Real-time Rainfall Data Research Articles

Safer this way: Identifying flooded roads for facilitating mobility during floods

Severe storms and associated flooding pose a significant risk to urban mobility. Consequently, 40 to 63% of flood-related deaths are linked to roadway-related incidents in developed countries. The dynamic nature of flooding and the lack of real-time information make it challenging to sense flooding and its impact on roadways. Hence, existing state-of-the-art methods fall short of providing a robust, reliable, and affordable tool to facilitate situational awareness during storms. Such a tool is indispensable to aid emergency response, especially considering the potential increase in risk to flood exposure due to climate change and other factors. This study addresses this need by providing an open-source framework that couples real-time rainfall data, a physics-based flood model, and network and spatial analyses to sense real-time flood impact on the road transportation system. Case studies using three recent storms in Houston, Texas demonstrate the framework’s ability to provide vehicle-class specific roadway conditions for even minor roads and residential streets—a problem existing approaches struggle with. Aside from road-link conditions, the framework can also estimate network-level flood impacts, such as identifying regions without access to critical facilities like hospitals, giving decision-makers a more holistic view of network performance. Further, the framework is interoperable with existing situational awareness tools and could augment their ability to sense road conditions during flooding. Finally, the proposed framework can equip flood-prone communities and emergency responders with reliable and accessible situational awareness content using open-source tools and data to promote safer mobility during flooding—a key goal of intelligent transportation systems.

Landslip Warning System in Hong Kong - Over 40 Years of Evolution

Background: Hong Kong is situated on the coast of the south-eastern tip of China. A sub-tropical climate brings this coastal city a rainy season from April to October each year. Rainfall intensities as high as 50 mm to 100 mm per hour and 250 mm to 350 mm in 24 hours are not uncommon. With the hilly terrain and densely populated development, Hong Kong is susceptible to land-sliding during heavy rainstorms. Concerted efforts have been made by the Geotechnical Engineering Office (GEO) of the Hong Kong Special Administrative Region Government, to operate and enhance a territory-wide Landslip Warning System continuously, as a major component of landslide risk management tools in Hong Kong, since its establishment in 1977. Forewarning the public of possible landslide risks due to heavy rainstorms and triggering an emergency system within the government to deal with landslide incidents are the major objectives of the Landslip Warning System. This paper presents the development and enhancement of the comprehensive people-centred early warning system for landslides in Hong Kong, based on a framework of an early warning system with four major elements, including risk knowledge, monitoring and warning services, communication and dissemination, and response capability. Methods: With good quality landslide, rainfall and slope databases, landslide prediction models have been developed and continuously updated using statistical means to assess the severity of landslides based on real-time rainfall and rainfall forecast. For the purpose of an effective and robust operation of the Landslip Warning System, the latest Internet of Things (IoT) and cloud computing technologies have been deployed to operate the GEO Rainguage System collecting, transmitting and processing real-time rainfall data over the territory of Hong Kong, and to enhance system redundancy and resilience for data analysis and information dissemination. Results: To have an effective communication and dissemination of the Landslip Warning information to the community, various public education activities have been carried out regularly to promote public awareness of landslide risks and promote personal precautionary measures during heavy rainstorms. Conclusion: Well-established procedures for the operation of the Landslip Warning System have been developed to trigger the internal emergency mechanism within the government to deal with the landslide danger, with the support of innovative solutions to enhance the capability and efficiency of emergency responses.

Prediction of Time Diversity Gain – Comparison Between ITU-R P.618-13 Using a Concept of Rain Rate with Delay and Synthetic Storm Technique

Future satellite companies will use higher Ka and V bands. Rain fade is the most important issue in establishing reliable communication between Earth and satellites outside the 10 GHz band. The problem is exacerbated in the tropics by significant rainfall throughout the year. One way to deal with rainfall attenuation is to use a time-diversity strategy. Real-time rainfall attenuation data is needed to analyze time variability. However, data from higher frequency bands such as Ka and V bands cannot be used. As a result, the Synthetic Storm Technique (SST) was proposed to convert the measured real time rainfall data into rainfall attenuation data and predict the time diversity gain. The measured rainfall data was converted to rainfall attenuation data using the traditional SST method. Time diversity gains were predicted using Converted rainfall attenuation and the Matriciani model, and the measurements were significantly overestimated. A new concept of realtime rainfall with and without time delay is proposed and used to predict time diversity gain using ITU-R P. 618-13 and the measured rainfall distribution with delay. Therefore, the proposed method recommends using the measured long-term precipitation data to predict the gain from time diversity at the desired frequency.

