Recently, emphasis has been placed on finding a reliable estimation of soil water content. In this study, two capacitance sensors EC5 and 5TE (METER Group) were utilized. These sensors provide many benefits relative to other sensors in that they are cost-effective and very economical regarding energy use, operate at a high measurement frequency of 70 MHz, and are dedicated to measuring at a small volume because of their small size. This makes them suitable for the context of use in this research, which consists of multiple sustainable drainage systems SuDS. Several studies have evaluated these two types of sensor but not for urban soils with specific characteristics. In addition, results from the literature are divergent and the published calibration data are limited. Therefore, an in-depth investigation of their accuracy is assessed in this paper. At first, the literature’s existing procedures and methods were examined. The sensor-to-sensor variability, as well as repeatability, were tested in soil and solutions. Additionally, a field calibration method was conducted to estimate the effects of soil texture on sensors readings. Two laboratory calibration methods having different principles were also applied, compared to each other and to the field calibration as well. Results revealed weak sensor-to-sensor variability (coefficient of variation up to 15% in soil) and also good repeatability (0.1%), for both sensors. A soil-specific calibration equation has improved the estimation of the volumetric water content. In case of soil having high field bulk density, the undisturbed soil calibration method described and proposed in this paper gives promising results. The latter method yields a volumetric water content (VWC) prediction accuracy of 0.025 m³∙m⁻³ on a sandy loam soil. This paper presents a large knowledge of capacitance sensors measurement technique as well as their calibration procedures and methods. Limitations of existing procedures have been identified and key elements for selecting the appropriate one are suggested. Derived calibration equations have also been provided for three urban soils with different particle size distribution, ranging from sandy loam to silt loam. Accurate monitoring of soil moisture content in urban soils is thus achievable.

Sustainable drainage systems (SuDS) have become a promising solution for increasing imperviousness by reducing water runoff volumes and flow rates, and improving water quality. However, the efficiency of these systems is dependent on soil hydraulic and physical properties, which in turn are spatially variable; however, this variability has been sparsely documented for urban areas, especially for road-side SuDS. In this study, the spatial variability of soil hydraulic properties, along with the uncertainty of these properties due to estimation methods, were investigated for three roadside SuDS in France (Paris region). Estimation methods were based on both in-situ infiltration tests and pedotransfer functions (PTFs). Results show high spatial variability in saturated hydraulic conductivity K_s (up to 160% coefficient of variation), which is dominant relative to uncertainties in PTFs predictions and those induced by experimental errors. Many specific factors might be responsible for this variability, especially in the urban context, such as construction techniques, CaCO₃ precipitation, and vegetation development. In order to evaluate the effects of this variability on hydrological performance, a hydrological model of a bioretention cell was tested. Simulations revealed that peak flows and volumes are highly affected by the spatial variability of soil hydraulic properties; notably, vertical variability increases the overflow by 50%. The number of infiltration measurements required to evaluate a representative average K_s with an uncertainty of a factor of two or less was found to be four/eight, depending on the studied site. This study provides considerable insight into the spatial variability of soil hydraulic properties and its implications for hydrological performance of roadside SuDS, as it is based on a sound understanding of both theory and practice.

Stormwater quality modeling has arisen as a promising tool to develop mitigation strategies. The aim of this paper is to assess the build-up and wash-off processes and investigate the capacity of several water quality models to accurately simulate and predict the temporal variability of suspended solids concentrations in runoff, based on a long-term data set. A Markov Chain Monte-Carlo (MCMC) technique is applied to calibrate the models and analyze the parameter’s uncertainty. The short-term predictive capacity of the models is assessed based on inter- and intra-event approaches. Results suggest that the performance of the wash-off model is related to the dynamic of pollutant transport where the best fit is recorded for first flush events. Assessment of SWMM (Storm Water Management Model) exponential build-up model reveals that better performance is obtained on short periods and that build-up models relying only on the antecedent dry weather period as an explanatory variable, cannot predict satisfactorily the accumulated mass on the surface. The predictive inter-event capacity of SWMM exponential model proves its inability to predict the pollutograph while the intra-event approach based on data assimilation proves its efficiency for first flush events only. This method is very interesting for management practices because of its simplicity and easy implementation.

Remote sensing (RS) and geographic information system (GIS) data is increasingly used in urban stormwater modeling. The undirected use of such data may waste economic and human resources. In order to provide guidance for practitioners to efficiently use different data collection resources, as well as give a reference for future works, this paper aims to assess the effects of using free access GIS data and ad hoc RS data on urban 2D–1D stormwater modeling. The 2D-surface Two-dimensional Runoff, Erosion, and Export model (TREX) model was published in Science of the Total Environment in 2008. The 1D-sewer CANOE (Logiciel intégré de conception et de diagnostic des réseaux d’assainissement) model was published in Journal of Hydrology in 2004. The two models are integrated in the TRENOE (TREX-CANOE) platform. The modeling approach is applied to a small urban catchment near Paris (Le Perreux sur Marne, 0.12 km²). Simulation results reveal that the detailed land-use information derived from multiple data sources is a crucial factor for accurate simulations. Nevertheless, using the very high resolution LiDAR (light detection and ranging) data is not equally significant for the water flow simulations at sewage outlets. Finally, we suggest that using the free access GIS data accompanying the urban sewer network design might be an acceptable low-cost solution for accurate urban 2D–1D stormwater modeling during moderate rainfall events. Further studies of urban stormwater modeling could focus on the development of “suitable” models with “enough” input data, depending on the management/research objectives.

The widespread use of separate stormwater systems requires better understanding of the interactions between urban landscapes and drainage systems. This paper describes a novel attempt of developing urban 2D-surface and 1D-drainage model “TRENOE” for urban stormwater quantity and quality modelling. The physically-based TREX model and the conceptual CANOE model are integrated into the TRENOE platform, highlighting that the roofs of buildings are represented separately from the surface model, but simulated as virtual “sub-basins” in the CANOE model. The modelling approach is applied to a small urban catchment near Paris (Le Perreux sur Marne, 0.12 km²). Simulation scenarios are developed for assessing the influences of different “internal” (model structure, numerical issues) and “external” (parameters, input data) factors on model performance. The adequate numerical precision and the detailed information of land use data are identified as crucial elements of water quantity modelling. Contrarily, the high-resolution topographic data and the common variations of the water flow parameters are not equally significant at the scale of a small urban catchment. Concerning water quality modelling, particle size distribution is revealed to be an important factor, while the empirical USLE equations need to be completed by a raindrop detachment process.