Influence of data spatial resolution in water resources management for oak-savanna distinctive vegetation patches

Abstract

In Mediterranean oak-savannas (known as dehesas in Spain), it is important to better understand the interactions between vegetation structure and local climate regulation at scales relevant to farm management and the dominant hydrological regime. This study evaluated the water use patterns of dehesa vegetation patches (open grasslands, lowland grasslands with high soil moisture, tree + grass, and riparian vegetation), estimating actual evapotranspiration (ET). We used different models, previously validated in the area, that integrate remotely sensed data. They apply (a) a soil water balance (Kc-FAO56), (b) a surface energy balance (ALEXI/DisALEXI, and SEBS), and (c) a sharpening algorithm (STARFM), obtaining products at multiple spatial resolutions (30 m, 1 km, 5 km). The conceptual and operational differences between the methodologies reinforce the idea of a combined application of models. We demonstrated the need for high spatial and temporal resolution for on-farm livestock management due to the importance of the grasslands layer. This scale is crucial to determine the grass's emergence/drying cycle, which is key for livestock feeding planning. In humid/denser areas that provide essential ecosystem services (e.g., refuge, pasture rotation), transpiration rates are higher throughout the year and were underestimated when coarser spatial scale data was used. Over the typical system (grass with dispersed trees), the ET maps at low spatial resolution reflected the water use trends, and all models correlated well. Higher differences were found when comparing the models' performance over open grasslands.