scholarly journals The Impact of Error Accounting in a Bayesian Approach to Calibrating Modeled Turbulent Fluxes in an Open-Canopy Forest

2017 ◽  
Vol 18 (7) ◽  
pp. 2029-2042
Author(s):  
Tony E. Wong ◽  
William Kleiber ◽  
David C. Noone
Keyword(s):  
Markov Chain ◽  
Land Surface ◽  
Hydrological Modeling ◽  
Soil Layer ◽  
Turbulent Fluxes ◽  
Surface Model ◽  
Model Parameters ◽  
Top Soil ◽  
The Impact ◽  
Calibration Approach

Abstract Land surface models are notorious for containing many parameters that control the exchange of heat and moisture between land and atmosphere. Properly modeling the partitioning of total evapotranspiration (ET) between transpiration and evaporation is critical for accurate hydrological modeling, but depends heavily on the treatment of turbulence within and above canopies. Previous work has constrained estimates of evapotranspiration and its partitioning using statistical approaches that calibrate land surface model parameters by assimilating in situ measurements. These studies, however, are silent on the impacts of the accounting of uncertainty within the statistical calibration framework. The present study calibrates the aerodynamic, leaf boundary layer, and stomatal resistance parameters, which partially control canopy turbulent exchange and thus the evapotranspiration flux partitioning. Using an adaptive Metropolis–Hastings algorithm to construct a Markov chain of draws from the joint posterior distribution of these resistance parameters, an ensemble of model realizations is generated, in which latent and sensible heat fluxes and top soil layer temperature are optimized. A set of five calibration experiments demonstrate that model performance is sensitive to the accounting of various sources of uncertainty in the field observations and model output and that it is critical to account for model structural uncertainty. After calibration, the modeled fluxes and top soil layer temperature are largely free from bias, and this calibration approach successfully informs and characterizes uncertainty in these parameters, which is essential for model improvement and development. The key points of this paper are 1) a Markov chain Monte Carlo calibration approach successfully improves modeled turbulent fluxes; 2) ET partitioning estimates hinge on the representation of uncertainties in the model and data; and 3) despite these inherent uncertainties, constrained posterior estimates of ET partitioning emerge.

scholarly journals High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation

2020 ◽  
Author(s):  
Benjamin Fersch ◽  
Alfonso Senatore ◽  
Bianca Adler ◽  
Joël Arnault ◽  
Matthias Mauder ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Land Surface ◽  
Hydrological Modeling ◽  
Weather Prediction ◽  
Surface Model ◽  
Near Surface ◽  
Ground Heat Flux ◽  
The Impact ◽  
Fully Coupled ◽  
Regional Water

<p>The land surface and the atmospheric boundary layer are closely intertwined with respect to the exchange of water, trace gases and energy. Nonlinear feedback and scale dependent mechanisms are obvious by observations and theories. Modeling instead is often narrowed to single compartments of the terrestrial system or bound to traditional viewpoints of definite scientific disciplines. Coupled terrestrial hydrometeorological modeling systems attempt to overcome these limitations to achieve a better integration of the processes relevant for regional climate studies and local area weather prediction. We examine the ability of the hydrologically enhanced version of the Weather Research and Forecasting Model (WRF-Hydro) to reproduce the regional water cycle by means of a two-way coupled approach and assess the impact of hydrological coupling with respect to a traditional regional atmospheric model setting. It includes the observation-based calibration of the hydrological model component (offline WRF-Hydro) and a comparison of the classic WRF and the fully coupled WRF-Hydro models both with identical calibrated parameter settings for the land surface model (Noah-MP). The simulations are evaluated based on extensive observations at the pre-Alpine Terrestrial Environmental Observatory (TERENO Pre-Alpine) for the Ammer (600 km²) and Rott (55 km²) river catchments in southern Germany, covering a five month period (Jun–Oct 2016).</p><p>The sensitivity of 7 land surface parameters is tested using the <em>Latin-Hypercube One-factor-At-a-Time</em> (LH-OAT) method and 6 sensitive parameters are subsequently optimized for 6 different subcatchments, using the Model-Independent <em>Parameter Estimation and Uncertainty Analysis software</em> (PEST).</p><p>The calibration of the offline WRF-Hydro leads to Nash-Sutcliffe efficiencies between 0.56 and 0.64 and volumetric efficiencies between 0.46 and 0.81 for the six subcatchments. The comparison of classic WRF and fully coupled WRF-Hydro shows only tiny alterations for radiation and precipitation but considerable changes for moisture- and energy fluxes. By comparison with TERENO Pre-Alpine observations, the fully coupled model slightly outperforms the classic WRF with respect to evapotranspiration, sensible and ground heat flux, near surface mixing ratio, temperature, and boundary layer profiles of air temperature. The subcatchment-based water budgets show uniformly directed variations for evapotranspiration, infiltration excess and percolation whereas soil moisture and precipitation change randomly.</p>

