The dependence of arid and semiarid ecosystems on seasonal rainfall is not well understood when sites have access to groundwater. Gradients in terrain conditions in northwest México can help explore this dependence as different ecosystems experience rainfall during the North American monsoon (NAM), but can have variations in groundwater access as well as in soil and microclimatic conditions that depend on elevation. In this study, we analyze water‐energy‐carbon fluxes from eddy covariance (EC) systems deployed at three sites: a subtropical scrubland, a riparian mesquite woodland, and a mountain oak savanna to identify the relative roles of soil and microclimatic conditions and groundwater access. We place datasets during the NAM season of 2017 into a wider context using previous EC measurements, nearby rainfall data, and remotely‐sensed products. We then characterize differences in soil, vegetation, and meteorological variables; latent and sensible heat fluxes; and carbon budget components. We find that lower elevation ecosystems exhibited an intense and short greening period leading to a net carbon release, while the high elevation ecosystem showed an extensive water use strategy with delayed greening of longer duration leading to net carbon uptake during the NAM. Access to groundwater appears to reduce the dependence of deep‐rooted riparian trees at low elevation and mountain trees on seasonal rainfall, allowing for a lower water use efficiency as compared to subtropical scrublands sustained by water in shallow soils. Thus, a transition from intensive to extensive water use strategies can be expected where there is reliable access to groundwater.

Evapotranspiration (ET) estimates are particularly needed for monitoring the available water of arid lands. Remote sensing data offer the ideal spatial and temporal coverage needed by irrigation water management institutions to deal with increasing pressure on available water. Low spatial resolution (LR) products present strong advantages. They cover larger zones and are acquired more frequently than high spatial resolution (HR) products. Current sensors such as Moderate-Resolution Imaging Spectroradiometer (MODIS) offer a long record history. However, validation of ET products at LR remains a difficult task. In this context, the objective of this study is to evaluate scaling properties of ET fluxes obtained at high and low resolution by two commonly used Energy Balance models, the Surface Energy Balance System (SEBS) and the Two-Source Energy Balance model (TSEB). Both are forced by local meteorological observations and remote sensing data in Visible, Near Infra-Red and Thermal Infra-Red spectral domains. Remotely sensed data stem from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODIS sensors, respectively, resampled at 100 m and 1000 m resolutions. The study zone is a square area of 4 by 4 km² located in a semi-arid irrigated agricultural zone in the northwest of Mexico. Wheat is the dominant crop, followed by maize and vegetables. The HR ASTER dataset includes seven dates between the 30 December 2007 and 13 May 2008 and the LR MODIS products were retrieved for the same overpasses. ET retrievals from HR ASTER products provided reference ET maps at LR once linearly aggregated at the km scale. The quality of this retrieval was assessed using eddy covariance data at seven locations within the 4 by 4 km² square. To investigate the impact of input aggregation, we first compared to the reference dataset all fluxes obtained by running TSEB and SEBS models using ASTER reflectances and radiances previously aggregated at the km scale. Second, we compared to the same reference dataset all fluxes obtained with SEBS and TSEB models using MODIS data. LR fluxes obtained by both models driven by aggregated ASTER input data compared well with the reference simulations and illustrated the relatively good accuracy achieved using aggregated inputs (relative bias of about 3.5% for SEBS and decreased to less than 1% for TSEB). Results also showed that MODIS ET estimates compared well with the reference simulation (relative bias was down to about 2% for SEBS and 3% for TSEB). Discrepancies were mainly related to fraction cover mapping for TSEB and to surface roughness length mapping for SEBS. This was consistent with the sensitivity analysis of those parameters previously published. To improve accuracy from LR estimates obtained using the 1 km surface temperature product provided by MODIS, we tested three statistical and one deterministic aggregation rules for the most sensible input parameter, the surface roughness length. The harmonic and geometric averages appeared to be the most accurate.