circabc.europa.eu€¦ · Web viewDevelopment of an NRCS curve number global dataset using the latest geospatial remote sensing data for worldwide hydrologic applications. Remote

EUROSTAT GRANT FOR 2017EUROSTAT UNIT: E.2 – ENVIRONMENTAL STATISTICS AND ACCOUNTS; SUSTAINABLE DEVELOPMENT

OBJECTIVE: 05.1 – PROVIDE ENVIRONMENTAL ACCOUNTS AND CLIMATE CHANGE RELATED STATISTICS

TITLE: KNOWLEDGE INNOVATION PROJECT ON ACCOUNTING FOR ECOSYSTEMS

PROJECT ON ECOSYSTEM ACCOUNTING FOR ITALY

GRANT AGREEMENT N. 05122.2017.003-2017.648

COORDINATOR: ISPRACO-PARTNER: ISTAT

PROJECT COORDINATOR: ALESSIO CAPRIOLO

AUTHORS OF THE REPORT: STEFANO BALBI2, RICCARDO GIUSEPPE BOSCHETTO1, ALESSIO CAPRIOLO1 , ROSA ANNA MASCOLO1, FERDINANDO VILLA2

CONTRIBUTORS: ALDO FEMIA, ANGELICA TUDINI3

1 Istituto Superiore per la Protezione e Ricerca Ambientale (ISPRA).2 Basque Centre for Climate Change (BC3).3 Istituto Nazionale di Statistica (ISTAT)

1

CONTENTS1. OUTDOOR RECREATION.....................................................................................................1

1.1 Introduction........................................................................................................................11.2 Biophysical Model.............................................................................................................11.3 Economic Valuation...........................................................................................................51.4 Extent table, supply table and use table.............................................................................8

2. CROP POLLINATION...........................................................................................................112.1 Introduction......................................................................................................................112.2 Biophysical Model...........................................................................................................112.3 Economic Valuation.........................................................................................................142.4 Extent table, supply table and use table...........................................................................16

3. FLOOD REGULATION.........................................................................................................213.1 Biophysical Model...........................................................................................................213.2 Economic Valuation.........................................................................................................253.3 Extent table, supply table and use table...........................................................................29

4. WATER PROVISION............................................................................................................314.1 Introduction......................................................................................................................314.2 Biophysical Model...........................................................................................................314.3 Economic Valuation.........................................................................................................334.4 Extent table, supply table and use table...........................................................................35

5. Future Steps and Conclusions.................................................................................................376. ANNEXES..............................................................................................................................387. REFERENCES........................................................................................................................42

7.1 Outdoor Recreation..........................................................................................................427.2 Crop Pollination...............................................................................................................437.3 Flood Regulation..............................................................................................................447.4 Water Provision................................................................................................................457.5 Model Data Sources.........................................................................................................46

2

1. OUTDOOR RECREATION

1.1 Introduction

The Outdoor Recreation is a cultural ecosystem service that includes all physical and intellectual interactions with biota, ecosystems, land-/seascapes. It covers the biophysical characteristics or qualities of ecosystems that are viewed, observed, experienced or enjoyed in a passive, or active way by people on a daily basis.

The recreation model is inspired by the ESTIMAP model of nature based outdoor recreation developed by Paracchini et al. (2014) for Europe4, but it is run by using ARIES’ technology5. The model computes the Recreation Supply (eq.1) as a multiplicative function of naturalness and the distance-driven accessibility of nature-based factors of attractiveness, computed as Euclidean distance to protected areas6, mountain peaks (based on the top fraction of terrain height values in the study area), and water bodies (including streams, lakes and oceans).

1.2 Biophysical Model

The model computes the degree of naturalness (see table 1 in Annex I) as a reclassification of land cover types into numerical values ranging from 1 to 7, with higher values representing increasing land use intensity/human influence (Paracchini et al., 2014). Finally, the model discretizes normalized recreation supply values into three recreation potential classes (<0.75, 0.75 to 0.88, and >0.88), which produces values 4 Currently used by the Joint Research Centre of the European Commission.5 This work has been carried out using the networked software technology of ARIES (Artificial Intelligence for Ecosystem Service) that helps users map and value ecosystems and the benefits they provide to specific human beneficiary groups across specific geographies. Aries contains an extensive database of spatial data and ecosystem service models across local to global scales. Data are “tagged” with relevant concepts so that ecosystem service models automatically call on, transform, and integrate the needed data into each model for a region’s ecological and socioeconomic context. When semantically annotated data covering new spatial and temporal extents or resolutions are made available, the annotated concept, described in the data, enables ARIES to automatically substitute local data for global where appropriate. The software is a context aware modelling system where the best available knowledge will be used for the context analysed. The system assembles a computational strategy, based on a set of rules under which data, models, and model parameterizations are selectively applied in order to produce the desired outputs. Model and data customization are important for capturing local knowledge, improving credibility, and reducing the inherent inaccuracies of global and other large-scale data. ARIES’ approach to automated model customization expands the role of global ecosystem service models, enabling navigation between different model tiers based on assessment needs, time and data availability. 6 Official List of Protected Areas (EAUP) MATTM- National Geoportal http://www.pcn.minambiente.it/.

1

similar to those from the ESTIMAP implementation of the Recreation Opportunity Spectrum (ROS; Clark and Stankey, 1979; Joyce and Sutton, 2009; Paracchini et al., 2014).

Supply(S)=HI ∙¿ (eq. 1)

HI is the human influence and d is the calculated Euclidean distance respectively from Protected Areas (PA), Coast (C), Streams (St), Lakes (L) and Mountains ‘peaks (Mp). In order to offer a greater spatial resolution, in fig. 1.1 outdoor recreation supply is shown only for a particular region.

Fig.1.1 Example of Outdoor Recreation Supply for Veneto Region

As alternative to ESTIMAP's ROS classification, the model quantifies Recreation Demand (eq.2) as a weighted sum of two normalized indices, one related to a recreation-driven mobility function F (d ) (eq. 3) and the other one related to population density ¿) with a constant reduction factor β , that takes into consideration the greater or lesser inclination of the population to travel, according to the belonging registry class:

Demand (D )=F (d )+β Popdensity (eq. 2)

2

In order to offer a greater spatial resolution, in fig. 1.2 outdoor recreation demand is shown only for a particular region.

Fig.1.2 Example of Outdoor Recreation Demand for Veneto Region

The mobility function, adapted from (Paracchini et al. (2014)) and originally based on Geurs and van Eck (2001), describes how far an individual is likely to travel for recreation on one-day-trip basis and models the probability of traveling to a site as a function of distance, assuming high probability of trips within 30 Km and very low probability at 80 Km and beyond:

F (d )= (1+K )(K+e (a ∙ d ))

(eq. 3)

where d is the distance from a site and K and a are parameters describing the shape (S-shape) and scale of the log-logistic function (Geurs and van Eck, 2001), respectively. The function assumes that the average citizen has a higher probability to travel to sites that are closer compared to those that are farther away. The value of each cell of the resulting layer corresponds to the number of inhabitants who can reach the cell from cities with over 50000 inhabitants (Uchida and Nelson, 2010).

More specifically d is the distance to main cities automatically queried in OpenStreetMap at every single runtime and, when travel time is more than 30 min then we assumes that every new distance becomes d =d i+30 km. This

3

creates a 30 km buffer for short trips around main cities, where the likelihood of high recreation demand is much greater.

The mobility function parameter values are constants set to K = 450 and a =1.12E − 04, which is a combination of the short-distance (8 km) and long-distance (80 km) mobility functions (Paracchini et al. 2014).

Along with the evaluation of the distance to main cities we consider the travel time to those cities in order to assess inequalities in accessibility. We used a dataset for travel time7 (fig. 1.3) and then we normalized and discretized into three classes (easily accessible: ≤ 0.25; accessible: 0.25 to 0.5; and not accessible >0.5).

Fig. 1.3: Travel Time database

Recreation Demand takes into account the likelihood of taking a day trip to a certain point and the population density in the areas serving as a source of visitors for that location, thus describing the relative number of trips taken from each grid cell within the context. In this way, the model uses estimated travel time to calculate the flow of recreation demand from population centers to recreational sites.

