Measurement of meteorological variables is essential to assess and analyze extreme events, such as droughts and floods, and even more so when the purpose is to generate early warnings of such natural phenomena. Nowadays, several mechanisms can estimate climatic variables like precipitation and temperature. However, no device measures precipitation values in real-time and at a low-cost, much less are these installed in remote areas of difficult access. Therefore, an Automated Hydrological Information System was developed based on low-cost meteorological stations with two communication protocols, Wi-Fi and GSM. The devices are equipped with a self-sustainable power supply, including a solar panel and energy storage that can last for up to three cloudy days. The precipitation, temperature, and relative humidity values are sent to a database, where they are then processed and displayed on a web page, accessible for download. Users can easily access the data from an official application that redirects them to the website without the need for a computer or a mobile browser. Warning systems are feasible due to the use of IoT services such as ThingSpeak and Ubidots. Ultimately, they allow the analysis of information and immediately send alerts if it exceeds the tolerance ranges.

The study of urban floods is increasingly crucial due to their growing frequency and impact on densely populated areas, often characterized by inadequate drainage and located in flood-prone zones. The consequences extend beyond physical damage, significantly affecting economies and livelihoods, necessitating substantial economic resources for recovery and infrastructure rebuilding. Urban planning now must integrate flood risk management, emphasizing not only infrastructural resilience but also comprehensive policies that address environmental and social vulnerabilities to better prepare and protect urban environments against future flood risks. This study addresses the critical issue of urban flood vulnerability through a focused analysis of Morelia, a city known for its susceptibility to flooding due to its geographical and hydrological characteristics and accelerated urban growth. Employing a multifaceted approach that integrates hydrological, socio-economic, and land use data within a Geographic Information Systems (GIS) framework, the research develops a Spatial Flood Vulnerability Index (SFVI). This index is meticulously applied at the urban block level, offering a precise mapping of flood risks across the city. By correlating the SFVI results with historical flood data, the study identifies the most vulnerable areas in Morelia, which are primarily impacted due to their proximity to water bodies, economic density, and infrastructural settings. The methodology not only highlights immediate flood risks but also aids in strategic urban planning to enhance resilience against future flooding events. This paper contributes a novel approach to flood risk assessment, providing a replicable model for similarly affected cities worldwide, aiming to balance structural measures with strategic planning tailored to local needs.

Flood prediction has become essential to hydrology and natural disaster management due to the increasing frequency and severity of extreme hydrological events driven by climate change. This study compares two methodologies for predicting flood events in Morelia, Mexico: theoretical distribution functions and stochastic weather generators. The methodology integrates maximum runoff results for different return periods into a drainage network hydraulic model, using the Soil Conservation Service Curve Number (SCS-CN) method and a multivariate stochastic model (MASVC). Hydrodynamic modeling with HEC-RAS, incorporating two-dimensional shallow water equations, was used to simulate flood inundation areas. The study reveals that while both modeling approaches similarly replicate the system’s behavior, they produce different water levels due to variations in maximum flow values. The stochastic model tends to generate higher maximum water levels. High-resolution digital elevation models (DEMs) with a pixel size of five m in urban areas and 0.5 m in drainage network zones, and land use data were crucial in improving the accuracy of the hydraulic simulations. Findings indicate that unregulated urban growth in flood-prone areas significantly exacerbates the impact of flooding. The generated hazard maps and flood simulations provide valuable tools for urban planning and decision-making, highlighting the need for strategic interventions to mitigate flood risks. This research underscores the importance of integrating advanced modeling techniques in flood risk management to enhance the precision and reliability of flood predictions.

Producto de la crisis hídrica vivida en el área Metropolitana de Monterrey durante el año 2022, se realizó una modelación hidrológica superficial y subterránea para analizar los niveles piezométricos del acuífero y establecer las causas probables de la problemática. Se recopiló información climática, hidrométrica, fisiográfica, hidrogeológica y de extracciones. Mediante métodos estadísticos se validaron y completaron los datos climáticos e hidrométricos y se calculó la evapotranspiración potencial. Se montaron y calibraron los modelos superficial y subterráneo, siendo los modelos HBV y MODFLOW, respectivamente, para obtener los niveles piezométricos. Se observó que el acuífero presenta signos de recuperación de alrededor del 2% en sus niveles en años recientes, indicando que la problemática probablemente sea la creciente demanda originada por el aumento poblacional y que no se debe únicamente a factores climáticos.