Several sessions at AGU 2017 conference will adress (hydrologic) uncertainty. An overview is given here: http://aguhu.blogspot.nl/2017/07/which-session-to-submit-to-hydrologic.html
DMDU draws on a range of approaches to deal with uncertainty. Methods for living with (deep) uncertainty is a key focus, but not all aspects of a situation are deeply uncertain. Even if we don’t necessarily think about it, we also use methods to reduce uncertainty in model structure, and perhaps to characterise uncertainty in parameters that can be estimated from data without controversy. This context-sensitive approach to uncertainty is a strength of DMDU.
Submissions to any of these sessions are warmly invited (abstracts due 2nd August).
The deadline for submitting abstracts for this years annual meeting is extended to 9th of June.
To register or submit an abstract, please go to the conference website.
by: Patrick Reed
This blog reports on one of the session of the annual meeting of 2016. The sessoin was organized by Patrick Reed (Cornell University), Jan Kwakkel (TU Delft), Andrea Castelletti (Politecnico di Milano), Laura Bonzanigo (World Bank). Invited speakers were Julie Quinn (Cornell University) and Marc Jaxa-Rozen (TU Delft).
Session Focus: This session explored the interplay between short-term adaptive operations and their influence on long-term planning is particularly relevant for irreversible decisions for long-lived infrastructures that present complex ecological impacts, and must reliably meet multi-sectoral demands (e.g., reservoirs, energy production/transmission, transportation networks, etc.). A core theme throughout this whole session is that current DMDU frameworks that truly seek robustness must better exploit information feedbacks, tailor adaptivity so that triggered actions are contextually appropriate, and minimize lock in. The session was organized into three case study presentations, five posters, and an interactive serious table top game. This suite of multi-sector examples helped clarify emerging innovations and persistent challenges related to bridging the planning and management divide.
•We performed global sensitivity analyses of a land use model under deep uncertainty.
•Deep uncertainty was characterised by internally consistent global change scenarios.
•The influence of scenarios on output uncertainty and parameter sensitivity was significant.
•Sensitivity indicators robust to deep uncertainty were calculated using four decision criteria.
•Our methods can better inform efforts to improve model outputs under deep uncertainty.
Buchanan, M. K., Kopp, R. E., Oppenheimer, M., & Tebaldi, C. (2015). Allowances for evolving coastal flood risk under uncertain local sea-level rise.Climatic Change, 1-16. URL: http://link.springer.com/article/10.1007/s10584-016-1664-7
Abstract: Estimates of future flood hazards made under the assumption of stationary mean sea level are biased low due to sea-level rise (SLR). However, adjustments to flood return levels made assuming fixed increases of sea level are also inadequate when applied to sea level that is rising over time at an uncertain rate. SLR allowances—the height adjustment from historic flood levels that maintain under uncertainty the annual expected probability of flooding—are typically estimated independently of individual decision-makers’ preferences, such as time horizon, risk tolerance, and confidence in SLR projections. We provide a framework of SLR allowances that employs complete probability distributions of local SLR and a range of user-defined flood risk management preferences. Given non-stationary and uncertain sea-level rise, these metrics provide estimates of flood protection heights and offsets for different planning horizons in coastal areas. We illustrate the calculation of various allowance types for a set of long-duration tide gauges along U.S. coastlines.
Kingsborough, A., Borgomeo, E. and Hall, J. (2016) Adaptation pathways in practice: Mapping options and trade-offs for London’s water resources. Sustainable Cities and Society.http://dx.doi.org/10.1016/j.scs.2016.08.013
London’s ability to remain a world-leading city in an increasingly globalised economy is dependent on it being an efficient, low-risk place to do business and a desirable place to live. However, increasing climate risk from flooding, overheating and water scarcity threatens this, creating the need for adaptation. An adaption pathway describes a structured sequence of adaptation decisions that are designed to manage climate risk in a wide range of possible future conditions. Analysis of sequential adaptation decision ‘pathways’ helps to demonstrate how climate risk can (or cannot) be managed, whilst retaining the flexibility to respond to future uncertainties. Whilst adaptive planning has gained increasing attention, the uptake of such methods has been relatively limited compared to the scale of the adaptation challenge due to institutional, financial and methodological barriers. This paper introduces a framework for adaptation planning in urban water supply systems that links existing risk-based decision-making with the development of long-term adaptation pathways. We present a quantified assessment of how the risk of water shortages in London is predicted to vary dynamically through to 2100 depending on the choice of adaptation pathways and under different long-term transient population and climate scenarios. This approach helps to reconcile multiple decision timescales and demonstrates the value of strategic long-term adaptation planning to stakeholders by outlining long-term futures that may influence medium-term decision-making. Adopting a flexible approach to adaptation will be critical to the management of risk under uncertainty. This adaptation pathways approach demonstrates an effective framework for informing such decision processes.
