Testing stomatal models at stand level in deciduous angiosperm and evergreen gymnosperm forests using CliMA Land (v0.1)

Abstract. At the leaf level, stomata control the exchange of water and carbon across the air-leaf interface. Stomatal conductance is typically modeled empirically, based on environmental conditions at the leaf surface. Recently developed stomatal optimization models show great skills at predicting carbon and water fluxes at both the leaf and tree levels. However, it has not been evaluated how well the optimization models perform at larger scales. Furthermore, stomatal models are often used with simple single-leaf representations of canopy radiative transfer (RT), such as big-leaf models. Nevertheless, the single-leaf canopy RT schemes do not have the capability to model optical properties of the leaves or the entire canopy. As a result, they are unable to evaluate the impact of vertical gradients within the canopy, or directly link canopy optical properties with light distribution within the canopy to remote sensing data observed from afar. Here we incorporated one optimization-based and two empirical stomatal models with a comprehensive RT model in the land component of a new Earth System model within CliMA, the Climate Modelling Alliance. The model allowed us to simultaneously simulate carbon and water fluxes as well as leaf and canopy reflectance and fluorescence spectra. We tested our model by comparing our modeled carbon and water fluxes and solar-induced chlorophyll fluorescence (SIF) to two flux tower observations (a gymnosperm forest and an angiosperm forest) and satellite SIF retrievals, respectively. All three stomatal models quantitatively predicted the carbon and water fluxes for both forests. The optimization model, in particular, showed increased skill in predicting the water flux given the lower error (c. 14.2 % and 21.8 % improvement for the gymnosperm and angiosperm forests, respectively) and better 1 : 1 comparison (slope increases from c. 0.34 to 0.91 for the gymnosperm forest, and from c. 0.38 to 0.62 for the angiosperm forest). Our model also predicted the SIF yield, quantitatively reproducing seasonal cycles for both forests. We found that using stomatal optimization with a comprehensive RT model showed high accuracy in simulating land surface processes. The ever-increasing number of regional and global datasets of terrestrial plants, such as leaf area index and chlorophyll contents, will help parameterize the land model and improve future Earth System modeling in general.

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Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling

Remote Sensing of Environment ◽

10.1016/j.rse.2011.01.001 ◽

2011 ◽

Vol 115 (5) ◽

pp. 1171-1187 ◽

Cited By ~ 168

Author(s):

Hua Yuan ◽

Yongjiu Dai ◽

Zhiqiang Xiao ◽

Duoying Ji ◽

Wei Shangguan

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Land Surface ◽

Climate Modelling ◽

Area Index

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Effects of high spatial and temporal resolution Earth observations on simulated hydrometeorological variables in a cropland (southwestern France)

Hydrology and Earth System Sciences ◽

10.5194/hess-21-5693-2017 ◽

2017 ◽

Vol 21 (11) ◽

pp. 5693-5708 ◽

Cited By ~ 3

Author(s):

Jordi Etchanchu ◽

Vincent Rivalland ◽

Simon Gascoin ◽

Jérôme Cros ◽

Tiphaine Tallec ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Land Surface ◽

Agricultural Landscapes ◽

Surface Model ◽

Spatial And Temporal Variability ◽

Area Index ◽

The Impact ◽

Sentinel 2

Abstract. Agricultural landscapes are often constituted by a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes and must be taken into account in simulations of land surface and distributed hydrological models. The Sentinel-2 mission allows for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20 m and 5 days, respectively) that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy fluxes via the Interactions between the Surface Biosphere Atmosphere (ISBA) land surface model included in the EXternalized SURface (SURFEX) modeling platform. The study focuses on the effect of the leaf area index (LAI) spatial and temporal variability on these fluxes. We compare the use of the LAI climatology from ECOCLIMAP-II, used by default in SURFEX-ISBA, and time series of LAI derived from the high-resolution Formosat-2 satellite data (8 m). The study area is an agricultural zone in southwestern France covering 576 km2 (24 km  ×  24 km). An innovative plot-scale approach is used, in which each computational unit has a homogeneous vegetation type. Evaluation of the simulations quality is done by comparing model outputs with in situ eddy covariance measurements of latent heat flux (LE). Our results show that the use of LAI derived from high-resolution remote sensing significantly improves simulated evapotranspiration with respect to ECOCLIMAP-II, especially when the surface is covered with summer crops. The comparison with in situ measurements shows an improvement of roughly 0.3 in the correlation coefficient and a decrease of around 30 % of the root mean square error (RMSE) in the simulated evapotranspiration. This finding is attributable to a better description of LAI evolution processes with Formosat-2 data, which further modify soil water content and drainage of soil reservoirs. Effects on annual drainage patterns remain small but significant, i.e., an increase roughly equivalent to 4 % of annual precipitation levels with simulations using Formosat-2 data in comparison to the reference simulation values. This study illustrates the potential for the Sentinel-2 mission to better represent effects of crop management on water budgeting for large, anthropized river basins.

