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Facing uncertainty in ecosystem services-based resource management
Journal of Environmental Management 127 (2013) S145eS154
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Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Facing uncertainty in ecosystem services-based resource management Adrienne Grêt-Regamey a, *, Sibyl H. Brunner a, Jürg Altwegg a, Peter Bebi b a Planning of Landscape and Urban Systems, Swiss Federal Institute of Technology ETH Zurich, HIL H 51.4, Wolfgang-Pauli-Str. 15, 8093 Zurich, Switzerland b Community Ecology, WSL Institute for Snow and Avalanche Research SLF, Flüelastr. 11, 7260 Davos Dorf, Switzerland
a r t i c l e i n f o
a b s t r a c t
Article history: Received 25 November 2011 Received in revised form 24 July 2012 Accepted 25 July 2012 Available online 22 August 2012
The concept of ecosystem services is increasingly used as a support for natural resource management decisions. While the science for assessing ecosystem services is improving, appropriate methods to address uncertainties in a quantitative manner are missing. Ignoring parameter uncertainties, modeling uncertainties and uncertainties related to humaneenvironment interactions can modify decisions and lead to overlooking important management possibilities. In this contribution, we present a new approach for mapping the uncertainties in the assessment of multiple ecosystem services. The spatially explicit risk approach links Bayesian networks to a Geographic Information System for forecasting the value of a bundle of ecosystem services and quantifies the uncertainties related to the outcomes in a spatially explicit manner. We demonstrate that mapping uncertainties in ecosystem services assessments provides key information for decision-makers seeking critical areas in the delivery of ecosystem services in a case study in the Swiss Alps. The results suggest that not only the total value of the bundle of ecosystem services is highly dependent on uncertainties, but the spatial pattern of the ecosystem services values changes substantially when considering uncertainties. This is particularly important for the long-term management of mountain forest ecosystems, which have long rotation stands and are highly sensitive to pressing climate and socio-economic changes. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: Uncertainty Mapping Ecosystem services Risk Bayesian network Forest management
1. Introduction The concept of ecosystem services is increasingly used as a support for natural resource management decisions. Its suitability to bridge human welfare and the natural environment gives a common platform for communicating the value of ecosystems to stakeholders (Farley, 2008; Daily et al., 2009; Sutton and Constanza, 2002). While the number of papers mentioning “ecosystem services” has risen exponentially since 1990 (Fisher et al., 2009), the science of ecosystem services has not yet had the time to develop approaches to deal with the complexity of the operationalization of the concept (Holling, 2001; Ghazoul, 2007; Kienast et al., 2009). Several authors provide valuable conceptual ideas for improving ecosystem services’ research (Boyd and Banzhaf, 2007; Ghazoul, 2007; Luck et al., 2009; McCauley, 2006; Armsworth et al., 2007), but lack practical suggestions for implementation. Especially the management of multiple ecosystem services still relies on assumptions related to spatial trade-offs over time.
* Corresponding author. Tel.: þ41 44 633 29 57. E-mail addresses: [email protected] (A. Grêt-Regamey), [email protected] (S.H. Brunner), [email protected] (J. Altwegg), [email protected] (P. Bebi). 0301-4797/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jenvman.2012.07.028
Decisions in ecosystem management are based on assumptions of a potential development and a future state of the ecosystems, and are thus inherently laden with uncertainty. In order to mitigate risks of ecosystem changes, decision analyses require estimates of the consequences of mitigation actions with some stated degree of confidence (Carpenter et al., 2009). Uncertainty can result because drivers (and consequently model structures) are uncertain, parameters are uncertain, and unknown human responses to ecosystem change (or to forecasts of ecosystem change) affect outcomes. Most daunting is the uncertainty that results from strong nonlinearities and stochasticity (Clark et al., 2001). Omitting the communication of such uncertainties could lead to overlooking important management possibilities, thus providing misleading decision-support information. In ecosystem services’ research, uncertainty has been addressed by stating the degree of scientific consensus (MA, 2005), but rigorous quantitative methods are missing. Over the past two decades, many different formal techniques to quantify specific aspects of uncertainty and several integrative approaches have been developed for dealing with uncertain information in other disciplines (Ascough et al., 2008). One of the traditional ways to address uncertainty is risk analysis, where risk is defined as the set of possibilities each with quantified probabilities and quantified consequences (Hubbard, 2009). While
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such approaches have been applied successfully in ecosystem research (e.g. Scholze et al., 2006), using quantitative risk assessments for estimating uncertainties in predicting a bundle of ecosystem services is a most pressing area for ecosystem services’ research. Managing ecosystem services needs the explicit consideration of spatial synergies and trade-offs. Mapping multiple ecosystem services helps showing the spatial distribution of ecosystem services benefits and costs, thus allowing to decide where efforts can be of most value (Chan et al., 2006; Naidoo and Rickets, 2006; Grêt-Regamey et al., 2008; Egoh et al., 2008; Nelson et al., 2009; Chen et al., 2009). Especially in mountain areas, where the provision of ecosystem services is highly heterogeneous (Grêt-Regamey et al., 2012), information about the supply of and demand for ecosystem services must be available in a spatially explicit manner. Uncertainties partitioning has been conducted on a pixel-by-pixel basis to account for spatial variation in natural resources and environmental systems (Gertner et al., 2004; Wang et al., 2009). But despite advances in the spatially explicit quantification and valuation of ecosystems services, the spatial outputs of the ecosystem services models have been considered as hard labeling until now. New computational approaches represented by hierarchical models accommodate multiple stochastic elements, but have not yet been exploited in ecosystem services’ research. Especially, Bayesian networks (BN) are known to facilitate the explicit modeling of involved uncertainties in a probabilistic framework (Friis-Hansen, 2000; Faber et al., 2002). The basic idea behind Bayesian statistics is that the lack of knowledge is an uncertainty that should be treated by probabilistic reasoning in a similar way to other types of uncertainty. BN have the advantages that (1) they picture the explicit relationships between the variables of the model, thus, results can be presented to decision-makers in a concise matter, (2) quantitative data and expert knowledge can be taken into account simultaneously, and (3) they can be updated as soon as new evidence becomes available enabling iterative decision processes and adaptive resource management (e.g. Ellison, 1996; Ascough et al., 2008). In the last decade, applications of Bayesian statistics have spread into many areas in environmental and resource management (for a review, see Ascough et al., 2008), but were mostly non-spatial. Aspinall (1992) was one of the pioneers trying to explicitly address uncertainties in a Geographic Information System (GIS) using a Bayesian method. Other attempts at incorporating BN in spatially explicit decision support tools are found in other disciplines, e.g. in risk assessment of desertification of burned forest (Strassopoulou et al., 1998), in avalanche risk assessment (Grêt-Regamey and Straub, 2006), in vulnerability assessment of marine landscapes (Stelzenmüller et al., 2010) and in prediction of land-use change for reforestation planning (Ordóñez Galán et al., 2009), but we are not aware of any study investigating the effectiveness of the approach in mapping uncertainties of future ecosystem services provisions. In this contribution, we present a new approach for mapping the uncertainties in the assessment of multiple ecosystem services. The spatially explicit risk approach links Bayesian networks (BN) to a GIS for forecasting the value of a bundle of ecosystem services and quantifies the uncertainties related to the outcomes in a spatially explicit manner. We illustrate the approach in a case study in the Swiss Alps e the ‘Landschaft Davos’. We focus on ecosystem services provided by forests including carbon sequestration, wood production and avalanche protection. On the one hand, these ecosystem services are key for the regional economy (GrêtRegamey and Kytzia, 2007). On the other hand, they cover the three main ecosystem services valuation scales: carbon sequestration is valued on a global scale, wood production on a regional
