Abstract
Cosmic-Ray Neutron Sensing (CRNS) offers a non-invasive method for estimating soil moisture at the field scale, in our case a few tens of hectares. The current study uses the Ensemble Adjustment Kalman Filter (EAKF) to assimilate neutron counts observed at four locations within a 655 km2 pre-alpine river catchment into the Noah-MP land surface model (LSM) to improve soil moisture simulations and to optimize model parameters. The model runs with 100 m spatial resolution and uses the EU-SoilHydroGrids soil map along with the Mualem–van Genuchten soil water retention functions. Using the state estimation (ST) and joint state–parameter estimation (STP) technique, soil moisture states and model parameters controlling infiltration and evaporation rates were optimized, respectively. The added value of assimilation was evaluated for local and regional impacts using independent root zone soil moisture observations. The results show that during the assimilation period both ST and STP significantly improved the simulated soil moisture around the neutron sensors locations with improvements of the root mean square errors between 60 and 62% for ST and 55–66% for STP. STP could further enhance the model performance for the validation period at assimilation locations, mainly by reducing the Bias. Nevertheless, due to a lack of convergence of calculated parameters and a shorter evaluation period, performance during the validation phase degraded at a site further away from the assimilation locations. The comparison of modeled soil moisture with field-scale spatial patterns of a dense network of CRNS observations showed that STP helped to improve the average wetness conditions (reduction of spatial Bias from –0.038 cm3 cm−3 to –0.012 cm3 cm−3) for the validation period. However, the assimilation of neutron counts from only four stations showed limited success in enhancing the field-scale soil moisture patterns.
Highlights
The amount of water present in the root zone of the soil is an essential climate variable and a common link between the carbon, water, and energy cycles
To address these research gaps, the key focus of the present study is to evaluate the potential of sparsely distributed Cosmic-Ray Neutron Sensing (CRNS) data to improve the soil moisture states and dynamics of a mesoscale land surface model
The cosmicray neutron probes (CRNPs) were placed with overlapping footprints to facilitate spatial maps of field scale Soil water content (SWC) dynamics for the headwater catchment area (Fersch et al, 2020a)
Summary
The amount of water present in the root zone of the soil is an essential climate variable and a common link between the carbon, water, and energy cycles. Soil water content (SWC) influences multiple processes like runoff generation, evapotranspiration, sensible and latent heat fluxes, groundwater recharge, plant water stress and vegetation development (Mishra et al, 2014; McColl et al, 2017) It acts as a regulator of the hydrological cycle and the global radiation budget (Small and Kurc, 2003; Brocca et al, 2017). The difference between the thermal microwave emissions of dry soil (dielectric constant of ∼3.5) and that of water (dielectric constant of ∼80) is significant and can be detected with a high signal to noise ratio within 1–5 GHz of sensing frequency This variation in the brightness temperature is used for the estimation of SWC at the land surface. Most of the land surface, crop and hydrology models operate at finer spatial resolution (100 m–1 km) and they rely on root zone soil moisture information to efficiently evaluate water and energy balances
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