Orals

Ensuring long-term water sustainability for increasing human populations is a common goal for water resource managers. Measuring evapotranspiration (ET) at watershed or river-reach scales, upland or urban areas is required to estimate how much water can be apportioned for human needs while maintaining healthy vegetation and habitat for wildlife.
Consequently, much research has been devoted to this topic. However although there have been many advances in meteorological equipment and observations, more universal recognition of the impact of climate and land cover changes on evaporation and hydrology, and the increased accessibility of many parts of the world, evaporation from much of the globe remains elusive to quantify. This is particularly true in areas with few meteorological observations, in regions where precipitation is particularly hard to predict such as in arid and semi-arid or mountain environments. ET measurements are often made on local scales, but scaling up has been problematic due to spatial and temporal variability.
There are challenges associated with handling temporal variability over complex agro-climatic regions and in places with strong effects of unpredictable climate oscillations. For instance, crop/plant coefficients vary seasonally, particularly for riparian, upland vegetation, and urban greenery; traditional approaches of ET estimation commonly neglect the heterogeneity of microclimate, density, species, and phenology that have often led to gross overestimates of plant water use.
In this session, we want to focus on quantifying evapotranspiration dynamics in diverse climates and environments as a tool for improving hydrologic assessments and predictions at a catchment scale. Remote sensing products in many cases are the only spatially distributed information available to account for seasonal climate and vegetation variability and are thus extremely valuable data sources for ET estimation on larger scales.
We invite researchers to contribute theoretical and empirical ET model applications for a variety of dryland vegetation associations and other sensitive environments. We welcome studies that estimate ET using both prognostic and diagnostic approaches from process-based models that rely on the integration of precipitation and soil-vegetation dynamics to a more direct estimation of ET using e.g. remote sensing based data streams. Applications in drought-prone forests, rangelands, mountain and urban areas at a range of spatial and temporal scales are encouraged.