scale and avalanche protection is selected as an ecosystem service which is locally available and cannot be traded with other regions. We compare the monetary risks calculated using the BN approach to those obtained by a traditional ecosystem services valuation. We show how expert knowledge helps diminish uncertainties in the prediction of ecosystem services values. We finally map risks and most notably uncertainties related to the outcomes showing vulnerability patterns that decision-makers must consider when managing a bundle of ecosystem services. Such information will support the success of ecosystem services-based management by increasing transparency in the information provided to resource managers. 2. Risk assessment using Bayesian networks Risk analysis has traditionally been applied to estimate the dangers from particular natural hazards (e.g. Einstein, 1988; Cruden and Fell, 1997). Risk R is defined as the function of the probabilities P(Ai) of all potential events Ai and the expected consequences under these events C(Ai):
R ¼
X ðPðAi Þ$CðAi ÞÞ
(1)
i
In ecosystem services-based resource management risk corresponds to the sum of all probabilities of occurrence of a particular land use P(Ai) and the consequences in the flow of ecosystem services given this land use C(Ai). R can be understood as the risk of a change in the ecosystem services values under land-use changes. Since the Bayesian probability theory belongs to the most consistent probabilistic framework for decision-making subject to uncertainty (Faber and Maes, 2005), we embed the quantification of risk into a BN. Pearl (1988) and Jensen (2001) give a comprehensive summary of BN. In short, BN are directed acyclic graphs in which nodes represent random variables (X ¼ X1,., Xn), and arcs represent causal relations among these variables. The nodes are characterized by their associated conditional probability tables, conditional on the states of any parent node that interact with it. The joint probability distribution P(x) of a BN is given as:
PðxÞ ¼ Pðx1 ; :::; xn Þ ¼
Yn i¼1
Pðxi jpaðxi ÞÞ
(2)
where pa(xi) is a set of values of the parents of a variable Xi. The distribution of Xi given its parents may have any form, but efficient algorithms for solving the BN are available only for the case where the nodes have discrete or Gaussian distributions. Thus, we restrict ourselves to variables with discrete states. BN enhance the utilization of expert knowledge in risk assessment: probabilities in the network can be updated when new information becomes available. The goal of inference is to find the posterior conditional distribution of a subset of the variables, conditional on known values for some other subset (the evidence e). For example, when the state of a variable is observed to be e, this information will propagate through the network and the joint probability of all nodes will change to its posterior:
PðxjeÞ ¼ Pðx; eÞ=PðeÞ
(3)
In order to calculate the expected ecosystem services values in a spatially explicit manner, the BN is linked to a GIS. Fig. 1 illustrates the framework: for each pixel, the evidence provided by the spatially explicit datasets is propagated through the BN, which calls GIS-based process models for the quantification and economic valuation models for the valuation of the ecosystem services. The results of the quantification process and the economic models fill the nodes with the probabilistic distributions. The uncertainty is
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Fig. 1. Conceptual framework to map uncertainties in ecosystem services (ESS) values under future land use linking a Bayesian network (BN) to a Geographic Information System (GIS). In step 1, a land-use scenario is set up by assigning probabilities of different land uses to each grid cell; in step 2, we quantify the resulting probabilities of the change in the flow of ESS using GIS-based models; in step 3, we convert these quantities into monetary units applying different economic valuation methods.
given in terms of the standard deviation of the estimated ecosystem services values. The effectiveness of the approach is showed by comparing the ecosystem services values calculated using the BN risk approach and a traditional approach for risk analysis. The data used is the same as the data in the BN approach, but as opposed to the calculation described above, the risk is computed from the expected value of each variable, thus, ignoring the probability distribution of the single variables and their joint probability distribution. 3. Case study The ‘Landschaft Davos’ is a 254 km2 landscape around the mountain town Davos in the Swiss Alps (Fig. 2). While the altitude of the valley bottom lies between 1400 and 1600 m.a.s.l., the highest peaks reach over 3000 m.a.s.l. The area is a popular tourist destination that can accommodate up to 28,000 visitors during winter peak season in addition to its 11,000 permanent residents. The main valley is NEeSW oriented and hosts the urban core settlement and most of the tourist infrastructure. The four side valleys have maintained their rural character with a few small, scattered settlements and a typical Alpine landscape shaped by mountain agriculture. The BN network that represents the causal relations among the factors that influence the expected forest ecosystem services values (carbon sequestration, avalanche protection, and wood production) is given in Fig. 3. The most important variable in the BN is the type of forest, which drives the provision of all three ecosystem services. Along with the elevation it determines the carbon storage potential of the forest and through the carbon tax the value of carbon sequestration. The value of wood production depends directly on the costs and benefits of harvesting the resource. For valuing avalanche protection, modeled snow pressures and the type of buildings under risk are particularly important. The states and the content of the conditional probability tables corresponding to each node are described in Appendix A.
The land-use scenario considered in the case study corresponds to a trend scenario that assumes a continuous land-use change as observed between 1950 and 2000. We model the forest in the year 2050 based on a forest map in 2000 using transition matrices for a 50-years period derived from land-use maps of 1950 and 2000 (Kulakowski et al., 2011). The model assigns to each grid cell an expected forest type by a random generator, and separately lists the probabilities of conversion to all other considered forest types. Most noticeable changes are an expansion of forest cover at altitude where Alpine pastures are abandoned as well as a slight densification of the forest. We quantify the changes in the selected ecosystem services by using state-of-the-art GIS-based models. For the quantification of avalanche protection, we use a numerical 2-D avalanche model (Gruber, 1999), that calculates the avalanche run-out areas and associated pressures in a spatially explicit manner (for details see Grêt-Regamey and Straub, 2006). By overlaying the run-out areas with a building types layer, we identify the potentially endangered dwellings. Carbon sequestration is quantified through the carbon storage capacity of the forest, and wood production is quantified through the harvestable amount of wood. Both measures are estimated based on the change in wood stock over the 50-years modeling period (details of the calculations are given in Appendix B and C). Main spatially explicit data sources include the map of different forest types from Kulakowski et al. (2011) on a 25 m 25 m grid, the vector25 dataset based on the National Map 1:25 000, which provides information on locations of buildings on a 5 m 5 m grid (Swisstopo, 2004), the 25 m digital elevation model (DEM25, Swiss Federal Office of Topography) and a map of optimal harvesting techniques modeled using Bont (2009). The data delivered by the quantification step is converted into monetary units applying different valuation methods in accordance with the ecosystem services to be valued. We monetize avalanche protection by pricing damages to the buildings and fatalities using a risk-analysis approach (Grêt-Regamey and
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Fig. 2. Map of the ‘Landschaft Davos’. The insert on the top left shows the location of the study area in Switzerland. The green forest area of the year 2000 is denser in regions of darker color.
Straub, 2006) and transferring the total value back to the protective forest area. In order to value carbon sequestration, we use the carbon market price (values are given in Appendix A). The value of wood production is the difference between the timber market price and the costs for the harvest of the resource (Appendix A and C). We update our network with expert knowledge on land-use change and future carbon and wood prices, respectively (Fig. 3, nodes tagged with ‘e’). For each variable, we asked five independent experts for an estimate of the probability distribution of the variable. The posterior distributions that fed the BN are calculated as an average of the five expert opinions. Several algorithms exist to facilitate the updating of a prior probabilistic model using Bayes’ theorem (Murphy, 2001). In our study, we select the BN modeling shell Hugin (Hugin Expert, 2005). The propagation of the input uncertainties related to the modeling uncertainties and inaccuracies in the spatially explicit input layers as well as uncertainties in the socio-economic variables results in a probability distribution of expected monetary values of the selected ecosystem services. The risk of change in the ecosystem services values in each 25 m 25 m raster cell is expressed as the function of these values and their occurrence probabilities and is presented in a map (ArcGIS 8.3, ESRI, 2000).