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Use of satellite-based sensing in land surface climatology

Progress in Physical Geography Earth and Environment ◽

10.1177/030913339401800101 ◽

1994 ◽

Vol 18 (1) ◽

pp. 1-15 ◽

Cited By ~ 4

Author(s):

David Greenland

Keyword(s):

Satellite Data ◽

Land Surface ◽

Climate Models ◽

Climate Model ◽

Meteorological Data ◽

Sensible Heat Flux ◽

Longwave Radiation ◽

Climate Modelling ◽

Area Index ◽

Surface Climate

Common types of satellite-derived measurements are reviewed with respect to how they are used to provide information on variables important to land surface climatology. The variables considered include solar radiation, surface albedo, surface temperature, outgoing longwave radiation, cloud cover, net radiation, soil moisture, latent and sensible heat flux, surface cover and leaf area index. A selection of land surface climate modelling schemes is identified and considered with a view to their practicality for use with satellite-derived data. Issues arising from the foregoing considerations include the absence from satellite data of some variables required by land surface climate models, the importance of extreme pixel values in model parameterization, the importance of matching spatial resolution in satellite data and climate model, and the need to have concurrent, independently observed, meteorological data in order to make full use of the satellite data.

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Improving arable land heterogeneity information in available land cover products for land surface modelling using MERIS NDVI data

Hydrology and Earth System Sciences ◽

10.5194/hess-14-2073-2010 ◽

2010 ◽

Vol 14 (10) ◽

pp. 2073-2084 ◽

Cited By ~ 5

Author(s):

F. Zabel ◽

T. B. Hank ◽

W. Mauser

Keyword(s):

Land Cover ◽

Central Europe ◽

Land Surface ◽

Vegetation Index ◽

Arable Land ◽

Area Index ◽

Temporal Behaviour ◽

Danube Catchment ◽

Crop Types ◽

The Impact

Abstract. Regionalization of physical land surface models requires the supply of detailed land cover information. Numerous global and regional land cover maps already exist but generally, they do not resolve arable land into different crop types. However, arable land comprises a huge variety of different crops with characteristic phenological behaviour, demonstrated in this paper with Leaf Area Index (LAI) measurements exemplarily for maize and winter wheat. This affects the mass and energy fluxes on the land surface and thus its hydrology. The objective of this study is the generation of a land cover map for central Europe based on CORINE Land Cover (CLC) 2000, merged with CORINE Switzerland, but distinguishing different crop types. Accordingly, an approach was developed, subdividing the land cover class arable land into the regionally most relevant subclasses for central Europe using multiseasonal MERIS Normalized Difference Vegetation Index (NDVI) data. The satellite data were used for the separation of spring and summer crops due to their different phenological behaviour. Subsequently, the generated phenological classes were subdivided following statistical data from EUROSTAT. This database was analysed concerning the acreage of different crop types. The impact of the improved land use/cover map on evapotranspiration was modelled exemplarily for the Upper Danube catchment with the hydrological model PROMET. Simulations based on the newly developed land cover approach showed a more detailed evapotranspiration pattern compared to model results using the traditional CLC map, which is ignorant of most arable subdivisions. Due to the improved temporal behaviour and spatial allocation of evapotranspiration processes in the new land cover approach, the simulated water balance more closely matches the measured gauge.

