Table of contents
2. Supporting information
Indicator definition
The WEI+ provides a measure of total water consumption as a percentage of the renewable freshwater resources available for a given territory and period.
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Add the definition for "water consumption"
Italy: we agree
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Thank you for your comment, we will make proper adjustments in the text
Add the definition for "water consumption"
Italy: we agree
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We also agree that it would be helpful to clarify hypothesis
Add the definition for "water consumption"
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Thank you for your comment, we will make proper adjustments in the text
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Thank you for your comment, we will make proper adjustments in the text
Add the definition for "water consumption"
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The WEI+ is an advanced geo-referenced version of the WEI. It quantifies how much water is abstracted monthly or seasonally and how much water is returned before or after use to the environment via river basins. The difference between water abstraction and return is regarded as the amount of water used.
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lastsentence: .....as the amount of water used. better use the term ‘consumed’.
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lastsentence: .....as the amount of water used. better use the term ‘consumed’.
Italy: we agree
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Thank for your comment, the respective text will be revised accordingly
lastsentence: .....as the amount of water used. better use the term ‘consumed’.
Italy: we agree
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Thank you for the comment, well-noted
lastsentence: .....as the amount of water used. better use the term ‘consumed’.
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In case of Latvia, there is annual data collection on water abstraction, use and return. Only water users with seasonal activity have an obligation to report monthly data, while largest majority of respondents report only annual values.
Also, there are common situations when water, abstracted in the one river basin, can be returned to environment in another one. Thus, there will be territorial units, for which calculating of WEI+ indicator will be impossible, as it will return absurd results.
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Thank you for your feedback and sharing your experiences, we deal with similar cases across the scales and resolutions we elaborate the WEI+. Under the current implementation of the WEI+, temporal disaggregation of annual data is performed based on constant coefficient per sector by using surrogate data. We are fully aware of potential uncertainties around such methodological approach. However, we are continuously improving our methods and approaches to provide more accurate indicator results. Recently, we have started employing the Artificial Intelligence with the computation of the WEI+ based on multi-parametric variables of drivers and pressures to come up with more accurate approximation to the spatial and temporal disaggregation of the data on water abstraction. However, it should be noted here that still there is a long way to achieve such convincing outcomes.
In case of Latvia, there is annual data collection on water abstraction, use and return. Only water users with seasonal activity have an obligation to report monthly data, while largest majority of respondents report only annual values.
Also, there are common situations when water, abstracted in the one river basin, can be returned to environment in another one. Thus, there will be territorial units, for which calculating of WEI+ indicator will be impossible, as it will return absurd results.
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Describe the difference between WEI and WEI+.
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Describe the difference between WEI and WEI+.
Italy: we agree
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Thank you for the comment. Given the text adjustments based on the indicator definition, we will re-consider to provide more background on the development from WEI to WEI+
Describe the difference between WEI and WEI+.
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Rationale
Justification for indicator selection
The WEI+ is a water scarcity indicator that provides information on the level of pressure exerted by human activities on the natural water resources of a territory. This helps to identify the areas, which are prone to water stress problems (Feargemann, 2012). The WEI+ values on the country and annual scales are provided in line with the directions of UN SDG indicator 6.4.2 (“Level of water stress”), which is used to track progress towards target 6.4, addressing water scarcity and resource efficiency (UN, 2021) (however, ecological flows are not yet included in WEI+). Furthermore, computing and assessing the WEI+ at finer spatial scale (e.g. river basin districts) and finer temporal scale (e.g. seasonal), compared to the country-scale annual averages, helps to improve the monitoring and assessment of water scarcity issues, occurring regionally/locally and seasonally. Finally, computation and assessment of the WEI+ at the European level, would hide the large regional and local differences that exist across the continent. Therefore, it would be misleading. Instead, the computation and assessment of the proportional area and population being affected by water scarcity conditions (either seasonally or throughout an entire year) better capture the significance of water scarcity conditions on the continental scale.
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Austrian comment (Helga Lindinger)
after 2nd sentence: ....efficiency (UN, 2021) (however, ecological flows are not yet included in WEI+).
Including the e-flows would raise the awareness for water scarcity, The low WEI+ conveys a too comforting situation although the challenges exist and rise.
last sentence: ....proportional area and population
affected population: people who live in this region or people who rely on water from water scarce regions – water transfer)
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important to precise, effectively
Austrian comment (Helga Lindinger)
after 2nd sentence: ....efficiency (UN, 2021) (however, ecological flows are not yet included in WEI+).
Including the e-flows would raise the awareness for water scarcity, The low WEI+ conveys a too comforting situation although the challenges exist and rise.
