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 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.

ECLand: an ECMWF land surface modelling platform

2021 ◽  
Author(s):  
Gianpaolo Balsamo ◽  
Souhail Boussetta
Keyword(s):  
Land Surface ◽  
Urban Areas ◽  
Land Surface Model ◽  
Open Water ◽  
Surface Fluxes ◽  
Surface Model ◽  
New Developments ◽  
Surface Modelling ◽  
Land Surface Modelling ◽  
New Research

<p>The ECMWF operational land surface model, based on the Carbon-Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (CHTESSEL) is the baseline for global weather, climate and environmental applications at ECMWF. In order to expedite its progress and benefit from international collaboration, an ECLand platform has been designed to host advanced and modular schemes. ECLand is paving the way toward a land model that could support a wider range of modelling applications, facilitating global kilometer scales testing as envisaged in the Copernicus and Destination Earth programmes. This presentation introduces the CHTESSEL and its recent new developments that aims at hosting new research applications.</p><p>These new improvements touch upon different components of the model: (i) vegetation, (ii) snow, (iii) soil hydrology, (iv) open water/lakes (v) rivers and (vi) urban areas. The developments are evaluated separately with either offline simulations or coupled experiments, depending on their level of operational readiness, illustrating the benchmarking criteria for assessing process fidelity with regards to land surface fluxes and reservoirs involved in water-energy-carbon exchange, and within the Earth system prediction framework, as foreseen to enter upcoming ECMWF operational cycles.</p><p>Reference: Souhail Boussetta, Gianpaolo Balsamo*, Anna Agustì-Panareda, Gabriele Arduini, Anton Beljaars, Emanuel Dutra, Glenn Carver, Margarita Choulga, Ioan Hadade, Cinzia Mazzetti, Joaquìn Munõz-Sabater, Joe McNorton, Christel Prudhomme, Patricia De Rosnay, Irina Sandu, Nils Wedi, Dai Yamazaki, Ervin Zsoter, 2021: ECLand: an ECMWF land surface modelling platform, MDPI Atmosphere, (in prep).</p>

scholarly journals Modeling Land Surface Fluxes from Uncertain Rainfall: A Case Study in the Sahel with Field-Driven Stochastic Rainfields

Atmosphere ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 465
Author(s):  
Bernard Cappelaere ◽  
Denis Feurer ◽  
Théo Vischel ◽  
Catherine Ottlé ◽  
Hassane Bil-Assanou Issoufou ◽  
...  
Keyword(s):  
Land Surface ◽  
Monsoon Season ◽  
Land Surface Model ◽  
Surface Fluxes ◽  
Energy Budgets ◽  
Substantial Part ◽  
Surface Model ◽  
Time Space ◽  
Flux Variability ◽  
Sahel Region

In distributed land surface modeling (LSM) studies, uncertainty in the rainfields that are used to force models is a major source of error in predicted land surface response variables. This is particularly true for applications in the African Sahel region, where weak knowledge of highly time/space-variable convective rainfall in a poorly monitored region is a considerable obstacle to such developments. In this study, we used a field-based stochastic rainfield generator to analyze the propagation of the rainfall uncertainty through a distributed land surface model simulating water and energy fluxes in Sahelian ecosystems. Ensemble time/space rainfields were generated from field observations of the local AMMA-CATCH-Niger recording raingauge network. The rainfields were then used to force the SEtHyS-Savannah LSM, yielding an ensemble of time/space simulated fluxes. Through informative graphical representations and innovative diagnosis metrics, these outputs were analyzed to separate the different components of flux variability, among which was the uncertainty represented by ensemble-wise variability. Scale dependence was analyzed for each flux type in the water and energy budgets, producing a comprehensive picture of uncertainty propagation for the various flux types, with its relationship to intrinsic space/time flux variability. The study was performed over a 2530 km2 domain over six months, covering an entire monsoon season and the subsequent dry-down, using a kilometer/daily base resolution of analysis. The newly introduced dimensionless uncertainty measure, called the uncertainty coefficient, proved to be more effective in describing uncertainty patterns and relationships than a more classical measure based on variance fractions. Results show a clear scaling relationship in uncertainty coefficients between rainfall and the dependent fluxes, specific to each flux type. These results suggest a higher sensitivity to rainfall uncertainty for hydrological than for agro-ecological or meteorological applications, even though eddy fluxes do receive a substantial part of that source uncertainty.

