Suction irrigation in ornamental species provides only the amount of water required by the soil-plant-atmosphere system, reducing water losses due to evaporation, runoff, and percolation. In this work, an experiment was conducted in subdivided plots arranged in randomized blocks to evaluate the daily and cumulative water consumption of Dimorphotheca ecklonis DC. and Graptopetalum paraguayense (N.E.Br.) E. Walther. Two substrate mixtures were used: tezontle (3–4 mm) with peat (2:1) and tezontle (4–6 mm) with peat (2:1), with suction heights of 8 and 15 cm (H8 and H15). The results showed significant statistical differences (p < 0.05) in cumulative water consumption, suction height, and plant type. The ornamental species showed significant statistical differences in terms of water consumption (daily and cumulative), plant height, cover, and total fresh and dry matter production, where D. ecklonis recorded the highest values, while G. paraguayense maintained higher substrate moisture. These findings show that suction irrigation is an efficient water supply option in green infrastructure systems because water consumption is self-regulating.

Currently, water erosion represents one of the main environmental degradation issues in tropical watersheds. Therefore, the main objective of this research was to determine the spatial distribution of erosion susceptibility by integrating multi-criteria decision-making (MCDM) and Geographic Information Systems (GIS) in the “Las Flores” Watershed, Chiapas, Mexico, which covers an area of 2519 km². The Analytic Hierarchy Process (AHP) was implemented in combination with twelve spatial variables: watershed curve number (CNW), topographic wetness index (TWI), sediment transport index (STI), slope (SLP), soil type (STF), distance to rivers (DTR), precipitation (PCP), drainage density (DDN), terrain curvature (CUR), land use and vegetation cover (LCI), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI). The model was evaluated using a Receiver Operating Characteristic (ROC) curve based on 135 erosion points identified through multitemporal image analysis. The results reveal that the variables of SLP (19.30%), PCP (16.70%), CNW (11.89%), and LCI (10.04%) had the most influence on the modeling of erosion susceptibility. In this regard, the high and very high susceptibility categories encompass a significant portion of the basin’s territory, representing 29.20% (735.41 km²), mostly concentrated in areas near the watershed, where the highest precipitation occurs, soils with shallower layers, and areas dedicated to pastures and agriculture. The model achieved very good predictive performance, with an area under the ROC curve (AUC) of 0.82. Therefore, the resulting zoning indicates that spatial modeling can reliably represent erosion susceptibility, serving as an essential technical tool for decision-makers to optimize resource distribution and develop intervention plans in high-priority areas.