scholarly journals Effects of vegetation heterogeneity and surface topography on spatial scaling of net primary productivity

2013 ◽  
Vol 10 (7) ◽  
pp. 4879-4896 ◽  
Author(s):  
J. M. Chen ◽  
X. Chen ◽  
W. Ju
Keyword(s):  
Surface Topography ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Shanxi Province ◽  
Mean Bias Error ◽  
Bias Error ◽  
Spatial Scaling ◽  
Area Index ◽  
Vegetation Heterogeneity ◽  
The Mean

Abstract. Due to the heterogeneous nature of the land surface, spatial scaling is an inevitable issue in the development of land models coupled with low-resolution Earth system models (ESMs) for predicting land-atmosphere interactions and carbon-climate feedbacks. In this study, a simple spatial scaling algorithm is developed to correct errors in net primary productivity (NPP) estimates made at a coarse spatial resolution based on sub-pixel information of vegetation heterogeneity and surface topography. An eco-hydrological model BEPS-TerrainLab, which considers both vegetation and topographical effects on the vertical and lateral water flows and the carbon cycle, is used to simulate NPP at 30 m and 1 km resolutions for a 5700 km2 watershed with an elevation range from 518 m to 3767 m in the Qinling Mountain, Shanxi Province, China. Assuming that the NPP simulated at 30 m resolution represents the reality and that at 1 km resolution is subject to errors due to sub-pixel heterogeneity, a spatial scaling index (SSI) is developed to correct the coarse resolution NPP values pixel by pixel. The agreement between the NPP values at these two resolutions is improved considerably from R2 = 0.782 to R2 = 0.884 after the correction. The mean bias error (MBE) in NPP modelled at the 1 km resolution is reduced from 14.8 g C m−2 yr−1 to 4.8 g C m−2 yr−1 in comparison with NPP modelled at 30 m resolution, where the mean NPP is 668 g C m−2 yr−1. The range of spatial variations of NPP at 30 m resolution is larger than that at 1 km resolution. Land cover fraction is the most important vegetation factor to be considered in NPP spatial scaling, and slope is the most important topographical factor for NPP spatial scaling especially in mountainous areas, because of its influence on the lateral water redistribution, affecting water table, soil moisture and plant growth. Other factors including leaf area index (LAI) and elevation have small and additive effects on improving the spatial scaling between these two resolutions.

scholarly journals Effects of vegetation heterogeneity and surface topography on spatial scaling of net primary productivity

2013 ◽  
Vol 10 (3) ◽  
pp. 4225-4270 ◽  
Author(s):  
J. M. Chen ◽  
X. Chen ◽  
W. Ju
Keyword(s):  
Surface Topography ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Shaanxi Province ◽  
Mean Bias Error ◽  
Bias Error ◽  
Spatial Scaling ◽  
Area Index ◽  
Vegetation Heterogeneity ◽  
The Mean

Abstract. Due to the heterogeneous nature of the land surface, spatial scaling is an inevitable issue in the development of land models coupled with low-resolution Earth system models (ESMs) for predicting land-atmosphere interactions and carbon-climate feedbacks. In this study, a simple spatial scaling algorithm is developed to correct errors in net primary productivity (NPP) estimates made at a coarse spatial resolution based on sub-pixel information of vegetation heterogeneity and surface topography. An eco-hydrological model BEPS-TerrainLab, which considers both vegetation and topographical effects on the vertical and lateral water flows and the carbon cycle, is used to simulate NPP at 30 m and 1 km resolutions for a 5700 km2 watershed with an elevation range from 518 m to 3767 m in the Qinling Mountain, Shaanxi Province, China. Assuming that the NPP simulated at 30 m resolution represents the reality and that at 1 km resolution is subject to errors due to sub-pixel heterogeneity, a spatial scaling index (SSI) is developed to correct the coarse resolution NPP values pixel by pixel. The agreement between the NPP values at these two resolutions is improved considerably from R2 = 0.782 to R2 = 0.884 after the correction. The mean bias error (MBE) in NPP modeled at the 1 km resolution is reduced from 14.8 g C m−2 yr−1 to 4.8 g C m−2 yr−1 in comparison with NPP modeled at 30 m resolution, where the mean NPP is 668 g C m−2 yr−1. The range of spatial variations of NPP at 30 m resolution is larger than that at 1 km resolution. Land cover fraction is the most important vegetation factor to be considered in NPP spatial scaling, and slope is the most important topographical factor for NPP spatial scaling especially in mountainous areas, because of its influence on the lateral water redistribution, affecting water table, soil moisture and plant growth. Other factors including leaf area index (LAI), elevation and aspect have small and additive effects on improving the spatial scaling between these two resolutions.

