High resolution grid of potential incoming solar radiation for Serbia

Solar radiation is a key driving force for many natural processes. At the Earth?s surface solar radiation is the result of complex interactions between the atmosphere and Earth?s surface. Our study highlights the development and evaluation of a data base of potential solar radiation that is based on a digital elevation model (DEM) with a resolution of 90 m over Serbia. The main aim of this paper is to map solar radiation in Serbia using DEM. This is so far the finest resolution being applied and presented using DEM. The final results of the potential direct, diffuse and total solar radiation as well as duration of insolation databases of Serbia are portrayed as thematic maps that can be communicated and shared easily through the cartographic web map-based service.

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Integration of Remotely Sensed Digital Elevation Model Data and Geographic Information System Facilities to Estimate Net Solar Radiation Variances in Iraq (1987-2017)

Iraqi Journal of Science ◽

10.24996/ijs.2021.62.2.32 ◽

2021 ◽

pp. 689-698

Author(s):

Mustafa E. Homadi ◽

Laith A. Jawad

Keyword(s):

Solar Radiation ◽

Model Data ◽

Surface Solar Radiation ◽

Spatially Correlated ◽

Digital Elevation ◽

Soil Temperatures ◽

Standard Rate ◽

Topographical Factors ◽

Elevation Model ◽

Digital Elevation Model Data

The calculation of potential earth's surface solar radiation is imperative for analyzing the atmosphere-vegetation-soil interaction process. Therefore, many schemes were introduced with direct (using net radiometer) or indirect (using air temperature or air plus soil temperatures) formulas. Three combinations of factors are known to control the Rn value; the astronomical based factors which determine the general spatial distribution of Rn values, the climatological factors which determine the assigned spatial variation of those values, and the topographical factors that influence climatological factors rates ( i.e. have indirect effects on Rn values). For Iraq, the ecosystem influences of global warming were obvious in the 1980s and the Rn rates approached peak values .. Thereafter, the general behavior of Rn rates was geographically-based , i.e. increasing rates in the middle and southern regions and descending rates in the northern parts, since it was spatially correlated in a reverse manner with RH values. In the present study, this issue was clarified by utilizing the standard annual mean Rn rate known for Iraq’s weather, which was 9.8MJ.m-2.year-1. The results showed that, in 1987, the area with annual mean Rn equal or higher than this annual standard rate was 305088.098 km2. The area was reduced to 241984.77 km2 in 1997, followed by an expansion to 294491.136 km2 in 2007, and another reduction to 277272.542 km2 in 2017.

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Assessing the Surface Solar Radiation Budget in the WRF Model: A Spatiotemporal Analysis of the Bias and Its Causes

Monthly Weather Review ◽

10.1175/mwr-d-15-0262.1 ◽

2016 ◽

Vol 144 (2) ◽

pp. 703-711 ◽

Cited By ~ 23

Author(s):

José A. Ruiz-Arias ◽

Clara Arbizu-Barrena ◽

Francisco J. Santos-Alamillos ◽

Joaquín Tovar-Pescador ◽

David Pozo-Vázquez

Keyword(s):

Solar Radiation ◽

Wrf Model ◽

Cloud Amount ◽

Principal Component ◽

Spatiotemporal Analysis ◽

Radiation Budget ◽

Weather Research And Forecasting ◽

Total Solar Radiation ◽

Surface Solar Radiation ◽

Solar Radiation Flux

Abstract Solar radiation plays a key role in the atmospheric system but its distribution throughout the atmosphere and at the surface is still very uncertain in atmospheric models, and further assessment is required. In this study, the shortwave downward total solar radiation flux (SWD) predicted by the Weather Research and Forecasting (WRF) Model at the surface is validated over Spain for a 10-yr period based on observations of a network of 52 radiometric stations. In addition to the traditional pointwise validation of modeled data, an original spatially continuous evaluation of the SWD bias is also conducted using a principal component analysis. Overall, WRF overestimates the mean observed SWD by 28.9 W m−2, while the bias of ERA-Interim, which provides initial and boundary conditions to WRF, is only 15.0 W m−2. An important part of the WRF SWD bias seems to be related to a very low cumulus cloud amount in the model and, possibly, a misrepresentation of the radiative impact of this type of cloud.

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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

Canadian Journal of Forest Research ◽

10.1139/x03-213 ◽

2004 ◽

Vol 34 (3) ◽

pp. 519-530 ◽

Cited By ~ 19

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.

