Mapping current and future European public water withdrawals and consumption

Vandecasteele, Ine; Bianchi, Alessio Belmondo; Silva, F. Batista e; Lavalle, Carlo; Batelaan, Okke

doi:10.5194/hessd-10-9889-2013

Cited by 10 publications

(17 citation statements)

References 12 publications

(3 reference statements)

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“…For example, in Southwestern Australia, a 50% reduction in discharge over the past 35 years has been observed for some rivers [135]. This trend will continue with predicted climate change and increased water demand [31,132,135,136], and likely will exacerbate current impacts on freshwater biodiversity [79]. Despite numerous efforts in developing environmental policies aiming at protecting freshwater ecosystems and their sustainable use in most of the Mediterranean-climate countries, major challenges persist in implementation [132].…”

Section: Conservation Management In Mediterranean Regionsmentioning

confidence: 99%

High Variability Is a Defining Component of Mediterranean-Climate Rivers and Their Biota

Cid

Bonada

Carlson

et al. 2017

Water

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Abstract:Variability in flow as a result of seasonal precipitation patterns is a defining element of streams and rivers in Mediterranean-climate regions of the world and strongly influences the biota of these unique systems. Mediterranean-climate areas include the Mediterranean Basin and parts of Australia, California, Chile, and South Africa. Mediterranean streams and rivers can experience wet winters and consequent floods to severe droughts, when intermittency in otherwise perennial systems can occur. Inter-annual variation in precipitation can include multi-year droughts or consecutive wet years. Spatial variation in patterns of precipitation (rain vs. snow) combined with topographic variability lead to spatial variability in hydrologic patterns that influence populations and communities. Mediterranean streams and rivers are global biodiversity hotspots and are particularly vulnerable to human impacts. Biomonitoring, conservation efforts, and management responses to climate change require approaches that account for spatial and temporal variability (including both intra-and inter-annual). The importance of long-term data sets for understanding and managing these systems highlights the need for sustained and coordinated research efforts in Mediterranean-climate streams and rivers.

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Section: Conservation Management In Mediterranean Regionsmentioning

confidence: 99%

High Variability Is a Defining Component of Mediterranean-Climate Rivers and Their Biota

Cid

Bonada

Carlson

et al. 2017

Water

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“…The average estimated ratio between consumption and abstraction is around 45 %, according to Vandecasteele et al (2014). Therefore, if we had interpreted the target in a less stringent way by applying the threshold of 20 % withdrawals to the available renewable resources only to consumptive uses (i.e., not accounting for water released to the water body after its use), the resulting NF2020B would have been 1.41E+ 10 m 3 , with a resulting WF2020B of 2.88.…”

Section: Resultsmentioning

confidence: 99%

A distance-to-target weighting method for Europe 2020

Castellani

Benini

Sala

et al. 2016

Int J Life Cycle Assess

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Purpose Distance-to-target (DTT) methods are weighting methods aimed at assessing the distance of an existing situation from a desired state (the target). Weighting factors in DTT methods could be based on calculation which is performed on normalization factors (NFs) developed for life cycle assessment (LCA). At present, some DTT weighting sets have been developed. However, there is no DTT weighting set assessing the distance of EU domestic impacts from the desired state set by EU binding or non-binding policy targets (e.g., those related to the "Climate and Energy Package" and the "Roadmap to a Resource Efficient Europe"). Methods In the present work, a methodology to derive target references from policy-based targets in 2020 (TRs2020), both binding (A) and non-binding (B), is presented. Resulting target factors and DTT weighting factors are then compared to the current normalisation factors (based on 2010 normalization references). The resulting weighting factor (WF) sets are presented and discussed in light of their use for decision support in policy and business contexts. We applied the WF sets to characterization results to an example (the EU energy mix process) aiming at illustrating key differences and effects on the results.Results and discussion The three reference sets (NRs2010, TRs2020A, and TRs2020B) show, in some impact categories, a relatively small difference. WFs referred to set A and set B result to be quite similar, with the only exception of water depletion impact category, for which a very relevant change is foreseen when considering the effect of the non-binding target of limiting the abstraction of water resource to 20 % of the available renewable water resources. This is mainly due to the higher difficulty in deriving quantitative targets from non-binding strategies and policies rather than from binding ones. Conclusions The resulting weighting sets present strengths and limitations. The translation of policy targets into quantitative modifications to the baseline inventories appeared to be not a straightforward task, due to several reasons discussed in the paper (e.g., not all the policy targets are expressed in quantitative terms or can be translated into quantitative reductions and modifications of the elementary flows in the existing baseline inventories). Aiming at improving the effectiveness in supporting policies, further development of the methodology may be the integration with other DTT approaches such those based on carrying capacity, developed to integrate Earth's carrying capacity concept and planetary boundaries.

