scholarly journals Comparison between remote sensing and a dynamic vegetation model for estimating terrestrial primary production of Africa

2015 ◽  
Vol 10 (1) ◽  
Author(s):  
Jonas Ardö
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Vegetation Model ◽  
Dynamic Vegetation Model

scholarly journals Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model

2014 ◽  
Vol 11 (7) ◽  
pp. 2027-2054 ◽  
Author(s):  
B. Smith ◽  
D. Wårlind ◽  
A. Arneth ◽  
T. Hickler ◽  
P. Leadley ◽  
...  
Keyword(s):  
Primary Production ◽  
Goodness Of Fit ◽  
Net Primary Production ◽  
N Cycling ◽  
Cold Climate ◽  
N Mineralisation ◽  
Vegetation Model ◽  
Dynamic Vegetation Model ◽  
N Limitation ◽  
Forest Stand Structure

Abstract. The LPJ-GUESS dynamic vegetation model uniquely combines an individual- and patch-based representation of vegetation dynamics with ecosystem biogeochemical cycling from regional to global scales. We present an updated version that includes plant and soil N dynamics, analysing the implications of accounting for C–N interactions on predictions and performance of the model. Stand structural dynamics and allometric scaling of tree growth suggested by global databases of forest stand structure and development were well reproduced by the model in comparison to an earlier multi-model study. Accounting for N cycle dynamics improved the goodness of fit for broadleaved forests. N limitation associated with low N-mineralisation rates reduces productivity of cold-climate and dry-climate ecosystems relative to mesic temperate and tropical ecosystems. In a model experiment emulating free-air CO2 enrichment (FACE) treatment for forests globally, N limitation associated with low N-mineralisation rates of colder soils reduces CO2 enhancement of net primary production (NPP) for boreal forests, while some temperate and tropical forests exhibit increased NPP enhancement. Under a business-as-usual future climate and emissions scenario, ecosystem C storage globally was projected to increase by ca. 10%; additional N requirements to match this increasing ecosystem C were within the high N supply limit estimated on stoichiometric grounds in an earlier study. Our results highlight the importance of accounting for C–N interactions in studies of global terrestrial N cycling, and as a basis for understanding mechanisms on local scales and in different regional contexts.

scholarly journals Assessing the effects of climate and land use land cover changes on recent carbon storage in terrestrial ecosystem using model-satellite approach over Wallonia, Belgium

2021 ◽  
Author(s):  
Arpita Verma ◽  
Louis Francois ◽  
Ingrid Jacquemin ◽  
Merja Tölle ◽  
Huan Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Carbon Sequestration ◽  
Land Cover ◽  
Land Cover Change ◽  
Carbon Storage ◽  
Terrestrial Ecosystem ◽  
Land Use Land Cover ◽  
Vegetation Model ◽  
Dynamic Vegetation Model

<p>The use of a dynamic vegetation model, CARAIB, to estimate carbon sequestration from land-use and land-cover change (LULCC) offers a new approach for spatial and temporal details of carbon sink and for terrestrial ecosystem productivity affected by LULCC. Using the remote sensing satellite imagery (Landsat) we explore the role of land use land cover change (LULCC) in modifying the terrestrial carbon sequestration. We have constructed our LULCC data over Wallonia, Belgium, and compared it with the ground-based statistical data. However, the results from the satellite base LULCC are overestimating the forest data due to the single isolated trees. We know forests play an important role in mitigating climate change by capturing and sequestering atmospheric carbon. Overall, the conversion of land and increase in urban land can impact the environment. Moreover, quantitative estimation of the temporal and spatial pattern of carbon storage with the change in land use land cover is critical to estimate. The objective of this study is to estimate the inter-annual variability in carbon sequestration with the change in land use land cover. Here, with the CARAIB dynamic vegetation model, we perform simulations using remote sensing satellite-based LULCC data to analyse the sensitivity of the carbon sequestration. We propose a new method of using satellite and machine learning-based observation to reconstruct historical LULCC. It will quantify the spatial and temporal variability of land-use change during the 1985-2020 periods over Wallonia, Belgium at high resolution. This study will give the space to analyse past information and hence calibrate the dynamic vegetation model to minimize uncertainty in the future projection (until 2070). Further, we will also analyse the change in other climate variables, such as CO<sub>2</sub>, temperature, etc. Overall, this study allows us to understand the effect of changing land-use patterns and to constrain the model with an improved input dataset which minimizes the uncertainty in model estimation.</p>

Estimation of forest Leaf Area Index using remote sensing and GIS data for modelling net primary production

1997 ◽  
Vol 18 (16) ◽  
pp. 3459-3471 ◽  
Author(s):  
S. E. Franklin ◽  
M. B. Lavigne ◽  
M. J. Deuling ◽  
M. A. Wulder ◽  
E. R. Hunt
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Net Primary Production ◽  
Remote Sensing And Gis ◽  
Area Index ◽  
Gis Data

scholarly journals Gross Primary Production and False Spring: a spatio-temporal analysis