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Thank you for your comment, well-noted. We will make proper adjustments in the text
important to precise, effectively
Austrian comment (Helga Lindinger)
after 2nd sentence: ....efficiency (UN, 2021) (however, ecological flows are not yet included in WEI+).
Including the e-flows would raise the awareness for water scarcity, The low WEI+ conveys a too comforting situation although the challenges exist and rise.
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Thank you for your comment, well-noted. We will make proper adjustments in the text
Austrian comment (Helga Lindinger)
after 2nd sentence: ....efficiency (UN, 2021) (however, ecological flows are not yet included in WEI+).
Including the e-flows would raise the awareness for water scarcity, The low WEI+ conveys a too comforting situation although the challenges exist and rise.
last sentence: ....proportional area and population
affected population: people who live in this region or people who rely on water from water scarce regions – water transfer)
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River basin districts when talking about Danube is too large. For Slovakia, where the Danube River is flowing just on the South-West border of the country, the source is not available for majority of the country, however in WEI+ for Slovakia it causes very small values (0,3 for years 2018 and 2019). This does not reflect the real situation in the country.
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Thank you for your feedback. As mentioned earlier, we are aware of some shortcomings in the case of Danube basin. Our computation system is not capable for such a distinction of inflow from Danube for Slovakia. Could you be so kind to provide an estimate on WEI+ for SK other than our estimations so as we have a basis for comparison or we can simply replace the EEA calculation with your calculation.
River basin districts when talking about Danube is too large. For Slovakia, where the Danube River is flowing just on the South-West border of the country, the source is not available for majority of the country, however in WEI+ for Slovakia it causes very small values (0,3 for years 2018 and 2019). This does not reflect the real situation in the country.
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Hungary:
We agree with the Slovak colleague's comment. In the case either of Hungary or the Danube River Basin District, the EU-level WEI+ also gives a false impression, because it masks the smaller-scaled territorial (hidrological) differences. Therefore, it would be useful to calculate the WEI+ for smaller-scaled catchments (e.g. sub-units according to the WFD) in order to reveal territorial (regional) differences.-
Thank you for your feedback, for the moment we do not provide EU-wide WEI+ values. As mentioned earlier, due to missing information on topology in the WFD spatial data, Ecrins is used by EEA as spatial reference data for assessing the water scarcity conditions in Europe. All compiled data on renewable freshwater resources, water abstraction and returns are primarily disaggregated to the catchment scale and then aggregated to any spatial scale of interest e.g. subbasin, country. The average size of catchments in Ecrins is around 60 km2. The Ecrins sub-basin delineation is the closest delineation to the WFD sub-unit, although some differences exist. It is in our long-term planning to integrate WEI+ under WFD compatible scales.
Hungary:
We agree with the Slovak colleague's comment. In the case either of Hungary or the Danube River Basin District, the EU-level WEI+ also gives a false impression, because it masks the smaller-scaled territorial (hidrological) differences. Therefore, it would be useful to calculate the WEI+ for smaller-scaled catchments (e.g. sub-units according to the WFD) in order to reveal territorial (regional) differences.
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Policy context and targets
Context description
The WEI is part of the set of water indicators published by several international organisations, such as the Food and Agricultural Organization of the United Nations (FAO), the Organisation for Economic Co-operation and Development (OECD), Eurostat and the Mediterranean Blue Plan. The WEI is also used to measure progress towards UN SDG target 6.4 at the global level (UN, 2021). Therefore, the WEI is an internationally accepted indicator for assessing the pressure of the economy on water resources, i.e. water scarcity.
The indicator and its underlying data may be used for assessments related to the decoupling of resource use from population and growth, which is one of the key objectives of the European Green Deal. In addition, it may support assessments on the improvement of socio-economic resilience against water scarcity, in line with the requirements of the EU Adaptation Strategy to climate change. Furthermore, the EU’s 8th EAP aims at ensuring the protection, conservation and enhancement of the EU’s natural capital. Monitoring the pressure of water consumption by different economic sectors at national, regional and local levels is necessary to achieve this.
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Furthermore, the EU’s 8th Environment Action Program (EAP) aims at....
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Thank you for your comment, well-noted. We will make proper adjustments in the text
Furthermore, the EU’s 8th Environment Action Program (EAP) aims at....