Guivarch, Céline, Julie Rozenberg, and Vanessa Schweizer. 2016. The diversity of socio-economic pathways and CO2 emissions scenarios: Insights from the investigation of a scenarios database. Environmental Modelling & Software 80, p 336-353.
The new scenario framework developed by the climate change research community rests on the fundamental logic that a diversity of socio-economic pathways can lead to the same radiative forcing, and therefore that a given level of radiative forcing can have very different socio-economic impacts. We propose a methodology that implements a “scenario discovery” cluster analysis and systematically identifies diverse groups of scenarios that share common outcomes among a database of socio-economic scenarios. We demonstrate the methodology with two examples using the Shared Socio-economic Pathways framework. We find that high emissions scenarios can be associated with either high or low per capita GDP growth, and that high productivity growth and catch-up are not necessarily associated with high per capita GDP and high emissions.
Girard, C., Pulido-Velazquez, M., Rinaudo, J.-D., Page, C., and Caballero, Y., 2015, Integrating top-down and bottom-up approaches to design global change adaptation at the river basin scale, Global Environmental Change 34,132-146 http://dx.doi.org/10.1016/j.gloenvcha.2015.07.002
The high uncertainty associated with the effect of global change on water resource systems calls for a better combination of conventional top–down and bottom–up approaches, in order to design robust adaptation plans at the local scale. The methodological framework presented in this article introduces “bottom–up meets top–down” integrated approach to support the selection of adaptation measures at the river basin level by comprehensively integrating the goals of economic efficiency, social acceptability, environmental sustainability and adaptation robustness. The top–down approach relies on the use of a chain of models to assess the impact of global change on water resources and its adaptive management over a range of climate projections. Future demand scenarios and locally prioritised adaptation measures are identified following a bottom–up approach through a participatory process with the relevant stakeholders and experts. The optimal combinations of adaptation measures are then selected using a hydro-economic model at basin scale for each climate projection. The resulting adaptation portfolios are, finally, climate checked to define a robust least-regret programme of measures based on trade-offs between adaptation costs and the reliability of supply for agricultural demands.
This innovative approach has been applied to a Mediterranean basin, the Orb river basin (France). Mid-term climate projections, downscaled from 9 General Climate Models, are used to assess the uncertainty associated with climate projections. Demand evolution scenarios are developed to project agricultural and urban water demands on the 2030 time horizon. The results derived from the integration of the bottom–up and top–down approaches illustrate the sensitivity of the adaptation strategies to the climate projections, and provide an assessment of the trade-offs between the performance of the water resource system and the cost of the adaptation plan to inform local decision-making. The article contributes new methodological elements for the development of an integrated framework for decision-making under climate change uncertainty, advocating an interdisciplinary approach that bridges the gap between bottom–up and top–down approaches.
Lempert, Robert J., Drake Warren, Ryan Henry, Robert W. Button, Jonathan Klenk and Kate Giglio. Defense Resource Planning Under Uncertainty: An Application of Robust Decision Making to Munitions Mix Planning. Santa Monica, CA: RAND Corporation, 2016. http://www.rand.org/pubs/research_reports/RR1112.html. Also available in print form.
Robust decision making stress-tests plans over a wide range of plausible futures and identifies scenarios in which strategies do and do not meet their goals. It can help identify and evaluate adaptive strategies—ones designed to evolve over time—and can help decision makers use this information to develop more robust plans and evaluate the tradeoffs among them. One strategy investigated was shown to be robust over a wide range of plausible futures.
Borgomeo, E., G. Pflug, J. W. Hall, and S. Hochrainer-Stigler (2015), Assessing water resource system vulnerability to unprecedented hydrological drought using copulas to characterize drought duration and deficit, Water Resour. Res., 51, 8927–8948, doi:10.1002/2015WR017324.
Global climate models suggest an increase in evapotranspiration, changing storm tracks, and moisture delivery in many parts of the world, which are likely to cause more prolonged and severe drought, yet the weakness of climate models in modeling persistence of hydroclimatic variables and the uncertainties associated with regional climate projections mean that impact assessments based on climate model output may underestimate the risk of multiyear droughts. In this paper, we propose a vulnerability-based approach to test water resource system response to drought. We generate a large number of synthetic streamflow series with different drought durations and deficits and use them as input to a water resource system model. Marginal distributions of the streamflow for each month are generated by bootstrapping the historical data, while the joint probability distributions of consecutive months are constructed using a copula-based method. Droughts with longer durations and larger deficits than the observed record are generated by perturbing the copula parameter and by adopting an importance sampling strategy for low flows. In this way, potential climate-induced changes in monthly hydrological persistence are factored into the vulnerability analysis. The method is applied to the London water system (England) to investigate under which drought conditions severe water use restrictions would need to be imposed. Results indicate that the water system is vulnerable to drought conditions outside the range of historical events. The vulnerability assessment results were coupled with climate model information to compare alternative water management options with respect to their vulnerability to increasingly long and severe drought.