On the use of field measurements of energy fluxes to evaluate land surface models

2008 ◽  
Vol 214 (2-4) ◽  
pp. 293-304 ◽  
Author(s):  
Mustapha El Maayar ◽  
Jing M. Chen ◽  
David T. Price
Keyword(s):  
Land Surface ◽  
Field Measurements ◽  
Land Surface Models ◽  
Energy Fluxes ◽  
Surface Models

Phenology-induced energy and carbon fluxes in land surface models

2020 ◽  
Author(s):  
Jan De Pue ◽  
José Miguel Barrios ◽  
Fabienne Maignan ◽  
Liyang Liu ◽  
Philippe Ciais ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Terrestrial Ecosystems ◽  
Carbon Fluxes ◽  
Land Surface Models ◽  
Energy Fluxes ◽  
Wide Range ◽  
Surface Models ◽  
Modelling Strategies ◽  
Phenological Cycle

<p>The annual phenological cycle is of key importance for the carbon and energy fluxes in terrestrial ecosystems. Although the processes controlling budburst and leaf senescence are fairly well known, the connection between plant phenology and the carbon fluxes remains a challenging aspect in land surface modelling (LSM). In this study, the modelling strategies of three well stablished LSM are compared. The LSM considered in this study were: ORCHIDEE, ISBA-A-gs and the model driving the LSA-SAF evapotranspiration product (https://landsaf.ipma.pt). The latter model does not simulate the carbon fluxes but focuses on the computation of evapotranspiration and energy fluxes.<br>The phenological cycle is simulated explicitly in the ORCHIDEE model, using empirical relations based on temperature sum, water availability, and other variables. In the ISBA-A-gs model, phenology and LAI development is fully photosynthesis-driven. The phenology in the LSA-SAF model is driven by remote sensing forcing variables, such as LAI observations. Alternatively, the assimilation of remote sensing LAI products is a convenient method to improve the simulated phenological cycle in land surface models. A dedicated module for this operation is available in ISBA-A-gs.<br>Simulations were performed over a wide range of climatological conditions and plant functional types. The results were then validated with in-situ measurements conducted at Fluxnet stations. In addition to the comparison between measured and modelled carbon fluxes, the validation in this study included the intra-annual variation in the simulated phenological cycle.</p>

scholarly journals Calibration of LaD Model in the Northeast United States Using Observed Annual Streamflow

2007 ◽  
Vol 8 (5) ◽  
pp. 1098-1110 ◽  
Author(s):  
Y. Xia
Keyword(s):  
United States ◽  
Land Surface ◽  
Land Surface Models ◽  
Northeastern United States ◽  
Energy Fluxes ◽  
Annual Streamflow ◽  
Reasonable Estimate ◽  
Model Parameter ◽  
Resources Management ◽  
Surface Models

Abstract Calibration of land surface models improves simulations of surface water and energy fluxes and provides important information for water resources management. However, most calibration studies focus on local sites and/or small catchments because of computational limitations, lack of atmospheric forcing data, and lack of observed water and energy fluxes. Even though a well-established streamflow gauge network exists, its data are not well suited to the calibration of land surface models in cold regions because of large systematic precipitation biases. This study provides a newly developed method to adjust systematic precipitation biases arising from gauge undercatch (e.g., wind blowing, wetting loss, and evaporation loss). The new method estimates model parameter and precipitation errors simultaneously through the use of observed annual streamflow in the northeastern United States. The results show that this method improves streamflow simulations and gives a reasonable estimate for systematic precipitation bias. In addition, the impacts of model parameter errors on the calibration of the Land Dynamics (LaD) model and on the estimation of systematic precipitation biases are investigated in the northeastern United States.

scholarly journals Eco‐evolutionary optimality as a means to improve vegetation and land‐surface models

2021 ◽  
Author(s):  
Sandy P. Harrison ◽  
Wolfgang Cramer ◽  
Oskar Franklin ◽  
Iain Colin Prentice ◽  
Han Wang ◽  
...  
Keyword(s):  
Land Surface ◽  
Land Surface Models ◽  
Surface Models ◽  
Evolutionary Optimality

scholarly journals The CarboEurope Regional Experiment Strategy

2006 ◽  
Vol 87 (10) ◽  
pp. 1367-1380 ◽  
Author(s):  
A. J. Dolman ◽  
J. Noilhan ◽  
P. Durand ◽  
C. Sarrat ◽  
A. Brut ◽  
...  
Keyword(s):  
Land Surface ◽  
Major Product ◽  
State Variables ◽  
Land Surface Models ◽  
Quality Metric ◽  
Key Characteristics ◽  
Surface Models ◽  
Global Soil ◽  
Standard Deviations ◽  
Model Agreement