scholarly journals Identification of key parameters controlling demographicallystructured vegetation dynamics in a Land Surface Model [CLM4.5(ED)]

2019 ◽  
Author(s):  
Elias C. Massoud ◽  
Chonggang Xu ◽  
Rosie Fisher ◽  
Ryan Knox ◽  
Anthony Walker ◽  
...  
Keyword(s):  
Land Surface ◽  
Canopy Structure ◽  
Land Surface Model ◽  
Carbon Stocks ◽  
Surface Model ◽  
Model Parameters ◽  
Growth Strategies ◽  
Community Land Model ◽  
Vegetation Models ◽  
The Impact

Abstract. Vegetation plays a key role in regulating global carbon cycles and is a key component of the Earth System Models (ESMs) aimed to project Earth's future climates. In the last decade, the vegetation component within ESMs has witnessed great progresses from simple 'big-leaf' approaches to demographically-structured approaches, which has a better representation of plant size, canopy structure, and disturbances. The demographically-structured vegetation models are typically controlled by a large number of parameters, and sensitivity analysis is generally needed to quantify the impact of each parameter on the model outputs for a better understanding of model behaviors. In this study, we use the Fourier Amplitude Sensitivity Test (FAST) to diagnose the Community Land Model coupled to the Ecosystem Demography Model, or CLM4.5(ED). We investigate the first and second order sensitivities of the model parameters to outputs that represent simulated growth and mortality as well as carbon fluxes and stocks. While the photosynthetic capacity parameter Vc,max25 is found to be important for simulated carbon stocks and fluxes, we also show the importance of carbon storage and allometry parameters, which are shown here to determine vegetation demography and carbon stocks through their impacts on survival and growth strategies. The results of this study highlights the importance of understanding the dynamics of the next generation of demographically-enabled vegetation models within ESMs toward improved model parameterization and model structure for better model fidelity.

scholarly journals Baseline Evaluation of the Impact of Updates to the MIT Earth System Model on its Model Parameter Estimates

2018 ◽  
Author(s):  
Alex G. Libardoni ◽  
Chris E. Forest ◽  
Andrei P. Sokolov ◽  
Erwan Monier
Keyword(s):  
Land Surface ◽  
Probability Distributions ◽  
Land Surface Model ◽  
Response Surfaces ◽  
Surface Model ◽  
Model Parameters ◽  
Ocean Heat Uptake ◽  
Aerosol Forcing ◽  
Baseline Evaluation ◽  
The Impact

Abstract. For over twenty years, the Massachusetts Institute of Technology Earth System Model (MESM) has been used extensively for climate change research. The model is under continuous development with components being added or updated. To provide transparency in the model development, we perform a baseline evaluation of the newest version by comparing model behavior and properties to the previous model version. In particular, the impacts resulting from updates to the land surface model component and the input forcings used in historical simulations of climate change are investigated. We run an 1800-member ensemble of MESM historical climate simulations where the model parameters that set climate sensitivity, ocean heat uptake, and the net anthropogenic aerosol forcing are systematically varied. By comparing model output to observed patterns of surface temperature changes, the linear trend in the increase in ocean heat content, and upper-air temperature changes, we derive probability distributions for the three model parameters. Furthermore, we run a 372-member ensemble of transient climate simulations where model forcings are held fixed, absent an increase in carbon dioxide concentrations at the rate of 1 % per year. From these runs, we derive a response surface for transient climate response and thermosteric sea level rise as a function of climate sensitivity and ocean heat uptake. We compare the probability distributions and response surfaces derived using the current version of MESM to the preceding version to evaluate the impact of the updated land surface model and forcing suite. We show that the probability distributions shift towards higher climate sensitivities and weaker aerosol forcing in response to the new forcing suite. The climate response surfaces are relatively unchanged between model versions, indicating that the updated land surface model has limited impact on temperature evolution in the model.

