The effective environmental management of combined sewer systems requires reliable estimation of discharge and pollutant loads conveyed at the outlet during rainstorms. This study investigates how, with a lumped modelling approach, it is possible to reproduce the quality characteristics of discharged water, provided that high temporal resolution experimental data of pollutant concentrations are available. The methodology is applied to the combined sewer of a real urban drainage network where a continuous high resolution monitoring campaign of water quality and quantity has been carried out at an overflow structure location near the outlet of the drainage system. The lumped modelling approach has been implemented in the Storm Water Management Model (SWMM) with hydrological parameters estimated from cartographic information, based on recently proposed methodology that allows reliably simulating the storm hydrographs without model calibration. A semi-distributed model has been also developed using the SWMM with hydrologic parameters randomly sampled to fit the measured hydrographs of different training and validation data. The results obtained show that the uncalibrated lumped model simulates the observed hydrographs with similar performance as with the semi-distributed model (i.e., the normalized Nash-Sutcliff efficiency index of the validation set is 0.753 for the uncalibrated lumped model and 0.765 for the best-performing sampled parameter set of the semi-distributed model). The water quality parameters describing the build-up and wash-off of total dissolved solids (TDS) in a lumped model have been calibrated too, as well as those describing the mixing and consumption of dissolved oxygen (DO). The results show that a lumped modelling approach can reproduce the water quality dynamics in a combined sewer system, representing a promising tool for effective environmental management. However, event-specific calibrated parameter values have been obtained in some cases, which require further investigation and still limit the general applicability of the obtained results, thus confirming that setting up a reliable model requires water quality measurements.

Occurrence of rainfall-induced landslides is increasing worldwide, owing to land use and climate changes. Although the connection between hydrology and rainfall-induced landslides might seem obvious, hydrological processes have been only marginally considered in landslide research for decades. In 2016, an advanced review paper published in WIREs Water [Bogaard and Greco (2016), WIREs Water, 3(3), 439–459] pointed out several challenging issues for landslide hydrology research: considering large-scale hydrological processes in the assessment of slope water balance; including antecedent hydrological information in landslide hazard assessment; understanding and quantifying the feedbacks between deformation and infiltration/drainage processes; overcoming the conceptual mismatch of soil mechanics models and hydrological models. While little progress has been made on the latter two issues, a variety of studies have been published, focusing on the role of hydrological processes in landslide initiation and prediction. The importance of the identification of the origin of water to understand the processes leading to landslide activation is largely acknowledged. Techniques and methodologies for the definition of landslide catchments and for the assessment of landslide water balance are progressing fast, often considering the hydraulic effect of vegetation. The use of hydrological information in landslide prediction models has also progressed enormously. Empirical predictive tools, to be implemented in early warning systems for shallow landslides, benefit from the inclusion of antecedent soil moisture, extracted from different sources depending on the scale of the prediction, leading to significant improvement of their predictive skill. However, this kind of information is generally still missing in operational LEWS. This article is categorized under: Science of Water > Hydrological Processes.

Empirical thresholds indicating the meteorological conditions leading to shallow landslide triggering are one of the most important components of landslide early warning systems (LEWS). Thresholds have been determined for many parts of the globe and present significant margins of improvement, especially for the high number of false alarms they produce. The use of soil moisture information to define hydro-meteorological thresholds is a potential way of improvement. Such information is becoming increasingly available from remote sensing and sensor networks, but to date, there is a lack of studies that quantify the possible improvement of the performance of LEWS. In this study, we investigate this issue by modelling the response of slopes to precipitations, introducing also the possible influence of uncertainty in soil moisture provided by either field sensors or remote sensing, and investigating various soil depths at which the information may be available. Results show that soil moisture information introduced within hydro-meteorological thresholds can significantly reduce the false alarm ratio of LEWS, while keeping at least unvaried the number of missed alarms. The degree of improvement is particularly significant in the case of soils with small water storage capacity.