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 Multi-objective parameter optimization of common land model using adaptive surrogate modelling

2014 ◽  
Vol 11 (6) ◽  
pp. 6715-6751 ◽  
Author(s):  
W. Gong ◽  
Q. Duan ◽  
J. Li ◽  
C. Wang ◽  
Z. Di ◽  
...  
Keyword(s):  
Parameter Optimization ◽  
Land Surface ◽  
Dynamic Models ◽  
Optimal Parameter ◽  
Surface Model ◽  
Multi Objective Optimization ◽  
Land Surface Models ◽  
Multi Objective ◽  
Common Land Model ◽  
Surface Models

Abstract. Parameter specification usually has significant influence on the performance of land surface models (LSMs). However, estimating the parameters properly is a challenging task due to the following reasons: (1) LSMs usually have too many adjustable parameters (20–100 or even more), leading to the curse of dimensionality in the parameter input space; (2) LSMs usually have many output variables involving water/energy/carbon cycles, so that calibrating LSMs is actually a multi-objective optimization problem; (3) regional LSMs are expensive to run, while conventional multi-objective optimization methods needs a huge number of model runs (typically 105~106). It makes parameter optimization computationally prohibitive. An uncertainty qualification framework was developed to meet the aforementioned challenges: (1) use parameter screening to reduce the number of adjustable parameters; (2) use surrogate models to emulate the response of dynamic models to the variation of adjustable parameters; (3) use an adaptive strategy to promote the efficiency of surrogate modeling based optimization; (4) use a weighting function to transfer multi-objective optimization to single objective optimization. In this study, we demonstrate the uncertainty quantification framework on a single column case study of a land surface model – Common Land Model (CoLM) and evaluate the effectiveness and efficiency of the proposed framework. The result indicated that this framework can achieve optimal parameter set using totally 411 model runs, and worth to be extended to other large complex dynamic models, such as regional land surface models, atmospheric models and climate models.

scholarly journals Modeling sugarcane yield with a process-based model from site to continental scale: uncertainties arising from model structure and parameter values

2014 ◽  
Vol 7 (3) ◽  
pp. 1225-1245 ◽  
Author(s):  
A. Valade ◽  
P. Ciais ◽  
N. Vuichard ◽  
N. Viovy ◽  
A. Caubel ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Land Surface ◽  
Surface Model ◽  
Land Surface Models ◽  
Continental Scale ◽  
Surface Models ◽  
Input Parameters ◽  
Sugarcane Yield ◽  
Parameter Values ◽  
Sensitive Parameters

Abstract. Agro-land surface models (agro-LSM) have been developed from the integration of specific crop processes into large-scale generic land surface models that allow calculating the spatial distribution and variability of energy, water and carbon fluxes within the soil–vegetation–atmosphere continuum. When developing agro-LSM models, particular attention must be given to the effects of crop phenology and management on the turbulent fluxes exchanged with the atmosphere, and the underlying water and carbon pools. A part of the uncertainty of agro-LSM models is related to their usually large number of parameters. In this study, we quantify the parameter-values uncertainty in the simulation of sugarcane biomass production with the agro-LSM ORCHIDEE–STICS, using a multi-regional approach with data from sites in Australia, La Réunion and Brazil. In ORCHIDEE–STICS, two models are chained: STICS, an agronomy model that calculates phenology and management, and ORCHIDEE, a land surface model that calculates biomass and other ecosystem variables forced by STICS phenology. First, the parameters that dominate the uncertainty of simulated biomass at harvest date are determined through a screening of 67 different parameters of both STICS and ORCHIDEE on a multi-site basis. Secondly, the uncertainty of harvested biomass attributable to those most sensitive parameters is quantified and specifically attributed to either STICS (phenology, management) or to ORCHIDEE (other ecosystem variables including biomass) through distinct Monte Carlo runs. The uncertainty on parameter values is constrained using observations by calibrating the model independently at seven sites. In a third step, a sensitivity analysis is carried out by varying the most sensitive parameters to investigate their effects at continental scale. A Monte Carlo sampling method associated with the calculation of partial ranked correlation coefficients is used to quantify the sensitivity of harvested biomass to input parameters on a continental scale across the large regions of intensive sugarcane cultivation in Australia and Brazil. The ten parameters driving most of the uncertainty in the ORCHIDEE–STICS modeled biomass at the 7 sites are identified by the screening procedure. We found that the 10 most sensitive parameters control phenology (maximum rate of increase of LAI) and root uptake of water and nitrogen (root profile and root growth rate, nitrogen stress threshold) in STICS, and photosynthesis (optimal temperature of photosynthesis, optimal carboxylation rate), radiation interception (extinction coefficient), and transpiration and respiration (stomatal conductance, growth and maintenance respiration coefficients) in ORCHIDEE. We find that the optimal carboxylation rate and photosynthesis temperature parameters contribute most to the uncertainty in harvested biomass simulations at site scale. The spatial variation of the ranked correlation between input parameters and modeled biomass at harvest is well explained by rain and temperature drivers, suggesting different climate-mediated sensitivities of modeled sugarcane yield to the model parameters, for Australia and Brazil. This study reveals the spatial and temporal patterns of uncertainty variability for a highly parameterized agro-LSM and calls for more systematic uncertainty analyses of such models.

