Through litter decomposition enormous amounts of carbon is emitted to the atmosphere. Numerous large-scale decomposition experiments have been conducted focusing on this fundamental soil process in order to understand the controls on the terrestrial carbon transfer to the atmosphere. However, previous studies were mostly based on site-specific litter and methodologies, adding major uncertainty to syntheses, comparisons and meta-analyses across different experiments and sites. In the TeaComposition initiative, the potential litter decomposition is investigated by using standardized substrates (Rooibos and Green tea) for comparison of litter mass loss at 336 sites (ranging from -9 to +26 °C MAT and from 60 to 3113 mm MAP) across different ecosystems. In this study we tested the effect of climate (temperature and moisture), litter type and land-use on early stage decomposition (3 months) across nine biomes. We show that litter quality was the predominant controlling factor in early stage litter decomposition, which explained about 65% of the variability in litter decomposition at a global scale. The effect of climate, on the other hand, was not litter specific and explained <0.5% of the variation for Green tea and 5% for Rooibos tea, and was of significance only under unfavorable decomposition conditions (i.e. xeric versus mesic environments). When the data were aggregated at the biome scale, climate played a significant role on decomposition of both litter types (explaining 64% of the variation for Green tea and 72% for Rooibos tea). No significant effect of land-use on early stage litter decomposition was noted within the temperate biome. Our results indicate that multiple drivers are affecting early stage litter mass loss with litter quality being dominant. In order to be able to quantify the relative importance of the different drivers over time, long-term studies combined with experimental trials are needed.

The objective of this research was to determine the relationship between the inefficiency of furrow irrigation, the geometric characteristics of agricultural plots and the depletion of the aquifer conditions of the Laguna Bustillos basin (Chihuahua, Mexico) in recent decades (1991-2012). The main results were: a) the analysis of geometric characteristics showed that the Mennonite properties have the least favorable conditions for the use of furrow irrigation; b) the index of inefficiency of furrow irrigation demonstrated the existence of agricultural plots that exceed 2.5 to 13 times the maximum allowed 2019, Instituto Mexicano de Tecnología del Agua.

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Waterbody evaporation (E) within endorheic basins in semiarid areas is critical in determining the water balance. Accurate E measurements can provide valuable information for the sustainable management of water resources in the face of climate change scenarios. However, evaporation can be estimated through methods as efficient as Penman using variables from agroclimatic stations, such as wind velocity, net radiation, relative humidity, and air temperature, which have spatiotemporal variability. Within the evaporation models based on remote sensing (RS) is the surface energy balance model (SEB), which has been applied to different methodologies and extends the measurements of evapotranspiration (ET) at a regional level. SEB-based methodologies use physical principles with minimal weather data requirements to estimate ET. Hence, this article compares two RS methodologies that estimate evaporation: The Regional Evapotranspiration Estimate Model (REEM) and the Earth Engine Evapotranspiration Flux (EEFlux). Comparing ET measurements obtained from REEM and EEFlux for seven Landsat OLI scenes in the agriculture cycle of April to September applied against the simplified Penman equation showed that the REEM performed better (d = 94 %) than the EEFlux (d = 68 %) for the indicated period. Although the comparison of REEM and EEFlux shows accurate E measurements (REEM), gridded weather data (EEFlux) needs to improve, increasing the scale using local information.

Since there are no mathematical models that can calculate the Laguna de Bustillos’ water storage levels, water balance requires this data to understand the connectivity between this water body and the Cuauhtemoc aquifer. This article presents a new three-dimensional reconstruction technique based on a time series of multispectral remote sensing images, bathymetry, a topographic survey with high precision GPS, and regional contours. With the images of Landsat ETM+/OLI and Sentinel 2A from 2012 to 2013, 2016, and 2017, the contours of the water surface were extracted using the MNDWI and were associated with an elevation received from GPS. An Autonomous Surface Vehicle was also used to obtain the bathymetry of the lake. A topographic survey was carried out using GPS in populated areas, and the contour lines extracted from the INEGI Continuous Elevations Model 3.0. A DEM was constructed using ArcGIS 10.5.1, and surfaces and volumes were calculated at different elevations and compared with 16 Landsat TM/ETM+/OLI multispectral images from 1999 to 2018. The results showed that the mean of the average intersection area between the test images and the area extracted from the 3D model is above 90.9% according to the confidence interval, kappa overall accuracy 95.2–99.7 %, and a coefficient 89.9–99.3 %. This model proved to be very accurate on a regional scale when the water level exceeded 1971.32 meters above mean sea level and useful to evaluate and administer water resources. DOI: https://doi.org/10.54167/tch.v12i1.129