scholarly journals Performance of the Remotely-Derived Products in Monitoring Gross Primary Production across Arid and Semi-Arid Ecosystems in Northwest China

Land ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 288 ◽  
Author(s):  
Qing Gu ◽  
Hui Zheng ◽  
Li Yao ◽  
Min Wang ◽  
Mingguo Ma ◽  
...  
Keyword(s):  
Primary Production ◽  
Large Scale ◽  
Meteorological Data ◽  
Gross Primary Production ◽  
Carbon Fluxes ◽  
Arid Ecosystems ◽  
Model Parameters ◽  
Net Ecosystem Carbon Exchange ◽  
Dryland Ecosystems ◽  
Semi Arid

As an important component to quantify the carbon budget, accurate evaluation of terrestrial gross primary production (GPP) is crucial for large-scale applications, especially in dryland ecosystems. Based on the in situ data from six flux sites in northwestern China from 2014 to 2016, this study compares seasonal and interannual dynamics of carbon fluxes between these arid and semi-arid ecosystems and the atmosphere. Meanwhile, the reliability of multiple remotely-derived GPP products in representative drylands was examined, including the Breathing Earth System Simulator (BESS), the Moderate Resolution Imaging Spectroradiometer (MODIS) and data derived from the OCO-2 solar-induced chlorophyll fluorescence (GOSIF). The results indicated that the carbon fluxes had clear seasonal patterns, with all ecosystems functioning as carbon sinks. The maize cropland had the highest GPP with 1183 g C m−2 y−1. Although the net ecosystem carbon exchange (NEE) in the Tamarix spp. ecosystem was the smallest among these flux sites, it reached 208 g C m−2 y−1. Furthermore, distinct advantages of GOSIF GPP (with R2 = 0.85–0.98, and RMSE = 0.87–2.66 g C m−2 d−1) were found with good performance. However, large underestimations in three GPP products existed during the growing seasons, except in grassland ecosystems. The main reasons can be ascribed to the uncertainties in the key model parameters, including the underestimated light use efficiency of the MODIS GPP, the same coarse land cover product for the BESS and MODIS GPP, the coarse gridded meteorological data, and distribution of C3 and C4 plants. Therefore, it still requires more work to accurately quantify the GPP across these dryland ecosystems.

scholarly journals Improving Estimation of Gross Primary Production in Dryland Ecosystems by a Model-Data Fusion Approach

2019 ◽  
Vol 11 (3) ◽  
pp. 225 ◽  
Author(s):  
Haibo Wang ◽  
Xin Li ◽  
Mingguo Ma ◽  
Liying Geng
Keyword(s):  
Carbon Cycle ◽  
Arid Region ◽  
Arid Regions ◽  
Meteorological Data ◽  
Gross Primary Production ◽  
Arid Ecosystems ◽  
Desert Ecosystems ◽  
Dryland Ecosystems ◽  
Semi Arid

Accurate and continuous monitoring of the production of arid ecosystems is of great importance for global and regional carbon cycle estimation. However, the magnitude of carbon sequestration in arid regions and its contribution to the global carbon cycle is poorly understood due to the worldwide paucity of measurements of carbon exchange in arid ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) product provides worldwide high-frequency monitoring of terrestrial GPP. While there have been a large number of studies to validate the MODIS GPP product with ground-based measurements over a range of biome types. Few studies have comprehensively validated the performance of MODIS estimates in arid and semi-arid ecosystems, especially for the newly released Collection 6 GPP products, whose resolution have been improved from 1000 m to 500 m. Thus, this study examined the performance of MODIS-derived GPP by compared with eddy covariance (EC)-observed GPP at different timescales for the main ecosystems in arid and semi-arid regions of China. Meanwhile, we also improved the estimation of MODIS GPP by using in situ meteorological forcing data and optimization of biome-specific parameters with the Bayesian approach. Our results revealed that the current MOD17A2H GPP algorithm could, on the whole, capture the broad trends of GPP at eight-day time scales for the most investigated sites. However, GPP was underestimated in some ecosystems in the arid region, especially for the irrigated cropland and forest ecosystems (with R2 = 0.80, RMSE = 2.66 gC/m2/day and R2 = 0.53, RMSE = 2.12 gC/m2/day, respectively). At the eight-day time scale, the slope of the original MOD17A2H GPP relative to the EC-based GPP was only 0.49, which showed significant underestimation compared with tower-based GPP. However, after using in situ meteorological data to optimize the biome-based parameters of MODIS GPP algorithm, the model could explain 91% of the EC-observed GPP of the sites. Our study revealed that the current MODIS GPP model works well after improving the maximum light-use efficiency (εmax or LUEmax), as well as the temperature and water-constrained parameters of the main ecosystems in the arid region. Nevertheless, there are still large uncertainties surrounding GPP modelling in dryland ecosystems, especially for desert ecosystems. Further improvements in GPP simulation in dryland ecosystems are needed in future studies, for example, improvements of remote sensing products and the GPP estimation algorithm, implementation of data-driven methods, or physiology models.