In addition to the previously described analysis, we propose a multiplicative Cobb-Douglas-type function to relate recreation supply and demand, which takes the following form (Fuleky, 2006):

F (S , D )=pSxD y (eq. 4)

where p=1, x= y=12 and S and D are recreation supply and demand, respectively. It is

a symmetric function with constant return to scale (the service increases by the same proportional change as supply and demand change) and diminishing marginal utility. This estimates the spatial overlay of supply and demand expressed with a weakly concave function representing landscape recreational utility. This output facilitates the identification of sites with high recreation supply and demand at the same time, where outdoor recreation daily trips are most likely to happen. The following figure (fig. 1.4) describes areas where a value is closer to one (red color). Areas with low supply or demand receive values closer to zero (blue color).

7 http://forobs.jrc.ec.europa.eu/products/gam/4

http://forobs.jrc.ec.europa.eu/products/gam/

Fig.1.4 Use of the Outdoor Recreation Service

1.3 Economic Valuation

As one of the disputes in the context of valuation methods for natural capital accounting concerns the adequacy and relevance of the exchange value versus welfare value concepts, it is useful to briefly discuss the relationship between the value concept recommended by the SEEA EEA (2017) and the valuation methods used in this report, in order to assess the relationship between the values of stocks and flows related to the man-made capital with the values of natural capital thus estimated. The choice of valuation approach and methods depends mainly on the aim of natural capital assessment. When the purpose of constructing natural capital accounts is to integrate the ecosystem values with System of National Accounts (SNA), then the exchange value method remains the only one eligible.Obst et al. (2015) argues that the exchange value represents “the value at which goods, services and assets are exchanged regardless of the prevailing market

5

conditions”. In the case of ecosystem services, for which markets often do not exist, exchange values essentially represent an assumed transaction between an ecosystem asset and an economic beneficiary, and the estimated values include only components that are already present in national accounts or may be compatible with their metrics.When the primary aim is, instead, to highlight the contribution of the ecosystems to well-being, welfare value concept and welfare analysis which are related to changes in consumer surplus, relative either to market or shadow price, become eligible. In order to carry out the valuation of outdoor recreation services we applied the so-called ‘travel cost’ method aimed to estimate travel costs actually incurred by visitors, as a proxy of the contribution of the ecosystem to values included in National Accounts. The ‘travel cost’ method is the most commonly used methodology in literature for such a type of service. The travel cost method here applied (which use travel expenditures to a site to assess the site’s value, but no entry price to the facility/area has been considers due to lack of data) has been greatly refined over the years due to the increased use and capability of Geographical Information Systems (GIS). Starting from the travel time of the recreation model and considering an average speed of 60 km/h, under a combined urban and extra-urban route, with a fuel cost equal to 1.65 € / L referred to a gasoline-fueled car, and a cost of about € 0.4/Kw/h for an electric vehicle, we assumed (tab 1.1) that the cost per km associated to a recreation experience (travel cost) is equal to:

Energy consuption of electric vehicle (Kw/h)

Cost (€/km)

0.28 0.11Fuel consumption of gasoline-fueled car (L/km)

Cost ( €/km)

11.8 0.14

Tab.1.1: Energy consumption and costs for different sources of energy

It can be assumed, in the case of outdoor recreational activities, that a daily trip includes an average occupancy value of 2 people per vehicle. Fig. 1.5 shows the Monetized Value of Use in the Outdoor Recreation Service.

6

Fig.1.5 Monetized Value of Use in the Outdoor Recreation Service

In order to adapt the recreation model for national based needs, we made several simplifications that preclude the direct comparability of results to European ESTIMAP recreation model outputs (Paracchini et al., 2014). These include the use of Italian-derived, land cover-based estimates of naturalness and proximity thresholds, which may differ by eco-region and socioeconomic setting, respectively.Both would be best informed by local parameterizations provided by region-specific experts. Finally, the concept of outdoor recreation and the model's parameterization, which implies travel by vehicle to outdoor recreation sites, makes the existing model most suitable to be developed in different contexts. However, the model's basic components (attractiveness, demand, and accessibility) could be adjusted to account for non vehicular access to green space in cities or tourist access to protected areas, which may be useful for short trips daily recreation.

The whole process of assessment is described in the flow-chart below:

7

Human Influence

Distance to featuresof actrativeness

Travel TimeCalculation

Distance toHuman Settlement

MobilityFunction

Population

Demand

USE: Number of

Visitors

Supply

Outdoor RecreationValue(€)

Travel CostMethod

OUTDOOR RECREATION MODEL FLOW CHART

NaturalCapital

Accounts.

Extent Table

Supply Table

Use Table

1.4 Extent table, supply table and use table

Extent table

The extent of the outdoor recreation ecosystem service (table 1.2.) is represented by the areas where the service is provided to a Demand that requires it, and spatially

8

correspond to the polygons or cells identified by the overlay of supply (eq.1) and demand (eq.2).

Ecosystem Service/Type of Ecosystem Units

Outdoor Recreation (ha)(2018)

Green urban areas 7875

Cropland 14.155.164

Grassland 2.112.489

Heatland and shurbs 2.307.123

Woodland and forest 10.629.819

Wetlands 88.029

Rivers and lakes 248.274

Others 585.027

Total Extent 30.133.800

Tab.1.2: Outdoor Recreation Extent Table

Supply table

The Supply table (tab.1.3) describes which type of ecosystem provide different quantities of the ecosystem service (UN et al., 2014). As a result it is possible to understand the origin of the service from the various types of ecosystem (tab.1.3). The provision of the ecosystem service, expressed in monetary terms, is given by the output value of equation 4 of the model associated to the travel cost of each visit (Badura et al., 2017). Two scenarios have been considered for the monetary calculation: the first one sees households moving with gasoline-fueled cars while the second one uses the electric cars as mean of transportation.


Outdoor Recreation (Visits number - 2018)

Outdoor RecreationGasoline-fueled

(M€- 2018)

Outdoor RecreationElectric-fueled

(M€- 2018)

Green urban areas 634.981 0,66 0,52Cropland 882.060.376 2.486 1953,22Grassland 116.265.336 658 516,98

Heatland and shurbs 119.104.297 745 585,34Woodland and forest 586.452.548 4.091 3214,25

Wetlands 7.015.505 10 7,86Rivers and lakes 17.398.793 40 31,43

Others 36.725.571 325 255,35Total 1.765.657.407 8.357 6.565

Tab.1.3: Outdoor Recreation Supply Table

9

Use tableThe Use table (tab 1.4) indicates which economic sectors (including households) benefit from the ecosystem service (La Notte et al. 2017). The same total output value already distributed among ecosystems of origin in the Supply table, is now allocated to the recipients of the outdoor recreation service, in this case households (UN et al., 2014).

Ecosystem Service/Type of Economic

Units (M€ - 2018)

Prim

ary

sect

or

Seco

ndar

y se

ctor

Terti

ary

sect

or

Hous

ehol

ds

Tota

l U

se

Outdoor Recreation(gasoline-fueled car) 8.357 8.357

Outdoor Recreation(electric vehicle) 6.565 6.565

Tab.1.4: Outdoor Recreation Use Table

10

2. CROP POLLINATION

2.1 Introduction

Crop Pollination is a regulating ecosystem service defined as the fertilisation of crops by wild insects and other animals that maintains or increases the crop production.

Crop pollination by bees and other animals is a potentially valuable ecosystem service in many landscapes of mixed agricultural and natural habitats. Pollination can increase the yield, quality, and stability of fruit and seed crops. Indeed, Klein et al. (2007) found that 87 of 115 globally important crops (70%) benefit from animal pollination. Despite these numbers, it is important to realize that not all crops need animal pollination. Some crop plants are wind-(e.g., staple grains such as rice, corn, wheat) or self-pollinated (e.g., lentils and other beans), with no need of animal pollinators to successfully produce fruits or seeds. Klein et al. (2007) provides a list of crops and their pollination requirements that can help identify whether crops in a region of interest may benefit from wild animal pollinators.

Decision-makers can use information on crop pollinators, their abundance across a landscape, and the pollination services they provide to crops in several ways. Firstly, with maps of pollinator abundance and crops that need them, land use planners could predict consequences of different policies on pollination services and income to farmers (for an example, see Priess et al. 2007). Secondly, farmers could use these maps to locate crops efficiently, given their pollination requirements and predictions of pollinator availability. Thirdly, institutions could use this tool to optimize conservative investments that benefit both biodiversity and farmers. Finally, governments or other organizations proposing payment schemes for ecosystem services could use the results to estimate who should pay whom, and how much.

