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China has begun a rapid move towards Sponge City implementation, which will include widespread installation of various Low Impact Development (LID) stormwater infrastructure. Ambitious goals have been set for all Sponge Cities, including a target capture rate of 80–85% of annual precipitation in Beijing. However, there has so far been limited investigation into whether these goals are obtainable at the catchment scale given the physical restrictions of the existing urban landscape. This paper demonstrates the use of long-term continuous hydrologic modelling to evaluate the potential of meeting rainfall capture goals over a long time period for a case study area in Beijing. Detailed land cover classification derived from satellite imagery aided in the parameterization of a SWMM (Stormwater Management Model) model representing a 133 km2 urban study area. The calibrated and validated model demonstrated satisfactory agreement between computed and observed runoff for a variety of precipitation events ranging from 10 to 207 mm. A synthetic high resolution rainfall time series was generated from historic daily observations then used as input to run the model continuously for a 35 year period. Feasible LID model scenarios based on varying degrees of implementation of rain gardens, permeable pavements and green roofs were compared in terms of their potential volume capture of annual rainfall. Modelling results demonstrated that the conversion of 30% of roof area to green roof, 10% of green areas to rain gardens and 35% of paved areas to permeable pavement could increase the baseline volume capture ratio from 59.9% to 82.2%, indicating that Sponge City goals can be met within the case study area with realistic levels of LID implementation. This study provides a decision-making basis for future planning in the study area. • Satellite imagery based hydrologic model shows good agreement with observed flows. • Continuous modelling approach suggested for evaluation of Volume Capture Ratio. • >80% of annual rainfall captured with moderate catchment wide implementation of LID. [ABSTRACT FROM AUTHOR]

China's Sponge City initiative will involve widespread installation of new stormwater infrastructure including green roofs, permeable pavements and rain gardens in at least 30 cities. Hydrologic modelling can support the planning of Sponge Cities at the catchment scale, however, highly detailed spatial data for model input can be challenging to compile from the various authorities, or, if available, may not be sufficiently detailed or updated. Remote sensing methods show great promise for mitigating this challenge due to their ability to efficiently classify satellite images into categories relevant to a specific application. In this study Geographic Object Based Image Analysis (GEOBIA) was applied to WorldView-3 satellite imagery (2017) to create a detailed land cover map of an urban catchment area in Beijing. While land cover classification results based on a Bayesian machine learning classifier alone provided an overall land cover classification accuracy of 63%, the subsequent inclusion of a series of refining rules in combination with supplementary data (including elevation and parcel delineations), yielded the significantly improved overall accuracy of 76%. Results of the land cover classification highlight the limitations of automated classification based on satellite imagery alone and the value of supplementary data and additional rules to refine classification results. Catchment scale hydrologic modelling based on the generated land cover results indicated that 61 to 82% of rainfall volume could be captured for a range of 24 h design storms under varying degrees of Sponge City implementation. [ABSTRACT FROM AUTHOR]