scholarly journals Developing a sequential cropping capability in the JULESvn5.2 land–surface model

2019 ◽  
Author(s):  
Camilla Mathison ◽  
Andrew J. Challinor ◽  
Chetan Deva ◽  
Pete Falloon ◽  
Sébastien Garrigues ◽  
...  
Keyword(s):  
Land Surface ◽  
Crop Production ◽  
Asian Summer Monsoon ◽  
Cropping System ◽  
Surface Model ◽  
Adaptation To Climate Change ◽  
Indian States ◽  
Area Index ◽  
Tropical Regions ◽  
Sequential Cropping

Abstract. Sequential cropping (also known as multiple or double cropping) is a common feature, particularly for tropical regions, where the crop seasons are largely dictated by the main wet season such as the Asian summer monsoon (ASM). The ASM provides the water resources for crops grown for the whole year, thereby influencing crop production outside the ASM period. Land surface models (LSMs) typically simulate a single crop per year, however, in order to understand how sequential cropping influences demand for resources, we need to simulate all of the crops grown within a year in a seamless way. In this paper we implement sequential cropping in a branch of the Joint UK Land Environment Simulator (JULES) and demonstrate its use at Avignon, a site that uses the sequential cropping system and provides over 15-years of continuous flux observations which we use to evaluate JULES with sequential cropping. In order to implement the method in future regional simulations where there may be large variations in growing conditions, we apply the same method to four locations in the North Indian states of Uttar Pradesh and Bihar to simulate the rice--wheat rotation and compare model yields to observations at these locations. JULES is able to simulate sequential cropping at Avignon and the four India locations, representing both crops within one growing season in each of the crop rotations presented. At Avignon the maxima of LAI, above ground biomass and canopy height occur at approximately the correct time for both crops. The magnitudes of biomass, especially for winter wheat, are underestimated and the leaf area index is overestimated. The JULES fluxes are a good fit to observations (r-values greater than 0.7), either using grasses to represent crops or the crop model, implying that both approaches represent the surface coverage correctly. For the India simulations, JULES successfully reproduces observed yields for the eastern locations, however yields are under estimated for the western locations. This development is a step forward in the ability of JULES to simulate crops in tropical regions, where this cropping system is already prevalent, while also providing the opportunity to assess the potential for other regions to implement it as an adaptation to climate change.

scholarly journals Implementation of sequential cropping into JULESvn5.2 land-surface model

2021 ◽  
Vol 14 (1) ◽  
pp. 437-471
Author(s):  
Camilla Mathison ◽  
Andrew J. Challinor ◽  
Chetan Deva ◽  
Pete Falloon ◽  
Sébastien Garrigues ◽  
...  
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Surface Fluxes ◽  
Surface Model ◽  
Adaptation To Climate Change ◽  
Wet Season ◽  
Indian States ◽  
Tropical Regions ◽  
Growing Period ◽  
Sequential Cropping

Abstract. Land-surface models (LSMs) typically simulate a single crop per year in a field or location. However, actual cropping systems are characterized by a succession of distinct crop cycles that are sometimes interspersed with long periods of bare soil. Sequential cropping (also known as multiple or double cropping) is particularly common in tropical regions, where the crop seasons are largely dictated by the main wet season. In this paper, we implement sequential cropping in a branch of the Joint UK Land Environment Simulator (JULES) and demonstrate its use at sites in France and India. We simulate all the crops grown within a year in a field or location in a seamless way to understand how sequential cropping influences the surface fluxes of a land-surface model. We evaluate JULES with sequential cropping in Avignon, France, providing over 15 years of continuous flux observations (a point simulation). We apply JULES with sequential cropping to simulate the rice–wheat rotation in a regional 25 km resolution gridded simulation for the northern Indian states of Uttar Pradesh and Bihar and four single-grid-box simulations across these states, where each simulation is a 25 km grid box. The inclusion of a secondary crop in JULES using the sequential cropping method presented does not change the crop growth or development of the primary crop. During the secondary crop growing period, the carbon and energy fluxes for Avignon and India are modified; they are largely unchanged for the primary crop growing period. For India, the inclusion of a secondary crop using this sequential cropping method affects the available soil moisture in the top 1.0 m throughout the year, with larger fluctuations in sequential crops compared with single-crop simulations even outside the secondary crop growing period. JULES simulates sequential cropping in Avignon, the four India locations and the regional run, representing both crops within one growing season in each of the crop rotations presented. This development is a step forward in the ability of JULES to simulate crops in tropical regions where this cropping system is already prevalent. It also provides the opportunity to assess the potential for other regions to implement sequential cropping as an adaptation to climate change.