scholarly journals Remote Sensing to Study Mangrove Fragmentation and Its Impacts on Leaf Area Index and Gross Primary Productivity in the South of Peninsular Malaysia

2021 ◽  
Vol 13 (8) ◽  
pp. 1427
Author(s):  
Kasturi Devi Kanniah ◽  
Chuen Siang Kang ◽  
Sahadev Sharma ◽  
A. Aldrie Amir
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Primary Productivity ◽  
Peninsular Malaysia ◽  
Area Index ◽  
Mangrove Area ◽  
The Mean ◽  
Shape Complexity ◽  
The Impact

Mangrove is classified as an important ecosystem along the shorelines of tropical and subtropical landmasses, which are being degraded at an alarming rate despite numerous international treaties having been agreed. Iskandar Malaysia (IM) is a fast-growing economic region in southern Peninsular Malaysia, where three Ramsar Sites are located. Since the beginning of the 21st century (2000–2019), a total loss of 2907.29 ha of mangrove area has been estimated based on medium-high resolution remote sensing data. This corresponds to an annual loss rate of 1.12%, which is higher than the world mangrove depletion rate. The causes of mangrove loss were identified as land conversion to urban, plantations, and aquaculture activities, where large mangrove areas were shattered into many smaller patches. Fragmentation analysis over the mangrove area shows a reduction in the mean patch size (from 105 ha to 27 ha) and an increase in the number of mangrove patches (130 to 402), edge, and shape complexity, where smaller and isolated mangrove patches were found to be related to the rapid development of IM region. The Moderate Resolution Imaging Spectro-radiometer (MODIS) Leaf Area Index (LAI) and Gross Primary Productivity (GPP) products were used to inspect the impact of fragmentation on the mangrove ecosystem process. The mean LAI and GPP of mangrove areas that had not undergone any land cover changes over the years showed an increase from 3.03 to 3.55 (LAI) and 5.81 g C m−2 to 6.73 g C m−2 (GPP), highlighting the ability of the mangrove forest to assimilate CO2 when it is not disturbed. Similarly, GPP also increased over the gained areas (from 1.88 g C m−2 to 2.78 g C m−2). Meanwhile, areas that lost mangroves, but replaced them with oil palm, had decreased mean LAI from 2.99 to 2.62. In fragmented mangrove patches an increase in GPP was recorded, and this could be due to the smaller patches (<9 ha) and their edge effects where abundance of solar radiation along the edges of the patches may increase productivity. The impact on GPP due to fragmentation is found to rely on the type of land transformation and patch characteristics (size, edge, and shape complexity). The preservation of mangrove forests in a rapidly developing region such as IM is vital to ensure ecosystem, ecology, environment, and biodiversity conservation, in addition to providing economical revenue and supporting human activities.

scholarly journals A representation of the phosphorus cycle for ORCHIDEE (revision 3985)

2017 ◽  
Author(s):  
Daniel S. Goll ◽  
Nicolas Vuichard ◽  
Fabienne Maignan ◽  
Albert Jornet-Puig ◽  
Jordi Sardans ◽  
...  
Keyword(s):  
Primary Productivity ◽  
Land Surface ◽  
Net Primary Productivity ◽  
Nutrient Content ◽  
Soil Development ◽  
Nutrient Concentrations ◽  
Surface Model ◽  
Phosphorus Cycle ◽  
Area Index ◽  
Leaf Level