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“…In Table S3 (Electronic Supplementary Material) the statistics on water withdrawal retrieved from different sources Vandecasteele et al 2014) are provided. Within the uncertainty and sensitivity analysis carried out in this paper, all of the data sources reported in Tables S1, S2, S3 (Electronic Supplementary Material) were given the same likelihood.…”

Section: Sources Of Uncertainty In the Inventorymentioning

confidence: 99%

“…The normalization factor calculated by Sala et al (2015) for water depletion is proven to be very sensitive to the choice of regional characterization factors instead of average ones, as almost 100 % of the observed uncertainty is due to this choice (see Table 1). Therefore, better normalization factors could be calculated by making use of regionalized CFs, no matter whether the underlying statistics on water withdrawals normalization inventory is based on the data collected by Sala et al (2014Sala et al ( , 2015 or by Vandecasteele et al (2014).…”

Section: Water Depletion (Wd)mentioning

confidence: 99%

Uncertainty and sensitivity analysis of normalization factors to methodological assumptions

Benini

Sala

2015

Int J Life Cycle Assess

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Purpose Normalisation is an optional step of a life cycle assessment, supporting the interpretation of the results of the characterization in terms of relative environmental relevance of the impacts. Normalisations factors (NFs) are calculated as results of regional/global inventories of emission and resources characterized through impact assessment methods. Several methodological assumptions are needed for building the inventory, as presented in Sala et al. (Int J Life Cycle Assess 20:1568-1585, 2015. NFs for EU27 have been calculated for 2010 compliant with ILCD recommendations defining a methodological approach for sources selection and the use of proxy indicators. Qualitative and quantitative uncertainty evaluation is needed for assessing the robustness of final figures. The present work aims at quantifying the influence of key methodological choices on the variability of the normalisation factors. Materials and methods Five sources of uncertainty have been analyzed in this work: (F1) the selection of the sources of data; (F2) the classification of data as life cycle inventory (LCI) elementary flows; (F3) the classification of substances for characterization; (F4) the specification of the emission compartments and (F5) the use of spatially differentiated characterization factors. The sensitivity of the normalization factors to such uncertainties were assessed through a global sensitivity method, for the impact categories acidification (ACID), terrestrial eutrophication (ET), marine eutrophication (EM), photochemical ozone formation (POF), respiratory inorganics/particulate matter (RIPM) and water depletion (WD). Results and discussionThe results demonstrate the need of thorough uncertainty and sensitivity analysis for supporting the use of NFs. Uncertainties are high for the impact categories respiratory inorganics (RIPM) and water depletion (WD) and improvement of these categories is a priority. For RIPM this is explained by the high variability amongst the characterization factors for PM 2.5 and PM 10 , together with the contextual lack of information about the height of the emission source in the inventory. For WD this is explained by variability of the regionalized factors available within the ILCD. For ACID, ET and EM the uncertainty is relatively low and generally completely led by factors F1 and F2. However, regionalized characterization factors were not tested for ACID and ET, therefore the results might be underestimating the overall uncertainty. For what concerns POF, the main source of uncertainty-amongst those included in the analysis-is the selection of the data source. Overall, improvements in the spatial resolution of the inventory are needed in order to confine uncertainty. This would allow the use of characterization factors specific for emission source typology and geographical location. 20:1568-1585, 2015).

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Mapping current and future European public water withdrawals and consumption

Cited by 10 publications

References 12 publications

High Variability Is a Defining Component of Mediterranean-Climate Rivers and Their Biota

High Variability Is a Defining Component of Mediterranean-Climate Rivers and Their Biota

A distance-to-target weighting method for Europe 2020

Uncertainty and sensitivity analysis of normalization factors to methodological assumptions

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