scholarly journals A representation of the phosphorus cycle for ORCHIDEE (revision 3985)

2017 ◽  
Author(s):  
Daniel S. Goll ◽  
Nicolas Vuichard ◽  
Fabienne Maignan ◽  
Albert Jornet-Puig ◽  
Jordi Sardans ◽  
...  
Keyword(s):  
Primary Productivity ◽  
Land Surface ◽  
Net Primary Productivity ◽  
Nutrient Content ◽  
Soil Development ◽  
Nutrient Concentrations ◽  
Surface Model ◽  
Phosphorus Cycle ◽  
Area Index ◽  
Leaf Level

Abstract. Land surface models rarely incorporate the terrestrial phosphorus cycle and its interactions with the carbon cycle, despite the extensive scientific debate about the importance of nitrogen and phosphorus supply for future land carbon uptake. We describe a representation of the terrestrial phosphorus cycle for the land surface model ORCHIDEE, and evaluate it with data from nutrient manipulation experiments along a soil formation chronosequence in Hawaii. ORCHIDEE accounts for influence of nutritional state of vegetation on tissue nutrient concentrations, photosynthesis, plant growth, biomass allocation, biochemical (phosphatase-mediated) mineralization and biological nitrogen fixation. Changes in nutrient content (quality) of litter affect the carbon use efficiency of decomposition and in return the nutrient availability to vegetation. The model explicitly accounts for root zone depletion of phosphorus as a function of root phosphorus uptake and phosphorus transport from soil to the root surface. The model captures the observed differences in the foliage stoichiometry of vegetation between an early (300yr) and a late stage (4.1 Myr) of soil development. The contrasting sensitivities of net primary productivity to the addition of either nitrogen, phosphorus or both among sites are in general reproduced by the model. As observed, the model simulates a preferential stimulation of leaf level productivity when nitrogen stress is alleviated, while leaf level productivity and leaf area index are stimulated equally when phosphorus stress is alleviated. The nutrient use efficiencies in the model are lower as observed primarily due to biases in the nutrient content and turnover of woody biomass. We conclude that ORCHIDEE is able to reproduce the shift from nitrogen to phosphorus limited net primary productivity along the soil development chronosequence, as well as the contrasting responses of net primary productivity to nutrient addition.

scholarly journals Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area

2017 ◽  
Author(s):  
Clément Albergel ◽  
Simon Munier ◽  
Delphine Jennifer Leroux ◽  
Hélène Dewaele ◽  
David Fairbairn ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Gross Primary Production ◽  
Mediterranean Area ◽  
Surface Model ◽  
Strong Impact ◽  
Control Variables ◽  
Area Index ◽  
Model Control ◽  
Global Land

