scholarly journals Multi-site, multi-crop measurements in the soil-vegetation-atmosphere continuum: A comprehensive dataset from two climatically contrasting regions in South West Germany for the period 2009–2018

2021 ◽  
Author(s):  
Tobias K. D. Weber ◽  
Joachim Ingwersen ◽  
Petra Högy ◽  
Arne Poyda ◽  
Hans-Dieter Wizemann ◽  
...  
Keyword(s):  
Land Surface ◽  
Heat Fluxes ◽  
Data Availability ◽  
West Germany ◽  
Annual Precipitation ◽  
Data Set ◽  
Area Index ◽  
Carbon And Nitrogen ◽  
South West ◽  
Mean Temperature

Abstract. We present a comprehensive, high-quality dataset characterising soil-vegetation and land-surface processes from continuous measurements conducted in two climatically contrasting study regions in South West Germany: the warmer and drier Kraichgau region with a mean temperature of 9.7 °C and annual precipitation of 890 mm, and the cooler and wetter Swabian Alp with mean temperature 7.5 °C and annual precipitation 1042 mm. In each region, measurements were conducted over a time period of nine cropping seasons from 2009 to 2018. The backbone of the investigation was formed by six eddy-covariance stations (EC) which measured fluxes of water, energy and carbon dioxide between the land surface and the atmosphere at half-hourly resolution. This resulted in a dataset containing measurements from a total of 54 site*years containing observations with a multitude of crops, as well as considerable variation in local growing season climates. The presented multi-site, multi-year data set is composed of crop-related data on phenological development stages, canopy height, leaf area index, vegetative and generative biomass and their respective carbon and nitrogen content. Time series of soil temperature and soil water content were monitored with 30-min resolution at various points in the soil profile, including ground heat fluxes. Moreover, more than 1,200 soil samples were taken to study changes of carbon and nitrogen contents. The data set was uploaded to the Pangaea database and can be accessed at https://doi.org/10.20387/bonares-a0qc-46jc (for the review process, please refer to the data availability section). One station in each region has now been set up as continuous observatories of state variables and fluxes in intensively managed agricultural fields.

Hydrological functioning of irrigated maize crops in southwest France using Eddy Covariance measurements and a land surface model

2021 ◽  
Author(s):  
Oluwakemi Dare-Idowu ◽  
Lionel Jarlan ◽  
Aurore Brut ◽  
Valerie Le-Dantec ◽  
Vincent Rivalland ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Land Surface ◽  
Sap Flow ◽  
Heat Fluxes ◽  
Added Value ◽  
Surface Model ◽  
Data Set ◽  
Flow Measurements ◽  
Ground Heat Flux

<p>This study aims to analyze the main components of the energy and hydric budgets of irrigated maize in southwestern France. To this objective, the ISBA-A-gs (<span>Interactions between Soil, Biosphere, and Atmosphere) </span>is run over six maize growing seasons. As a preliminary step, the ability of the ISBA-A-gs model to predict the different terms of the energy and water budgets is assessed thanks to a large database of <em>in situ</em> measurements by comparing the single budget version of the model with the new Multiple Energy Balance version solving an energy budget separately for the soil and the vegetation. The <em>in situ</em> data set acquired at the Lamasquere site (43.48<sup>o</sup> N, 1.249<sup>o</sup> E) includes half-hourly measurements of sensible (H) and latent heat fluxes (LE) estimated by an Eddy Covariance system. Measurements also include net radiation (Rn), ground heat flux (G), plant transpiration with sap flow sensors, meteorological variables, and 15-days measurements of vegetation characteristics. The seasonal dynamics of the turbulent fluxes were properly reproduced by both configurations of the model with an R² ranging from 0.66 to 0.89, and a root mean square error lower than 48 W m<sup>-2</sup>. Statistical metrics showed that H was better predicted by MEB with R² of 0.80 in comparison to ISBA-Ags (0.73). However, the difference between the RMSE of ISBA-Ags and MEB during the well-developed stage of the plants for both H and LE does not exceed 8 W m<sup>-2</sup>. This implies that MEB only has a significant added value over ISBA-Ags when the soil and the canopy are not fully coupled, and over a heterogeneous field. Furthermore, this study made a comparison between the sap flow measurements and the transpiration simulated by ISBA-A-gs and MEB. A good dynamics was reproduced by ISBA-A-gs and MEB, although, MEB (R²= 0.91) provided a slightly more realistic estimation of the vegetation transpiration. Consequently, this study investigated the dynamics of the water budget during the growing maize seasons. Results indicated that drainage is almost null on the site, while the observed values of cumulative evapotranspiration that was higher than the water inputs are related to a shallow ground table that provides supplement water to the crop. This work provides insight into the modeling of water and energy exchanges over maize crops and opens perspectives for better water management of the crop in the future.</p>

