Sensitivity analysis and calibration of multi energy balance land surface model parameters

Flows of energy between the atmosphere, the oceans and the land surfaces drive weather and climate on Earth. Increased understanding of these processes is crucial to successfully predict and address the challenges of climate change. Land surface models (LSM) are mathematical models designed to mimic natural processes and evolution of land surfaces with the basic task to simulate surface-atmosphere energy flows. Within the SURFace EXternalisée modeling platform (SURFEX), developed by Météo-France and a suite of international partners, a new LSM called the Interaction Soil Biosphere Atmosphere model - Multi Energy Balance (ISBA-MEB) has been developed. There are however still uncertainties in how to accurately prescribe model parameters used to numerically define the physiography and natural processes of modelled land surfaces which consequently results in uncertainties in modelled outputs. In the present study, Quasi-Monte Carlo simulations based on Sobol sensitivity analysis was applied to explore the uncertainty contribution of individual parameters to modelled surface-atmosphere turbulent sensible and latent heat fluxes in forest environments. Those parameters to which modelled fluxes were identified as significantly sensitive were then calibrated by generating multiple sets of parameter values with the Latin Hypercube sampling technique on which the model was run to identify what parameter values generated the least amount of model output bias and to evaluate how much model output uncertainty could be reduced. To explore variations in parameter sensitivity and optimal parameter prescriptions between forest environments, four separate forest areas with varying vegetation types and climate classifications were modelled. Results disclose that the level of uncertainty contribution of individual parameters varies between forest environments. Three parameters were however identified to contribute with significantly output uncertainty; 1) the ration between roughness length of momentum and thermal roughness length, 2) the heat capacity of vegetation and soil and 3) the leaf orientation at canopy bottom. Calibrating these parameters marginally reduced model output uncertainty at all study areas.Jorden tar emot ett konstant flöde av energi via solinstrålning som sedan cirkulerar mellan atmosfären, haven och markytan innan den slutligen strålas ut i rymden. Dessa energiflöden är bränslet som driver planetens väder- och klimatfenomen och det vetenskapliga samfundet efterfrågar ökad kunskap om detta system för att utmaningarna med klimatförändringar ska kunna förutspås och hanteras. En grundläggande komponent i klimatsystemet är markytans energiutbyte med atmosfären. Hur stora dessa energiflöden är och i vilken form som energin transporteras avgörs av väderförhållanden och markens fysiska egenskaper. Inom exempelvis meteorologi och hydrologi simuleras dessa processer med hjälp av Markytamodeller. I ett internationellt samarbete med utgångspunkt i Frankrikes meteorologiska institut Météo France har en ny Markytamodell för simulering av naturmiljöer utvecklats. Denna modell möjliggör en mer detaljerad beskrivning av markytans fysiska komponenter, så som karaktären på jord och vegetation, än sina förgångare. Markytamodeller är matematiska och lanskapets karaktär beskrivs därför numeriska parametrar. I nuläget råder det osäkerhet kring hur vissa av dessa parametrar bäst definieras i olika skogstyper. Eftersom markytans olika fysiska komponenter har olika inflytande på energiflöden har även Markytamodellers parmetrar olika inflytande på simuleringen av dessa energiflöden. Detta uttrycks även som att modellen är olika känslig för olika parametrar. Syftet med denna studie var att undersöka hur känslig den nya Markmodellen är för olika vegetationsparametrar i olika skogsmiljöer. Vidare var syftet att undersöka hur mycket simuleringar kan förbättras genom att finna det optimala värdet på de mest känsliga parametrarna i respektive skogsområde. Skillnader i parameterkänslighet och optimala parametervärden för fyra olika skogsmiljöer identifierades med så kallade Monte-Carlo simuleringar. Kortfattat innebar detta att skogsmiljöerna modellerades upprepade gånger med olika parametervärden. Slutsatserna är att parameterkänsligheten varierar mellan de inkluderade skogsområdena, men att modellen är mycket känsliga för tre av de analyserade parametrarna. Genom att identifiera optimala värden för dessa mycket känsliga parametrar i respektive skogsmiljö kunde mer realistiska simuleringar av energiflöden uppnås

