Irrigation quantification through backscatter data assimilation with a
buddy check approach
Abstract
Irrigation is an important component of the terrestrial water cycle, but
it is often poorly accounted for in models. Recent studies have
attempted to integrate satellite data and land surface models via data
assimilation (DA) to (1) detect and quantify irrigation, and (2) better
model the related land surface variables such as soil moisture,
vegetation, and evapotranspiration. In this study, different synthetic
DA experiments are tested to advance satellite DA for the estimation of
irrigation. We assimilate synthetic Sentinel-1 backscatter observations
into the Noah-MP model coupled with an irrigation scheme. When updating
soil moisture, we found that the DA sets better initial conditions to
trigger irrigation in the model. However, large DA updates to wetter
conditions can inhibit irrigation simulation. Building on this
limitation, we propose an improved DA algorithm using a buddy check
approach. The method still updates the land surface, but now the
irrigation trigger is not based on the evolution of soil moisture, but
on an adaptive innovation outlier detection. The new method was tested
with different levels of model and observation error. For mild model and
observation errors, the DA outperforms the model-only 14-day irrigation
estimates by about 30% in terms of root-mean-squared differences, when
frequent (daily or every other day) observations are available. The
improvements can surpass 50% for high forcing errors. However, with
longer observation intervals (7 days), the system strongly
underestimates the irrigation amounts. The method is flexible and can be
expanded to other DA systems and to a real world case.