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

2017 ◽  
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.

scholarly journals Dynamic Anthropogenic activitieS impacting Heat emissions (DASHv1.0): Development and evaluation

2020 ◽  
Author(s):  
Isabella Capel-Timms ◽  
Stefán Thor Smith ◽  
Ting Sun ◽  
Sue Grimmond
Keyword(s):  
Land Surface ◽  
Urban Form ◽  
Anthropogenic Activities ◽  
Heat Fluxes ◽  
Outdoor Environment ◽  
Data Availability ◽  
Anthropogenic Heat ◽  
Surface Model ◽  
Spatial And Temporal Patterns ◽  
Top Down

Abstract. Thermal emissions or anthropogenic heat fluxes (QF) from human activities impact the local and larger scales urban climate. DASH considers both urban form and function in simulating QF by use of an agent-based structure that includes behavioural characteristics of city populations. This allows social practices to drive the calculation of QF as occupants move, varying by day type, demographic, location, activity, socio-economic factors and in response to environmental conditions. The spatial resolution depends on data availability. DASH has simple transport and building energy models to allow simulation of dynamic vehicle use, occupancy and heating/cooling demand, with subsequent release of energy to the outdoor environment through the building fabric. Building stock variations are captured using archetypes. Evaluation of DASH in Greater London for various periods in 2015 uses a top-down inventory model (GQF) and national energy consumption statistics. DASH reproduces the expected spatial and temporal patterns of QF but the annual average is smaller than published energy data. Overall the model generally performs well, including for domestic appliance energy use against top down model results. DASH could be coupled to an urban land surface model and/or used offline for developing coefficients for simpler/faster models.

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

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.

scholarly journals A leaf area index data set acquired in Sahelian rangelands of Gourma in Mali over the 2005–2017 period

2019 ◽  
Vol 11 (2) ◽  
pp. 675-686
Author(s):  
Eric Mougin ◽  
Mamadou Oumar Diawara ◽  
Nogmana Soumaguel ◽  
Ali Amadou Maïga ◽  
Valérie Demarez ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Model Development ◽  
Land Surface Model ◽  
Product Evaluation ◽  
Tree Canopy ◽  
Surface Model ◽  
Data Set ◽  
Area Index

Abstract. The leaf area index of Sahelian rangelands and related variables such as the vegetation cover fraction, the fraction of absorbed photosynthetically active radiation and the clumping index were measured between 2005 and 2017 in the Gourma region of northern Mali. These variables, known as climate essential variables, were derived from the acquisition and the processing of hemispherical photographs taken along 1 km linear sampling transects for five contrasted canopies and one millet field. The same sampling protocol was applied in a seasonally inundated Acacia open forest, along a 0.5 km transect, by taking photographs of the understorey and the tree canopy. These observations collected over more than a decade, in a remote and not very accessible region, provide a relevant and unique data set that can be used for a better understanding of the Sahelian vegetation response to the current rainfall changes. The collected data can also be used for satellite product evaluation and land surface model development and validation. This paper aims to present the field work that was carried out during 13 successive rainy seasons, the measured vegetation variables, and the associated open database. Finally, a few examples of data use are shown. DOI of the referenced data set: https://doi.org/10.17178/AMMA-CATCH.CE.Veg_Gh.

