The relative importance of solar radiation and soil origin in cactus seedling survivorship at two spatial scales: plant association and microhabitat

2013 ◽  
Vol 25 (3) ◽  
pp. 668-680 ◽  
Author(s):  
Juan H. García-Chávez ◽  
Carlos Montaña ◽  
Yareni Perroni ◽  
Vinicio J. Sosa ◽  
José B. García-Licona
Keyword(s):  
Solar Radiation ◽  
Spatial Scales ◽  
Relative Importance ◽  
Plant Association ◽  
Seedling Survivorship ◽  
Soil Origin

scholarly journals Shortwave Spectral Radiative Signatures and Their Physical Controls

2019 ◽  
Vol 32 (15) ◽  
pp. 4805-4828 ◽  
Author(s):  
Jake J. Gristey ◽  
J. Christine Chiu ◽  
Robert J. Gurney ◽  
Keith P. Shine ◽  
Stephan Havemann ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Physical Properties ◽  
Spectral Reflectance ◽  
Spatial Scales ◽  
Monsoon Season ◽  
Spectral Characteristics ◽  
Great Promise ◽  
Earth System ◽  
Spectral Signatures ◽  
Cloud Regimes

AbstractThe spectrum of reflected solar radiation emerging at the top of the atmosphere is rich with Earth system information. To identify spectral signatures in the reflected solar radiation and directly relate them to the underlying physical properties controlling their structure, over 90 000 solar reflectance spectra are computed over West Africa in 2010 using a fast radiation code employing the spectral characteristics of the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY). Cluster analysis applied to the computed spectra reveals spectral signatures related to distinct surface properties, and cloud regimes distinguished by their spectral shortwave cloud radiative effect (SWCRE). The cloud regimes exhibit a diverse variety of mean broadband SWCREs, and offer an alternative approach to define cloud type for SWCRE applications that does not require any prior assumptions. The direct link between spectral signatures and distinct physical properties extracted from clustering remains robust between spatial scales of 1, 20, and 240 km, and presents an excellent opportunity to understand the underlying properties controlling real spectral reflectance observations. Observed SCIAMACHY spectra are assigned to the calculated spectral clusters, showing that cloud regimes are most frequent during the active West African monsoon season of June–October in 2010, and all cloud regimes have a higher frequency of occurrence during the active monsoon season of 2003 compared with the inactive monsoon season of 2004. Overall, the distinct underlying physical properties controlling spectral signatures show great promise for monitoring evolution of the Earth system directly from solar spectral reflectance observations.

scholarly journals Evaluation of Multi-Reanalysis Solar Radiation Products Using Global Surface Observations

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 42 ◽  
Author(s):  
Xiaomin Peng ◽  
Jiangfeng She ◽  
Shuhua Zhang ◽  
Junzhong Tan ◽  
Yang Li
Keyword(s):  
Solar Radiation ◽  
Large Scale ◽  
Spatial Scales ◽  
Global Solar Radiation ◽  
Hydrological Process ◽  
China Meteorological Administration ◽  
Surface Solar Radiation ◽  
Climate Zones ◽  
Tropical Regions ◽  
Spatial Coverage