scholarly journals Using Cosmic-Ray Neutron Probes in Validating Satellite Soil Moisture Products and Land Surface Models

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1362 ◽  
Author(s):  
Mustafa Berk Duygu ◽  
Zuhal Akyürek
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Surface Model ◽  
High Temporal Resolution ◽  
Land Surface Models ◽  
Statistical Measures ◽  
Surface Models ◽  
Global Land

Soil moisture content is one of the most important parameters of hydrological studies. Cosmic-ray neutron sensing is a promising proximal soil moisture sensing technique at intermediate scale and high temporal resolution. In this study, we validate satellite soil moisture products for the period of March 2015 and December 2018 by using several existing Cosmic Ray Neutron Probe (CRNP) stations of the COSMOS database and a CRNP station that was installed in the south part of Turkey in October 2016. Soil moisture values, which were inferred from the CRNP station in Turkey, are also validated using a time domain reflectometer (TDR) installed at the same location and soil water content values obtained from a land surface model (Noah LSM) at various depths (0.1 m, 0.3 m, 0.6 m and 1.0 m). The CRNP has a very good correlation with TDR where both measurements show consistent changes in soil moisture due to storm events. Satellite soil moisture products obtained from the Soil Moisture and Ocean Salinity (SMOS), the METOP-A/B Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer 2 (AMSR2), Climate Change Initiative (CCI) and a global land surface model Global Land Data Assimilation System (GLDAS) are compared with the soil moisture values obtained from CRNP stations. Coefficient of determination ( r 2 ) and unbiased root mean square error (ubRMSE) are used as the statistical measures. Triple Collocation (TC) was also performed by considering soil moisture values obtained from different soil moisture products and the CRNPs. The validation results are mainly influenced by the location of the sensor and the soil moisture retrieval algorithm of satellite products. The SMAP surface product produces the highest correlations and lowest errors especially in semi-arid areas whereas the ASCAT product provides better results in vegetated areas. Both global and local land surface models’ outputs are highly compatible with the CRNP soil moisture values.

scholarly journals Evaluation of the snow regime in dynamic vegetation land surface models using field measurements

2013 ◽  
Vol 7 (3) ◽  
pp. 2333-2372
Author(s):  
E. Kantzas ◽  
M. Lomas ◽  
S. Quegan ◽  
E. Zakharova
Keyword(s):  
Land Surface ◽  
Field Data ◽  
Climate Models ◽  
Optimization Techniques ◽  
The Arctic ◽  
Surface Model ◽  
Land Surface Models ◽  
Snow Density ◽  
Data Set ◽  
Surface Models