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A Scale‐Aware Parameterization for Estimating Subgrid Variability of Downward Solar Radiation Using High‐Resolution Digital Elevation Model Data

Journal of Geophysical Research Atmospheres ◽

10.1029/2019jd031563 ◽

2019 ◽

Vol 124 (24) ◽

pp. 13680-13692

Author(s):

Siwei He ◽

Tatiana G. Smirnova ◽

Stanley G. Benjamin

Keyword(s):

High Resolution ◽

Solar Radiation ◽

Digital Elevation Model ◽

Model Data ◽

Digital Elevation ◽

Elevation Model ◽

Digital Elevation Model Data

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Deep learning-based approach for landform classification from integrated data sources of digital elevation model and imagery

10.5194/egusphere-egu2020-22334 ◽

2020 ◽

Author(s):

Sijin Li ◽

Liyang Xiong ◽

Guoan Tang ◽

Josef Strobl

Keyword(s):

Deep Learning ◽

Loess Plateau ◽

Data Sources ◽

The Loess Plateau ◽

Transitional Area ◽

Landform Classification ◽

The Earth ◽

Digital Elevation ◽

Elevation Model ◽

External Forces

Landform classification is one of the most important aspects in geomorphological research, dividing the Earth&#8217;s surface into diverse geomorphological types. Thus, an accurate classification of landforms is a key procedure in describing the topographic characteristics of a given area and understanding their inner geomorphological formation processes. However, landform types are not always independent of one another due to the complexity and dynamics of interior and external forces. Furthermore, transitional landforms with gradually changing surface morphologies are widely distributed on the Earth&#8217;s surface. With this situation, classifying these complex and transitional landforms with traditional landform classification methods is hard. In this study, a deep learning (DL) algorithm was introduced, aiming at automatically classifying complex and transitional landforms. This algorithm was trained to learn and extract landform features from integrated data sources. These integrated data sources contain different combinations of imagery, digital elevation models (DEMs), and terrain derivatives. The Loess Plateau in China, which contains complex and transitional loess landforms, was selected as the study area for data training. In addition, two sample areas in the Loess Plateau with complex and transitional loess hill and ridge landforms were used to validate the classified landform types by using the proposed DL method. Meanwhile, a comparative analysis between the proposed DL and random forest (RF) methods was also conducted to investigate their capabilities in landform classification. The proposed DL approach can achieve the highest landform classification accuracy of 87% in the transitional area with data combination of DEMs and images. In addition, the proposed DL method can achieve a higher accuracy of landform classification with better defined landform boundaries compared to the RF method. The classified loess landforms indicate the different landform development stages in this area. Finally, the proposed DL method can be extended to other landform areas for classifying their complex and transitional landforms.

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Impacts of Dem Uncertainty on Estimated Surface Solar Radiation and Extracted River Network

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-13-00285.1 ◽

2015 ◽

Vol 96 (2) ◽

pp. 297-304 ◽

Cited By ~ 6

Author(s):

Laigang Wang ◽

Kaicun Wang

Keyword(s):

Solar Radiation ◽

Pearl River ◽

River Networks ◽

Pearl River Basin ◽

Surface Solar Radiation ◽

Digital Elevation ◽

Absolute Height ◽

The Pearl River ◽

Dem Uncertainty ◽

The Pearl River Basin

Abstract Digital elevation models (DEMs) have important meteorological, hydrological, and climatological applications. This research studies the uncertainties of six widely accepted global DEM datasets over China and their derivative parameters, including slope and aspect, in calculating the surface-received solar radiation and extracting the river networks. The authors’ results indicate that, although the absolute height values of the six DEM data are nearly identical, substantial and significant differences are introduced when estimating the surface-received solar radiation. The extracted drainage streamflows of the Pearl River basin in South China are close to the actual river networks in general but are quite different in some details that cannot be ignored. Results herein highlight that the uncertainties of DEM themselves as well as their derived parameters must be considered in analogous study.

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Solar Radiation Estimation Using Data Mining Techniques for Remote Areas—A Case Study in Ethiopia

Energies ◽

10.3390/en13215714 ◽

2020 ◽

Vol 13 (21) ◽

pp. 5714

Author(s):

Bizuayehu Abebe Worke ◽

Hans Bludszuweit ◽

José A. Domínguez-Navarro

Keyword(s):

Data Mining ◽

Solar Radiation ◽

Digital Elevation Model ◽

Interpolation Method ◽

Inference System ◽

Remote Areas ◽

Radiation Data ◽

Digital Elevation ◽

Elevation Model

High quality of solar radiation data is essential for solar resource assessment. For remote areas this is a challenge, as often only satellite data with low spatial resolution are available. This paper presents an interpolation method based on topographic data in digital elevation model format to improve the resolution of solar radiation maps. The refinement is performed with a data mining method based on first-order Sugeno type Adaptive Neuro-Fuzzy Inference System. The training set contains topographic characteristics such as terrain aspect, slope and elevation which may influence the solar radiation distribution. An efficient sampling method is proposed to obtain representative training sets from digital elevation model data. The proposed geographic information system based approach makes this method reproducible and adaptable for any region. A case study is presented on the remote Amhara region in North Shewa, Ethiopia. Results are shown for interpolation of solar radiation data from 10 km × 10 km to a resolution of 1 km × 1 km and are validated with data from the PVGIS and SWERA projects.

