Precipitation is influential in determining runoff at different scales of analysis, whether in minutes, hours, or days. This paper proposes the use of a multisite multivariate model of precipitation at a daily scale. Stochastic models allow the generation of maximum precipitation and its association with different return periods. The modeling is carried out in three phases. The first is the estimation of precipitation occurrence by using a two-state multivariate Markov model to calculate the non-rainfall periods. Once the rainfall periods of various storms have been identified, the amount of precipitation is estimated through a process of normalization, standardization of the series, acquisition of multivariate parameters, and generation of synthetic series. In comparison, the analysis applies probability density functions that require fewer data and, consequently, represent greater certainty. The maximum values of surface runoff show consistency for different observed return periods, therefore, a more reliable estimation of maximum surface runoff. Our approach enhances the use of stochastic models for generating synthetic series that preserve spatial and temporal variability at daily, monthly, annual, and extreme values. Moreover, the number of parameters reduces in comparison to other stochastic weather generators.

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.

Reference evapotranspiration (ETo) refers to the combined processes of evaporation and transpiration, which are relevant for hydrology, climate change research, and irrigation system design. The ETo is considered for different climatological studies, agriculture-focused studies, drought indices and climate change as well. From the ETo, water needs can be obtained, and along with precipitation, it is important to determine water availability and drought indices like the Standardized Precipitation Evapotranspiration Index (SPEI). Currently, there are different methods to estimate the ETo based on various climatic variables, which have been proposed for different climates and applied in different regions worldwide. The method standardized by most studies for determining the ETo is the “modified Penman–Monteith” method by the Food and Agriculture Organization (FAO). This method is versatile as it considers different climatic conditions and global latitudes. Due to limited climate data in developing countries like Mexico, alternative methods are used. The present study analyzed 40 comparative methods for determining ETo and their influence on SPEI. The best methods for the study area were chosen, including Hansen, Hargreaves and Samani, and Trajkovic, as they are the best based on the available information in Mexico. Additionally, each equation was adjusted to reduce errors and achieve closer approximations to actual ETo values to obtain the most accurate values possible. The influence on SPEI calculation indicates overestimations in temperature-based methods and underestimations in radiation and mass-transfer-based methods. The SPEI calculation showed fewer errors when using the modified HANSEN equations. In the absence of information, Allen’s temperature-based method is recommended.

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.

In recent years, human activities have impacted surface water and groundwater and their interactions with natural water bodies. Lake Cuitzeo is one of Mexico’s most important water bodies but has significantly reduced its flooded area in recent years. Previous studies did not explicitly evaluate the combined effects of hydrological variables on lake dynamics, limiting the understanding of how basin-scale processes influence lake-level. The objective of this study is to evaluate the change in spatio-temporal patterns of hydrological variables under climatic and anthropogenic stress in the Lake Cuitzeo endorheic basin. The proposed methodology uses the SWAT model to analyze at the basin scale, land use and land cover changes, and trends in precipitation and their effect on hydrological processes. Consequently, groundwater flow interactions were assessed for the first time for the Cuitzeo Lake Basin using an automatically coupled SWAT-MODFLOW (v3, 2019), despite limited observational data. A statistically significant change in mean precipitation was detected beginning in 2015, with a decrease of 10.22% compared to the 1973–2014 mean. Land use and land cover changes between 1997 and 2013 resulted in a 26.20% increase in surface runoff. In contrast, estimated evapotranspiration decreased by 1.77%, potentially associated with the reduction in forest cover. As a combined effect of decreased precipitation and land use and land cover change, groundwater percolation declined by 6.34%. Overall, the combined effects of climatic variables and anthropogenic activities have altered lake–aquifer interaction.