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Algal blooms are one of the most serious threats to water resources, and their early detection remains a challenge in eutrophication management worldwide. In recent years, with more widely available real-time auto-monitoring data and the advancement of computational capabilities, fuzzy logic has become a robust tool to establish early warning systems. In this study, a framework for an early warning system was constructed, aiming to accurately predict algae blooms in a river containing several water conservation areas and in which the operation of two tidal sluices has altered the tidal currents. Statistical analysis of sampled data was first conducted and suggested the utilization of dissolved oxygen, velocity, ammonia nitrogen, total phosphorus, and water temperature as inputs into the fuzzy logic model. The fuzzy logic model, which was driven by biochemical data sampled by two auto-monitoring sites and numerically simulated velocity, successfully reproduced algae bloom events over the past several years (i.e., 2011, 2012, 2013, 2017, and 2019). Considering the demands of management, several key parameters, such as onset threshold and prolongation time and subsequent threshold, were additionally applied in the warning system, which achieved a critical success index and positive hit rate values of 0.5 and 0.9, respectively. The differences in the early warning index between the two auto-monitoring sites were further illustrated in terms of tidal influence, sluice operation, and the influence of the contaminated water mass that returned from downstream during flood tides. It is highlighted that for typical tidal rivers in urban areas of South China with sufficient nutrient supply and warm temperature, dissolved oxygen and velocity are key factors for driving early warning systems. The study also suggests that some additional common pollutants should be sampled and utilized for further analysis of water mass extents and data quality control of auto-monitoring sampling.

Rainfall-induced landslides are among the most devastating natural disasters in hilly terrains and the reduction of the related risk has become paramount for public authorities. Between the several possible approaches, one of the most used is the development of early warning systems, so as the population can be rapidly warned, and the loss related to landslide can be reduced. Early warning systems which can forecast such disasters must hence be developed for zones which are susceptible to landslides, and have to be based on reliable scientific bases such as the SIGMA (sistema integrato gestione monitoraggio allerta&mdash, integrated system for management, monitoring and alerting) model, which is used in the regional landslide warning system developed for Emilia Romagna in Italy. The model uses statistical distribution of cumulative rainfall values as input and rainfall thresholds are defined as multiples of standard deviation. In this paper, the SIGMA model has been applied to the Kalimpong town in the Darjeeling Himalayas, which is among the regions most affected by landslides. The objectives of the study is twofold: (i) the definition of local rainfall thresholds for landslide occurrences in the Kalimpong region, (ii) testing the applicability of the SIGMA model in a physical setting completely different from one of the areas where it was first conceived and developed. To achieve these purposes, a calibration dataset of daily rainfall and landslides from 2010 to 2015 has been used, the results have then been validated using 2016 and 2017 data, which represent an independent dataset from the calibration one. The validation showed that the model correctly predicted all the reported landslide events in the region. Statistically, the SIGMA model for Kalimpong town is found to have 92% efficiency with a likelihood ratio of 11.28. This performance was deemed satisfactory, thus SIGMA can be integrated with rainfall forecasting and can be used to develop a landslide early warning system.