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this paragraph is about WEI (and not WEI+) but it introduces a bit of confusion as long as it is stated : "Monitoring the pressure of water consumption by different economic sectors at national, regional and local levels is necessary to achieve this"
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Thank you for your comment, well-noted. We will make proper adjustments in the text
this paragraph is about WEI (and not WEI+) but it introduces a bit of confusion as long as it is stated : "Monitoring the pressure of water consumption by different economic sectors at national, regional and local levels is necessary to achieve this"
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Targets
There are no specific targets directly related to this indicator. However, the Water Framework Directive (Directive 2000/60/EC) (EU, 2000b) requires Member States to promote the sustainable use of water resources based on the long-term protection of available water resources, and to ensure a balance between abstraction and the recharge of groundwater, with the aim of achieving good groundwater status.
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....achieving good groundwater status and good ecological status or potential for surface waters.
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Thank you for your comment, well-noted. We will make proper adjustments in the text
....achieving good groundwater status and good ecological status or potential for surface waters.
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Regarding WEI+ thresholds, it is important that agreement is reached on how to delineate non-stressed and stressed areas. Raskin et al. (1997) suggested that a WEI value of more than 20 % should be used to indicate water scarcity, whereas a value of more than 40 % would indicate severe water scarcity. These thresholds are commonly used in scientific studies (Alcamo et al., 2000). Smakhtin et al. (2004) suggested that a 60 % reduction in annual total run-off would cause environmental water stress. The FAO uses a water abstraction value of above 25 % to indicate water stress and of above 75 % to indicate serious water scarcity (FAO, 2017). Since no formally agreed thresholds are available for assessing water stress conditions across Europe, in the current assessment, the 20 % WEI+ threshold proposed by Raskin at al. (1997) is considered to distinguish stressed from non-stressed areas, while a value of 40 % is used as the highest threshold for mapping purposes.
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last sentence: ....threshold proposed by Raskin at al. (1997) originally for WEI is considered....
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Thank you for your comment, well-noted. We will make proper adjustments in the text
last sentence: ....threshold proposed by Raskin at al. (1997) originally for WEI is considered....
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Are the thresholds presented by Raskin et al., 1997 within the scope of the WEI indicator also applicable, without adaptation, to the WEI+ indicator?
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Thank you for your comment. We are aware of the issue. As there is no agreed trehsolds values available for the EU assessment, we indicatively keep the same threshold values, until any new are proposed.
Are the thresholds presented by Raskin et al., 1997 within the scope of the WEI indicator also applicable, without adaptation, to the WEI+ indicator?
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As no formally agreed thresholds are available for WEI+, we agree with previous comments about applicability of WEI tresholds.
We wonder if there is any ongoing work on specific WEI+ thresholds
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Thank you for your comment. We are aware of the issue. As there is no agreed trehsolds values available for the EU assessment, we indicatively keep the same threshold values, until any new are proposed.
As no formally agreed thresholds are available for WEI+, we agree with previous comments about applicability of WEI tresholds.
We wonder if there is any ongoing work on specific WEI+ thresholds
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Italy:
we agree with previous comments about the applicability of WEI thresholds to the WEI+ evaluations
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Thank you for your comment. We are aware of the issue. We assume that having an agreed set of WEI+ thresholds could be achieved by wider consultation between the Commission and the MSs with the contributions from the Scientific community.
Italy:
we agree with previous comments about the applicability of WEI thresholds to the WEI+ evaluations
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Data sources and data providers:
The European Pollutant Release and Transfer Register (E-PRTR), Member States reporting under Article 7 of Regulation (EC) No 166/2006 provided by the European Environment Agency (EEA, 2019).
Waterbase — UWWTD: Urban Waste Water Treatment Directive — reported data provided by the Directorate-General for Environment (DG ENV) and the European Environment Agency (EEA, 2020).
European Catchments and Rivers Network System (Ecrins) provided by the European Environment Agency (EEA, 2018).
Urban morphological zones 2006 provided by the European Environment Agency (EEA, 2014).
Waterbase — Water Quantity provided by the European Environment Agency (EEA, 2021).
Water statistics (Eurostat) provided by the Statistical Office of the European Union (Eurostat, 2021b).
Eurostat statistics on population provided by the Statistical Office of the European Union (Eurostat, 2020b).
Lisflood. Distributed Water Balance and Flood Simulation Model provided by the Joint Research Centre (JRC) (Burek et al., 2013).
National Statistical offices (dataset URL is not available) provided by the Statistical Office of the European Union (Eurostat, 2021a).
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We consider that the diversity of data sources (EEA/EIONET, OECD, Eurostat, AQUAstat,…), all with different purposes, assumptions and statistical treatment, can cause problems of coherence and comparability. For this reason it was important to ask each member state to validate its baseline data, whether this data is correct vis-à-vis the intended target, before calculating the indicator.
