The structural pattern of rainfall data exhibits random fluctuations over time and space. Utilizing concepts of fractal theory, it has been possible to identify characteristics of rainfall data beyond simple statistical indicators of their randomness. The objective of this research was to identify the spatial variation of the Hurst exponent, extracted through standard wavelet techniques from time series of daily rainfall data in the state of Zacatecas, Mexico. The Hurst exponent was extracted for 26 locations using the reference techniques for auto-affine traces—in particular, the wavelets method. Results have shown that the Hurst exponents of rainfall time series are negatively influenced by altitude; thus, stations located at higher altitudes were characterized by Hurst exponents indicating more nonpersistent behavior. The trends among geographical variables (west longitude and latitude) and climatic parameters (annual rainfall and number of rainy days) and their relationship with the Hurst exponent were also analyzed.

The vegetation indices (VIs) estimated from remotely sensed data are simple and based on effective algorithms for quantitative and qualitative evaluations of the dynamics of biophysical crop variables such as vegetation cover, leaf area, vigor and development, and many others. Over the last decade, many VIs have been proposed and validated to enhance the vegetation signal by reducing the noise from effects produced either by the soil or by vegetation such as brightness, shadows, color, etc. VIs are commonly calculated from satellite images such as ones from Landsat and Sentinel-2 because of their medium resolution and free availability. However, despite the VIs being fairly simple algorithms, it can take hours to calculate them for an established agricultural area, mainly due to the pre-processing of the images (including atmospheric corrections, the detection of clouds and shadows), size and download time of the images, and the capacity of the computer equipment used. Time increases as the number of images increases. In this sense, the free to use Google Earth Engine (GEE) platform was here used to develop an application called VICAL to calculate 23 VIs map (VIs commonly used in agricultural applications) and time series of any agricultural area in the world with images (cloud-free) from Landsat and Sentinel-2 data. It was found that VICAL can calculate these 23 VIs accurately, and shows the potential of the GEE cloud-based tools using multispectral dataset to assess many spectral VIs. This tool is very beneficial for researchers with poor access to satellite data or in institutions with a lack of computational infrastructure to handle the large volumes of satellite datasets, since it is not necessary for the user writing a single line of code. The VICAL is open-access image analysis platform that can be modified to carry out more complex analysis or adapt it to a specific VI application.

Water scarcity for agriculture is increasingly evident due to climatic alterations and inadequate management of this resource. Therefore, developing digital models that help improve water resource management to provide solutions to agronomic problems in northern Mexico is necessary. In this context, the objective of the present research is to calibrate the Optical Trapezoid (OPTRAM) and Thermal-Optical Trapezoid (TOTRAM) models to estimate the volumetric soil moisture at different depths through vegetation indices derived from Landsat-8 and Sentinel-2 satellite images using Google Earth Engine (GEE). Agricultural areas under gravity irrigation and rainfed runoff in the Comarca Lagunera, the lower part of the Hydrological Region No. 36 of the Nazas and Aguanaval rivers were selected for in-situ measurements. The OPTRAM and TOTRAM normalized moisture content (W) estimates were compared with in-situ volumetric soil moisture (Ɵ) data. Results indicate that the predictions of OPTRAM errors using Sentinel-2 images showed RMSE between 0.033 to 0.043 cm3 cm-3 and R2 between 0.66 to 0.75, whereas Landsat-8 errors showed RSME from 0.036 to from 0.036 to 0.057 cm3 cm-3 and R2 between 0.70 to 0.81. On the other hand, TOTRAM errors showed RMSE between 0.045 to 0.053 cm3 cm-3 and R2 between 0.62 to 0.85 through calibrations. This study made it possible to evaluate the most accurate combinations of the pixel distributions of each model and vegetation indices for the estimation of volumetric soil moisture within the different phenological stages of the crops.

En el suroeste del estado de Chihuahua, México, en la reserva “Cerro El Mohinora” se desarrollaron series de tiempo dendrocronológicas de madera temprana, tardía y anillo total de Pseudotsuga menziesii con una longitud de tres siglos y medio (1657-2005), con el objetivo de analizar la variabilidad hidroclimática histórica de la región. La reconstrucción de precipitación invierno-primavera indica gran variabilidad interanual, decenal y multidecenal de los patrones de precipitación para la región. Sequías severas se reconstruyeron para los periodos 1695-1715, 1753-1760, 1785-1792, 1798-1806, 1819-1830, 1841-1870, 1890-1897, 1906-1912, 1924-1941, 1971-1977 y 1994-2005, aunque las sequías más prolongadas ocurrieron en los periodos 1695-1715, 1841-1870 y 1924-1941. Las últimas tres décadas del siglo XX y los años corrientes de la primera década del siglo XXI (1971-2005) indican un periodo de intensa sequía para la región, con impactos en lo ecológico y socioeconómico aun no cuantificados.

This paper describes a simple, few-parameters simulation model for a water harvesting-strip farming system. The model includes runoff threshold functions for different soil types, and for different initial soil water contents. The evapotranspiration component of the model accounts for dynamic root water extraction using regression equations that correlate canopy cover with root growth as a function of time. The model is simple to operate and uses readily available inputs. It can work with several crops when leaf area indices and crop stage coefficients are provided. The agreement between simulation outputs and field-observed data indicates that the model accurately describes the water balance within the system. The model is then recommended for obtaining preliminary design of water harvesting systems. A FORTRAN77 source code has been written for the simulation procedure.