Uncertainty in climate change impacts on water resources

Kundzewicz, Zbigniew W.; Krysanova, Valentina; Benestad, Rasmus E.; Hov, Øystein; Piniewski, Mikołaj; Otto, Ilona M.

doi:10.1016/j.envsci.2017.10.008

Cited by 294 publications

(150 citation statements)

References 42 publications

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“…We have used the example of climate impacts on forests but our framework is also useful for other areas of climate impact science. The types of models used to simulate climate impacts on forests and the types of methods to assess uncertainties as well as our conceptualisation of uncertainty are very similar to those used in hydrology (Kundzewicz et al 2018), health (Wardekker et al 2012), agricultural modelling (Asseng et al 2013) or climate impact science in general (Falloon et al 2014). Likewise are the management challenges inherently complex in these areas.…”

Section: Discussionmentioning

confidence: 99%

Inconsistent recognition of uncertainty in studies of climate change impacts on forests

Petr

Vacchiano

Thom

et al. 2019

Environ. Res. Lett.

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Background. Uncertainty about climate change impacts on forests can hinder mitigation and adaptation actions. Scientific enquiry typically involves assessments of uncertainties, yet different uncertainty components emerge in different studies. Consequently, inconsistent understanding of uncertainty among different climate impact studies (from the impact analysis to implementing solutions) can be an additional reason for delaying action. In this review we (a) expanded existing uncertainty assessment frameworks into one harmonised framework for characterizing uncertainty, (b) used this framework to identify and classify uncertainties in climate change impacts studies on forests, and (c) summarised the uncertainty assessment methods applied in those studies. Methods. We systematically reviewed climate change impact studies published between 1994 and 2016. We separated these studies into those generating information about climate change impacts on forests using models -'modelling studies', and those that used this information to design management actions-'decision-making studies'. We classified uncertainty across three dimensions: nature, level, and location, which can be further categorised into specific uncertainty types. Results. We found that different uncertainties prevail in modelling versus decision-making studies. Epistemic uncertainty is the most common nature of uncertainty covered by both types of studies, whereas ambiguity plays a pronounced role only in decision-making studies. Modelling studies equally investigate all levels of uncertainty, whereas decision-making studies mainly address scenario uncertainty and recognised ignorance. Finally, the main location of uncertainty for both modelling and decision-making studies is within the driving forces-representing, e.g. socioeconomic or policy changes. The most frequently used methods to assess uncertainty are expert elicitation, sensitivity and scenario analysis, but a full suite of methods exists that seems currently underutilized. Discussion & Synthesis. The misalignment of uncertainty types addressed by modelling and decision-making studies may complicate adaptation actions early in the implementation pathway. Furthermore, these differences can be a potential barrier for communicating research findings to decision-makers.

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Section: Discussionmentioning

confidence: 99%

Inconsistent recognition of uncertainty in studies of climate change impacts on forests

Petr

Vacchiano

Thom

et al. 2019

Environ. Res. Lett.

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“…Under non-stationarity, or changing flood frequency and magnitude over time, design-flood estimates based on the most recent record may be reasonable for projects with shorter design lives but not over longer design lives. Top-down modeling using downscaled climate projections to predict future hydrologic conditions results in a cascading effect on uncertainty (Wilby and Dessai 2010) and scenario analysis may be helpful (Kundzewicz et al 2018). Another approach to addressing non-stationarity in design, known as decision scaling, first characterizes the climatic conditions that result in project failure (e.g., levee or bridge overtopping) and then compares these to the distribution of future projected climate conditions informing the probability of failure (Brown et al 2012).…”

Section: Jawra Journal Of the American Water Resources Associationmentioning

confidence: 99%

Managing Infrastructure in the Stream Environment

Sholtes

Ubing

Randle

et al. 2018

J American Water Resour Assoc

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Riverine infrastructure provides essential services for the operation and development of the world's nations and their economies. When much of this infrastructure was built in the United States, fluvial processes and stream ecology were not well understood, putting it in conflict with and at risk from the stream environment. High maintenance costs are often required to keep such infrastructure viable and some of it has led to the degradation of aquatic and riparian ecosystems. This commentary paper lays the foundation for infrastructure designers and managers to build and manage infrastructure in a manner both resilient to riverine hazards and more compatible with aquatic and riparian ecosystem needs. We introduce fundamental fluvial geomorphic and ecosystem concepts and provide a decision‐making framework to replace or repair existing infrastructure or build new infrastructure. Common management challenges associated with 11 riverine infrastructure types are discussed and we provide suggestions on how each infrastructure type can be better built and managed within stream corridors. We close with a discussion on managing infrastructure under future hydrologic uncertainty and in response to natural disasters.

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“…There are substantial uncertainties associated with projections of climate change, which mainly originate from global climate model (GCM), regional climate model (RCM), greenhouse gas emission scenario, downscaling method, hydrological model, as well as observational data (Kiparsky et al, 2012). They are primarily caused by limits of our knowledge in process understanding and modelling (Kundzewicz et al, 2018). The uncertainties related to GCM and downscaling methods (statistical or dynamic) are the principal ones (Vetter et al, 2017) while those related to hydrological model are relatively smaller (Nearing et al, 2016).…”

mentioning

confidence: 99%

Downscaling climate change of water availability, sediment yield and extreme events: Application to a Mediterranean climate basin

Zhang

Corte‐Real

Moreira

et al. 2019

Intl Journal of Climatology

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A robust hydrological impact assessment is indispensable for mitigation and adaptation planning. This study presents an integrated modelling methodology for evaluating climate change impacts on water availability, sediment yield and extreme events at the catchment scale. We propose the use of the spatial–temporal Neyman–Scott Rectangular Pulses (STNSRP) model—RainSim V3 and the rainfall conditioned daily weather generator—ICAAM‐WG, as well as the physically based spatially distributed hydrological model—SHETRAN. The change factor approach was applied for obtaining unbiased rainfall and temperature statistics. The ICAAM‐WG was developed based on the modified Climate Research Unit daily Weather Generator (CRU‐WG). The methodology is proposed to generate synthetic series of hourly precipitation, daily temperature and potential evapotranspiration, hourly runoff and hourly sediment discharge. We demonstrated a possible application in a 705‐km2 Mediterranean climate basin in southern Portugal. The case study showed the evaluation of future climate change impacts on annual and monthly water balance components and sediment yield, annual and seasonal flow duration curves, empirical extreme value distributions and the theoretical fits. It did not consider the possible uncertainty due to the limit of computational resources. The methodology can be well justified as follows: (a) the use of synthetic hourly instead of daily precipitation enables SHETRAN to be more capable of reproducing reliable storm runoff processes and the consequent sediment transport processes; (b) the use of SHETRAN makes possible the impact assessment to be accessible for any model grid square within the study basin; (c) the use of a statistical–stochastic downscaling method facilitates the generation of the synthetic series with unlimited length. It makes possible robust hydrological impact assessments if uncertainties related to the global climate model, regional climate model, greenhouse gas emission scenario, downscaling method, hydrological model and observational data are considered.

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Uncertainty in climate change impacts on water resources

Cited by 294 publications

References 42 publications

Inconsistent recognition of uncertainty in studies of climate change impacts on forests

Inconsistent recognition of uncertainty in studies of climate change impacts on forests

Managing Infrastructure in the Stream Environment

Downscaling climate change of water availability, sediment yield and extreme events: Application to a Mediterranean climate basin

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