scholarly journals The impact of model and rainfall forcing errors on characterizing soil moisture uncertainty in land surface modeling

2012 ◽  
Vol 16 (10) ◽  
pp. 3499-3515 ◽  
Author(s):  
V. Maggioni ◽  
E. N. Anagnostou ◽  
R. H. Reichle
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Parameter Uncertainty ◽  
Land Surface ◽  
Accurate Estimation ◽  
Surface Model ◽  
Model Parameters ◽  
Model Parameter ◽  
Parameter Perturbations ◽  
The Impact

Abstract. The contribution of rainfall forcing errors relative to model (structural and parameter) uncertainty in the prediction of soil moisture is investigated by integrating the NASA Catchment Land Surface Model (CLSM), forced with hydro-meteorological data, in the Oklahoma region. Rainfall-forcing uncertainty is introduced using a stochastic error model that generates ensemble rainfall fields from satellite rainfall products. The ensemble satellite rain fields are propagated through CLSM to produce soil moisture ensembles. Errors in CLSM are modeled with two different approaches: either by perturbing model parameters (representing model parameter uncertainty) or by adding randomly generated noise (representing model structure and parameter uncertainty) to the model prognostic variables. Our findings highlight that the method currently used in the NASA GEOS-5 Land Data Assimilation System to perturb CLSM variables poorly describes the uncertainty in the predicted soil moisture, even when combined with rainfall model perturbations. On the other hand, by adding model parameter perturbations to rainfall forcing perturbations, a better characterization of uncertainty in soil moisture simulations is observed. Specifically, an analysis of the rank histograms shows that the most consistent ensemble of soil moisture is obtained by combining rainfall and model parameter perturbations. When rainfall forcing and model prognostic perturbations are added, the rank histogram shows a U-shape at the domain average scale, which corresponds to a lack of variability in the forecast ensemble. The more accurate estimation of the soil moisture prediction uncertainty obtained by combining rainfall and parameter perturbations is encouraging for the application of this approach in ensemble data assimilation systems.

scholarly journals High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation

2019 ◽  
Author(s):  
Benjamin Fersch ◽  
Alfonso Senatore ◽  
Bianca Adler ◽  
Joël Arnault ◽  
Matthias Mauder ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Land Surface ◽  
Hydrological Modeling ◽  
Weather Prediction ◽  
Surface Model ◽  
Near Surface ◽  
Ground Heat Flux ◽  
The Impact ◽  
Fully Coupled ◽  
Regional Water

Abstract. The land surface and the atmospheric boundary layer are closely intertwined with respect to the exchange of water, trace gases and energy. Nonlinear feedback and scale dependent mechanisms are obvious by observations and theories. Modeling instead is often narrowed to single compartments of the terrestrial system or largely bound to traditional disciplines. Coupled terrestrial hydrometeorological modeling systems attempt to overcome these limitations to achieve a better integration of the processes relevant for regional climate studies and local area weather prediction. This study examines the ability of the hydrologically enhanced version of the Weather Research and Forecasting Model (WRF-Hydro) to reproduce the regional water cycle by means of a two-way coupled approach and assesses the impact of hydrological coupling with respect to a traditional regional atmospheric model setting. It includes the observation-based calibration of the hydrological model component (offline WRF-Hydro) and a comparison of the classic WRF and the fully coupled WRF-Hydro models both with identical calibrated parameter settings for the land surface model (Noah-MP). The simulations are evaluated based on extensive observations at the preAlpine Terrestrial Environmental Observatory (TERENO-preAlpine) for the Ammer (600 km2) and Rott (55 km2) river catchments in southern Germany, covering a five month period (Jun–Oct 2016). The sensitivity of 7 land surface parameters is tested using the Latin-Hypercube One-factor-At-a-Time (LH-OAT) method and 6 sensitive parameters are subsequently optimized for 6 different subcatchments, using the Model-Independent Parameter Estimation and Uncertainty Analysis software (PEST). The calibration of the offline WRF-Hydro gives Nash-Sutcliffe efficiencies between 0.56 and 0.64 and volumetric efficiencies between 0.46 and 0.81 for the six subcatchments. The comparison of classic WRF and fully coupled WRF-Hydro, both using the calibrated parameters from the offline model, shows nominal alterations for radiation and precipitation but considerable changes for moisture- and heat fluxes. By comparison with TERENO-preAlpine observations, the fully coupled model slightly outperforms the classic WRF with respect to evapotranspiration, sensible and ground heat flux, near surface mixing ratio, temperature, and boundary layer profiles of air temperature. The subcatchment-based water budgets show uniformly directed variations for evapotranspiration, infiltration excess and percolation whereas soil moisture and precipitation change randomly.