2020 ◽  
Author(s):  
Emma Izquierdo-Verdiguier ◽  
Raúl Zurita-Milla ◽  
Álvaro Moreno-Martinez ◽  
Gustau Camps-Valls ◽  
Anja Klisch ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Primary Production ◽  
Gross Primary Production ◽  
Damage Index ◽  
Temporal Analysis ◽  
Environmental Stressors ◽  
Impact Of Climate Change ◽  
False Spring ◽  
Spatial Temporal Analysis

<p>Phenological information can be obtained from different sources of data. For instance, from remote sensing data or products and from models driven by weather variables. The former typically allows analyzing land surface phenology whereas the latter provide plant phenological information. Analyzing relationships between both sources of data allows us to understand the impact of climate change on vegetation over space and time. For example, the onset of spring is advanced or delayed by changes in the climate. These alterations affect plant productivity and animal migrations.</p><p>Spring onset monitoring is supported by the Extended Spring Index (SI-x), which are a suite of regression-based models for key indicator plant species. These models (Schwartz et al. in 2013) are based on daily maximum and minimum temperature from the first day of the year (January 1<sup>st</sup>). The primary products of these models are the timing of first leaf and first bloom, but they also provide derivative products such as the timing of last freeze day and the risk of frost damage day (damage index) for each year. This information helps to understand if vegetation could have suffered from environmental stressors such as droughts or a late frost events. The effects of environmental stressors in vegetation could be captured by the false spring index, which relates the first leaf day and the last freeze day. Moreover, this information could be used to understand plant productivity as well as to evaluate the economic impact of climate change.</p><p>Previous works studied the relationship between remote sensing and plant level products by means of spatial-temporal analysis between Gross Primary Production (GPP) and a spring onset index. However, they did not consider the possible impact of false spring effect in these relationships. Here, we present a spatial-temporal analysis between GPP and the damage index to better understand the effect of false springs (in annual gross photosynthesis data). The analysis is done for the period 2000 to 2015 over the contiguous US and at spatial resolution of 1 km. We used the MODIS annual sum of GPP and the damage and false spring indices derived from the SI-x models.</p>

scholarly journals Introducing an Irrigation Scheme to a Regional Climate Model: A Case Study over West Africa

2014 ◽  
Vol 27 (15) ◽  
pp. 5708-5723 ◽  
Author(s):  
Marc P. Marcella ◽  
Elfatih A. B. Eltahir
Keyword(s):  
Heat Flux ◽  
Land Cover ◽  
West Africa ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Vegetation Model ◽  
Irrigation Scheme ◽  
Dynamic Vegetation Model ◽  
Niger River

Abstract This article presents a new irrigation scheme and biome to the dynamic vegetation model, Integrated Biosphere Simulator (IBIS), coupled to version 3 of the Regional Climate Model (RegCM3-IBIS). The new land cover allows for only the plant functional type (crop) to exist in an irrigated grid cell. Irrigation water (i.e., negative runoff) is applied until the soil root zone reaches relative field capacity. The new scheme allows for irrigation scheduling (i.e., when to apply water) and for the user to determine the crop to be grown. Initial simulations show a large sensitivity of the scheme to soil texture types, how the water is applied, and the climatic conditions over the region. Application of the new scheme is tested over West Africa, specifically Mali and Niger, to simulate the potential irrigation of the Niger River. A realistic representation of irrigation of the Niger River is performed by constraining the land irrigated by the annual flow of the Niger River and the amount of arable land in the region as reported by the Food and Agriculture Organization of the United Nations (FAO). A 30-yr simulation including irrigated cropland is compared to a 30-yr simulation that is identical but with no irrigation of the Niger. Results indicate a significant greening of the irrigated land as evapotranspiration over the crop fields largely increases—mostly via increases in transpiration from plant growth. The increase in the evapotranspiration, or latent heat flux (by 65–150 W m−2), causes a significant decrease in the sensible heat flux while surface temperatures cool on average by nearly 5°C. This cooling is felt downwind, where average daily temperatures outside the irrigation are reduced by 0.5°–1.0°C. Likewise, large increases in 2-m specific humidity are experienced across the irrigated cropland (on the order of 5 g kg−1) but also extend farther north and east, reflecting the prevailing surface southwesterlies. Changes (decreases) in rainfall are found only over the irrigated lands of west Mali. The decrease in rainfall can be explained by the large surface cooling and collapse of the boundary layer (by approximately 500 m). Both lead to a reduction in the triggering of convection as the convective inhibition, or negative buoyant energy, is never breached. Nevertheless, the new scheme and land cover allows for a novel line of research that can accurately reflect the effects of irrigation on climate and the surrounding environment using a dynamic vegetation model coupled to a regional climate model.

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