Hydrologic and water-quality impacts of agricultural land use changes incurred from bioenergy policies

Lin, Zhulu; Anar, Mohammad J.; Zheng, Haochi

doi:10.1016/j.jhydrol.2015.04.001

Cited by 28 publications

(22 citation statements)

References 34 publications

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“…With the development of the real estate and housing construction, many farmland, forest, and mountains change to residential or commercial buildings [41]. This impacts the farmland tillage and use of fertilizer and farm manure, which, in turn, affects TC, TBC, arsenic, and nitrate in rural drinking water [42]. The fluoride was mainly affected by secondary industries.…”

Section: Relationship Between the Individual Water Quality Indicator mentioning

confidence: 99%

Effects of Urbanization on Rural Drinking Water Quality in Beijing, China

Zhang¹,

Gao²,

Wei

et al. 2017

Sustainability

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Abstract:Urbanization is an inevitable trend in historical development, but eco-environmental problems, including drinking water safety, have gradually become more and more outstanding during the process of rural urbanization. Ten districts in rural areas of Beijing, China were selected to study the effects of urbanization on drinking water quality. The relation between the urbanization index and drinking water quality indicators were explored. The influence of the urbanization process on drinking water quality showed that housing construction, population urbanization, energy consumption, and industrialization during urban development were closely related to drinking water quality. The paired t-test showed the total electricity consumption, living electricity consumption, tertiary industry, and the GDP growth rate had boundary (p = 0.06) or significantly positive (p < 0.05) relations with the qualified rate of rural drinking water. The grey correlation analysis showed that the growth rates of the value-added of housing construction areas were the most important factor affecting comprehensive water quality of Beijing rural areas, followed by the growth rates of the value-added by secondary industry and total electricity consumption, and then the growth rates of the value-added by the tertiary industry and GDP. Urbanization had a significant impact on individual water quality indicators. The results of this study provided some supports for drinking water security in the face of urbanization.

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Section: Relationship Between the Individual Water Quality Indicator mentioning

confidence: 99%

Effects of Urbanization on Rural Drinking Water Quality in Beijing, China

Zhang¹,

Gao²,

Wei

et al. 2017

Sustainability

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show abstract

“…Managing water quality is challenging and implies to deal with both point and non-point source and ineffective for larger areas. Hydrological and water quality models used in several studies [26][27][28] require vast amounts of data, are complex, costly and time consuming to calibrate, and therefore only applied on one single or few catchments. Statistical methods tend to be simpler, easier to apply, and more efficient than physically-based hydrologic/water quality models when observed data are limited in time and when datasets are covering many different catchments [29].…”

Section: Introductionmentioning

confidence: 99%

Linking Forest Cover to Water Quality: A Multivariate Analysis of Large Monitoring Datasets

Brogna

Michez

Jacobs

et al. 2017

Water

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Forested catchments are generally assumed to provide higher quality water. However, this hypothesis must be validated in various contexts as interactions between multiple land use and land cover (LULC) types, ecological variables and water quality variables render this relationship highly complex. This paper applies a straightforward multivariate approach on a typical large monitoring dataset of a highly managed and densely populated area (Wallonia, Belgium; 10-year dataset), quantifying forest cover effects on nine physico-chemical water quality variables. Results show that forest cover explains about one third of the variability of water quality and is positively correlated with higher quality water. When controlling for spatial autocorrelation, forest cover still explains 9% of water quality. Unlike needle-leaved forest cover, broad-leaved forest cover presents an independent effect from ecological variables and explains independently 4.8% of water quality variability while it shares 5.8% with cropland cover. This study demonstrates clear independent effects of forest cover on water quality, and presents a method to tease out independent LULC effects from typical large multivariate monitoring datasets. Further research on explanatory variables, spatial distribution effects and water quality datasets could lead to effective strategies to mitigate pollution and reach legal targets.

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“…Especially in the currently context in that agriculture and urban growth have been appointed as the primary cause of water quality degradation Lin et al, 2015;.…”

Section: Introductionmentioning

confidence: 99%

“…Land use impacts on water quantity and quality are assessed using hydrological models in urban and agricultural watersheds (Wangpimool et al, 2013;Pereira et al, 2014;Lin et al, 2015;Wagner et al, 2016;Zhao et al, 2016). Lin et al (2015) assessed the hydrologic and water quality impacts of agricultural land changes in North America.…”

Section: Introductionmentioning

confidence: 99%

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Forest cover and water quality in tropical agricultural watersheds

Mello¹

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Hydrologic and water-quality impacts of agricultural land use changes incurred from bioenergy policies

Cited by 28 publications

References 34 publications

Effects of Urbanization on Rural Drinking Water Quality in Beijing, China

Effects of Urbanization on Rural Drinking Water Quality in Beijing, China

Linking Forest Cover to Water Quality: A Multivariate Analysis of Large Monitoring Datasets

Forest cover and water quality in tropical agricultural watersheds

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