Climate variability and its effects on regional hydrology: a case study for the Baltic Sea drainage basin
(2009) Combined IMACS World Congress/Modelling and Simulation SocietyofAustraliaandNewZealand (MSSANZ)/18th Biennial Conference on Modelling and Simulation In 18th World Imacs Congress and Modsim09 International Congress on Modelling and Simulation  Interfacing Modelling and Simulation With Mathematical and Computational Sciences p.38933899 Abstract
 As climate models can be used to reproduce historical climates, the outcomes can be used to put climate extremes in to a proper historical perspective. This also allows investigation of nonlinear properties of hydrologic processes (e. g. precipitation, runoff) to better understand regional hydrologic dynamics. To this end, the present study uses results from a socalled 'paleosimulation' (i.e. simulation of climate during periods prior to the development of measuring instruments, including historic and geologic time, for which only proxy climate records are available) covering the Baltic Sea drainage basin and the surrounding areas. Time series of annual temperature, precipitation, and runoff are simulated to study their dynamic... (More)
 As climate models can be used to reproduce historical climates, the outcomes can be used to put climate extremes in to a proper historical perspective. This also allows investigation of nonlinear properties of hydrologic processes (e. g. precipitation, runoff) to better understand regional hydrologic dynamics. To this end, the present study uses results from a socalled 'paleosimulation' (i.e. simulation of climate during periods prior to the development of measuring instruments, including historic and geologic time, for which only proxy climate records are available) covering the Baltic Sea drainage basin and the surrounding areas. Time series of annual temperature, precipitation, and runoff are simulated to study their dynamic characteristics. Three different simulation periods between years 1000 and 1929 are considered: 10001199, 15511749, and 17511929; these three periods represent a warm, a cool, and an intermediate climate episode, respectively. Both linear (autocorrelation function) and nonlinear (phase space reconstruction) methods are employed. The autocorrelation function is a normalized measure of the linear correlation among successive values in a time series, while the basic idea behind the phase space reconstruction is that the past history of a single variable contains important information about the dynamics of the multivariable system. The 30year average for all the three variables seems to follow a quasiperiodic behavior. An increasing trend is noted for temperature and precipitation during the later periods, but no such pronounced trend is evident for runoff. There is a general linear correlation between annual temperature and precipitation equal to 0.53, and between precipitation and runoff equal to 0.77; however, the correlation between temperature and runoff is as low as 0.30. The annual temperature series has one significant autocorrelation coefficient (lag 1 year), but precipitation and runoff series have no significant coefficients. Significant and slowly decreasing autocorrelation may be an indication of chaotic dynamics and temporal persistence that could be related to fractals. Due to the small autocorrelation, further analyses are carried out using serial time series (i.e. the simulated data are assumed continuous in time). The 30year moving average for these serial time series reveals linear correlations between the variables; the crosscorrelation between temperature and precipitation is 0.88, between precipitation and runoff is 0.83, and between temperature and runoff is 0.52. For these serial time series, phase space reconstruction is carried out to investigate the possible presence of attractors. Univariate (temperature, precipitation and runoff, independently) as well as multivariate (temperatureprecipitation, temperaturerunoff, precipitationrunoff) reconstructions are performed. For reconstruction, a delay time value of 5 years is considered for the univariate cases, while two delay time values (0 and 5 years) are considered for the multivariate cases. The results generally indicate clear attractors for all the variables and combinations, suggesting nonlinear relationships between temperature, precipitation, and runoff. These relationships could be exploited in prediction schemes, in both univariate and multivariate senses. Such an analysis would contribute to a better understanding of regional runoff dynamics due to climate effects. This is especially important for the Baltic Basin, since transport of nutrients, for example, are strongly correlated to the runoff conditions. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/record/1983977
 author
 Berndtsson, Ronny ^{LU} ; Sivakumar, B.; Olsson, J, and Graham, L. P.