11


A wide range of animals can be important pollinators (e.g. flies, birds, bats), but bees are the most important group for most crops (Free 1993). In order for bees to live in a habitat, they need two elements: suitable places to nest, and sufficient food (provided by flowers) near their nesting sites. If provided, pollinators are available to fly to nearby crops and pollinate them as they collect nectar and pollen. As a result, this pollination model focuses on the resource needs and flight behaviors of wild bees and the pollination service associated with some crops.

The model, that uses ARIES’ technology, calculates pollination supply (eq.1) as pollinator occurrence, or the ability of the environment to support wild insect pollinators, as dependent by the insect forage activity (eq.5) and the habitat suitability (HS) (eq.2), which is in turn a function of nesting suitability (NS), floral availability (FA) and proximity to water (rivers, lakes and streams). We assigned NS and FA values to land cover categories (table n.1 and n.2 in Annex II) based on expert opinion from previous studies (Lonsdorf et al. 2009; Zulian et al. 2014).

Supply =A(%)∙ habitat suitability HShabitat suitability max (Eq. 1)

HS= (FA+spe+lpe )(NS+spe+lpe) (Eq.2)

The model estimates and maps pollinator habitat suitability (HS) as a proxy for pollinator abundance, assuming that both variables, NS and FA, need to be simultaneously present (multiplicative function) to support pollinator populations. Then, the distance to streams (spe) and lakes (lpe) is added (eq.3 and eq.4) to account for the better habitat suitability belonging to areas close to water supply. In this regard, the model assumes a positive effect of water bodies on the probability of pollinator presence, based on the inverse weighted distance from them.

Stream proximity effect (spe )=12∙ e−2d (Eq.3)

Lake proximity effect (lpe)=0,7e−2d (Eq.4)

Beyond the presence of pollinators, insect forage activity represents another relevant variable that can be greatly affected by atmospheric temperature and solar radiation, which can modify the number of active individuals (Corbet et al. 1993). We thus calculated the proportion of active individuals foraging (A) as:

A (% )=−39,3+4,01T Blackglobe (Eq. 5)

where T Blackglobe represents the temperature of an insect’s body simulated as a black spherical model with a function of annual mean ambient temperature (T in °C) and annual mean solar irradiance (R in W m-2):

T Blackglobe=−0,62+1,027T+0,006R (Eq. 6)

In eq.1, we then multiplied the normalized habitat suitability by the proportion of active individuals foraging to account for spatial differences in pollinator activity

12

levels. Relative pollinator occurrence (fig.2.1) is estimated based on landscape suitability and average potential annual activity of insects.

Fig. 2.1: Example of Insects Occurrence (Supply) for Sicily Region

Next, the model estimates pollination demand in each cell of the grid as the product of each pollination dependency rate (cdp i) (Klein et al. 2007, or see table n.3 in the Annex II) and the relative production for 30 crop types (CPi) requiring insect pollination for optimal yields (Monfreda et al. 2008). The model normalizes pollination-dependent crop production based on the values found within the selected spatial context. All pollination analyses are run at 1 km resolution, which is comparable to the maximum distance of the most insect pollinator flights (Gathmann and Tscharntke 2002; Danner et al. 2016). In order to simplify the computation, we assume the flow of the pollination service to be restricted to the grid cell within which the pollinator resides (i.e., no supply is received from adjoining cells).

Demand=cdpicdpmax

∙CPi (eq. 7)

The model combines demand and supply to produce grid-scale pollination Use values.

Use=Supply Demand (eq. 8)

The pollination status values range from 0 to 1 and are marked in different colors (fig.2.2). These values represent the areas where the service is present with different intensity. Value 0 represents the un-met demand.

13

Fig.2.2: Use normalized values of crop pollination


In order to carry out this valuation we applied a ‘market-based’ method using the crop price to set the monetary value referred to the crop pollination service. As we apply a market-based approach, in this context we ensure the alignment with the national accounting system, as pollination service contributes to the value of agricultural production that is already included in the national accounts (UN, 2014). The increased quantity of crop production can be computed as a share of the crop production attributable to the pollination flow, that is calculated multiplying the outcome from the USE equation (eq. 8) and the market price8 for each of the 30 different crops (La Notte et al., 2017). This component of production would not exist without the ecosystem service, and therefore it represents as a whole the additional value deriving from the presence of pollinators.

The whole process of the assessment is described in the flow-chart below:

8 http://arearica.crea.gov.it.

14

http://arearica.crea.gov.it/report_d.php

NestingSuitability

FlorealAvaliability

SolarIrradiance

TemperatureInsect

Activity

CropsPollination

Dependency

DEMAND

Habitat Suitability

Water nearness(Lakes , Streams) SUPPLY

Production

CROP POLLINATION MODEL FLOW CHART

USE: Pollinated

Crops

CropsPrice[€ T-1]

CropsPollinationValue [€]

NaturalCapital

Accounts.

Extent Table

Supply Table

Use Table

15


Excessive use, unsustainable management practices, soil change or the effects of climate change are anthropogenic pressures that affect ecosystem supply of Ecosystem Services (ES). The loss of ES supply results in economic losses. ES accounting can be used as a tool for assessing the loss of economic value by the socio-economic system. The methodology for defining the ES flows in biophysical and monetary value terms vary depending on the service and its physical and fruition characteristics. All the approaches ideally contribute to the construction of SEEA-EEA accounting supply and use tables, first in biophysical terms and then in monetary terms (UN et al., 2014).

Extent tableThe extent of crop pollination ecosystem service is represented by the areas where the service is present, that is where the service supply and demand spatially overlap (tab.2.1). The pollination model produces spatially explicit information on the distribution of insect pollination services based on land cover cropland and climatic patterns. According to the predicted changes in climatic patterns, some species of wild pollinators will move towards northern ranges and the vast majority of bumblebees will suffer from range contractions (Potts et al., 2015). Land cover is the most important driver, but its relative importance differs among the taxonomic groups, reflecting their ecological requirements.

Ecosystem Service/Type of Ecosystem

Units (ha)2018 Gr

een

urba

n ar

eas

Crop

land

Gras

sland

Heat

land

and

sh

urbs

Woo

dlan

d an

d fo

rest

Spar

sely

ve

geta

ted

land

Wet

land

s

Rive

rs a

nd la

kes

Coas

tal a

nd

inte

rtida

l are

as

Tota

l Ex

tent

Crop pollination

1.448.4541.

448.

454

Tab.2.1: Crop Pollination Extent Table

Supply table

Starting from what is supplied by the ecosystem in terms of ‘Met Demand’ (tab. 2.2 and cartography in fig.2.3), the Supply table (tab 2.3) shows from which ecosystem type the service is produced. Biophysical evaluation is fundamental to estimate the contribution made by the pollinators that defines the "quantity" of the service offered by the ecosystem.

Met Demand Met Demand

16

[t] [M€]

Crop

land

almond 29.928 45

apple 925.706 333

apricot 91.504 63

cherry 136.291 144

figs 116.795 27

citrusnes 40.323 69

kiwi 843.472 531

Lemon lime 169.162 78

melon 218.823 94

orange 11.356 11

peach 2.864 2

pear 163.827 95

persimmon 21.719 2

plum 2.058.637 844

rasberry 167.300 85

strawberry 7.912.002 2.294

tangerine 548.180 280

watermelon 265.336 157

bean 228.573 94

broadbean 85.570 44

chili 111.701 61

eggplant 28.869 9

flax 876 5

legumenes 961.625 346

pumpkin 32.387 90

rapeseed 253.580 76

soybean 3.692.448 1.219

sunflower 27.786.774 11.393

tomato 885.677 37

turnipfor 136.328 30Green urban areas

GrasslandHeatland and shurbsWoodland and forest

WetlandsRivers and lakes

Total 18.560Tab.2.2: Outdoor Recreation “Met Demand” Table

17

Fig.2.3: Crop pollination Met-Demand monetized


Crop pollination (M€ - 2018)

Green urban areas

Cropland 1.939

Grassland

Heatland and shurbs

18

Woodland and forest

Wetlands

Rivers and lakes

Total 1.939Tab.2.3: Crop pollination Supply Table

Use tableThe Use table (tab 2.4) indicates which economic sectors benefit from ecosystem services. The share of the ‘Met Demand’ values, that represents the contribution of the pollination service to crop production (eq. 8), is now allocated to the recipients of the crop pollination service, in this case Agriculture-Primary sector (mapped in fig.2.4).