4. Results Including uncertainties in ecosystem services quantification and valuation modifies significantly the predicted value of the bundle of ecosystem services. Table 1 presents the total ecosystem services values and related standard deviations in the case study region estimated using a traditional risk approach, a Bayesian network, and including expert update. Protection from avalanches is by far the most highly valued service, making up 98% of the total ecosystem services. The value of carbon sequestration is more than an order of magnitude lower and wood production is not profitable in Davos. Accounting for uncertainties in estimating carbon sequestration modifies its total value by 48%. The difference is mainly a consequence of including uncertainty in the variable ‘carbon price’: while the traditional approach is based on a single fix carbon tax of 50 CHF/t, experts believe that the carbon price will increase in the future and higher market prices are used in the BN approach. Similarly, considering experts’ estimates of the future wood price in the corresponding variable enhances the total value of the wood production service, even though the value remains negative. In contrast, the total value of the avalanche protection service is lower if calculated with the BN approach. This results mainly from the uncertainties introduced in the
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Fig. 3. Bayesian network of the valuation of three forest ecosystem services (ESS). The yellow and gray oval nodes are the variables used in the quantification and valuation of the ESS, the darker shade show the spatially explicit input variables; the green rhomboids are the utilities in terms of changes in ESS values; the red box is a decision node to set choices of possible land-use changes. Nodes tagged with an “e” are updated by expert knowledge. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
variables ‘building damage’, ‘persons per building’ and ‘lethality in building’ that reduce the total cost of a destroyed building, and the probability distribution introduced in the variable ‘forest type’ that decreases the forest stock on avalanche run-out areas and therefore the potential of these areas to protect the endangered buildings. As the latter probability is confirmed by the evidence of experts the total value of the ecosystem services increases after the update of the network. Although the influence of accounting for uncertainties on the total ecosystem service value varies between ecosystem services, the update of the BN by experts consistently reduces the uncertainties in the valuation. Fig. 4 maps the future value of the carbon sequestration service. The decrease in uncertainty due to integration of expert knowledge in the ecosystem service valuation is revealed in a sharpening of the spatial pattern of the ecosystem service value. If the ecosystem services value is calculated using a traditional risk approach, high values in currently forested areas are visible, but there is no clear spatial pattern in the ecosystem service value (Fig. 4a). Certain unfavorable locations to sequester carbon show a high carbon sequestration capacity, while others in the middle of a forested area have a zero storage potential. This scattering is due to the calculation of land-use changes using a transition probability matrix-based approach, thus, generating randomly different forest types based on preceding patterns of land-use change. By considering uncertainties in the input data and propagating them
through the BN, the pattern becomes clearer (Fig. 4b): high ecosystem service values in remote regions disappear and the pattern within the forested areas is much more homogenous with larger patches of equal value. The update of the BN with expert knowledge even sharpens this pattern and highlights locations of maximum carbon storage potential (Fig. 4c). An overlay of such maps representing the different ecosystem services would thus support forest managers in identifying ecosystem services hotspots as well as spatial synergies and trade-offs between the multiple ecosystem services while addressing uncertainties. Since a map of expected ecosystem services values alone would give a false impression of precision, we map the standard deviation of the value of the different ecosystem services. Fig. 5 visualizes the uncertainty for carbon sequestration. In addition to the color range that shows the expected value, we introduce a shading as a measure for the uncertainty of each value. Grid cells with dark shading have a large standard deviation as compared to their expected value, pointing to the large uncertainties in the estimation of the ecosystem service benefit at these locations. Such zones predominantly lie near the current tree line, where a large variety of biophysical and anthropogenic factors influence tree establishment (Gehrig-Fasel et al., 2007; Harsch et al., 2009) and predictions on the extent of forest expansion are difficult (without the specific local knowledge of experts). We observe some uncertainty in the outcomes on currently forested areas, mainly resulting from the
Table 1 Total annual value of three ecosystem services (ESS) in the region Davos, estimated by (1) a traditional risk approach, (2) by a Bayesian network (BN) and (3) by BN including expert update. ESS
Carbon sequestration Wood production Avalanche protection Sum
Total value [CHF/year] (1) Traditional risk approach [CHF/year]
(2) With BN [CHF/year]
Average standard deviation/grid cell
(3) With BN and expert update [CHF/year]
Average standard deviation/grid cell
1,203,599 368,640 143,849,316 144,684,275
1,668,183 437,356 53,351,248 54,582,075
5.80 2.41 732.06
1,779,213 290,115 74,393,653 75,882,751
2.55 1.15 455.37
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Fig. 4. Annual value of the ecosystem service carbon sequestration in 2050 calculated (a) by a traditional risk approach, (b) using a Bayesian network, (c) including expert update.
lack of knowledge regarding the development of the carbon price. In remote areas, where the growth of forest is very unlikely, the uncertainties are lowest. 5. Discussion Parameter uncertainties, modeling uncertainties and uncertainties related to humaneenvironment interactions are common in
ecosystem services’ research. The analyses presented here show that mapping uncertainties in ecosystem services assessments provides key information for decision-makers seeking critical areas in the delivery of ecosystem services. The results suggest that not only the total value of the bundle of ecosystem services is highly depend on uncertainties, but the spatial pattern of the ecosystem services values changes substantially when considering uncertainties. This is particularly important for the long-term management of mountain
Fig. 5. Annual value of the ecosystem service carbon sequestration in 2050 using a Bayesian network updated by expert knowledge. The dark shading indicates high uncertainty in the estimations.
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forest ecosystems, which have long rotation stands and are highly sensitive to climate and socio-economic changes. By integrating probabilistic approaches such as a BN into a GIS, we present a new way of quantifying uncertainties in a spatially explicit manner. The resulting maps visualize the geographical variation of uncertainties, which can increase confidence in the modeling results. In addition, BN allow integrating expert knowledge into ecosystem services assessments. While accounting for uncertainties improves the spatially explicit estimations of the ecosystem services values, we showed that updating a BN with expert knowledge even refines the spatial pattern and reduces uncertainties of the outcomes, and thus allows an adaptive resource management when new information becomes available. This can considerably improve the identification of ecosystem services hotspots and make transparent the tradeoffs between multiple ecosystem services, thus engaging stakeholders and decision makers (Nelson et al., 2009). Finally, BN allow a traceable and concise representation of the causal relationships between the considered variables and explicitly represent both data and modeling uncertainties in form of conditional probability tables. A glance at these tables allows
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decision-makers not only to understand the influence factors on the ecosystem services, but also the degree of uncertainty related to each variable. In order to fully exploit the potential of BN, stakeholders can already be integrated early in the quantification and valuation process, helping to weigh influence factors, identify key variables and their causal connections, determine the states of the variables and fill the conditional probability tables. Such a participatory process might also enhance the acceptance of the management plans and allows continuous adaptation to local conditions. In conclusion, mapping uncertainties related to ecosystem services assessments is important for the sustainable management of natural resources, and integrating probabilistic approaches into a geographical information system supports such effort. However, ways to communicate uncertainties related to ecosystem services quantification and valuation in a useful manner to decision-makers remains largely unstudied. Beside applying and improving the approach presented here to move beyond these preliminary and illustrative analyses, an ambitious research effort is thus needed to develop appropriate methods for risk communication in ecosystem services’ research.
