Sustainability Assessment of Lake Babati Based on Land-Use and Land-Cover Change Patterns and Future Projections

Globally, Land use and land cover changes pose significant threats to the sustainability of reservoirs and lakes particularly in East Africa due to siltation which reduces storage capacity. This study employed the MOLUSCE plugin in QGIS to analyze historical LULC changes from 2016 to 2019 and project future scenarios to 2050 for Lake Babati catchment area. Using Cellular Automata-Artificial Neural Network (CA-ANN) with five driving factors; slope, elevation, distance to road, distance to river and distance to settlement. The model achieved high validation accuracy with Kappa coefficient of 0.99377 and overall accuracy of 99%. Results of projections from 2026 to 2050 revealed continuation of agricultural expansion which increased by 4.53% substantial built-up growth by 0.84% and forest loss by 1.8%. The transition matrix revealed that agriculture as the dominant driver of landscape transformation with 75% of bare land and 15% of forest converted to cropland. Sustainability assessment suggests the catchment is currently moderately sustainable but trending toward unsustainability without intervention. This study provides critical evidence for integrated catchment management and land-use planning to safeguard Lake Babati's ecological integrity.