A TECHNIQUE TO PREDICT HOURLY POTENTIAL SOLAR-RADIATION AND TEMPERATURE FOR A MOSTLY UNMONITORED AREA IN THE CAPE-BRETON HIGHLANDS

Citation
Cpa. Bourque et Jj. Gullison, A TECHNIQUE TO PREDICT HOURLY POTENTIAL SOLAR-RADIATION AND TEMPERATURE FOR A MOSTLY UNMONITORED AREA IN THE CAPE-BRETON HIGHLANDS, Canadian Journal of Soil Science, 78(3), 1998, pp. 409-420
Citations number
27
Categorie Soggetti
Agriculture Soil Science
ISSN journal
00084271
Volume
78
Issue
3
Year of publication
1998
Pages
409 - 420
Database
ISI
SICI code
0008-4271(1998)78:3<409:ATTPHP>2.0.ZU;2-D
Abstract
A technique was developed to obtain predictions of potential solar rad iation and temperature for a prescribed, mostly unmonitored, area in t he Cape Breton Highlands region of northeastern Nova Scotia (46 degree s 39'N 60 degrees 57'W to 46 degrees 40'N 60 degrees 24'W). Hourly pre dictions of incoming solar radiation are based on relations of sun-ear th geometry, clear-sky atmospheric transmittance, and land-surface att ributes resolved from digital terrain and vegetation models. The digit al vegetation model characterizes vegetation cover and is used to defi ne the average midday albedoes for the area in question. Hourly albedo es are calculated according to assigned mid-day albedo and sun-illumin ation angles. Land-surface characteristics (elevation, slope, aspect, horizon angles, terrain configuration factor, and view factor) affect total incident solar radiation by affecting the direct, diffused, and reflected energy components. Hourly spatial variability in aboveground daytime temperature is captured by way of a fully trained artificial neural network (ANN) that describes hourly fluctuations of interior hi ghland temperatures according to i) reference temperatures taken at tw o lowland locations, one at Ingonish Beach and the other at Grande Ans e; ii) distance from a north-south line representing the east coast of the study area and from the Grande Anse location; iii) time of day; a nd iv) land-surface attributes. Training the ANN involves supplying th e network with actual data and having the network adjust its internal weights iteratively so that the output values are sufficiently close t o the supplied target values. Comparison of predicted and observed hou rly spring-summer (1997) temperatures revealed that the constructed AN N explained over 88% of the variability exhibited in the observed temp eratures and that the standard error of estimate was 2.0 degrees C (me an absolute error = 1.5 degrees C).