Coupling large-scale climate indices with a stochastic weather generator to improve long-term streamflow forecasts in a Canadian watershed

Understanding the spatiotemporal regime of summer precipitation at local scales plays a key role in regional prevention and mitigation of floods disasters and water resources management. Previous works focused on spatiotemporal characteristics of a region as a whole but left the influence of associated physical factors on sub-regions unexplored. Based on the precipitation data of 77 meteorological stations in the Poyang Lake basin (PYLB) from 1959 to 2013, we have investigated regional characteristics of summer precipitation in the PYLB by integrating the rotated empirical orthogonal function (REOF) analysis with hierarchical clustering algorithm (HCA). Then the long-term variability of summer precipitation in sub-regions of the PYLB and possible links with large-scale circulations was investigated using multiple trend analyses, wavelet analysis and correlation analysis. The results indicate that summer precipitation variations in the PYLB were of very striking regional characteristics. The PYLB was divided into three independent sub-regions based on two leading REOF modes and silhouette coefficient (SC). These sub-regions were located in northern PYLB (sub-region I), central PYLB (sub-region II), and southern PYLB (sub-region III). The summer precipitation in different sub-regions exhibited distinct variation trends and periodicities, which was associated with different factors. All sub-regions show no trends over the whole period 1959–2013, rather they show trends in different periods. Trends per decade in annual summer precipitation in sub-region I and sub-region II were consistent for all periods with different start and end years. The oscillations periods with 2–3 years were found in summer precipitation of all the three sub-regions. Summer precipitation in sub-region I was significantly positively correlated with the previous Indian Ocean Dipole (IOD) event, but negatively correlated with East Asian Summer Monsoon (EASM). While summer precipitation in sub-region II and sub-region III showed weak teleconnections with climate indices. All of the results of this study are conducive to further understand both the regional climate variations in the PYLB and response to circulation patterns variations.

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Probabilistic streamflow forecasts based on hydrologic persistence and large-scale climate signals in central Texas

Journal of Hydroinformatics ◽

10.2166/hydro.2010.133 ◽

2010 ◽

Vol 13 (4) ◽

pp. 760-774 ◽

Cited By ~ 6

Author(s):

Wenge Wei ◽

David W. Watkins

Keyword(s):

Large Scale ◽

Southern Oscillation ◽

River System ◽

Forecast Model ◽

Climate Indices ◽

Lead Times ◽

Forecast Performance ◽

Streamflow Forecasts ◽

Central Texas ◽

Multiple Predictor

Skillful streamflow forecasts at seasonal lead times may be useful to water managers seeking to provide reliable water supplies and maximize system benefits. In this study, streamflow autocorrelation and large-scale climate information are used to generate probabilistic streamflow forecasts for the Lower Colorado River system in central Texas. A number of potential predictors are evaluated for forecasting flows in various seasons, including large-scale climate indices related to the El Niño/Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO) and others. Results indicate that, of the predictors evaluated, only hydrologic persistence and Pacific Ocean sea surface temperature patterns associated with ENSO and PDO provide forecasts which are statistically better than climatology. An ordinal polytomous logistic regression approach is proposed as a means of incorporating multiple predictor variables into a probabilistic forecast model. Forecast performance is assessed through a cross-validation procedure, using distribution-oriented metrics, and implications for decision making are discussed.

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Assessment of meteorological extremes using a synoptic weather generator and a downscaling model based on analogs

10.5194/hess-2019-557 ◽

2019 ◽

Cited By ~ 1

Author(s):

Damien Raynaud ◽

Benoit Hingray ◽

Guillaume Evin ◽

Anne-Catherine Favre ◽

Jérémy Chardon

Keyword(s):

Time Series ◽

Large Scale ◽

Weather Generator ◽

Time Step ◽

Stochastic Weather Generator ◽

Natural Risk ◽

Synoptic Weather ◽

Weather Generators ◽

Long Time ◽

Precipitation And Temperature

Abstract. Natural risk studies such as flood risk assessments require long series of weather variables. As an alternative to observed series, which have a limited length, these data can be provided by weather generators. Among the large variety of existing ones, resampling methods based on analogues have the advantage of guaranteeing the physical consistency between local variables at each time step. However, they cannot generate values of predictands exceeding the range of observed values. Moreover, the length of the simulated series is typically limited to the length of the synoptic meteorology records used to characterize the large-scale atmospheric configuration of the generation day. To overcome those limitations, the stochastic weather generator proposed in this study combines two sampling approaches based on atmospheric analogues: (1) a synoptic weather generator in a first step, which recombines days in the 20th century to generate a 1000-year sequence of new atmospheric trajectories and (2) a stochastic downscaling model in a second step, applied to these atmospheric trajectories, in order to simulate long time series of daily regional precipitation and temperature. The method is applied to daily time series of mean areal precipitation and temperature in Switzerland. It is shown that the climatological characteristics of observed precipitation and temperature are adequately reproduced. It also improves the reproduction of extreme precipitation values, overcoming previous limitations of standard analog-based weather generators.

