The origins of river and floodplain waters (groundwater, rainfall, and snowmelt) and their extent during overbank flow events strongly impact ecological processes such as denitrification and vegetation development. However, the long-term sensitivity of floodplain water signatures to climate change remains elusive. We examined how the integrated hydrological model HydroGeoSphere and the Hydraulic Mixing-Cell method could help us understand the long-term impact of climate change on water signatures and their spatial distribution in the protected Biebrza River Catchment in northeastern Poland. Our model relied on 20th century Reanalysis Data from 1881 to 2015 and an ensemble of EURO-CORDEX simulations for RCP 2.6, 4.5, and 8.5 from 2006 to 2099. The historical component of the simulations was subjected to extensive multiple-variable validation from 1881 to 2019. The results show that the extents of water sources were rather stable in the floodplain in the 1881-2015 period. The projected future impacts were variable with each analyzed RCP, but in all cases, different significant trends were present for the spatial distribution of water sources and for the river-floodplain mixing. However, the total volume of water from different sources was less sensitive to climate change than the dominant sources and spatial distribution of water. The simulation results highlight the impact of climate change on the extent of water sources in temperate zone wetlands with significant implications for ecological processes and management. These results also underscore the urgent need to leverage such modeling studies to inform protective and preservation strategies of floodplain wetlands.

The determination of flow state remains an important challenge in non-perennial river catchments. Previous studies interpreted stream channel temperature time series data using the moving standard deviation method to identify the timing and duration of flow. However, the performance of this technique requires the user to specify multiple subjective constraints. We implemented six variations of time-frequency analysis from three categories: (1) Fourier transform methods, (2) wavelet transform methods, and (3) Empirical Mode Decomposition methods. We evaluated and compared their ability to discern periods of flow from synthetic and field data of stream temperature time series data. Overall, all methods performed reasonably well, with performance of 63–99 % success in matching flow and no-flow periods. Greater variability in performance was observed when evaluating field data. Differences between methods include the ease of implementation and evaluation of results, computational needs, and ability to handle discontinuous data. We suggest five primary areas for future research to improve the general understanding of these time-frequency analysis techniques.