Appendix A. Values of the Bayesian network Table A1 States of the Bayesian network nodes, organized into the categories input nodes, nodes representing quantification procedures, nodes representing valuation procedures and nodes that are updated by expert knowledge. Node
# States
Description of states
Source of probability distribution
Digital elevation model (DEM25, Swiss Federal Office of Topography) Predicted spatially explicitly by forest model described in section ‘case study’
Input nodes Height
2
<1800, >1800 [m]
Forest type
5
Harvesting method
4
Modeled pressure
6
No forest, small wood stock, medium wood stock, large wood stock, very large wood stock From ground, mobile cable way, conventional cable way, helicopter 0, >0 and 3, >3 and <10, >10 and <20, >20 and <30, >30 [kPa] Agricultural building þ garage, one-family house, multiple-family house, administration, school, hotel, industry, hospital, living þ work, chair-lift, appart hotel, staff house, restaurant, trafo, reservoir, shop, church, depot
Building type
18
Nodes representing quantification procedures CO2 storage capacity 5 Harvestable amount 5 of wood People’s presence in 2 buildings
Yes, no
Presence “yes” ¼ T*D/24*7 (Borter, 1999, p. 64), where T is average presence time in hours per day, D is average presence time in days per week Mean based on Wilhelm (1997), variance proportional to mean (normal distributed variance) Barbolini et al. (2004, Fig. 5), for the category “some damage”: assumed 50% lethality of “total damage” Borter (1999, p. 125)
Lethality in buildings
2
Yes, no
House construction
6
Building damage
3
Agricultural building, administration building, one-family house, multiple-family house, armed concrete, safety construction Yes, some, no
Numeric: 0e80
Nodes representing valuation procedures CO2 price 8
10, 30, 50, 75, 100, 150, 200, 250 [CHF/t]
Wood price
86, 96, 106, 115, 120, 150, 170 [CHF/m3]
11
Hard labeling based on location of buildings from Communal cadastral register of Davos (unpublished data)
See Appendix B See Appendix C
81
Benefit wood production
Deterministic relations, calculated with AVAL-2D
Numeric: 0e6.88 [t/ha] Numeric: 0e5.28 [m3/ha]
Persons per building
7
Bont, 2009
Numeric: 0e54 [CHF]
For one-family and multiple-family houses: Barbolini et al. (2004, Fig. 4), otherwise Borter (1999, p. 125), added state “some damage” (50% of damage ¼ “yes”) Assumed based on literature (EcoSecurities, 2009; Stern, 2006; Tol, 2005) Assumed based on current price and literature (SHL, 2010) See Appendix C (continued on next page)
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Table A1 (continued ) Node
# States
Description of states
Source of probability distribution
Numeric: 0e51 [CHF] 90, 105, 115, 160 [CHF/m3] 5,000,000 [CHF]
See Appendix C See Appendix C Life Quality Index approach according to Merz et al. (1995) Communal cadastral register Davos (unpublished data), for the category “some damage” assumed 50% cost of “total damage” Communal cadastral register Davos (unpublished data), Wilhelm (1997, p. 230e236)
Input nodes Cost wood production Harvesting cost Cost of human death
8 4 1
Cost of destroyed building
37
Numeric: 0e17,402,000 [CHF]
Indirect cost of destroyed building
37
Belongings: 24%, infrastructure: 15%, socio-economic: 10% of building value [CHF]
Nodes updated by expert knowledge Forest type by experts 5
CO2 price by experts
8
No forest, small wood stock, medium wood stock, large wood stock, very large wood stock 10, 30, 50, 75, 100, 150, 200, 250 [CHF/t]
Wood price by experts
7
86, 96, 106, 115, 120, 150, 170 [CHF/m3]
Appendix B. Quantification and valuation of carbon sequestration We quantify the carbon sequestration service based on estimations of the change in biomass, i.e. the stock of wood, between 2000 and 2050: we consider (1) the increase in forested areas and (2) the growth in areas that have been woodless in 2000. The biomass stored in the present forest is not considered as a sink. While the potential of soils to store carbon is considerable, we do not include it in our model because of lack of data and of adequate process models. In forested areas, we calculate the growth of wood by allocating the average growth rate of the forest of Davos between 1980 and 2006 (4.87 m3/ha and y; LFI, 2008) to different forest types proportionally to their stock of wood (Bebi, 1999; Table B1). The growth in woodless areas is considerably slower as compared to already forested areas. We thus set the average yearly growth rate to 50% of the rate observed between 1980 and 2006.
Table B1 Stock and annual growth rate for different forest types. Forest type
No forest Small stock of wood Medium stock of wood Large stock of wood Very large stock of wood
Stock of Growth on Growth on forested forested areas areas assumed for wood 3 [m /ha] 1980e2006 2000e2050 [m3/ha and y] [m3/ha and y]
Growth on woodless areas assumed for 2000e2050 [m3/ha and y]
0 259.2
0 2.88
0 2.88
0 1.44
388.8
4.32
4.32
2.24
529.6
5.92
5.92
2.88
561.6
6.24
6.24
3.20
The spatially explicit carbon storage quantity is calculated according to Thürig and Schmid (2008):
i h i h CO2 ½t=ha ¼ G m3 =ha $r kg=m3 $BEF$C=biomass½kg=kg$C1 $C2 (C1) G is the growth volume of a specific forest type (Table B1), r the density of coniferous wood (394 kg/m3), BMF the biomass expansion factor of 1.49 for areas below 1800 m.a.s.l. and of 1.57 for areas
Prior: assumed spatially explicitly, based on forest type Posterior: spatially explicitly by experts Prior: Assumed based on price CO2 Posterior: experts Prior: assumed based on wood price Posterior: experts
above 1800 m.a.s.l. (Thürig et al., 2005), C/biomass is 0.5 kg/kg and C1 and C2 the conversion factors to CO2 (44/12) and to tons (0.001), respectively. We value the carbon sequestration service by the market price for carbon equivalents, based on literature values in the prior distribution (EcoSecurities, 2009; Stern, 2006; Tol, 2005) and based on expert opinions in the posterior distribution. Appendix C. Quantification and valuation of wood production The harvestable amount of wood is derived from the yearly growth in forested and woodless areas (Table B1), by multiplying the growth with a reduction factor of 0.85 (LFI, 2008; Table C1).
