A variety of statistical methods are available to detect sudden changes, or breakpoints, in time series when used as multi-temporal change detection technique. However, these methods are unreliable in the presence of noise. Neural nets might detect breakpoints better. These deep learning models are able to generalize and optimize well, even in the presence of noise. This research tests the feasibility of different neural net architectures to detect breakpoints in generic linear time series. Two relatively simple neural nets are proposed, combined with four different descriptions of breakpoint, and trained on synthetic
data. The neural nets are tested on two datasets: On a separate synthetic dataset and on Australian rainuse-efficieny (RUE) time series, a surrogate for dryland ecosystem functioning. Some of the neural nets built performed exceptionally well on synthetic data, outperforming a benchmark statistical method with
margin. The direct translation to RUE time series was less successful. The results shows great promise for the use of neural nets in change detection. A generalist change detection approach by use of neural nets is likely not optimal. Current developments in deep learning, as well as choosing the right user-case, show
great promise to unlock the full potential of neural nets in time series analysis.

Terminos Lagoon is the biggest and ecologically most important fluvial-lagoon system of the southern Gulf of Mexico. Rivers, sea and meteorology all influence the lagoon, variable over the year, resulting in a complex situation. To protect this area, it is crucial to know how different hydrological processes, hydrodynamic processes and spatial characteristics influence each other in this context. Using a multidisciplinary approach, this research focused on the question: What is the influence of hydrological and hydrodynamic processes on spatial characteristics of Terminos Lagoon, now and in the future? The study has shown that evaporation has a larger part in the water balance during dry season, where during other seasons the water balance is similar to the annual mean. It is found that the western part of Terminos Lagoon shows different characteristics
than the eastern part, as river discharge plays a larger role in the western part of the lagoon. Secchi depth, temperature, dissolved oxygen, sediments and salinity are all different here compared to the eastern part of the lagoon. Salinity and river discharge, as well as air and water temperatures, show to be highly correlated. A tidal watershed divides the lagoon in two approximately equal areas, following the mentioned separation of east and west. Residual currents flow along the boundaries of the lagoon from east to west. A circular
residual current in the lagoon is observed near the Puerto Real inlet in created temperature and Secchi depth maps. Nortes season shows highest salinity and lowest Secchi depths, where dry season shows lowest salinity. Both inlets are expected to sedimentate and sediments outside the lagoon move westward. Climatological influences are uncertain, though likely effects are increased water temperature, salinity, flushing time and a decrease in residual current. Mentioned effects are likely most noticeable in the eastern part of the lagoon.
Further research is necessary to achieve ecological goals in the region.