scholarly journals Influence of thermodynamic soil and vegetation parameterizations on the simulation of soil temperature states and surface fluxes by the Noah LSm over a Tibetan plateau site

2009 ◽  
Vol 6 (1) ◽  
pp. 455-499 ◽  
Author(s):  
R. van der Velde ◽  
Z. Su ◽  
M. Ek ◽  
M. Rodell ◽  
Y. Ma
Keyword(s):  
Thermal Properties ◽  
Tibetan Plateau ◽  
Land Surface ◽  
Land Surface Model ◽  
Soil Heat Flux ◽  
Stomatal Resistance ◽  
Surface Fluxes ◽  
Surface Model ◽  
The Tibetan Plateau ◽  
Soil Thermal Properties

Abstract. In this paper, we investigate the ability of the Noah Land Surface model (LSm) to simulate temperature states in the soil profile and surface fluxes measured during a 7-day dry period at a micrometeorological station on the Tibetan Plateau. Adjustments in soil and vegetation parameterizations required to ameliorate the Noah simulation on these two aspects are presented, which include: (1) Differentiating the soil thermal properties of top- and subsoils, (2) Investigation of the different numerical soil discretizations and (3) Calibration of the parameters utilized to describe the transpiration dynamics of the Plateau vegetation. Through the adjustments in the parameterization of the soil thermal properties (STP) simulation of the soil heat transfer is improved, which results in a reduction of Root Mean Squared Differences (RMSD's) by 14%, 18% and 49% between measured and simulated skin, 5-cm and 25-cm soil temperatures, respectively. Further, decreasing the minimum stomatal resistance (Rc, min) and the optimum temperature for transpiration (Topt) of the vegetation parameterization reduces RMSD's between measured and simulated energy balance components by 30%, 20% and 5% for the sensible, latent and soil heat flux, respectively.

Assessment of the uncertainty related to irrigation modeling by a land surface model across India

2020 ◽  
Author(s):  
Dazhi Li ◽  
Xujun Han ◽  
Dhanya C.t. ◽  
Stefan Siebert ◽  
Harry Vereecken ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Monsoon Season ◽  
Land Surface Model ◽  
Surface Fluxes ◽  
Spatiotemporal Pattern ◽  
Surface Model ◽  
Irrigation Requirement ◽  
Community Land Model ◽  
Surface Models

<p>Irrigation is very important for maintaining the agricultural production and sustaining the increasing population of India. The irrigation requirement can be estimated with land surface models by modeling water storage changes but the estimates are affected by various uncertainties such as regarding the spatiotemporal distribution of areas where and when irrigation is potentially applied. In the present work, this uncertainty is analyzed for the whole Indian domain. The irrigation requirements and hydrological fluxes over India were reconstructed by multiple simulation experiments with the Community Land Model (CLM) version 4.5 for the year of 2010.</p><p>These multiple simulation scenarios showed that the modeled irrigation requirement and the land surface fluxes differed between the scenarios, representing the spatiotemporal uncertainty of the irrigation maps. Using a season-specific irrigation map resulted in a higher transpiration-evapotranspiration ratio (T/ET) in the pre-monsoon season compared to the application of a static irrigation map, which implies a higher irrigation efficiency. The remote sensing based evapotranspiration products GLEAM and MODIS ET were used for comparison, showing a similar increasing ET-trend in the pre-monsoon season as the irrigation induced land surface modeling. The correspondence is better if the seasonal irrigation map is used as basis for simulations with CLM. We conclude that more accurate temporal information on irrigation results in modeled evapotranspiration closer to the spatiotemporal pattern of evapotranspiration deduced from remote sensing. Another conclusion is that irrigation modeling should consider the sub-grid heterogeneity to improve the estimation of soil water deficit and irrigation requirement.</p>

scholarly journals WRFv3.2-SPAv2: development and validation of a coupled ecosystem–atmosphere model, scaling from surface fluxes of CO<sub>2</sub> and energy to atmospheric profiles

