Dealing with uncertainty in water scarcity footprints

Scherer, Laura; Pfister, Stephan

doi:10.1088/1748-9326/11/5/054008

Cited by 45 publications

(42 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An adjustment in the food consumption structure could also affect agriculture production, and thus change the WF. Scherer and Pfister's calculated global water scarcity indices for the decades 1981-1990 and 2001-2010, and concluded that increased water consumption, rather than climate change, plays a more important role in the increase of water scarcity [45]. Our study supports this, illustrating that other factors, rather than climate change, are more responsible for the increase of the WF of food consumption.…”

Section: Discussionsupporting

confidence: 78%

Decomposition of the Urban Water Footprint of Food Consumption: A Case Study of Xiamen City

et al. 2017

View full text Add to dashboard Cite

Decomposition of the urban water footprint can provide insight for water management. In this paper, a new decomposition method based on the log-mean Divisia index model (LMDI) was developed to analyze the driving forces of water footprint changes, attributable to food consumption. Compared to previous studies, this new approach can distinguish between various factors relating to urban and rural residents. The water footprint of food consumption in Xiamen City, from 2001 to 2012, was calculated. Following this, the driving forces of water footprint change were broken down into considerations of the population, the structure of food consumption, the level of food consumption, water intensity, and the population rate. Research shows that between 2001 and 2012, the water footprint of food consumption in Xiamen increased by 675.53 Mm 3 , with a growth rate of 88.69%. Population effects were the leading contributors to this change, accounting for 87.97% of the total growth. The food consumption structure also had a considerable effect on this increase. Here, the urban area represented 94.96% of the water footprint increase, driven by the effect of the food consumption structure. Water intensity and the urban/rural population rate had a weak positive cumulative effect. The effects of the urban/rural population rate on the water footprint change in urban and rural areas, however, were individually significant. The level of food consumption was the only negative factor. In terms of food categories, meat and grain had the greatest effects during the study period. Controlling the urban population, promoting a healthy and less water-intensive diet, reducing food waste, and improving agriculture efficiency, are all elements of an effective approach for mitigating the growth of the water footprint.

show abstract

Section: Discussionsupporting

confidence: 78%

Decomposition of the Urban Water Footprint of Food Consumption: A Case Study of Xiamen City

et al. 2017

View full text Add to dashboard Cite

show abstract

“…We therefore did not apply model performance indicators. However, from the comparison of fully model derived water footprints to footprints using only simulated ET and observed crop yields we have to state that no model performed best on all sites and treatments and that, similar to other model inter-comparisons [19][20][21][22][23], the ensemble means were in most cases among the estimates closest to the footprints with observed yields.…”

Section: Discussionmentioning

confidence: 80%

“…While uncertainty analyses of models addressing the first point are usually using combinations of stochastically distributed inputs by using, e.g., Monte-Carlo simulations (e.g., [22]), for the other two aspects recent studies have shown that the application of ensembles of complex simulation models is a valuable tool to assess the uncertainty in the estimation of climate impact on crop growth [23][24][25][26][27] and water consumption [28]. To assess the uncertainty of model based assessments of WF an ensemble of different crop models was applied to field data sets from five locations from across Europe.…”

Section: Introductionmentioning

confidence: 99%

Assessing Uncertainties of Water Footprints Using an Ensemble of Crop Growth Models on Winter Wheat

Kersebaum

Kroes

Gobin

et al. 2016

Water

View full text Add to dashboard Cite

Crop productivity and water consumption form the basis to calculate the water footprint (WF) of a specific crop. Under current climate conditions, calculated evapotranspiration is related to observed crop yields to calculate WF. The assessment of WF under future climate conditions requires the simulation of crop yields adding further uncertainty. To assess the uncertainty of model based assessments of WF, an ensemble of crop models was applied to data from five field experiments across Europe. Only limited data were provided for a rough calibration, which corresponds to a typical situation for regional assessments, where data availability is limited. Up to eight models were applied for wheat. The coefficient of variation for the simulated actual evapotranspiration between models was in the range of 13%-19%, which was higher than the inter-annual variability. Simulated yields showed a higher variability between models in the range of 17%-39%. Models responded differently to elevated CO 2 in a FACE (Free-Air Carbon Dioxide Enrichment) experiment, especially regarding the reduction of water consumption. The variability of calculated WF between models was in the range of 15%-49%. Yield predictions contributed more to this variance than the estimation of water consumption. Transpiration accounts on average for 51%-68% of the total actual evapotranspiration.

show abstract

“…Considering the large competition for land (Wu et al 2014), severe water scarcity (Scherer and Pfister 2016), and other environmental pressures, achieving productivity gains (or compensation for productivity losses) is challenging without strong externalities or higher greenhouse gas emissions and energy use. Meeting the twin challenge of climate change mitigation and food security requires a portfolio of interventions at both the supply and demand side.…”

Section: Discussionmentioning

confidence: 99%

Mapping and linking supply- and demand-side measures in climate-smart agriculture. A review

Scherer

Verburg

2017

Agron. Sustain. Dev.

Self Cite

View full text Add to dashboard Cite

Climate change and food security are two of humanity's greatest challenges and are highly interlinked. On the one hand, climate change puts pressure on food security. On the other hand, farming significantly contributes to anthropogenic greenhouse gas emissions. This calls for climate-smart agriculture-agriculture that helps to mitigate and adapt to climate change. Climate-smart agriculture measures are diverse and include emission reductions, sink enhancements, and fossil fuel offsets for mitigation. Adaptation measures include technological advancements, adaptive farming practices, and financial management. Here, we review the potentials and trade-offs of climate-smart agricultural measures by producers and consumers. Our two main findings are as follows: (1) The benefits of measures are often site-dependent and differ according to agricultural practices (e.g., fertilizer use), environmental conditions (e.g., carbon sequestration potential), or the production and consumption of specific products (e.g., rice and meat). (2) Climate-smart agricultural measures on the supply side are likely to be insufficient or ineffective if not accompanied by changes in consumer behavior, as climate-smart agriculture will affect the supply of agricultural commodities and require changes on the demand side in response. Such linkages between demand and supply require simultaneous policy and market incentives. It, therefore, requires interdisciplinary cooperation to meet the twin challenge of climate change and food security. The link to consumer behavior is often neglected in research but regarded as an essential component of climate-smart agriculture. We argue for not solely focusing research and implementation on one-sided measures but designing good, sitespecific combinations of both demand-and supply-side measures to use the potential of agriculture more effectively to mitigate and adapt to climate change.

show abstract

Dealing with uncertainty in water scarcity footprints

Cited by 45 publications

References 43 publications

Decomposition of the Urban Water Footprint of Food Consumption: A Case Study of Xiamen City

Decomposition of the Urban Water Footprint of Food Consumption: A Case Study of Xiamen City

Assessing Uncertainties of Water Footprints Using an Ensemble of Crop Growth Models on Winter Wheat

Mapping and linking supply- and demand-side measures in climate-smart agriculture. A review

Contact Info

Product

Resources

About