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Thank you for your comment, all data reported to EU or other international organizations are considered as validated ones. We've gone through an extensive assessment of potential different definitions of parameters under these datasets and in very limited cases it required additional data curation. Other than this, these data sources are fit for calculating the WEI+ indicator
We consider that the diversity of data sources (EEA/EIONET, OECD, Eurostat, AQUAstat,…), all with different purposes, assumptions and statistical treatment, can cause problems of coherence and comparability. For this reason it was important to ask each member state to validate its baseline data, whether this data is correct vis-à-vis the intended target, before calculating the indicator.
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We agree with previous comments, that data used for each country should be consulted. For Slovakia we found out (when comparing the data we have reported to EEA for years 2018 and 2019), that for abstractions there was omitted one part of water used for electricity production (only water for cooling was taken in account).
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Thank you for your comment, we will re-assess the Slovakian case in the context of underlying data and potentially correct the WEI+ results. Please be informed that the underlying data on water abstraction used for computing the WEI+ is the same set of data used for developing the indicator on water abstraction by source and by sector which was consulted with the Eionet member countries until 31st January 2022;
Freshwater - Public content available for wider Water Community (europa.eu)
The underlying data and the document clarifying the method for data gap filling can also be seen on here;
We agree with previous comments, that data used for each country should be consulted. For Slovakia we found out (when comparing the data we have reported to EEA for years 2018 and 2019), that for abstractions there was omitted one part of water used for electricity production (only water for cooling was taken in account).
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Italy:
we agree with previous comments
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We kindly ask you to consult with the EEA feedback to Slovakia on the same comment.
Italy:
we agree with previous comments
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Unit of measure:
WEI+ values are given as percentages, i.e. water use as a percentage of renewable water resources. Absolute water volumes are presented as millions of cubic meters (million m3 or hm3).
Methodology for indicator calculation
The WEI+ is an advanced version of the WEI. It is geo-referenced and developed for use on a seasonal scale. It also takes into account water abstraction (gross) and return (net abstraction) to reflect water consumption.
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Specify the difference between WEI and WEI+.
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Specify the difference between WEI and WEI+.
Italy:
we agree
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Thank you, well-noted, the necessary adjustment with the text will be made.
Specify the difference between WEI and WEI+.
Italy:
we agree
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Thank you for the comment. Given the text adjustments based on the indicator definition, we will re-consider to provide more background on the development from WEI to WEI+
Specify the difference between WEI and WEI+.
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Hungary:
A question to be considered is whether it would be useful to provide a separate WEI+ index for surface and groundwater. In the case of Hungary, the use of water from underground water can significantly reduce the base yield of surface water, especially in the dry summer period.-
Thank you for you comment, we are aware on current developments to elaborate SW and GW WEI+ indicators separately. However, the WISE SoE database is not sufficiently capable to compute the WEI+ for GW and SW seperately and thus for the moment this is not planned.
Hungary:
A question to be considered is whether it would be useful to provide a separate WEI+ index for surface and groundwater. In the case of Hungary, the use of water from underground water can significantly reduce the base yield of surface water, especially in the dry summer period.
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In 2011, a technical working group, developed under the Water Framework Directive Common Implementation Strategy, proposed the implementation of a regional WEI+. This differed from the previous approach, as the WEI+ was able to depict more seasonal and regional aspects of water stress conditions across Europe (see EEA’s updated conceptual model of WEI+ computation). This proposal was approved by the Water Directors in 2012 as one of the awareness-raising indicators (Faergemann, H., 2012).
The regional WEI+ is calculated according to the following formula:
WEI+ = (abstractions - returns)/renewable freshwater resources.
Renewable freshwater resources are calculated as ‘ExIn + P - Eta ± ΔS’ for natural and semi-natural areas, and as ‘outflow + (abstraction - return) ± ΔS’ for densely populated areas.
Where:
ExIn = external inflow
P = precipitation
Eta = actual evapotranspiration
ΔS = change in storage (lakes and reservoirs)
Outflow = outflow to downstream/sea.
It is assumed that there are no pristine or semi-natural river basin districts or sub-basins in Europe. Therefore, the formula ‘outflow + (abstraction - return) ± ΔS’ is used to estimate renewable water resources.
Climate data and streamflow data have been integrated from the Joint Research Centre (JRC) Lisflood model (Burek et al., 2013). The cover Europe in a homogeneous way for the years 2000-2019 on a monthly scale.
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For Slovakia, we report the data to EEA for our main sub-basins. As we mentioned in previous comments, the location of Danube River is only on the part of South-West state border, so this largest part of renewable water resources (counted into WEI+) is not applicable for the whole country, only for the smaller part. Therefore for an objective assessment of the situation of the country the WEI+ maybe should be represented by the weighted average of its values for main sub-basins in the country.
