A modelling framework and R-package for evaluating system performance under hydroclimate variability and change

Bennett, Bree; Devanand, Anjana; Culley, Sam; Westra, Seth; Guo, Danlu; Maier, Holger R.

doi:10.1016/j.envsoft.2021.104999

Cited by 15 publications

(5 citation statements)

References 68 publications

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“…The second step is to stress-test the defined system and identify the climate conditions under which it will fail. This process is implemented using the foreSIGHT software tool (Bennett et al, 2021) and its associated framework (Section 3.2). The third step is to analyse climate projections and other lines of evidence to determine the reliability of the system, by considering the likelihood and possible timing of failure occurring (Section 3.3).…”

Section: Methodsmentioning

confidence: 99%

“…This has previously been a barrier to the uptake of scenario-neutral approaches, with a particular challenge involved in creating time series of hydroclimatic data for use in system modelling (Culley et al, 2019;Guo et al, 2018). As a means to overcome these challenges, an open source software tool called foreSIGHT has been developed (Bennett et al, 2021). This modelling tool works by first defining a series of climate statistics of a future scenario (referred to as climate attributes), and then using formal optimization techniques to identify the parameters of a stochastic weather generator that will provide the desired time series.…”

Section: Introductionmentioning

confidence: 99%

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Water security analysis of the Middle River supply system: a foreSIGHT application

2021

MODSIM2021, 24th International Congress on Modelling and Simulation.

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Scenario-neutral approaches have become the preferred method of interpreting climate driven impacts on natural and engineered systems when analysing the reliability of water resource systems facing climate change. Scenario-neutral approaches help deal with uncertainty and potential decision paralysis, by stress-testing systems across a range of plausible future conditions, instead of analysing systems under a narrower selection of climate projections. This results in better identification of failure modes and a richer elucidation of system behaviour. A case-study application of a methodology that supports the bottom-up approach-driven by a software tool 'foreSIGHT'-is presented here. Applied to the Middle River Reservoir system, the scenario-neutral approach adopted here follows three steps to: i) provide insights into system dynamics, ii) reveal modes of failure due to changes in climate, and iii) understand when intervention may be required given multiple lines of evidence. The Middle River system (situated in Kangaroo Island, South Australia) comprises of a single reservoir with relatively small storage volume relative to the annual catchment inflows, resulting in large seasonal fluctuations in reservoir levels with regular filling in winter and drawdown over the dry season. The reservoir can hold approximately 200 ML more than the mean annual demand (450 ML), which is highly variable year to year. The occurrence of draw down below 6 and 2 m levels were of interest to the stakeholders, as they broadly indicate, the need to augment the water supply due to a decrease in water quality (below 6 m), and the reservoir emptying (below 2 m). The reliability of the water supply system in 2030 was the focus of the analysis, as this aligns with opportunities to upgrade infrastructure.Observed hydrological data for the Middle River catchment was analysed, and a coupled hydrological, water balance and demand model was used in order to determine a historical baseline of system performance over the period of 1961-2005. When modelling the system in response to the historical baseline as a benchmark of expected performance, it was found that in dry years the demand can be high enough to draw down the reservoir below 6 m. The modelled probability of that failure under a historical climate was ~2%. It was also found that following this period of stationary climate there has been an observed increase in PET of 8.3%, indicating that the system is already experiencing a 'changed' climate compared to the historical baseline.A climate stress-test of the Middle River water supply system following the foreSIGHT framework demonstrated that the system is sensitive to annual changes in rainfall and PET. These metrics both influence supply and demand, with decreases in rainfall primarily affecting streamflow, and increases in PET increasing the demand placed on the system. In many of these failures, the reservoir had not filled the previous year, therefore providing little warning to enable water augmentation strategies to be put into plac...

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Water security analysis of the Middle River supply system: a foreSIGHT application

2021

MODSIM2021, 24th International Congress on Modelling and Simulation.