Recurring landslides in the Western Ghats have become an important concern for authorities, considering the recent disasters that occurred during the 2018 and 2019 monsoons. Wayanad is one of the highly affected districts in Kerala State (India), where landslides have become a threat to lives and properties. Rainfall is the major factor which triggers landslides in this region, and hence, an early warning system could be developed based on empirical rainfall thresholds considering the relationship between rainfall events and their potential to initiate landslides. As an initial step in achieving this goal, a detailed study was conducted to develop a regional scale rainfall threshold for the area using intensity and duration conditions, using the landslides that occurred during the years from 2010 to 2018. Detailed analyses were conducted in order to select the most effective method for choosing a reference rain gauge and rainfall event associated with the occurrence of landslides. The study ponders the effect of the selection of rainfall parameters for this data-sparse region by considering four different approaches. First, a regional scale threshold was defined using the nearest rain gauge. The second approach was achieved by selecting the most extreme rainfall event recorded in the area, irrespective of the location of landslide and rain gauge. Third, the classical definition of intensity was modified from average intensity to peak daily intensity measured by the nearest rain gauge. In the last approach, four different local scale thresholds were defined, exploring the possibility of developing a threshold for a uniform meteo-hydro-geological condition instead of merging the data and developing a regional scale threshold. All developed thresholds were then validated and empirically compared to find the best suited approach for the study area. From the analysis, it was observed that the approach selecting the rain gauge based on the most extreme rainfall parameters performed better than the other approaches. The results are useful in understanding the sensitivity of Intensity&ndash, Duration threshold models to some boundary conditions such as rain gauge selection, the intensity definition and the strategy of subdividing the area into independent alert zones. The results were discussed with perspective on a future application in a regional scale Landslide Early Warning System (LEWS) and on further improvements needed for this objective.

Distributed physically based slope stability models usually provide outputs representing, on a pixel basis, the probability of failure of each cell. This kind of result, although scientifically sound, from an operational point of view has several limitations. First, the procedure of validation lacks standards. As instance, it is not straightforward to decide above which percentage of failure probability a pixel (or larger spatial units) should be considered unstable. Second, the validation procedure is a time-consuming task, usually requiring a long series of GIS operations to overlap landslide inventories and model outputs to extract statistically significant performance metrics. Finally, if model outputs are conceived to be used in the operational management of landslide hazard (e.g., early warning procedures), the pixeled probabilistic output is difficult to handle and a synthesis to characterize the hazard scenario over larger spatial units is usually required to issue warnings aimed at specific operational procedures. In this work, a tool is presented that automates the validation procedure for physically based distributed probabilistic slope stability models and translates the pixeled outputs in warnings released over larger spatial units like small watersheds. The tool is named DTVT (double-threshold validation tool) because it defines a warning criterion on the basis of two threshold values—the probability of failure above which a pixel should be considered stable (failure probability threshold, FPT) and the percentage of unstable pixels needed in each watershed to consider the hazard level widespread enough to justify the issuing of an alert (instability diffusion threshold, IDT). A series of GIS operations were organized in a model builder to reaggregate the raw instability maps from pixels to watershed; draw the warning maps; compare them with an existing landslide inventory; build a contingency matrix counting true positives, true negatives, false positive, and false negatives; and draw in a map the results of the validation. The DTVT tool was tested in an alert zone of the Aosta Valley (northern Italy) to investigate the high sensitivity of the results to the values selected for the two thresholds. Moreover, among 24 different configurations tested, we performed a quantitative comparison to identify which criterion (in the case of our study, there was an 85% or higher failure probability in 5% or more of the pixels of a watershed) produces the most reliable validation results, thus appearing as the most promising candidate to be used to issue alerts during civil protection warning activities.

Desalinated seawater enters the urban water supply network on a large scale, which brings new challenges to water quality assurance. In order to strengthen the safety supervision of the pipeline network, ensure the stability of water quality, prevent pipeline corrosion, and avoid the “red water” problem, this study constructed a safety supervision system for desalinated seawater entering the urban water supply pipeline network. In this system, the on-line monitoring system can monitor water quality, water quantity, water pressure and the corrosion of pipeline network in real-time. Early warning system can quickly identify problems and initiate based on the threshold exceeding, statistical analysis, and model prediction. The safety regulation system (including water source regulation system, water quality adjustment system and operation management system) is used to regulate and control water quality problems in the urban water supply network. The application of this safety supervision system is conducive to improving regulation efficiency and ensuring water supply safety.