Abstract. In this study, a global Land Data Assimilation system (LDAS-Monde) is tested over Europe and the Mediterranean basin to increase monitoring accuracy for land surface variables. LDAS-Monde is able to ingest information from satellite-derived surface Soil Moisture (SM) and Leaf Area Index (LAI) observations to constrain the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model (LSM) coupled with the CNRM (Centre National de Recherches Météorologiques) version of the Total Runoff Integrating Pathways (ISBA-CTRIP) continental hydrological system. It makes use of the CO2-responsive version of ISBA which models leaf-scale physiological processes and plant growth. Transfer of water and heat in the soil rely on a multilayer diffusion scheme. Surface SM and LAI observations are assimilated using a simplified extended Kalman filter (SEKF), which uses finite differences from perturbed simulations to generate flow-dependence between the observations and the model control variables. The latter include LAI and seven layers of soil (from 1 cm to 100 cm depth). A sensitivity test of the Jacobians over 2000–2012 exhibits effects related to both depth and season. It also suggests that observations of both LAI and surface SM have an impact on the different control variables. From the assimilation of surface SM, the LDAS is more effective in modifying soil-moisture from the top layers of soil as model sensitivity to surface SM decreases with depth and has almost no impact from 60 cm downwards. From the assimilation of LAI, a strong impact on LAI itself is found. The LAI assimilation impact is more pronounced in SM layers that contain the highest fraction of roots (from 10 cm to 60 cm). The assimilation is more efficient in summer and autumn than in winter and spring. Assimilation impact shows that the LDAS works well constraining the model to the observations and that stronger corrections are applied to LAI than to SM. The assimilation impact's evaluation is successfully carried out using (i) agricultural statistics over France, (ii) river discharge observations, (iii) satellite-derived estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project and (iv) spatially gridded observations based estimates of up-scaled gross primary production and evapotranspiration from the FLUXNET network. Comparisons with those four datasets highlight neutral to highly positive improvement.

scholarly journals Evaluation of MEGAN-CLM parameter sensitivity to predictions of isoprene emissions from an Amazonian rainforest

2014 ◽  
Vol 14 (17) ◽  
pp. 23995-24041 ◽  
Author(s):  
J. A. Holm ◽  
K. Jardine ◽  
A. B. Guenther ◽  
J. Q. Chambers ◽  
E. Tribuzy
Keyword(s):  
Land Surface ◽  
Field Measurements ◽  
Leaf Temperature ◽  
Surface Model ◽  
Amazon Forest ◽  
Biophysical Parameters ◽  
Area Index ◽  
Central Amazon ◽  
Community Land Model ◽  
Environmental Controls

Abstract. Tropical trees are known to be large emitters of biogenic volatile organic compounds (BVOC), accounting for up to 75% of the global isoprene budget. Once in the atmosphere, these compounds influence multiple processes associated with air quality and climate. However, uncertainty in biogenic emissions is two-fold, (1) the environmental controls over isoprene emissions from tropical forests remain highly uncertain; and (2) our ability to accurately represent these environmental controls within models is lacking. This study evaluated the biophysical parameters that drive the global Model of Emissions of Gases and Aerosols from Nature (MEGAN) embedded in a biogeochemistry land surface model, the Community Land Model (CLM), with a focus on isoprene emissions from an Amazonian forest. Upon evaluating the sensitivity of 19 parameters in CLM that currently influence isoprene emissions by using a Monte Carlo analysis, up to 61% of the uncertainty in mean isoprene emissions was caused by the uncertainty in the parameters related to leaf temperature. The eight parameters associated with photosynthetic active radiation (PAR) contributed in total to only 15% of the uncertainty in mean isoprene emissions. Leaf temperature was strongly correlated with isoprene emission activity (R2 = 0.89). However, when compared to field measurements in the Central Amazon, CLM failed to capture the upper 10–14 °C of leaf temperatures throughout the year (i.e., failed to represent ~32 to 46 °C), and the spread observed in field measurements was not representative in CLM. This is an important parameter to accurately simulate due to the non-linear response of emissions to temperature. MEGAN-CLM 4.0 overestimated isoprene emissions by 60% for a Central Amazon forest (5.7 mg m−2 h−1 vs. 3.6 mg m−2 h−1), but due to reductions in leaf area index (LAI) by 28% in MEGAN-CLM 4.5 isoprene emissions were within 7% of observed data (3.8 mg m−2 h−1). When a slight adjustment to leaf temperature was made to match observations, isoprene emissions increased 24%, up to 4.8 mg m−2 h−1. Air temperatures are very likely to increase in tropical regions as a result of human induced climate change. Reducing the uncertainty of leaf temperature in BVOC algorithms, as well as improving the accuracy of replicating leaf temperature output in land surface models is warranted in order to improve estimations of tropical BVOC emissions.

scholarly journals An Improved Canopy Integration Scheme for a Land Surface Model with Prognostic Canopy Structure

