scholarly journals Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference

2021 ◽  
pp. 1-19
Author(s):  
Douglas Brinkerhoff ◽  
Andy Aschwanden ◽  
Mark Fahnestock
Keyword(s):  
Probability Distributions ◽  
Water Pressure ◽  
Model Development ◽  
Computational Cost ◽  
Hydrologic Model ◽  
Surface Velocity ◽  
Small Scale ◽  
Model Parameters ◽  
Stress Regime ◽  
Ice Dynamics

Abstract Basal motion is the primary mechanism for ice flux in Greenland, yet a widely applicable model for predicting it remains elusive. This is due to the difficulty in both observing small-scale bed properties and predicting a time-varying water pressure on which basal motion putatively depends. We take a Bayesian approach to these problems by coupling models of ice dynamics and subglacial hydrology and conditioning on observations of surface velocity in southwestern Greenland to infer the posterior probability distributions for eight spatially and temporally constant parameters governing the behavior of both the sliding law and hydrologic model. Because the model is computationally expensive, characterization of these distributions using classical Markov Chain Monte Carlo sampling is intractable. We skirt this issue by training a neural network as a surrogate that approximates the model at a sliver of the computational cost. We find that surface velocity observations establish strong constraints on model parameters relative to a prior distribution and also elucidate correlations, while the model explains 60% of observed variance. However, we also find that several distinct configurations of the hydrologic system and stress regime are consistent with observations, underscoring the need for continued data collection and model development.

scholarly journals A double continuum hydrological model for glacier applications

2014 ◽  
Vol 8 (1) ◽  
pp. 137-153 ◽  
Author(s):  
B. de Fleurian ◽  
O. Gagliardini ◽  
T. Zwinger ◽  
G. Durand ◽  
E. Le Meur ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Water Pressure ◽  
Computational Cost ◽  
Ice Streams ◽  
Ice Dynamics ◽  
Ice Flow ◽  
Wide Range ◽  
Hydrological System ◽  
Speed Up ◽  
Model Components

Abstract. The flow of glaciers and ice streams is strongly influenced by the presence of water at the interface between ice and bed. In this paper, a hydrological model evaluating the subglacial water pressure is developed with the final aim of estimating the sliding velocities of glaciers. The global model fully couples the subglacial hydrology and the ice dynamics through a water-dependent friction law. The hydrological part of the model follows a double continuum approach which relies on the use of porous layers to compute water heads in inefficient and efficient drainage systems. This method has the advantage of a relatively low computational cost that would allow its application to large ice bodies such as Greenland or Antarctica ice streams. The hydrological model has been implemented in the finite element code Elmer/Ice, which simultaneously computes the ice flow. Herein, we present an application to the Haut Glacier d'Arolla for which we have a large number of observations, making it well suited to the purpose of validating both the hydrology and ice flow model components. The selection of hydrological, under-determined parameters from a wide range of values is guided by comparison of the model results with available glacier observations. Once this selection has been performed, the coupling between subglacial hydrology and ice dynamics is undertaken throughout a melt season. Results indicate that this new modelling approach for subglacial hydrology is able to reproduce the broad temporal and spatial patterns of the observed subglacial hydrological system. Furthermore, the coupling with the ice dynamics shows good agreement with the observed spring speed-up.

Multi-objective Optimization of Catchment Reforestation Robust to Uncertainty in Bayesian-Calibrated Watershed Model Parameters

2021 ◽  
Author(s):  
Jared Smith ◽  
Laurence Lin ◽  
Julianne Quinn ◽  
Lawrence Band
Keyword(s):  
Computational Cost ◽  
Bayesian Framework ◽  
Hydrologic Model ◽  
Low Flow ◽  
Critical Parameters ◽  
Model Parameters ◽  
Calibration Data ◽  
Baltimore County ◽  
True Parameter ◽  
Multi Objective

