Understanding the impacts of land-use change on landscape-hydrological dynamics is one of the main challenges in the Northern Brazilian Cerrado biome, where the Amazon agricultural frontier is located. Motivated by the gap in literature assessing these impacts, we characterized the soil hydro-physical properties and quantified surface water fluxes from catchments under contrasting land-use in this region. We used data from field measurements in two headwater micro-catchments with similar physical characteristics and different land use, i.e. cerrado sensu stricto vegetation and pasture for extensive cattle ranching. We determined hydraulic and physical properties of the soils, applied ground-based remote sensing techniques to estimate evapotranspiration, and monitored streamflow from October 2012 to September 2014. Our results show significant differences in soil hydro-physical properties between the catchments, with greater bulk density and smaller total porosity in the pasture catchment. We found that evapotranspiration is smaller in the pasture (639 ± 31% mm yr^-1) than in the cerrado catchment (1,004 ± 24% mm yr^-1), and that streamflow from the pasture catchment is greater with runoff coefficients of 0.40 for the pasture and 0.27 for the cerrado catchment. Overall, our results confirm that conversion of cerrado vegetation to pasture causes soil hydro-physical properties deterioration, reduction in evapotranspiration reduction, and increased streamflow.

Many remote sensing‐based evapotranspiration (RSBET) algorithms have been proposed in the past decades and evaluated using flux tower data, mainly over North America and Europe. Model evaluation across South America has been done locally or using only a single algorithm at a time. Here, we provide the first evaluation of multiple RSBET models, at a daily scale, across a wide variety of biomes, climate zones, and land uses in South America. We used meteorological data from 25 flux towers to force four RSBET models: Priestley–Taylor Jet Propulsion Laboratory (PT‐JPL), Global Land Evaporation Amsterdam Model (GLEAM), Penman–Monteith Mu model (PM‐MOD), and Penman–Monteith Nagler model (PM‐VI). was predicted satisfactorily by all four models, with correlations consistently higher () for GLEAM and PT‐JPL, and PM‐MOD and PM‐VI presenting overall better responses in terms of percent bias (%). As for PM‐VI, this outcome is expected, given that the model requires calibration with local data. Model skill seems to be unrelated to land‐use but instead presented some dependency on biome and climate, with the models producing the best results for wet to moderately wet environments. Our findings show the suitability of individual models for a number of combinations of land cover types, biomes, and climates. At the same time, no model outperformed the others for all conditions, which emphasizes the need for adapting individual algorithms to take into account intrinsic characteristics of climates and ecosystems in South America.

Drought is predicted to increase in the future due to climate change, bringing with it myriad impacts on ecosystems. Plants respond to drier soils by reducing stomatal conductance in order to conserve water and avoid hydraulic damage. Despite the importance of plant drought responses for the global carbon cycle and local and regional climate feedbacks, land surface models are unable to capture observed plant responses to soil moisture stress. We assessed the impact of soil moisture stress on simulated gross primary productivity (GPP) and latent energy flux (LE) in the Joint UK Land Environment Simulator (JULES) vn4.9 on seasonal and annual timescales and evaluated 10 different representations of soil moisture stress in the model. For the default configuration, GPP was more realistic in temperate biome sites than in the tropics or high-latitude (cold-region) sites, while LE was best simulated in temperate and high-latitude (cold) sites. Errors that were not due to soil moisture stress, possibly linked to phenology, contributed to model biases for GPP in tropical savanna and deciduous forest sites. We found that three alternative approaches to calculating soil moisture stress produced more realistic results than the default parameterization for most biomes and climates. All of these involved increasing the number of soil layers from 4 to 14 and the soil depth from 3.0 to 10.8 m. In addition, we found improvements when soil matric potential replaced volumetric water content in the stress equation (the “soil14_psi” experiments), when the critical threshold value for inducing soil moisture stress was reduced (“soil14_p0”), and when plants were able to access soil moisture in deeper soil layers (“soil14_dr*2”). For LE, the biases were highest in the default configuration in temperate mixed forests, with overestimation occurring during most of the year. At these sites, reducing soil moisture stress (with the new parameterizations mentioned above) increased LE and increased model biases but improved the simulated seasonal cycle and brought the monthly variance closer to the measured variance of LE. Further evaluation of the reason for the high bias in LE at many of the sites would enable improvements in both carbon and energy fluxes with new parameterizations for soil moisture stress. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES or as a general way to improve land surface carbon and water fluxes in other models. In addition, using soil matric potential presents the opportunity to include plant functional type-specific parameters to further improve modeled fluxes.