Owing to their short duration and high intensity, flash floods are among the most devastating natural disasters in metropolises. The existing warning tools&mdash, flood potential maps and two-dimensional numerical models&mdash, are disadvantaged by time-consuming computation and complex model calibration. This study develops a data-driven, probabilistic rainfall-inundation model for flash-flood warnings. Applying a modified support vector machine (SVM) to limited flood information, the model provides probabilistic outputs, which are superior to the Boolean functions of the traditional rainfall-flood threshold method. The probabilistic SVM-based model is based on a data preprocessing framework that identifies the expected durations of hazardous rainfalls via rainfall pattern analysis, ensuring satisfactory training data, and optimal rainfall thresholds for validating the input/output data. The proposed model was implemented in 12 flash-flooded districts of the Xindian River. It was found that (1) hydrological rainfall pattern analysis improves the hazardous event identification (used for configuring the input layer of the SVM), (2) brief hazardous events are more critical than longer-lasting events, and (3) the SVM model exports the probability of flash flooding 1 to 3 h in advance.

Sediment deposition issues for reservoirs are important in Taiwan because the severe deposition could excessively decrease the reservoir lifecycle. Extreme storm events usually can carry a massive amount of sediment into reservoirs, and deposition will happen unless the incoming material can pass through sluice gates. When it comes with high concentration, the density current flow is prone to be generated, and the bottom outlets are the most effective sluice gate to release the sediment. In order to improve the sediment release efficiency, an accurate estimation of arriving concentration and time of the density current can be useful for the reservoir management. This study develops a two-stage approach which combines a numerical model (SRH2D) and the modified Rouse equation to predict the sediment flux of the reservoir. The numerical model was verified and applied to establish the relation between inflow and dam face concentration. The modified Rouse equation then adopted this relation to estimate the proper exponential parameter. As a result, the sediment flux amount at each bottom outlet can be accurately predicted by this equation. With this means, an early warning system can be established for reservoir operation, which can improve release efficiency during typhoons.

Climate, weather and water hazards do not recognize national boundaries. Transboundary/regional programs and cooperation are essential to reduce the loss of lives and damage to livelihoods when facing these hazards. The development and implementation of systems to provide early warnings for severe weather events such as cyclones and flash floods requires data and information sharing in real time, and coordination among the government agencies at all levels. Within a country, this includes local, municipal, provincial-to-national levels as well as regional and international entities involved in hydrometeorological services and Disaster Risk Reduction (DRR). Of key importance are the National Meteorological and Hydrologic Services (NMHSs). The NMHS is generally the authority solely responsible for issuing warnings for these hazards. However, in many regions of the world, the linkages and interfaces between the NMHS and other agencies are weak or non-existent. Therefore, there is a critical need to assess, strengthen, and formalize collaborations when addressing the concept of reducing risk and impacts from severe weather and floods. The U.S. Agency for International Development/Office of U.S. Foreign Disaster Assistance; the United Nations World Meteorological Organization (WMO); the WMO Southern Africa Regional Specialized Meteorological Center, hosted by the South African Weather Service; the U.S. National Oceanic and Atmospheric Administration/National Weather Service and the Hydrologic Research Center (a non-profit corporation) are currently implementing a project working with Southern Africa NMHSs on addressing this gap. The project aims to strengthen coordination and collaboration mechanisms from national to local levels. The project partners are working with the NMHSs to apply and implement appropriate tools and infrastructure to enhance currently operational severe weather and flash flood early warning systems in each country in support of delivery and communication of warnings for the DRR entities at the regional, national and local levels in order to reduce the loss of life and property.

Abstract: Simple, automated methods are required for rapid detection of wastewater contamination in urban recreational water. The activity of the enzyme b-D-galactosidase (GAL) can rapidly (5 LOG10. Furthermore, the GAL-activity per gram feces from birds, sheep and cattle was 2–3 LOG10 lower than the activity from human feces, indicating that high GAL-activity in water may reflect human fecal pollution more than the total fecal pollution. The rapid method can only be used to quantify high levels of human fecal pollution, corresponding to about 0.1 mg human feces/liter (or 103 E. coli/100 mL), since below this limit GAL-activity from non-fecal environmental sources may interfere.