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Thank you for your comment, as mentioned before we will re-assess the Slovakian case.
For Slovakia, we report the data to EEA for our main sub-basins. As we mentioned in previous comments, the location of Danube River is only on the part of South-West state border, so this largest part of renewable water resources (counted into WEI+) is not applicable for the whole country, only for the smaller part. Therefore for an objective assessment of the situation of the country the WEI+ maybe should be represented by the weighted average of its values for main sub-basins in the country.
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Switzerland:
What is the original source of the precipitation data? The Lisflood model? Not the water quantity data reported annually to the EEA (WISE-3)?
We cannot understand why the precipitation data and thus the index for the Aare catchment (WSB0000010) are missing. Could you please check this again?
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Thankyoufor the feedback. Yes the precipitation data used as input for the CSI018 have been provided by JRC (LISFLOOD model outputs). We will recheck the case for the subbasin WSB0000010.
Switzerland:
What is the original source of the precipitation data? The Lisflood model? Not the water quantity data reported annually to the EEA (WISE-3)?
We cannot understand why the precipitation data and thus the index for the Aare catchment (WSB0000010) are missing. Could you please check this again?
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Once the data series are complete, the flow linearisation calculation is implemented, followed by a water asset accounts calculation, which is done to fill the gaps in the data for the parameters requested for the estimation of renewable water resources. The computations are implemented at different scales independently, from sub-basin scale to river basin district scale.
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Portugal reports its data by river basin districts (8 rivers basin districts in mainland Portugal). The values for Portugal of the variables used in the computation of the WEI+ indicator presented in the Excel files are not always recognized by us. Even when the total value coincides with that reported by Portugal, a breakdown is made that we do not know how it is computed.
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If this is about "code_ea" breakdown, which I did not understand either, after much searching, it seems to me that it is related to types of renewable water resources (type of source, i.e. distinction between Groundwater, Rivers, Artificial reservoirs and Lakes) as long as this items are used in DeltaS calculation (change in storage)... this is a supposition, but it would be very helpful to give metadata or data dictionary in order to permit full comprehension of available files. In the same way, we would have appreciated to have data with economic sectors breakdown (agriculture, public water supply...) at subbasins level, in oder to analyze where differences with our data could come from. Unless I am mistaken, these elements have not been provided)
Portugal reports its data by river basin districts (8 rivers basin districts in mainland Portugal). The values for Portugal of the variables used in the computation of the WEI+ indicator presented in the Excel files are not always recognized by us. Even when the total value coincides with that reported by Portugal, a breakdown is made that we do not know how it is computed.
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Thank you for the feedback. We apologize for overlooking the necessary clarification on the coding system used in the excel files. It had to be a part of the ReadMe file. The EEA Water accounting system is aligned with the UN SEEA Water (2012) standard. The Environmental Accounting codes (Code_EA) used in the excel tables are defining the following water bodies:
1311 – Artificial reservoirs
1312- Lakes
1313 – Rivers
1314 – Snow, Ice and Glaciers
132 – Groundwater
133 – Soil water
21 – Sea
DeltaS – change in water storage (as required by the formula for calculating the Renewable freshwater resourcesIf this is about "code_ea" breakdown, which I did not understand either, after much searching, it seems to me that it is related to types of renewable water resources (type of source, i.e. distinction between Groundwater, Rivers, Artificial reservoirs and Lakes) as long as this items are used in DeltaS calculation (change in storage)... this is a supposition, but it would be very helpful to give metadata or data dictionary in order to permit full comprehension of available files. In the same way, we would have appreciated to have data with economic sectors breakdown (agriculture, public water supply...) at subbasins level, in oder to analyze where differences with our data could come from. Unless I am mistaken, these elements have not been provided)
Portugal reports its data by river basin districts (8 rivers basin districts in mainland Portugal). The values for Portugal of the variables used in the computation of the WEI+ indicator presented in the Excel files are not always recognized by us. Even when the total value coincides with that reported by Portugal, a breakdown is made that we do not know how it is computed.
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Thank you for your feedback. The datasets that have been used in this indicator assessment are cited in the respective section, however methodological approach to temporal disaggregation of the annual data might expose some uncertainties and unmatching with the national data that national water agencies hold. We kindly encourage the Eionet Member Countries to share with us any additional information that might be available from the national datasets so that we can use as baseline for such a comparison and further potential correction in the EEA computation of the WEI+.