Table C1 Annual harvestable amount of wood for different forest types. Forest type
Harvestable amount of wood on forested areas assumed for 2000e2050 [m3/ha and y]
Harvestable amount of wood on woodless areas assumed for 2000e2050 [m3/ha and y]
No forest Small stock of wood Medium stock of wood Large stock of wood Very large stock of wood
0 2.40 3.68 4.96 5.28
0 1.28 1.92 2.40 2.72
We value the wood production as the difference of the value of selling the harvestable amount of wood and the harvesting costs. The values are calculated by an average price for different types of wood. The prior distribution was assumed considering the current average wood price of 86 CHF/m3 for the region Davos (77% construction timber à 100 CHF/m3, 8% pulpwood à 55 CHF/m3, 15% firewood à 30 CHF/m3; personal communication local forester) as well as estimates of the range of price fluctuations of the past (SHL, 2010). Expert opinions determined the posterior distribution. The harvesting costs are calculated based on a spatially explicit distribution of harvesting methods (Bont, 2009). Depending on soil texture, infrastructure and slope, the following categories are distinguished: harvesting from the ground, with a mobile cable way, with a conventional cable way and with helicopter. Each method is given a price based on general experience from the
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region and expert knowledge (AfW Gr, 2008; personal communication with local forester; Table C2). Table C2 Costs of different harvesting methods. Harvesting method
Harvesting costs [CHF/m3]
From ground Mobile cable way Conventional cable way Helicopter
90 105 115 160
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Contents lists available at SciVerse ScienceDirect
Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Facing uncertainty in ecosystem services-based resource management Adrienne Grêt-Regamey a, *, Sibyl H. Brunner a, Jürg Altwegg a, Peter Bebi b a Planning of Landscape and Urban Systems, Swiss Federal Institute of Technology ETH Zurich, HIL H 51.4, Wolfgang-Pauli-Str. 15, 8093 Zurich, Switzerland b Community Ecology, WSL Institute for Snow and Avalanche Research SLF, Flüelastr. 11, 7260 Davos Dorf, Switzerland
a r t i c l e i n f o
a b s t r a c t
Article history: Received 25 November 2011 Received in revised form 24 July 2012 Accepted 25 July 2012 Available online 22 August 2012
The concept of ecosystem services is increasingly used as a support for natural resource management decisions. While the science for assessing ecosystem services is improving, appropriate methods to address uncertainties in a quantitative manner are missing. Ignoring parameter uncertainties, modeling uncertainties and uncertainties related to humaneenvironment interactions can modify decisions and lead to overlooking important management possibilities. In this contribution, we present a new approach for mapping the uncertainties in the assessment of multiple ecosystem services. The spatially explicit risk approach links Bayesian networks to a Geographic Information System for forecasting the value of a bundle of ecosystem services and quantifies the uncertainties related to the outcomes in a spatially explicit manner. We demonstrate that mapping uncertainties in ecosystem services assessments provides key information for decision-makers seeking critical areas in the delivery of ecosystem services in a case study in the Swiss Alps. The results suggest that not only the total value of the bundle of ecosystem services is highly dependent on uncertainties, but the spatial pattern of the ecosystem services values changes substantially when considering uncertainties. This is particularly important for the long-term management of mountain forest ecosystems, which have long rotation stands and are highly sensitive to pressing climate and socio-economic changes. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: Uncertainty Mapping Ecosystem services Risk Bayesian network Forest management
1. Introduction The concept of ecosystem services is increasingly used as a support for natural resource management decisions. Its suitability to bridge human welfare and the natural environment gives a common platform for communicating the value of ecosystems to stakeholders (Farley, 2008; Daily et al., 2009; Sutton and Constanza, 2002). While the number of papers mentioning “ecosystem services” has risen exponentially since 1990 (Fisher et al., 2009), the science of ecosystem services has not yet had the time to develop approaches to deal with the complexity of the operationalization of the concept (Holling, 2001; Ghazoul, 2007; Kienast et al., 2009). Several authors provide valuable conceptual ideas for improving ecosystem services’ research (Boyd and Banzhaf, 2007; Ghazoul, 2007; Luck et al., 2009; McCauley, 2006; Armsworth et al., 2007), but lack practical suggestions for implementation. Especially the management of multiple ecosystem services still relies on assumptions related to spatial trade-offs over time.
* Corresponding author. Tel.: þ41 44 633 29 57. E-mail addresses: [email protected] (A. Grêt-Regamey), [email protected] (S.H. Brunner), [email protected] (J. Altwegg), [email protected] (P. Bebi). 0301-4797/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jenvman.2012.07.028
Decisions in ecosystem management are based on assumptions of a potential development and a future state of the ecosystems, and are thus inherently laden with uncertainty. In order to mitigate risks of ecosystem changes, decision analyses require estimates of the consequences of mitigation actions with some stated degree of confidence (Carpenter et al., 2009). Uncertainty can result because drivers (and consequently model structures) are uncertain, parameters are uncertain, and unknown human responses to ecosystem change (or to forecasts of ecosystem change) affect outcomes. Most daunting is the uncertainty that results from strong nonlinearities and stochasticity (Clark et al., 2001). Omitting the communication of such uncertainties could lead to overlooking important management possibilities, thus providing misleading decision-support information. In ecosystem services’ research, uncertainty has been addressed by stating the degree of scientific consensus (MA, 2005), but rigorous quantitative methods are missing. Over the past two decades, many different formal techniques to quantify specific aspects of uncertainty and several integrative approaches have been developed for dealing with uncertain information in other disciplines (Ascough et al., 2008). One of the traditional ways to address uncertainty is risk analysis, where risk is defined as the set of possibilities each with quantified probabilities and quantified consequences (Hubbard, 2009). While
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such approaches have been applied successfully in ecosystem research (e.g. Scholze et al., 2006), using quantitative risk assessments for estimating uncertainties in predicting a bundle of ecosystem services is a most pressing area for ecosystem services’ research. Managing ecosystem services needs the explicit consideration of spatial synergies and trade-offs. Mapping multiple ecosystem services helps showing the spatial distribution of ecosystem services benefits and costs, thus allowing to decide where efforts can be of most value (Chan et al., 2006; Naidoo and Rickets, 2006; Grêt-Regamey et al., 2008; Egoh et al., 2008; Nelson et al., 2009; Chen et al., 2009). Especially in mountain areas, where the provision of ecosystem services is highly heterogeneous (Grêt-Regamey et al., 2012), information about the supply of and demand for ecosystem services must be available in a spatially explicit manner. Uncertainties partitioning has been conducted on a pixel-by-pixel basis to account for spatial variation in natural resources and environmental systems (Gertner et al., 2004; Wang et al., 2009). But despite advances in the spatially explicit quantification and valuation of ecosystems services, the spatial outputs of the ecosystem services models have been considered as hard labeling until now. New computational approaches represented by hierarchical models accommodate multiple stochastic elements, but have not yet been exploited in ecosystem services’ research. Especially, Bayesian networks (BN) are known to facilitate the explicit modeling of involved uncertainties in a probabilistic framework (Friis-Hansen, 2000; Faber et al., 2002). The basic idea behind Bayesian statistics is that the lack of knowledge is an uncertainty that should be treated by probabilistic reasoning in a similar way to other types of uncertainty. BN have the advantages that (1) they picture the explicit relationships between the variables of the model, thus, results can be presented to decision-makers in a concise matter, (2) quantitative data and expert knowledge can be taken into account simultaneously, and (3) they can be updated as soon as new evidence becomes available enabling iterative decision processes and adaptive resource management (e.g. Ellison, 1996; Ascough et al., 2008). In the last decade, applications of Bayesian statistics have spread into many areas in environmental and resource management (for a review, see Ascough et al., 2008), but were mostly non-spatial. Aspinall (1992) was one of the pioneers trying to explicitly address uncertainties in a Geographic Information System (GIS) using a Bayesian method. Other attempts at incorporating BN in spatially explicit decision support tools are found in other disciplines, e.g. in risk assessment of desertification of burned forest (Strassopoulou et al., 1998), in avalanche risk assessment (Grêt-Regamey and Straub, 2006), in vulnerability assessment of marine landscapes (Stelzenmüller et al., 2010) and in prediction of land-use change for reforestation planning (Ordóñez Galán et al., 2009), but we are not aware of any study investigating the effectiveness of the approach in mapping uncertainties of future ecosystem services provisions. In this contribution, we present a new approach for mapping the uncertainties in the assessment of multiple ecosystem services. The spatially explicit risk approach links Bayesian networks (BN) to a GIS for forecasting the value of a bundle of ecosystem services and quantifies the uncertainties related to the outcomes in a spatially explicit manner. We illustrate the approach in a case study in the Swiss Alps e the ‘Landschaft Davos’. We focus on ecosystem services provided by forests including carbon sequestration, wood production and avalanche protection. On the one hand, these ecosystem services are key for the regional economy (GrêtRegamey and Kytzia, 2007). On the other hand, they cover the three main ecosystem services valuation scales: carbon sequestration is valued on a global scale, wood production on a regional
