Author(s): Roberto Abraham Vazquez Martinez; Ramon Dominguez Mora; Maritza Liliana Arganis Juarez; Andres Olaf Santana Soto And Eliseo Carrizosa Elizondo
Linked Author(s): Maritza Liliana Arganis Juárez
Keywords: IDW spatial interpolation influence parameter p nonlinear programming
Abstract: The real-time pluviographic measurement systems provide crucial information for the coordination and efficient operation of the control structures in a hydrological system, especially under extreme storm conditions. Unfortunately, the stations sometimes present measurement errors or, in certain cases, do not record the event, which makes it more difficult to accurately determine both the height and intensity of the storm, producing inconveniences in the operation of the system. Therefore, it is crucial to have a reliable tool to estimate rainfall at stations affected by measurement problems. In hydrology, spatial interpolation techniques are used to estimate the value of a variable at an unmeasured point from a weighted average of the recorded values. They are based on the premise that the points closer to the location of interest have a better influence than those farther away. Therefore, the proximity of the measured points directly affects the calculation of the variable at the site of interest. One of the most common interpolation techniques for interpolating precipitation data at unmeasured points is the Inverse Distance Weighted (IDW). This technique assumes that measured points have an influence inversely proportional to their distance from the point of interest, and that such influence is constant and accurate (Echavarria, 2013). This paper presents a methodology to improve the spatial interpolation of precipitation using the IDW technique, through the continuous adjustment of the influence parameter p. Although the proposed methodology has been developed for the case study of the Valley of Mexico Basin (VMB), its application can be extended to any rainfall measurement network, which makes it a versatile tool for the accurate estimation of precipitation at unmeasured sites.
Year: 2025