Portugal reports its data by river basin districts (8 rivers basin districts in mainland Portugal). The values for Portugal of the variables used in the computation of the WEI+ indicator presented in the Excel files are not always recognized by us. Even when the total value coincides with that reported by Portugal, a breakdown is made that we do not know how it is computed.
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Overall, annually reported data are available for water abstraction by source (surface water and groundwater) and water abstraction by sector with temporal and spatial gaps. Gap-filling methods are applied to obtain harmonised time series.
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We would like to point out that Portugal does not have water abstraction data available on an annual basis, but in accordance with the WFD planning cycles. Therefore, the values reported annually for Portugal are the same between planning cycles. So this values are not known to us.
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Thank you for your comment. Data for computing the WEI+ are mainly reported as annual under the WISE SoE and Eurostat dataflows. As mentioned earlier, in order to obtain a harmonized baseline on monthly resolution across all data required for WEI+ computation, we implement a disaggregation method by employing the surrogate data/coefficients. We acknowledge that that may cause some uncertainties particularly in those cases where no sufficient data are available. In that context, we had to perform large data gap filling exercise for Portuguese case. We kindly ask you about the next steps to be taken. We would happily replace the EEA computation with your results for WEI+ in the case that is available. If you have spotted large inconsistencies of WEI+ results in some certain river basins with the EEA calculation, with the potential baseline you would provide, we can explore how to improve the WEI+ results for Portuguese basins. Another option would be omitting that basin(s) from the final dataset and the map.
We would like to point out that Portugal does not have water abstraction data available on an annual basis, but in accordance with the WFD planning cycles. Therefore, the values reported annually for Portugal are the same between planning cycles. So this values are not known to us.
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The methodology is not very detailed, although there are additional elements in several other documents. It seems to me that there are at least two types of calculations concerning abstractions for the local level (sub-basins), (based on demand modelling?) and the national level (based on the national report?) but is there any harmonisation between these methodologies and if so, at what level?
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Thank you for your feedback. Supporting information section of the indicator provides references to the methodological implementation of the WEI+ computation including the methods for data aggregation and disaggregation processes. In addition to that, we kindly recommend to consult with ETC/ICM Report 1/2016:
The methodology is not very detailed, although there are additional elements in several other documents. It seems to me that there are at least two types of calculations concerning abstractions for the local level (sub-basins), (based on demand modelling?) and the national level (based on the national report?) but is there any harmonisation between these methodologies and if so, at what level?
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No sufficient data are available at the European scale on ‘return’. To fill gap in data on return, urban waste water treatment plant data, the European Pollutant Release and Transfer Register (E-PRTR) database, Eurostat population data, JRC data on the crop coefficient of water consumption and satellite-observed phenology data have been used as proxies to quantify the water demand and water use by different economic sectors. Eurostat tourism data (Eurostat, 2020a) and data on industry in production have been used to estimate the actual water abstraction and return on a monthly scale. Where available, Waterbase — Water Quantity database (EEA, 2021) and Eurostat data (Eurostat, 2020c) on water availability and water use have also been used at aggregated scales for further validation purposes.
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The methodology used for calculating the returns is not well explained. The values of the returns cannot simply be estimated on the basis of WWTP discharges but also on the basis of their location. Keep in mind that many of the sewage treatment plants in large cities located on the coast discharge into the sea and therefore do not account for their return to inland waters. And this calculation at monthly level seems even more difficult.
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The data on UWWTP is used in identifying potential locations of the water discharge from the UWWTPs back to the surface water and sea water. As you also know that the UWWTP data is not providing the information on the volume of discharge water, but rather the capacity of the water treatment of UWWTPs on population equivalent. We use constant coefficient to estimate the water returns per sector. The set of coefficients used in estimating the water returns per economic sector for each country has been provided in the excel file “Reference data on water abstraction and returns” under the current Eionet consultation;
The methodology used for calculating the returns is not well explained. The values of the returns cannot simply be estimated on the basis of WWTP discharges but also on the basis of their location. Keep in mind that many of the sewage treatment plants in large cities located on the coast discharge into the sea and therefore do not account for their return to inland waters. And this calculation at monthly level seems even more difficult.
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Once water asset accounts have been implemented according to the United Nations System of Environmental- Economic Accounting for Water (UN, 2012), the necessary parameters for calculating water use and renewable freshwater resources are harvested.
Following this, bar and pie charts are produced, together with static and dynamic maps.
Methodology for gap filling
For each parameter of water abstraction, return and renewable freshwater resources, primarily data from the Waterbase — Water Quantity database have been used (EEA, 2021). Eurostat, OECD and Aquastat (FAO) databases have also been used to fill the gaps in the data sets. Furthermore, the statistical office websites (Eurostat, 2021a) of all European countries have each been visited several times to get the most up-to-date data from these national open sources. Despite this, some gaps still needed to be filled by applying certain statistical or geospatial methodologies (see EEA (undated), Table 1 — Reference data sources for gap filling and modulation coefficients).