Examples on Simulation Model

Abstract: A new methodology was developed Further real-time determination gate control operations of a river-reservoir system to minimize flooding conditions. The methodology is based upon an optimization-simulation model approach interfacing the genetic algorithm within simulation software for short-term rainfall forecasting, rainfall–runoff modeling (HEC-HMS), and a one-dimensional (1D), two-dimensional (2D), and combined 1D and 2D combined unsteady flow models (HEC-RAS). Both realtime rainfall data from next-generation radar (NEXRAD) and gaging stations, and forecasted rainfall are needed to make gate control decisions (reservoir releases) in real-time so that at timet, rainfall is known and rainfall over the future timeperiod(∆t)totimet+ ∆t can be forecasted. This new model can be used to manage reservoir release schedules (optimal gate operations) before, during, and after a rainfall event. Through real-time observations and optimal gate controls, downstream water surface elevations are controlled to avoid exceedance of threshold flood levels at target locations throughout a riverreservoir system to minimize the damage. In an example application, an actual river reach with a hypothetical upstream flood control reservoir is modeled in real-time to test the optimization-simulation portion of the overall model. Keywords: Simulation – Random numbers- Steps for simulation – Problems.

Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting

The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed.

An Impacts-Based Flood Decision Support System for a Tropical Pacific Island Catchment with Short Warnings Lead Time

Early warnings decision support systems are recognized as effective soft adaptation tools to prepare for the impacts of imminent flooding and minimize potential injuries and/or loss of life in flood-prone regions. This paper presents a case study of a pilot project that aimed to establish an impacts-based flood monitoring, early warnings, and decision support system for the Vaisigano River which flows through Apia, the capital of Samoa. This river is located in a characteristic short and steep catchment with rapid critical flood peak durations following periods of intense rainfall. The developed system integrates numerical weather prediction rainfall forecasts, real-time rainfall, river level and flow monitoring data, precomputed rainfall-runoff simulations, and flood inundation estimates of exposure levels and threat to human safety at buildings and on roads for different return period events. Information is ingested into a centralized real-time, web-based, flood decision support system portal that enables hydrometeorological officers to monitor, forecast and alert relevant emergency or humanitarian responders of imminent flooding with adequate lead time. This includes nowcasts and forecasts of estimated flood peak time, magnitude and likely impacts of inundation. The occurrence of three distinct extreme rainfall and flood events over the 2020/2021 tropical cyclone season provided a means to operationally test the system. In each case, the system proved adequate in alerting duty officers of imminent flooding in the Vaisigano catchment with up to 24 h warnings and response lead time. Gaps for improvement of system capabilities and performance are discussed, with recommendations for future work suggested.

Flood inundation simulations based on GSMaP satellite rainfall data in Jakarta, Indonesia

Jakarta is the capital of Indonesia and is considered one of the most vulnerable cities to climate-related disasters, including flooding, sea-level rise, and storm surges. Therefore, the development of a flood-forecasting system for Jakarta is crucial. However, the accurate prediction of flooding in Jakarta is challenging because of the short flood concentration time in highly urbanized basins and the shortage of rainfall data in poorly gauged areas. The aim of this study is to simulate recent flood inundation using global satellite mapping of precipitation (GSMaP) products. The GSMaP products (NRT and Gauge V7) were evaluated and compared with hourly observation data from five ground stations in the Ciliwung River Basin. In addition, a rainfall-runoff and flood inundation model was applied to the target basin. The results of the analysis showed that the GSMaP Gauge data were more accurate than the GSMaP NRT data. However, the GSMaP Gauge cannot be used to provide real-time rainfall data and is, therefore, inadequate for real-time flood forecasting. We conclude that the GSMaP Gauge is suitable for replicating past flood events, but it is challenging to use the GSMaP NRT for real-time flood forecasting in Jakarta.