scholarly journals Impact of climate, vegetation, soil and crop management variables on multi-year ISBA-A-gs simulations of evapotranspiration over a Mediterranean crop site

2015 ◽  
Vol 8 (10) ◽  
pp. 3033-3053 ◽  
Author(s):  
S. Garrigues ◽  
A. Olioso ◽  
D. Carrer ◽  
B. Decharme ◽  
J.-C. Calvet ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Surface Model ◽  
Data Sets ◽  
Data Set ◽  
Area Index ◽  
Hydrodynamic Parameters ◽  
Land Surface Parameter ◽  
Crop Succession

Abstract. Generic land surface models are generally driven by large-scale data sets to describe the climate, the soil properties, the vegetation dynamic and the cropland management (irrigation). This paper investigates the uncertainties in these drivers and their impacts on the evapotranspiration (ET) simulated from the Interactions between Soil, Biosphere, and Atmosphere (ISBA-A-gs) land surface model over a 12-year Mediterranean crop succession. We evaluate the forcing data sets used in the standard implementation of ISBA over France where the model is driven by the SAFRAN (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie) high spatial resolution atmospheric reanalysis, the leaf area index (LAI) time courses derived from the ECOCLIMAP-II land surface parameter database and the soil texture derived from the French soil database. For climate, we focus on the radiations and rainfall variables and we test additional data sets which include the ERA-Interim (ERA-I) low spatial resolution reanalysis, the Global Precipitation Climatology Centre data set (GPCC) and the MeteoSat Second Generation (MSG) satellite estimate of downwelling shortwave radiations. The evaluation of the drivers indicates very low bias in daily downwelling shortwave radiation for ERA-I (2.5 W m−2) compared to the negative biases found for SAFRAN (−10 W m−2) and the MSG satellite (−12 W m−2). Both SAFRAN and ERA-I underestimate downwelling longwave radiations by −12 and −16 W m−2, respectively. The SAFRAN and ERA-I/GPCC rainfall are slightly biased at daily and longer timescales (1 and 0.5 % of the mean rainfall measurement). The SAFRAN rainfall is more precise than the ERA-I/GPCC estimate which shows larger inter-annual variability in yearly rainfall error (up to 100 mm). The ECOCLIMAP-II LAI climatology does not properly resolve Mediterranean crop phenology and underestimates the bare soil period which leads to an overall overestimation of LAI over the crop succession. The simulation of irrigation by the model provides an accurate irrigation amount over the crop cycle but the timing of irrigation occurrences is frequently unrealistic. Errors in the soil hydrodynamic parameters and the lack of irrigation in the simulation have the largest influence on ET compared to uncertainties in the large-scale climate reanalysis and the LAI climatology. Among climate variables, the errors in yearly ET are mainly related to the errors in yearly rainfall. The underestimation of the available water capacity and the soil hydraulic diffusivity induce a large underestimation of ET over 12 years. The underestimation of radiations by the reanalyses and the absence of irrigation in the simulation lead to the underestimation of ET while the overall overestimation of LAI by the ECOCLIMAP-II climatology induces an overestimation of ET over 12 years. This work shows that the key challenges to monitor the water balance of cropland at regional scale concern the representation of the spatial distribution of the soil hydrodynamic parameters, the variability of the irrigation practices, the seasonal and inter-annual dynamics of vegetation and the spatiotemporal heterogeneity of rainfall.