scale and avalanche protection is selected as an ecosystem service which is locally available and cannot be traded with other regions. We compare the monetary risks calculated using the BN approach to those obtained by a traditional ecosystem services valuation. We show how expert knowledge helps diminish uncertainties in the prediction of ecosystem services values. We finally map risks and most notably uncertainties related to the outcomes showing vulnerability patterns that decision-makers must consider when managing a bundle of ecosystem services. Such information will support the success of ecosystem services-based management by increasing transparency in the information provided to resource managers. 2. Risk assessment using Bayesian networks Risk analysis has traditionally been applied to estimate the dangers from particular natural hazards (e.g. Einstein, 1988; Cruden and Fell, 1997). Risk R is defined as the function of the probabilities P(Ai) of all potential events Ai and the expected consequences under these events C(Ai):
R ¼
X ðPðAi Þ$CðAi ÞÞ
(1)
i
In ecosystem services-based resource management risk corresponds to the sum of all probabilities of occurrence of a particular land use P(Ai) and the consequences in the flow of ecosystem services given this land use C(Ai). R can be understood as the risk of a change in the ecosystem services values under land-use changes. Since the Bayesian probability theory belongs to the most consistent probabilistic framework for decision-making subject to uncertainty (Faber and Maes, 2005), we embed the quantification of risk into a BN. Pearl (1988) and Jensen (2001) give a comprehensive summary of BN. In short, BN are directed acyclic graphs in which nodes represent random variables (X ¼ X1,., Xn), and arcs represent causal relations among these variables. The nodes are characterized by their associated conditional probability tables, conditional on the states of any parent node that interact with it. The joint probability distribution P(x) of a BN is given as:
PðxÞ ¼ Pðx1 ; :::; xn Þ ¼
Yn i¼1
Pðxi jpaðxi ÞÞ
(2)
where pa(xi) is a set of values of the parents of a variable Xi. The distribution of Xi given its parents may have any form, but efficient algorithms for solving the BN are available only for the case where the nodes have discrete or Gaussian distributions. Thus, we restrict ourselves to variables with discrete states. BN enhance the utilization of expert knowledge in risk assessment: probabilities in the network can be updated when new information becomes available. The goal of inference is to find the posterior conditional distribution of a subset of the variables, conditional on known values for some other subset (the evidence e). For example, when the state of a variable is observed to be e, this information will propagate through the network and the joint probability of all nodes will change to its posterior:
PðxjeÞ ¼ Pðx; eÞ=PðeÞ
(3)
In order to calculate the expected ecosystem services values in a spatially explicit manner, the BN is linked to a GIS. Fig. 1 illustrates the framework: for each pixel, the evidence provided by the spatially explicit datasets is propagated through the BN, which calls GIS-based process models for the quantification and economic valuation models for the valuation of the ecosystem services. The results of the quantification process and the economic models fill the nodes with the probabilistic distributions. The uncertainty is
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Fig. 1. Conceptual framework to map uncertainties in ecosystem services (ESS) values under future land use linking a Bayesian network (BN) to a Geographic Information System (GIS). In step 1, a land-use scenario is set up by assigning probabilities of different land uses to each grid cell; in step 2, we quantify the resulting probabilities of the change in the flow of ESS using GIS-based models; in step 3, we convert these quantities into monetary units applying different economic valuation methods.
given in terms of the standard deviation of the estimated ecosystem services values. The effectiveness of the approach is showed by comparing the ecosystem services values calculated using the BN risk approach and a traditional approach for risk analysis. The data used is the same as the data in the BN approach, but as opposed to the calculation described above, the risk is computed from the expected value of each variable, thus, ignoring the probability distribution of the single variables and their joint probability distribution. 3. Case study The ‘Landschaft Davos’ is a 254 km2 landscape around the mountain town Davos in the Swiss Alps (Fig. 2). While the altitude of the valley bottom lies between 1400 and 1600 m.a.s.l., the highest peaks reach over 3000 m.a.s.l. The area is a popular tourist destination that can accommodate up to 28,000 visitors during winter peak season in addition to its 11,000 permanent residents. The main valley is NEeSW oriented and hosts the urban core settlement and most of the tourist infrastructure. The four side valleys have maintained their rural character with a few small, scattered settlements and a typical Alpine landscape shaped by mountain agriculture. The BN network that represents the causal relations among the factors that influence the expected forest ecosystem services values (carbon sequestration, avalanche protection, and wood production) is given in Fig. 3. The most important variable in the BN is the type of forest, which drives the provision of all three ecosystem services. Along with the elevation it determines the carbon storage potential of the forest and through the carbon tax the value of carbon sequestration. The value of wood production depends directly on the costs and benefits of harvesting the resource. For valuing avalanche protection, modeled snow pressures and the type of buildings under risk are particularly important. The states and the content of the conditional probability tables corresponding to each node are described in Appendix A.
The land-use scenario considered in the case study corresponds to a trend scenario that assumes a continuous land-use change as observed between 1950 and 2000. We model the forest in the year 2050 based on a forest map in 2000 using transition matrices for a 50-years period derived from land-use maps of 1950 and 2000 (Kulakowski et al., 2011). The model assigns to each grid cell an expected forest type by a random generator, and separately lists the probabilities of conversion to all other considered forest types. Most noticeable changes are an expansion of forest cover at altitude where Alpine pastures are abandoned as well as a slight densification of the forest. We quantify the changes in the selected ecosystem services by using state-of-the-art GIS-based models. For the quantification of avalanche protection, we use a numerical 2-D avalanche model (Gruber, 1999), that calculates the avalanche run-out areas and associated pressures in a spatially explicit manner (for details see Grêt-Regamey and Straub, 2006). By overlaying the run-out areas with a building types layer, we identify the potentially endangered dwellings. Carbon sequestration is quantified through the carbon storage capacity of the forest, and wood production is quantified through the harvestable amount of wood. Both measures are estimated based on the change in wood stock over the 50-years modeling period (details of the calculations are given in Appendix B and C). Main spatially explicit data sources include the map of different forest types from Kulakowski et al. (2011) on a 25 m 25 m grid, the vector25 dataset based on the National Map 1:25 000, which provides information on locations of buildings on a 5 m 5 m grid (Swisstopo, 2004), the 25 m digital elevation model (DEM25, Swiss Federal Office of Topography) and a map of optimal harvesting techniques modeled using Bont (2009). The data delivered by the quantification step is converted into monetary units applying different valuation methods in accordance with the ecosystem services to be valued. We monetize avalanche protection by pricing damages to the buildings and fatalities using a risk-analysis approach (Grêt-Regamey and
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Fig. 2. Map of the ‘Landschaft Davos’. The insert on the top left shows the location of the study area in Switzerland. The green forest area of the year 2000 is denser in regions of darker color.
Straub, 2006) and transferring the total value back to the protective forest area. In order to value carbon sequestration, we use the carbon market price (values are given in Appendix A). The value of wood production is the difference between the timber market price and the costs for the harvest of the resource (Appendix A and C). We update our network with expert knowledge on land-use change and future carbon and wood prices, respectively (Fig. 3, nodes tagged with ‘e’). For each variable, we asked five independent experts for an estimate of the probability distribution of the variable. The posterior distributions that fed the BN are calculated as an average of the five expert opinions. Several algorithms exist to facilitate the updating of a prior probabilistic model using Bayes’ theorem (Murphy, 2001). In our study, we select the BN modeling shell Hugin (Hugin Expert, 2005). The propagation of the input uncertainties related to the modeling uncertainties and inaccuracies in the spatially explicit input layers as well as uncertainties in the socio-economic variables results in a probability distribution of expected monetary values of the selected ecosystem services. The risk of change in the ecosystem services values in each 25 m 25 m raster cell is expressed as the function of these values and their occurrence probabilities and is presented in a map (ArcGIS 8.3, ESRI, 2000).
