Estimation of distributed parameters at regional scale by history-matching of a multi-layered sedimentary aquifer

2020 ◽  
Author(s):  
Ryma Aissat ◽  
Alexandre Pryet ◽  
Marc Saltel ◽  
Alain Dupuy
Keyword(s):  
Parameter Estimation ◽  
Hydraulic Properties ◽  
Large Scale ◽  
Classical Method ◽  
Regional Scale ◽  
Scale Model ◽  
Estimation Methods ◽  
Model Parameters ◽  
Convergence Criteria ◽  
Distributed Parameters

<p>Large scale, physically-based groundwater models have been used for many years for water resources management and decision-support. Improving the accuracy and reliability of these models is a constant objective. The characterization of model parameters, in particular hydraulic properties, which are spatially heterogeneous is a challenge. Parameter estimation algorithms can now manage numerous model runs in parallel, but the operation remains, in practice, largely constrained by the computational burden. A large-scale model of the sedimentary, multilayered aquifer system of North Aquitania (MONA), in South-West France, developed by the French Geological Survey (BRGM) is used here to illustrate the case. We focus on the estimation of distributed parameters and investigate the optimum parameterization given the level of spatial heterogeneity we aim to characterize, available observations, model run time, and computational resources. Hydraulic properties are estimated with pilot points. Interpolation is conducted by kriging, the variogram range and pilot point density are set given modeling purposes and a series of constraints. The popular gradient-based parameter estimation methods such as the Gauss–Marquard–Levenberg algorithm (GLMA) are conditioned by the integrity of the Jacobian matrix. We investigate the trade-off between strict convergence criteria, which insure a better integrity of derivatives, and loose convergence criteria, which reduce computation time. The results obtained with the classical method (GLMA) are compared with the results of an emerging method, the Iterative Ensemble Smoother (IES). Some guidelines are eventually provided for parameter estimation of large-scale multi-layered groundwater models.</p>

Parameter estimation using relay feedback

2019 ◽  
Vol 35 (4) ◽  
pp. 505-529
Author(s):  
Kalpana Dharmalingam ◽  
Thyagarajan Thangavelu
Keyword(s):  
Parameter Estimation ◽  
Closed Loop ◽  
Parametric Estimation ◽  
Estimation Methods ◽  
Model Parameters ◽  
Relay Feedback ◽  
Research Activity ◽  
Process Industries ◽  
Single Input Single Output ◽  
Estimation Techniques

Abstract In process industries, closed-loop step and closed-loop relay feedback tests are popularly used for estimating model parameters. In this paper, different methods available in the literature for parameter estimation using conventional techniques and techniques based on relay feedback test are surveyed by reviewing around 152 research articles published during the past three decades. Through a comprehensive survey of available literature, the parameter estimation methods are classified into two broad groups, namely conventional techniques and relay-based parametric estimation techniques. These relay-based techniques are further classified into two subgroups, namely single-input-single-output (SISO) systems and multi-input-multi-output systems (both square and nonsquare), and are revealed in a lucid manner with the help of benchmark examples and case studies. For the above categorized methods, the procedural steps involved in relay-based parametric estimation methods are also presented. To facilitate the readers, comparison tables are included to comprehend the results of different parametric estimation techniques available in the literature. The incorporation of quantitative and qualitative analysis of papers published in various journals in the above area with the help of pie charts and graphs would enable the readers to grasp the overview of the research activity being carried out in the relay feedback domain. At the end, the challenging issues in relay-based parametric estimation methods and the directions for future investigations that can be explored are also highlighted.

scholarly journals An application of a large scale conceptual hydrological model over the Elbe region

1999 ◽  
Vol 3 (3) ◽  
pp. 363-374 ◽  
Author(s):  
M. Lobmeyr ◽  
D. Lohmann ◽  
C. Ruhe
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Sunshine Duration ◽  
Regional Scale ◽  
Scale Model ◽  
Surface Parameterization ◽  
Extreme Flood ◽  
Daily Sunshine ◽  
Surface Scheme ◽  
The Baltic

Abstract. This paper investigates the ability of the VIC-2L model coupled to a routing model to reproduce streamflow in the catchment of the lower Elbe River, Germany. The VIC-2L model, a hydrologically-based land surface scheme (LSS) which has been tested extensively in the Project for Intercomparison of Land-surface Parameterization Schemes (PILPS), is put up on the rotated grid of 1/6 degree of the atmospheric regional scale model (REMO) used in the Baltic Sea Experiment (BALTEX). For a 10 year period, the VIC-2L model is forced in daily time steps with measured daily means of precipitation, air temperature, pressure, wind speed, air humidity and daily sunshine duration. VIC-2L model output of surface runoff and baseflow is used as input for the routing model, which transforms modelled runoff into streamflow, which is compared to measured streamflow at selected gauge stations. The water balance of the basin is investigated and the model results on daily, monthly and annual time scales are discussed. Discrepancies appear in time periods where snow and ice processes are important. Extreme flood events are analyzed in more dital. The influence of calibration with respect to runoff is examined.