USE [t]2018

USE [M€]2018

Agric

ultu

re -

Prim

ary

Sect

or

almond 19.453 29 apple 601.709 217 apricot 59.478 41 cherry 34.073 36 figs 29.199 7 citrusnes 26.210 45 kiwi 42.174 27 Lemon lime 8.458 4 melon 54.706 24 orange 2.839 3 peach 143 0 pear 147.444 86 persimmon 5.430 1 plum 102.932 42 rasberry 150.570 77 strawberry 395.600 115tangerine 356.317 182watermelon 172.469 102 bean 11.429 5 broadbean 55.621 28 chili 100.531 55 eggplant 7.217 2 flax 569 3 legumenes 240.406 87 pumpkin 8.097 23 rapeseed 63.395 19 soybean 184.622 61sunflower 1.389.339 570tomato 575.690 24

19

turnipfor 122.695 27Secondary SectorTertiary SectorHouseholdsOthersTotal 1.939

Tab.2.4: Crop pollination Use Table

Fig.2.4: Crop pollination Use monetized

20

3. FLOOD REGULATION

Flood Regulation is a regulating ecosystem service that estimates the capacity of the vegetation and soils to retain excess runoff from rainfall. The reduction in the speed and volumes of water flows by virtue of the presence of ecosystem features (i.e. vegetation) mitigates or prevents damage to the human environment. The service should be present where there is ideally a lower o medium risk of flooding where areas are able to mitigate naturally this risk through water retention.


The flood regulation ecosystem service is modeled with ARIES’ technology and is aimed to quantify ranked values for Flood Regulation (FR) Supply (S) and Demand (D). The Supply (FRS) (fig. 3.1) is based on the Flood Hazard Probability Index (FHP), that accounts for physical and bioclimatic parameters characterizing the ecosystem able to control a potential flood, mitigated by a variable (CN9) representing water retention by soil and vegetation to control the excess rainfall:

FRS=FHP−CN100

FHP=FHP (1− CN100

) (eq. 1)

9 At low CN values correspond soils with very high water capacity and conductivity, 100 value corresponds to total outflow.

21

Fig. 3.1 Flood Regulation Supply (FRS): an example of Lazio Region

The estimate of FHP index (fig. 3.2) combines the rainfall with the value of the water accumulation flow for each altitude along the slope (DEM10) and the temperature of the wettest quarter of the year. Here below follows the Supply equation:

FHP=13[

PA−PAmin .

PA max.−PA min.+

TWI−TWImin .TWImax.−TWImin .

+TWQ

TWQmax .] (eq. 2)

Fig.3.2 Potential Probability of Flood (FHP): an example of Lazio Region

where PA is total annual precipitation;TWI is the topographic wetness index (Kirkby and Beven, 1979; Manfreda et al., 2011) expressed as:

10 Digital Elevation Model.22

TWI=ln atanB (eq. 3)

It includes the upslope contributing area (a) and B that is a geometric function indicating the slope. The value of a for each cell in the output raster is the value in a flow accumulation raster for the corresponding DEM. Higher values represent drainage depressions, lower values represent crests and ridges.TWQ is the mean temperature of the wettest quarter (Hijmans et al., 2005). Temperature is included in the equation to account for the role of the Clausius- Clapeyron relationship (Trenberth et al., 2003), which predicts greater rainfall intensity at higher temperatures. Based on Utsumi et al. (2011), the model uses mean atmospheric temperature in the wettest quarter to predict an increase in the temperature-rainfall intensity relationship in polar regions (high latitudes), a decreasing relationship in equatorial regions (tropics), and a peaked relationship in temperate regions (intermediate latitudes). Studies based on numerical models have revealed that the rate of increase in the extreme daily precipitation is associated with atmospheric warming (Allen and Ingram, 2002; Pall et al., 2007; Kharin et al., 2007).

The model computes the overall Flood Regulation Supply (FRS) using the Curve Number (CN) method, which estimates the capacity of vegetation and soils to retain excess runoff from rainfall. The CN is a function of land cover, soil hydrologic group data, and in some contexts slope (Zeng et al., 2017; Soil Conservation Service, 1985). The model then reduces flood hazard probability by the CN (as shown in eq.1).

The Flood Regulation Demand (FRD) is represented by the population or the assets placed in the area targeted by the service. This provides an assessment of people and property exposure to potential flood risk.

FRD=Population∨Assets (eq. 5)

Finally, the model estimates the overall ecosystem service (Use) through a multiplicative function between the Supply and the Demand (fig.3.3 for population, fig.3.4 for residential assets, fig.3.5 for commercial/industrial assets).

Use=Supply Demand (eq. 6)

This model thus constitutes a simplification of previously published global or continental-scale ones (Stürck et al., 2014; Ward et al., 2015), but it is fast and easily replicable even in data-scarce contexts.

23

Fig. 3.3: Flood Regulation Service Use for Population

Fig 3.4: Flood Regulation Service Use for Residential Assets

24

Fig 3.5: Flood Regulation Service Use for Commercial/Industrial Assets


In order to carry out this valuation we applied a particular ‘cost-based’ method which, although rather innovative in literature, belongs to the ‘avoided cost/damage’ methodology.The approach proposed for a quantitative economic estimate of the flood control service, is to evaluate the expected damage for some categories of assets (starting from residential and commercial buildings), affected by a potential flooding in the areas identified by equation 6, if the ecosystem service - currently present - should be removed or no longer exist.The level of approximation with which the vulnerability and the exposure of these assets are quantified, remains extremely variable depending on the element considered. Depending on the type of building and the state of maintenance, the damage to the structure caused by a flood event theoretically can vary from small to complete destruction. The assessment of the expected damage is even more problematic in complex urban areas, with the presence of artistic and cultural heritage.

The intensity of flooding is usually given by an indicator of the height level reached by the water above the road level, which is assumed to be not above 3 meters high (ground floor). The potential damage is calculated by crossing the potential floodable areas with the presence of buildings throughout the national territory (CLC 2012), and considering the corresponding recovery value of the buildings concerned.This methodology for the economic assessment, applied on an experimental basis, takes into consideration only potential damage to residential and commercial

25

structures, leaving to future analysis the estimate of damage to infrastructures (cost of restoration or cost of disruption in the infrastructures' network service), to companies (disruption in production activity):

V=∑x i=1

n

Sx i ∙(Q(R)i−Q(NR)i) (eq. 7)

where the sum of the Sxi represents the buildings surface affected by the potential flooding identified by eq. 6, Q(R)i is the real estate value11 of a renovated unit expressed in €/m2 in the i area, Q(NR)i is the real estate value12 of a non-renovated unit in the same area expressed in €/m2. Following a flood event we assume that the property needs complete maintenance. In this circumstance the difference between the market value of an apartment to be restored and one in perfect conditions may be considered as a proxy of the restoration cost for the damaged structures (fig. 3.6 for residential assets and 3.7 for commercial/industrial assets).

Fig 3.6: Monetized Value for Flood Regulation Service Use (Residential)

11 Real Estate Observatory (Tax Revenue Agency). 12 Real Estate Observatory (Tax Revenue Agency).

26

Fig 3.7: Monetized Value for Flood Regulation Service Use (Commercial/Industrial)

The whole process of the assessment is described in the flow-chart below:

27

TopographicWetnessIndex

Total Rainfall

Curve Number

Population

Δ Estate Quotation

[€ m-2]

DEMAND

FloodHazard

ProbabilityTemperature of the wettest quarter

SUPPLY

FLOOD REGULATION MODEL FLOW CHART

Flood Control Valuation [€ ]

Commercial Industrial

Residential Assets

USE: Assets affected

by potentialFlood [m2]

NaturalCapital

Accounts

Extent Table

Supply Table

Use Table

28


Extent table

The extent of the flood control ecosystem service is represented by the areas where the service is present (UN et al., 2014), that is where the cells of Supply (eq.1) and Demand (eq.5) spatially overlap (eq.6), as showed in tab 3.1.