4. Results Including uncertainties in ecosystem services quantification and valuation modifies significantly the predicted value of the bundle of ecosystem services. Table 1 presents the total ecosystem services values and related standard deviations in the case study region estimated using a traditional risk approach, a Bayesian network, and including expert update. Protection from avalanches is by far the most highly valued service, making up 98% of the total ecosystem services. The value of carbon sequestration is more than an order of magnitude lower and wood production is not profitable in Davos. Accounting for uncertainties in estimating carbon sequestration modifies its total value by 48%. The difference is mainly a consequence of including uncertainty in the variable ‘carbon price’: while the traditional approach is based on a single fix carbon tax of 50 CHF/t, experts believe that the carbon price will increase in the future and higher market prices are used in the BN approach. Similarly, considering experts’ estimates of the future wood price in the corresponding variable enhances the total value of the wood production service, even though the value remains negative. In contrast, the total value of the avalanche protection service is lower if calculated with the BN approach. This results mainly from the uncertainties introduced in the
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Fig. 3. Bayesian network of the valuation of three forest ecosystem services (ESS). The yellow and gray oval nodes are the variables used in the quantification and valuation of the ESS, the darker shade show the spatially explicit input variables; the green rhomboids are the utilities in terms of changes in ESS values; the red box is a decision node to set choices of possible land-use changes. Nodes tagged with an “e” are updated by expert knowledge. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
variables ‘building damage’, ‘persons per building’ and ‘lethality in building’ that reduce the total cost of a destroyed building, and the probability distribution introduced in the variable ‘forest type’ that decreases the forest stock on avalanche run-out areas and therefore the potential of these areas to protect the endangered buildings. As the latter probability is confirmed by the evidence of experts the total value of the ecosystem services increases after the update of the network. Although the influence of accounting for uncertainties on the total ecosystem service value varies between ecosystem services, the update of the BN by experts consistently reduces the uncertainties in the valuation. Fig. 4 maps the future value of the carbon sequestration service. The decrease in uncertainty due to integration of expert knowledge in the ecosystem service valuation is revealed in a sharpening of the spatial pattern of the ecosystem service value. If the ecosystem services value is calculated using a traditional risk approach, high values in currently forested areas are visible, but there is no clear spatial pattern in the ecosystem service value (Fig. 4a). Certain unfavorable locations to sequester carbon show a high carbon sequestration capacity, while others in the middle of a forested area have a zero storage potential. This scattering is due to the calculation of land-use changes using a transition probability matrix-based approach, thus, generating randomly different forest types based on preceding patterns of land-use change. By considering uncertainties in the input data and propagating them
through the BN, the pattern becomes clearer (Fig. 4b): high ecosystem service values in remote regions disappear and the pattern within the forested areas is much more homogenous with larger patches of equal value. The update of the BN with expert knowledge even sharpens this pattern and highlights locations of maximum carbon storage potential (Fig. 4c). An overlay of such maps representing the different ecosystem services would thus support forest managers in identifying ecosystem services hotspots as well as spatial synergies and trade-offs between the multiple ecosystem services while addressing uncertainties. Since a map of expected ecosystem services values alone would give a false impression of precision, we map the standard deviation of the value of the different ecosystem services. Fig. 5 visualizes the uncertainty for carbon sequestration. In addition to the color range that shows the expected value, we introduce a shading as a measure for the uncertainty of each value. Grid cells with dark shading have a large standard deviation as compared to their expected value, pointing to the large uncertainties in the estimation of the ecosystem service benefit at these locations. Such zones predominantly lie near the current tree line, where a large variety of biophysical and anthropogenic factors influence tree establishment (Gehrig-Fasel et al., 2007; Harsch et al., 2009) and predictions on the extent of forest expansion are difficult (without the specific local knowledge of experts). We observe some uncertainty in the outcomes on currently forested areas, mainly resulting from the
Table 1 Total annual value of three ecosystem services (ESS) in the region Davos, estimated by (1) a traditional risk approach, (2) by a Bayesian network (BN) and (3) by BN including expert update. ESS
Carbon sequestration Wood production Avalanche protection Sum
Total value [CHF/year] (1) Traditional risk approach [CHF/year]
(2) With BN [CHF/year]
Average standard deviation/grid cell
(3) With BN and expert update [CHF/year]
Average standard deviation/grid cell
1,203,599 368,640 143,849,316 144,684,275
1,668,183 437,356 53,351,248 54,582,075
5.80 2.41 732.06
1,779,213 290,115 74,393,653 75,882,751
2.55 1.15 455.37
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Fig. 4. Annual value of the ecosystem service carbon sequestration in 2050 calculated (a) by a traditional risk approach, (b) using a Bayesian network, (c) including expert update.
lack of knowledge regarding the development of the carbon price. In remote areas, where the growth of forest is very unlikely, the uncertainties are lowest. 5. Discussion Parameter uncertainties, modeling uncertainties and uncertainties related to humaneenvironment interactions are common in
ecosystem services’ research. The analyses presented here show that mapping uncertainties in ecosystem services assessments provides key information for decision-makers seeking critical areas in the delivery of ecosystem services. The results suggest that not only the total value of the bundle of ecosystem services is highly depend on uncertainties, but the spatial pattern of the ecosystem services values changes substantially when considering uncertainties. This is particularly important for the long-term management of mountain
Fig. 5. Annual value of the ecosystem service carbon sequestration in 2050 using a Bayesian network updated by expert knowledge. The dark shading indicates high uncertainty in the estimations.
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forest ecosystems, which have long rotation stands and are highly sensitive to climate and socio-economic changes. By integrating probabilistic approaches such as a BN into a GIS, we present a new way of quantifying uncertainties in a spatially explicit manner. The resulting maps visualize the geographical variation of uncertainties, which can increase confidence in the modeling results. In addition, BN allow integrating expert knowledge into ecosystem services assessments. While accounting for uncertainties improves the spatially explicit estimations of the ecosystem services values, we showed that updating a BN with expert knowledge even refines the spatial pattern and reduces uncertainties of the outcomes, and thus allows an adaptive resource management when new information becomes available. This can considerably improve the identification of ecosystem services hotspots and make transparent the tradeoffs between multiple ecosystem services, thus engaging stakeholders and decision makers (Nelson et al., 2009). Finally, BN allow a traceable and concise representation of the causal relationships between the considered variables and explicitly represent both data and modeling uncertainties in form of conditional probability tables. A glance at these tables allows
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decision-makers not only to understand the influence factors on the ecosystem services, but also the degree of uncertainty related to each variable. In order to fully exploit the potential of BN, stakeholders can already be integrated early in the quantification and valuation process, helping to weigh influence factors, identify key variables and their causal connections, determine the states of the variables and fill the conditional probability tables. Such a participatory process might also enhance the acceptance of the management plans and allows continuous adaptation to local conditions. In conclusion, mapping uncertainties related to ecosystem services assessments is important for the sustainable management of natural resources, and integrating probabilistic approaches into a geographical information system supports such effort. However, ways to communicate uncertainties related to ecosystem services quantification and valuation in a useful manner to decision-makers remains largely unstudied. Beside applying and improving the approach presented here to move beyond these preliminary and illustrative analyses, an ambitious research effort is thus needed to develop appropriate methods for risk communication in ecosystem services’ research.
