Future prediction of scenario based land use land cover (LU&LC) using DynaCLUE model for a river basin

Human activities that cause changes to the surface of the Earth lead to alterations in Land Use and Land Cover (LU&LC) which have an impact on biodiversity, ecosystem functioning, and the well-being of humans. In order to comprehend and manage the effects of human activities on the environment, prediction of scenario-based LU&LC in future periods are crucial. Scenario-based predictions of LU&LC provide valuable insights for decision-makers in the sustainable governance of land and water resources. In the present study, the Dynamic Conversion of Land Use and its Effects (DynaCLUE) modelling platform was used to predict future LU&LC for Munneru river basin, India. Using six different user defined scenarios LU&LC change patterns were analyzed in 2030, 2050 and 2080 so as to understand the pressure on the natural resources and to plan sustainable Land Use Planning by preserving the important land use classes. The connection between LU&LC classes and input driving factors was quantified using Binary Logistic Regression (BLR) analysis. The β-coefficient was estimated using LU&LC type as a dependent variable and driving factors as independent variables. The demands of each LU&LC type, spatial policies and constraints, characteristics of each location and land use conversions are used as inputs for prediction of future LU&LC maps. Major conversions in LU&LC observed in this basin from last two decades are the rapid increase in built-up area due to urbanization in the outskirts of cities and towns. The major LU&LC changes projected for the period of 2019–2080 are expansion of built-up area ranging from 42.5% to 88.5%, and a reduction in barren land ranging from 57.3% to 74.5% across all six scenarios in the entire basin. The projected LU&LC maps under different scenarios provide valuable insights that could aid local communities, government agencies, and stakeholders in systematically allocating resources at the local level and in preparing the policies for long-term benefits.