High-resolution projections of surface water availability for Tasmania, Australia

Bennett, James; Ling, Fiona; Post, D. A.; Grose, Michael; Corney, SP; Graham, B; Holz, Greg; Katzfey, Jack; Bindoff, Nathaniel L.

doi:10.5194/hess-16-1287-2012

Cited by 33 publications

(28 citation statements)

References 54 publications

(71 reference statements)

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“…The largest cross‐validation Tmin and Tmax biases after bias‐correction are within ±0.1 °C for most of Tasmania (Figure (i) and (j)). We note that the rainfall biases under ensemble cross‐validation (Figure (f)) are very similar to those reported by Bennett et al () for the same RCM simulations, who used conventional split‐sample cross‐validation. The largest rainfall biases after bias‐correction (up to 8%, Figure (f)) are similar to the cross‐validated interpolation errors reported for AWAP rainfall data over Tasmania by Jones et al ().…”

Section: Resultssupporting

confidence: 84%

Performance of an empirical bias‐correction of a high‐resolution climate dataset

Bennett

Grose

Corney

et al. 2013

Intl Journal of Climatology

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We describe the method and performance of a bias-correction applied to high-resolution (10 km) simulations from a stretched-grid Regional Climate Model (RCM) over Tasmania, Australia. The bias-correction is a quantile mapping of empirical cumulative frequency distributions. Corrections are applied at a daily time step to five variables: rainfall, potential evaporation (PE), solar radiation, maximum temperature and minimum temperature. Corrections are calculated independently for each season.We show that quantile mapping of empirical distributions can be highly effective in correcting biases in RCM outputs. Cross-validation shows biases are effectively reduced across the range of cumulative frequency distributions, with few exceptions. The bias-correction is not as effective at correcting biases for values at or near zero (e.g. in rainfall simulations), although even here the bias-correction improves biases evident in the uncorrected simulations. In addition, the biascorrection improves frequency characteristics of variables such as the number of rain days.We use a detrending technique to apply the bias-correction to 140-year time series of RCM variables. We show that the bias-correction effectively preserves long-term changes (e.g. to the mean and variance) to variables projected by the uncorrected RCM simulations. Correlations between key variables are also largely preserved, thus the bias-corrected outputs reflect the dynamics of the underlying RCM. However, the bias-corrected simulations still exhibit some of the deficiencies of the RCM simulations, e.g. the tendency to underestimate the magnitude and duration of large, multi-day rain events, and the tendency to underestimate the duration of dry spells.The bias-corrected simulations for six downscaled GCMs for the A2 and B1 emissions scenarios are available to researchers from http://www.tpac.org.au.

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Section: Resultssupporting

confidence: 84%

Performance of an empirical bias‐correction of a high‐resolution climate dataset

Bennett

Grose

Corney

et al. 2013

Intl Journal of Climatology

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“…In practice, reservoirs have long service lives (typically decades), leaving them vulnerable to possible changes to the inflow regime beyond their construction (e.g. Bennett et al, 2012). In severe cases, an older reservoir may no longer be able to buffer inflows as effectively as when it was constructed, even if demand is static.…”

Section: Discussionmentioning

confidence: 99%

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

Turner

Bennett

Robertson

et al. 2017

Hydrol. Earth Syst. Sci.

108

105

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Abstract. Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strong relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made -namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.

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“…In brief, it is modelling with a regional climate model, or RCM. With advances in RCMs and the increasing availability of RCM simulations, this type of downscaling is gaining more and more popularity in hydrological impact studies (Dosio et al, 2012;Argüeso et al, 2013;Seaby et al, 2013;Teutschbein and Seibert, 2010;Maraun et al, 2010;Bennett et al, 2012). A drawback, however, is that precipitation simulations from RCMs are "biased": in addition to errors inherited from the driving GCM, there are systematic RCM model errors, due to imperfect conceptualisation and parameterisation, inadequate length and quality of reference data sets, and insufficient spatial resolution (Wilby et al, 2000;Wood et al, 2004;Piani et al, 2010b;Chen et al, 2011a;Christensen et al, 2008;Teutschbein and Seibert, 2010).…”

Section: Introductionmentioning

confidence: 99%

How does bias correction of regional climate model precipitation affect modelled runoff?

Teng

Potter

Chiew

et al. 2015

Hydrol. Earth Syst. Sci.

150

103

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Abstract. Many studies bias correct daily precipitation from climate models to match the observed precipitation statistics, and the bias corrected data are then used for various modelling applications. This paper presents a review of recent methods used to bias correct precipitation from regional climate models (RCMs). The paper then assesses four bias correction methods applied to the weather research and forecasting (WRF) model simulated precipitation, and the follow-on impact on modelled runoff for eight catchments in southeast Australia. Overall, the best results are produced by either quantile mapping or a newly proposed two-state gamma distribution mapping method. However, the differences between the methods are small in the modelling experiments here (and as reported in the literature), mainly due to the substantial corrections required and inconsistent errors over time (non-stationarity). The errors in bias corrected precipitation are typically amplified in modelled runoff. The tested methods cannot overcome limitations of the RCM in simulating precipitation sequence, which affects runoff generation. Results further show that whereas bias correction does not seem to alter change signals in precipitation means, it can introduce additional uncertainty to change signals in high precipitation amounts and, consequently, in runoff. Future climate change impact studies need to take this into account when deciding whether to use raw or bias corrected RCM results. Nevertheless, RCMs will continue to improve and will become increasingly useful for hydrological applications as the bias in RCM simulations reduces.

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High-resolution projections of surface water availability for Tasmania, Australia

Cited by 33 publications

References 54 publications

Performance of an empirical bias‐correction of a high‐resolution climate dataset

Performance of an empirical bias‐correction of a high‐resolution climate dataset

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

How does bias correction of regional climate model precipitation affect modelled runoff?

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