## 1 Introduction  
Over the past 10 years, the increasing availability of distributed remote sensing data has led to a sudden shift of hydrological modeling from data-sparse to data-rich research [1; 2]. For instance, high-resolution topographic data and high resolution multi-view aerial images are accessible by using airborne laser altimetry of LiDAR (light detection and ranging), flood extent maps are achievable by using satellites' synthetic aperture radar (SAR), accurate information on land-use is available by analyzing digital orthophotos. Such data sources allowed a significant breakthrough in urban spatially distributed modeling [3; 4; 5].  
Due to the high heterogeneity of urban surfaces, various city objects such as roads and buildings are hardly specified in urban hydrological models. Consequently, researchers often argue that more detailed remote sensing data should be considered for improving the accuracy of the simulation results [6; 7]. Nevertheless, as for the operational modeling applications, the acquisition of such data usually requires expensive ad hoc instruments, ample data storage capacity, and post-treatment analysis [2,8]. Therefore, the efficiency of using highly detailed remote sensing (RS) data for common practices of urban hydrological modeling need to be discussed.  
In order to support urban infrastructure design and to control urban non-point source pollutants, the integrated modeling of two-dimensional (2D) surface runoff and one-dimensional (1D) urban sewer network has received growing attention in recent years. Several research models [9, 10, 11, 12] and commercial tools [13,14] have been developed and applied to various case studies. However, current applications of urban 2D/1D models focus on applying increasingly detailed remote sensing data [1,2]. Few studies have attempted to quantify the gains made by using high-resolution data compared with only using free access data sources (for example, low-resolution topographic data and coarse land-use information). A reference for the economical use of data collection efforts and human resources is required for urban stormwater governors and practitioners. Such work is crucial for recognizing the real needs of collecting remote sensing data for improving urban stormwater modeling.  
In this study, the 2D-surface TREX (Two-dimensional Runoff, Erosion, and Export model) model [15] and the 1D-sewer CANOE (Logiciel integre de conception et de diagnostic des reseaux d'assainissement) model [16] are integrated in TRENOE platform. TREX is an open source code, and both the TREX and CANOE models are well-documented, with robust numerical schemes. That makes these models suitable for modifications and adaptations in an urban context. The coupling between TREX and CANOE models is designed to be able to simulate the 2D overland flows and the 1D sewer network routing.  
Four different types of remote sensing (RS) and geographic information system (GIS) data are applied to the modeling platform: (i) high resolution topographic data derived from LiDAR; (ii) low-resolution digital terrain model (DTM) data obtained from the free access database; (iii) detailed land-use data proceeded from multiple data sources; (iv) coarse land-use information extracted from the free access database. The influences of using such RS and GIS data on urban stormwater 2D-1D modeling are analyzed for a small urban catchment near Paris (Le Perreux sur Marne, Val de Marne, France). This paper provides the first rigorous discussion on the use of RS and GIS data from different sources for urban 2D-1D modeling.