Testing of an Optimization-Simulation Model for Real-Time Flood Operation of River-Reservoir Systems

A new methodology was developed for the real-time determination gate control operations of a river-reservoir system to minimize flooding conditions. The methodology is based upon an optimization-simulation model approach interfacing the genetic algorithm within MATLAB with simulation software for short-term rainfall forecasting, rainfall–runoff modeling (HEC-HMS), and a one-dimensional (1D), two-dimensional (2D), and combined 1D and 2D combined unsteady flow models (HEC-RAS). Both real-time rainfall data from next-generation radar (NEXRAD) and gaging stations, and forecasted rainfall are needed to make gate control decisions (reservoir releases) in real-time so that at time t, rainfall is known and rainfall over the future time-period (Δt) to time t + Δt can be forecasted. This new model can be used to manage reservoir release schedules (optimal gate operations) before, during, and after a rainfall event. Through real-time observations and optimal gate controls, downstream water surface elevations are controlled to avoid exceedance of threshold flood levels at target locations throughout a river-reservoir system to minimize the damage. In an example application, an actual river reach with a hypothetical upstream flood control reservoir is modeled in real-time to test the optimization-simulation portion of the overall model.

Developing Real-Time Nowcasting System for Regional Landslide Hazard Assessment under Extreme Rainfall Events

In this research, a real-time nowcasting system for regional landslide-hazard assessment under extreme-rainfall conditions was established by integrating a real-time rainfall data retrieving system, a landslide-susceptibility analysis program (TRISHAL), and a real-time display system to show the stability of regional slopes in real time and provide an alert index under rainstorm conditions for disaster prevention and mitigation. The regional hydrogeological parameters were calibrated using a reverse-optimization analysis based on an RGA (Real-coded Genetic Algorithm) of the optimization techniques and an improved version of the TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-Stability) model. The 2009 landslide event in the Xiaolin area of Taiwan, associated with Typhoon Morakot, was used to test the real-time regional landslide-susceptibility system. The system-testing results showed that the system configuration was feasible for practical applications concerning disaster prevention and mitigation.

Effect of Check Dam on Phosphorus Transport and Retention in the Qingshui River, in Zhangjiakou City

The transport and retention of phosphorus is a key process in nutrient cycling in river ecosystems and one of the main indicators used to evaluate river health. To explore the effect of check dams on the transport and retention of phosphorus, water samples were collected from four representative monitoring sections of a check-dammed reach of the Qingshui River in Zhangjiakou City. Various forms of phosphorus and runoff process were monitored, and local real-time rainfall data were also collected. The results showed that:① The average concentration ratios of total phosphorus (TP), total dissolved phosphorus (TDP), total particulate phosphorus (TPP), and total suspended solids (TSS) downstream to upstream of the dam were 50%, 79%, 47%, and 58%, respectively, indicating that the check dam has a retention effect on phosphorus and sediment. ② During non-rainfall periods, TP, TDP, and TPP were retained in the check dam reach, while these forms of phosphorus were both retained and transported in the check dam reach during period of rainfall; the ratio of TP, TDP, and TPP retaining rainfall events to total rainfall events was 63.6%, 9%, and 81.8%, respectively, indicating that the transport-retention process was affected by the time interval, duration, and intensity of rainfall events. ③ When a single rainfall event or continuous event exceeded 56 mm, the retention effect of the check dam notably decreased.

Wetting/drying cycles increase arsenic bioaccessibility in mine-impacted sediments

Physical and chemical weathering of arsenic-enriched mine wastes can lead to the mobilization and dispersal of contaminated sediments into surrounding (residential) areas. Additionally, seasonal effects during dry and rainy seasons affect contaminant bioaccessibility in ways that may significantly influence risk estimates of chronic arsenic exposure for residents living near mining sites. Rainwater-transported sediments from the Red Hill Mercury Mine in Tustin, California were collected monthly over a period of 25 months and the ingestible size fraction (≤250 μm) analyzed for bulk arsenic concentration (239 ± 149 mg/kg, n = 32, 114 to 678 mg/kg), assessed for arsenic bioaccessibility (11.2 ± 6.3%, n = 32, 2.7 to 24.9%), and compared with realtime rainfall data. Arsenic bioaccessibility was highly variable and strongly negatively correlated with cumulative rainfall over the preceding 40 days (r2 = 0.51, P < 0.001). Controlled wetting/drying experiments and As speciation analyses demonstrated that this correlation primarily arose through the removal of soluble/surface-bound arsenic phases from sediments during rainwater exposure/transport, but also suggested the secondary formation of soluble arsenic phases resulting from diel humidity and temperature fluctuations during dry periods, which gradually increases the proportion of bioaccessible arsenic with time. These findings demonstrate significant seasonal variability in arsenic bioaccessibility and indicate that arsenic exposure risks through incidental ingestion in these and similar systems are highest following extended dry periods.

