Application of geostatistical models for aridity scenarios in northern Mexico

Abstract

An annual mean temperature map was calculated using the Kriging interpolation method for the north-central zone of Mexico to obtain the current aridity, as well as, possible scenarios for the near and distant future. The altitudinal gradient was estimated by linear regression and it was used to estimate the mean temperature. Climate Influence Areas (CIA) were obtained by superimposing the official precipitation layer and the annual mean temperature layer with help of Geographic Information Systems tools. Monthly databases of climatic variables were generated for each CIA and potential evapotranspiration was estimated using the Thorthwaite methodology. The Aridity Index (AI) was calculated and mapped for a base scenario (1970-2000). Subsequently, the aridity behavior of some scenarios was projected and mapped using the global climate models HADGEM 2.0, GFDLCM 3.0, MIP_ESM, and CRNMCM5. Some scenarios were predicted, in the best scenario, aridity will weaken the humid ecosystems and in the worst scenario, hyper-arid climates will appear in the study region.

The Socioeconomic Dimensions of Water Scarcity in Urban and Rural Mexico: A Comprehensive Assessment of Sustainable Development

“…Nonetheless, the weighed components resulting from the AHP exhibited results for the water poverty of these municipalities that agree with the estimations argued by other studies and experts [8,24,25,36,48,52,65]. The WPIs calculated on the basis of AHP multicriteria decision making indicated that Dr. Arroyo and Aramberri were the most vulnerable municipalities in terms of water poverty.…”

“…We propose the WPI as a versatile and replicable model for evaluating the causes and impacts of water scarcity and its relation to poverty and socioeconomic conditions. By employing the WPI, studies have evaluated water scarcity and vulnerability at different scales and seasons, national or regional [6,23,31,[44][45][46][47][48]. Despite these evaluations demonstrating the significance of socioeconomic conditions in water scarcity, comparability is hindered by variations in methodology and scale.…”

“…The NDVI, one of the most common indicators in land use assessments, quantifies vegetation cover using data collected from satellite imaging [47,48,61]. Based on how the plant absorbs and reflects light, this index measures the difference between two spectral bands, near-infrared (reflected by vegetation) and red light (absorbed by vegetation).…”

“…It addresses the challenge of performing WPI calculations using the available data by applying it to the municipality scale, because datasets related to demographics, economic activities, or infrastructure are frequently collected at the municipal level. Data on vegetation cover were calculated using remote sensing data, which offers advantages in filling data gaps for various applications, including environmental assessment and monitoring [48].…”

Review of water scarcity assessments: Highlights of Mexico's water situation

“…However, limitations in aridity measurements have been prevalent in developing countries due to the lack of measured PET data and challenges with accurate PET estimations (Sahin, 2012). A more comprehensive evaluation of aridity aims to overcome these limitations through the application of geostatistical interpolation models (De Jesus Correa‐Islas et al, 2023). This study predicted future aridity scenarios in northern Mexico that may worsen the current situation, potentially leading to a gradual decline in humidity and environmental resilience.…”