Quantitative evaluation of spatial scale effects on regional water footprint in crop production

2021 ◽  
Vol 173 ◽  
pp. 105709
Author(s):  
Ying Mao ◽  
Yilin Liu ◽  
La Zhuo ◽  
Wei Wang ◽  
Meng Li ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Quantitative Evaluation ◽  
Crop Production ◽  
Water Footprint ◽  
Scale Effects ◽  
Regional Water

scholarly journals Agro-economic and socio-environmental assessments of food and virtual water trades of Iran

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fatemeh Karandish ◽  
Hamideh Nouri ◽  
Marcela Brugnach
Keyword(s):  
Food Security ◽  
Water Consumption ◽  
Crop Production ◽  
Water Footprint ◽  
Virtual Water ◽  
Environmental Indicators ◽  
Blue Water ◽  
Water Crisis ◽  
Food Trade ◽  
Water Limitations

AbstractEnding hunger and ensuring food security are among targets of 2030’s SDGs. While food trade and the embedded (virtual) water (VW) may improve food availability and accessibility for more people all year round, the sustainability and efficiency of food and VW trade needs to be revisited. In this research, we assess the sustainability and efficiency of food and VW trades under two food security scenarios for Iran, a country suffering from an escalating water crisis. These scenarios are (1) Individual Crop Food Security (ICFS), which restricts calorie fulfillment from individual crops and (2) Crop Category Food Security (CCFS), which promotes “eating local” by suggesting food substitution within the crop category. To this end, we simulate the water footprint and VW trades of 27 major crops, within 8 crop categories, in 30 provinces of Iran (2005–2015). We investigate the impacts of these two scenarios on (a) provincial food security (FSp) and exports; (b) sustainable and efficient blue water consumption, and (c) blue VW export. We then test the correlation between agro-economic and socio-environmental indicators and provincial food security. Our results show that most provinces were threatened by unsustainable and inefficient blue water consumption for crop production, particularly in the summertime. This water mismanagement results in 14.41 and 8.45 billion m3 y−1 unsustainable and inefficient blue VW exports under ICFS. “Eating local” improves the FSp value by up to 210% which lessens the unsustainable and inefficient blue VW export from hotspots. As illustrated in the graphical abstract, the FSp value strongly correlates with different agro-economic and socio-environmental indicators, but in different ways. Our findings promote “eating local” besides improving agro-economic and socio-environmental conditions to take transformative steps toward eradicating food insecurity not only in Iran but also in other countries facing water limitations.

Water resources efficiency assessment in crop production from the perspective of water footprint

2021 ◽  
pp. 127371
Author(s):  
Xinchun Cao ◽  
Wen Zeng ◽  
Mengyang Wu ◽  
Tingyu Li ◽  
Sheng Chen ◽  
...  
Keyword(s):  
Water Resources ◽  
Crop Production ◽  
Water Footprint ◽  
Efficiency Assessment

Spatial scale effects of water erosion dynamics: Complexities, variabilities, and uncertainties

2012 ◽  
Vol 22 (2) ◽  
pp. 127-143 ◽  
Author(s):  
Wei Wei ◽  
Liding Chen ◽  
Lei Yang ◽  
Bojie Fu ◽  
Ranhao Sun
Keyword(s):  
Spatial Scale ◽  
Scale Effects ◽  
Water Erosion

Spatial scale effects on model parameter estimation and predictive uncertainty in ungauged basins

2012 ◽  
Vol 44 (3) ◽  
pp. 441-453 ◽  
Author(s):  
Denis A. Hughes ◽  
Evison Kapangaziwiri ◽  
Jane Tanner
Keyword(s):  
Parameter Estimation ◽  
Spatial Scale ◽  
Scale Effects ◽  
Physical Property ◽  
Hydrological Modelling ◽  
Ungauged Basins ◽  
Estimation Equations ◽  
Property Data ◽  
Physical Property Data ◽  
Parameter Values

