Given the critical role of the streamflow regime for instream, riparian, and floodplain ecosystem sustainability, modeling the long-term effect of urbanization on streamflow is important to predict possible changes in stream ecosystems. Since flow duration curves are largely used to characterize the streamflow regime and define indices for stream ecosystem health, we present two stochastic models, with different levels of complexity, that link the key physical features of urbanized basins with rainfall variability to determine the resulting flow duration curves. The two models are tested against 11 basins with various degrees of urban development, characterized by the percentage of impervious areas in the basin. Results show that the more complex model needs to be used to reproduce accurately the entire flow duration curve. The analysis performed suggests that the transformation of green (i.e., water used in evapotranspiration) to blue (i.e., streamflow) water in urbanized basins is an important long-term source of ecohydrological alteration. The modeling scheme also provides useful links between rainfall variability, urbanization levels, and some streamflow indices of high and low flows.

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Due to the COVID-19 pandemic, citizens of the United Kingdom were required to stay at home for many months in 2020. In the weeks before and months following lockdown, including when it was not being enforced, citizens were advised to stay at home where possible. As a result, in a megacity such as London, where long-distance commuting is common, spatial and temporal changes to patterns of water demand are inevitable. This, in turn, may change where people's waste is treated and ultimately impact the in-river quality of effluent receiving waters. To assess large scale impacts, such as COVID-19, at the city scale, an integrated modelling approach that captures everything between households and rivers is needed. A framework to achieve this is presented in this study and used to explore changes in water use and the associated impacts on wastewater treatment and in-river quality as a result of government and societal responses to COVID-19. Our modelling results revealed significant changes to household water consumption under a range of impact scenarios, however, they only showed significant impacts on pollutant concentrations in household wastewater in central London. Pollutant concentrations in rivers simulated by the model were most sensitive in the tributaries of the River Thames, highlighting the vulnerability of smaller rivers and the important role that they play in diluting pollution. Modelled ammonia and phosphates were found to be the pollutants that rivers were most sensitive to because their main source in urban rivers is domestic wastewater that was significantly altered during the imposed mobility restrictions. A model evaluation showed that we can accurately validate individual model components (i.e., water demand generator) and emphasised need for continuous water quality measurements. Ultimatly, the work provides a basis for further developments of water systems integration approaches to project changes under never-before seen scenarios.

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As complex systems, urban stormwater networks (USNs) may reveal emergent features (e.g., scaling) and sudden changes in behavior, which can lead to unanticipated impacts. We explored this through the USN properties of connectivity, heterogeneity, and scaling, which were quantified using outputs from a hydrological model and network dispersion mechanisms. The network properties were determined retrospectively in space and time by reconstructing the contemporary history of urban development and stormwater infrastructure in an arid, urban catchment in the City of Scottsdale, Arizona, USA. We found that the relative importance to USN functioning of both network structure (geomorphology) and dynamics (spatial celerity pattern) changed with the spatial scale, with network geomorphology being more dominant at larger spatial scales. The importance of network geomorphology suggested that the structure of the USN itself could potentially serve as a stormwater control measure, for example, by enhancing flow dispersion. The temporal evolution of the USN revealed a sudden change in the hydrological functioning of the network, which seemed to be a consequence of the combined effects of patchy urban development and changes in network connectivity. The interactions between the urban spatial pattern, stormwater infrastructure, and surface runoff may result in threshold‐like behavior. A spatial multiscale approach to stormwater management may be beneficial to ensure that hydrological benefits at one scale do not cause unintended consequences at another. Overall, the retrospective modeling and network analysis approach used in this study may be useful for understanding emergent urban stormwater impacts.

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Abstract. Urbanisation has been associated with a reduction in the long-term correlation within a streamflow series, quantified by the Hurst exponent (H). This presents an opportunity to use the H exponent as an index for the classification of catchments on a scale from natural to urbanised conditions. However, before using the H exponent as a general index, the relationship between this exponent and level of urbanisation needs to be further examined and verified on catchments with different levels of imperviousness and from different climatic regions. In this study, the H exponent is estimated for 38 (deseasonalised) mean daily runoff time series, 22 from the USA and 16 from Australia, using the traditional rescaled-range statistic (R∕S) and the more advanced multifractal detrended fluctuation analysis (MF-DFA). Relationships between H and catchment imperviousness, catchment size, annual rainfall and specific mean discharge were investigated. No clear relationship with catchment area was found, and a weak negative relationship with annual rainfall and specific mean streamflow was found only when the R∕S method was used. Conversely, both methods showed decreasing values of H as catchment imperviousness increased. The H exponent decreased from around 1.0 for catchments in natural conditions to around 0.6 for highly urbanised catchments. Three significantly different ranges of H exponents were identified, allowing catchments to be parsed into groups with imperviousness lower than 5 % (natural), catchments with imperviousness between 5 and 15 % (peri-urban) and catchments with imperviousness larger than 15 % (urban). The H exponent thus represents a useful metric to quantitatively assess the impact of catchment imperviousness on streamflow regime.

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