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The impact of urban expansion on achieving sustainable development goals (SDGs) has become a significant research topic in the field of geographic information science. In this article, we describe a coupled cellular automata (CA)—‐What If? model to explore SDG11 “Sustainable cities and communities.” The model calculates overall residential land use demand based on historical data archives using the What If? planning support system (PSS), and then allocates it using a CA model that incorporates variables related to SDG11.2.1 and 11.7.1. Historical datasets for years 2016 and 2021 from Southwest Sydney, Australia were used to assess model accuracy, after which two residential expansion scenarios (years 2021 and 2026) were generated. Based on the modeling results, the SDG‐related spatial variables can improve the overall accuracy of CA sub‐models using an XGBoost machine learning training methodology. The simulation results of these scenarios confirm the effectiveness of the coupled CA‐What If? model, which has the potential to generate more reliable scenario results than the standalone What If? PSS for modeling urban growth of cities across Australia and internationally. [ABSTRACT FROM AUTHOR]

• Long-term monitored cave drip water hydrology time-series for years 2014–––2017. • Functional Principal Component Analysis (FPCA) identified modes of drip variation. • Three flow types classified: storage baseflow, fracture baseflow, and overflow. • FPCA results are validated with stable isotope drip water data. Understanding the mechanisms controlling spatial heterogeneity of drip water percolation into caves is essential for interpreting karst aquifer recharge and speleothem isotopic and geochemical records for paleoclimate analyses. Here we present the first analysis of drip rate variability using a novel time-varying Functional Principal Component Analysis (FPCA), validated against drip water stable isotope composition. Twenty-six drip sites were monitored across Harrie Wood Cave, south-east Australia, over a 2.5 year period. A positive relationship of cave drip water hydrology with precipitation and soil moisture was identified, with soil moisture recording the strongest relationship. FPCA was used to classify drip-water flow (percolation) pathways based on temporal shifts in the drip rate time series. Our results reveal that three percolation classes can be used to explain water movement within the cave: storage baseflow, fracture baseflow and overflow. The successful application of FPCA in this study suggests that this statistical technique will be useful for the analysis and interpretation of other large, discontinuous hydrological datasets. [ABSTRACT FROM AUTHOR]