Evaluating enhanced hydrological representations in Noah LSM over transition zones : an ensemble-based approach to model diagnostics

textThis work introduces diagnostic methods for land surface model (LSM) evaluation that enable developers to identify structural shortcomings in model parameterizations by evaluating model 'signatures' (characteristic temporal and spatial patterns of behavior) in feature, cost-function, and parameter spaces. The ensemble-based methods allow researchers to draw conclusions about hypotheses and model realism that are independent of parameter choice. I compare the performance and physical realism of three versions of Noah LSM (a benchmark standard version [STD], a dynamic-vegetation enhanced version [DV], and a groundwater-enabled one [GW]) in simulating high-frequency near-surface states and land-to-atmosphere fluxes in-situ and over a catchment at high-resolution in the U.S. Southern Great Plains, a transition zone between humid and arid climates. Only at more humid sites do the more conceptually realistic, hydrologically enhanced LSMs (DV and GW) ameliorate biases in the estimation of root-zone moisture change and evaporative fraction. Although the improved simulations support the hypothesis that groundwater and vegetation processes shape fluxes in transition zones, further assessment of the timing and partitioning of the energy and water cycles indicates improvements to the movement of water within the soil column are needed. Distributed STD and GW underestimate the contribution of baseflow and simulate too-flashy streamflow. This work challenges common practices and assumptions in LSM development and offers researchers more stringent model evaluation methods. I show that, because of equifinality, ad-hoc evaluation using single parameter sets provides insufficient information for choosing among competing parameterizations, for addressing hypotheses under uncertainty, or for guiding model development. Posterior distributions of physically meaningful parameters differ between models and sites, and relationships between parameters themselves change. 'Plug and play' of modules and partial calibration likely introduce error and should be re-examined. Even though LSMs are 'physically based,' model parameters are effective and scale-, site- and model-dependent. Parameters are not functions of soil or vegetation type alone: they likely depend in part on climate and cannot be assumed to be transferable between sites with similar physical characteristics. By helping bridge the gap between the model identification and model development, this research contributes to the continued improvement of our understanding and modeling of environmental processes.Geological Science

Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES)

Vegetation plays an important role in regulating global carbon cycles and is a key component of the Earth system models (ESMs) that aim to project Earth's future climate. In the last decade, the vegetation component within ESMs has witnessed great progress from simple "big-leaf" approaches to demographically structured approaches, which have a better representation of plant size, canopy structure, and disturbances. These demographically structured vegetation models typically have a large number of input parameters, and sensitivity analysis is needed to quantify the impact of each parameter on the model outputs for a better understanding of model behavior. In this study, we conducted a comprehensive sensitivity analysis to diagnose the Community Land Model coupled to the Functionally Assembled Terrestrial Simulator, or CLM4.5(FATES). Specifically, we quantified 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 for a tropical site with an extent of 1×1°. 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 determine survival and growth strategies within the model. The parameter sensitivity changes with different sizes of trees and climate conditions. The results of this study highlight the importance of understanding the dynamics of the next generation of demographically enabled vegetation models within ESMs to improve model parameterization and structure for better model fidelity

Seasonal adaptation of the thermal‐based two‐source energy balance model for estimating evapotranspiration in a semiarid tree‐grass ecosystem

© 2020 by the authors.The thermal-based two-source energy balance (TSEB) model has accurately simulated energy fluxes in a wide range of landscapes with both remote and proximal sensing data. However, tree-grass ecosystems (TGE) have notably complex heterogeneous vegetation mixtures and dynamic phenological characteristics presenting clear challenges to earth observation and modeling methods. Particularly, the TSEB modeling structure assumes a single vegetation source, making it difficult to represent the multiple vegetation layers present in TGEs (i.e., trees and grasses) which have different phenological and structural characteristics. This study evaluates the implementation of TSEB in a TGE located in central Spain and proposes a new strategy to consider the spatial and temporal complexities observed. This was based on sensitivity analyses (SA) conducted on both primary remote sensing inputs (local SA) and model parameters (global SA). The model was subsequently modified considering phenological dynamics in semi-arid TGEs and assuming a dominant vegetation structure and cover (i.e., either grassland or broadleaved trees) for different seasons (TSEB-2S). The adaptation was compared against the default model and evaluated against eddy covariance (EC) flux measurements and lysimeters over the experimental site. TSEB-2S vastly improved over the default TSEB performance decreasing the mean bias and root-mean-square-deviation (RMSD) of latent heat (LE) from 40 and 82 W m−2 to −4 and 59 W m−2, respectively during 2015. TSEB-2S was further validated for two other EC towers and for different years (2015, 2016 and 2017) obtaining similar error statistics with RMSD of LE ranging between 57 and 63 W m−2. The results presented here demonstrate a relatively simple strategy to improve water and energy flux monitoring over a complex and vulnerable landscape, which are often poorly represented through remote sensing models.The research received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 721995. It was also funded by Ministerio de Economía y Competitividad through FLUXPEC CGL2012-34383 and SynerTGE CGL2015-G9095-R (MINECO/FEDER, UE) projects. The research infrastructure at the measurement site in Majadas de Tiétar was partly funded through the Alexander von Humboldt Foundation, ELEMENTAL (CGL 2017-83538-C3-3-R, MINECO-FEDER) and IMAGINA (PROMETEU 2019; Generalitat Valenciana).Peer reviewe