2013 ◽  
Vol 6 (4) ◽  
pp. 1079-1093 ◽  
Author(s):  
T. L. Smallman ◽  
J. B. Moncrieff ◽  
M. Williams
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
Net Ecosystem Exchange ◽  
Terrestrial Ecosystem ◽  
Surface Fluxes ◽  
Surface Model ◽  
Terrestrial Ecosystem Model ◽  
Atmospheric Profiles

Abstract. The Weather Research and Forecasting meteorological (WRF) model has been coupled to the Soil–Plant–Atmosphere (SPA) terrestrial ecosystem model, to produce WRF-SPA. SPA generates realistic land–atmosphere exchanges through fully coupled hydrological, carbon and energy cycles. The addition of a~land surface model (SPA) capable of modelling biospheric CO2 exchange allows WRF-SPA to be used for investigating the feedbacks between biosphere carbon balance, meteorology, and land use and land cover change. We have extensively validated WRF-SPA using multi-annual observations of air temperature, turbulent fluxes, net radiation and net ecosystem exchange of CO2 at three sites, representing the dominant vegetation types in Scotland (forest, managed grassland and arable agriculture). For example air temperature is well simulated across all sites (forest R2 = 0.92, RMSE = 1.7 °C, bias = 0.88 °C; managed grassland R2 = 0.73, RMSE = 2.7 °C, bias = −0.30 °C; arable agriculture R2 = 0.82, RMSE = 2.2 °C, bias = 0.46 °C; RMSE, root mean square error). WRF-SPA generates more realistic seasonal behaviour at the site level compared to an unmodified version of WRF, such as improved simulation of seasonal transitions in latent heat flux in arable systems. WRF-SPA also generates realistic seasonal CO2 exchanges across all sites. WRF-SPA is also able to realistically model atmospheric profiles of CO2 over Scotland, spanning a 3 yr period (2004–2006), capturing both profile structure, indicating realistic transport, and magnitude (model–data residual

scholarly journals Quantifying Land Surface Temperature Variability for Two Sahelian Mesoscale Regions during the Wet Season

2013 ◽  
Vol 14 (5) ◽  
pp. 1605-1619 ◽  
Author(s):  
Martin G. De Kauwe ◽  
Christopher M. Taylor ◽  
Philip P. Harris ◽  
Graham P. Weedon ◽  
Richard. J. Ellis
Keyword(s):  
Spectral Analysis ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Wet Season ◽  
Rainfall Events ◽  
Spectral Variance ◽  
High Level

Abstract Land–atmosphere feedbacks play an important role in the weather and climate of many semiarid regions. These feedbacks are strongly controlled by how the surface responds to precipitation events, which regulate the return of heat and moisture to the atmosphere. Characteristics of the surface can result in both differing amplitudes and rates of warming following rain. Spectral analysis is used to quantify these surface responses to rainfall events using land surface temperature (LST) derived from Earth observations (EOs). The authors analyzed two mesoscale regions in the Sahel and identified distinct differences in the strength of the short-term (&lt;5 days) spectral variance, notably, a shift toward lower-frequency variability in forest pixels relative to nonforest areas and an increase in amplitude with decreasing vegetation cover. Consistent with these spectral signatures, areas of forest and, to a lesser extent, grassland regions were found to warm up more slowly than sparsely vegetated or barren pixels. The authors applied the same spectral analysis method to simulated LST data from the Joint UK Land Environment Simulator (JULES) land surface model. A reasonable level of agreement was found with the EO spectral analysis for two contrasting land surface regions. However, JULES shows a significant underestimate in the magnitude of the observed response to rain compared to EOs. A sensitivity analysis of the JULES model highlights an unrealistically high level of soil water availability as a key deficiency, which dampens the models response to rainfall events.

scholarly journals Evaluation of the Noah land-surface model using data from a fair-weather IHOP_2002 day with heterogeneous surface fluxes