Designing Deep-Based Learning Flood Forecast Model With ConvLSTM Hybrid Algorithm

Efficient, robust, and accurate early flood warning is a pivotal decision support tool that can help save lives and protect the infrastructure in natural disasters. This research builds a hybrid deep learning (ConvLSTM) algorithm integrating the predictive merits of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Network to design and evaluate a flood forecasting model to forecast the future occurrence of flood events. Derived from precipitation dataset, the work adopts a Flood Index ( I <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> ), in form of a mathematical representation, to capture the gradual depletion of water resources over time, employed in a flood monitoring system to determine the duration, severity, and intensity of any flood situation. The newly designed predictive model utilizes statistically significant lagged I <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> , improved by antecedent and real-time rainfall data to forecast the next daily I <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> value. The performance of the proposed ConvLSTM model is validated against 9 different rainfall datasets in flood prone regions in Fiji which faces flood-driven devastations almost annually. The results illustrate the superiority of ConvLSTM-based flood model over the benchmark methods, all of which were tested at the 1-day, 3-day, 7-day, and the 14-day forecast horizon. For instance, the Root Mean Squared Error (RMSE) for the study sites were 0.101, 0.150, 0.211 and 0.279 for the four forecasted periods, respectively, using ConvLSTM model. For the next best model, the RMSE values were 0.105, 0.154, 0.213 and 0.282 in that same order for the four forecast horizons. In terms of the difference in model performance for individual stations, the Legate-McCabe Efficiency Index (LME) were 0.939, 0.898, 0.832 and 0.726 for the four forecast horizons, respectively. The results demonstrated practical utility of ConvLSTM in accurately forecasting I <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> and its potential use in disaster management and risk mitigation in the current phase of extreme weather events.

Correcting bias of satellite rainfall data using physical empirical model

The provision of high resolution near real-time rainfall data has made satellite rainfall products very potential for monitoring hydrological hazards. However, a major challenge in their direct-use can be problematic due to measurement error. In this study, an attempt was made to correct the bias of Global Satellite Mapping of Precipitation near-real-time (GSMaP_NRT) product. Physical factors, including topography, season, windspeed and cloud types were accounted for correcting bias. Peninsular Malaysia was used as the case study area. Gridded rainfall, developed from 80 gauges for the period 2000–2018, was used along with physical factors in a two-stage procedure. The model consisted of a classifier to categorise rainfall of different intensity and regression models to predict rainfall amount of different intensity class. An ensemble tree-based learning algorithm, called random forest, was used for classification and regression. The results revealed a big improvement of near-real-time GSMaP_NRT product after bias correction (GSMaP_BC) compared to the gauge corrected version (GSMaP_GC). Accuracy evaluation for complete timeseries indicated about 110% reduction of normalized root-mean-square error (NRMSE) in GSMaP_BC (0.8) compared to GSMaP_NRT (1.7) and GSMaP_GC (1.75). On the other hand, the bias of GSMaP_BC became nearly zero (0.3) compared to 2.1 and − 3.1 for GSMaP_NRT and GSMaP_GC products. The spatial correlation of GSMaP_BC was >0.7 with observed rainfall data for all months compared to 0.2–0.78 for GSMaP_NRT and GSMaP_GC, indicating capability of GSMaP_BC to replicate spatial pattern of rainfall. The bias-corrected near-real-time GSMaP data can be used for monitoring and forecasting floods and hydrological phenomena in the absence of dense rain-gauge network in areas, frequently experience hydro-meteorological hazards.

A Study on the Improvement of Flood Forecasting Techniques in Urban Areas by Considering Rainfall Intensity and Duration

Frequent localized torrential rains, excessive population density in urban areas, and increased impervious areas have led to massive flood damage that has been causing overloading of drainage systems (watersheds, reservoirs, drainage pump sites, etc.). Flood concerns are raised around the world in the events of rain. Flood forecasting, a typical nonstructural measure, was developed to help prevent repetitive flood damage. However, it is difficult to apply flood prediction techniques using training processes because training needs to be applied at every usage. Other techniques that use predicted rainfall data are also not appropriate for small watershed, such as single drainage area. Thus, in this paper, a flood prediction method is proposed by improving four criteria (50% water level, 70% water level, 100% water level, and first flooding of water pipes) in an attempt to reduce flooding in urban areas. The four criteria nodes are generated using a rainfall runoff simulation with synthetic rainfall at various durations. When applying real-time rainfall data, these nodes have the advantage of simple application. The improved flood nomograph made in this way is expected to help predict and prepare for rainstorms that can potentially cause flood damage.