Ecosystem Service/Type of ecosystem

Unit

Urban (2018)

Population(number of Inhabitants)

Residential Areas (m2)

Commercial/Industrial areas (m2)

Flood Regulation 3.596.805 124.031.033 31.457.272

Tab. 3.1 Flood Regulation Extent Table

Supply table

The Supply table (tab. 3.2) does not show from which ecosystem type the service is produced, as it was for the previous applications, but the ecosystem on which the service has an impact. The biophysical values derive from the USE equation outputs (eq. 6) and represent the affected asset surface. The monetary values either in the Supply or Use table are computed in equation 7.

Ecosystem Service/Type of ecosystem

Unit

Urban (2018)

Residential Areas (M€) Commercial/Industrial areas (M€)

Flood Regulation 39.070 7.770

29

Tab. 3.2 Flood Regulation Supply Table

Use tableThe Use table (tab.3.3) indicates which economic sectors benefit from ecosystem services. The same total value attributed to flood control service already distributed among the ecosystems of origin in the Supply table, is now allocated to the recipients of the service, in this case households/tertiary sector and secondary sector.

Ecosystem Service/Type of ecosystem Unit (M€)

2018

Primary Sector (M€)

Secondary and

Tertiary Sector (M€)

Households

(M€)

Total USE(M€)

Flood Regulation 7.770 39.070 46.840

Tab. 3.3 Flood Regulation Use Table

30

4. WATER PROVISION

4.1 Introduction

Water Provisioning is a provisioning ecosystem service defined as the natural, surface and ground water bodies that provide drinking and non-drinking water.

The national provision of the law assigns to ISPRA the task of defining the water-hydrological national balance through the following decrees:

Legislative Decree 112/98 Art. 88 Tasks of national relevance Legislative Decree 152/2006 Art. 60. Competences of the Institute for

Environmental Protection and Research - ISPRAThe Hydrological Balance GIS BAsed (BIGBANG) at national scale on regular grid, developed by ISPRA (Braca, 2018), provides the estimate of the total hydrological components such as total precipitation, real evapotranspiration, recharge of the aquifers or infiltration and surface runoff, covering the whole national territory.


The BIGBANG model is based on the Thornthwaite and Mather approach (Thornthwaite, C.W., e Mather, J.R. 1955) and simulates on each element of the grid the hydrological components using precipitation and temperature data along with land use data and information on hydraulic and geological characteristics of the land.

The BIGBANG equation is written as follows:

P−E=R+G+∆V with∆V=0 (eq. 1)

where P is the total precipitation, E is the real evapotranspiration, R is the superficial outflow, G is the recharge in the groundwater and ΔV is the variation of the water content in the soil, whose (cumulated and balanced) contribution is considered to be approximately zero on annual basis (fig. 4.1).

31

ΔV=0Fig.4.1 Equation of the hydrological balance

The automation in the GIS environment and the analysis of the monthly data directly bring the equation of the hydrological balance on the grid: it is evaluated on a regular grid for each month and the calculations are run separately for each of the 300000 grid cells (fig. 4.2). Aggregating in space and time, the balance can be obtained for any reference territory and for any multiple time frame (seasonal, annual, LTAA) 13.

Fig. 4.2 Calculation process

The above equation is applied for each month and on each cell represented as a 1-km-side tank for 1 m depth, whose maximum capacity is given by the accumulation of available water content (Available Water Storage AWS or Available Water Content AWC) depending on the type of soil. The current version of the model uses the data produced by the LUCAS_TOPSOIL project of the Joint Research Center as values of AWS (in mm) (Toth et al., 2013).Starting from the evaluation of the amount of water that exceeds the storage capacity of the ground, it is possible to evaluate the surface runoff R and the groundwater recharge G, based on the potential infiltration coefficient (CIP, Celico 1988).The main sources of input data used by the model are listed below in tab 4.1:

Data ISPRA sourcePrecipitation map from rainfall stations Hydrological Annals of the National

Hydrographic and Mareographic Service and Network of Functional Centers

Average monthly temperature map Grid format by SCIA-ISPRA (Fioravanti et al., 2010)

Available Water Content map LUCAS_TOPSOIL (Toth et al. 2013)Map of the hydrogeological complexes (ISPRA) to which the potential infiltration coefficient is associated

(Celico, 1988)

13 LTAA is the Long Term Annual Average. The minimum period taken into account for the calculation of long term annual averages is 20 consecutive years.

32

Map of the degree of soil sealing Soil sealing rate (Munafò et altri, 2013)

Tab. 4.1 Main data source

The hydrological balance is strongly affected by the value of the meteoric flow and by the assessment of evapotranspiration. Data are interpolated using geostatistical techniques, therefore a particular care must be given to the spatial interpolation procedure of the monthly rainfall data in the first case and temperature in the second case. As mentioned, the balance scheme works at a resolution of 1 km which is generally not very adequate for local assessments. Furthermore, the model faces a critical issue: all recharge and runoff do not depend on the soil but on the potential infiltration coefficient parameterized on hydrogeological basis (Celico P., 1988). These are only 15 values that cover all the diversity of the Italian territory. In addition, the storage of water in artificial lakes and water bodies or horizontal exchanges between cells is not modeled (Braca, 2018). In any case, thanks to the capacity of the model to integrate data on land cover and use, it is possible to estimate the variation in the variables of the hydrological balance according to the soil consumption in different periods. The increase in surface runoff is considered in this case a proxy of the water volume to be further managed.The ecosystem water accounts have been carried out following the SEEA-EEA accounting tables only where feasible, due to considerable lack of spatial data on water withdrawals (especially the direct ones), storage in artificial basins and network losses.


According to literature, the most suited method for a market-based evaluation of the so-called provisioning ecosystem services is the “resource rent” method (Badura T., Ferrini S., Agarwala M., Turner K. 2017). The ‘resource rent’ value is defined as the difference between the benefit price and the unit costs of labour, produced assets and intermediate inputs.

No ‘resource rent’ properly said officially exists in Italy on the water resource, which is per se a public good. However, it can be argued that the return on invested capital, applied by private water management companies, is in reality a vested rent deriving from the control of the resource. Indeed, although the results of the referendum in Italy (June 2011) established to change the methodology for defining the tariff on water by eliminating the component "return on invested capital", the rate of ‘return on capital invested’, which had been set by the national legislation of 199614 at 7% (maximum), is still applied by companies all over the country. It is on this basis that we use the current return on invested capital as reference (proxy for the resource rent) for our estimate of the monetary value of the water provision service provided by the environment and already included in the national accounts. On average the “return on invested capital” corresponds to the 10% of whole tariff. Therefore, considering the volumes of water collected (Focus ISTAT 'World Water Day' 2018) and the percentages of consumption assigned to the different classes of users (Focus ISTAT 'World Water Day' 2017), with a water tariff of 1,29 €/m3 (elaborations on ARERA data - national average) for potable use and an average value between 0.04 and 0.07 € / m³ for irrigation use, as indicated by ARCADIS (Arcadis, 2012), an estimated monetary value of about 1,22 billion euros (households) and around 88 million euros (primary sector), respectively, is estimated and shown in the USE table. 14 Standardized method for defining cost components and determining the reference tariff of the integrated water service (GU General Series n.243 of 16-10-1996).

33


Extent TableThe extent of the water provision ecosystem service is quantified in tab. 4.2 by the areas where we can identify spatially the ‘Potential Flow’ that represents here only the superficial outflow plus the net groundwater recharge (eq. 1). We do not consider in the computation neither the external inflow nor change in artificial reservoirs.


(Km2)2018

Gree

n ur

ban

area

s

Crop

land

Gras

sland

Heat

land

and

sh

urbs

Woo

dlan

d an

d fo

rest

Wet

land

s

Rive

rs a

nd la

kes

Tota

l Ex

tent

Water Provision

37.8

89

31.4

22.4

52

9.54

3.56

0

7.65

5.29

4

48.4

66,3

2

101.

617

974,

48

98.2

02.0

21

Tab. 4.2 Water Provision Extent Table

Supply Table

The Supply table (tab 4.3) shows from which ecosystem type the service flows (UN et al., 2014). In this case the flow of renewable water, including superficial outflow plus the net groundwater recharge, that is annually and naturally produced (m3/year), represents the ‘Potential Flow’ as described in the Extent Table (tab. 4.2) and spatially shown in fig. 4.3.