Lisflood data from the JRC have been used to gap fill the streamflow data set (see EEA (undated), Table 1 — Reference data sources for gap filling and modulation coefficients). The spatial reference data for the WEI+ are the European Catchments and Rivers Network System (Ecrins) data (250-m vector resolution). Ecrins is a vector spatial data set, while Lisflood data are in 5-km raster format. To fill the gaps in the streamflow data, centroids of the Lisflood raster have been identified as fictitious (virtual) stations. The topological definition of the drainage network in Ecrins has been used to match the most relevant and nearest fictitious Lisflood stations with EEA-Eionet stations and the Ecrins river network. After this, the locations of stations between Eionet and Lisflood stations were compared and overlapping stations were selected for gap filling. For the remaining stations, the following criteria were adhered to: fictitious stations had to be located within the same catchment as the Eionet station and have the same main river segment; in addition, both stations had to show a strong correlation.
A substantial amount of gap filling has been performed on the data on water abstraction for irrigation. First, a mean factor between utilised agricultural areas and irrigated areas has been used to fill the gaps in the data on irrigated areas. Then, a multiannual mean factor of water density (m3/ha) in irrigated areas per country has been used to fill the gaps in the data on water abstraction for irrigation.
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Comment AT: Irrigation data for Austria mainly rely on the very limited irrigation volumes reported for the Farm Structure Survey in 2010. In 2010 the climate was wetter than on average especially in the eastern part of the country where the irrigation areas are situated.
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Thank you for your comment. Data gap on water abstraction particularly for agriculture and irrigation is exposing a big challenge for accurately calculating the actual pressure of agriculture on renewable freshwater resources. We have recently started using the Artificial Intelligence tools to undertake potential correlation among multi-parametric variables of drivers and pressures in water use by agriculture to improve the data quality. The quality of the data , however, can only be improved if more reported data would be available. We are working on to further improve all the sectorial data input for water in the short term for more accurate results.
Comment AT: Irrigation data for Austria mainly rely on the very limited irrigation volumes reported for the Farm Structure Survey in 2010. In 2010 the climate was wetter than on average especially in the eastern part of the country where the irrigation areas are situated.
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The gaps in the data on water abstraction for manufacturing and construction have been filled using Eurostat data on production in industry (Eurostat [sts_inpr_a]) and the E-PRTR database, with the methodologies in the best available techniques reference document (BREF) being used to convert the production level into the volume of water.
Uncertainties
Methodology uncertainty
Reported data on water abstraction and water use do not have sufficient spatial or temporal coverage. Therefore, estimates based on country coefficients are required to assess water use. First, water abstraction values are calculated and, second, these values are compared with the production level in industry and in relation to tourist movements to approximate actual water use for a given time resolution. This approach cannot be used to assess the variations (i.e. the resource efficiency) in water use within the time series.
Spatial data on lakes and reservoirs are incomplete. However, as reference volumes for reservoirs, lakes and groundwater aquifers are not available, the water balance can be quantified as only a relative change, and not the actual volume of water. This masks the actual volume of water stored in, and abstracted from, reservoirs. Thus, the impact of the residence time, between water storage and use, in reservoirs is unknown.
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Altough we note that the overall objective with this indicator is to provide a European overview, and we are also fine with the share presented for Sweden at national level (Figure 2), we are surprised to see the very high share (above 40) for the river basin around Lake Mälaren (west of Stockholm) in Figure 1. There's no water scarcity in this area and Lake Mälaren in itself is in fact a fresh water reservoir for the Stockholm region and surrounding areas. Perhaps this issue is due to that, as you write, "Spatial data on lakes and reservoirs are incomplete" and not taken into account in this context? The generally accepted view in Sweden is rather that water scarcity exists in South-Eastern Sweden, as well as on the Baltic Sea islands of Gotland and Öland.
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Thank you for your comment, we will further analyze the reason behind. It might be either related to the delineation of the basin in Ecrins or any error with the computation. Meanwhile, in order to make proper adjustments in the text, could you share with us your assessment in order to further improve the assessment and our data inputs?