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“…Examples include the R package for scenario discovery (sdtoolkit; Bryant, 2015;Bryant & Lempert, 2010), the Exploratory Modeling and Analysis workbench developed by researchers at Delft University of Technology (Kwakkel & Pruyt, 2013), Open MORDM for multi-objective optimization (Hadka et al, 2015), PRIM (Duong, 2021), and other packages such as the R package foresight (B. Bennett et al, 2019).…”

Section: Modeling and Computational Complexitymentioning

confidence: 99%

A Review of Decision Making Under Deep Uncertainty Applications Using Green Infrastructure for Flood Management

Webber

Samaras

2022

Earth's Future

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Decision making under deep uncertainty (DMDU) approaches are well‐suited for making decisions about infrastructure to manage flooding exacerbated by climate change. One important system for climate resilience and flood management is green infrastructure, which refers to a network of natural and semi‐engineered areas that provides ecosystem functions. Green infrastructure is often characterized as a low‐regret strategy with multiple co‐benefits under uncertainty. These attributes enable green infrastructure to be an important adaptation strategy under DMDU frameworks for flood management. However, DMDU analyses that include green infrastructure are still relatively limited, perhaps due to computational or modeling complexity and other barriers. This paper identifies and reviews publications in the flood management literature that use DMDU frameworks and refer to green infrastructure adaptation strategies, in order to identify trends and inform future research. The reviewed publications are categorized according to a variety of performance metrics, climate change scenarios, DMDU metrics, and hydrologic modeling techniques, and represent several adaptation strategies applied to case studies on five continents using a range of data sources and assumptions. This paper highlights a number of solutions that can be employed to facilitate additional research at the intersection of these fields. Primary among these is the transparent documentation and use of open source models, methods, and data. Future research should also focus on communication among different stakeholders, particularly in ensuring definitions, assumptions, and data requirements are clear. These partnerships can facilitate effective application of robust strategies such as green infrastructure for urban adaptation to the effects of climate change.

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“…Guo et al [4] also added that the scenarioneutral approach can identify hydrometeorological variables significantly influencing water resource systems. Bennet et al [5] explained that modelling and analysis of the components of climate impact assessment using scenario-neutral forms a framework with the following five stages: identify attributes for perturbation and create an exposure space, generate hydroclimate scenarios; simulate system performance; analyse system performance; and evaluate system options.…”

Section: Introductionmentioning

confidence: 99%

“…The hydroclimate scenario generation process will be conducted using the R-Package foresight (Systems Insights from the Generation of Hydroclimate Time series). This R-Package implements the stages of the five climate impact assessment framework using the Scenario-Neutral approach [5]. The R-Package contains code and sample data stored in the library directory of the R program.…”

Section: Introductionmentioning

confidence: 99%

Flood modelling due to climate change impact in the Logawa watershed

Nabil Hakim,

Suroso,

Bekti Santoso

et al. 2023

E3S Web Conf.

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Rainfall change is one of the results of global climate change. It is required to analyse the impact of rainfall changes on flood risks. The aims of this research are (i) to model the watershed using the HECHMS hydrological model, (ii) to apply the scenario-neutral approach, (iii) to perform the frequency analysis using simulated discharges, and (iv) to determine the response and sensitivity of the watershed due to rainfall changes. This research uses a quantitative method using secondary data. The data required in this research are the watershed's geospatial and biogeophysical data. The simulation methods used in this model are the user Hyetograph Model, SCS Curve Number, Clark’s Unit Hydrograph, Recession Baseflow, and Muskingum Routing. The results of this research are (i) the hydrological model of the watershed with a percent bias score of 19.18% and an NSE score of 0.218; (ii) 130 total scenarios as the result of the RStudio using the scenario-neutral approach; (iii) the discharge result from frequency analysis with return periods of 1.1, 2, 5, 10, 20, 30, 40, 50, 100, and 200 years; and (iv) the watershed’s sensitivity with the average highest maximum discharge from November to April and the average lowest maximum discharge from May to October.

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A modelling framework and R-package for evaluating system performance under hydroclimate variability and change

Cited by 15 publications

References 68 publications

Water security analysis of the Middle River supply system: a foreSIGHT application

Water security analysis of the Middle River supply system: a foreSIGHT application

A Review of Decision Making Under Deep Uncertainty Applications Using Green Infrastructure for Flood Management

Flood modelling due to climate change impact in the Logawa watershed

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