Ramahaimandimby, Z.; Randriamaherisoa, A.; Vanclooster, M.; Bielders, C.L. Driving Factors of the Hydrological Response of a Tropical Watershed: The Ankavia River Basin in Madagascar. Water2023, 15, 2237.
Ramahaimandimby, Z.; Randriamaherisoa, A.; Vanclooster, M.; Bielders, C.L. Driving Factors of the Hydrological Response of a Tropical Watershed: The Ankavia River Basin in Madagascar. Water 2023, 15, 2237.
Ramahaimandimby, Z.; Randriamaherisoa, A.; Vanclooster, M.; Bielders, C.L. Driving Factors of the Hydrological Response of a Tropical Watershed: The Ankavia River Basin in Madagascar. Water2023, 15, 2237.
Ramahaimandimby, Z.; Randriamaherisoa, A.; Vanclooster, M.; Bielders, C.L. Driving Factors of the Hydrological Response of a Tropical Watershed: The Ankavia River Basin in Madagascar. Water 2023, 15, 2237.
Abstract
Understanding the hydrological behavior of watersheds and their driving factors is crucial for sustainable water resources management. However, at large scales, this task remains challenging due to the spatial heterogeneity in landscapes, topography, land use, geology, and soil properties. In this context, the aim of this study was to identify the key factors that influence the hydrological system of four watersheds: Ankavia (WS1: 55% forest cover), Ankaviabe (WS2: 77% forest cover), Sahafihitry (WS3: 41% forest cover), and Antsahovy (WS4: 48% forest cover), over a 10-month study period. These catchments are located within the SAVA region of northeastern Madagascar and have a humid-tropical climate. We investigated the relationship between selected catchment descriptors (CD) and hydrological signatures (HS) by using a Pearson coefficient-based correlation matrix. More specifically, CD extracted from topography/morphology (T), land use (LU), soil (S), and geological characteristics (G) were correlated with HS, including base flow index (BFI), runoff coefficient (rc), peak flow (Qp), runoff event time scales (ts), high flows (Q5), low flows (Q95), and mean discharge (q_mean). The analysis revealed that land use, soil properties, and geology seem to be the best predictors for BFI and Q95, while soil properties mainly govern rc, Qp, Q5, ts, and q_mean. These findings provide valuable insights into the key drivers of hydrologic behavior that can inform water resource management strategies. In particular, WS2 has better flood buffering capacity but suffers from lower base flows in the dry season potentially due to higher evapotranspiration. Conversely, WS3 and WS4 (and to a lesser extent WS1) have lower flood buffering capacity, but these watersheds experience less pronounced low flows in the dry season due to higher base flow index resulting from lower evapotranspiration. The results emphasize the importance of sustainable land use practices and conservation efforts, which are essential for the sustainable development of the region. By incorporating these practices into water management strategies, we can help ensure a more stable and reliable water supply for communities and ecosystems within the region.
Environmental and Earth Sciences, Water Science and Technology
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