Abstract. An increasing number of studies have demonstrated the significant climatic and ecological changes occurring in the northern latitudes over the past decades. As coupled, earth-system models attempt to describe and simulate the dynamics and complex feedbacks of the Arctic environment, it is important to reduce their uncertainties in short-term predictions by improving the description of both the systems processes and its initial state. This study focuses on snow-related variables and extensively utilizes a historical data set (1966–1996) of field snow measurements acquired across the extend of the Former Soviet Union (FSU) to evaluate a range of simulated snow metrics produced by a variety of land surface models, most of them embedded in IPCC-standard climate models. We reveal model-specific issues in simulating snow dynamics such as magnitude and timings of SWE as well as evolution of snow density. We further employ the field snow measurements alongside novel and model-independent methodologies to extract for the first time (i) a fresh snow density value (57–117 kg m–3) for the region and (ii) mean monthly snowpack sublimation estimates across a grassland-dominated western (November–February) [9.2, 6.1, 9.15, 15.25] mm and forested eastern sub-sector (November–March) [1.53, 1.52, 3.05, 3.80, 12.20] mm; we subsequently use the retrieved values to assess relevant model outputs. The discussion session consists of two parts. The first describes a sensitivity study where field data of snow depth and snow density are forced directly into the surface heat exchange formulation of a land surface model to evaluate how inaccuracies in simulating snow metrics affect important modeled variables and carbon fluxes such as soil temperature, thaw depth and soil carbon decomposition. The second part showcases how the field data can be assimilated with ready-available optimization techniques to pinpoint model issues and improve their performance.

scholarly journals Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

2020 ◽  
Author(s):  
Elizabeth Cooper ◽  
Eleanor Blyth ◽  
Hollie Cooper ◽  
Rich Ellis ◽  
Ewan Pinnington ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Pedotransfer Functions ◽  
Surface Model ◽  
Climate Projections ◽  
Land Surface Models ◽  
Surface Models

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological and meteorological applications. In recent years the availability of wide-area soil-moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in-situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the JULES land surface model using field scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

scholarly journals Evaluating a land surface model at a water-limited site: implications for land surface contributions to droughts and heatwaves

2021 ◽  
Author(s):  
Mengyuan Mu ◽  
Martin De Kauwe ◽  
Anna Ukkola ◽  
Andy Pitman ◽  
Teresa Gimeno ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Heat Fluxes ◽  
Surface Model ◽  
Strong Impact ◽  
Water Fluxes ◽  
Land Surface Models ◽  
Groundwater Aquifer ◽  
Surface Models

<p>Land surface models underpin coupled climate model projections of droughts and heatwaves. However, the lack of simultaneous observations of individual components of evapotranspiration, concurrent with root-zone soil moisture, has limited previous model evaluations. Here, we use a comprehensive set of observations from a water-limited site in southeastern Australia including both evapotranspiration and soil moisture to a depth of 4.5 m to evaluate the Community Atmosphere-Biosphere Land Exchange (CABLE) land surface model. We demonstrate that alternative process representations within CABLE had the capacity to improve simulated evapotranspiration, but not necessarily soil moisture dynamics - highlighting problems of model evaluations against water fluxes alone. Our best simulation was achieved by resolving a soil evaporation bias; a more realistic initialisation of the groundwater aquifer state; higher vertical soil resolution informed by observed soil properties; and further calibrating soil hydraulic conductivity. Despite these improvements, the role of the empirical soil moisture stress function in influencing the simulated water fluxes remained important: using a site calibrated function reduced the soil water stress on plants by 36 % during drought and 23 % at other times. These changes in CABLE not only improve the seasonal cycle of evapotranspiration, but also affect the latent and sensible heat fluxes during droughts and heatwaves. The range of parameterisations tested led to differences of ~150 W m<sup>-2</sup> in the simulated latent heat flux during a heatwave, implying a strong impact of parameterisations on the capacity for evaporative cooling and feedbacks to the boundary layer (when coupled). Overall, our results highlight the opportunity to advance the capability of land surface models to capture water cycle processes, particularly during meteorological extremes, when sufficient observations of both evapotranspiration fluxes and soil moisture profiles are available.</p>

scholarly journals Using the anomaly forcing Community Land Model (CLM 4.5) for crop yield projections

2020 ◽  
Author(s):  
Yaqiong Lu ◽  
Xianyu Yang
Keyword(s):  
Crop Yield ◽  
Temporal Resolution ◽  
Land Surface ◽  
Crop Yields ◽  
Surface Model ◽  
High Temporal Resolution ◽  
Climate Scenarios ◽  
Climate Data ◽  
Land Surface Models ◽  
Surface Models