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Effects of multi-temporal high-resolution remote sensing products on simulated hydrometeorological variables in a cultivated area (southwestern France)

10.5194/hess-2016-661 ◽

2017 ◽

Cited By ~ 1

Author(s):

Jordi Etchanchu ◽

Vincent Rivalland ◽

Simon Gascoin ◽

Jérôme Cros ◽

Aurore Brut ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Land Cover ◽

Land Surface ◽

Agricultural Landscapes ◽

Annual Precipitation ◽

Surface Model ◽

Area Index ◽

The Impact ◽

Sentinel 2

Abstract. Agricultural landscapes often include a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes as simulated by land surface and distributed hydrological models. Sentinel-2 mission satellite remote sensing products allow for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20 m and 5 days, respectively) that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy flux via the ISBA-SURFEX land surface model. The study area is a 24 km by 24 km agricultural zone in southwestern France. An initial reference simulation was conducted from 2006–2010 using the ECOCLIMAP-II database. This global numerical land ecosystem database was created at a 1 km resolution and includes an ecosystem classification with a consistent set of land surface parameters required for the model, such as the Leaf Area Index (LAI) and albedo measures. The LAI of ECOCLIMAP is climatologic and derived from a 2000–2005 analysis of MODIS satellite products. This low resolution induces that several vegetation covers can be mixed in a model cell. The climatic construction of LAI dynamics also suggests that there is no interannual variability in the vegetation cycle. A second simulation was performed by forcing the same model with annual land cover maps and monthly LAI values derived from a series of 105 8 m-resolution Formosat-2 images for the same period. Both simulations were conducted at the parcel scale, i.e., a computation unit covers an area of connected pixels of the same vegetation type (a crop field, forest patch, etc.). To evaluate our simulations, we used in situ measurements of evapotranspiration and latent and sensible heat flux from two eddy covariance stations in the study area. Our results show that the use of Formosat-2 high-resolution products significantly improves simulated evapotranspiration results with respect to ECOCLIMAP-II, especially when a surface is covered with summer crops (the correlation coefficient with monthly measurements is increased by roughly 0.3 and the root mean square error is decreased by roughly 31 %). This finding is attributable to a better description of LAI evolution processes reflected by Formosat-2 data, which further modify soil water content and drainage levels of deep soil reservoirs. Effects on annual drainage patterns remain small but significant, i.e., an increase roughly equivalent to 4 % of annual precipitation levels from Formosat-2 data in comparison to reference values. In smaller proportions, runoff is also increased by roughly 1 % of annual precipitation when using Formosat-2 data. This study illustrates the potential for the Sentinel-2 mission to better represent effects of crop management on water budgeting for large, anthropized river basins.

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Implementation and validation of a new irrigation scheme in the ISBA land surface model

10.5194/gmd-2021-332 ◽

2021 ◽

Author(s):

Arsène Druel ◽

Simon Munier ◽

Anthony Mucia ◽

Clément Albergel ◽

Jean-Christophe Calvet

Keyword(s):

Land Surface ◽

Vegetation Type ◽

Spatial Scales ◽

Terrestrial Ecosystems ◽

Moisture Stress ◽

Surface Model ◽

Irrigation Scheme ◽

Area Index ◽

Natural Processes ◽

The Impact

Abstract. With an increase in the number of natural processes represented, global land surface models (LSMs) have become more and more accurate in representing natural terrestrial ecosystems. However, they are still limited, especially in the representation of the impact of agriculture on land surface variables. This is particularly true for agro-hydrological processes related to a strong human control on freshwater. While most LSMs consider natural processes only, the development of human-related processes, e.g. crop phenology and irrigation in LSMs, is key. In this study we present the implementation of a new irrigation scheme in the ISBA (Interaction between Soil, Biosphere, and Atmosphere) LSM. This highly flexible scheme is designed to account for various configurations and can be applied at different spatial scales. For each vegetation type within a model grid cell, three irrigation systems can be used at the same time. A limited number of parameters are used to control (1) the amount of water used for irrigation, (2) irrigation triggering (based on the soil moisture stress) and (3) crop seasonality (emergence, harvesting). After a presentation of the simulations of the new scheme at a plot scale, an evaluation is proposed over Nebraska (USA). This region is chosen for its high irrigation density and because independent observations of irrigation practices can be used to verify the simulated irrigation amounts. The ISBA simulations with and without the irrigation scheme are compared to different satellite-based observations. The comparison shows that the irrigation scheme improves the simulated vegetation variables such as leaf area index and gross primary productivity and other variables largely impacted by irrigation such as evapotranspiration and land surface temperature. In addition to a better representation of land surface processes, the results point to potential applications of this new version of the ISBA model for water resource monitoring and climate change impact studies.