(mln m3/year)2018

Gree

n ur

ban

area

s

Crop

land

Gras

sland

Heat

land

and

sh

urbs

Woo

dlan

d an

d fo

rest

Wet

land

s

Rive

rs a

nd la

kes

Coas

tal a

nd

inte

rtida

l are

as

Tota

l Su

pply

Supply (Potential Flow)

37.8

89

31.4

22,4

5

9.54

3.56

0

7.65

5.24

9

48.4

66.7

32

101.

617

974.

447

1.07

1.68

2

115.218,84

Tab. 4.3 Water Provision Supply Table

34

Fig.4.3 Water Potential Flow

Use TableUse table (tab. 4.4) indicates which economic sectors benefit from the ecosystem service. The Actual Flow of water abstraction for a given period (m3/year) is allocated to the recipients of the service, in this case households and primary sector. Data availability is provided only for two sectors: for 15975 mln∙m3 (primary sector) and 9490 mln∙ m3 (household)15.

Ecosystem Service/Type of

Economic Units

(2018)

Primary Sector Secondary Sector Households Total USE

Agriculture Livestock Industrial Energy

Use [mln m3](Abstractions)

15975 989 6076 1604 9490 34136

Use [mln €](Abstractions)

87,87 1224,21 1312,08

Tab. 4.4 Water provision Use Table

15 Data processing on www.istat.it. 35

5. Future Steps and Conclusions

This work has been carried out with ISTAT’s support in the collection of some basic data and with the technical advisory of BC3 for the use of ARIES technology.

The Results of this project will stimulate the involvement of all the regional network of environmental agencies coordinated by ISPRA to work on ecosystem services modeling and assessment and on the environmental monitoring of natural capital data in order to develop a permanent data flows to carry on the mapping at national level.

Enhancement on the quality of the models and results will be achieved once the effects of the ecosystems condition will be integrated in the assessment model.

The project outcomes will be integrated into existing policy domains, starting from the next ‘Report on the State of the Natural Capital in Italy’ as required yearly by the National Law 221/2015 through The Italian Natural Capital Committee (INCC).

Participation to future EUROSTAT Grants will be assured by ISPRA thanks to the stimulus of the good results achieved with the present project and to the opportunity offered by the new machine reasoning technology adopted by Aries software to customized model for different ecosystem services.

This project represents a first attempt at national level to measure in biophysical and economic terms four ecosystem services in order to provide for the first time accounting tables as proposed by the SEEA-EEA. Data and model customization is important to improve accuracy, transparency, and reliability in results used for applications at different spatial levels.Nevertheless temporal comparability will keep on suffering as long as more accurate models or data inputs with a better quality will modify the final assessment of the ecosystem service even in absence of any significant changes in their real availability.

36

6. ANNEXES

Annex I

Landcover HIArtificialSurface, 7landcover:ArableLand, 6landcover:NonIrrigatedArableLand, 5landcover:PermanentlyIrrigatedArableLand, 5landcover:RiceField, 5landcover:PermanentCropland, 4landcover:Vineyard, 4landcover:FruitAndBerryPlantation, 4landcover:OliveGrove, 4landcover:Pastureland, 4landcover:AnnualCroplandAssociatedWithPermanent, 4landcover:ComplexCultivationPatternedLand, 4landcover:AgriculturalLandWithNaturalVegetation, 4landcover:AgroForestryLand, 4landcover:BroadleafForest, 3landcover:ConiferousForest, 3landcover:MixedForest, 3landcover:NaturalGrassland, 3landcover:MoorAndHeathland, 2landcover:SclerophyllousVegetation, 2landcover:TransitionalWoodlandScrub, 2landcover:BeachDuneAndSand, 2landcover:BareArea, 1landcover:BareRock, 1landcover:LichenMoss, 1landcover:SparseVegetation, 2landcover:BurnedLand, 5landcover:GlacierAndPerpetualSnow, 1landcover:Wetland, 1landcover:Mangrove, 1landcover:InlandMarsh, 2landcover:PeatBog, 2landcover:SaltMarsh, 2landcover:Saline, 5landcover:IntertidalFlat, 1landcover:WaterBody, 1

Table 1: Human influence (HI) on natural environment, or alternatively called Naturalness

37

Annex II

Table n. 1 – NESTING_SUITABILITYlandcover:ContinuousUrbanFabric, 0.1landcover:DiscontinuousUrbanFabric, 0.3landcover:IndustrialCommercialUnits, 0.1landcover:RoadRailNetwork, 0.3landcover:PortArea, 0.3landcover:Airport, 0.3landcover:MineralExtraction, 0.3landcover:DumpArea, 0.05landcover:ConstructionArea, 0.1landcover:GreenUrbanArea, 0.3landcover:SportLeisureFacility, 0.3landcover:NonIrrigatedArableLand, 0.2landcover:PermanentlyIrrigatedArableLand, 0.2landcover:RiceField, 0.2landcover:Vineyard, 0.4landcover:FruitAndBerryPlantation, 0.4landcover:OliveGrove, 0.5landcover:Pastureland, 0.3landcover:AnnualCroplandAssociatedWithPermanent, 0.4landcover:ComplexCultivationPatternedLand, 0.4landcover:AgriculturalLandWithNaturalVegetation, 0.7landcover:AgroForestryLand, 1landcover:BroadleafForest, 0.8landcover:ConiferousForest, 0.8landcover:MixedForest, 0.8landcover:NaturalGrassland, 0.8landcover:MoorAndHeathland, 0.9landcover:SclerophyllousVegetation, 0.9landcover:TransitionalWoodlandScrub, 1landcover:BeachDuneAndSand, 0.3landcover:BareRock, 0landcover:SparseVegetation, 0.7landcover:BurnedLand, 0.3landcover:GlacierAndPerpetualSnow, 0landcover:InlandMarsh, 0.3landcover:PeatBog, 0.3landcover:SaltMarsh, 0.3landcover:Saline, 0landcover:IntertidalFlat, 0landcover:Watercourse, 0landcover:NonVegetatedStillWaterBody, 0landcover:CoastalLagoon, 0.2landcover:Estuary, 0landcover:SeaAndOcean, 0

Table n.2 - FLOWER_AVAILABILITYlandcover:ContinuousUrbanFabric, 0.05landcover:DiscontinuousUrbanFabric, 0.3landcover:IndustrialCommercialUnits, 0.05landcover:RoadRailNetwork, 0.25landcover:PortArea, 0landcover:Airport, 0.1landcover:MineralExtraction, 0.05landcover:DumpArea, 0landcover:ConstructionArea, 0landcover:GreenUrbanArea, 0.25

38

landcover:SportLeisureFacility, 0.05landcover:NonIrrigatedArableLand, 0.2landcover:PermanentlyIrrigatedArableLand, 0.05landcover:RiceField, 0.05landcover:Vineyard, 0.6landcover:FruitAndBerryPlantation, 0.9landcover:OliveGrove, 0.4landcover:Pastureland, 0.2landcover:AnnualCroplandAssociatedWithPermanent, 0.5landcover:ComplexCultivationPatternedLand, 0.4landcover:AgriculturalLandWithNaturalVegetation, 0.75landcover:AgroForestryLand, 0.5landcover:BroadleafForest, 0.9landcover:ConiferousForest, 0.3landcover:MixedForest, 0.6landcover:NaturalGrassland, 1landcover:MoorAndHeathland, 1landcover:SclerophyllousVegetation, 0.75landcover:TransitionalWoodlandScrub, 0.85landcover:BeachDuneAndSand, 0.1landcover:BareRock, 00landcover:SparseVegetation, 0.35landcover:BurnedLand, 0.2landcover:GlacierAndPerpetualSnow, 0landcover:InlandMarsh, 0.75landcover:PeatBog, 0.5landcover:SaltMarsh, 0.55landcover:Saline, 0landcover:IntertidalFlat, 0landcover:Watercourse, 0landcover:NonVegetatedStillWaterBody, 0landcover:CoastalLagoon, 0landcover:Estuary, 0landcover:SeaAndOcean, 0

Table 3. Dependency rates on insect pollination (CPi) of 30 crops types from Klein et al 2007

Crops Dependency Rates

almond 0,65

apple 0,65

apricot 0,65

cherry 0,65

figs 0,25

citrusnes 0,05

kiwi 0,9

Lemon lime 0,05

melon 0,9

orange 0,05

peach 0,65

pear 0,65

persimmon 0,05

plum 0,65

rasberry 0,65

strawberry 0,25

39

tangerine 0,05

watermelon 0,9

bean 0,25

broadbean 0,25

chilly 0,05

eggplant 0,25

flax 0,05

legumenes 0,25

pumpkin 0,9

rapeseed 0,25

soybean 0,25

sunflower 0,25

tomato 0,05

turnipfor 0,65

40

7. REFERENCES

7.1 Outdoor Recreation

Badura T., Ferrini S., Agarwala M. and Turner K. (2017) Valuation for Natural Capital and Ecosystem Accounting. Synthesis report for the European Commission. Centre for Social and Economic Research on the Global Environment, University of East Anglia. Norwich 2017.