Abstract. Crop growth in land surface models normally requires high temporal resolution climate data (3-hourly or 6-hourly), but such high temporal resolution climate data are not provided by many climate model simulations due to expensive storage, which limits modeling choice if there is an interest in a particular climate simulation that only saved monthly outputs. The Community Land Surface Model (CLM) has proposed an alternative approach for utilizing monthly climate outputs as forcing data since version 4.5, and it is called the anomaly forcing CLM. However, such an approach has never been validated for crop yield projections. In our work, we created anomaly forcing datasets for three climate scenarios (1.5 °C warming, 2.0 °C warming, and RCP4.5) and validated crop yields against the standard CLM forcing with the same climate scenarios using 3-hourly data. We found that the anomaly forcing CLM could not produce crop yields identical to the standard CLM due to the different submonthly variations, and crop yields were underestimated by 5–8 % across the three scenarios (1.5 °C, 2.0 °C, and RCP4.5) for the global average, and 28–41 % of cropland showed significantly different yields. However, the anomaly forcing CLM effectively captured the relative changes between scenarios and over time, as well as regional crop yield variations. We recommend that such an approach be used for qualitative analysis of crop yields when only monthly outputs are available. Our approach can be adopted by other land surface models to expand their capabilities for utilizing monthly climate data.

Climate controlled root zone parameters show potential to improve water flux simulations by land surface models

2021 ◽  
Author(s):  
Fransje van Oorschot ◽  
Ruud van der Ent ◽  
Andrea Alessandri ◽  
Markus Hrachowitz
Keyword(s):  
Land Surface ◽  
Root Zone ◽  
Surface Model ◽  
Water Fluxes ◽  
Land Surface Models ◽  
Climate Control ◽  
Local Climate ◽  
Climate Conditions ◽  
Surface Models ◽  
Zone Parameters

<p>The root zone storage capacity (S<sub>r</sub> ) is the maximum volume of water in the subsurface that can potentially be accessed by vegetation for transpiration. It influences the seasonality of transpiration as well as fast and slow runoff processes. Many studies have shown that S<sub>r</sub> is heterogeneous as controlled by local climate conditions, which affect vegetation strategies in sizing their root system able to support plant growth and to prevent water shortages. Root zone parameterization in most land surface models does not account for this climate control on root development, being based on look-up tables that prescribe worldwide the same root zone parameters for each vegetation class. These look-up tables are obtained from measurements of rooting structure that are scarce and hardly representative of the ecosystem scale. The objective of this research was to quantify and evaluate the effects of a climate-controlled representation of S<sub>r</sub> on the  water fluxes modeled by the HTESSEL land surface model. Climate controlled S<sub>r</sub> was here estimated with the "memory method" (hereinafter MM) in which S<sub>r</sub> is derived from the vegetation's memory of past root zone water storage deficits. S<sub>r,MM</sub> was estimated for 15 river catchments over Australia across three contrasting climate regions: tropical, temperate and Mediterranean. Suitable representations of S<sub>r,MM</sub> were then implemented in HTESSEL (hereinafter MD) by accordingly modifying the soil depths to obtain a model S<sub>r,MD </sub>that matches S<sub>r,MM</sub> in the 15 catchments. In the control version of HTESSEL (hereinafter CTR), S<sub>r,CTR</sub> was larger than S<sub>r,MM</sub> in 14 out of 15 catchments. Furthermore, the variability among the individual catchments of S<sub>r,MM</sub> (117—722 mm) was considerably larger than of S<sub>r,CTR</sub> (491—725 mm). The HTESSEL MD version resulted in a significant and consistent improvement version of the modeled monthly seasonal climatology (1975--2010) and inter-annual anomalies of river discharge compared with observations. However, the effects on biases in long-term annual mean fluxes were small and mixed. The modeled monthly seasonal climatology of the catchment discharge improved in MD compared to CTR: the correlation with observations increased significantly from 0.84 to 0.90 in tropical catchments, from 0.74 to 0.86 in temperate catchments and from 0.86 to 0.96 in Mediterranean catchments. Correspondingly, the correlations of the inter-annual discharge anomalies improved significantly in MD from 0.74 to 0.78 in tropical catchments, from 0.80 to 0.85 in temperate catchments and from 0.71 to 0.79 in Mediterranean catchments. Based on these results, we believe that a global application of climate controlled root zone parameters has the potential to improve the timing of modeled water fluxes by land surface models, but a significant reduction of biases is not expected. </p>

