Testing for nonlinearity of streamflow processes at different timescales

Wang, Wen; Vrijling, J.K.; Gelder, Pieter van; Ma, Jun

doi:10.1016/j.jhydrol.2005.02.045

Cited by 85 publications

(56 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the calculation of N-S efficiency, the original N-S formula uses the overall average of the streamflows irrespective of seasons or months considered, whereas the SANS efficiency formula uses the individual seasonal averages of streamflows in the same N-S formula. When there is a considerable difference between the overall average of the streamflow and the individual seasonal averages of the streamflows, the original N-S formula tends to produce overall model performance values higher than those of four seasons (Wang, 2006). However, due to the inclusion of seasonal averages of streamflows, SANS efficiency formula produces overall model performances, which are comparable with those of seasons.…”

Section: Generic Methodologymentioning

confidence: 98%

“…The model performances in calibration and validation during each calendar month and four seasons were measured using the original Nash-Sutcliffe (N-S) efficiency formula defined by Nash and Sutcliffe (1970). Both original N-S efficiency and seasonally adjusted Nash-Sutcliffe (SANS) efficiency proposed by Wang (2006) were computed for the estimation of the model performance over the entire calibration and validation period, covering all four seasons and years. In the calculation of N-S efficiency, the original N-S formula uses the overall average of the streamflows irrespective of seasons or months considered, whereas the SANS efficiency formula uses the individual seasonal averages of streamflows in the same N-S formula.…”

Section: Generic Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Least square support vector and multi‐linear regression for statistically downscaling general circulation model outputs to catchment streamflows

Sachindra

Huang

Barton

et al. 2012

Intl Journal of Climatology

112

View full text Add to dashboard Cite

This study employed least square support vector machine regression (LS-SVM-R) and multi-linear regression (MLR) for statistically downscaling monthly general circulation model (GCM) outputs directly to monthly catchment streamflows. The scope of the study was limited to calibration and validation of the downscaling models. The methodology was demonstrated by its application to a streamflow site in the Grampian water supply system in northwestern Victoria, Australia. Probable predictors for the study were selected from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data set based on the past literature and hydrology. Probable variables that displayed the best significant correlations, consistently with the streamflows over the entire period of the study and under three 20-year time slices (1950-1969, 1970-1989 and 1990-2010) were selected as potential predictors. To better capture seasonal variations of streamflows, downscaling models were developed for each calendar month. The standardized potential predictors were introduced to the LS-SVM-R and MLR models, starting from the best correlated three and then, others one by one, based on their correlations with the streamflows, until the model performance in validation was maximized. This stepwise model development enabled the identification of the optimum number of potential variables for each month. The model calibration was performed over the period 1950-1989 and validation was done for 1990-2010. LS-SVM-R model parameter optimization was achieved using simplex algorithm and leave-one-out cross-validation. The MLR models were optimized by minimizing the sum of squared errors. In both modelling techniques, validation was performed as an independent simulation. In calibration, LS-SVM-R and MLR models displayed equally good performances with a trend of under-predicting high flows. During validation, LS-SVM-R outperformed MLR, though both techniques over-predicted most of the streamflows. It was concluded that LS-SVM-R is a better technique for statistically downscaling GCM outputs to streamflows than MLR, but still MLR is a potential technique for the same task.

show abstract

Section: Generic Methodologymentioning

confidence: 98%

Section: Generic Methodologymentioning

confidence: 99%

Least square support vector and multi‐linear regression for statistically downscaling general circulation model outputs to catchment streamflows

Sachindra

Huang

Barton

et al. 2012

Intl Journal of Climatology

112

View full text Add to dashboard Cite

show abstract

“…Therefore, three statistical tests, which are augmented Dickey-Fuller (ADF), Kwiatkowski-Phillips-Schmidt-Shin (KPSS) and Phillips-Perron (PP), were adopted in this study to investigate non-stationarity in extreme rainfall time series data. These tests were selected due to their common use in hydrological studies (Wang et al 2005;Wang et al 2006;Yoo 2007). The null hypothesis of ADF and PP tests is nonstationarity of the time series data, whereas the null hypothesis of the KPSS test is stationarity of the data series.…”

