Observed hydrologic non-stationarity in far south-eastern Australia: implications for modelling and prediction

Chiew, Francis H. S.; Potter, Nick; Vaze, Jai; Petheram, Cuan; Zhang, Lu; Teng, Jin; Post, David

doi:10.1007/s00477-013-0755-5

Cited by 128 publications

(136 citation statements)

References 33 publications

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“…The average rainfall shows a decreasing trend over the period. The decreasing trend in streamflow is much more significant than rainfall, highlighting the non-stationarity in the rainfall-streamflow relationship in the MDB as reported in Chiew et al (2014). As can be seen from the streamflow plots and trend lines for the current and predevelopment scenarios, the decreasing trend in streamflow is exacerbated by the anthropogenic development and water use in the basin over the modelling period.…”

Section: Impacts Of Climate Variability/change and Anthropogenic Actimentioning

confidence: 67%

“…There are numerous studies that have been conducted to assess the potential impacts of climate change on rainfall and runoff at global to regional to catchment scales (Chiew et al, 2014;Vaze et al, 2010;Kamruzzaman et al, 2011;Rossi et al, 2009). Many of these studies have demonstrated the impact of climate change on global and regional climatic systems and alteration of hydrological processes as a result of that.…”

Section: Introductionmentioning

confidence: 99%

“…Many of these studies have demonstrated the impact of climate change on global and regional climatic systems and alteration of hydrological processes as a result of that. It is acknowledged by many scientists and water managers that the assumption of stationarity in rainfall-runoff modelling is no longer valid (Chiew et al, 2014). Most of the studies related to hydrological non-stationarity have mainly focussed on un-regulated headwater catchments, which are less influenced by anthropogenic water use.…”

Section: Introductionmentioning

confidence: 99%

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Streamflow predictions in regulated river systems: hydrological non-stationarity versus anthropogenic water use

et al. 2015

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Abstract. Streamflow in a regulated river system is highly influenced by storage regulations and anthropogenic water use in addition to climate variability. Thus, changes in climate-streamflow relationships and dominant hydrological processes over time are difficult to quantify in a regulated system without partitioning influence of storage regulation and anthropogenic water uses. This requires a robust regulated river system model, which takes into consideration of both hydrological and man-made flow regulation processes, as well as anthropogenic water uses. In this study, a newly developed large-scale river system model (called "AWRA-R") was used to assess the influence of both anthropogenic and climate variability/change on streamflow non-stationarity in the Murray Darling Basin (MDB). MDB is one of the highly regulated basins in Australia with multiple large and small storages developed primarily for supplying water to irrigated agriculture. The modelling was undertaken for the period of 1950-2010, which includes rapid water resources development and both wet and dry climate. The AWRA-R model was calibrated for a reasonably long period and then, validated on an independent period. The calibrated parameters were used to simulate streamflow under current and pre-development conditions to analyse the streamflow variability and influence of climate variability and anthropogenic development on streamflow trend. This paper briefly introduces the model and the method used for assessing streamflow variability under natural and developed conditions and presents the results and findings.

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Section: Impacts Of Climate Variability/change and Anthropogenic Actimentioning

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Streamflow predictions in regulated river systems: hydrological non-stationarity versus anthropogenic water use

et al. 2015

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“…so that the data are approximately Gaussian distributed) using the Box-Cox power transformation (Box and Cox 1964, Siriwardena et al 2006, Chiew et al 2014. As the Box-Cox power transformation requires that all data be greater than zero, the zero rainfall values of the aggregated datasets were replaced with a value of 0.2 mm prior to the power transformation.…”

Section: Autocorrelation Analysismentioning

confidence: 99%

Statistical testing of dynamically downscaled rainfall data for the Upper Hunter region, New South Wales, Australia

Manage¹,

Lockart²,

Willgoose³

et al. 2016

JSHESS

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“…Chiew et al (2014) presents an example of hydrologic nonstationarity and the implications on hydrologic prediction exposed by the prolonged 1997-2009 Millennium Drought in far south-eastern Australia (Fig. 1).…”

Section: Hydrologic Nonstationarity and Implicationsoverviewmentioning

confidence: 99%

Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding

Chiew

Vaze

2015

Proc. IAHS

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Abstract. This paper provides an overview of this IAHS symposium and PIAHS proceeding on "hydrologic nonstationarity and extrapolating models to predict the future". The paper provides a brief review of research on this topic, presents approaches used to account for nonstationarity when extrapolating models to predict the future, and summarises the papers in this session and proceeding. Hydrologic nonstationarity and implicationsOverviewThe commentary by Milly et al. (2008) has initiated significant discussions and continuing progression of research on hydrologic nonstationarity. The term "hydrologic nonstationarity" has been used to describe many things, ranging from different climate-runoff relationships evident in different periods within a long hydroclimate time series to changes in hydroclimate characteristics and dominant hydrological processes in an increasingly warmer and higher CO 2 world. Hydrologists have always represented stationarity and nonstationarity (which is difficult to distinguish statistically in natural systems) as best they could and their implications on water resources and related systems, but modelling this adequately will become increasingly challenging in a world driven by anthropogenic changes. The constancy of laws and patterns has always been and will always be "stationary". It is our understanding or lack of these and the constancy of variables or characteristics at different times that may appear "nonstationary". For example, a hydroclimate time series can be considered "stationary" over thousands or millions of years, in that we can represent statistically or stochastically the characteristics and variability over time and space scales or even develop a precise understanding of the processes from the very long record. But of course, the characteristics of the different periods will always be different (exhibiting variability over different time and space scales), that is, nonstationary over time. The practical issue then is not whether hydroclimate systems are stationary or nonstationary, but whether the nonstationarity is substantial enough to require a change in existing system characterisation, conceptualisation or modelling for a particular hydrologic design, operation and planning.Hydrologists have excelled in developing models for numerous applications, through analysing and interpreting climate and hydrologic data to understand hydrologic processes, conceptualising the processes in hydrological models, and calibrating and testing models against observations. These models are particularly good in predicting the streamflow response to changes in the climate inputs and catchment characteristics. These models, when developed adequately using relatively long historical records that encapsulate the range of hydroclimate conditions, should be able to predict hydrologic responses to changes in the climate inputs over the near and medium term.However, extrapolating hydrological models to predict further into the future that is influenced by anthropogenic change is challenging as ...

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Observed hydrologic non-stationarity in far south-eastern Australia: implications for modelling and prediction

Cited by 128 publications

References 33 publications

Streamflow predictions in regulated river systems: hydrological non-stationarity versus anthropogenic water use

Streamflow predictions in regulated river systems: hydrological non-stationarity versus anthropogenic water use

Statistical testing of dynamically downscaled rainfall data for the Upper Hunter region, New South Wales, Australia

Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding

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