<p>Urban land expansion is expected for our changing world, which unmitigated will result in increased flooding and nutrient exports that already wreak havoc on the wellbeing of coupled human-natural systems worldwide. Reforestation of urbanized catchments is one green infrastructure strategy to reduce stormwater volumes and nutrient exports. Reforestation designs must balance the benefits of flood flow reduction against the costs of implementation and the chance to exacerbate droughts via reduction in recharge that supplies low flows. Optimal locations and numbers of trees depend on the spatial distribution of runoff and streamflow in a catchment; however, calibration data are often only available at the catchment outlet. Equifinal model parameterizations for the outlet can result in uncertainty in the locations and magnitudes of streamflows across the catchment, which can lead to different optimal reforestation designs for different parameterizations.</p><p>Multi-objective robust optimization (MORO) has been proposed to discover reforestation designs that are robust to such parametric model uncertainty. However, it has not been shown that this actually results in better decisions than optimizing to a single, most likely parameter set, which would be less computationally expensive. In this work, the utility of MORO is assessed by comparing reforestation designs optimized using these two approaches with reforestation designs optimized to a synthetic true set of hydrologic model parameters. The spatially-distributed RHESSys ecohydrological model is employed for this study of a suburban-forested catchment in Baltimore County, Maryland, USA. Calibration of the model’s critical parameters is completed using a Bayesian framework to estimate the joint posterior distribution of the parameters. The Bayesian framework estimates the probability that different parameterizations generated the synthetic streamflow data, allowing the MORO process to evaluate reforestation portfolios across a probability-weighted sample of parameter sets in search of solutions that are robust to this uncertainty.</p><p>Reforestation portfolios are designed to minimize flooding, low flow intensity, and construction costs (number of trees). Comparing the Pareto front obtained from using MORO with the Pareto fronts obtained from optimizing to the estimated maximum a posteriori (MAP) parameter set and the synthetic true parameter set, we find that MORO solutions are closer to the synthetic solutions than are MAP solutions. This illustrates the value of considering parametric uncertainty in designing robust water systems despite the additional computational cost.</p>

scholarly journals Temporal variations in the flow of a large Antarctic ice-stream controlled by tidally induced changes in the subglacial water system

2015 ◽  
Vol 9 (2) ◽  
pp. 2397-2429 ◽  
Author(s):  
S. H. R. Rosier ◽  
G. H. Gudmundsson ◽  
J. A. M. Green
Keyword(s):  
Water Pressure ◽  
Water System ◽  
Surface Velocity ◽  
Decay Length ◽  
Model Parameters ◽  
Effective Pressure ◽  
Tidal Period ◽  
Ice Stream ◽  
Highly Nonlinear ◽  
Basal Sliding

Abstract. Observations show that the flow of Rutford Ice Stream (RIS) is strongly modulated by the ocean tides, with the strongest tidal response at the 14.77 day tidal period (Msf). This is striking because this period is absent in the tidal forcing. A number of mechanisms have been proposed to account for this effect, yet previous modeling studies have struggled to match the observed large amplitude and decay length scale. We use a nonlinear 3-D viscoelastic full-Stokes model of ice-stream flow to investigate this open issue. We find that the long period Msf modulation of ice-stream velocity observed in data cannot be reproduced quantitatively without including a coupling between basal sliding and tidal subglacial water pressure variations. Furthermore, the subglacial water system must be highly conductive and at low effective pressure, and the relationship between sliding velocity and effective pressure highly nonlinear in order for the model results to match GPS measurements. Hydrological and basal sliding model parameters that produced a best fit to observations were a mean effective pressure N of 105 kPa, subglacial drainage system conductivity K of 7 × 109 m2d-1, with sliding law exponents m = 3 and q =10. Coupled model results show the presence of tides result in a ~ 12% increase in mean surface velocity. Observations of tidally-induced variations in flow of ice-streams provide stronger constraints on basal sliding processes than provided by any other set of measurements.

scholarly journals Generation of Scenarios from Calibrated Ensemble Forecasts with a Dual-Ensemble Copula-Coupling Approach

2016 ◽  
Vol 144 (12) ◽  
pp. 4737-4750 ◽  
Author(s):  
Zied Ben Bouallègue ◽  
Tobias Heppelmann ◽  
Susanne E. Theis ◽  
Pierre Pinson
Keyword(s):  
Probability Distributions ◽  
Forecast Error ◽  
Computational Cost ◽  
Joint Probability ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Probabilistic Forecasts ◽  
Joint Probability Distributions

Abstract Probabilistic forecasts in the form of ensembles of scenarios are required for complex decision-making processes. Ensemble forecasting systems provide such products but the spatiotemporal structures of the forecast uncertainty is lost when statistical calibration of the ensemble forecasts is applied for each lead time and location independently. Nonparametric approaches allow the reconstruction of spatiotemporal joint probability distributions at a small computational cost. For example, the ensemble copula coupling (ECC) method rebuilds the multivariate aspect of the forecast from the original ensemble forecasts. Based on the assumption of error stationarity, parametric methods aim to fully describe the forecast dependence structures. In this study, the concept of ECC is combined with past data statistics in order to account for the autocorrelation of the forecast error. The new approach, called d-ECC, is applied to wind forecasts from the high-resolution Consortium for Small-Scale Modeling (COSMO) ensemble prediction system (EPS) run operationally at the German Weather Service (COSMO-DE-EPS). Scenarios generated by ECC and d-ECC are compared and assessed in the form of time series by means of multivariate verification tools and within a product-oriented framework. Verification results over a 3-month period show that the innovative method d-ECC performs as well as or even outperforms ECC in all investigated aspects.