 organization
 publishing date
 2009
 type
 Chapter in Book/Report/Conference proceeding
 publication status
 published
 subject
 keywords
 Regional hydrology, Baltic sea, climate, temperature, precipitation, runoff, nonlinearity, chaos
 in
 18th World Imacs Congress and Modsim09 International Congress on Modelling and Simulation  Interfacing Modelling and Simulation With Mathematical and Computational Sciences
 pages
 3893  3899
 publisher
 Univ Western Australia
 conference name
 Combined IMACS World Congress/Modelling and Simulation SocietyofAustraliaandNewZealand (MSSANZ)/18th Biennial Conference on Modelling and Simulation
 external identifiers

 WOS:000290045003134
 Scopus:80052981501
 language
 English
 LU publication?
 yes
 id
 55d92877b03a492abd623ae14e84a22d (old id 1983977)
 alternative location
 http://www.mssanz.org.au/modsim09/I13/berndtsson.pdf
 date added to LUP
 20110630 09:28:57
 date last changed
 20161013 04:47:42
@misc{55d92877b03a492abd623ae14e84a22d, abstract = {As climate models can be used to reproduce historical climates, the outcomes can be used to put climate extremes in to a proper historical perspective. This also allows investigation of nonlinear properties of hydrologic processes (e. g. precipitation, runoff) to better understand regional hydrologic dynamics. To this end, the present study uses results from a socalled 'paleosimulation' (i.e. simulation of climate during periods prior to the development of measuring instruments, including historic and geologic time, for which only proxy climate records are available) covering the Baltic Sea drainage basin and the surrounding areas. Time series of annual temperature, precipitation, and runoff are simulated to study their dynamic characteristics. Three different simulation periods between years 1000 and 1929 are considered: 10001199, 15511749, and 17511929; these three periods represent a warm, a cool, and an intermediate climate episode, respectively. Both linear (autocorrelation function) and nonlinear (phase space reconstruction) methods are employed. The autocorrelation function is a normalized measure of the linear correlation among successive values in a time series, while the basic idea behind the phase space reconstruction is that the past history of a single variable contains important information about the dynamics of the multivariable system. The 30year average for all the three variables seems to follow a quasiperiodic behavior. An increasing trend is noted for temperature and precipitation during the later periods, but no such pronounced trend is evident for runoff. There is a general linear correlation between annual temperature and precipitation equal to 0.53, and between precipitation and runoff equal to 0.77; however, the correlation between temperature and runoff is as low as 0.30. The annual temperature series has one significant autocorrelation coefficient (lag 1 year), but precipitation and runoff series have no significant coefficients. Significant and slowly decreasing autocorrelation may be an indication of chaotic dynamics and temporal persistence that could be related to fractals. Due to the small autocorrelation, further analyses are carried out using serial time series (i.e. the simulated data are assumed continuous in time). The 30year moving average for these serial time series reveals linear correlations between the variables; the crosscorrelation between temperature and precipitation is 0.88, between precipitation and runoff is 0.83, and between temperature and runoff is 0.52. For these serial time series, phase space reconstruction is carried out to investigate the possible presence of attractors. Univariate (temperature, precipitation and runoff, independently) as well as multivariate (temperatureprecipitation, temperaturerunoff, precipitationrunoff) reconstructions are performed. For reconstruction, a delay time value of 5 years is considered for the univariate cases, while two delay time values (0 and 5 years) are considered for the multivariate cases. The results generally indicate clear attractors for all the variables and combinations, suggesting nonlinear relationships between temperature, precipitation, and runoff. These relationships could be exploited in prediction schemes, in both univariate and multivariate senses. Such an analysis would contribute to a better understanding of regional runoff dynamics due to climate effects. This is especially important for the Baltic Basin, since transport of nutrients, for example, are strongly correlated to the runoff conditions.}, author = {Berndtsson, Ronny and Sivakumar, B. and Olsson, J, and Graham, L. P.}, keyword = {Regional hydrology,Baltic sea,climate,temperature,precipitation,runoff,nonlinearity,chaos}, language = {eng}, pages = {38933899}, publisher = {ARRAY(0x9dc9eb0)}, series = {18th World Imacs Congress and Modsim09 International Congress on Modelling and Simulation  Interfacing Modelling and Simulation With Mathematical and Computational Sciences}, title = {Climate variability and its effects on regional hydrology: a case study for the Baltic Sea drainage basin}, year = {2009}, }