Clark, R.N., Stankey, G.H., 1979. The recreation opportunity spectrum: a framework for planning, management, and research. The Recreation Opportunity Spectrum: A Framework for Planning, Management, And Research.

Fuleky, P., 2006. Anatomy of a Cobb-Douglas Type Production/Utility Function in Three Dimensions. Department of Economics, University of Washington.

Geurs, K.T., van Eck, J.R., 2001. Accessibility measures: review and applications. Evaluation of Accessibility Impacts of Land-use Transportation Scenarios, And Related Social and Economic Impact.

Joyce, K., Sutton, S., 2009. A method for automatic generation of the recreation opportunity spectrum in New Zealand. Appl. Geogr. 29 (3), 409–418 (Jul).

La Notte A, Vallecillo S, Polce C, Zulian G, Maes J. 2017. Implementing an EU system of accounting for ecosystems and their services. Initial proposals for the implementation of ecosystem services accounts, EUR 28681 EN; Publications Office of the European Union, Luxembourg, doi:10.2760/214137, JRC107150.

Obst, C., Hein, L., & Edens, B. (2015) National Accounting and the Valuation of Ecosystem Assets and Their Services. Environmental and Resource Economics. http://doi.org/10.1007/s10640-015-9921-1

Paracchini, M.L., Zulian, G., Kopperoinen, L., Maes, J., Schägner, J.P., Termansen, M., et al., 2014. Mapping cultural ecosystem services: a framework to assess the potential for outdoor recreation across the EU. Ecol. Indic. 45, 371–385 (Oct).

Uchida, H., Nelson, A., 2010. Agglomeration Index: Towards a NewMeasure of Urban Concentration. No. 2010, 29. (Working paper).World Institute for Development Economics Research.

UN et al., 2014 The System of Environmental-Economic Accounting 2012—Experimental Ecosystem Accounting (SEEA Experimental Ecosystem Accounting), Copyright © 2014 United Nations, European Union, Food and Agriculture Organization of the United Nations, Organisation for Economic Co-operation and Development, World Bank Group

UNEP / UNSD / CBD project on Advancing Natural Capital Accounting SEEA Experimental Ecosystem Accounting: Technical Recommendations, 2017.

UNEP-WCMC, IUCN, 2016. The World Database on Protected Areas (WDPA). UK, Cambridge (Oct).

41

7.2 Crop Pollination


Corbet, S.A., Fussell,M., Ake, R., Fraser, A., Gunson, C., Savage, A., et al., 1993. Temperature and the pollinating activity of social bees. Ecol. Entomol. 18 (1), 17–30 (Feb).

Danner, N., Molitor, A.M., Schiele, S., Härtel, S., Steffan-Dewenter, I., 2016. Season and landscape composition affect pollen foraging distances and habitat use of honeybees. Ecol. Appl. 26 (6), 1920–1929 (Sep).

FREE, J B (1993) John Brand Free: Insect pollination of crops. 2nd ed. Academic Press, London, pp. 96–104

Gathmann, A., Tscharntke, T., 2002. Foraging ranges of solitary bees. J. Anim. Ecol. 71 (5), 757–764 (Sep).

Klein, A.M., Vaissiere, B.E., Cane, J.H., Steffan-Dewenter, I., Cunningham, S.A., Kremen, C., et al., 2007. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. Lond. B Biol. Sci. 274 (1608), 303–313.

Krupke, C.H., Hunt, G.J., Eitzer, B.D., Andino, G. & Given, K. 2012. Multiple routes of pesticide exposure for honey bees living near agricultural fields. PLoS One, 7(1): e29268.

La Notte A, Vallecillo S, Polce C, Zulian G, Maes J. 2017. Implementing an EU system of accounting for ecosystems and their services. Initial proposals for the implementation of ecosystem services accounts, EUR 28681 EN; Publications Office of the European Union, Luxembourg, doi:10.2760/214137, JRC107150

Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R.,Williams, N., Greenleaf, S., 2009. Modelling pollination services across agricultural landscapes. Ann. Bot. 103 (9), 1589–1600 (Jun 1).

Monfreda, C., Ramankutty, N., Foley, J.A., 2008. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Glob. Biogeochem. Cycles 22 (1) (Mar).

Moron, D., Grzes, I.M., Skorka, P., Szentgyörgyi, H., Laskowski, R., Potts, S.G. & Woyciechowski, M. 2012. Abundance and diversity of wild bees along gradients of heavy metal pollution. J. Appl. Ecol., 49(1): 118-125.

Moron, D., Szentgyörgyi, H., Skórka, P., Potts, S.G. & Woyciechowski, M. 2014. Survival, reproduction and population growth of the bee pollinator, Osmia rufa (Hymenoptera: Megachilidae), along gradients of heavy metal pollution. Insect Conserv. Diver., 7(2): 113-121.

42

Potts S., Biesmeijer K., Bommarco R., Breeze T., Carvalheiro L., Franzén M., González-Varo J.P., Holz-schuh A., Kleijn D., Klein A.-M., Kunin, B., Lecocq T., Lundin O., Michez D., Neumann P., Nieto A., Penev L., Ras-mont P., Ratamäki O., Riedinger V., Roberts S.P.M., Rundlöf M., Scheper J., Sørensen P., Steffan-Dewenter I., Stoev P., Vilà M., Schweiger O. (2015) Status and trends of European pollinators. Key findings of the STEP project. Pensoft Publishers Priess, J.A., Mimler, M., Klein, A.M., Schwarze, S., Tscharntke, T.& Steffan-Dewenter, I. (2007). Linking deforestation scenarios to pollination services and economic returns in coffee agrofor-estry systems. Ecol. Appl., 17, 407–417.

Reganold, J.P., Glover, J.D., Andrews, P.K., & Hinman, H.R. 2001. Sustainability of three apple production systems. Nature, 410(6831): 926-930.

UN et al., 2014 The System of Environmental-Economic Accounting 2012—Experimental Ecosystem Accounting (SEEA Experimental Ecosystem Accounting), Copyright © 2014 United Nations, European Union, Food and Agriculture Organization of the United Nations, Organisation for Economic Co-operation and Development, World Bank Group.


Zulian, G., Polce, C., Maes, J., 2014. ESTIMAP: a GIS-based model to map ecosystem services in the European union. Ann. Bot. 4, 1–7.

7.3 Flood Regulation

Allen, M. R., and W. J. Ingram, 2002: Constraints on the future changes in climate and the hydrological cycle. Nature, 419, 224–232.


Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A., 2005. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25 (15), 1965–1978 (Dec).

Kharin, V. V., F. W. Zwiers, X. Zhang, and G. C. Hegerl (2007), Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations, J. Clim., 20, 1419–1444, doi:10.1175/JCLI4066.1.

Kirkby, M.J., Beven, K.J., 1979. A physically based, variable contributing area model of basin hydrology. Hydrol. Sci. J. 24 (1), 43–69.

Manfreda, S., Di Leo, M., Sole, A., 2011. Detection of flood-prone areas using digital elevation models. J. Hydrol. Eng. 16 (10), 781–790 (Oct).

Pall, P., M. R. Allen, and D. A. Stone (2007), Testing the Clausius–Clapeyron constraint on changes in extreme precipitation under CO2 warming, Clim. Dyn., 28, 351–363, doi:10.1007/s00382-006-0180-2.

43

Soil Conservation Service, 1985. National Engineering Handbook Section. vol. 4. Hydrology, Washington, DC.

