Due to its geographical position, Mexico is exposed annually to cold fronts and tropical cyclones, registering extremely high values that are atypical in the series of maximum annual flows. Univariate mixed probability distribution functions have been developed based on the theory of extreme values, which require techniques to determine their parameters. Therefore, this paper explores a function that considers three populations to analyze maximum annual flows. According to the structure of the Generalized Extreme-Value Distribution (GEV), the simultaneous definition of nine parameters is required: three of location, three of scale, and three of probability of occurrence. Thus, the use of a meta-heuristic technique was proposed (harmonic search). The precision of the adjustment was increased through the optimization of the parameters, and with it came a reduction in the uncertainty of the forecast, particularly for cyclonic events. It is concluded that the use of an extreme value distribution (Type I) structured with three populations and accompanied by the technique of harmonic search improves the performance in respect to classic techniques for the determination of its parameters.

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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.

Among surface hydrologic phenomena, it is common to find series of events of random occurrence in time. Poisson processes lead to probabilistic models that are appropriate to explain the number of events produced by certain phenomena. For instance, in surface hydrology, it is quite frequent to relate the Poisson distribution to the occurrence of rainfall events. The so-called leak distribution consists of the simultaneous use of a Poisson law to represent the probability of occurrence of an event and an exponential distribution applied to the mean magnitude of such event.Originally introduced to simulate gas leaks in distribution networks in France, from where it takes its name, the leak distribution has important applications in hydrology. In this paper, the theoretical basis of the law and the method for the estimation of its parameters are introduced. Some applications are included, such as further knowledge of the precipitation regime of hydrologic region No. 10 in Mexico. In this case, through the knowledge of the two parameters of this law, which can be associated to physical variables, it is possible to determine the temporal and spatial distribution of precipitation in detail. As an additional application, the use of this law in drought analysis is shown. Here, the distribution parameters are related to the Standardized Precipitation Index, SPI, allowing the construction of a modified SPI that much better represents the spatial variability of drought periods in the watershed. According to the

La cartografía en hidrología es una de las formas tradicionales de representar la variabilidad espacial de eventos climáticos y ambientales. Sin embargo, pocas veces se pone atención a cómo se construyen, por ejemplo los mapas de isoyetas. Una incorrecta selección del método de interpolación espacial, puede ocasionar errores en la estimación de magnitudes. Se realiza una caracterización espacial y temporal de la variabilidad en la precipitación horaria de la zona metropolitana de Santiago de Querétaro. Se utilizan láminas de lluvia medidas a cada minuto de las diez tormentas más intensas registradas en los años 2013, 2014 y 2015. Utilizando variogramas direccionales, distancia entre estaciones y un esquema de anisotropía; se obtienen los parámetros óptimos a emplear en una interpolación por Kriging. Los resultados muestran que en el mes de junio existe una mayor variabilidad de la precipitación en la zona metropolitana y que la lámina de precipitación tiene una correlación directa con la distancia entre estaciones. El análisis permite seleccionar de forma correcta los variogramas direccionales que deben emplearse para la interpolación espacial y la cartografía de campos de lluvia.