Determination of Rain-/Wind-Dominant Type for Typhoons Approaching South Korea Based on Satellite-Estimated Rainfall and Best-Track Data

Kim, H.-J. and Moon, I.-J., 2019. Determination of rain-/wind-dominant type for typhoons approaching South Korea based on satellite-estimated rainfall and best-track data. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue, No. 90, pp. 340-345. Coconut Creek (Florida), ISSN 0749-0208Typhoons are one of the most influential natural hazards in South Korea. When a typhoon approaches the Korean Peninsula (KP), detailed disaster plans are needed to prevent typhoon-induced damage. In general, the official forecast in South Korea provides basic information on the track and the intensity of the typhoon, but the forecast does not provide detailed information on the impact of the typhoon on the KP based on the type of damage (i.e., rain-, wind-, or rain-wind-dominant). Impact-based information is crucial to prevent disasters, because preparations for an approaching typhoon should be planned differently depending on the type. Based on satellite-estimated rainfall and best-track wind data, this study developed an algorithm for estimating the rainfall and maximum wind speed (MWS) percentiles induced by a typhoon, compared to those induced by previous tropical cyclones (TCs) that have moved through the same area, at intervals of 1° latitude. This information is used to determine the dominant types of typhoons along their tracks. From the estimated rainfall and MWS percentiles, all TCs that approached the KP during 2001–2016 were classified as wind-, rain-, and wind-rain-dominant types. As a result, Maemi in 2003 was the most wind-dominant type, while Nabi in 2005 was the most rain-dominant type. This result has important implications for providing a guidance tool based on impact-based TC prediction for decision makers preparing disaster prevention plans based on real-time satellite-estimated rainfall data.

Real-Time Operation of Pumping Systems for Urban Flood Mitigation: Single-Period vs. Multi-Period Optimization

To reduce flood risk in urban regions, it is important to optimize the performance of operational elements such as gates and pumps. This paper compares the performances of two approaches of multi-period and single-period simulation-optimization that are used to derive real-time control policies for operating urban drainage systems. The EPA storm water management model (SWMM), converting real-time rainfall data to surface runoff at network control points, i.e. pump stations, is linked to the particle swarm optimization (PSO) algorithm, evaluating the system operation performance measure (objective function) for different sets of control policies. A prototype network in a portion of the Seoul urban drainage system is used to investigate the efficiency of the proposed approaches. Results justify the high efficiency of multi-period optimization, leading to 32 and 29% average reductions in peak water level violations from a pre-defined permissible threshold at target points and the number of pump switches, respectively, in comparison with the online single-period optimization. The myopic policies derived by single-period optimization are not reliable, and in some cases, they even perform worse than ad-hoc policies applied by system operators based on their past experiences.

From landslide susceptibility to landslide frequency: A territory-wide study in Hong Kong

Rain-induced shallow landslide is the major type of landslide that happens on natural terrain in Hong Kong due to its high seasonal rainfall and deep weathering soil profile. The Geotechnical Engineering Office has been in a leading role to steer the risk management of natural terrain landslides in Hong Kong. Recently, the territory-wide rainfall-based landslide susceptibility model (in terms of landslide density per year) has been developed to predict the number of natural terrain landslides that may occur in an anticipated rainfall event. Subsequently, the storm-based landslide density has been transformed to the annual landslide frequency to compile the territory-wide landslide frequency map by incorporating the mean annual frequency of occurrence of different probable rainfall scenarios. The annual rainfall frequency of a particular rainfall scenario is derived from its return period, determined based on the abundant real-time rainfall data at a five-minute interval from 110 automatic raingauges across Hong Kong at an average density of 10 km2/gauge. The transformation is discussed in this paper, together with the evaluation of the performance of the landslide frequency map. In addition, the potential applications of the map in Hong Kong and the pitfalls of the common evaluation methods, including the receiver operating characteristic curve and the success-rate curve, are highlighted.

