scholarly journals Algorithm to Predict the Rainfall Starting Point as a Function of Atmospheric Pressure, Humidity, and Dewpoint

Climate ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 131
Author(s):  
Alfonso Gutierrez-Lopez ◽  
Ivonne Cruz-Paz ◽  
Martin Muñoz Mandujano
Keyword(s):  
Early Warning ◽  
Atmospheric Pressure ◽  
Urban Areas ◽  
Early Warning System ◽  
Warning System ◽  
Forecast Model ◽  
Flood Damage ◽  
Precipitation Forecast ◽  
Rainfall Forecasting ◽  
Starting Point

Forecasting extreme precipitations is one of the main priorities of hydrology in Latin America and the Caribbean (LAC). Flood damage in urban areas increases every year, and is mainly caused by convective precipitations and hurricanes. In addition, hydrometeorological monitoring is limited in most countries in this region. Therefore, one of the primary challenges in the LAC region the development of a good rainfall forecasting model that can be used in an early warning system (EWS) or a flood early warning system (FEWS). The aim of this study was to provide an effective forecast of short-term rainfall using a set of climatic variables, based on the Clausius–Clapeyron relationship and taking into account that atmospheric water vapor is one of the variables that determine most meteorological phenomena, particularly regarding precipitation. As a consequence, a simple precipitation forecast model was proposed from data monitored at every minute, such as humidity, surface temperature, atmospheric pressure, and dewpoint. With access to a historical database of 1237 storms, the proposed model allows use of the right combination of these variables to make an accurate forecast of the time of storm onset. The results indicate that the proposed methodology was capable of predicting precipitation onset as a function of the atmospheric pressure, humidity, and dewpoint. The synoptic forecast model was implemented as a hydroinformatics tool in the Extreme Precipitation Monitoring Network of the city of Queretaro, Mexico (RedCIAQ). The improved forecasts provided by the proposed methodology are expected to be useful to support disaster warning systems all over Mexico, mainly during hurricanes and flashfloods.

scholarly journals MIDAS: A New Integrated Flood Early Warning System for the Miño River

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2319 ◽  
Author(s):  
Diego Fernández-Nóvoa ◽  
Orlando García-Feal ◽  
José González-Cao ◽  
Carlos de Gonzalo ◽  
José Antonio Rodríguez-Suárez ◽  
...  
Keyword(s):  
Early Warning ◽  
Urban Areas ◽  
Early Warning System ◽  
River Flow ◽  
Warning System ◽  
Early Warning Systems ◽  
Precipitation Forecast ◽  
Mitigation Measures ◽  
Flood Alert ◽  
The Impact

Early warning systems have become an essential tool to mitigate the impact of river floods, whose frequency and magnitude have increased during the last few decades as a consequence of climate change. In this context, the Miño River Flood Alert System (MIDAS) early warning system has been developed for the Miño River (Galicia, NW Spain), whose flood events have historically caused severe damage in urban areas and are expected to increase in intensity in the next decades. MIDAS is integrated by a hydrologic (HEC-HMS) and a hydraulic (Iber+) model using precipitation forecast as input data. The system runs automatically and is governed by a set of Python scripts. When any hazard is detected, an alert is issued by the system, including detailed hazards maps, to help decision makers to take precise and effective mitigation measures. Statistical analysis supports the accuracy of hydrologic and hydraulic modules implemented to forecast river flow and flooded critical areas during the analyzed period of time, including some of the most extreme events registered in the Miño River. In fact, MIDAS has proven to be capable of predicting most of the alert situations occurred during the study period, showing its capability to anticipate risk situations.

An Urban Air Pollution Early Warning System Based on PM2.5 Prediction Applied in Ploiesti City

2017 ◽  
Vol 68 (4) ◽  
pp. 858-863
Author(s):  
Mihaela Oprea ◽  
Marius Olteanu ◽  
Radu Teodor Ianache
Keyword(s):  
Air Pollution ◽  
Early Warning ◽  
Urban Areas ◽  
Early Warning System ◽  
Urban Air Pollution ◽  
Warning System ◽  
Air Pollutant ◽  
Pollution Level ◽  
Urban Air ◽  
Prediction Approach

