Hamid Darabi, Ehsan Moradi, Ali Akbar Davudirad, Mohammad Ehteram, Artemi Cerda, Ali Torabi Haghighi, Efficient rainwater harvesting planning using socio-environmental variables and data-driven geospatial techniques, Journal of Cleaner Production, Volume 311, 2021, 127706, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2021.127706
Efficient rainwater harvesting planning using socio-environmental variables and data-driven geospatial techniques
|Author:||Darabi, Hamid1; Moradi, Ehsan2; Davudirad, Ali Akbar3;|
1Water, Energy and Environmental Engineering Research Unit, University of Oulu, P.O. Box 4300, FIN-90014, Oulu, Finland
2Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran
3Agricultural Research, Education & Extension Organization (AREEO), Agricultural and Natural Resources Research and Education Center of Markazi Province, Arak, P. O. Box: 38188-9-3811, Iran
4Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran
5Soil Erosion and Degradation Research Group. Department of Geography, Valencia University, Blasco Ibàñez, 28, 46010, Valencia, Spain
|Online Access:||PDF Full Text (PDF, 0.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021120158195
|Publish Date:|| 2021-12-01
Water scarcity is increasing worldwide due to population growth and climate variability/change.
As a supplementary water resource, Rainwater harvesting (RWH) is a possible solution for dealing with water scarcity, particularly in arid and semi-arid regions with considerable water demand and high variability in precipitation and unexpected extreme events (floods and droughts). The success of RWH systems significantly depends on the location of RWH structures and usually selecting suitable sites is challenging for decision-makers and managers. This paper presents an approach for mapping suitable sites for RWH structures using socio-environmental variables and artificial intelligence algorithms (AIAs). Based on FAO recommendations, the most important conditioning variables for RWH systems are elevation, slope, aspect, precipitation, temperature, distance from the river, curve number (CN), land use, geology, soil type, population density, distance from road, and distance from lakes. An ensemble model was developed based on AIAs, socio-environmental variables, and existing RWH projects, and used for RWH suitability mapping in the large Maharloo-Bakhtegan basin, Iran. Model performance was evaluated using receiver operating characteristic (ROC) and Kappa index. Using the best-performing model, threshold values for conditioning variables were determined from probability curves (PC). The results showed that land use, precipitation, soil type, CN and slope were the most important variables for RHW sites, with the lowest correlation and autocorrelation. The suitability map indicated that 9.7% (3070 km²) of Maharloo-Bakhtegan basin had very high suitability for RWH systems. Thus, in RWH suitability mapping for large area, climate, hydrological, geological, agricultural, topographical, human and socio-economic parameters should be considered to enable efficient RWH planning. Probability curves revealed that the optimum parameter range (α) in Maharloo-Bakhtegan basin was precipitation 357–428 mm, temperature 12.80–15.16 °C, slope 3–6%, elevation 1612–1975 m asl, distance from lake 32–45 km, distance from river 11.4–15.9 km, distance from road 2.59–4.80 km. The RWH suitability map presented can assist decision-makers, hydrologists, and natural resources planners in finding suitable locations for constructing RWH systems.
Journal of cleaner production
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
The authors would like to acknowledge the Iranian Meteorological Organization (IRIMO) and Fars Water Authority for their contributions in providing data and express special thanks to Maa_ja VESITEKNIIKAN TUKI project number 41878 for the financial support of this study.
© 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.