scholarly journals Modelling spatial and temporal dynamics of gross primary production in the Sahel from earth-observation-based photosynthetic capacity and quantum efficiency

2017 ◽  
Vol 14 (5) ◽  
pp. 1333-1348 ◽  
Author(s):  
Torbern Tagesson ◽  
Jonas Ardö ◽  
Bernard Cappelaere ◽  
Laurent Kergoat ◽  
Abdulhakim Abdi ◽  
...  
Keyword(s):  
Primary Production ◽  
Quantum Efficiency ◽  
Vegetation Index ◽  
Photosynthetic Capacity ◽  
Gross Primary Production ◽  
Earth Observation ◽  
Arid Ecosystems ◽  
Spatially Explicit ◽  
Equivalent Water Thickness ◽  
Semi Arid

Abstract. It has been shown that vegetation growth in semi-arid regions is important to the global terrestrial CO2 sink, which indicates the strong need for improved understanding and spatially explicit estimates of CO2 uptake (gross primary production; GPP) in semi-arid ecosystems. This study has three aims: (1) to evaluate the MOD17A2H GPP (collection 6) product against GPP based on eddy covariance (EC) for six sites across the Sahel; (2) to characterize relationships between spatial and temporal variability in EC-based photosynthetic capacity (Fopt) and quantum efficiency (α) and vegetation indices based on earth observation (EO) (normalized difference vegetation index (NDVI), renormalized difference vegetation index (RDVI), enhanced vegetation index (EVI) and shortwave infrared water stress index (SIWSI)); and (3) to study the applicability of EO upscaled Fopt and α for GPP modelling purposes. MOD17A2H GPP (collection 6) drastically underestimated GPP, most likely because maximum light use efficiency is set too low for semi-arid ecosystems in the MODIS algorithm. Intra-annual dynamics in Fopt were closely related to SIWSI being sensitive to equivalent water thickness, whereas α was closely related to RDVI being affected by chlorophyll abundance. Spatial and inter-annual dynamics in Fopt and α were closely coupled to NDVI and RDVI, respectively. Modelled GPP based on Fopt and α upscaled using EO-based indices reproduced in situ GPP well for all except a cropped site that was strongly impacted by anthropogenic land use. Upscaled GPP for the Sahel 2001–2014 was 736 ± 39 g C m−2 yr−1. This study indicates the strong applicability of EO as a tool for spatially explicit estimates of GPP, Fopt and α; incorporating EO-based Fopt and α in dynamic global vegetation models could improve estimates of vegetation production and simulations of ecosystem processes and hydro-biochemical cycles.

scholarly journals Understanding the effect of fire on vegetation composition and gross primary production in a semi-arid shrubland ecosystem using the Ecosystem Demography (EDv2.2) model

2020 ◽  
Author(s):  
Karun Pandit ◽  
Hamid Dashti ◽  
Andrew T. Hudak ◽  
Nancy F. Glenn ◽  
Alejandro N. Flores ◽  
...  
Keyword(s):  
Primary Production ◽  
Temporal Dynamics ◽  
Gross Primary Production ◽  
Spatial Behavior ◽  
Landsat Image ◽  
Arid Ecosystems ◽  
Vegetation Composition ◽  
Fire Scenario ◽  
Grass Growth ◽  
Semi Arid