Solar radiation incident at the Earth’s surface is an essential driver of the energy exchange between the atmosphere and the surface and is also an important input variable in the research on the surface eco-hydrological process. The reanalysis solar radiation dataset is characterized by a long time series and wide spatial coverage and is used in the research of large-scale eco-hydrological processes. Due to certain errors in their production process of the reanalysis of solar radiation products, reanalysis products should be evaluated before application. In this study, three global solar-radiation reanalysis products (ERA-Interim; JRA-55; and NCEP-DOE) in different temporal scales and climate zones were evaluated using surface solar-radiation observations from the National Meteorological Information Center of the China Meteorological Administration (CMA, Beijing, China) and the Global Energy Balance Archive (GEBA, Zürich, Switzerland) from 2000 to 2009. All reanalysis products (ERA-Interim; JRA-55; and NCEP-DOE) overestimated with an annual bias of 14.86 W/m2, 22.61 W/m2, and 31.85 W/m2; monthly bias of 15.17 W/m2, 21.29 W/m2, and 36.91 W/m2; and seasonal bias of 15.08 W/m2, 21.21 W/m2, and 36.69 W/m2, respectively. In different Köppen climate zones, the annual solar radiation of ERA-Interim performed best in cold regions with a bias of 10.30 W/m2 and absolute relative error (ARE) of 8.98%. However, JRA-55 and NCEP-DOE showed the best performance in tropical regions with a bias of 20.08 W/m2 and −0.12 W/m2, and ARE of 11.00% and 9.68%, respectively. Overall, through the evaluations across different temporal and spatial scales, the rank of the three reanalysis products in order was the ERA-Interim, JRA-55, and NCEP-DOE. In addition, based on the evaluation, we analyzed the relationship between the error (ARE) of the reanalysis products and cloud cover, aerosol, and water vapor, which significantly influences solar radiation and we found that cloud was the main cause for errors in the three solar radiation reanalysis products. The above can provide a reference for the application and downscaling of the three solar radiation reanalysis products.

The effects of spatial aggregation of complex topography on hydroecological process simulations within a rugged forest landscape: development and application of a satellite-based topoclimatic model

2004 ◽  
Vol 34 (3) ◽  
pp. 519-530 ◽  
Author(s):  
S Kang ◽  
D Lee ◽  
J S Kimball
Keyword(s):  
Solar Radiation ◽  
Scale Effects ◽  
Net Primary Production ◽  
Spatial Scales ◽  
Spatial Aggregation ◽  
Area Index ◽  
Topographic Complexity ◽  
Digital Elevation ◽  
Process Simulations ◽  
Elevation Model

We evaluated the effects of topographic complexity on landscape carbon and hydrologic process simulations within a rugged mixed hardwood forest by developing and applying a satellite-based hydroecological model at multiple spatial scales. The effects of topographic variability were evaluated by aggregating raster-based digital elevation model and satellite-derived leaf area index inputs across eight different spatial resolutions from 30 m (62 208 pixels) to 2160 m (12 pixels). Our modeling analysis showed that the effect of topography was the strongest on solar radiation and temperature, intermediate on soil water and evapotranspiration, and ambiguous on soil respiration. Spatial aggregation of model inputs smoothed heterogeneous spatial patterns of modeled output variables relative to fine-scale results. Model outputs varied nonlinearly with different levels of spatial aggregation, while spatial variability of model inputs and outputs were dampened at increasingly coarse aggregation levels. Biases in spatially aggregated model predictions were generally less than ±10%, except for solar radiation, which showed biases of up to +50% at coarser spatial scales. The large positive bias in the solar radiation implies that overestimation of biophysical variables that are sensitive to solar radiation (e.g., photosynthesis and net primary production) may be considerable in rugged forested landscapes unless subgrid scale effects are accounted for.

scholarly journals Relative importance of habitat characteristics at multiple spatial scales for wood-dependent beetles in boreal forest

2015 ◽  
Vol 30 (10) ◽  
pp. 1931-1942 ◽  
Author(s):  
Thomas Ranius ◽  
Victor Johansson ◽  
Martin Schroeder ◽  
Alexandro Caruso
Keyword(s):  
Boreal Forest ◽  
Spatial Scales ◽  
Habitat Characteristics ◽  
Relative Importance ◽  
Multiple Spatial Scales

scholarly journals Testing the relationship between the solar radiation dose and surface DMS concentrations using in situ data

2009 ◽  
Vol 6 (9) ◽  
pp. 1927-1934 ◽  
Author(s):  
C. J. Miles ◽  
T. G. Bell ◽  
T. M. Lenton
Keyword(s):  
Radiation Dose ◽  
Solar Radiation ◽  
Spatial Scales ◽  
Mixed Layer Depth ◽  
Light Attenuation ◽  
Climate Feedback ◽  
Solar Ultraviolet Radiation ◽  
In Situ Data ◽  
Linear Relationships