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Assessment of meteorological extremes using a synoptic weather generator and a downscaling model based on analogues

Hydrology and Earth System Sciences ◽

10.5194/hess-24-4339-2020 ◽

2020 ◽

Vol 24 (9) ◽

pp. 4339-4352

Author(s):

Damien Raynaud ◽

Benoit Hingray ◽

Guillaume Evin ◽

Anne-Catherine Favre ◽

Jérémy Chardon

Keyword(s):

Time Series ◽

Large Scale ◽

Weather Generator ◽

Weather Variables ◽

Time Step ◽

Stochastic Weather Generator ◽

Natural Risk ◽

Synoptic Weather ◽

Weather Generators ◽

Precipitation And Temperature

Abstract. Natural risk studies such as flood risk assessments require long series of weather variables. As an alternative to observed series, which have a limited length, these data can be provided by weather generators. Among the large variety of existing ones, resampling methods based on analogues have the advantage of guaranteeing the physical consistency between local weather variables at each time step. However, they cannot generate values of predictands exceeding the range of observed values. Moreover, the length of the simulated series is typically limited to the length of the synoptic meteorological records used to characterize the large-scale atmospheric configuration of the generation day. To overcome these limitations, the stochastic weather generator proposed in this study combines two sampling approaches based on atmospheric analogues: (1) a synoptic weather generator in a first step, which recombines days of the 20th century to generate a 1000-year sequence of new atmospheric trajectories, and (2) a stochastic downscaling model in a second step applied to these atmospheric trajectories, in order to simulate long time series of daily regional precipitation and temperature. The method is applied to daily time series of mean areal precipitation and temperature in Switzerland. It is shown that the climatological characteristics of observed precipitation and temperature are adequately reproduced. It also improves the reproduction of extreme precipitation values, overcoming previous limitations of standard analogue-based weather generators.

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Assessing the predictability of MLR models for long-term streamflow using lagged climate indices as predictors: a case study of NSW (Australia)

Hydrology Research ◽

10.2166/nh.2018.171 ◽

2018 ◽

Vol 50 (1) ◽

pp. 262-281 ◽

Cited By ~ 5

Author(s):

Rijwana I. Esha ◽

Monzur A. Imteaz

Keyword(s):

Large Scale ◽

Southern Oscillation ◽

Pearson Correlation ◽

Absolute Error ◽

Climate Indices ◽

South Wales ◽

Forecast Models ◽

Input Variables ◽

Combined Impact

Abstract The current study aims to assess the potential of statistical multiple linear regression (MLR) techniques to develop long-term streamflow forecast models for New South Wales (NSW). While most of the past studies were concentrated on revealing the relationship between streamflow and single concurrent or lagged climate indices, this study intends to explore the combined impact of large-scale climate drivers. Considering their influences on the streamflow of NSW, several major climate drivers – IPO (Inter Decadal Pacific Oscillation)/PDO (Pacific Decadal Oscillation), IOD (Indian Ocean Dipole) and ENSO (El Niño-Southern Oscillation) are selected. Single correlation analysis is exploited as the basis for selecting different combinations of input variables for developing MLR models to examine the extent of the combined impacts of the selected climate drivers on forecasting spring streamflow several months ahead. The developed models with all the possible combinations show significantly good results for all selected 12 stations in terms of Pearson correlation (r), root mean square error (RMSE), mean absolute error (MAE) and Willmott index of agreement (d). For each region, the best model with lower errors provides statistically significant maximum correlation which ranges from 0.51 to 0.65.

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Application of Deterministic-stochastic Simulation for Frequency Curves Estimation: the Pasha River (Russia) as a Case Study

Water sector of Russia: problems, technologies, management - Методы оценки и механизмы регулирования водопользования в современных условиях ◽

10.35567/1999-4508-2012-4-4 ◽

2012 ◽

pp. 43-52

Author(s):

Keyword(s):

Stochastic Simulation ◽

Hydrological Model ◽

Weather Generator ◽

Distributed Hydrological Model ◽

Stochastic Weather Generator ◽

Runoff Model ◽

Frequency Curves

An approach that combines runoff model & stochastic weather generator in order to get the coordinates of yearly, monthly, daily, maximal & minimal runoff frequency curves is considered. The distributed hydrological model “Hydrograph” and stochastic weather generator were applied to the catchment of the Pasha River (5710 km2) located in the Northwest Russia. The frequency curves are compared with analogous ones that have been built on the basis of the long-term runoff observations.

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Evolution of Large-Scale Coronal Structures

International Astronomical Union Colloquium ◽

10.1017/s0252921100024945 ◽

1994 ◽

Vol 144 ◽

pp. 29-33

Author(s):

P. Ambrož

Keyword(s):

Magnetic Field ◽

Field Line ◽

Large Scale ◽

Coronal Magnetic Field ◽

Source Surface ◽

Solar Cycles ◽

Characteristic Relationship ◽

The Magnetic Field ◽

Coronal Structures

AbstractThe large-scale coronal structures observed during the sporadically visible solar eclipses were compared with the numerically extrapolated field-line structures of coronal magnetic field. A characteristic relationship between the observed structures of coronal plasma and the magnetic field line configurations was determined. The long-term evolution of large scale coronal structures inferred from photospheric magnetic observations in the course of 11- and 22-year solar cycles is described.Some known parameters, such as the source surface radius, or coronal rotation rate are discussed and actually interpreted. A relation between the large-scale photospheric magnetic field evolution and the coronal structure rearrangement is demonstrated.

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