Abstract. Land surface models rarely incorporate the terrestrial phosphorus cycle and its interactions with the carbon cycle, despite the extensive scientific debate about the importance of nitrogen and phosphorus supply for future land carbon uptake. We describe a representation of the terrestrial phosphorus cycle for the land surface model ORCHIDEE, and evaluate it with data from nutrient manipulation experiments along a soil formation chronosequence in Hawaii. ORCHIDEE accounts for influence of nutritional state of vegetation on tissue nutrient concentrations, photosynthesis, plant growth, biomass allocation, biochemical (phosphatase-mediated) mineralization and biological nitrogen fixation. Changes in nutrient content (quality) of litter affect the carbon use efficiency of decomposition and in return the nutrient availability to vegetation. The model explicitly accounts for root zone depletion of phosphorus as a function of root phosphorus uptake and phosphorus transport from soil to the root surface. The model captures the observed differences in the foliage stoichiometry of vegetation between an early (300yr) and a late stage (4.1 Myr) of soil development. The contrasting sensitivities of net primary productivity to the addition of either nitrogen, phosphorus or both among sites are in general reproduced by the model. As observed, the model simulates a preferential stimulation of leaf level productivity when nitrogen stress is alleviated, while leaf level productivity and leaf area index are stimulated equally when phosphorus stress is alleviated. The nutrient use efficiencies in the model are lower as observed primarily due to biases in the nutrient content and turnover of woody biomass. We conclude that ORCHIDEE is able to reproduce the shift from nitrogen to phosphorus limited net primary productivity along the soil development chronosequence, as well as the contrasting responses of net primary productivity to nutrient addition.

scholarly journals The influence of Carboniferous palaeoatmospheres on plant function: an experimental and modelling assessment

1998 ◽  
Vol 353 (1365) ◽  
pp. 131-140 ◽  
Author(s):  
D. J. Beerling ◽  
F. I. Woodward ◽  
M. R. Lomas ◽  
M. A. Wills ◽  
W. P. Quick ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Soil Carbon ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Global Scale ◽  
Carbon Accumulation ◽  
Late Carboniferous ◽  
Atmospheric Composition ◽  
Area Index ◽  
The Impact

Geochemical models of atmospheric evolution predict that during the late Carboniferous, ca . 300 Ma, atmospheric oxygen and carbon dioxide concentrations were 35% and 0.03%, respectively. Both gases compete with each other for ribulose–1,5–bisphosphate carboxylase/oxygenase–the primary C–fixing enzyme in C 3 land plants: and the absolute concentrations and the ratio of the two in the atmosphere have the potential to strongly influence land–plant function. The Carboniferous therefore represents an era of potentially strong feedback between atmospheric composition and plant function. We assessed some implications of this ratio of atmospheric gases on plant function using experimental and modelling approaches. After six weeks growth at 35% O 2 and 0.03% carbon dioxide, no photosynthetic acclimation was observed in the woody species Betula pubescens and Hedera helix relative to those plants grown at 21% O 2 . Leaf photosynthetic rates were 29% lower in the high O 2 environment compared to the controls. A global–scale analysis of the impact of the late Carboniferous climate and atmospheric composition on vegetation function was determined by driving a process–based vegetation–biogeochemistry model with a Carboniferous global palaeoclimate simulated by the Universities Global Atmospheric Modelling Programme General Circulation Model. Global patterns of net primary productivity, leaf area index and soil carbon concentration for the equilibrium model solutions showed generally low values everywhere, compared with the present day, except for a central band in the northern land mass extension of Gondwana, where high values were predicted. The areas of high soil carbon accumulation closely match the known distribution of late Carboniferous coals. Sensitivity analysis with the model indicated that the increase in O 2 concentration from 21% to 35% reduced global net primary productivity by 18.7% or by 6.3 GtC yr –1 . Further work is required to collate and map at the global scale the distribution of vegetation types, and evidence for wildfires, for the late Carboniferous to test our predictions.

scholarly journals Climate Change Impacts on the Future of Forests in Great Britain

2021 ◽  
Vol 9 ◽  
Author(s):  
Jianjun Yu ◽  
Pam Berry ◽  
Benoit P. Guillod ◽  
Thomas Hickler
Keyword(s):  
Climate Change ◽  
Great Britain ◽  
Tree Species ◽  
Net Primary Productivity ◽  
Climate Model ◽  
Climate Change Impacts ◽  
Physiological Effects ◽  
Area Index ◽  
Projected Change ◽  
The Mean