Abstract. The proposed strong positive relationship between dimethylsulphide (DMS) concentration and the solar radiation dose (SRD) received into the surface ocean is tested using data from the Atlantic Meridional Transect (AMT) programme. In situ, daily data sampled concurrently with DMS concentrations is used for the component variables of the SRD (mixed layer depth, MLD, surface insolation, I0, and a light attenuation coefficient, k) to calculate SRDinsitu. This is the first time in situ data for all of the components, including k, has been used to test the SRD-DMS relationship over large spatial scales. We find a significant correlation (ρ=0.55 n=65 p<0.01) but the slope of this relationship (0.006 nM/W m−2) is less than previously found at the global (0.019 nM/W m−2) and regional scales (Blanes Bay, Mediterranean, 0.028 nM/W m−2; Sargasso Sea 0.017 nM/W m−2). The correlation is improved (ρ=0.74 n=65 p<0.01) by replacing the in situ data with an estimated I0 (which assumes a constant 50% removal of the top of atmosphere value; 0.5×TOA), a MLD climatology and a fixed value for k following previous work. Equally strong, but non-linear relationships are also found between DMS and both in situ MLD (ρ=0.61 n=65 p<0.01) and the estimated I0 (ρ=0.73 n=65 p<0.01) alone. Using a satellite-retrieved, cloud-adjusted surface UVA irradiance to calculate a UV radiation dose (UVRD) with a climatological MLD also provides an equivalent correlation (ρ=0.67 n=54 p<0.01) to DMS. With this data, MLD appears the dominant control upon DMS concentrations and remains a useful shorthand to prediction without fully resolving the biological processes involved. However, the implied relationship between the incident solar/ultraviolet radiation (modulated by MLD), and sea surface DMS concentrations, is critical for closing a climate feedback loop.

Relative importance of environmental variables for the distribution of the invasive marsh species Spartina alterniflora across different spatial scales

2018 ◽  
Vol 69 (5) ◽  
pp. 790 ◽  
Author(s):  
Huiyu Liu ◽  
Haibo Gong ◽  
Xiangzhen Qi ◽  
Yufeng Li ◽  
Zhenshan Lin
Keyword(s):  
Organic Carbon ◽  
Spartina Alterniflora ◽  
Environmental Variables ◽  
Spatial Scales ◽  
Regional Scale ◽  
Species Distribution Modelling ◽  
Relative Importance ◽  
Soil Drainage ◽  
Variable Type ◽  
Chinese Coast

The relative importance of environmental variables for Spartina alterniflora distribution was investigated across different spatial scales using maximum entropy modelling (MaxEnt), a species distribution modelling technique. The results showed that elevation was the most important predictor for species presence at each scale. Mean diurnal temperature range and isothermality were the second most important predictors at national and regional scales respectively. Soil drainage class, pH and organic carbon were important on the northern Chinese coast. The importance of climatic variable type was highest at global and national scales and declined as the scale decreased. The importance of soil variable type was lower at coarser scales, but varied greatly at finer scales. The relationships between environmental variables and species presence changed as the variables’ ranges changed across different scales. Climatic and soil variables were substantially affected by interactions among variables, which changed their relationships with species presence and relative importance. The modelled suitable area on the Chinese coast decreased from 54.16 to 12.64% limited by elevation from the global to national scale, and decreased to 8.04% limited by soil drainage, pH and organic carbon from the national to regional scale. The findings of the present study emphasise the importance of spatial scale for understanding relationships between environmental variables and the presence of S. alterniflora.

scholarly journals The contribution of environmental and dispersal filters on phylogenetic and taxonomic beta diversity patterns in Amazonian tree communities

Oecologia ◽  
2021 ◽  
Author(s):  
Juan Ernesto Guevara Andino ◽  
Nigel C. A. Pitman ◽  
Hans ter Steege ◽  
Manuel Peralvo ◽  
Carlos Cerón ◽  
...  
Keyword(s):  
Beta Diversity ◽  
Spatial Scales ◽  
Dispersal Limitation ◽  
Comprehensive Analysis ◽  
Environmental Drivers ◽  
Relative Importance ◽  
Spatial Variables ◽  
Ecuadorian Amazon ◽  
Tree Communities