Fine particulate matter with a diameter less than 2.5 �m (i.e. PM2.5) is an air pollutant of special concern for urban areas due to its potential significant negative effects on human health, especially on children and elderly people. In order to reduce these effects, new tools based on PM2.5 monitoring infrastructures tailored to specific urban regions are needed by the local and regional environmental management systems for the provision of an expert support to decision makers in air quality planning for cities and also, to inform in real time the vulnerable population when PM2.5 related air pollution episodes occur. The paper focuses on urban air pollution early warning based on PM2.5 prediction. It describes the methodology used, the prediction approach, and the experimental system developed under the ROKIDAIR project for the analysis of PM2.5 air pollution level, health impact assessment and early warning of sensitive people in the Ploiesti city. The PM2.5 concentration evolution prediction is correlated with PM2.5 air pollution and health effects analysis, and the final result is processed by the ROKIDAIR Early Warning System (EWS) and sent as a message to the affected population via email or SMS. ROKIDAIR EWS is included in the ROKIDAIR decision support system.

Research on the Early Warning Mechanism of Financial Risk by Computer Technology

2014 ◽  
Vol 687-691 ◽  
pp. 4922-4925
Author(s):  
Liang Liu ◽  
Chun Ling Li
Keyword(s):  
Financial Markets ◽  
Empirical Analysis ◽  
Early Warning ◽  
Early Warning System ◽  
Financial Risk ◽  
Warning System ◽  
Warning Signal ◽  
Financial Risks ◽  
Early Warning Mechanism ◽  
Starting Point

The briefly review and the development of the financial risk early warning theory is first discussed in this study and the domestic and foreign research is analyzed as a brief summary. Secondly, the concept of financial risks, financial crisis and the financial early warning is defined. Financial fragility as a starting point is used to establish the rationality model of the financial risk early warning system. The early warning indicators is selected on the basis of the 12 indicators of macro-financial risks, 15 net financial indicators is selected to represent the financial markets according to the characteristics of China's financial markets. In the empirical part, the previous empirical analysis method is chosen to build the financial risk early warning signal system. In order to display China's financial risk profile, the proper model for the calculation is made on the basis of empirical analysis. Thus, in order to minimize the local financial risk, the early warning system should be established by the local government, together with some other necessary measures.

scholarly journals Application of FLaIR model for early warning system in Chibo Pashyor, Kalimpong, India for rainfall-induced landslides

2017 ◽  
Author(s):  
Abhirup Dikshit ◽  
Neelima Satyam
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Risk Mitigation ◽  
Warning System ◽  
Landslide Risk ◽  
Rainfall Forecasting ◽  
Landslide Occurrence ◽  
Mobility Function ◽  
Hourly Rainfall ◽  
Rainfall Model

Abstract. The development of an early warning system for landslides due to rainfall has become an indispensable part for landslide risk mitigation. This paper explains the application of the hydrological FLaIR (Forecasting of Landslides Induced by Rainfall) model, correlating rainfall amount and landslide events. The FLaIR model comprises of two modules: RL (Rainfall-Landslide) which correlates rainfall and landslide occurrence and RF (Rainfall-Forecasting) which allows simulation of future rainfall events. The model can predetermine landslides based on identification of mobility function Y(.) which links actual rainfall and incidence of landslide occurrence. The critical value of mobility function was analyzed using 1st July 2015 event and applying it to 2016 monsoon to validate the results. These rainfall thresholds presented can be improved with intense hourly rainfall and landslide inventory data. This paper describes the details of the model and its performance for the study area.

scholarly journals Rainfall Thresholds for Prediction of Landslides in Idukki, India: An Empirical Approach

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2113 ◽  
Author(s):  
Minu Treesa Abraham ◽  
Deekshith Pothuraju ◽  
Neelima Satyam
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Regional Scale ◽  
Warning System ◽  
Antecedent Rainfall ◽  
Rainfall Forecasting ◽  
Study Region ◽  
Landslide Occurrence ◽  
South Indian ◽  
Landslide Early Warning

Idukki is a South Indian district in the state of Kerala, which is highly susceptible to landslides. This hilly area which is a hub of a wide variety of flora and fauna, has been suffering from slope stability issues due to heavy rainfall. A well-established landslide early warning system for the region is the need of the hour, considering the recent landslide disasters in 2018 and 2019. This study is an attempt to define a regional scale rainfall threshold for landslide occurrence in Idukki district, as the first step of establishing a landslide early warning system. Using the rainfall and landslide database from 2010 to 2018, an intensity-duration threshold was derived as I = 0.9D-0.16 for the Idukki district. The effect of antecedent rainfall conditions in triggering landslide events was explored in detail using cumulative rainfalls of 3 days, 10 days, 20 days, 30 days, and 40 days prior to failure. As the number of days prior to landslide increases, the distribution of landslide events shifts towards antecedent rainfall conditions. The biasness increased from 72.12% to 99.56% when the number of days was increased from 3 to 40. The derived equations can be used along with a rainfall forecasting system for landslide early warning in the study region.

