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Abstract Urbanisation is an important driver of changes in streamflow. These changes are not uniform across catchments due to the diverse nature of water sources, storage, and pathways in urban river systems. While land cover data are typically used in urban hydrology analyses, other characteristics of urban systems (such as water management practices) are poorly quantified which means that urbanisation impacts on streamflow are often difficult to detect and quantify. Here, we assess urban impacts on streamflow dynamics for 711 catchments across England and Wales. We use the CAMELS-GB dataset, which is a large-sample hydrology dataset containing hydro-meteorological timeseries and catchment attributes characterising climate, geology, water management practices and land cover. We quantify urban impacts on a wide range of streamflow dynamics (flow magnitudes, variability, frequency, and duration) using random forest models. We demonstrate that wastewater discharges from sewage treatment plants and urban land cover dominate urban hydrology signals across England and Wales. Wastewater discharges increase low flows and reduce flashiness in urban catchments. In contrast, urban land cover increases flashiness and frequency of medium and high flow events. We highlight the need to move beyond land cover metrics and include other features of urban river systems in hydrological analyses to quantify current and future drivers of urban streamflow. 

Abstract Understanding drivers of river mobility—temporal shifts in river channel positions—is critical for managing fluvial landscapes sustainably and for interpreting past river responses to climate change. However, direct observations linking river mobility and water discharge variability are scarce. Here, we pair multi‐annual measurements of daily water discharge and river mobility, estimated from Landsat, for 48 rivers worldwide. We show that, across climates and planforms, river mobility is correlated with water discharge variability over daily, intra‐annual, and inter‐annual timescales. For similar mean discharge, higher discharge variability is associated with up to an order‐of‐magnitude faster floodplain reworking. A random forest regression model indicates that discharge variability is the primary predictor of river mobility, when compared to mean water discharge, sediment concentration, and channel‐bed slope. Our results suggest that enhanced hydro‐climatic extremes could accelerate future river mobility, and that past changes to discharge variability may explain the fabric of fluvial strata.