Abstract. Wildfire incidents in sagebrush (Artemisia spp.) dominated semi-arid ecosystems in the western United States have risen dramatically in the last few decades. Severe wildfires often lead to the loss of native sagebrush communities and change the biogeochemical conditions which make it difficult for sagebrush to regenerate. Invasion of cheatgrass (Bromus tectorum) accentuates the problem by making the ecosystem more susceptible to frequent burns. Managers have implemented several techniques to cope with the cheatgrass-fire cycle, ranging from controlling undesirable fire effects by removing fuel loads either mechanically or via prescribed burns, to seeding the fire-affected areas with shrubs and native perennial forbs. There have been a number of studies at local scales to understand the direct impacts of wildfire on vegetation, however there is a larger gap in understanding these impacts at broad spatial and temporal scales. This need highlights the importance of global dynamic vegetation models (DGVMs) and remote sensing. In this study, we explored the influence of fire on vegetation composition and gross primary production (GPP) in the sagebrush ecosystem using the Ecosystem Demography (EDv2.2) model, a dynamic vegetation model. We selected Reynold Creek Experimental Watershed (RCEW) to run our simulation study, which represents sagebrush dominated ecosystems in the northern Great Basin. We ran point-based simulations at four existing flux-tower sites in the study area for a total 150 years after turning on the fire module in the 25th year. Results suggest dominance of shrub in a non-fire scenario, however under the fire scenario we observed contrasting phases of high and low shrub and C3 grass growth. Regional model simulations showed a gradual decline in gross primary production (GPP) for fire-introduced areas through the initial couple of years instead of killing all the vegetation in the affected area in the first year itself. We also compared the results from EDv2.2 with satellite data for the areas in RCEW affected by the 2015 Soda Fire. We observed a good spatial agreement between modeled GPP and a Landsat image-derived index for the study area with moderate to marginally strong correlations at the pixel level between maps of post-fire recovery GPP and the vegetation response observed in a post-fire Landsat image. This study contributes in understanding the application of ecosystem models to investigate temporal dynamics of vegetation under alternative fire regimes and the spatial behavior of post-fire ecosystem restoration.

scholarly journals Understanding the effect of fire on vegetation composition and gross primary production in a semi-arid shrubland ecosystem using the Ecosystem Demography (EDv2.2) model

2021 ◽  
Vol 18 (6) ◽  
pp. 2027-2045
Author(s):  
Karun Pandit ◽  
Hamid Dashti ◽  
Andrew T. Hudak ◽  
Nancy F. Glenn ◽  
Alejandro N. Flores ◽  
...  
Keyword(s):  
Primary Production ◽  
Temporal Dynamics ◽  
Gross Primary Production ◽  
Arid Ecosystems ◽  
Vegetation Composition ◽  
Dynamic Global Vegetation Model ◽  
Fire Scenario ◽  
Grass Growth ◽  
Global Vegetation ◽  
Semi Arid

Abstract. Wildfires in sagebrush (Artemisia spp.)-dominated semi-arid ecosystems in the western United States have increased dramatically in frequency and severity in the last few decades. Severe wildfires often lead to the loss of native sagebrush communities and change the biogeochemical conditions which make it difficult for sagebrush to regenerate. Invasion of cheatgrass (Bromus tectorum) accentuates the problem by making the ecosystem more susceptible to frequent burns. Managers have implemented several techniques to cope with the cheatgrass–fire cycle, ranging from controlling undesirable fire effects by removing fuel loads either mechanically or via prescribed burns to seeding the fire-affected areas with shrubs and native perennial forbs. There have been a number of studies at local scales to understand the direct impacts of wildfire on vegetation; however there is a larger gap in understanding these impacts at broad spatial and temporal scales. This need highlights the importance of dynamic global vegetation models (DGVMs) and remote sensing. In this study, we explored the influence of fire on vegetation composition and gross primary production (GPP) in the sagebrush ecosystem using the Ecosystem Demography (EDv2.2) model, a dynamic global vegetation model. We selected the Reynolds Creek Experimental Watershed (RCEW) to run our simulation study, an intensively monitored sagebrush-dominated ecosystem in the northern Great Basin. We ran point-based simulations at four existing flux tower sites in the study area for a total of 150 years after turning on the fire module in the 25th year. Results suggest dominance of shrubs in a non-fire scenario; however under the fire scenario we observed contrasting phases of high and low shrub density and C3 grass growth. Regional model simulations showed a gradual decline in GPP for fire-introduced areas through the initial couple of years instead of killing all the vegetation in the affected area in the first year itself. We also compared the results from EDv2.2 with satellite-derived GPP estimates for the areas in the RCEW burned by a wildfire in 2015 (Soda Fire). We observed moderate pixel-level correlations between maps of post-fire recovery EDv2.2 GPP and MODIS-derived GPP. This study contributes to understanding the application of ecosystem models to investigate temporal dynamics of vegetation under alternative fire regimes and post-fire ecosystem restoration.

scholarly journals How drought severity constrains gross primary production(GPP) and its partitioning among carbon pools in a <i>Quercus ilex</i> coppice?

