Knowing the spatiotemporal variability of temperature and precipitation, as well as their future projections, is essential to assess environmental risks and plan long-term mitigation and adaptation. In this study, the General Circulation Models (GCMs) HadGEM3-GC31-LL, MRI-ESM2-0 and ACCESS-ESM1-5 of the Coupled Model Intercomparison Project (CMIP6) have been used, under two Shared Socioeconomic Pathways (SPP2-4.5 and SSP5-8.5), to project the minimum and maximum air temperature and the mean annual precipitation in Atlacomulco Rural Development District (ARDD). 30 meteorological stations were used to perform regionalized climate change projections by downscaling using LARS-WG 7. The expected changes were evaluated for three time horizons: near horizon (2021–2040), mid horizon (2041–2060) and far horizon (2061–2080); compared to the historical period (1985–2017). Based on the results, the annual minimum temperature towards the far horizon is projected to increase by 2.0 ºC with SSP2-4.5, and 3.1 ºC with SSP5-8.5; while the annual maximum temperature towards the far horizon is projected to increase between 2.5 ºC with SSP2-4.5 and 3.8 ºC using SSP5-8.5. Precipitation shows an increase for SSP2-4.5 in the near and middle horizons with 15.7 and 42.5 mm.year⁻¹, respectively, while the largest decrease is projected towards the middle horizon of SSP5-8.5 with − 26.3 mm.year⁻¹. The findings of this study provide detailed information on climate change projections in the ARDD and can serve as a useful guide for the management of different ecosystems.

Revista Tecnología y Ciencias del Agua (TyCA) del Instituto Mexicano de Tecnología del Agua (IMTA)

Con la evapotranspiración de referencia (ETo) y los Kc de un cultivo se estiman y planean los volúmenes de agua de riego y frecuencia de aplicación. En México la superficie de agricultura protegida se incrementa de 20 al 25% anualmente, 44% son invernaderos y predominan las unidades de producción menores a 0.5 ha, lo que sugiere que tienen tecnología limitada. Para los invernaderos no hay un método estándar para estimar la ETo como el de Penman-Monteith para campo abierto. Con una estación automática Campbell instalada dentro de un invernadero en los terrenos de la Universidad Autónoma Chapingo, se midieron dos series de datos meteorológicos. Con la primera se modificaron los parámetros de varios modelos reportados en la literatura para calcular la ETo, unos basados en temperatura (Baier-Robertson, Romanenko y Hargreaves) y otros en radiación (Abtew, Jensen-Haise, Caprio, Irmak, Sthepen, Priestley-Taylor y Makkink) y con la segunda se evaluaron todos. La ETo obtenida por cada uno de los métodos con sus parámetros modificados, se comparó con la ETo calculada con el método de Penman-Monteith, determinándose que los modelos de radiación mostraron mejores ajustes que los de temperatura. En la evaluación los basados en temperatura mostraron ajustes no adecuados con R² menores a 0.461 y RSE mayores a 0.31 mm d^-1 y los de radiación tuvieron R² mayores a 0.909 y RSE menores a 0.21. El modelo modificado de Abtew fue el mejor para estimar la ETo en invernadero con un R² de 0.947 y un RSE de 0.06 mm d^-1.