Can planting trees make it rain?: the impact of trees on precipitation through land-atmosphere feedback

Land surface atmosphere feedback is the interaction between land and atmosphere. Three studies have been conducted to investigate the vegetation-rainfall feedback. The first study is to detect the signal of land cover change from rainfall data in two regions in the Murray Darling Basin (MDB) with known land surface intervention. A semi-parametric regression model and a regression model with non-parametric step trend test are applied to identify changes in rainfall data and correlate these changes to land cover intervention. Both methods have identified some rainfall changes in the Snowy Mountain ranges in NSW/VIC after the 2003 severe bushfires in this area. A simple model, “CLASS”, is used in the second study to simulate day time boundary layer dynamics and atmospheric conditions. The model is applied to Kyeamba (short grasses) and Tumbarumba (Eucalyptus forest), during 09 - 11 November, 2006. Sensitivity experiments do not find a preferential land cover type for the lifting condensation level. Even though latent heat flux and humidity are usually higher over forest, the boundary layer development over forest is not significantly stronger than the grassland. In the last study, rainfall sensitivity to land surface conditions is assessed using a simple equilibrium model. A Sobol’s sensitivity analysis shows the lateral moisture fluxes have the highest impact on rainfall, followed by initial soil moisture and initial LAI. Afforestation cannot enhance long term summer precipitation in the MDB, unless there is a constant supply of high lateral moisture. The current research does not find a strong relationship between the forest and precipitation. Loss of tree covers might decrease precipitation in some areas of the MDB but this relation is not significant, especially in areas with low rainfall. High moisture convergence is required for afforestation to affect rainfall

Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES)

Vegetation plays an important role in regulating global carbon cycles and is a key component of the Earth system models (ESMs) that aim to project Earth\u27s future climate. In the last decade, the vegetation component within ESMs has witnessed great progress from simple “big-leaf” approaches to demographically structured approaches, which have a better representation of plant size, canopy structure, and disturbances. These demographically structured vegetation models typically have a large number of input parameters, and sensitivity analysis is needed to quantify the impact of each parameter on the model outputs for a better understanding of model behavior. In this study, we conducted a comprehensive sensitivity analysis to diagnose the Community Land Model coupled to the Functionally Assembled Terrestrial Simulator, or CLM4.5(FATES). Specifically, we quantified 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 for a tropical site with an extent of 1×1∘. 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 determine survival and growth strategies within the model. The parameter sensitivity changes with different sizes of trees and climate conditions. The results of this study highlight the importance of understanding the dynamics of the next generation of demographically enabled vegetation models within ESMs to improve model parameterization and structure for better model fidelity

One Stomatal Model to Rule Them All?:Toward Improved Representation of Carbon and Water Exchange in Global Models

Stomatal conductance schemes that optimize with respect to photosynthetic and hydraulic functions have been proposed to address biases in land-surface model (LSM) simulations during drought. However, systematic evaluations of both optimality-based and alternative empirical formulations for coupling carbon and water fluxes are lacking. Here, we embed 12 empirical and optimization approaches within a LSM framework. We use theoretical model experiments to explore parameter identifiability and understand how model behaviors differ in response to abiotic changes. We also evaluate the models against leaf-level observations of gas-exchange and hydraulic variables, from xeric to wet forest/woody species spanning a mean annual precipitation range of 361–3,286 mm yr−1. We find that models differ in how easily parameterized they are, due to: (a) poorly constrained optimality criteria (i.e., resulting in multiple solutions), (b) low influence parameters, (c) sensitivities to environmental drivers. In both the idealized experiments and compared to observations, sensitivities to variability in environmental drivers do not agree among models. Marked differences arise in sensitivities to soil moisture (soil water potential) and vapor pressure deficit. For example, stomatal closure rates at high vapor pressure deficit range between −45% and +70% of those observed. Although over half the new generation of stomatal schemes perform to a similar standard compared to observations of leaf-gas exchange, two models do so through large biases in simulated leaf water potential (up to 11 MPa). Our results provide guidance for LSM development, by highlighting key areas in need for additional experimentation and theory, and by constraining currently viable stomatal hypotheses