@phdthesis{AlegueFeugo2008, author = {Alegue Feugo, Jean Duclos}, title = {Investigating ecological indicators of freshwater ecosystems using signal analysis methods}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus-4396}, school = {BTU Cottbus - Senftenberg}, year = {2008}, abstract = {Human life depends on water of good quality. Freshwater ecosystems are being degraded as a result of anthropogenic activities. Managing freshwater resources require a good understanding of the dynamics of the processes affecting water quality and the interrelationship existing between them. Signal analysis methods are used to extract information from water quality time series. The information obtained from such analysis is required for developing dynamic models as an aid in investigating the outcome of different management scenarios. They are equally useful in determining the appropriate sampling frequency required for monitoring ecological indicators. Modern methods developed in other field such as mathematical statistics need to be investigated in the fields of water quality management so as to enhance the knowledge of the functioning and the structure of water bodies. Classical methods consisting of time domain and frequency domain methods in combination with a modern method, wavelet analysis, were used to extract information from water quality indicators from the River Havel in the State of Brandenburg in Germany. The indicators were dissolved oxygen, chlorophyll-a and water temperature which are respectively chemical, biological and physical indicators. The time domain methods revealed the behavior of the signal across time as well as the relationship between them. The frequency domain methods proved quite inadequate because the signals from water quality are non-stationary with changing variance across time. The wavelet methods were quite good in unraveling the behavior of these signals at different time-scales. This analysis revealed that the high frequency changes have no significant effect on the long term dynamics of water quality signals. Given that only the low frequency components influence the long term behavior of these signals, it was found that it makes more sense to sample most of the signals at a weekly or two weekly intervals so as to avoid noisy or redundant information. In addition, the time-scale decomposition allows for noisy or redundant information that blurs the long term dynamics thereby negatively affecting the quality of models to be detected and kicked out. Moreover, it was also found that the use of a time delay in a dynamic model should be based on the delay from the time-scale that influence the long term dynamics the most in the freshwater body by the help of the wavelet cross-correlation rather than the classical cross-correlation. Finally, the same indicator from different water bodies of the same watershed portrayed different time and frequency dependent behavior implying that each freshwater body needs to be uniquely investigated. Applying the same management techniques to different water bodies without prior investigation will not be sound. Freshwater ecosystems are more and more threatened by phenomena such as global warming, pharmaceuticals in water bodies, invasive species requiring more investigation on the applicability of tools developed in other fields of science in the field of water resource management. Enhanced knowledge will improve the existing knowledge on the structure and functioning of freshwater bodies, the quality of models developed as management decision aid and the quality of the data used for decision making.}, subject = {S{\"u}ßwasser; {\"O}kosystem; Verschmutzung; Indikatoren; Zeitreihenanalyse; wavelet; Pollution; Indicators; Time series; Modeling; Wavelet}, language = {en} }