Facing uncertainty in ecosystem services-based resource management

Grêt‐Regamey, Adrienne; Brunner, Sibyl Hanna; Altwegg, Jürg; Bebi, Peter

doi:10.1016/j.jenvman.2012.07.028

Cited by 102 publications

(63 citation statements)

References 38 publications

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“…This rapid overview of attempts to model soil functions/services, either using direct, targeted measurements of soil properties or using pedotransfer functions to predict soil characteristics of interest on the basis of soil survey data, suggests that this modeling effort is accompanied by significant uncertainties (Barnaud et al, 2011;Grêt-Regamey et al, 2013;Tancoigne et al, 2013;Barnaud and Antona, 2014). A traditional way to insure that the uncertainties associated with initial measurements or "guestimates" based on survey data, as well as the inaccuracies resulting from spatial or temporal interpolations, do not vitiate model predictions entirely, is to carry out detailed uncertainty analyses.…”

Section: Uncertainties Bayesian Belief Network and The Top-down Apmentioning

confidence: 99%

“…Since their introduction in the mid-80s, BBNs have been adopted in various scientific fields, and are becoming increasingly popular tools in environmental modeling (e.g., Sadoddin et al, 2009;Haines-Young, 2011;Farmani et al, 2012;Henriksen et al, 2012;Troldborg et al, 2013), in particular to quantify and map ecosystem services (Grêt-Regamey et al, 2013;Landuyt et al, 2013Landuyt et al, , 2015Celio et al, 2014;Rositano and Ferraro, 2014;Taalab et al, 2015). Formally, a BBN is a probabilistic model that represents graphically a set of random variables and their conditional dependencies via a network, referred to technically as a directed acyclic graph (DAG).…”

Section: Uncertainties Bayesian Belief Network and The Top-down Apmentioning

confidence: 99%

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Soil “Ecosystem” Services and Natural Capital: Critical Appraisal of Research on Uncertain Ground

Baveye

Gowdy

2016

Front. Environ. Sci.

307

168

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Section: Uncertainties Bayesian Belief Network and The Top-down Apmentioning

confidence: 99%

Section: Uncertainties Bayesian Belief Network and The Top-down Apmentioning

confidence: 99%

Soil “Ecosystem” Services and Natural Capital: Critical Appraisal of Research on Uncertain Ground

Baveye

Gowdy

2016

Front. Environ. Sci.

307

168

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“…Despite recognition of these and other sources of uncertainty in perceptions assessments, this uncertainty is frequently ignored when translated into a spatial representation. Variation in perceptions can be captured by using multiple maps for different user groups and by mapping uncertainty associated with mapped values (e.g., [131,132]). Lechner and others [133] have devised a framework for characterizing uncertainty propagation when incorporating spatially referenced social data in conservation planning, including uncertainty in the spatial distribution of social and cultural values.…”

Section: Research Directions and Challengesmentioning

confidence: 99%

Integrating Spatially Explicit Representations of Landscape Perceptions into Land Change Research

Dorning

Berkel

Semmens

2017

Curr Landscape Ecol Rep

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Purpose of Review Human perceptions of the landscape can influence land-use and land-management decisions. Recognizing the diversity of landscape perceptions across space and time is essential to understanding land change processes and emergent landscape patterns. We summarize the role of landscape perceptions in the land change process, demonstrate advances in quantifying and mapping landscape perceptions, and describe how these spatially explicit techniques have and may benefit land change research. Recent Findings Mapping landscape perceptions is becoming increasingly common, particularly in research focused on quantifying ecosystem services provision. Spatial representations of landscape perceptions, often measured in terms of landscape values and functions, provide an avenue for matching social and environmental data in land change studies. Integrating these data can provide new insights into land change processes, contribute to landscape planning strategies, and guide the design and implementation of land change models. Summary Challenges remain in creating spatial representations of human perceptions. Maps must be accompanied by descriptions of whose perceptions are being represented and the validity and uncertainty of those representations across space. With these considerations, rapid advancements in mapping landscape perceptions hold great promise for improving representation of human dimensions in landscape ecology and land change research.

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“…Specific approaches exist in BN modeling to cope with such spatial and temporal dynamics. They may either be considered by an additional node [82], or separate casual networks can be prepared for each temporal and spatial state [83], which is tedious [38] and adds additional complexity in the participatory process. Similarly, the potential integration of empirical models into BN modeling, as suggested by Castelletti and Soncini-Sessa [84], was considered improper in the present study, due to increasing complexity being undesirable for the participatory approach applied.…”

Section: Challenges and Limitations Of The Participatory Bn Modeling mentioning

confidence: 99%

“…BN modeling is a popular tool, especially when dealing with decision-making in the face of uncertainty and limited data availability [37]. It has been widely used in environmental sciences and natural resources management with a focus on ecosystem service modeling [38,39], climate change impact assessment [40,41], watershed management [42][43][44], and ground water protection [45,46]. Its ability to clearly explain complex relations, easily compare alternative management scenarios, and determine the important driving factors makes BN modeling an extremely useful approach to support natural resources management under complex settings [47].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Network Modeling to Improve Water Pricing Practices in Northwest China

Mamitimin

Feike

Doluschitz

2015

Water

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Water pricing is regarded as the most important and simplest economic instrument to encourage more efficient use of irrigation water in crop production. In the extremely water-scarce Tarim River basin in northwest China, improving water use efficiency has high relevance for research and policy. A Bayesian network modeling approach was applied, which is especially suitable under data-scarce conditions and the complex geo-hydrological, socioeconomic, and institutional settings of the study region, as it allows the integration of data from various types of sources. The transdisciplinary approach aimed at understanding the actual water pricing practices, the shortcomings of the current system, and possible ways of improvement. In an iterative procedure of expert interviews and group workshops, the key factors related to water pricing and water use efficiency were identified. The interactions among specific factors were defined by the respective experts, generating a causal network, which describes all relevant aspects of the investigated system. This network was finally populated with probabilistic relationships through a second round of expert interviews and group discussions. The Bayesian modeling exercise was then conducted using Netica software. The modeling results show that the mere increase of water price does not lead to significant increases in water use efficiency in crop production. Additionally, the model suggests a shift to volumetric water pricing, subsidization of water saving irrigation technology, and advancing agricultural extension to enable the farmer to efficiently react to increased costs for water. The applied OPEN ACCESSWater 2015, 7 5618 participatory modeling approach helped to stimulate communication among relevant stakeholders from different domains in the region, which is necessary to create mutual understanding and joint targeted action. Finally, the challenges related to the applied transdisciplinary Bayesian modeling approach are discussed in the Chinese context.

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Facing uncertainty in ecosystem services-based resource management

Cited by 102 publications

References 38 publications

Soil “Ecosystem” Services and Natural Capital: Critical Appraisal of Research on Uncertain Ground

Soil “Ecosystem” Services and Natural Capital: Critical Appraisal of Research on Uncertain Ground

Integrating Spatially Explicit Representations of Landscape Perceptions into Land Change Research

Bayesian Network Modeling to Improve Water Pricing Practices in Northwest China

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