The Second Global Soil Wetness Project (GSWP-2) is an initiative to compare and evaluate 10-year simulations by a broad range of land surface models under controlled conditions. A major product of GSWP-2 is the first global gridded multimodel analysis of land surface state variables and fluxes for use by meteorologists, hydrologists, engineers, biogeochemists, agronomists, botanists, ecologists, geographers, climatologists, and educators. Simulations by 13 land models from five nations have gone into production of the analysis. The models are driven by forcing data derived from a combination of gridded atmospheric reanalyses and observations. The resulting analysis consists of multimodel means and standard deviations on the monthly time scale, including profiles of soil moisture and temperature at six levels, as well as daily and climatological (mean annual cycle) fields for over 50 land surface variables. The monthly standard deviations provide a measure of model agreement that may be used as a quality metric. An overview of key characteristics of the analysis is presented here, along with information on obtaining the data.

scholarly journals On the utility of land surface models for agricultural drought monitoring

2012 ◽  
Vol 16 (9) ◽  
pp. 3451-3460 ◽  
Author(s):  
W. T. Crow ◽  
S. V. Kumar ◽  
J. D. Bolten
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Cross Correlation ◽  
Root Zone ◽  
Vegetation Indices ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Global Evaluation ◽  
Land Surface Models ◽  
Surface Models

Abstract. The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely sensed vegetation indices (VI) is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agricultural drought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modern land surface models (LSMs) based on complex water and energy balance formulations. A quasi-global evaluation of lagged VI/soil moisture cross-correlation suggests, when globally averaged across the entire annual cycle, soil moisture estimates obtained from complex LSMs provide little added skill (< 5% in relative terms) in anticipating variations in vegetation condition relative to a simplified water accounting procedure based solely on observed precipitation. However, larger amounts of added skill (5–15% in relative terms) can be identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired by averaging across a multi-model ensemble.

scholarly journals Exploring the Sensitivity of Photosynthesis and Stomatal Resistance Parameters in a Land Surface Model

2017 ◽  
Vol 18 (3) ◽  
pp. 897-915 ◽  
Author(s):  
Jennifer L. Jefferson ◽  
Reed M. Maxwell ◽  
Paul G. Constantine
Keyword(s):  
Land Surface ◽  
Stomatal Resistance ◽  
Hydrologic Model ◽  
Surface Model ◽  
Land Surface Models ◽  
Common Land Model ◽  
Surface Models ◽  
Input Parameters ◽  
Rate Of Photosynthesis ◽  
Quantities Of Interest

Abstract Land surface models, like the Common Land Model component of the ParFlow integrated hydrologic model (PF-CLM), are used to estimate transpiration from vegetated surfaces. Transpiration rates quantify how much water moves from the subsurface through the plant and into the atmosphere. This rate is controlled by the stomatal resistance term in land surface models. The Ball–Berry stomatal resistance parameterization relies, in part, on the rate of photosynthesis, and together these equations require the specification of 20 input parameters. Here, the active subspace method is applied to 2100 year-long PF-CLM simulations, forced by atmospheric data from California, Colorado, and Oklahoma, to identify which input parameters are important and how they relate to three quantities of interest: transpiration, stomatal resistance from the sunlit portion of the canopy, and stomatal resistance from the shaded portion. The slope (mp) and intercept (bp) parameters associated with the Ball–Berry parameterization are consistently important for all locations, along with five parameters associated with ribulose bisphosphate carboxylase/oxygenase (RuBisCO)- and light-limited rates of photosynthesis [CO2 Michaelis–Menten constant at 25°C (kc25), maximum ratio of oxygenation to carboxylation (ocr), quantum efficiency at 25°C (qe25), maximum rate of carboxylation at 25°C (vcmx25), and multiplier in the denominator of the equation used to compute the light-limited rate of photosynthesis (wj1)]. The importance of these input parameters, quantified by the active variable weight, and the relationship between the input parameters and quantities of interest vary seasonally and diurnally. Input parameter values influence transpiration rates most during midday, summertime hours when fluxes are large. This research informs model users about which photosynthesis and stomatal resistance parameters should be more carefully selected. Quantifying sensitivities associated with the stomatal resistance term is necessary to better understand transpiration estimates from land surface models.

scholarly journals Attribution of Flux Partitioning Variations between Land Surface Models over the Continental U.S.

2018 ◽  
Vol 10 (5) ◽  
pp. 751 ◽  
Author(s):  
Sujay Kumar ◽  
Thomas Holmes ◽  
David Mocko ◽  
Shugong Wang ◽  
Christa Peters-Lidard
Keyword(s):  
Land Surface ◽  
Land Surface Models ◽  
Flux Partitioning ◽  
Surface Models
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