Part 2 of 2 special seminar series
| Thu Sep 9, 10:00-11:35 | Livestream on Zoom |
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Abstract
To support future-oriented decision makings in water and agricultural sectors, access to accurate and up-to-date information on the current state of the global water system is critical. In water- limited ecosystems, soil moisture provides the main connection between climate and vegetation dynamics in space and time. The spatial pattern of soil moisture can vary significantly due to the heterogeneous spatial distribution of rainfall and variability in soil properties, land cover type and topography. Due to this large spatial variability, the utility of ground-based, point-scale measurements is limited. Soil moisture estimates from land surface models are adversely affected by the uncertainties of atmospheric forcing, model dynamics and model parameterization. Remotely sensed data can provide spatially and temporally varying constraints on the modelling of biophysical landscape variables that are often superior to that achieved by a single static set of model parameters. Data assimilation merges models and observations in a way that take advantage of their respective strengths (e.g., uncertainty, coverage), resulting in improved accuracy, coverage, and ultimately forecasting capability. The development of the space-based, hyper resolution soil moisture products would enhance timely decision making and forward planning by farmers, fire agencies and other land and water managers.