Droughts are hydrometeorological hazards that are characterized by an abnormal and persistent humidity deficit. In the last years, this hazard has been present more frequently and with more severity levels, producing negative impacts on the ecosystem, agriculture, livestock, and society. Therefore, its monitoring and forecast, must be part of integral planning, preparation, and mitigation of its adverse effects at local, regional, and national levels. In Mexico, most of the drought studies are focused on characterization and analysis. Thus, in this research, we evaluated the application of artificial neural networks (ANN) to forecast the meteorological droughts in the medium and high parts of the Sonora River watershed. SPI and SPEI index were used, on scales of 3, 6, 12, and, 24 months, for the 1974 to 2013 period of years. Forecast results showed that ANN has a satisfactory level of prediction, with an average determination coefficient (R2) in the validation phase, of 0.76. It was observed that the statistical efficiency of SPEI was better than that of SPI, and that this efficiency increased with the longer temporal scale; maybe because in a short term, climate variability is greater. Las sequías son un fenómeno hidrometeorológico extremo que se caracteriza por la deficiencia de humedad de manera anormal y persistente. En los últimos años, este fenómeno se ha presentado con mayor frecuencia y con niveles de gravedad cada vez más intensos, lo que ha provocado numerosos impactos negativos en los sistemas ecológicos, agrícolas, ganaderos y sociales. Por lo tanto, su monitoreo y pronóstico deben ser parte integral de la planeación, preparación y mitigación de sus efectos adversos a nivel local, regional e incluso nacional. En México, los estudios se han enfocado mayoritariamente en la caracterización y el análisis de los eventos de sequía, por lo que el objetivo de este estudio fue evaluar la aplicabilidad de las redes neuronales artificiales (RNA) para pronosticar las sequías meteorológicas en la parte media y alta de la cuenca del río Sonora, México. Para ello se utilizaron los índices SPI y SPEI a escalas temporales de 3, 6, 12 y 24 meses, para el periodo de 1974 a 2013. De manera general, los resultados mostraron que las habilidades predictivas de los modelos de RNA fueron satisfactorias, con un coeficiente de determinación (R2) promedio de 0.76 en la etapa final de validación de los modelos. Se observó que el rendimiento estadístico de los modelos para el pronóstico del SPEI fue superior al SPI y que éste aumentaba conforme la escala temporal era mayor, probablemente debido a que a corto plazo existe mayor variabilidad de las condiciones climáticas.

Agricultural drought is a condition that threatens natural ecosystems, water security, and food security. The timely identification of an agricultural drought event is essential to mitigating its effects. However, achieving a reliable and accurate assessment is challenging due to the interannual variability of precipitation in a region. Therefore, the objective of this study was to identify the months with drought during the agricultural cycle of the maize crop (Zea mays L.) in the Atlacomulco Rural Development District (ARDD) as a study area using the SPI and SPEI indices and their impact on each phenological stage. The results show that when analyzing the historical period (1985–2017), the ARDD is a region prone to agricultural droughts with a duration of one month. The stages of grain filling and ripening were the most vulnerable, since SPI and SPEI-1 quantify that 25% and 31% of the total months with drought occur during those stages, respectively. Towards the 2041–2080 horizon, the MCG ACCESS-ESM1-5 with the SSP2-4.5 scenario identified an occurrence of dry periods with 17% and 20% by SPI and SPEI, respectively, while for SSP5-8.5, 17% and 22% of the total number of periods corresponded to dry months with SPI and SPEI, respectively. Greater recurrence will be observed in the future, specifically after the year 2061, meaning an increase in the frequency of agricultural drought events in the region, causing difficult and erratic productive conditions for each agricultural cycle and threatening sustainable development. Therefore, it is necessary to take action to mitigate the effects of climate change in this sector.

Climate change has put the agricultural industry under enormous pressure, as rising temperatures and changing rainfall patterns are affecting crop yields and productivity. The temporal variability of the irrigation water requirement (IWR) as a function of crop evapotranspiration (ETc) and effective rainfall (Peff) was analyzed for forage corn cultivation from a climate-change perspective in the “Comarca Lagunera” region, located in the north of Mexico. The time periods 1975–2016 and 2061–2080 were analyzed, the latter using the forcings of the climate-change scenario SSP5-8.5, from the meteorological data. The Peff, ETc, and IWR for the maize crop were modeled with CROPWAT software, and the Rodionov test was applied to detect points of change in the three variables mentioned above. The historical values of IWR, ETc, and Peff values for spring were estimated at 511, 571, and 57, while for summer, they were 336, 450, and 122, respectively. The climate-change scenario toward the distant horizon projects increases in IWR of 11.9% and 3.5% and in ETc of 7.7% and 0.6%, respectively, for both spring and summer agricultural cycles, as well as decreases in Peff of −30% and −12%, respectively. These results emphasize the combined impact of rising temperatures and reduced rainfall on crop water needs, a crucial factor for crop production in regions that depend on agricultural irrigation. This study provides a foundation for planning irrigation water management in anticipation of an imminent increase in demand due to erratic weather patterns in arid zones.