Improved representation of soil moisture processes through incorporation of cosmic-ray neutron count measurements in a large-scale hydrologic model

Abstract

Abstract. Profound knowledge of soil moisture and its variability plays a crucial role in hydrological modelling to support agricultural management, flood and drought monitoring and forecasting, and groundwater recharge estimation. Cosmic-ray neutron sensing (CRNS) has been recognised as a promising tool for soil moisture monitoring due to its hectare-scale footprint and decimetre-scale measurement depth. But since CRNS provides an integral measurement over several soil horizons, a direct comparison of observed and simulated soil moisture products is not possible. This study establishes a framework to assess the accuracy of soil moisture simulated by the mesoscale Hydrologic Model (mHM) by generating simulated neutron counts and comparing these with observed neutron measurements for the first time. We included three different approaches to estimate CRNS neutron counts in the mHM as a function of the simulated soil moisture profiles: two methods based on the Desilets equation and one based on the forward operator COSMIC (COsmic-ray Soil Moisture Interaction Code). For the Desilets method, we tested two different approaches to average the vertical soil moisture profiles: a uniform vs. a non-uniform weighting scheme depending on the CRNS measurement depth. The methods were tested at two agricultural sites, namely one pasture site and one forest site, in Germany. To explore the prior and posterior distributions of the mHM parameters when constrained by CRNS observations, we used a Monte Carlo method based on Latin hypercube sampling with a large sample size (S = 100 000). We found that all three methods performed well, with a Kling–Gupta efficiency > 0.75 and a percent bias < ± 10 % across the majority of investigated sites and for the best 1 % of parameter sets. The performance of the neutron forward models varied slightly across different land cover types. The non-uniform approach generally showed good performance, particularly at the agricultural sites. On the other hand, the COSMIC method performed slightly better at the forest site. The uniform approach showed slightly better results at the grassland site. We also demonstrated for the first time that the incorporation of CRNS measurements into the mHM could improve both the soil moisture and the evapotranspiration products of the mHM. This suggests that CRNS is capable of improving the model parameter space in general and adds a broader perspective on the potential of CRNS to support large-scale hydrological and land surface models.