Improvement of the simulation of the water and energy cycle using Multiscale Parameter Regionalization (MPR)

2020 ◽  
Author(s):  
Stephan Thober ◽  
Matthias Kelbling ◽  
Florian Pappenberger ◽  
Christel Prudhomme ◽  
Gianpaolo Balsamo ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
High Resolution ◽  
Parameter Space ◽  
Land Surface ◽  
Hydrologic Model ◽  
Estimation Methods ◽  
Surface Model ◽  
Model Parameters ◽  
Environmental Models ◽  
Energy Cycle

<p>The representation of the water and energy cycle in environmental models is closely linked to the parameter values used in the process parametrizations. The dimension of the parameter space in spatially distributed environmental models corresponds to the number of grid cells multiplied by the number of parameters per grid cell. For large-scale simulations on national and continental scales, the dimensionality of the parameter space is too high for efficient parameter estimation using inverse estimation methods. A regularization of the parameter space is necessary to reduce its dimensionality. The Multiscale Parameter Regionalization (MPR) is one approach to achieve this.</p><p>MPR translates local geophysical properties into model parameters. It consists of two steps: 1) local high-resolution geophysical data sets (e.g. soil maps) are translated into model parameters using a transfer function. 2) the high-resolution model parameters are scaled to the model resolution using suitable upscaling operators (e.g., harmonic mean). The MPR technique was introduced into the mesoscale hydrologic model (mHM, Samaniego et al. 2010, Kumar et al. 2013) and it is key factor for its success on transferring parameters across scales and locations.  </p><p>In this study, we apply MPR to vegetation and soil parameters in the land surface model HTESSEL. This model is the land-surface component of the European Centre for Medium-Range Weather Forecasting seasonal forecasting system. About 100 hard-coded parameters have been extracted to allow for a comprehensive sensitivity analysis and parameter estimation.</p><p>We analyze simulated evaporation and runoff fluxes by HTESSEL using parameters estimated by MPR in comparison to a default HTESSEL setup over Europe. The magnitude of simulated long-term fluxes deviates the most (up to 10% and 20% for evapotranspiration and runoff, respectively) in regions with a large subgrid variability in geophysical attributes (e.g., soil texture). The choice of transfer functions and upscaling operators influences the magnitude of these differences and governs model performance assessed after calibration against observations (e.g. streamflow).</p><p><strong>References:</strong></p><p>Samaniego L., et al.  <strong>https://doi.org/10.1029/2008WR007327</strong></p><p>Kumar, R., et al.  <strong>https://doi.org/10.1029/2012WR012195</strong></p>

Li-Ion Cell Aging Model Online Parameter Estimation for Improved Prognosis

2016 ◽  
Author(s):  
Punit Tulpule ◽  
Chin-Yao Chang ◽  
Giorgio Rizzoni
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Estimation ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Estimation Methods ◽  
Cell Aging ◽  
Model Parameters ◽  
Positive Electrodes ◽  
Aging Model ◽  
Estimation Algorithms

In this paper, a semi-empirical aging model of lithium-ion pouch cells containing blended spinel and layered-oxide positive electrodes is calibrated using aging campaigns. Sensitivity analysis is done on this model to identify the effect of parameter variations on the State of Health (SOH) prediction. The sensitivity analysis shows that the aging model alone is not robust enough to perform long term predictions, hence we propose to use online parameter estimation algorithms to adapt the model parameters. Four different estimation methods are compared using aging campaign. It is demonstrated that the estimation algorithms improve aging model leading to significant improvement in Remaining Useful Life (RUL) prediction.

scholarly journals Data assimilation in integrated hydrological modelling in the presence of observation bias

2015 ◽  
Vol 12 (8) ◽  
pp. 8131-8173 ◽  
Author(s):  
J. Rasmussen ◽  
H. Madsen ◽  
K. H. Jensen ◽  
J. C. Refsgaard
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Stream Flow ◽  
Groundwater Modeling ◽  
Optimal Parameter ◽  
Estimation Methods ◽  
Model Parameters ◽  
Stream Discharge ◽  
Groundwater Head ◽  
Observation Bias