Altough we note that the overall objective with this indicator is to provide a European overview, and we are also fine with the share presented for Sweden at national level (Figure 2), we are surprised to see the very high share (above 40) for the river basin around Lake Mälaren (west of Stockholm) in Figure 1. There's no water scarcity in this area and Lake Mälaren in itself is in fact a fresh water reservoir for the Stockholm region and surrounding areas. Perhaps this issue is due to that, as you write, "Spatial data on lakes and reservoirs are incomplete" and not taken into account in this context? The generally accepted view in Sweden is rather that water scarcity exists in South-Eastern Sweden, as well as on the Baltic Sea islands of Gotland and Öland.
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The sectoral use of water does not always reflect the relative importance of the sectors to the economy of a given country. It is, rather, an indicator that describes which sectors environmental measures should focus on in order to enhance the protection of the environment. A number of iterative computations based on identified proxies are applied to different data sets, i.e. urban waste water treatment plant data, E-PRTR data, Eurostat population data, JRC data on the crop coefficient of water consumption and satellite-observed phenology data have been used as proxies to quantify water demand and water use by different economic sectors. This creates a high level of uncertainty in the quantification of water return from economic sectors, thus also leading to uncertainty with regard to the ‘water use’ component.
To distribute population data across Europe, the Geostat 2011 grid data set from Eurostat (Eurostat, 2011) was used. Further aggregations were then performed in the spatial dimension to give the sub-basin and functional river basin district scales of Ecrins spatial reference data. The population within the time frame of 1 calendar year is regarded as stable. Variations are taken into account only for the annual scale. Deviations from officially reported data are expected because of the nature of the methodological steps followed.
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are overnight stays included in the population data?
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Yes they are included aggregated from monthly level based on the ‘Nights spent at tourist accommodation establishments by NUTS 2 regions (tgs00111)’ dataset (<a href="https://ec.europa.eu/eurostat/data/database?node_code=tin00175" rel="nofollow">https://ec.europa.eu/eurostat/data/database?node_code=tin00175</a>).
are overnight stays included in the population data?
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Data set uncertainty
Data are very sparse on some particular parameters of the WEI+. For instance, current streamflow data reported by the EEA member countries to the WISE SoE — Water Quantity database (EEA, 2021) do not have sufficient temporal or spatial coverage to provide a strong enough basis for estimating renewable water resources for all of Europe. Such data are not available elsewhere at the European level either. Therefore, JRC Lisflood data are used intensively as surrogates.
Data on water abstraction by economic sector have better spatial and temporal coverage. However, the representativeness of data for some sectors is also poor, such as the data on water abstraction for mining. In addition to the WISE SoE — Water Quantity database, intensive efforts to compile data from open data sources such as Eurostat, OECD, Aquastat (FAO) and national statistical offices have also been made
Quantifying water exchanges between the environment and the economy is, conceptually, very complex. A complete quantification of the water flows from the environment to the economy and, at a later stage, back to the environment, requires detailed data collection and processing, which have not been done at the European level. Thus, reported data have to be used in combination with modelling to obtain data that can be used to quantify such water exchanges, with the purpose of developing a good approximation of ‘ground truth’. However, the most challenging issue is related to water abstraction and water use data, as the water flow within the economy is quite difficult to monitor and assess given the current lack of data availability. Therefore, several interpolation, aggregation or disaggregation procedures have to be implemented at finer scales, with both reported and modelled data. The main consequences of data set uncertainty are the following:
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This paragraph is a duplication and should be deleted.
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Well-noted, the paragraph will be deleted.
This paragraph is a duplication and should be deleted.
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Quantifying water exchanges between the environment and the economy is, conceptually, very complex. A complete quantification of the water flows from the environment to the economy and, at a later stage, back to the environment, requires detailed data collection and processing, which have not been done at the European level. Thus, reported data have to be used in combination with modelling to obtain data that can be used to quantify such water exchanges, with the purpose of developing a good approximation of ‘ground truth’. However, the most challenging issue is related to water abstraction and water use data, as the water flow within the economy is quite difficult to monitor and assess given the current lack of data availability. Therefore, several interpolation, aggregation or disaggregation procedures have to be implemented at finer scales, with both reported and modelled data. The main consequences of data set uncertainty are the following:
- The water accounts and WEI+ results have been implemented in the EEA member and Western Balkan countries. However, regional data availability was an issue for some river basins (e.g. in Turkish river basins), which had to be removed from the assessment.
- Because of technical issues in estimating the variable outflow to the sea, the seasonal WEI+ calculation at NUTS2 level could not be performed for Netherlands and some of other NUTS regions from other countries. This has been made explicit without presenting the WEI results for such entities in the final WEI tab.
Rationale uncertainty
Because of the aggregation procedure used, differences exist between Country, NUTS2 and basin level for total renewable water resources and water use; and in turn on the estimated water exploitation index.
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