Explore the vast range of titles available.

Diffusive losses of nitrogen and phosphorus from agricultural areas have detrimental effects on freshwater and marine ecosystems. Mitigation measures treating drainage water before it enters streams hold a high potential for reducing nitrogen and phosphorus losses from agricultural areas. To achieve a better understanding of the opportunities and challenges characterising current and new drainage mitigation measures in oceanic and continental climates, we reviewed the nitrate and total phosphorus removal efficiency of: (i) free water surface constructed wetlands, (ii) denitrifying bioreactors, (iii) controlled drainage, (iv) saturated buffer zones and (v) integrated buffer zones. Our data analysis showed that the load of nitrate was substantially reduced by all five drainage mitigation measures, while they mainly acted as sinks of total phosphorus, but occasionally, also as sources. The various factors influencing performance, such as design, runoff characteristics and hydrology, differed in the studies, resulting in large variation in the reported removal efficiencies.

The performance of urban drainage systems is typically examined using hydrological and hydrodynamic models where rainfall input is uniformly distributed, i.e., derived from a single or very few rain gauges. When models are fed with a single uniformly distributed rainfall realization, the response of the urban drainage system to the rainfall variability remains unexplored. The goal of this study was to understand how climate variability and spatial rainfall variability, jointly or individually considered, affect the response of a calibrated hydrodynamic urban drainage model. A stochastic spatially distributed rainfall generator (STREAP – Space-Time Realizations of Areal Precipitation) was used to simulate many realizations of rainfall for a 30-year period, accounting for both climate variability and spatial rainfall variability. The generated rainfall ensemble was used as input into a calibrated hydrodynamic model (EPA SWMM – the US EPA's Storm Water Management Model) to simulate surface runoff and channel flow in a small urban catchment in the city of Lucerne, Switzerland. The variability of peak flows in response to rainfall of different return periods was evaluated at three different locations in the urban drainage network and partitioned among its sources. The main contribution to the total flow variability was found to originate from the natural climate variability (on average over 74 %). In addition, the relative contribution of the spatial rainfall variability to the total flow variability was found to increase with longer return periods. This suggests that while the use of spatially distributed rainfall data can supply valuable information for sewer network design (typically based on rainfall with return periods from 5 to 15 years), there is a more pronounced relevance when conducting flood risk assessments for larger return periods. The results show the importance of using multiple distributed rainfall realizations in urban hydrology studies to capture the total flow variability in the response of the urban drainage systems to heavy rainfall events.

Sustainable urban drainage systems (SuDS) are decentralized stormwater management practices that mimic natural drainage processes. The hydrological processes of SuDS are often modeled using process-based models. However, it can require considerable effort to set up these models. This study thus proposes a machine learning (ML) method to directly learn the statistical correlations between the hydrological responses of SuDS and the forcing variables at sub-hourly timescales from observation data. The proposed methods are applied to two SuDS catchments with different sizes, SuDS practice types, and data availabilities in the USA for discharge prediction. The resulting models have high prediction accuracies (Nash–Sutcliffe efficiency, NSE, >0.70). ML explanation methods are then employed to derive the basis of each ML prediction, based on which the hydrological processes being modeled are then inferred. The physical realism of the inferred hydrological processes is then compared to that would be expected based on the domain-specific knowledge of the system being modeled. The inferred processes of some models, however, are found to be physically implausible. For instance, negative contributions of rainfall to runoff have been identified in some models. This study further empirically shows that an ML model's ability to provide accurate predictions can be uncorrelated with its ability to offer plausible explanations to the physical processes being modeled. Finally, this study provides a high-level overview of the practices of inferring physical processes from the ML modeling results and shows both conceptually and empirically that large uncertainty exists in every step of the inference processes. In summary, this study shows that ML methods are a useful tool for predicting the hydrological responses of SuDS catchments, and the hydrological processes inferred from modeling results should be interpreted cautiously due to the existence of large uncertainty in the inference processes.