2007 ◽  
Vol 20 (15) ◽  
pp. 3902-3923 ◽  
Author(s):  
Peter E. Thornton ◽  
Niklaus E. Zimmermann
Keyword(s):  
Leaf Area ◽  
Specific Leaf Area ◽  
Land Surface ◽  
Temperate Climate ◽  
Integration Scheme ◽  
Surface Model ◽  
Area Index ◽  
Climate System Model ◽  
Community Land Model ◽  
Canopy Model

Abstract A new logical framework relating the structural and functional characteristics of a vegetation canopy is presented, based on the hypothesis that the ratio of leaf area to leaf mass (specific leaf area) varies linearly with overlying leaf area index within the canopy. Measurements of vertical gradients in specific leaf area and leaf carbon:nitrogen ratio for five species (two deciduous and three evergreen) in a temperate climate support this hypothesis. This new logic is combined with a two-leaf (sunlit and shaded) canopy model to arrive at a new canopy integration scheme for use in the land surface component of a climate system model. An inconsistency in the released model radiation code is identified and corrected. Also introduced here is a prognostic canopy model with coupled carbon and nitrogen cycle dynamics. The new scheme is implemented within the Community Land Model and tested in both diagnostic and prognostic canopy modes. The new scheme increases global gross primary production by 66% (from 65 to 108 Pg carbon yr−1) for diagnostic model simulations driven with reanalysis surface weather, with similar results (117 PgC yr−1) for the new prognostic model. Comparison of model predictions to global syntheses of observations shows generally good agreement for net primary productivity (NPP) across a range of vegetation types, with likely underestimation of NPP in tundra and larch communities. Vegetation carbon stocks are higher than observed in forest systems, but the ranking of stocks by vegetation type is accurately captured.

The Role of Different Sources of Uncertainty in Data Assimilation with Coupled Land Surface - Subsurface Models

2021 ◽  
Author(s):  
Eduardo Emilio Sanchez-Leon ◽  
Natascha Brandhorst ◽  
Bastian Waldowski ◽  
Ching Pui Hung ◽  
Insa Neuweiler ◽  
...  
Keyword(s):  
Plant Physiology ◽  
Data Assimilation ◽  
Land Surface ◽  
Reference Model ◽  
Land Surface Model ◽  
Open Loop ◽  
Surface Model ◽  
Model Parameters ◽  
Area Index ◽  
Sources Of Uncertainty

<p>The success of data assimilation systems strongly depends on the suitability of the generated ensembles. While in theory data assimilation should correct the states of an ensemble of models, especially if model parameters are included in the update, its effectiveness will depend on many factors, such as ensemble size, ensemble spread, and the proximity of the prior ensemble simulations to the data. In a previous study, we generated an ensemble-based data-assimilation framework to update model states and parameters of a coupled land surface-subsurface model. As simulation system we used the Terrestrial Systems Modeling Platform TerrSysMP, with the community land-surface model (CLM) coupled to the subsurface model Parflow. In this work, we used the previously generated ensemble to assess the effect of uncertain input forcings (i.e. precipitation), unknown subsurface parameterization, and/or plant physiology in data assimilation. The model domain covers a rectangular area of 1×5km<sup>2</sup>, with a uniform depth of 50m. The subsurface material is divided into four units, and the top soil layers consist of three different soil types with different vegetation. Streams are defined along three of the four boundaries of the domain. For data assimilation, we used the TerrsysMP PDAF framework. We defined a series of data assimilation experiments in which sources of uncertainty were considered individually, and all additional settings of the ensemble members matched those of the reference. To evaluate the effect of all sources of uncertainty combined, we designed an additional test in which the input forcings, subsurface parameters, and the leaf area index of the ensemble were all perturbed. In all these tests, the reference model had homogenous subsurface units and the same grid resolution as all models of the ensemble. We used point measurements of soil moisture in all data assimilation experiments. We concluded that precipitation dominates the dynamics of the simulations, and perturbing the precipitation fields for the ensemble have a major impact in the performance of the assimilation. Still, considerable improvements are observed compared to open-loop simulations. In contrast, the effect of variable plant physiology was minimal, with no visible improvement in relevant fluxes such as evapotranspiration. As expected, improved ensemble predictions are propagated longer in time when parameters are included in the update.</p>

scholarly journals Ability of the land surface model ISBA-A-gs to simulate leaf area index at the global scale: Comparison with satellites products

