Project description
Low flow prediction is important for water management to support ecosystem functioning and biodiversity preservation in dry season and during drought events. Low flow comes from shallow groundwater storage, which can be reflected in land surface temperature (LST) via groundwater dependent vegetation. We will test the idea based on statistical analysis of remotely sensed LST and streamflow time series, and develop algorithms for low flow prediction with a one-season lead time.
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