Changes in precipitation characteristics directly affect society through their impacts on drought and floods, hydro-dams, and urban drainage systems. Global warming increases the water holding capacity of the atmosphere and thus the risk of heavy precipitation. Here, daily precipitation records from over 700 Chinese stations from1956 to 2005 are analyzed. The results show a significant shift from light to heavy precipitation over eastern China. An optimal fingerprinting analysis of simulations from 11 climate models driven by different combinations of historical anthropogenic (greenhouse gases, aerosols, land use, and ozone) and natural (volcanic and solar) forcings indicates that anthropogenic forcing on climate, including increases in greenhouse gases (GHGs), has had a detectable contribution to the observed shift toward heavy precipitation. Some evidence is found that anthropogenic aerosols (AAs) partially offset the effect of the GHG forcing, resulting in a weaker shift toward heavy precipitation in simulations that include the AA forcing than in simulations with only the GHG forcing. In addition to the thermodynamic mechanism, strengthened water vapor transport from the adjacent oceans and by midlatitude westerlies, resulting mainly from GHG-induced warming, also favors heavy precipitation over eastern China. Further GHG-induced warming is predicted to lead to an increasing shift toward heavy precipitation, leading to increased urban flooding and posing a significant challenge for mega-cities in China in the coming decades. Future reductions in AA emissions resulting from air pollution controls could exacerbate this tendency toward heavier precipitation.

Surface meltwater reaching the bed of the Greenland ice sheet imparts a fundamental control on basal motion. Sliding speed depends on ice/bed coupling, dictated by the configuration and pressure of the hydrologic drainage system. In situ observations in a four-site transect containing 23 boreholes drilled to Greenland's bed reveal basal water pressures unfavorable to water-draining conduit development extending inland beneath deep ice. This finding is supported by numerical analysis based on realistic ice sheet geometry. Slow meltback of ice walls limits conduit growth, inhibiting their capacity to transport increased discharge. Key aspects of current conceptual models for Greenland basal hydrology, derived primarily from the study of mountain glaciers, appear to be limited to a portion of the ablation zone near the ice sheet margin.

In the last few years, some regions of the Mediterranean area have witnessed a progressive increase in extreme events, such as urban and flash floods, as a response to the increasingly frequent and severe extreme rainfall events, which are often exacerbated by the ever-growing urbanization. In such a context, the urban drainage systems may not be sufficient to convey the rainwater, thus increasing the risk deriving from the occurrence of such events. This study focuses on a particularly intense urban flood that occurred in Palermo (Italy) on 15 July 2020; it represents a typical pluvial flood due to extreme rainfall on a complex urban area that many cities have experienced in recent years, especially in the Mediterranean region. A conceptual hydrological model and a 2D hydraulic model, particularly suitable for simulations in a very complex urban context, have been used to simulate the event. Results have been qualitatively validated by means of crowdsourced information and satellite images. The experience of Palermo, which has highlighted the urgent need for a shift in the way stormwater in urban settlements is managed, can be assumed to be a paradigm for modeling pluvial floods in complex urban areas under extreme rainfall conditions. Although the approaches and the related policies cannot be identical for all cities, the modeling framework used here to assess the impacts of the event under study and some conclusive remarks could be easily transferred to other, different urban contexts.

Ongoing and potential future changes in precipitation will affect water management infrastructure. Urban drainage systems are particularly vulnerable. Design standards for many stormwater practices rely on design storms based on precipitation intensity-duration-frequency (IDF) curves. In many locations, climate projections suggest relatively small changes in total precipitation volume, but increased magnitude of extreme events. We develop an approach for estimating future IDF curves that is efficient, can use widely available downscaled GCM output, and is consistent with published IDF curves for the USA that are used in local stormwater regulations and design guides. The method is GCM-agnostic and provides a relatively simple way to develop scenarios in a format directly useful to assessing risk to stormwater management infrastructure. Model biases are addressed through equidistant quantile mapping, in which the modeled change in both the location and scale of the cumulative distribution of storm events from historical to future conditions is used to adjust the extreme value fit used for IDF curve development. The approach requires only precipitation annual maxima, is readily automated, and hits a mid-point between theoretical rigor and ease of application that will be of practical use for the rapid screening of vulnerabilities across projections. We demonstrate estimation of future IDF curves at locations throughout the USA and link IDF-derived design storms to a rainfall-runoff model to evaluate the potential change in storage volume requirements for capture-based stormwater management practices by 2065.

