TY - JOUR
T1 - Complexity and information propagation in hydrological time series of mountain forest catchments
AU - Engelhardt, Stefan
AU - Matyssek, Rainer
AU - Huwe, Bernd
N1 - Funding Information:
The research reported in this article was conducted in the context of the DFG-funded umbrella project ‘Methodologies in Linking Hydrological and Biological Processes at the Landscape Level—A Contribution to IGBP/BAHC Research in Germany’ (DFG: Deutsche Forschungsgemeinschaft, German Research Foundation).
Funding Information:
Acknowledgments We express our gratitude to our colleagues in the research unit of the Ammerprojekt. The workgroups of Prof. Dr. C. Bernhofer (University of Dresden), Prof. R. Matyssek (Technical University of Munich) and Dr. A. Becker (Potsdam Institute for Climate Impact Research) supported our study by contributing time series of precipitation, sapflow and catchment discharge. Especially, we thank Dr. K. Patzner from the TUM for the collaboration on the monitoring plots. We also express our gratitude to the colleagues of the research unit Ecuador who contributed time series of precipitation, sapflow and catchment discharge. The workgroups are Prof. Dr. M. Küppers (University of Hohenheim), Prof. Dr. M. Richter (University of Erlangen) and Prof. Dr. W. Wilcke (University of Mainz). Especially, we name Matthias Oesker and Christian Oh-lemacher from the University of Hohenheim for their collaboration on the monitoring plot. We wish to thank the German Research Fundacion (DFG) for financial support of our projects.
PY - 2009
Y1 - 2009
N2 - Ecosystem analysis is typically done by determination of species composition, structural exploration, determination of matter and energy fluxes and/or system analyses based on deterministic or probabilistic/stochastic model approaches. However, regarding ecosystem dynamics, temporal structure, information content, complexity of signals, and their modifications when subsequently passing through different subsystems, have not intensively been studied to date. Structure in time series characterised by information and complexity measures may provide additional, powerful tools to analyse state and dynamics of ecosystems. Along their path through ecosystem compartments, e.g., hydrological signals are transformed in several ways, comprising changes in randomness, autocorrelation structures, and smoothness. Thus, time series analyses with complexity and information measures are of interest for a holistic understanding of ecosystem behaviour and early indications of natural and anthropogenic disturbances of ecosystems such as ecosystem degradation and climate change. Further, these measures provide additional criteria for the calibration of model parameters, tests of model validity, and determination of the necessary degree of complexity of process models. In this paper, we present the outcome from applications of information and complexity measures to hydrological time series in two climatically different forest ecosystems in South Germany and southern Ecuador. Information and complexity measures are different for different parameters but ecosystems of the same type such as mountain forests exhibit similar behaviour. We hypothesise that complexity of hydraulic time series increases with the number of abiotic and biotic variables involved in the generating process of the time series. Thus, complexity should reach a minimum in the precipitation signal which is controlled by abiotic, atmospheric factors only, and reach a maximum in the root zone where the interaction of abiotic and biotic variables is high. Hydrological time series under study cover the sequence of hydrological signals from open precipitation, throughfall, sapflow, water fluxes in the soil compartment and system discharge. We detected pronounced data aggregation and transformation effects of hydrological signals along their path through subsystems in terms of information propagation. We further found similar patterns in different ecosystems of the same general type. As a result of intensive abiotic and biotic interactions, a pronounced maximum of complexity was found in the moisture signal of the soil compartment.
AB - Ecosystem analysis is typically done by determination of species composition, structural exploration, determination of matter and energy fluxes and/or system analyses based on deterministic or probabilistic/stochastic model approaches. However, regarding ecosystem dynamics, temporal structure, information content, complexity of signals, and their modifications when subsequently passing through different subsystems, have not intensively been studied to date. Structure in time series characterised by information and complexity measures may provide additional, powerful tools to analyse state and dynamics of ecosystems. Along their path through ecosystem compartments, e.g., hydrological signals are transformed in several ways, comprising changes in randomness, autocorrelation structures, and smoothness. Thus, time series analyses with complexity and information measures are of interest for a holistic understanding of ecosystem behaviour and early indications of natural and anthropogenic disturbances of ecosystems such as ecosystem degradation and climate change. Further, these measures provide additional criteria for the calibration of model parameters, tests of model validity, and determination of the necessary degree of complexity of process models. In this paper, we present the outcome from applications of information and complexity measures to hydrological time series in two climatically different forest ecosystems in South Germany and southern Ecuador. Information and complexity measures are different for different parameters but ecosystems of the same type such as mountain forests exhibit similar behaviour. We hypothesise that complexity of hydraulic time series increases with the number of abiotic and biotic variables involved in the generating process of the time series. Thus, complexity should reach a minimum in the precipitation signal which is controlled by abiotic, atmospheric factors only, and reach a maximum in the root zone where the interaction of abiotic and biotic variables is high. Hydrological time series under study cover the sequence of hydrological signals from open precipitation, throughfall, sapflow, water fluxes in the soil compartment and system discharge. We detected pronounced data aggregation and transformation effects of hydrological signals along their path through subsystems in terms of information propagation. We further found similar patterns in different ecosystems of the same general type. As a result of intensive abiotic and biotic interactions, a pronounced maximum of complexity was found in the moisture signal of the soil compartment.
KW - Ammer mountains
KW - Andes
KW - Complexity and information measures
KW - Ecosystem response
KW - Information propagation
KW - Mountain forests
KW - Soil hydrology
KW - Time series analysis
KW - Tropical mountain rain forest
UR - http://www.scopus.com/inward/record.url?scp=76749150873&partnerID=8YFLogxK
U2 - 10.1007/s10342-009-0306-2
DO - 10.1007/s10342-009-0306-2
M3 - Article
AN - SCOPUS:76749150873
SN - 1612-4669
VL - 128
SP - 621
EP - 631
JO - European Journal of Forest Research
JF - European Journal of Forest Research
IS - 6
ER -