Forests provide important ecosystem services but are being affected by climate change, not only changes in temperature and precipitation but potentially also directly through the plant-physiological effects of increases in atmospheric CO2. We applied a tree-species-based dynamic model (LPJ-GUESS) at a high 5-km spatial resolution to project climate and CO2 impacts on tree species and thus forests in Great Britain. Climatic inputs consisted of a novel large climate scenario ensemble derived from a regional climate model (RCM) under an RCP 8.5 emission scenario. The climate change impacts were assessed using leaf area index (LAI) and net primary productivity (NPP) for the 2030s and the 2080s compared to baseline (1975–2004). The potential CO2 effects, which are highly uncertain, were examined using a constant CO2 level scenario for comparison. Also, a climate vulnerability index was developed to assess the potential drought impact on modeled tree species. In spite of substantial future reductions in rainfall, the mean projected LAI and NPP generally showed an increase over Britain, with a larger increment in Scotland, northwest England, and west Wales. The CO2 increase led to higher projected LAI and NPP, especially in northern Britain, but with little effect on overall geographical patterns. However, without accounting for plant-physiological effects of elevated CO2, NPP in Southern and Central Britain and easternmost parts of Wales showed a decrease relative to 2011, implying less ecosystem service provisioning, e.g., in terms of timber yields and carbon storage. The projected change of LAI and NPP varied from 5 to 100% of the mean change, due to the uncertainty arising from natural weather-induced variability, with Southeast England being most sensitive to this. It was also the most susceptible to climate change and drought, with reduced suitability for broad-leaved trees such as beech, small-leaved lime, and hornbeam. These could lead to important changes in woodland composition across Great Britain.

scholarly journals Evaluation of models for the dew point temperature determination

2017 ◽  
Vol 3 (20) ◽  
pp. 241-257
Author(s):  
Krzysztof Górnicki ◽  
Radosław Winiczenko ◽  
Agnieszka Kaleta ◽  
Aneta Choińska
Keyword(s):  
Dew Point ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square ◽  
Ann Model ◽  
Chi Square ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Temperature Determination ◽  
The Mean

The accuracy of the available from the literature models for the dew point temperature determination was compared. The proposal of the modelling using artificial neural networks was also given. The experimental data were taken from the psychrometric tables. The accuracies of the models were measured using the mean bias error MBE, root mean square error RMSE, correlation coefficient R, and reduced chi-square χ2. Model M3, especially with constants A=237, B=7.5, gave the best results in determining the dew point temperature (MBE: -0.0229 – 0.0038 K, RMSE: 0.1259 – 0.1286 K, R=0.9999, χ2: 0.0159 – 0.0166 K2). Model M1 with constants A=243.5, B=17.67 and A=243.3, B=17.269 can be also considered as appropriate (MBE=-0.0062 and -0.0078 K, RMSE=0.1277 and 0.1261 K, R=0.9999, χ2=0.0163 and 0.0159 K2). Proposed ANN model gave the good results in determining the dew point temperature (MBE=-0.0038 K, RMSE=0.1373 K, R=0.9999, χ2=0.0189 K2).

scholarly journals Power Prediction and Technoeconomic Analysis of a Solar PV Power Plant by MLP-ABC and COMFAR III, considering Cloudy Weather Conditions

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
M. Khademi ◽  
M. Moadel ◽  
A. Khosravi
Keyword(s):  
Power Plant ◽  
Output Power ◽  
Weather Conditions ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Solar Pv ◽  
Economic Point ◽  
Cloudy Weather ◽  
The Mean

The prediction of power generated by photovoltaic (PV) panels in different climates is of great importance. The aim of this paper is to predict the output power of a 3.2 kW PV power plant using the MLP-ABC (multilayer perceptron-artificial bee colony) algorithm. Experimental data (ambient temperature, solar radiation, and relative humidity) was gathered at five-minute intervals from Tehran University’s PV Power Plant from September 22nd, 2012, to January 14th, 2013. Following data validation, 10665 data sets, equivalent to 35 days, were used in the analysis. The output power was predicted using the MLP-ABC algorithm with the mean absolute percentage error (MAPE), the mean bias error (MBE), and correlation coefficient (R2), of 3.7, 3.1, and 94.7%, respectively. The optimized configuration of the network consisted of two hidden layers. The first layer had four neurons and the second had two neurons. A detailed economic analysis is also presented for sunny and cloudy weather conditions using COMFAR III software. A detailed cost analysis indicated that the total investment’s payback period would be 3.83 years in sunny periods and 4.08 years in cloudy periods. The results showed that the solar PV power plant is feasible from an economic point of view in both cloudy and sunny weather conditions.

Sign in / Sign up

Export Citation Format

Share Document