Abstract. The use of bias-aware Kalman filters for estimating and correcting observation bias in groundwater head observations is evaluated using both synthetic and real observations. In the synthetic test, groundwater head observations with a constant bias and unbiased stream discharge observations are assimilated in a catchment scale integrated hydrological model with the aim of updating stream discharge and groundwater head, as well as several model parameters relating to both stream flow and groundwater modeling. The Colored Noise Kalman filter (ColKF) and the Separate bias Kalman filter (SepKF) are tested and evaluated for correcting the observation biases. The study found that both methods were able to estimate most of the biases and that using any of the two bias estimation methods resulted in significant improvements over using a bias-unaware Kalman Filter. While the convergence of the ColKF was significantly faster than the convergence of the SepKF, a much larger ensemble size was required as the estimation of biases would otherwise fail. Real observations of groundwater head and stream discharge were also assimilated, resulting in improved stream flow modeling in terms of an increased Nash-Sutcliffe coefficient while no clear improvement in groundwater head modeling was observed. Both the ColKF and the SepKF tended to underestimate the biases, which resulted in drifting model behavior and sub-optimal parameter estimation, but both methods provided better state updating and parameter estimation than using a bias-unaware filter.

scholarly journals Streamflow trends in Europe: evidence from a dataset of near-natural catchments

2010 ◽  
Vol 14 (12) ◽  
pp. 2367-2382 ◽  
Author(s):  
K. Stahl ◽  
H. Hisdal ◽  
J. Hannaford ◽  
L. M. Tallaksen ◽  
H. A. J. van Lanen ◽  
...  
Keyword(s):  
Large Scale ◽  
Regional Scale ◽  
Low Flow ◽  
Scale Model ◽  
Model Simulations ◽  
Continental Scale ◽  
Low Flows ◽  
Spatial Coverage ◽  
Monthly Streamflow ◽  
Streamflow Trends

Abstract. Streamflow observations from near-natural catchments are of paramount importance for detection and attribution studies, evaluation of large-scale model simulations, and assessment of water management, adaptation and policy options. This study investigates streamflow trends in a newly-assembled, consolidated dataset of near-natural streamflow records from 441 small catchments in 15 countries across Europe. The period 1962–2004 provided the best spatial coverage, but analyses were also carried out for longer time periods (with fewer stations), starting in 1932, 1942 and 1952. Trends were calculated by the slopes of the Kendall-Theil robust line for standardized annual and monthly streamflow, as well as for summer low flow magnitude and timing. A regionally coherent picture of annual streamflow trends emerged, with negative trends in southern and eastern regions, and generally positive trends elsewhere. Trends in monthly streamflow for 1962–2004 elucidated potential causes for these changes, as well as for changes in hydrological regimes across Europe. Positive trends were found in the winter months in most catchments. A marked shift towards negative trends was observed in April, gradually spreading across Europe to reach a maximum extent in August. Low flows have decreased in most regions where the lowest mean monthly flow occurs in summer, but vary for catchments which have flow minima in winter and secondary low flows in summer. The study largely confirms findings from national and regional scale trend analyses, but clearly adds to these by confirming that these tendencies are part of coherent patterns of change, which cover a much larger region. The broad, continental-scale patterns of change are mostly congruent with the hydrological responses expected from future climatic changes, as projected by climate models. The patterns observed could hence provide a valuable benchmark for a number of different studies and model simulations.

scholarly journals Online dynamical downscaling of temperature and precipitation within the <i>i</i>LOVECLIM model (version 1.1)

2018 ◽  
Vol 11 (1) ◽  
pp. 453-466
Author(s):  
Aurélien Quiquet ◽  
Didier M. Roche ◽  
Christophe Dumas ◽  
Didier Paillard
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Atmospheric Model ◽  
Scale Model ◽  
Model Parameters ◽  
Temperature And Precipitation ◽  
Large Scale Model

Abstract. This paper presents the inclusion of an online dynamical downscaling of temperature and precipitation within the model of intermediate complexity iLOVECLIM v1.1. We describe the following methodology to generate temperature and precipitation fields on a 40 km  ×  40 km Cartesian grid of the Northern Hemisphere from the T21 native atmospheric model grid. Our scheme is not grid specific and conserves energy and moisture in the same way as the original climate model. We show that we are able to generate a high-resolution field which presents a spatial variability in better agreement with the observations compared to the standard model. Although the large-scale model biases are not corrected, for selected model parameters, the downscaling can induce a better overall performance compared to the standard version on both the high-resolution grid and on the native grid. Foreseen applications of this new model feature include the improvement of ice sheet model coupling and high-resolution land surface models.

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