Abstract: Present-day flood estimation practise is underpinned by the assumption that flood risk ... more Abstract: Present-day flood estimation practise is underpinned by the assumption that flood risk in a future climate will reflect historical flood risk as represented by the instrumental record. This assumption, which is commonly referred to as the assumption of stationarity, recently has been questioned as a result of both an increased appreciation of the natural variability in our hydroclimate at temporal scales beyond that of the instrumental record, as well as the projected intensification of the hydrologic cycle due to anthropogenic climate change. These developments have led some authors to suggest that the stationarity assumption should henceforth be considered invalid, thereby calling into question all the methods that are underpinned by it, including flood frequency analysis using observed streamflow records, and rainfall-runoff modelling informed by instrumental precipitation and streamflow records. In this paper we review a wide range of possible sources of non-stationari...
Abstract This study investigates the ability of a regional climate model (RCM) to simulate the di... more Abstract This study investigates the ability of a regional climate model (RCM) to simulate the diurnal cycle of precipitation over south-east Australia, to provide a basis for understanding the mechanisms which drive diurnal variability. When compared to 195 observation gauges, the RCM tends to simulate too many occurrences and too little intensity for precipitation events at the 3 hourly time scale. However, the overall precipitation amounts are well simulated and the diurnal variability in occurrences and intensities are generally well ...
Many problems in hydrology and agricultural science require extensive records of rainfall from mu... more Many problems in hydrology and agricultural science require extensive records of rainfall from multiple locations. Temporal and/or spatial coverage of rainfall data is often limited, so that stochastic models may be required to generate long synthetic rainfall records. This study describes a multi-site rainfall simulator (MRS) to stochastically generate daily rainfall at multiple locations. The MRS is available as an opensource software package in the R statistical computing environment. The software includes statistical analysis and graphics functions, and can display statistics and graphs at multiple time scales, including from individual sites and areal averages. The MRS thus provides a detailed set of modelling functions to simulate and analyse daily rainfall. The capabilities of the package are demonstrated using 30 gauges located in Sydney, Australia, and the results show that the model preserves observed year-to-year variability, interannual persistence and various daily distributional and spaceetime dependence attributes.
This paper presents a strategy for diagnosing and interpreting hydrological nonstationarity, aimi... more This paper presents a strategy for diagnosing and interpreting hydrological nonstationarity, aiming to improve hydrological models and their predictive ability under changing hydroclimatic conditions. The strategy consists of four elements: (i) detecting potential systematic errors in the calibration data; (ii) hypothesizing a set of ''nonstationary'' parameterizations of existing hydrological model structures, where one or more parameters vary in time as functions of selected covariates; (iii) trialing alternative stationary model structures to assess whether parameter nonstationarity can be reduced by modifying the model structure; and (iv) selecting one or more models for prediction. The Scott Creek catchment in South Australia and the lumped hydrological model GR4J are used to illustrate the strategy. Streamflow predictions improve significantly when the GR4J parameter describing the maximum capacity of the production store is allowed to vary in time as a combined function of: (i) an annual sinusoid; (ii) the previous 365 day rainfall and potential evapotranspiration; and (iii) a linear trend. This improvement provides strong evidence of model nonstationarity. Based on a range of hydrologically oriented diagnostics such as flow-duration curves, the GR4J model structure was modified by introducing an additional calibration parameter that controls recession behavior and by making actual evapotranspiration dependent only on catchment storage. Model comparison using an information-theoretic measure (the Akaike Information Criterion) and several hydrologically oriented diagnostics shows that the GR4J modifications clearly improve predictive performance in Scott Creek catchment. Based on a comparison of 22 versions of GR4J with different representations of nonstationarity and other modifications, the model selection approach applied in the exploratory period (used for parameter estimation) correctly identifies models that perform well in a much drier independent confirmatory period. Key Points: A strategy to diagnose and interpret hydrological nonstationarity is presented Time-varying parameters are used to represent model nonstationarity The strategy reduces predictive biases over an independent confirmatory period Correspondence to: S. Westra, seth.westra@adelaide.edu.au Citation: Westra, S., M. Thyer, M. Leonard, D. Kavetski, and M. Lambert (2014), A strategy for diagnosing and interpreting hydrological model nonstationarity, Water Resour. Res., 50, 5090-5113,
Abstract: Present-day flood estimation practise is underpinned by the assumption that flood risk ... more Abstract: Present-day flood estimation practise is underpinned by the assumption that flood risk in a future climate will reflect historical flood risk as represented by the instrumental record. This assumption, which is commonly referred to as the assumption of stationarity, recently has been questioned as a result of both an increased appreciation of the natural variability in our hydroclimate at temporal scales beyond that of the instrumental record, as well as the projected intensification of the hydrologic cycle due to anthropogenic climate change. These developments have led some authors to suggest that the stationarity assumption should henceforth be considered invalid, thereby calling into question all the methods that are underpinned by it, including flood frequency analysis using observed streamflow records, and rainfall-runoff modelling informed by instrumental precipitation and streamflow records. In this paper we review a wide range of possible sources of non-stationari...