scholarly journals Toward a cross-border early-warning system for Central Asia

2015 ◽  
Vol 58 (1) ◽  
Author(s):  
Jacek Stankiewicz ◽  
Dino Bindi ◽  
Adrien Oth ◽  
Stefano Parolai
Keyword(s):  
Central Asia ◽  
Early Warning ◽  
Urban Areas ◽  
Early Warning System ◽  
Large Scale ◽  
Damage Control ◽  
Warning System ◽  
Earthquake Early Warning ◽  
Successful Implementation ◽  
Cross Border

<p>Rapidly expanding urban areas in Central Asia are increasingly vulnerable to seismic risk; but at present, no earthquake early warning (EEW) systems exist in the region despite their successful implementation in other earthquake-prone areas. Such systems aim to provide short (seconds to tens of seconds) warnings of impending disaster, enabling the first risk mitigation and damage control steps to be taken. This study presents the feasibility of a large scale cross-border regional system for Central Asian countries. Genetic algorithms are used to design efficient EEW networks, computing optimal station locations and trigger thresholds in recorded ground acceleration. Installation of such systems within 3 years aims to both reducing the endemic lack of strong motion data in Central Asia that is limiting the possibility of improving seismic hazard assessment, and at providing the first regional earthquake early warning system in the area.</p>

scholarly journals In Action – An Early Warning System for the Detection of Unexpected or Novel Pathogens

2021 ◽  
Author(s):  
Pauline Dianne Santos ◽  
Ute Ziegler ◽  
Kevin Szillat ◽  
Claudia A Szentiks ◽  
Birte Strobel ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Molecular Diagnostic ◽  
Genome Sequences ◽  
Animal Populations ◽  
Starting Point ◽  
Pathogen Discovery ◽  
German Cities

Abstract Pro-active approaches in preventing future epidemics include pathogen discovery prior to their emergence in human and/or animal populations. Playing an important role in pathogen discovery, high-throughput sequencing (HTS) enables the characterization of microbial and viral genetic diversity within a given sample. In particular, metagenomic HTS allows the unbiased taxonomic profiling of sequences; hence, it can identify novel and highly divergent pathogens such as viruses. Newly discovered viral sequences must be further investigated using genomic characterization, molecular and serological screening, and/or in-vitro and in-vivo characterization. Several outbreak and surveillance studies apply unbiased generic HTS to characterize whole genome sequences of suspected pathogens. In contrast, this study aimed to screen for novel and unexpected pathogens in previously generated HTS datasets and use this information as a starting point for the establishment of an early warning system (EWS). As a proof of concept, the EWS was applied to HTS datasets and archived samples from the 2018-19 West Nile virus (WNV) epidemic in Germany. A metagenomics read classifier detected sequences related to genome sequences of various members of Riboviria. We focused the further EWS investigation on viruses belonging to the families Peribunyaviridae and Reoviridae, under suspicion of causing co-infections in WNV-infected birds. Phylogenetic analyses revealed that the reovirus genome sequences clustered with sequences assigned to the species Umatilla virus, whereas a new peribunyavirid, tentatively named “Hedwig virus” belonged to a putative novel genus of the family Peribunyaviridae. In follow up studies, newly developed molecular diagnostic assays detected fifteen Umatilla virus-positive wild birds from different German cities and eight Hedwig virus-positive captive birds from two zoological gardens. Umatilla virus was successfully cultivated in mosquito C6/36 cells inoculated with a blackbird liver. In conclusion, this study demonstrates the power of the applied EWS for the discovery and characterization of unexpected viruses in repurposed sequence datasets, followed by virus screening and cultivation using archived sample material. The EWS enhances the strategies for pathogen recognition before causing sporadic cases and massive outbreaks and proves to be a reliable tool for modern outbreak preparedness.

