# Flash Flood Early Warning System in Colima, Mexico  
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systems increases, effects are increasingly noticeable in the hydrological cycle and associated river system catchment areas. Flood prediction models may be tuned using historical hydrological data concerning fluvial floods [3; 4]. However, due to climate change and the increased levels of energy in weather systems, flash flooding is becoming more common and therefore, when considering flood alerting systems, historical data are less useful than real-time data [5; 6]. Flash floods concentrate their waters in small geographic areas within six hours of the rains or other events that spawned them and are characterized by a rapid rise of fast-moving water. Water moving at 10 m per second, a common speed for flash floods, can move rocks weighing almost a hundred pounds. Flash floods carry debris that elevates their potential to damage structures and injure people. The rest of this paper is laid out as follows. This section presents a short review of systems of sensors with similar applications to EWIN (Emergency Water Information Network). Section 2 shows materials and methods, Section 3 analyses the results, Section 4 presents a particular discussion, and Section 5 reports our conclusions. Several related projects are currently under development around the world. The authors in [6] and [7] present the development of the European Flood Warning System (EFAS), which aims at increasing preparedness for floods in trans-national European river basins by providing local water authorities with medium range and probabilistic flood forecast information, 3 to 10 days in advance. In Part 1, the approach used is based on quantiles. The core of EFAS consists of a grid-based distributed hydrological rainfall-runoff model with a routing component that simulates the hydrological processes in large river basins. This model is fed with several medium range weather forecasts, including full sets of the Ensemble Prediction System (EPS). Part 2 concerns the assessment of EFAS overall forecasting over a full two-year period of existing EFAS operational hydrological forecasts. In [8], the authors apply a regional flood frequency analysis on a global scale, exploring its suitability as a means of providing a preliminary characterization of flood behavior, while also identifying behavioral patterns between different catchments. The results suggest that regional flood frequency methods applied to suitably partitioned catchment groups can provide plausible discharge estimates on a global scale. The system in [9] uses rainfall intensity data from terrestrial microwave communication links and the geostationary Meteosat Second Generation (MSG) satellite. Based on publicly available meteorological data, this provides early warning through location-based assessments of precipitation forecasts and optional flood forecasts. The software is made up of the following components:  
1. Mobile Smartphone Application and VGI (Volunteered Geographic Information) data: Volunteers can share photographs, water levels, classification of the precipitation, and estimations of snow depths. The application runs on the Android operating system. 2. Forecasting System: The applied meteorological data originate from the free open data platform of the German Weather Service (DWD). The operational flood forecast is based on a model available from the DWD; periodically, the systems checks for enough VGI measurements to update the model state. 3. Web interface (dashboard): Allows authorized crisis managers to conveniently monitor the situation in the catchment area, map visualization, hydrological, and meteorological forecasts, and warnings as well as the user-generated VGI data. 4. Server: Based on a JavaScript runtime environment with a Node.js web server in combination with the Express.js web framework and a MongoDB NoSQL database.