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LDAS-Monde Sequential Assimilation of Satellite Derived Observations Applied to the CONtiguous US: An ERA-5 Driven Reanalysis of the Land Surface Variables

10.20944/preprints201809.0105.v1 ◽

2018 ◽

Author(s):

Clément Albergel ◽

Simon Munier ◽

Aymeric Bocher ◽

Bertrand Bonan ◽

Yongjun Zheng ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Initial Conditions ◽

The United States ◽

Agricultural Drought ◽

Reference Network ◽

Surface Model ◽

Area Index ◽

Medium Range Weather Forecast ◽

The Impact

LDAS-Monde, an offline land data assimilation system with global capacity, is applied over the CONtiguous US (CONUS) domain to enhance monitoring accuracy for water and energy states and fluxes. LDAS-Monde ingests satellite-derived Surface Soil Moisture (SSM) and Leaf Area Index (LAI) estimates 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 (CTRIP) continental hydrological system (ISBA-CTRIP). LDAS-Monde is forced by the ERA-5 atmospheric reanalysis from the European Center For Medium Range Weather Forecast (ECMWF) from 2010 to 2016 leading to a 7-yr, quarter degree spatial resolution offline reanalysis of Land Surface Variables (LSVs) over CONUS. The impact of assimilating LAI and SSM into LDAS-Monde is assessed over North America, by comparison to satellite-driven model estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project, and upscaled ground-based observations of gross primary productivity from the FLUXCOM project. Also, taking advantage of the relatively dense data networks over CONUS, we also evaluate the impact of the assimilation against in-situ measurements of soil moisture from the USCRN network (US Climate Reference Network) are used in the evaluation, together with river discharges from the United States Geophysical Survey (USGS) and the Global Runoff Data Centre (GRDC). Those data sets highlight the added value of assimilating satellite derived observations compared to an open-loop simulation (i.e. no assimilation). It is shown that LDAS-Monde has the ability not only to monitor land surface variables but also to forecast them, by providing improved initial conditions which impacts persist through time. LDAS-Monde reanalysis has a potential to be used to monitor extreme events like agricultural drought, also. Finally, limitations related to LDAS-Monde and current satellite-derived observations are exposed as well as several insights on how to use alternative datasets to analyze soil moisture and vegetation state.

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JULES-crop: a parametrisation of crops in the Joint UK Land Environment Simulator

Geoscientific Model Development Discussions ◽

10.5194/gmdd-7-6773-2014 ◽

2014 ◽

Vol 7 (5) ◽

pp. 6773-6809

Author(s):

T. Osborne ◽

J. Gornall ◽

J. Hooker ◽

K. Williams ◽

A. Wiltshire ◽

...

Keyword(s):

Climate Change ◽

Land Surface ◽

Land Surface Model ◽

Water Security ◽

Climate Change Impacts ◽

Heat Fluxes ◽

Surface Model ◽

Yield Model ◽

Area Index ◽

The Impact

Abstract. Studies of climate change impacts on the terrestrial biosphere have been completed without recognition of the integrated nature of the biosphere. Improved assessment of the impacts of climate change on food and water security requires the development and use of models not only representing each component but also their interactions. To meet this requirement the Joint UK Land Environment Simulator (JULES) land surface model has been modified to include a generic parametrisation of annual crops. The new model, JULES-crop, is described and evaluation at global and site levels for the four globally important crops; wheat, soy bean, maize and rice is presented. JULES-crop demonstrates skill in simulating the inter-annual variations of yield for maize and soy bean at the global level, and for wheat for major spring wheat producing countries. The impact of the new parametrisation, compared to the standard configuration, on the simulation of surface heat fluxes is largely an alteration of the partitioning between latent and sensible heat fluxes during the later part of the growing season. Further evaluation at the site level shows the model captures the seasonality of leaf area index and canopy height better than in standard JULES. However, this does not lead to an improvement in the simulation of sensible and latent heat fluxes. The performance of JULES-crop from both an earth system and crop yield model perspective is encouraging however, more effort is needed to develop the parameterisation of the model for specific applications. Key future model developments identified include the specification of the yield gap to enable better representation of the spatial variability in yield.

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ESM-Tools Version 4.0: A modular infrastructure for stand-alone and coupled Earth System Modelling (ESM)

10.5194/gmd-2020-100 ◽

2020 ◽

Author(s):

Dirk Barbi ◽

Nadine Wieters ◽

Paul Gierz ◽

Fatemeh Chegini ◽

Sara Khosravi ◽

...