The rise in smart water technologies has introduced new cybersecurity vulnerabilities for water infrastructures. However, the implications of cyber‐physical attacks on the systems like urban drainage systems remain underexplored. This research delves into this gap, introducing a method to quantify flood risks in the face of cyber‐physical threats. We apply this approach to a smart stormwater system—a real‐time controlled network of pond‐conduit configurations, fitted with water level detectors and gate regulators. Our focus is on a specific cyber‐physical threat: false data injection (FDI). In FDI attacks, adversaries introduce deceptive data that mimics legitimate system noises, evading detection. Our risk assessment incorporates factors like sensor noises and weather prediction uncertainties. Findings reveal that FDIs can amplify flood risks by feeding the control system false data, leading to erroneous outflow directives. Notably, FDI attacks can reshape flood risk dynamics across different storm intensities, accentuating flood risks during less severe but more frequent storms. This study offers valuable insights for strategizing investments in smart stormwater systems, keeping cyber‐physical threats in perspective. Furthermore, our risk quantification method can be extended to other water system networks, such as irrigation channels and multi‐reservoir systems, aiding in cyber‐defense planning. Key Points We proposed a mathematical framework for evaluating flood risks of cyber‐physical attacks in a smart stormwater system False data injection can maliciously increase inflow and reduce the outflow of a targeted detention pond in a smart stormwater system Additional flood risks caused by false data injection are higher with smaller, more frequent storms

Sustainable urban drainage systems are multi-functional nature-based solutions that can facilitate flood management in urban catchments while improving stormwater runoff quality. Traditionally, the evaluation of the performance of sustainable drainage infrastructure has been limited to a narrow set of design objectives to simplify their implementation and decision-making process. In this study, the spatial design of sustainable urban drainage systems is optimized considering five objective functions, including minimization of flood volume, flood duration, average peak runoff, total suspended solids, and capital cost. This allows selecting an ensemble of admissible portfolios that best trade-off capital costs and the other important urban drainage services. The impact of the average surface slope of the urban catchment on the optimal design solutions is discussed in terms of spatial distribution of sustainable drainage types. Results show that different subcatchment slopes result in non-uniform distributional designs of sustainable urban drainage systems, with higher capital costs and larger surface areas of green assets associated with steeper slopes. This has two implications. First, urban areas with different surface slopes should not have a one-size-fits-all design policy. Second, spatial equality must be taken into account when applying optimization models to urban subcatchments with different surface slopes to avoid unequal distribution of environmental and human health co-benefits associated with green drainage infrastructure.

Worldwide, landslides claim many lives each year, with an average of 162.6 deaths reported in Japan from 1945 to 2019. There is growing concern about a potential increase in this number due to climate change. The primary source of shallow and rapid landslides within watersheds is the 0-order basins, which are located above the 1st order drainage system. These active geomorphological locations govern the frequency of mass movement. Despite the recognition of their importance, little attention has been paid to the role of 0-order basins in initiating landslides. Drainage systems can be extracted using the Digital Elevation Model (DEM) in GIS software. However, the effect of DEM resolution on the extraction of 1st order basins remains unexplained. This research develops an algorithm to assess the impact of DEM resolution on the extraction of first-order basins, channel head points, and the identification of approximate 0-order basins. The study includes algorithms to evaluate the correlation between DEM resolution and 1st order drainage system extraction using fuzzy classification techniques for approximate 0-order basins. The algorithm was applied in Toho Village, Fukuoka, Japan, defining the most appropriate DEM and stream definition threshold with an 86.48% accuracy and ±30 m error margin for channel head points. Critical slip surfaces were identified inside the 0-order basins and validated with a landslide inventory map with a 91% accuracy. The developed algorithms support hazard management and land use planning, providing valuable tools for sustainable development.