# Flash Flood Early Warning System in Colima, Mexico
## 2 Materials and Methods
### Alert System  
The flow of a river undergoes variations over time, being subject to changes derived from its natural dynamics, as well as by human actions. For this reason, the topographic surveys that were carried out are necessary to obtain the flow using the Manning equation [28], and continuous monitoring of the water level to generate an accurate and reliable alert system. The design of the early warning level for a channel overflow is obtained by estimating the quantiles based on the maximum flow. The alert system is based on thresholds obtained from several hydraulic simulations with different scenarios through terrain elevation models, since a lot of computing power and time is required to perform a simulation. A method based on the real-time water level is used and instead of simulating each measurement, thresholds were set as a semaphore--green when the water level is at less than the 50% of the capacity of the river, yellow if the water level is between 50% and 75% of the capacity, and red if it exceeds 75% of its capacity. This allows it to alert population in real-time. Alert levels are calculated based on the flow measurement for each node. Every time the alert level changes state, it generates a notification in the system and sends an email notification to users with access to the system. Notifications change dynamically according to the risk of flooding. When the system estimates a higher risk, it will trigger the notifications with higher frequency, continuously alerting users to possible flood risk. The time between the alert and potential flooding can vary according to the location of the sensor that generates the alert and the distance with the community alerted, however, a good estimated value is 10 min, enough for people to take shelter or get away from the high-risk zones. Hydraulic models normally need 3 inputs--a field survey either in cross-sections or in a triangle irregular network, a rainfall-runoff analysis for different return periods, and a set of initial conditions such as roughness and normal depth. Field surveys were made to model the terrain through a Triangular Irregular Network [29] based on coordinated points. This is one of the main inputs for hydraulic models. Historical precipitation data were analyzed as well to obtain different scenarios for various return periods. These two elements are the basis of the hydraulic model which determines flooding zones based on the runoff path through the terrain. The sites evaluated for flooding were only the sites where the sensors were installed; these sites are well defined hydraulic sections that function as a checkpoint for the level measured and can be used to calibrate water level/discharge curves. One-dimensional hydraulics models are used broadly because of short processing time; this allows quick visualization of results such as water elevation, flow, velocity, and overflow in certain stations, but these models have some disadvantages, mainly the lack of precision in flooding zones and the lack of results between the stations added to the model. Bidimensional models, on the other hand, consider terrain information as an entity divided by small interconnected squares that its values depend on the results of the squares behind it. This method can predict the direction of the flow and can identify flooding zones based on the velocity and depth in more refined criteria; however, the modeling time is extended considerably, with a single run taking up to 20 h to complete. Figure 9 shows the location of Colima Mexico, where the study and data collection were carried out for this research. The simulation of the model in Figure 10 presents the rivers Colima and Gertrudis monitored within the city of Colima, showing the existing depth in the rivers, to determine the flow of the water and the point of loss. The color scale shows high depth in red color and low depth in blue color; the zones with high depth match are indeed problematic in real events, which shows the model is accurate.