Assimilation of Cosmogenic Neutron Counts for Improved Soil Moisture Prediction in a Distributed Land Surface Model

Cosmic-Ray Neutron Sensing (CRNS) offers a non-invasive method for estimating soil moisture at the field scale, in our case few tens of hectares. The current study uses Ensemble Adjustment Kalman Filter (EAKF) to assimilate neutron counts observed four locations within 655 km 2 pre-alpine river catchment into Noah-MP land surface model (LSM) improve simulations and optimize parameters. runs with 100 m spatial resolution EU-SoilHydroGrids map along Mualem–van Genuchten water retention functions. Using state estimation (ST) joint state–parameter (STP) technique, states parameters controlling infiltration evaporation rates were optimized, respectively. added value assimilation was evaluated local regional impacts using independent root zone observations. results show that during period both ST STP significantly improved simulated around sensors improvements mean square errors between 60 62% 55–66% STP. could further enhance performance validation locations, mainly by reducing Bias. Nevertheless, due lack convergence calculated shorter evaluation period, phase degraded site away from locations. comparison modeled field-scale patterns dense network CRNS observations showed helped average wetness conditions (reduction Bias –0.038 cm 3 −3 –0.012 ) period. However, only stations limited success enhancing patterns.