Abstract This study investigates the ability of a regional climate model (RCM) to simulate the di... more Abstract This study investigates the ability of a regional climate model (RCM) to simulate the diurnal cycle of precipitation over south-east Australia, to provide a basis for understanding the mechanisms which drive diurnal variability. When compared to 195 observation gauges, the RCM tends to simulate too many occurrences and too little intensity for precipitation events at the 3 hourly time scale. However, the overall precipitation amounts are well simulated and the diurnal variability in occurrences and intensities are generally well ...
Many problems in hydrology and agricultural science require extensive records of rainfall from mu... more Many problems in hydrology and agricultural science require extensive records of rainfall from multiple locations. Temporal and/or spatial coverage of rainfall data is often limited, so that stochastic models may be required to generate long synthetic rainfall records. This study describes a multi-site rainfall simulator (MRS) to stochastically generate daily rainfall at multiple locations. The MRS is available as an opensource software package in the R statistical computing environment. The software includes statistical analysis and graphics functions, and can display statistics and graphs at multiple time scales, including from individual sites and areal averages. The MRS thus provides a detailed set of modelling functions to simulate and analyse daily rainfall. The capabilities of the package are demonstrated using 30 gauges located in Sydney, Australia, and the results show that the model preserves observed year-to-year variability, interannual persistence and various daily distributional and spaceetime dependence attributes.
This paper presents a strategy for diagnosing and interpreting hydrological nonstationarity, aimi... more This paper presents a strategy for diagnosing and interpreting hydrological nonstationarity, aiming to improve hydrological models and their predictive ability under changing hydroclimatic conditions. The strategy consists of four elements: (i) detecting potential systematic errors in the calibration data; (ii) hypothesizing a set of ''nonstationary'' parameterizations of existing hydrological model structures, where one or more parameters vary in time as functions of selected covariates; (iii) trialing alternative stationary model structures to assess whether parameter nonstationarity can be reduced by modifying the model structure; and (iv) selecting one or more models for prediction. The Scott Creek catchment in South Australia and the lumped hydrological model GR4J are used to illustrate the strategy. Streamflow predictions improve significantly when the GR4J parameter describing the maximum capacity of the production store is allowed to vary in time as a combined function of: (i) an annual sinusoid; (ii) the previous 365 day rainfall and potential evapotranspiration; and (iii) a linear trend. This improvement provides strong evidence of model nonstationarity. Based on a range of hydrologically oriented diagnostics such as flow-duration curves, the GR4J model structure was modified by introducing an additional calibration parameter that controls recession behavior and by making actual evapotranspiration dependent only on catchment storage. Model comparison using an information-theoretic measure (the Akaike Information Criterion) and several hydrologically oriented diagnostics shows that the GR4J modifications clearly improve predictive performance in Scott Creek catchment. Based on a comparison of 22 versions of GR4J with different representations of nonstationarity and other modifications, the model selection approach applied in the exploratory period (used for parameter estimation) correctly identifies models that perform well in a much drier independent confirmatory period. Key Points: A strategy to diagnose and interpret hydrological nonstationarity is presented Time-varying parameters are used to represent model nonstationarity The strategy reduces predictive biases over an independent confirmatory period Correspondence to: S. Westra, seth.westra@adelaide.edu.au Citation: Westra, S., M. Thyer, M. Leonard, D. Kavetski, and M. Lambert (2014), A strategy for diagnosing and interpreting hydrological model nonstationarity, Water Resour. Res., 50, 5090-5113,
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Papers by Seth Westra