Section: Non-stationarity Analysismentioning

confidence: 99%

Extreme Rainfall Nonstationarity Investigation and Intensity–Frequency–Duration Relationship

2014

View full text Add to dashboard Cite

Non-stationary behaviour of recent climate increases concerns amongst hydrologists about the currently used design rainfall estimates. Therefore, it is necessary to perform analysis to confirm stationarity or detect non-stationarity of extreme rainfall data in order to derive accurate design rainfall estimates for infrastructure projects and flood mitigation works.Extreme rainfall non-stationarity analysis of the storm durations from 6 min to 72 hours was conducted in this study using data from the Melbourne Regional Office station in Melbourne (Australia) for the period of 1925-2010. Stationary Generalized Extreme Value (GEV) models were constructed to obtain Intensity-Frequency-Duration relationships for the above storm durations using data of two time periods : 1925-1966 and 1967-2010 after identifying the year 1967 as the change point year. Design rainfall estimates of the stationary models for the two periods were compared to identify the possible changes. Non-stationary GEV models, which were developed for storm durations that showed statistically significant extreme rainfall trends, did not show advantage over stationary GEV models. There was no evidence of non-stationarity according to stationarity tests, despite the presence of statistically significant extreme rainfall trends. The developed methodology consisting of trend and nonstationarity tests, change point analysis, and stationary and non-stationary GEV models was demonstrated successfully using the data of the selected station.

show abstract

“…The mean and variance of extreme rainfall data series may not change over time (i.e. stationarity), despite the presence of trends in extreme rainfall data series (Wang et al, 2006). Therefore, further analysis should be conducted to check whether the detected trends may correspond to extreme rainfall nonstationarity.…”

Section: A G Yilmaz Et Al: Climate Change and Variability On Rainfmentioning

confidence: 99%

Effect of climate change and variability on extreme rainfall intensity–frequency–duration relationships: a case study of Melbourne

Yilmaz

Hossain

Perera

2014

Hydrol. Earth Syst. Sci.

115

View full text Add to dashboard Cite

Abstract. The increased frequency and magnitude of extreme rainfall events due to anthropogenic climate change, and decadal and multi-decadal climate variability question the stationary climate assumption. The possible violation of stationarity in climate can cause erroneous estimation of design rainfalls derived from extreme rainfall frequency analysis. This may result in significant consequences for infrastructure and flood protection projects since design rainfalls are essential input for design of these projects. Therefore, there is a need to conduct frequency analysis of extreme rainfall events in the context of non-stationarity, when nonstationarity is present in extreme rainfall events. A methodology consisting of threshold selection, extreme rainfall data (peaks over threshold data) construction, trend and nonstationarity analysis, and stationary and non-stationary generalised Pareto distribution (GPD) models was developed in this paper to investigate trends and non-stationarity in extreme rainfall events, and potential impacts of climate change and variability on intensity-frequency-duration (IFD) relationships. The methodology developed was successfully implemented using rainfall data from an observation station in Melbourne (Australia) for storm durations ranging from 6 min to 72 h. Although statistically significant trends were detected in extreme rainfall data for storm durations of 30 min, 3 h and 48 h, statistical non-stationarity tests and non-stationary GPD models did not indicate non-stationarity for these storm durations and other storm durations. It was also found that the stationary GPD models were capable of fitting extreme rainfall data for all storm durations. Furthermore, the IFD analysis showed that urban flash flood producing hourly rainfall intensities have increased over time.

show abstract

Testing for nonlinearity of streamflow processes at different timescales

Cited by 85 publications

References 57 publications

Least square support vector and multi‐linear regression for statistically downscaling general circulation model outputs to catchment streamflows

Least square support vector and multi‐linear regression for statistically downscaling general circulation model outputs to catchment streamflows

Extreme Rainfall Nonstationarity Investigation and Intensity–Frequency–Duration Relationship

Effect of climate change and variability on extreme rainfall intensity–frequency–duration relationships: a case study of Melbourne

Contact Info

Product

Resources

About