Examining the Suitability of a Sparse In Situ Soil Moisture Monitoring Network for Assimilation into a Spatially Distributed Hydrologic Model

Abstract:

The continuous monitoring of soil moisture in a permanent network can yield an interesting data product for use in hydrological data assimilation. Major advantages of in situ observations compared to remote sensing products are the potential vertical extent of the measurements, the finer temporal resolution of the observation time series, the smaller impact of land cover variability on the observation bias, etc. However, two major disadvantages are the typical small integration volume of in situ measurements and the often large spacing between monitoring locations. This causes only a small part of the modelling domain to be directly observed. Furthermore, the spatial configuration of the monitoring network is typically temporally non-dynamic. Therefore two questions can be raised. Do spatially sparse in situ soil moisture observations contain a sufficient data representativeness to successfully assimilate them into the largely unobserved spatial extent of a distributed hydrological model? And if so, how is this assimilation best performed?

Consequently two important factors that can influence the success of assimilating in situ monitored soil moisture are the spatial configuration of the monitoring network and the applied assimilation algorithm. In this research the influence of those factors is examined by means of synthetic data-assimilation experiments. The study area is the ± 100 km² catchment of the Bellebeek in Flanders, Belgium.

The influence of the spatial configuration is examined by varying the amount of locations and their position in the landscape. The latter is performed using several techniques including temporal stability analysis and clustering. Furthermore the observation depth is considered by comparing assimilation of surface layer (5 cm) and deeper layer (50 cm) observations. The impact of the assimilation algorithm is assessed by comparing the performance obtained with two well-known algorithms: Newtonian nudging and the Ensemble Kalman Filter.