Abstract
Study region: Lake Urema and its floodplain in Gorongosa National Park, Mozambique Study focus: Tropical lowland lake-floodplain systems are increasingly threatened by climate change effects and other human-induced pressures. Determining the effect of these pressures on the water balance is challenging because of a lack of hydrological monitoring data, which impedes water management decisions. A collection of optical remote sensing and Synthetic Aperture Radar (SAR) scenes is used in combination with supervised classification algorithms and topographical data to derive lake volumes for the period 1984–2023, which are analyzed for trends and correlation with satellite-derived climate data. New hydrological insights for the region: Although lake volumes show strong interannual variability, no significant historical trend is identified. A precipitation response time of approximately two months is observed, suggesting a considerable contribution of groundwater to the lake's water balance. Minimum lake volumes found for the period 2014–2017 coincide with a prolonged period of below-average precipitation, indicating the effect of decreased groundwater recharge. Dry season lake volumes show weak correlation with cumulative precipitation in comparison to rainy season lake volumes, further indicating the importance of groundwater inflow for the dry season water balance. Results suggest that climate change effects and anthropogenic activities may have little short-term impact on the lake's dry season volume, while altering groundwater recharge may have more significant long-term effects.
| Original language | English |
|---|---|
| Article number | 102173 |
| Number of pages | 15 |
| Journal | Journal of Hydrology: Regional Studies |
| Volume | 57 |
| DOIs | |
| Publication status | Published - Feb 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 13 Climate Action
Keywords
- Gorongosa National Park
- Lake storage variability
- Lake-floodplain system
- Optical remote sensing
- Supervised classification
- Synthetic aperture radar
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