Beta effect of eCO2 can cause as much rainfall decrease as large-scale deforestation in the Amazon

2021 ◽  
Author(s):  
David Lapola ◽  
Gilvan Sampaio ◽  
Marília Shimizu ◽  
Carlos Guimarães-Júnior ◽  
Felipe Alexandre ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Strong Dependence ◽  
Physiological Effect ◽  
Heat Fluxes ◽  
Vegetation Coverage ◽  
Annual Rainfall ◽  
Area Index ◽  
Canopy Transpiration ◽  
Amazon Rainfall

<p>Amazon region’s climate is particularly sensitive to surface processes and properties such as heat fluxes and vegetation coverage. Rainfall is a key expression of such land surface-atmosphere interactions in the region due to its strong dependence on forest transpiration. While a large number of past studies have shown the impacts of large-scale deforestation on annual rainfall, studies on the isolated effects of elevated atmospheric CO<sub>2</sub> concentration (eCO<sub>2</sub>) on plant physiology (i.e. the β effect), for example on canopy transpiration and rainfall, are scarcer. Here we make a systematic comparison of the plant physiological effects of eCO<sub>2</sub> and deforestation on Amazon rainfall. We use the CPTEC-Brazilian Atmospheric Model (BAM) with dynamic vegetation under a 1.5xCO<sub>2</sub> and a 100% substitution of the forest by pasture grassland, with all other conditions held similar between the two scenarios. We find that both scenarios result in equivalent average annual rainfall reductions (Physiology: -252 mm,-12%; Deforestation: -292 mm, -13%) that are well above observed Amazon rainfall interannual variability of 5.1%. Rainfall decrease in the two scenarios are caused by a reduction of approximately 20% of canopy transpiration, but for different reasons: eCO<sub>2</sub>-driven reduction of stomatal conductance in the Physiology run; decreased leaf area index of pasture (-66%) and its dry-season lower surface vegetation coverage in the Deforestation run. Walker circulation is strengthened in the two scenarios (with enhanced convection over the Andes and a weak subsidence branch over east Amazon) but, again, through different mechanisms: enhanced west winds from the Pacific and reduced easterlies entering the basin in Physiology, and strongly increased easterlies in Deforestation. Although our results for the Deforestation scenario are in agreement with previous observational and modelling studies, the lack of direct field-based ecosystem-level experimental evidence on the effect of eCO<sub>2</sub> in moisture fluxes of tropical forests confers a substantial level of uncertainty to this and any other projections on the physiological effect of eCO<sub>2</sub> on Amazon rainfall. Furthermore, our results denote the incurred responsibilities of both Amazonian and non- Amazonian countries to mitigate potential future climatic change and its impacts in the region driven either by local deforestation (to be tackled by Amazonian countries) or global CO<sub>2</sub> emissions (to be handled by all countries).</p>

scholarly journals DEEP LEARNING-BASED RECONSTRUCTION OF SPATIOTEMPORALLY FUSED SATELLITE IMAGES FOR SMART AGRICULTURE APPLICATIONS IN A HETEROGENEOUS AGRICULTURAL REGION

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 249-254
Author(s):  
A. Htitiou ◽  
A. Boudhar ◽  
Y. Lebrini ◽  
T. Benabdelouahab
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Land Surface ◽  
Satellite Images ◽  
Vegetation Index ◽  
Data Availability ◽  
Series Data ◽  
Landsat 8 ◽  
Phenological Stages ◽  
Area Index

Abstract. Remote sensing offers spatially explicit and temporally continuous observational data of various land surface parameters such as vegetation index, land surface temperature, soil moisture, leaf area index, and evapotranspiration, which can be widely leveraged for various applications at different scales and contexts. One of the main applications is agricultural monitoring, where a smart system based on precision agriculture requires a set of satellite images with a high resolution, both in time and space to capture the phenological stages and fine spatial details, especially in landscapes with various spatial heterogeneity and temporal variation. These requirements sometimes cannot be provided by a single sensor due to the trade-off required between spatial and temporal resolutions and/or the influence of cloud cover. The data availability of new generation multispectral sensors of Landsat-8 (L8) and Sentinel-2 (S2) satellites offers unprecedented options for such applications. Given this, the current study aims to display how the synergistic use of these optical sensors can efficiently support such an application. Herein, this study proposes a deep learning spatiotemporal data fusion method to fill the need for predicting a dense time series of vegetation index with fine spatial resolution. The results show that the developed method creates more accurate fused NDVI time-series data that were able to derive phenological stages and characteristics in single-crop fields, while keeps more spatial details in such a heterogeneous landscape.