2006 ◽  
Vol 111 (D18) ◽  
Author(s):  
Anne-Laure Gibelin ◽  
Jean-Christophe Calvet ◽  
Jean-Louis Roujean ◽  
Lionel Jarlan ◽  
Sietse O. Los
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Land Surface Model ◽  
Global Scale ◽  
Surface Model ◽  
Area Index

The influence of the land surface on hydrometeorology and ecology: new advances from modeling and satellite remote sensing

2011 ◽  
Vol 42 (2-3) ◽  
pp. 95-112 ◽  
Author(s):  
Venkat Lakshmi ◽  
Seungbum Hong ◽  
Eric E. Small ◽  
Fei Chen
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Processes ◽  
Surface Model ◽  
Land Surface Processes ◽  
Area Index ◽  
Vegetation Fraction ◽  
Moisture Variation

The importance of land surface processes has long been recognized in hydrometeorology and ecology for they play a key role in climate and weather modeling. However, their quantification has been challenging due to the complex nature of the land surface amongst other reasons. One of the difficult parts in the quantification is the effect of vegetation that are related to land surface processes such as soil moisture variation and to atmospheric conditions such as radiation. This study addresses various relational investigations among vegetation properties such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), surface temperature (TSK), and vegetation water content (VegWC) derived from satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and EOS Advanced Microwave Scanning Radiometer (AMSR-E). The study provides general information about a physiological behavior of vegetation for various environmental conditions. Second, using a coupled mesoscale/land surface model, we examine the effects of vegetation and its relationship with soil moisture on the simulated land–atmospheric interactions through the model sensitivity tests. The Weather Research and Forecasting (WRF) model was selected for this study, and the Noah land surface model (Noah LSM) implemented in the WRF model was used for the model coupled system. This coupled model was tested through two parameterization methods for vegetation fraction using MODIS data and through model initialization of soil moisture from High Resolution Land Data Assimilation System (HRLDAS). Finally, this study evaluates the model improvements for each simulation method.

Impact of GVF Derivation Methods on Noah Land Surface Model Simulations and WRF Model Forecasts

2018 ◽  
Vol 19 (12) ◽  
pp. 1917-1933 ◽  
Author(s):  
Li Fang ◽  
Xiwu Zhan ◽  
Christopher R. Hain ◽  
Jifu Yin ◽  
Jicheng Liu
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Weather Prediction ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Vegetation Fraction ◽  
Data Products ◽  
Noah Land Surface Model

Abstract Green vegetation fraction (GVF) plays a crucial role in the atmosphere–land water and energy exchanges. It is one of the essential parameters in the Noah land surface model (LSM) that serves as the land component of a number of operational numerical weather prediction models at the National Centers for Environmental Prediction (NCEP) of NOAA. The satellite GVF products used in NCEP models are derived from a simple linear conversion of either the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) currently or the enhanced vegetation index (EVI) from the Visible Infrared Imaging Radiometer Suite (VIIRS) planned for the near future. Since the NDVI or EVI is a simple spectral index of vegetation cover, GVFs derived from them may lack the biophysical meaning required in the Noah LSM. Moreover, the NDVI- or EVI-based GVF data products may be systematically biased over densely vegetated regions resulting from the saturation issue associated with spectral vegetation indices. On the other hand, the GVF is physically related to the leaf area index (LAI), and thus it could be beneficial to derive GVF from LAI data products. In this paper, the EVI-based and the LAI-based GVF derivation methods are mathematically analyzed and are found to be significantly different from each other. Impacts of GVF differences on the Noah LSM simulations and on weather forecasts of the Weather Research and Forecasting (WRF) Model are further assessed. Results indicate that LAI-based GVF outperforms the EVI-based one when used in both the offline Noah LSM and WRF Model.

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