scholarly journals Quantification of the Uncertainties in Soil and Vegetation Parameterizations for Regional Climate Simulations in Europe

2017 ◽  
Vol 18 (5) ◽  
pp. 1535-1548 ◽  
Author(s):  
Marcus Breil ◽  
Gerd Schädler
Keyword(s):  
Land Surface ◽  
Regional Climate ◽  
Small Scale ◽  
Surface Model ◽  
Limited Area ◽  
Model Parameters ◽  
Subgrid Scale ◽  
Local Soil ◽  
Atmosphere Interaction ◽  
The Impact

Abstract The deterministic description of the subgrid-scale land–atmosphere interaction in regional climate model (RCM) simulations is changed by using stochastic soil and vegetation parameterizations. For this, the land–atmosphere interaction parameterized in a land surface model (LSM) is perturbed stochastically by adding a random value to the input parameters using a random number generator. In this way, a stochastic ensemble is created that represents the impact of the uncertainties in these subgrid-scale processes on the resolved scale circulation. In a first step, stochastic stand-alone simulations with the VEG3D LSM are performed to identify sensitive model parameters. Afterward, VEG3D is coupled to the Consortium for Small-Scale Modeling–Climate Limited-Area Modeling (COSMO-CLM) RCM and stochastically perturbed simulations driven by ERA-Interim (2001–10) are performed for the Coordinated Downscaling Experiment–European Domain (EURO-CORDEX) at a horizontal resolution of 0.22°. The simulation results reveal that the impact of stochastically varied soil and vegetation parameterizations on the simulated climate conditions differs regionally. In central Europe the impact on the mean temperature and precipitation characteristics is very weak. In southern Europe and North Africa, however, the resolved scale circulation is very sensitive to the local soil water conditions. Furthermore, it is demonstrated that the use of stochastic soil and vegetation parameterizations considerably improves the variability of monthly rainfall sums all over Europe by improving the representation of the land–atmosphere interaction in the stochastic ensemble on a daily basis. In particular, inland rainfall during summer is simulated much better.

scholarly journals The impact of land model structural, parameter, and forcing errors on the characterization of soil moisture uncertainty

2012 ◽  
Vol 9 (2) ◽  
pp. 2283-2319 ◽  
Author(s):  
V. Maggioni ◽  
E. N. Anagnostou ◽  
R. H. Reichle
Keyword(s):  
Soil Moisture ◽  
Model Uncertainty ◽  
Land Surface ◽  
Land Surface Model ◽  
Parametric Uncertainty ◽  
Structural Parameter ◽  
Surface Model ◽  
Model Parameters ◽  
Prognostic Variables ◽  
The Impact

Abstract. A sensitivity analysis is conducted to investigate the contribution of rainfall forcing relative to the model uncertainty in the prediction of soil moisture by integrating the NASA Catchment Land Surface Model (CLSM), forced with hydro-meteorological data, in the Oklahoma region. This study depicts different sources of uncertainty, namely, errors in the model input (i.e., rainfall estimates from satellite remote sensing observations) and errors in the land surface model itself. Specifically, rainfall-forcing uncertainty is introduced using a stochastic error model that generates reference-like ensemble rainfall fields from satellite rainfall products. The ensemble satellite rain fields are propagated through CLSM to produce soil moisture ensembles. Errors in CLSM are modeled with two different approaches: either by perturbing model parameters using the generalized likelihood uncertainty estimation (GLUE) technique or by adding randomly generated noise to the model prognostic variables. While the first method only addresses parametric uncertainty, the second one addresses both structural and parametric uncertainty. Despite this, a reasonable spread in soil moisture is achieved with relatively few parameter perturbations through GLUE, whereas the same ensemble width requires stronger prognostic perturbations with the standard random perturbation method. The probability of encapsulating the reference soil moisture simulation increases when the rainfall forcing uncertainty and the model uncertainty approaches are combined (compared with using only rainfall uncertainty). This improvement is more significant when using the GLUE technique to perturb CLSM parameters as opposed to perturbing the CLSM prognostic variables.

