Arid
DOI10.1016/j.jhydrol.2012.04.004
Rooftop dew, fog and rain collection in southwest Morocco and predictive dew modeling using neural networks
Lekouch, Imad1,2,3; Lekouch, Khalid1; Muselli, Marc3,4; Mongruel, Anne2; Kabbachi, Belkacem1; Beysens, Daniel2,3,5
通讯作者Beysens, Daniel
来源期刊JOURNAL OF HYDROLOGY
ISSN0022-1694
EISSN1879-2707
出版年2012
卷号448页码:60-72
英文摘要

Two coastal sites were investigated in an arid region of southwest Morocco to determine the amount of dew, fog and rain that could be collected from rooftops for household use. Systematic measurements were performed in Mirleft (43 m asl, 200 m from the coast) for 1 year (May 1, 2007 to April 30, 2008) and in Id Ouasskssou (240 m asl, 8 km from the coast) for three summer months July 1, 2007 to September 30, 2007). Dew water was collected using standard passive dew condensers and fog water by utilizing planar fog collectors. The wind flow was simulated on the rooftop to establish the location of the fog collector. At both sites, dew yields and, to a lesser extent, fog water yields, were found to be significant in comparison to rain events. Mirleft had 178 dew events (48.6% of the year, 18 +/- 2 L m(-2) cumulated amount) and 20 fog episodes (5.5% of the year, 1.4 L m(-2) with uncertainty -0.2/+0.4 L m(-2) cumulated amount), corresponding to almost 40% of the yearly rain contribution (31 rain events, 8.5% of the year, 49 +/- 7 mm cumulated amount). At Id Ouasskssou there were 50 dew events (7.1 +/- 0.3 L m(-2), 54.3% frequency), 16 fog events (6.5 L m(-2) with uncertainty -0.1/+1.8 L m(-2), 17.4% frequency) and six rain events (16 +/- 2 mm, 6.5% frequency).


Meteorological data (air and dew point temperature and/or relative humidity, wind speed and wind direction, cloud cover) were recorded continuously at Mirleft to assess the influence of local meteorological conditions on dew and fog formation. Using the set of collected data, a new model for dew yield prediction based on artificial neural networks was developed and tested for the Mirleft site. This model was then extrapolated to 15 major cities in Morocco to assess their potential for dew water collection. It was found that the location of the cities with respect to the Atlas mountain chain, which controls the circulation of the humid marine air, is the main factor that influences dew production. (C) 2012 Elsevier B.V. All rights reserved.


英文关键词Dew formation Artificial neural network Water production Radiative condenser Hydrometeorology
类型Article
语种英语
国家Morocco ; France
收录类别SCI-E
WOS记录号WOS:000306045500005
WOS关键词WATER COLLECTION ; SOLAR-RADIATION ; ISLANDS ; COASTAL ; VAPOR ; INDIA
WOS类目Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向Engineering ; Geology ; Water Resources
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/173678
作者单位1.Univ Ibn Zohr Agadir, Agadir 80000, Morocco;
2.Univ Paris 07, Univ Paris 06, ESPCI, UMR CNRS 7636, F-75231 Paris, France;
3.Int Org Dew Utilizat, F-33600 Pessac, France;
4.Univ Corse, UMR CNRS 6134, F-20000 Ajaccio, France;
5.CEA, Serv Basses Temp, Equipe Supercrit Environm Mat & Espace, F-38504 Grenoble, France
推荐引用方式
GB/T 7714
Lekouch, Imad,Lekouch, Khalid,Muselli, Marc,et al. Rooftop dew, fog and rain collection in southwest Morocco and predictive dew modeling using neural networks[J],2012,448:60-72.
APA Lekouch, Imad,Lekouch, Khalid,Muselli, Marc,Mongruel, Anne,Kabbachi, Belkacem,&Beysens, Daniel.(2012).Rooftop dew, fog and rain collection in southwest Morocco and predictive dew modeling using neural networks.JOURNAL OF HYDROLOGY,448,60-72.
MLA Lekouch, Imad,et al."Rooftop dew, fog and rain collection in southwest Morocco and predictive dew modeling using neural networks".JOURNAL OF HYDROLOGY 448(2012):60-72.
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