Land use land cover (LULC) modeling is considered as the best tool to comprehend and unravel the dynamics of future urban expansion. The present paper provides a comprehensive review of existing LULC modeling techniques and novel approaches used by the research community. Moreover, the review also compares each technique’s applications, utility, drawbacks, and broader differences. The rationale behind such a comparison is to highlight the strengths/weakness of individual techniques. The review further highlights the utility of the hybridization of different techniques (e.g., machine learning model combined with statistical models) to LULC modeling to complement their strengths. Although significant progress has been made in LULC modeling, the review highlights the need to incorporate the policy framework into LULC modeling for better urban planning and management. The present review will help researchers and policymakers to achieve better land management practices and ultimately assist in achieving Sustainable Development Goal-15 (SDG-15) (i.e., life on land).

Currently, land use and land cover change (LULCC) is of utmost concern for global environmental change and sustainability. Among the portfolio of techniques, modelling is considered as the best approach to explore LULC dynamics. We have performed a model inter-comparison exercise (including hybrid and non-hybrid models) using multiple performance metrics to identify the best modelling approach for the subsequent projection of future LULC. The methodology is demonstrated on the Subarnarekha basin of Eastern India by utilizing the LANDSAT imagery of 1989, 1994, 2006, and 2011. Before LULC modelling, the cross-tabulation and trend-surface analyses were performed to identify dominant land transitions in post-classification maps. Temporal mapping results over 1989–2011 exhibited a drastic decrease in the area under dense forest (25.7% to 19.0%), a substantial increase in the area under scrubland (21.0% to 26.1%) and a nominal reduction in the coverage of the agricultural land (51.2% to 49.0%). Four integrated models namely Multilayer perceptron-Markov Model (MLP-MC), Logistic Regression-Markov Model (LR-MC), and two hybrid models, i.e. Multilayer perceptron-Cellular automata-Markov model (MLP-CA-MC) and Logistic Regression-Cellular automata-Markov model (LR-CA-MC) were tested for their suitability for predicting future LULC for the basin. Based on the multiple model validation techniques, the MLP-MC model performed the best. MLP-MC model subsequently used a non-stationary relationship between selected explanatory variables and LULC to predict the future LULC for 2020 and 2030. The MLP-MC model projected that relative to the level of 2011, agricultural land, dense forest, and barren land may decrease by 8.3, 28.2 and 23.5%, respectively, and scrubland, built-up area, and water bodies may increase by 22.5, 87.3 and 13.3%, respectively, by 2030. Our findings contradict the prevalent view regarding the nationwide intensification of agriculture over the Indian subcontinent but are consistent with the national decreasing trend in the dense forest. The study provides a transferable methodology for the systematic comparison of LULC models (including hybrid and non-hybrid) against multiple performance metrics. The outcomes of the study may help land-use planners, environmentalist, and policymakers in framing better policies and management .recommendations.

This study presents climate change impacts on streamflow for the Subarnarekha basin at two gauging locations, Jamshedpur and Ghatshila, using the Soil and Water Assessment Tool (SWAT) model driven by an ensemble of four regional climate models (RCMs). The basin's hydrological responses to climate forcing in the projected period are analysed under two representative concentration pathways (RCPs). Trends in the projected period relative to the reference period are determined for medium, high and low flows. Flood characteristics are estimated using the threshold level approach. The analysis of variance technique (ANOVA) is used to segregate the contribution from RCMs, RCPs, and internal variability (IV) to the total uncertainty in streamflow projections. Results show a robust positive trend for streamflows. Flood volumes may increase by 11.7% in RCP4.5 (2006–2030), 76.4% in RCP4.5 (2025–2049), 20.3% in RCP8.5 (2006–2030), and 342.4% in RCP8.5 (2025–2049), respectively, for Jamshedpur. For Ghatshila, increment in flow volume is estimated as 15.7% in RCP4.5 (2006–2025), 24.2% in RCP4.5 (2025–2049), 35.9% in RCP8.5 (2006–2030), and 224.6% in RCP8.5 (2025–2049), respectively. Segregation results suggests that the uncertainty in climate prediction is dominated by RCMs followed by IV. These findings will serve as an early warning for the alarming extreme weather events India is currently facing.

The present study aims at illustrating the efficacy of socio-hydrology in understanding the interaction between coupled human and water systems for sustainable water resources management. The future LULC growth is projected under four scenarios, business-as-usual (BAU), sustainable growth (SG), exponential growth (EG-1&2), and combined sustainable and economic growth (CS-EG), for the Subarnarekha basin of India. The associated impacts of scenarios on hydrological processes are simulated using an integrated modelling system that combines a physically based distributed hydrological model (MIKE SHE/MIKE HYDRO RIVER) and a spatially explicit integrated LULC change model (Multilayer-perceptron Markov model). The magnitudes of different hydrological variables suggest SG as the most favourable scenario. In contrast, the CS-EG scenario is the most plausible scenario based on the signal-to-noise ratio. The findings may provide valuable knowledge for future LULC planning in the basin. The study suggests incorporating a citizen science approach in LULC planning to reflect local preferences in the decision-making process.