scholarly journals A subglacial hydrological model dedicated to glacier sliding

2013 ◽  
Vol 7 (4) ◽  
pp. 3449-3496 ◽  
Author(s):  
B. de Fleurian ◽  
O. Gagliardini ◽  
T. Zwinger ◽  
G. Durand ◽  
E. Le Meur ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Water Pressure ◽  
Computational Cost ◽  
Ice Streams ◽  
Ice Dynamics ◽  
Ice Flow ◽  
Wide Range ◽  
Hydrological System ◽  
Speed Up ◽  
Model Components

Abstract. The flow of glaciers and ice-streams is strongly influenced by the presence of water at the interface between ice and bedrock. In this paper, a hydrological model evaluating the subglacial water pressure is developed with the final aim of estimating the sliding velocities of glaciers. The global model fully couples the subglacial hydrology and the ice dynamics through a water-dependent friction law. The hydrological part of the model follows a double continuum approach which relies on the use of porous layers to compute water heads in inefficient and efficient drainage systems. This method has the advantage of a relatively low computational cost that would allow its application to large ice bodies such as Greenland or Antarctica ice-streams. The hydrological model has been implemented in the finite element code Elmer/Ice, which simultaneously computes the ice flow. Herein, we present an application to the Haut Glacier d'Arolla for which we have a large number of observations, making it well suited to the purpose of validating both the hydrology and ice flow model components. The selection of hydrological, under-determined parameters from a wide range of values is guided by comparison of the model results with available glacier observations. Once this selection has been performed, the coupling between subglacial hydrology and ice dynamics is undertaken throughout a melt season. Results indicate that this new modelling approach for subglacial hydrology is able to reproduce the broad temporal and spatial patterns of the observed subglacial hydrological system. Furthermore, the coupling with the ice dynamics shows good agreement with the observed spring speed-up.

scholarly journals Probabilistic Inference Coupled with Possibilistic Reasoning for Robust Estimation of Hydrologic Parameters and Piecewise Characterization of Interactive Uncertainties

2016 ◽  
Vol 17 (4) ◽  
pp. 1243-1260 ◽  
Author(s):  
S. Wang ◽  
G. H. Huang ◽  
B. W. Baetz ◽  
W. Huang
Keyword(s):  
Expert Knowledge ◽  
Probability Distributions ◽  
Statistical Significance ◽  
Probabilistic Inference ◽  
Hydrologic Model ◽  
Factorial Analysis ◽  
Model Parameters ◽  
Xiangxi River ◽  
Parametric Interactions

Abstract This paper presents a factorial possibilistic–probabilistic inference (FPI) framework for estimation of hydrologic parameters and characterization of interactive uncertainties. FPI is capable of incorporating expert knowledge into the parameter adjustment procedure for enhancing the understanding of the nature of the calibration problem. As a component of the FPI framework, a Monte Carlo–based fractional fuzzy–factorial analysis (MFA) method is also proposed to identify the best parameter set and its underlying probability distributions in a fuzzy probability space. Factorial analysis of variance (ANOVA) coupled with its multivariate extensions are performed to explore potential interactions among model parameters and among hydrological metrics in a systematic manner. The proposed methodology is applied to the Xiangxi River watershed by using the conceptual hydrological model (HYMOD) to demonstrate its validity and applicability. Results reveal that MFA is capable of deriving probability density functions (PDFs) of hydrologic model parameters. Moreover, the sequential inferences derived from the F test and its multivariate approximations disclose the statistical significance of parametric interactions affecting individual and multiple hydrological metrics, respectively. The findings presented here indicate that parametric interactions are complex in a fuzzy stochastic environment, and the magnitude and direction of interaction effects vary in different regions of the parameter space as well as vary temporally because of the dynamic behavior of hydrologic systems.

scholarly journals A hydro-climatological approach to predicting regional landslide probability using Landlab

2017 ◽  
Author(s):  
Ronda Strauch ◽  
Erkan Istanbulluoglu ◽  
Sai Siddhartha Nudurupati ◽  
Christina Bandaragoda ◽  
Nicole M. Gasparini ◽  
...  
Keyword(s):  
Soil Depth ◽  
Probability Distributions ◽  
Probability Model ◽  
Subsurface Flow ◽  
Grid Cell ◽  
Bimodal Distribution ◽  
Shallow Landslide ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Infinite Slope Stability Model