Keyword(s):

Land Surface ◽

High Performance ◽

Surface Model ◽

System Modelling ◽

Climate Modelling ◽

Earth System ◽

Coupled Models ◽

Wide Range ◽

Runtime Infrastructure ◽

Earth System Modelling

Abstract. Earth system and climate modelling involves the simulation of processes on a wide range of scales and within and across various components of the Earth system. In practice, component models are often developed independently by different research groups and then combined using a dedicated coupling software. This procedure not only leads to a strongly growing number of available versions of model components and coupled setups but also to model- and system-dependent ways of obtaining and operating them. Therefore, implementing these Earth System Models (ESMs) can be challenging and extremely time-consuming, especially for less experienced modellers, or scientists aiming to use different ESMs as in the case of inter-comparison projects. To assist researchers and modellers by reducing avoidable complexity, we developed the ESM-Tools software, which provides a standard way for downloading, configuring, compiling, running and monitoring different models - coupled ESMs and stand-alone models alike - on a variety of High-Performance Computing (HPC) systems. (The ESM-Tools are equally applicable and helpful for stand-alone as for coupled models. In fact, the ESM-Tools are used as standard compile and runtime infrastructure for FESOM2, and currently also applied for ECHAM and ICON standalone simulations. As coupled ESMs are technically the more challenging tasks, we will focus on coupled setups, always implying that stand-alone models can benefit in the same way.) With the ESM-Tools, the user is only required to provide a short script consisting of only the experiment specific definitions, while the software executes all the phases of a simulation in the correct order. The software, which is well documented and easy to install and use, currently supports four ocean models, three atmosphere models, two biogeochemistry models, an ice sheet model, an isostatic adjustment model, a hydrology model and a land-surface model. ESM-Tools has been entirely re-coded in a high-level programming language (Python) and provides researchers with an even more user-friendly interface for Earth system modelling lately. The ESM-Tools were developed within the framework of the project Advanced Earth System Model Capacity, supported by the Helmholtz Association.

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Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation

10.20944/preprints201901.0093.v1 ◽

2019 ◽

Author(s):

Clément Albergel ◽

Emanuel Dutra ◽

Bertrand Bonan ◽

Yongjun Zheng ◽

Simon Munier ◽

...

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Land Surface ◽

Western Europe ◽

Initial Conditions ◽

Added Value ◽

Area Index ◽

Long Time ◽

Land Data Assimilation System ◽

The Impact

This study aims to assess the potential of the LDAS-Monde a land data assimilation system developed by Météo-France to monitor the impact of the 2018 summer heatwave over western Europe vegetation state. The LDAS-Monde is forced by the ECMWF’s (i) ERA5 reanalysis, and (ii) the Integrated Forecasting System High Resolution operational analysis (IFS-HRES), used in conjunction with the assimilation of Copernicus Global Land Service (CGLS) satellite derived products, namely the Surface Soil Moisture (SSM) and the Leaf Area Index (LAI). Analysis of long time series of satellite derived CGLS LAI (2000-2018) and SSM (2008-2018) highlights marked negative anomalies for July 2018 affecting large areas of North Western Europe and reflects the impact of the heatwave. Such large anomalies spreading over a large part of the considered domain have never been observed in the LAI product over this 18-yr period. The LDAS-Monde land surface reanalyses were produced at spatial resolutions of 0.25°x0.25° (January 2008 to October 2018) and 0.10°x0.10° (April 2016 to December 2018). Both configuration of the LDAS-Monde forced by either ERA5 or HRES capture well the vegetation state in general and for this specific event, with HRES configuration exhibiting better monitoring skills than ERA5 configuration. The consistency of ERA5 and IFS HRES driven simulations over the common period (April 2016 to October 2018) allowed to disentangle and appreciate the origin of improvements observed between the ERA5 and HRES. Another experiment, down-scaling ERA5 to HRES spatial resolutions, was performed. Results suggest that land surface spatial resolution is key (e.g. associated to a better representation of the land cover, topography) and using HRES forcing still enhance the skill. While there are advantages in using HRES, there is added value in down-scaling ERA5, which can provide consistent, long term, high resolution land reanalysis. If the improvement from LDAS-Monde analysis on control variables (soil moisture from layers 2 to 8 of the model representing the first meter of soil and LAI) from the assimilation of SSM and LAI was expected, other model variables benefit from the assimilation through biophysical processes and feedbacks in the model. Finally, we also found added value of initializing 8-day land surface HRES driven forecasts from LDAS-Monde analysis when compared with model only initial conditions.

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