Simulation and analysis for flood early warning system in small catchments caused by rainfall-induced disaster

2020 ◽  
Author(s):  
Ke-Sin Yu ◽  
Jihn-Sung Lai ◽  
Yi-Huan Hsieh
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Meteorological Data ◽  
Warning System ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Flood Simulation ◽  
Rainfall Patterns ◽  
The Impact ◽  
Inundation Model

&lt;p&gt;Under the impact of climate change, rainfall-induced flood disasters have become more frequent in some areas. The development of an hourly rainfall forecast with higher time and spatial accuracy under different rainfall patterns and the connection between meteorological forecast and hydrological flood simulation are urgent issues. In this study, eight flood cases in 2019 in Taipei city, a high-risk urban area with high economic and social resource density, caused by different rainfall patterns were chosen to be analyzed. To improve the accuracy of meteorological data, WRF base ensemble prediction system (WEPS), a quantitative precipitation forecast (QPF) produced by Central Weather Bureau (CWB) of Taiwan was selected as the main meteorological data source, and after processed by objective quantitative analysis methods, the data then be input into the drainage&amp;#8211;inundation model. As a one-dimensional and two-dimensional flood simulation system, SOBEK was used to verify the depth and location of floods. Results indicated that the WEPS data would have better performance in drainage&amp;#8211;inundation model among the cases in 2019. Combining meteorological forecast data and hydrological simulation can somehow improve the accuracy of flood early warning system in a small catchment.&lt;/p&gt;

Geomorphologically based early warning system for flood events in minor hilly catchments and local urban areas: a case study in the Abruzzo region (Central Italy)

2020 ◽  
Author(s):  
Tommaso Piacentini ◽  
Enrico Miccadei ◽  
Cristiano Carabella ◽  
Fausto Boccabella ◽  
Silvia Ferrante ◽  
...  
Keyword(s):  
Early Warning ◽  
Urban Areas ◽  
Early Warning System ◽  
Heavy Rainfall ◽  
Central Italy ◽  
Warning System ◽  
Civil Protection ◽  
Rainfall Events ◽  
Critical Areas ◽  
Heavy Rainfall Events

&lt;p&gt;Urban and small catchments flooding is a common type of natural hazard caused by intense rainfall, which may cause inundation to roads, buildings, and infrastructure, interrupting transportation, power lines and, other critical urban infrastructure systems, damaging properties and threatening people&amp;#8217;s lives. The expansion of urban areas and infrastructure over the last 50 years has led to a marked increase in flood risk.&lt;/p&gt;&lt;p&gt;The coastal and hilly areas of Central Italy have been largely affected by heavy rainfall and flood/flash-flood events in recent times. The Apennine hilly piedmont and the coastal hills of Abruzzo have been affected by moderate to heavy events (rainfall &gt;35 mm/h and 100-220 mm/d), which caused damages to minor and major urban areas. In this study, the Feltrino Stream area and the Lanciano town were investigated for the realization of a local early warning system for heavy rainfall events and flooding. The project is funded by the Abruzzo Region within the frame of a regional Project named &amp;#8220;Communicate to protect&amp;#8221; and developed in collaboration with the Lanciano Municipality and with the Regional Civil Protection office.&lt;/p&gt;&lt;p&gt;The Feltrino Stream basin is located in the hilly area of southeastern Abruzzo, in the eastern piedmont of the Maiella massif (Central Apennines). The basin ranges from about 400 m a.s.l. to sea level, with an overall morphology characterized by a mesa and plateau relief and SW-NE elongated valleys. The Lanciano Town is developed on a mesa relief carved by minor valleys, largely modified and filled by anthropic activities.&lt;/p&gt;&lt;p&gt;In this work, the Feltrino Stream was investigated through a drainage basin scale geomorphological analysis incorporating (i) the morphometry of orography and hydrography, (ii) temperature and rainfall data analysis, (iii) acquisition of available geological, geomorphological, and hazard data, (iv) detail urban hydrography analysis and geomorphological field mapping, for the definition of a geodatabase of the geo-hydrological critical areas. The analysis allowed defining the arrangement of a rainfall, hydrometry and flood monitoring system integrating at local scale the existing regional monitoring network. The integration of the monitoring system and the critical areas in a web cloud digital system allowed to plan and realize an early warning system, based on the use of a digital app for smartphone. The warning system is being calibrated for the effectiveness during heavy rainfall events. After calibration, the system will support the local civil protection activities of the Lanciano Municipality. Moreover, under the supervision of the civil protection responsible, it is expected to be implemented as an automatic system for smartphone-based early warning of people exploiting the inbuilt geolocalization features of the recent smartphone.&lt;/p&gt;

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