Appendix A. Values of the Bayesian network Table A1 States of the Bayesian network nodes, organized into the categories input nodes, nodes representing quantification procedures, nodes representing valuation procedures and nodes that are updated by expert knowledge. Node
# States
Description of states
Source of probability distribution
Digital elevation model (DEM25, Swiss Federal Office of Topography) Predicted spatially explicitly by forest model described in section ‘case study’
Input nodes Height
2
<1800, >1800 [m]
Forest type
5
Harvesting method
4
Modeled pressure
6
No forest, small wood stock, medium wood stock, large wood stock, very large wood stock From ground, mobile cable way, conventional cable way, helicopter 0, >0 and 3, >3 and <10, >10 and <20, >20 and <30, >30 [kPa] Agricultural building þ garage, one-family house, multiple-family house, administration, school, hotel, industry, hospital, living þ work, chair-lift, appart hotel, staff house, restaurant, trafo, reservoir, shop, church, depot
Building type
18
Nodes representing quantification procedures CO2 storage capacity 5 Harvestable amount 5 of wood People’s presence in 2 buildings
Yes, no
Presence “yes” ¼ T*D/24*7 (Borter, 1999, p. 64), where T is average presence time in hours per day, D is average presence time in days per week Mean based on Wilhelm (1997), variance proportional to mean (normal distributed variance) Barbolini et al. (2004, Fig. 5), for the category “some damage”: assumed 50% lethality of “total damage” Borter (1999, p. 125)
Lethality in buildings
2
Yes, no
House construction
6
Building damage
3
Agricultural building, administration building, one-family house, multiple-family house, armed concrete, safety construction Yes, some, no
Numeric: 0e80
Nodes representing valuation procedures CO2 price 8
10, 30, 50, 75, 100, 150, 200, 250 [CHF/t]
Wood price
86, 96, 106, 115, 120, 150, 170 [CHF/m3]
11
Hard labeling based on location of buildings from Communal cadastral register of Davos (unpublished data)
See Appendix B See Appendix C
81
Benefit wood production
Deterministic relations, calculated with AVAL-2D
Numeric: 0e6.88 [t/ha] Numeric: 0e5.28 [m3/ha]
Persons per building
7
Bont, 2009
Numeric: 0e54 [CHF]
For one-family and multiple-family houses: Barbolini et al. (2004, Fig. 4), otherwise Borter (1999, p. 125), added state “some damage” (50% of damage ¼ “yes”) Assumed based on literature (EcoSecurities, 2009; Stern, 2006; Tol, 2005) Assumed based on current price and literature (SHL, 2010) See Appendix C (continued on next page)
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Table A1 (continued ) Node
# States
Description of states
Source of probability distribution
Numeric: 0e51 [CHF] 90, 105, 115, 160 [CHF/m3] 5,000,000 [CHF]
See Appendix C See Appendix C Life Quality Index approach according to Merz et al. (1995) Communal cadastral register Davos (unpublished data), for the category “some damage” assumed 50% cost of “total damage” Communal cadastral register Davos (unpublished data), Wilhelm (1997, p. 230e236)
Input nodes Cost wood production Harvesting cost Cost of human death
8 4 1
Cost of destroyed building
37
Numeric: 0e17,402,000 [CHF]
Indirect cost of destroyed building
37
Belongings: 24%, infrastructure: 15%, socio-economic: 10% of building value [CHF]
Nodes updated by expert knowledge Forest type by experts 5
CO2 price by experts
8
No forest, small wood stock, medium wood stock, large wood stock, very large wood stock 10, 30, 50, 75, 100, 150, 200, 250 [CHF/t]
Wood price by experts
7
86, 96, 106, 115, 120, 150, 170 [CHF/m3]
Appendix B. Quantification and valuation of carbon sequestration We quantify the carbon sequestration service based on estimations of the change in biomass, i.e. the stock of wood, between 2000 and 2050: we consider (1) the increase in forested areas and (2) the growth in areas that have been woodless in 2000. The biomass stored in the present forest is not considered as a sink. While the potential of soils to store carbon is considerable, we do not include it in our model because of lack of data and of adequate process models. In forested areas, we calculate the growth of wood by allocating the average growth rate of the forest of Davos between 1980 and 2006 (4.87 m3/ha and y; LFI, 2008) to different forest types proportionally to their stock of wood (Bebi, 1999; Table B1). The growth in woodless areas is considerably slower as compared to already forested areas. We thus set the average yearly growth rate to 50% of the rate observed between 1980 and 2006.
Table B1 Stock and annual growth rate for different forest types. Forest type
No forest Small stock of wood Medium stock of wood Large stock of wood Very large stock of wood
Stock of Growth on Growth on forested forested areas areas assumed for wood 3 [m /ha] 1980e2006 2000e2050 [m3/ha and y] [m3/ha and y]
Growth on woodless areas assumed for 2000e2050 [m3/ha and y]
0 259.2
0 2.88
0 2.88
0 1.44
388.8
4.32
4.32
2.24
529.6
5.92
5.92
2.88
561.6
6.24
6.24
3.20
The spatially explicit carbon storage quantity is calculated according to Thürig and Schmid (2008):
i h i h CO2 ½t=ha ¼ G m3 =ha $r kg=m3 $BEF$C=biomass½kg=kg$C1 $C2 (C1) G is the growth volume of a specific forest type (Table B1), r the density of coniferous wood (394 kg/m3), BMF the biomass expansion factor of 1.49 for areas below 1800 m.a.s.l. and of 1.57 for areas
Prior: assumed spatially explicitly, based on forest type Posterior: spatially explicitly by experts Prior: Assumed based on price CO2 Posterior: experts Prior: assumed based on wood price Posterior: experts
above 1800 m.a.s.l. (Thürig et al., 2005), C/biomass is 0.5 kg/kg and C1 and C2 the conversion factors to CO2 (44/12) and to tons (0.001), respectively. We value the carbon sequestration service by the market price for carbon equivalents, based on literature values in the prior distribution (EcoSecurities, 2009; Stern, 2006; Tol, 2005) and based on expert opinions in the posterior distribution. Appendix C. Quantification and valuation of wood production The harvestable amount of wood is derived from the yearly growth in forested and woodless areas (Table B1), by multiplying the growth with a reduction factor of 0.85 (LFI, 2008; Table C1).
Table C1 Annual harvestable amount of wood for different forest types. Forest type
Harvestable amount of wood on forested areas assumed for 2000e2050 [m3/ha and y]
Harvestable amount of wood on woodless areas assumed for 2000e2050 [m3/ha and y]
No forest Small stock of wood Medium stock of wood Large stock of wood Very large stock of wood
0 2.40 3.68 4.96 5.28
0 1.28 1.92 2.40 2.72
We value the wood production as the difference of the value of selling the harvestable amount of wood and the harvesting costs. The values are calculated by an average price for different types of wood. The prior distribution was assumed considering the current average wood price of 86 CHF/m3 for the region Davos (77% construction timber à 100 CHF/m3, 8% pulpwood à 55 CHF/m3, 15% firewood à 30 CHF/m3; personal communication local forester) as well as estimates of the range of price fluctuations of the past (SHL, 2010). Expert opinions determined the posterior distribution. The harvesting costs are calculated based on a spatially explicit distribution of harvesting methods (Bont, 2009). Depending on soil texture, infrastructure and slope, the following categories are distinguished: harvesting from the ground, with a mobile cable way, with a conventional cable way and with helicopter. Each method is given a price based on general experience from the
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region and expert knowledge (AfW Gr, 2008; personal communication with local forester; Table C2). Table C2 Costs of different harvesting methods. Harvesting method
Harvesting costs [CHF/m3]
From ground Mobile cable way Conventional cable way Helicopter
90 105 115 160
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