Hydrological similarity approach and rainfall satellite utilization for mini hydro power dam basic design (case study on the ungauged catchment at West Borneo, Indonesia)

An approach on flow duration and flood design estimation on the ungauged catchment with no rainfall and discharge data availability was been being develop with hydrological modelling including rainfall run off model implemented with watershed characteristic dataset. Near real time Rainfall data from multi satellite platform e.g. TRMM can be utilized for regionalization approach on the ungauged catchment. Watershed hydrologically similarity analysis were conducted including all of the major watershed in Borneo which was predicted to be similar with the Nanga Raun Watershed. It was found that a satisfactory hydrological model calibration could be achieved using catchment weighted time series of TRMM daily rainfall data, performed on nearby catchment deemed to be sufficiently similar to Nanga Raun catchment in hydrological terms. Based on this calibration, rainfall runoff parameters were then transferred to a model. Relatively reliable flow duration curve and extreme discharge value estimation were produced with reasonable several limitation. Further approach may be performed in order to deal with the primary limitations inherent in the hydrological and statistical analysis, especially to give prolongation to the availability of the rainfall and climate data with some novel approach like downscaling of global climate model.

Application of Flood Nomograph for Flood Forecasting in Urban Areas

Imperviousness has increased due to urbanization, as has the frequency of extreme rainfall events by climate change. Various countermeasures, such as structural and nonstructural measures, are required to prepare for these effects. Flood forecasting is a representative nonstructural measure. Flood forecasting techniques have been developed for the prevention of repetitive flood damage in urban areas. It is difficult to apply some flood forecasting techniques using training processes because training needs to be applied at every usage. The other flood forecasting techniques that use rainfall data predicted by radar are not appropriate for small areas, such as single drainage basins. In this study, a new flood forecasting technique is suggested to reduce flood damage in urban areas. The flood nomograph consists of the first flooding nodes in rainfall runoff simulations with synthetic rainfall data at each duration. When selecting the first flooding node, the initial amount of synthetic rainfall is 1 mm, which increases in 1 mm increments until flooding occurs. The advantage of this flood forecasting technique is its simple application using real-time rainfall data. This technique can be used to prepare a preemptive response in the process of urban flood management.

Evaluating a microbial water quality prediction model for beach management under the revised EU Bathing Water Directive

The revised Bathing Water Directive (2006/7/EC) requires EU member states to minimise the risk to public health from faecal pollution at bathing waters through improved monitoring and management approaches. While increasingly sophisticated measurement methods (such as microbial source tracking) assist in the management of bathing water resources, the use of deterministic predictive models for this purpose, while having the potential to provide decision making support, remains less common.This study explores an integrated, deterministic catchment-coastal hydro-environmental model as a decision-making tool for beach management which, based on advance predictions of bathing water quality, can inform beach managers on appropriate management actions (to prohibit bathing or advise the public not to bathe) in the event of a poor water quality forecast. The model provides a ‘moving window’ five-day forecast of Escherichia coli levels at a bathing water compliance point off the Irish coast and the accuracy of bathing water management decisions were investigated for model predictions under two scenarios over the period from the 11th August to the 5th September, 2012. Decisions for Scenario 1 were based on model predictions where rainfall forecasts from a meteorological source (www.yr.no) were used to drive the rainfall–runoff processes in the catchment component of the model, and for Scenario 2, were based on predictions that were improved by incorporating real-time rainfall data from a sensor network within the catchment into the forecasted meteorological input data. The accuracy of the model in the decision-making process was assessed using the contingency table and its metrics.The predictive model gave reasonable outputs to support appropriate decision making for public health protection. Scenario 1 provided real-time predictions that, on 77% of instances during the study period where both predicted and E. coli concentrations were available, would correctly inform a beach manager to either take action to mitigate for poor bathing water quality or take no action. However, Scenario 1 also provided data to support a decision to take action (when none was necessary – a type I error) in 4% of instances and to take no action (when action was required – a type II error) in 19% of the instances analysed. Type II errors are critical in terms of public health protection given that for this error, bathers can be exposed to risks from poor bathing water quality. Scenario 2, on the other hand, provided predictions that would support correct management actions for 79% of the instances but would result in type I and type II errors for 4% and 17% of the instances respectively.Comparison of Scenarios 1 and 2 for this study indicate that Scenario 2 gave a marginally better overall performance in terms of supporting correct management decisions, as it provided data that could result in a lower occurrence of the more critical type II errors.Given that the 28 member states of the European Union are required to engage with the public health provisions of the revised Bathing Water Directive, issues of compliance, pertaining particularly to the management of bathing water resources, remain topical. Decision supports for managing bathing waters in the context of the Directive are likely to become the focus of much attention and although, the current study has been validated in bathing waters off the east coast of Ireland, the approach of using a deterministic and integrated catchment-coastal model for such purposes is easily transferable to other bathing water jurisdictions.