scholarly journals The incorporation of an organic soil layer in the Noah-MP Land Surface Model and its evaluation over a Boreal Aspen Forest

2015 ◽  
Vol 15 (20) ◽  
pp. 29265-29302
Author(s):  
L. Chen ◽  
Y. Li ◽  
F. Chen ◽  
A. Barr ◽  
M. Barlage ◽  
...  
Keyword(s):  
Surface Energy ◽  
Land Surface ◽  
Soil Depth ◽  
Soil Layer ◽  
Land Surface Model ◽  
Organic Soil ◽  
Surface Model ◽  
Field Station ◽  
Aspen Forest ◽  
The Impact

Abstract. In this study, the multi-parameterization version of the Noah land-surface model (Noah-MP) was used to investigate the impact of adding a forest-floor organic soil layer on the simulated surface energy and water cycle components at a boreal aspen forest. The test site selected is BERMS Old Aspen Flux (OAS) field station in central Saskatchewan, Canada. The selection of different parameterization schemes for each process within the current Noah-MP model significantly affected the simulation results. The best combination options without incorporating organic soil is referred as the control experiment (CTL). By including an organic-soil parameterization within the Noah-MP model for the first time, the verification results (OGN) against site show significantly improved performance of the model in surface energy fluxes and hydrology simulation due to the lower thermal conductivity and greater porosity of the organic soil. The effects of including an organic soil layer on soil temperature are not uniform throughout the soil depth and year, and those effects are more prominent in summer and in deep soils. For drought years, the OGN simulation substantially modified the partition between direct soil evaporation and vegetation transpiration. For wet years, the OGN simulated latent heat fluxes are similar to CTL except for spring season where OGN produced less evaporation. The impact of the organic soil on sub-surface runoff is substantive with much higher runoff throughout the season.

scholarly journals Daily Precipitation Frequency Distributions Impacts on Land-Surface Simulations of CONUS

2021 ◽  
Vol 3 ◽  
Author(s):  
Daniel P. Sarmiento ◽  
Kimberly Slinski ◽  
Amy McNally ◽  
Jossy P. Jacob ◽  
Chris Funk ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Frequency Distribution ◽  
Land Surface ◽  
Surface Soil ◽  
Daily Precipitation ◽  
Soil Layer ◽  
Surface Model ◽  
Precipitation Frequency ◽  
Frequency Distributions ◽  
The Impact

Many precipitation-driven data products from land data assimilation systems support assessments of droughts, floods, and other societally-relevant land-surface processes. The accumulated precipitation used as input to these products has a significant impact on water budgets; however, the effects of daily distribution of precipitation on these products are not well known. A comparison of the Integrated Multi-satellite Retrievals for GPM (IMERG) and Climate Hazards Group InfraRed Precipitation with Stations version 2 (CHIRPS2) rainfall products over the continental United States (CONUS) was performed to quantify the impacts of the daily distribution of precipitation on biases and errors in soil moisture, runoff, and evapotranspiration (ET). Since the total accumulated precipitation between the IMERG and CHIRPS product differed, a third precipitation product, CHIRPS-to-IMERG (CHtoIM), was produced that used CHIRPS2 accumulated precipitation totals and the daily precipitation frequency distribution of IMERG. This new product supported a controlled analysis of the impact of precipitation frequency distribution on simulated hydrological fields. The CHtoIM had higher occurrences of precipitation in the 0–5 mm day−1 range, with a lower occurrence of dry days, which decreased soil moisture and surface runoff in the land-surface model. The surface soil layer had a tendency to reach saturation more often in the CHIRPS2 simulations, where the number of moderate to heavy precipitation days (>5 mm day−1) was increased. Using the blended CHtoIM product as input reduced errors in surface soil moisture by 5–15% when compared to Soil Moisture Active/Passive (SMAP) data. Similarly, ET errors were also slightly decreased (~2%) when compared to SSEBop data. Moderate changes in daily precipitation distributions had a quantifiable impact on soil moisture, runoff, and ET. These changes usually improved the model when compared to other modeled and observational datasets, but the magnitude of the improvements varied by region and time of year.

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