AbstractEnvironmental and dispersal filters are key determinants of species distributions of Amazonian tree communities. However, a comprehensive analysis of the role of environmental and dispersal filters is needed to understand the ecological and evolutionary processes that drive phylogenetic and taxonomic turnover of Amazonian tree communities. We compare measures of taxonomic and phylogenetic beta diversity in 41 one-hectare plots to test the relative importance of climate, soils, geology, geomorphology, pure spatial variables and the spatial variation of environmental drivers of phylogenetic and taxonomic turnover in Ecuadorian Amazon tree communities. We found low phylogenetic and high taxonomic turnover with respect to environmental and dispersal filters. In addition, our results suggest that climate is a significantly better predictor of phylogenetic turnover and taxonomic turnover than geomorphology and soils at all spatial scales. The influence of climate as a predictor of phylogenetic turnover was stronger at broader spatial scales (50 km2) whereas geomorphology and soils appear to be better predictors of taxonomic turnover at mid (5 km2) and fine spatial scales (0.5 km2) but a weak predictor of phylogenetic turnover at broad spatial scales. We also found that the combined effect of geomorphology and soils was significantly higher for taxonomic turnover at all spatial scales but not for phylogenetic turnover at large spatial scales. Geographic distances as proxy of dispersal limitation was a better predictor of phylogenetic turnover at distances of 50 < 500 km. Our findings suggest that climatic variation at regional scales can better predict phylogenetic and taxonomic turnover than geomorphology and soils.

scholarly journals Evaluating solar radiation forecast uncertainty

2018 ◽  
Author(s):  
Minttu Tuononen ◽  
Ewan J. O'Connor ◽  
Victoria A. Sinclair
Keyword(s):  
Solar Radiation ◽  
Cloud Cover ◽  
Spatial Scales ◽  
Forecast Error ◽  
Surface Radiation ◽  
Cloud Base ◽  
Strong Impact ◽  
Positive Bias ◽  
Radiative Balance ◽  
Clear Sky

Abstract. The presence of clouds, and their characteristics, has a strong impact on the radiative balance of the Earth and on the amount of solar radiation reaching the Earth's surface. Many applications require accurate forecasts of surface radiation on weather timescales, for example, solar energy and UV radiation forecasts. Here we investigate how operational forecasts of low and mid-level clouds affect the accuracy of solar radiation forecasts. Four years of cloud and solar radiation observations from one site – Helsinki, Finland, are analysed. Cloud observations are obtained from a ceilometer and therefore, we first develop algorithms to reliably detect cloud base, precipitation and fog. These new algorithms are widely applicable for both operational use and research, such as in-cloud icing detection for the wind energy industry and for aviation. The cloud and radiation observations are compared to forecasts from the Integrated Forecast System (IFS) run operationally and developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). We develop methods to evaluate the skill of the cloud and radiation forecasts. These methods can potentially be extended to hundreds of sites globally. Over Helsinki, the measured Global Horizontal Irradiance (GHI) is strongly influenced by its northerly location and the annual variation in cloudiness. Solar radiation forecast error is therefore larger in summer than in winter, but the relative error in the solar radiation forecast is more or less constant throughout the year. The mean overall bias in the GHI forecast is positive (8 W m−2). The observed and forecast distributions in cloud cover, at the spatial scales we are considering, are strongly skewed towards clear-sky and overcast situations. Cloud cover forecasts show more skill in winter when the cloud cover is predominantly overcast; in summer there are more clear-sky and broken cloud situations. A negative bias was found in forecast GHI for correctly forecast clear-sky cases and a positive bias in correctly forecast overcast cases. Temporal averaging improved the cloud cover forecast and hence decreased the solar radiation forecast error, but made little impact on the overall bias. The positive bias seen in overcast situations occurs when the model cloud has low values of liquid water path (LWP). We attribute this bias to the model having LWP values that are too low or that the model optical properties for clouds with low LWP are incorrect.

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