scholarly journals On the Proper Use of Satellite-Derived Leaf Area Index in Climate Modeling

2009 ◽  
Vol 22 (16) ◽  
pp. 4427-4433 ◽  
Author(s):  
Jianjun Ge
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Climate Modeling ◽  
Heat Fluxes ◽  
Sensible Heat ◽  
Area Index ◽  
Simulated Precipitation ◽  
Surface Models ◽  
Improper Use

Abstract Satellite-observed leaf area index (LAI) is increasingly being used in climate modeling. In common land surface models, LAI is specified for the vegetated part only. In contrast, satellite LAI is defined for the total area including both vegetated and nonvegetated fractions. Some recent modeling studies and model developments have not noticed this difference, which resulted in improper use of satellite LAI. This paper clarified this issue. A sensitivity test was carried out using a regional model to investigate the impacts of LAI definitions on simulated climates. This study showed that use of satellite LAI without considering the inconsistency in definition caused much smaller LAI values in the model. As a result, partitioning of surface energy into latent and sensible heat fluxes, as well as the model-simulated precipitation, was affected substantially. Overall, improper use of satellite LAI increased the model biases in simulated precipitation.

Modeling and Scaling Coupled Energy, Water, and Carbon Fluxes Based on Remote Sensing: An Application to Canada’s Landmass

2007 ◽  
Vol 8 (2) ◽  
pp. 123-143 ◽  
Author(s):  
Baozhang Chen ◽  
Jing M. Chen ◽  
Gang Mo ◽  
Chiu-Wai Yuen ◽  
Hank Margolis ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
General Circulation ◽  
Carbon Fluxes ◽  
General Circulation Models ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
Sensible Heat ◽  
Area Index ◽  
Up Scaling

Abstract Land surface models (LSMs) need to be coupled with atmospheric general circulation models (GCMs) to adequately simulate the exchanges of energy, water, and carbon between the atmosphere and terrestrial surfaces. The heterogeneity of the land surface and its interaction with temporally and spatially varying meteorological conditions result in nonlinear effects on fluxes of energy, water, and carbon, making it challenging to scale these fluxes accurately. The issue of up-scaling remains one of the critical unsolved problems in the parameterization of subgrid-scale fluxes in coupled LSM and GCM models. A new distributed LSM, the Ecosystem–Atmosphere Simulation Scheme (EASS) was developed and coupled with the atmospheric Global Environmental Multiscale model (GEM) to simulate energy, water, and carbon fluxes over Canada’s landmass through the use of remote sensing and ancillary data. Two approaches (lumped case and distributed case) for handling subgrid heterogeneity were used to evaluate the effect of land-cover heterogeneity on regional flux simulations based on remote sensing. Online runs for a week in August 2003 provided an opportunity to investigate model performance and spatial scaling issues. Comparisons of simulated results with available tower observations (five sites) across an east–west transect over Canada’s southern forest regions indicate that the model is reasonably successful in capturing both the spatial and temporal variations in carbon and energy fluxes, although there were still some biases in estimates of latent and sensible heat fluxes between the simulations and the tower observations. Moreover, the latent and sensible heat fluxes were found to be better modeled in the coupled EASS–GEM system than in the uncoupled GEM. There are marked spatial variations in simulated fluxes over Canada’s landmass. These patterns of spatial variation closely follow vegetation-cover types as well as leaf area index, both of which are highly correlated with the underlying soil types, soil moisture conditions, and soil carbon pools. The surface fluxes modeled by the two up-scaling approaches (lumped and distributed cases) differ by 5%–15% on average and by up to 15%–25% in highly heterogeneous regions. This suggests that different ways of treating subgrid land surface heterogeneities could lead to noticeable biases in model output.

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