2007 ◽  
Vol preprint (2008) ◽  
pp. 1
Author(s):  
Margaret A. LeMone ◽  
Mukul Tewari ◽  
Fei Chen ◽  
Joseph G. Alfieri ◽  
Dev Niyogi
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Surface Fluxes ◽  
Heterogeneous Surface ◽  
Surface Model ◽  
Fair Weather ◽  
Noah Land Surface Model ◽  
Using Data

scholarly journals Investigating the Impact of Soil Moisture and Atmospheric Stability on Cloud Development and Distribution Using a Coupled Large-Eddy Simulation and Land Surface Model

2011 ◽  
Vol 12 (5) ◽  
pp. 787-804 ◽  
Author(s):  
Hsin-Yuan Huang ◽  
Steven A. Margulis
Keyword(s):  
Thermal Stability ◽  
Soil Moisture ◽  
Land Surface ◽  
Atmospheric Stability ◽  
Land Surface Model ◽  
Coupled Model ◽  
Surface Fluxes ◽  
Surface Model ◽  
Eddy Simulation ◽  
Cloud Development

Abstract The influence of soil moisture and atmospheric thermal stability on surface fluxes, boundary layer characteristics, and cloud development are investigated using a coupled large-eddy simulation (LES)–land surface model (LSM) framework. The study day from the Cabauw site in the central part of the Netherlands has been studied to examine the soil moisture–cloud feedback using a parameterized single-column model (SCM) in previous work. Good agreement is seen in the comparison between coupled model results and observations collected at the Cabauw eddy-covariance tower. Simulation results confirm the hypothesis that both surface fluxes and atmospheric boundary layer (ABL) states are strongly affected by soil moisture and atmospheric stability, which was proposed by a previous study using an SCM with simple parameterization. While the ABL-top cloud development is a nonmonotonic function of surface water content under different thermal stability conditions, coupled model simulations find that weak thermal stability has significant impacts on both thermal and moisture fluxes and variances near the entrainment zone, especially for the dry surface cases. Additionally, the impacts of ABL-top stability on thermal and moisture entrainment processes are in a different magnitude. The explicitly resolved cloud cover fraction increases with increasing soil moisture only occurs in cases with strong atmospheric stability, and an opposite result is seen when weak atmospheric stability exists. The elevation of cloud base highly depends on the strength of sensible heat flux. However, results of cloud thickness show that a dry surface with weak thermal stability is able to form a large amount of cumulus cloud, even if the soil provides less water vapor.

scholarly journals Simulating carbon exchange using a regional atmospheric model coupled to an advanced land-surface model

2010 ◽  
Vol 7 (8) ◽  
pp. 2397-2417 ◽  
Author(s):  
H. W. Ter Maat ◽  
R. W. A. Hutjes ◽  
F. Miglietta ◽  
B. Gioli ◽  
F. C. Bosveld ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Land Surface ◽  
Land Surface Model ◽  
Atmospheric Model ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
Shortwave Radiation ◽  
Surface Model ◽  
Large Variability ◽  
Regional Atmospheric Modelling System

Abstract. This paper is a case study to investigate what the main controlling factors are that determine atmospheric carbon dioxide content for a region in the centre of The Netherlands. We use the Regional Atmospheric Modelling System (RAMS), coupled with a land surface scheme simulating carbon, heat and momentum fluxes (SWAPS-C), and including also submodels for urban and marine fluxes, which in principle should include the dominant mechanisms and should be able to capture the relevant dynamics of the system. To validate the model, observations are used that were taken during an intensive observational campaign in central Netherlands in summer 2002. These include flux-tower observations and aircraft observations of vertical profiles and spatial fluxes of various variables. The simulations performed with the coupled regional model (RAMS-SWAPS-C) are in good qualitative agreement with the observations. The station validation of the model demonstrates that the incoming shortwave radiation and surface fluxes of water and CO2 are well simulated. The comparison against aircraft data shows that the regional meteorology (i.e. wind, temperature) is captured well by the model. Comparing spatially explicitly simulated fluxes with aircraft observed fluxes we conclude that in general latent heat fluxes are underestimated by the model compared to the observations but that the latter exhibit large variability within all flights. Sensitivity experiments demonstrate the relevance of the urban emissions of carbon dioxide for the carbon balance in this particular region. The same tests also show the relation between uncertainties in surface fluxes and those in atmospheric concentrations.

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