Abstract. We develop a hydro-climatological approach to modeling of regional shallow landslide initiation that integrates spatial and temporal dimensions of parameter uncertainty to estimate an annual probability of landslide initiation. The physically-based model couples the infinite slope stability model with a steady-state subsurface flow representation and operates on a digital elevation model. Spatially distributed raster data for soil properties and a soil evolution model and vegetation classification from National Land Cover Data are used to derive parameters for probability distributions to represent input uncertainty. Hydrologic forcing to the model is through annual maximum recharge to subsurface flow obtained from a macroscale hydrologic model, routed on raster grid to develop subsurface flow. A Monte Carlo approach is used to generate model parameters at each grid cell and calculate probability of shallow landsliding. We demonstrate the model in a steep mountainous region in northern Washington, U.S.A., using 30-m grid resolution over 2,700 km2. The influence of soil depth on the probability of landslide initiation is investigated through comparisons among model output produced using three different soil depth scenarios reflecting uncertainty of soil depth and its potential long-term variability. We found elevation dependent patterns in probability of landslide initiation that showed the stabilizing effects of forests in low elevations, an increased landslide probability with forest decline at mid elevations (1,400 to 2,400 m), and soil limitation and steep topographic controls at high alpine elevations and post-glacial landscapes. These dominant controls manifest in a bimodal distribution of spatial annual landslide probability. Model testing with limited observations revealed similar model confidence for the three hazard maps, suggesting suitable use as relative hazard products. Validation of the model with observed landslides is hindered by the completeness and accuracy of the inventory, estimation of source areas, and unmapped landslides. The model is available as a component in Landlab, an open-source, Python-based landscape earth systems modeling environment, and is designed to be easily reproduced utilizing HydroShare cyberinfrastructure.

scholarly journals A Model-Based Tool for Assessing the Impact of Land Use Change Scenarios on Flood Risk in Small-Scale River Systems—Part 1: Pre-Processing of Scenario Based Flood Characteristics for the Current State of Land Use

Hydrology ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 102
Author(s):  
Frauke Kachholz ◽  
Jens Tränckner
Keyword(s):  
Land Use ◽  
River Flow ◽  
Land Use Changes ◽  
Small Rivers ◽  
Small Scale ◽  
Model Parameters ◽  
Ungauged Catchments ◽  
Model Based ◽  
Current State ◽  
The Impact

Land use changes influence the water balance and often increase surface runoff. The resulting impacts on river flow, water level, and flood should be identified beforehand in the phase of spatial planning. In two consecutive papers, we develop a model-based decision support system for quantifying the hydrological and stream hydraulic impacts of land use changes. Part 1 presents the semi-automatic set-up of physically based hydrological and hydraulic models on the basis of geodata analysis for the current state. Appropriate hydrological model parameters for ungauged catchments are derived by a transfer from a calibrated model. In the regarded lowland river basins, parameters of surface and groundwater inflow turned out to be particularly important. While the calibration delivers very good to good model results for flow (Evol =2.4%, R = 0.84, NSE = 0.84), the model performance is good to satisfactory (Evol = −9.6%, R = 0.88, NSE = 0.59) in a different river system parametrized with the transfer procedure. After transferring the concept to a larger area with various small rivers, the current state is analyzed by running simulations based on statistical rainfall scenarios. Results include watercourse section-specific capacities and excess volumes in case of flooding. The developed approach can relatively quickly generate physically reliable and spatially high-resolution results. Part 2 builds on the data generated in part 1 and presents the subsequent approach to assess hydrologic/hydrodynamic impacts of potential land use changes.

scholarly journals The ICON Single-Column Mode

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 906
Author(s):  
Ivan Bašták Ďurán ◽  
Martin Köhler ◽  
Astrid Eichhorn-Müller ◽  
Vera Maurer ◽  
Juerg Schmidli ◽  
...  
Keyword(s):  
Data Storage ◽  
Model Development ◽  
Computational Cost ◽  
Three Dimensional ◽  
Shallow Convection ◽  
Modeling Framework ◽  
Additional Tool ◽  
Eddy Simulation ◽  
Single Column ◽  
Stratocumulus Clouds

The single-column mode (SCM) of the ICON (ICOsahedral Nonhydrostatic) modeling framework is presented. The primary purpose of the ICON SCM is to use it as a tool for research, model evaluation and development. Thanks to the simplified geometry of the ICON SCM, various aspects of the ICON model, in particular the model physics, can be studied in a well-controlled environment. Additionally, the ICON SCM has a reduced computational cost and a low data storage demand. The ICON SCM can be utilized for idealized cases—several well-established cases are already included—or for semi-realistic cases based on analyses or model forecasts. As the case setup is defined by a single NetCDF file, new cases can be prepared easily by the modification of this file. We demonstrate the usage of the ICON SCM for different idealized cases such as shallow convection, stratocumulus clouds, and radiative transfer. Additionally, the ICON SCM is tested for a semi-realistic case together with an equivalent three-dimensional setup and the large eddy simulation mode of ICON. Such consistent comparisons across the hierarchy of ICON configurations are very helpful for model development. The ICON SCM will be implemented into the operational ICON model and will serve as an additional tool for advancing the development of the ICON model.

Sign in / Sign up

Export Citation Format

Share Document