2014 ◽  
Vol 11 (23) ◽  
pp. 6855-6869 ◽  
Author(s):  
S. Rambal ◽  
M. Lempereur ◽  
J. M. Limousin ◽  
N. K. Martin-StPaul ◽  
J. M. Ourcival ◽  
...  
Keyword(s):  
Primary Production ◽  
Water Deficit ◽  
Eddy Covariance ◽  
Quercus Ilex ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Carbon Fluxes ◽  
Stem Growth ◽  
Drought Severity ◽  
Mediterranean Ecosystem

Abstract. The partitioning of photosynthates toward biomass compartments plays a crucial role in the carbon (C) sink function of forests. Few studies have examined how carbon is allocated toward plant compartments in drought-prone forests. We analyzed the fate of gross primary production (GPP) in relation to yearly water deficit in an old evergreen Mediterranean Quercus ilex coppice severely affected by water limitations. Carbon fluxes between the ecosystem and the atmosphere were measured with an eddy covariance flux tower running continuously since 2001. Discrete measurements of litterfall, stem growth and fAPAR allowed us to derive annual productions of leaves, wood, flowers and acorns, and an isometric relationship between stem and belowground biomass has been used to estimate perennial belowground growth. By combining eddy covariance fluxes with annual net primary productions (NPP), we managed to close a C budget and derive values of autotrophic, heterotrophic respirations and carbon-use efficiency (CUE; the ratio between NPP and GPP). Average values of yearly net ecosystem production (NEP), GPP and Reco were 282, 1259 and 977 g C m−2. The corresponding aboveground net primary production (ANPP) components were 142.5, 26.4 and 69.6 g C m−2 for leaves, reproductive effort (flowers and fruits) and stems, respectively. NEP, GPP and Reco were affected by annual water deficit. Partitioning to the different plant compartments was also impacted by drought, with a hierarchy of responses going from the most affected – the stem growth – to the least affected – the leaf production. The average CUE was 0.40, which is well in the range for Mediterranean-type forest ecosystems. CUE tended to decrease less drastically in response to drought than GPP and NPP did, probably due to drought acclimation of autotrophic respiration. Overall, our results provide a baseline for modeling the inter-annual variations of carbon fluxes and allocation in this widespread Mediterranean ecosystem, and they highlight the value of maintaining continuous experimental measurements over the long term.

scholarly journals Relationships between primary production and crop yields in semi-arid and arid irrigated agro-ecosystems

2015 ◽  
Vol XL-7/W3 ◽  
pp. 27-30 ◽  
Author(s):  
H. H. Jaafar ◽  
F. A. Ahmad
Keyword(s):  
Primary Production ◽  
Crop Yield ◽  
Vegetation Index ◽  
Crop Yields ◽  
Gross Primary Production ◽  
Remotely Sensed ◽  
Early Assessment ◽  
Food Shortages ◽  
Semi Arid ◽  
Summer Crop

In semi-arid areas within the MENA region, food security problems are the main problematic imposed. Remote sensing can be a promising too early diagnose food shortages and further prevent the population from famine risks. This study is aimed at examining the possibility of forecasting yield before harvest from remotely sensed MODIS-derived Enhanced Vegetation Index (EVI), Net photosynthesis (net PSN), and Gross Primary Production (GPP) in semi-arid and arid irrigated agro-ecosystems within the conflict affected country of Syria. Relationships between summer yield and remotely sensed indices were derived and analyzed. Simple regression spatially-based models were developed to predict summer crop production. The validation of these models was tested during conflict years. A significant correlation (p<0.05) was found between summer crop yield and EVI, GPP and net PSN. Results indicate the efficiency of remotely sensed-based models in predicting summer yield, mostly for cotton yields and vegetables. Cumulative summer EVI-based model can predict summer crop yield during crisis period, with deviation less than 20% where vegetables are the major yield. This approach prompts to an early assessment of food shortages and lead to a real time management and decision making, especially in periods of crisis such as wars and drought.

scholarly journals The inter-annual variability of Africa's ecosystem productivity: a multi-model analysis

2008 ◽  
Vol 5 (5) ◽  
pp. 4035-4069 ◽  
Author(s):  
U. Weber ◽  
M. Jung ◽  
M. Reichstein ◽  
C. Beer ◽  
M. Braakhekke ◽  
...  
Keyword(s):  
Primary Production ◽  
Interannual Variability ◽  
Carbon Balance ◽  
Process Model ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Principal Component ◽  
Carbon Fluxes ◽  
Atmospheric Modeling ◽  
Eastern Africa

