budyko-type water balances, (iii) do not capture spatial heterogeneities in surface processes, and (iv) all suffer from similar patterns of process-level structural error. Because the PLUMBER intercomparison project did not report model parameter values, it is impossible to know whether process-level error patterns are due to model structural error or parameter error, although our proposed information-theoretic methodology could distinguish between these two issues if parameter values were reported. We conclude that there is room for significant improvement to the current generation of land models and their parameters. We also suggest two simple guidelines to make future community-wide model evaluation and intercomparison experiments more informative.

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