Stürck, J., Poortinga, A., Verburg, P.H., 2014. Mapping ecosystem services: the supply and demand of flood regulation services in Europe. Ecol. Indic. 38, 198–211 (Mar).

Trenberth, K.E., Dai, A., Rasmussen, R.M., Parsons, D.B., 2003. The changing character of precipitation. Bull. Am. Meteorol. Soc. 84 (9), 1205–1217 (Sep).

UN et al., 2014 The System of Environmental-Economic Accounting 2012—Experimental Ecosystem Accounting (SEEA Experimental Ecosystem Accounting), Copyright © 2014 United Nations, European Union, Food and Agriculture Organization of the United Nations, Organisation for Economic Co-operation and Development, World Bank Group


Utsumi, N., Seto, S., Kanae, S., Maeda, E.E., Oki, T., 2011. Does higher surface temperature intensify extreme precipitation? Geophys. Res. Lett. 38 (16).

Ward, P.J., Jongman, B., Salamon, P., Simpson, A., Bates, P., De Groeve, T., et al., 2015. Usefulness and limitations of global flood risk models. Nat. Clim. Chang. 5 (8).

Zeng, Z., Tang, G., Hong, Y., Zeng, C., Yang, Y., 2017. Development of an NRCS curve number global dataset using the latest geospatial remote sensing data for worldwide hydrologic applications. Remote Sens. Lett. 8 (6), 528–536.

7.4 Water Provision

Arcadis, The role of water pricing and water allocation in agriculture in delivering sustainable water use in Europe – final report – annexes, European Commission Project number 11589 | February 2012.


Braca, G., e Ducci, D., 2018, Development of a GIS Based Procedure (BIGBANG 1.0) for Evaluating Groundwater Balances at National Scale and Comparison with Groundwater Resources Evaluation at Local Scale. In Groundwater and Global Change in the Western Mediterranean Area, Calvache, M.L., Duque, C., Pulido-Velazquez, D. (Eds.), Springer, January 2018. DOI10.1007/978-3-319-69356-9_7.

Celico P., Prospezioni Idrogeologiche Vol. I e II. Liguori Editore, Napoli, 1988.

Fioravanti G, Toreti A, Fraschetti P, Perconti W, Desiato F (2010) Gridded monthly temperatures over Italy. EMS Annual Meeting Abstracts, 7, EMS2010 -306, ECAC Conference, Zurich, 13–17 Sept 2010.

La Notte A, Vallecillo S, Polce C, Zulian G, Maes J. 2017. Implementing an EU system of accounting for ecosystems and their services. Initial proposals for the implementation

44

of ecosystem services accounts, EUR 28681 EN; Publications Office of the European Union, Luxembourg, doi:10.2760/214137, JRC107150.

Munafò, M., Salvati, L., Zitti M., 2013: Estimating Soil Sealing Rate at National Level - Italy as a Case Study. Ecological Indicators, 26, 137–140.

Thornthwaite, C.W., e Mather, J.R., 1955: The water balance. Laboratory of Climatology, 8, Centerton NJ.

Toth G, Jones A, Montanarella L (eds) (2013) LUCAS topsoil survey. Methodology, data, results. JRC Technical Reports. Luxembourg. Publications office of the European Union, EUR 26102—scientific, technical research series—ISSN 1831-9424 online); ISBN 978-92-7932542-7.

UN et al., 2014 The System of Environmental-Economic Accounting 2012—Experimental Ecosystem Accounting (SEEA Experimental Ecosystem Accounting), Copyright © 2014 United Nations, European Union, Food and Agriculture Organization of the United Nations, Organisation for Economic Co-operation and Development, World Bank Group.


7.5 Model Data Sources

OUTDOOR RECREATION DATA

Land Cover Corine Land Cover 2012 http://www.sinanet.isprambiente.it/it

Naturalness DB Elenco Ufficiale Aree Protette (EAUP) MATTM- Geoportale Nazionale http://www.pcn.minambiente.it/Carta Nazionale di Copertura del Suolo 2017River and lakes ISPRA

Accessibility Open street map

Travel time to major cities: A global map of Accessibility Nelson , 2008 http://forobs.jrc.ec.europa.eu/products/gam/

Population density Gridded Population of the World v. 4.10 http://sedac.ciesin.columbia.edu/data/collection/gpw-v4

Dataset on natural areas visitors

Schägner, Maes, Brander, Paracchini, Hartje, Duboi (2017), Monitoring recreation across European nature areas: A geo-database of visitor counts, a review of literature and a call for a visitor counting reporting standard, Journal of Outdoor Recreation and Tourism

Travel cost Estimates on simulated average national data

CROP POLLINATION DATA

45

http://sedac.ciesin.columbia.edu/data/collection/gpw-v4

http://forobs.jrc.ec.europa.eu/products/gam/

http://www.pcn.minambiente.it/

http://www.sinanet.isprambiente.it/it

Land Cover Corine Land Cover 2012http://www.sinanet.isprambiente.it/itCarta Nazionale di Copertura del Suolo-ISPRA

Nesting suitability "Zulian, G.; Paracchini, M. L.; Maes, J.; Liquete, C. & others (2013), 'ESTIMAP ecosystem services mapping at European scale'

Flower availability Zulian, G.; Paracchini, M. L.; Maes, J.; Liquete, C. & others (2013), 'ESTIMAP ecosystem services mapping at European scale'

Irradiance and temperature WorldClim 2.0 http://www.worldclim.org/Annual average for 1970-2000

Croplands distribution Monfreda, C., N. Ramankutty, and J.A. Foley (2008). Farming the planet. Part 2: Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochemical Cycles 22, GB1022, http://www.earthstat.org/data-download/

Insect activity Corbet, S.A., Fussell,M., Ake, R., Fraser, A., Gunson, C., Savage, A., et al., 1993. Temperature and the pollinating activity of social bees. Ecol. Entomol. 18 (1), 17–30 (Feb).

Pollinated Crop yield Klein, A.M., Vaissiere, B.E., Cane, J.H., Steffan-Dewenter, I., Cunningham, S.A., Kremen, C., et al., 2007. Importance of pollinators in changing landscapes for world crops. Proc. R.Soc. Lond. B Biol. Sci. 274 (1608), 303–313.

Crop Production http://arearica.crea.gov.it/report_d.php

Crop price http://arearica.crea.gov.it

FLOOD REGULATION DATA

Land Cover Corine Land Cover 2012 http://www.sinanet.isprambiente.it/it

Rainfall Worldclim 2.0Mean temperature rainy season global

Worldclim 2.0

Latitude (wcs) World ClimCurve Number Zeng et al., 2017Demography Gridded Population of the World v. 4.10Residential e commercial assets

Corine Land Cover 2012 (commercial and residential)http://www.sinanet.isprambiente.it/it

Real estate quotation Real Estate Observatory (Italian Tax Revenue Agency)

WATER SUPPLY DATA

Land Cover Carta Nazionale di Copertura del Suolo 2017-ISPRAWater potential flow m3/anno

Big Bang Model, ISPRA, 2016Water bodies extension Sistema SINTAI- ISPRA 2017Water abstraction Focus ‘Giornata mondiale dell’acqua’ 2017 and 2018 ISTAT

46





http://www.earthstat.org/data-download/

http://www.earthstat.org/data-download/


Water pricing for potable use

Elaboration on Arera data

Water pricing in agriculture Arcadis, The role of water pricing and water allocation in agriculture in delivering sustainable water use in Europe – final report – annexes, European Commission Project number 11589, 2012.

NATURAL CAPITAL ACCOUNTS

(UN, 2014): The System of Environmental-Economic Accounting 2012—Experimental Ecosystem Accounting (SEEA Experimental Ecosystem Accounting), Copyright © 2014 United Nations, European Union, Food and Agriculture Organization of the United Nations, Organisation for Economic Co-operation and Development, World Bank Group

La Notte A, Vallecillo S, Polce C, Zulian G, Maes J. 2017. Implementing an EU system of accounting for ecosystems and their services. Initial proposals for the implementation of ecosystem services accounts, EUR 28681 EN; Publications Office of the European Union, Luxembourg, doi:10.2760/214137, JRC107150

47

Documents

circabc.europa.eu€¦ · Web viewDevelopment of an NRCS curve number global dataset using the latest geospatial remote sensing data for worldwide hydrologic applications. Remote