Abstract. We are comparing spatially explicit process-model based estimates of the terrestrial carbon balance and its components over Africa and confront them with remote sensing based proxies of vegetation productivity and atmospheric inversions of land-atmosphere net carbon exchange. Particular emphasis is on characterizing the patterns of interannual variability of carbon fluxes and analyzing the factors and processes responsible for it. For this purpose simulations with the terrestrial biosphere models ORCHIDEE, LPJ-DGVM, LPJ-Guess and JULES have been performed using a standardized modeling protocol and a uniform set of corrected climate forcing data. While the models differ concerning the absolute magnitude of carbon fluxes, we find several robust patterns of interannual variability among the models. Models exhibit largest interannual variability in southern and eastern Africa, regions which are primarily covered by herbaceous vegetation. Interannual variability of the net carbon balance appears to be more strongly influenced by gross primary production than by ecosystem respiration. A principal component analysis indicates that moisture is the main driving factor of interannual gross primary production variability for those regions. On the contrary in a large part of the inner tropics radiation appears to be limiting in two models. These patterns are corroborated by remotely sensed vegetation properties from the SeaWiFS satellite sensor. Inverse atmospheric modeling estimates of surface carbon fluxes are less conclusive at this point, implying the need for a denser network of observation stations over Africa.

scholarly journals P-model v1.0: An optimality-based light use efficiency model for simulating ecosystem gross primary production

2019 ◽  
Author(s):  
Benjamin D. Stocker ◽  
Han Wang ◽  
Nicholas G. Smith ◽  
Sandy P. Harrison ◽  
Trevor F. Keenan ◽  
...  
Keyword(s):  
Primary Production ◽  
Satellite Data ◽  
Vegetation Type ◽  
Meteorological Data ◽  
Gross Primary Production ◽  
Satellite Monitoring ◽  
Light Use Efficiency ◽  
Spatial And Temporal Scales ◽  
Wide Range ◽  
Use Efficiency

Abstract. Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth System Model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a gross primary production (GPP, photosynthesis per unit ground area) model, the P-model, that combines the Farquhar-von Caemmerer-Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation-transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model is forced here with satellite data for the fraction of absorbed photosynthetically active radiation and site-specific meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs and prescribed parameters, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8-day mean, 131 sites) – better than some state-of-the-art satellite data-driven light use efficiency models. The R2 is reduced to 0.69 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.37 (means by site) and 0.53 (means by vegetation type). The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosythesis across a wide range of conditions. The model is available as an R package (rpmodel).

Evaluating hydrological signatures and catchment similarities to estimate model parameters in Cauvery river basin, India

2020 ◽  
Author(s):  
Gowri Reghunath ◽  
Pradeep Mujumdar
Keyword(s):  
River Basin ◽  
Large Scale ◽  
Meteorological Data ◽  
Model Parameters ◽  
Catchment Scale ◽  
Infiltration Capacity ◽  
Cauvery River ◽  
Groundwater Availability ◽  
Cauvery River Basin ◽  
Hydrological Signatures

&lt;p&gt;Catchments are complex self-organizing environmental systems for which the form, drainage network, channel geometries, soil and vegetation, are all an outcome of co-evolution and adaptation to the ecological, geomorphologic and land-forming processes. Quantification of hydrological signatures provides vital information about the complex system properties and the functional behaviour of catchments. This work aims at evaluating catchment similarity with respect to geomorphology and hydrological signatures such as runoff ratio, flow duration curves and peak flows for calibrating and upscaling model parameters. The study is carried out on the sub-catchments of Cauvery river basin which is a major river basin in Peninsular India. The basin is characterized by extensive regional variability in surface and groundwater availability and large-scale shift in land use patterns in recent decades. With a significant number of anthropogenic interventions such as check dams and reservoirs, the basin faces water management challenges at the local, regional and basin scales. Hydrological signatures derived from elevation, streamflow and meteorological data are used to evaluate geomorphologic and hydrological similarity between the sub-catchments. We employ the physically based macroscale Variable Infiltration Capacity (VIC) model coupled with a routing model to simulate the streamflow. Streamflow simulations are carried out for various sub-catchments delineated based on discharge gauging stations. Model parameters are estimated and hydrological signatures are assessed for effective model calibration. Impact of interventions on flow signatures at the catchment scale is also assessed. This work can significantly improve the scientific understanding of variability of hydrological processes at various scales and provide useful insights for development of scaling relationships. It can also aid in examining the model parameter transferability across scales.&lt;/p&gt;

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