What Wets the Wetlands?: Reconstructing the Mara Wetland surface water dynamics through coupling satellite derived inundation patterns with hydrological field data

The Mara Wetland in Tanzania has an important role in regulating the quality, timing and magnitude of the flow of water into Lake Victoria. In addition, the wetland provides natural resources for local communities and habitat for variety of species. The planned dam construction upstream of the wetland and projected changes in the local climate could affect the physical and ecological equilibrium of the system. Baseline information on seasonal inundation dynamics is necessary to sustainably manage these potential threats. The wetland is sparsely instrumented, which has hampered a thorough temporal and spatial understanding of the local water balance. In addition, the highly vegetated nature of the wetland, and relatively frequent cloud-coverage, motivates multi-source integration of remotely sensed data to capture flood patterns at a high resolution.In this study, the spatiotemporal inundation pattern of the Mara Wetland in Tanzania is reconstructed using optical remote sensing data. The annual fluctuations in aerial wetland extent are analysed in parallel to the fluctuations of local water balance components: downstream water level of Lake Victoria, upstream discharge, direct precipitation and evaporation. The analyses aims to shed light on the underlying mechanisms and hydrological processes that control the hydric status of the wetland. Comparing the temporal changes in extent with surrounding physical processes provides insight on the responsiveness of the wetland to specific water balance components.The intra- and inter-annual trends in inundation of the Mara Wetland are reproduced for the years 2017, 2018, 2019. The Random Forests (RF) algorithm is trained bi-seasonally (using bands and derived water and vegetation indices from Sentinel-2 data and a Digital Elevation Model (DEM) as input variables), and used to classify the land-covers of the wetland region in a semi-automated way for a total of 73 Sentinel-2 scenes. The scenes are classified into 7 individual land-cover classes; 3 wetland classes (open water, flooded vegetation, wet floodplain) and 4 dryland classes (dry floodplain, wet agriculture, dry agriculture, bare land). The overall classification accuracy achieved (based on an independent validation set, not used to train the classification algorithm) is 98.6 %. The spatiotemporal variability of the inundated area is used in combination with available hydrological field-data to reproduce the local water balance.The seasonal expansion and contraction of the wetland follows a consistent bi-modal regime, and the results from the waterbalance affirm the importance of local precipitation in the seasonal expansion and contraction of the wetland. The base-flow supplied by the Mara River, together with the backwater from Lake Victoria appear to be at equilibrium at the extent of the permanent swamp during the dry season, insinuating the importance of the riverflow during these low-rainfall months. The occasional yet extreme flood events induced by high discharge rates are expected to play a specific ecological role in the wetland, and should be accounted for during future dam operations upstream.Civil Engineerin