Estimation of the Minimum Canopy Resistance for Croplands and Grasslands Using Data from the 2002 International H2O Project

2008 ◽  
Vol 136 (11) ◽  
pp. 4452-4469 ◽  
Author(s):  
Joseph G. Alfieri ◽  
Dev Niyogi ◽  
Peter D. Blanken ◽  
Fei Chen ◽  
Margaret A. LeMone ◽  
...  
Keyword(s):  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Moisture Flux ◽  
Mean Value ◽  
Surface Model ◽  
Canopy Resistance ◽  
Land Surface Models ◽  
Surface Models ◽  
The Mean

Abstract Vegetated surfaces, such as grasslands and croplands, constitute a significant portion of the earth’s surface and play an important role in land–atmosphere exchange processes. This study focuses on one important parameter used in describing the exchange of moisture from vegetated surfaces: the minimum canopy resistance (rcmin). This parameter is used in the Jarvis canopy resistance scheme that is incorporated into the Noah and many other land surface models. By using an inverted form of the Jarvis scheme, rcmin is determined from observational data collected during the 2002 International H2O Project (IHOP_2002). The results indicate that rcmin is highly variable both site to site and over diurnal and longer time scales. The mean value at the grassland sites in this study is 96 s m−1 while the mean value for the cropland (winter wheat) sites is one-fourth that value at 24 s m−1. The mean rcmin for all the sites is 72 s m−1 with a standard deviation of 39 s m−1. This variability is due to both the empirical nature of the Jarvis scheme and a combination of changing environmental conditions, such as plant physiology and plant species composition, that are not explicitly considered by the scheme. This variability in rcmin has important implications for land surface modeling where rcmin is often parameterized as a constant. For example, the Noah land surface model parameterizes rcmin for the grasslands and croplands types in this study as 40 s m−1. Tests with the coupled Weather Research and Forecasting (WRF)–Noah model indicate that the using the modified values of rcmin from this study improves the estimates of latent heat flux; the difference between the observed and modeled moisture flux decreased by 50% or more. While land surface models that estimate transpiration using Jarvis-type relationships may be improved by revising the rcmin values for grasslands and croplands, updating the rcmin will not fully account for the variability in rcmin observed in this study. As such, it may be necessary to replace the Jarvis scheme currently used in many land surface and numerical weather prediction models with a physiologically based estimate of the canopy resistance.

scholarly journals Observed nighttime conductance alters modeled global hydrology and carbon budgets

2015 ◽  
Vol 8 (12) ◽  
pp. 10339-10363 ◽  
Author(s):  
D. L. Lombardozzi ◽  
M. J. B. Zeppel ◽  
R. A. Fisher ◽  
A. Tawfik
Keyword(s):  
Stomatal Conductance ◽  
Land Surface ◽  
Arid Regions ◽  
Land Surface Model ◽  
Carbon Gain ◽  
Surface Model ◽  
Land Surface Models ◽  
Climate Simulations ◽  
Surface Models ◽  
Semi Arid

Abstract. The terrestrial biosphere regulates climate through carbon, water, and energy exchanges with the atmosphere. Land surface models estimate plant transpiration, which is actively regulated by stomatal pores, and provide projections essential for understanding Earth's carbon and water resources. Empirical evidence from 204 species suggests that significant amounts of water are lost through leaves at night, though land surface models typically reduce stomatal conductance to nearly zero at night. Here, we apply observed nighttime stomatal conductance values to a global land surface model, to better constrain carbon and water budgets. We find that our modifications increase transpiration up to 5 % globally, reduce modeled available soil moisture by up to 50 % in semi-arid regions, and increase the importance of the land surface on modulating energy fluxes. Carbon gain declines up to ~ 4 % globally and > 25 % in semi-arid regions. We advocate for realistic constraints of minimum stomatal conductance in future climate simulations, and widespread field observations to improve parameterizations.

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