The most appropriate scale to use for hydrological modelling depends on the model structure, the purpose of the results and the resolution of available data used to quantify parameter values and provide the climatic forcing. There is little consensus amongst the community of model users on the appropriate model complexity and number of model parameters that are needed for satisfactory simulations. These issues are not independent of modelling scale, the methods used to quantify parameter values, nor the purpose of use of the simulations. This paper reports on an investigation of spatial scale effects on the application of an approach to quantify the parameter values (with uncertainty) of a rainfall-runoff model with a relatively large number of parameters. The quantification approach uses estimation equations based on physical property data and is applicable to gauged and ungauged basins. Within South Africa the physical property data are available at a finer spatial resolution than is typically used for hydrological modelling. The results suggest that reducing the model spatial scale offers some advantages. Potential disadvantages are related to the need for some subjective interpretation of the available physical property data, as well as inconsistencies in some of the parameter estimation equations.

scholarly journals Spatial scale effects in environmental risk-factor modelling for diseases

2013 ◽  
Vol 7 (2) ◽  
pp. 169 ◽  
Author(s):  
Ram K. Raghavan ◽  
Karen M. Brenner ◽  
John A. Jr. Harrington ◽  
James J. Higgins ◽  
Kenneth R. Harkin
Keyword(s):  
Risk Factor ◽  
Spatial Scale ◽  
Environmental Risk ◽  
Scale Effects ◽  
Environmental Risk Factor

scholarly journals Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yang Wang ◽  
Moyang Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Information Extraction ◽  
Spatial Scale ◽  
Quantitative Evaluation ◽  
Urban Landscape ◽  
Kappa Coefficient ◽  
Deep Convolutional Neural Network ◽  
Landscape Information ◽  
Extraction Model

Modern urban landscape is a simple ecosystem, which is of great significance to the sustainable development of the city. This study proposes a landscape information extraction model based on deep convolutional neural network, studies the multiscale landscape convolutional neural network classification method, constructs a landscape information extraction model based on multiscale CNN, and finally analyzes the quantitative effect of deep convolutional neural network. The results show that the overall kappa coefficient is 0.91 and the classification accuracy is 93% by calculating the confusion matrix, production accuracy, and user accuracy. The method proposed in this study can identify more than 90% of water targets, the user accuracy and production accuracy are 99.78% and 91.94%, respectively, and the overall accuracy is 93.33%. The method proposed in this study is obviously better than other methods, and the kappa coefficient and overall accuracy are the best. This study provides a certain reference value for the quantitative evaluation of modern urban landscape spatial scale.

scholarly journals Water footprint of crop production for different crop structures in the Hebei southern plain, North China

2016 ◽  
Author(s):  
Yingmin Chu ◽  
Yanjun Shen ◽  
Zaijian Yuan
Keyword(s):  
Winter Wheat ◽  
Crop Production ◽  
North China ◽  
Water Footprint ◽  
Groundwater Resources ◽  
Arable Land ◽  
Summer Maize ◽  
The North ◽  
Groundwater Overdraft ◽  
Southern Plain

Abstract. The North China Plain (NCP) is serious lack of fresh water resources, while crop production consumed about 75 % of the region's water. To estimate water consumption of different crops and crop structures in the NCP, the Hebei southern plain (HSP) was selected as a study area because it is a typical region of groundwater overdraft in the NCP. In this study, water footprint (WF) was being used which was consisted of green, blue and grey components. The results showed: (1) the WF of the main crops production was about 51.0 km3 in 2012 and winter wheat, vegetables and summer maize were in the top three leading among the main crops in the HSP, while the water footprint intensity (WFI) of cotton was the largest and vegetables were the smallest; (2) winter wheat and vegetables consumed the main groundwater and their blue water footprint (WFblue) accounted for 66.0 % of the total WFblue in the HSP; (3) the crop structure scenarios analysis indicated that, with about 20 % of arable land cultivating winter wheat-summer maize in rotation, 40 % spring maize, 10 % vegetables and 10 % fruiters can promote the sustainable utilization of groundwater resources, at the same time can ensure sufficient supply of food, vegetables and fruits in the HSP.

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