Intensity-duration (ID) thresholds are used to identify rainfall conditions likely to initiate landslides. They consider the average rain intensity observed over the entire length (called duration) of user-defined wet periods that lead to the triggering. Intensity-duration-frequency (IDF) curves assign a probability to the intensity of precipitation observed over fixed-length temporal windows (also called durations). As the term duration refers to different concepts, ID thresholds and IDF curves cannot be compared directly, and should better not be plotted in one figure, and IDF curves should not be used to quantify the exceedance probability of ID thresholds.

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.

The management of an Urban Drainage System (UDS) is a complex task, as it requires extensive knowledge about precipitation regime, hydrological features of the catchment, hydraulic characteristics of the drainage network, and information about the water use by the served inhabitants. Complex semi-distributed hydrological and physically based hydraulic models are nowadays available to summarise such information and run simulations. However, in many cases, the uncertainty of the available hydrological information hampers the use of complex models. Hence, simple models with few parameters and small computational effort may be preferable, especially for UDS management problems requiring the execution of many simulations. This paper proposes a convenient approach to define effective lumped Simplified Models (SMs) of UDSs, the parameters of which can be estimated directly from cartographic information. For several case studies of UDS with different morphological and topological characteristics, SMs were built, capable of reproducing the hydrographs provided by available semi-distributed Detailed Models (DMs), assumed as benchmark in absence of measured hydrographs. To this aim, the SWMM simulation software was used, and the SM lumped parameters were calibrated by maximising the goodness of fit between the hydrograph of the DM and of the SM. The results show that SMs satisfactorily predict the hydrographs for all the case studies, and that robust relationships between the calibrated parameters and morphological and topological characteristics of the UDS can be established. This suggests that SMs can be used by decision makers for preliminary design, planning studies and management problems of UDSs, as their parameters can be soundly estimated from cartographic information. An example of application of SMs to Combined Sewer Overflow prediction is also presented.

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.

Undesired presence of water in historical masonries has a negative effect on the walls and causes deterioration of decorative works covering the walls, such as frescoes and valuable plasters. To prevent this, non-invasive moisture measurements are needed that avoid damage during masonry inspection caused by sample taking or probe insertion. Active heated distributed temperature sensing (DTS) with optical fibres is widely used in hydrology to assess soil moisture content. The aim of this study is to examine the potential of this technique for non-invasive water content measurements in a real scale wall. The tested masonry is made of yellow Neapolitan tuff bricks, a material widely used in historical buildings of Campania (Southern Italy). Distributed temperature measurements are carried out with three different heating strategies (different power and duration) during the drying process following the complete saturation of the wall. The acquired temperature data are then processed with three different methods (estimators), so to identify the best combination of heating strategy and data processing approach. Despite the presence of a significant bias, it is possible to identify relationships between the gravimetric moisture content and the different estimators. Those relationships are influenced to a large degree by the thermal contact between the DTS cable and the masonry. This research shows it is possible to measure water content in tuff masonry using non-invasive active heated fibre optic cable when establishing good thermal contact between the cable and the masonry.

Measuring water content in buildings of historical value requires non-invasive techniques to avoid the damage that sample taking or probe insertion may cause to the investigated walls. With this aim, a stepped frequency ground penetrating radar (GPR) system was tested to assess its applicability in moisture measurements of porous masonry elements. The technique was tested on a real scale wall made with yellow Neapolitan tuff bricks, a material commonly found in historical buildings of Campania (Southern Italy). First, the antenna was calibrated to find its characteristic transfer functions. Then 64 GPR acquisitions, coupled with gravimetric measurements of the volumetric water content, were performed on the tuff wall in laboratory controlled conditions. A full inverse modelling of the GPR signal on tuff was used to retrieve dielectric permittivity and electrical conductivity of tuff at various water contents. By linking these characteristic electromagnetic parameters to the water content, the calibration relationships specific for yellow Neapolitan tuff are defined, which can be used for moisture measurements by GPR in real case studies. The experimental results lead to a robust identification of clearly defined monotonic relationships for dielectric permittivity and electrical conductivity. These are characterized by high values of the correlation coefficient, indicating that both parameters are potentially good proxies for water content of tuff. The results indicate that GPR represents a promising indirect technique for reliable measurements of water content in tuff walls and, potentially, in other porous building materials.

Several recent studies have highlighted the potential of Actively Heated Fiber Optics (AHFO) for high resolution soil moisture mapping. In AHFO, the soil moisture can be calculated from the cumulative temperature (T_cum), the maximum temperature (T_max), or the soil thermal conductivity determined from the cooling phase after heating (λ). This study investigates the performance of the T_cum, T_max and λ methods for different heating strategies, i.e., differences in the duration and input power of the applied heat pulse. The aim is to compare the three approaches and to determine which is best suited to field applications where the power supply is limited. Results show that increasing the input power of the heat pulses makes it easier to differentiate between dry and wet soil conditions, which leads to an improved accuracy. Results suggest that if the power supply is limited, the heating strength is insufficient for the λ method to yield accurate estimates. Generally, the T_cum and T_max methods have similar accuracy. If the input power is limited, increasing the heat pulse duration can improve the accuracy of the AHFO method for both of these techniques. In particular, extending the heating duration can significantly increase the sensitivity of T_cum to soil moisture. Hence, the T_cum method is recommended when the input power is limited. Finally, results also show that up to 50% of the cable temperature change during the heat pulse can be attributed to soil background temperature, i.e., soil temperature changed by the net solar radiation. A method is proposed to correct this background temperature change. Without correction, soil moisture information can be completely masked by the background temperature error.