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Mapping monthly precipitation, temperature, and solar radiation for Ireland with polynomial regression and a digital elevation model

Climate Research ◽

10.3354/cr010035 ◽

1998 ◽

Vol 10 ◽

pp. 35-49 ◽

Cited By ~ 103

Author(s):

CL Goodale ◽

JD Aber ◽

SV Ollinger

Keyword(s):

Solar Radiation ◽

Digital Elevation Model ◽

Polynomial Regression ◽

Monthly Precipitation ◽

Precipitation Temperature ◽

Digital Elevation ◽

Elevation Model

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Agroclimatic atlas - prototype

10.5194/ems2021-475 ◽

2021 ◽

Author(s):

Karel Jedlička ◽

Pavel Hájek ◽

Tomáš Andrš ◽

Otakar Čerba ◽

Jiří Valeš ◽

...

Keyword(s):

Solar Radiation ◽

Climatic Factors ◽

Total Solar Radiation ◽

Climatic Data ◽

Comprehensive Overview ◽

Design Concepts ◽

Web Map ◽

Area Of Interest ◽

Precipitation Total ◽

Frost Free Period

Our contribution presents a prototype of Agroclimatic atlas - a web map application, presenting agroclimatic factors: Frost-free period, Water balance, Total precipitation, Total solar radiation, Last date with soil temperature above 10 &#176;C for nitrogen application, Number of days with growing temperatures for a crop, Number of days with optimal growing temperatures for a crop HSU - Heat stress units for a crop, The factors are calculated based on algorithms described in&#160;Calculation of Agro-Climatic Factors from Global Climatic Data&#160;(Jedli&#269;ka et al. 2021, doi:&#160;&#160;10.3390/app11031245).The agroclimatic atlas application aims to provide a comprehensive overview of agriculture-related climatic characteristics of an area of interest in a time retrospective.&#160; The application can be used by both an individual farmer or a precision farming expert exploring a wider area.The principal source of climatic variables (such as temperature, rainfall, evaporation, runoff, and solar radiation) used in the atlas is the&#160;ERA5-Land dataset&#160;(available as the&#160;Copernicus Climate Change Service (C3S) at its Climate Date Store).&#160;The contemporary version of the Agroclimatic Atlas application is accessible from here https://www.mdpi.com/2076-3417/11/3/1245#. This version is in Czech only and portrays data from Czechia 10 years backward. However, the application is under ongoing development driven by the H2020 projects&#160;Stargate,&#160;Sieusoil, and&#160;Smartagrihubs. Therefore a newer version will be presented at the conference. The first design concepts can be seen in the figure below.<img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.475eafd0808065334309161/sdaolpUECMynit/1202SME&app=m&a=0&c=31dbfa2ddfd3719b82491d259ccc4117&ct=x&pn=gnp.elif&d=1" alt="">Figure 1. - Mockup of Agroclimatic atlas application, accessible from https://xd.adobe.com/view/65199b72-db2f-420a-aee2-bc90dc83aaea-304a/

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Estimating spatial and temporal variations in solar radiation within Bordeaux winegrowing region using remotely sensed data

OENO One ◽

10.20870/oeno-one.2008.42.1.829 ◽

2008 ◽

Vol 42 (1) ◽

pp. 15 ◽

Cited By ~ 1

Author(s):

Benjamin Bois ◽

Lucien Wald ◽

Philippe Pieri ◽

Cornelis Van Leeuwen ◽

Loïc Commagnac ◽

...

Keyword(s):

Solar Radiation ◽

Temporal Variations ◽

Spatial And Temporal Variations ◽

Remotely Sensed Data ◽

High Solar Radiation ◽

Daily Data ◽

Digital Elevation ◽

Terrain Effect ◽

Resolution Mapping ◽

Elevation Model

Aims: This paper presents a study of spatial and temporal variations in solar radiation for the Bordeaux winegrowing region, over a 20 year period (1986-2005).Methods and results: Solar radiation data was retrieved from the HelioClim-1 database, elaborated from Meteosat satellite images, using the Heliosat-2 algorithm. Daily data was interpolated using ordinary kriging to produce horizontal solar radiation maps at a 500 m resolution. Then using a digital elevation model, 50 m resolution daily solar radiation maps with terrain integration were produced for the period 2001-2005. The long term (20 year) analysis of solar radiation at low spatial resolution (500 m) showed a west to east decreasing gradient within the Bordeaux winegrowing region. Mean August-to-September daily irradiation values, on horizontal surface, were used to classify the Bordeaux winegrowing region into three zones: low, medium, and high solar radiation areas. This initial zoning was downscaled to 50 m resolution, applying a local correction ratio, based on 2001-2005 solar radiation from the inclined surface analysis. Grapevine development and maturation potential of the different zones of appellation of origin of Bordeaux winegrowing regions are discussed in relation with this zoning.Conclusion: Solar radiation variability within the Bordeaux winegrowing region is mainly governed by terrain slopes and orientations, which induce considerable variations within the eastern part of Bordeaux vineyards. Significance and impact of study: Solar radiation has a major impact on vineyard water balance, grapevine development and berry ripening. However, irradiation data is seldom available in weather stations records. This paper highlights the need for high resolution mapping of solar radiation that uses remote sensing and terrain effect integration for agroclimatic studies in viticulture.

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