Abstract
Abstract. Soil moisture status in land surface models (LSMs) can be updated by assimilating cosmic-ray neutron intensity measured in air above the surface. This requires a fast and accurate model to calculate the neutron intensity from the profiles of soil moisture modeled by the LSM. The existing Monte Carlo N-Particle eXtended (MCNPX) model is sufficiently accurate but too slow to be practical in the context of data assimilation. Consequently an alternative and efficient model is needed which can be calibrated accurately to reproduce the calculations made by MCNPX and used to substitute for MCNPX during data assimilation. This paper describes the construction and calibration of such a model, COsmic-ray Soil Moisture Interaction Code (COSMIC), which is simple, physically based and analytic, and which, because it runs at least 50 000 times faster than MCNPX, is appropriate in data assimilation applications. The model includes simple descriptions of (a) degradation of the incoming high-energy neutron flux with soil depth, (b) creation of fast neutrons at each depth in the soil, and (c) scattering of the resulting fast neutrons before they reach the soil surface, all of which processes may have parameterized dependency on the chemistry and moisture content of the soil. The site-to-site variability in the parameters used in COSMIC is explored for 42 sample sites in the COsmic-ray Soil Moisture Observing System (COSMOS), and the comparative performance of COSMIC relative to MCNPX when applied to represent interactions between cosmic-ray neutrons and moist soil is explored. At an example site in Arizona, fast-neutron counts calculated by COSMIC from the average soil moisture profile given by an independent network of point measurements in the COSMOS probe footprint are similar to the fast-neutron intensity measured by the COSMOS probe. It was demonstrated that, when used within a data assimilation framework to assimilate COSMOS probe counts into the Noah land surface model at the Santa Rita Experimental Range field site, the calibrated COSMIC model provided an effective mechanism for translating model-calculated soil moisture profiles into aboveground fast-neutron count when applied with two radically different approaches used to remove the bias between data and model.
Highlights
Until recently area-average soil moisture at the hectometer horizontal scale has been difficult and costly to measure because of the need to take many point samples, but with the advent of the cosmic-ray method (Zreda et al, 2008, 2012; Desilets et al, 2010) it is feasible with a single instrument
The weighted may depend on the soil chemistry present, our simulations mean absolute error (MAE) is calculated based on the individual differences bewith Monte Carlo N-Particle eXtended (MCNPX) at the 42 COsmic-ray Soil Moisture Observing System (COSMOS) sites considered in this tween the COsmic-ray Soil Moisture Interaction Code (COSMIC) neutron flux and MCNPX neutron flux study suggest that L1 is only weakly related to soil chemistry, for each profile, in absolute terms, and weighted by the probwith site-to-site variability around the mean value for all sites ability density function of soil moisture historically observed being just ∼ 1 %
This study showed that COSMIC, a simple, physically based analytic model, can substitute for the time-consuming MCNPX model in data assimilation applications, and that COSMIC can be calibrated by multi-parameter optimization at 42 COSMOS sites to provide calculated neutron fluxes which are within a few percent of those given by the MCNPX model
Summary
Until recently area-average soil moisture at the hectometer horizontal scale has been difficult and costly to measure because of the need to take many point samples, but with the advent of the cosmic-ray method (Zreda et al, 2008, 2012; Desilets et al, 2010) it is feasible with a single instrument. One potentially important use of area-average soil moisture measured with the cosmic-ray method is through data assimilation methods to update the value of soil moisture states represented in the LSMs which are used to describe surface–atmosphere exchanges in meteorological and hydrological models. Such LSMs calculate (among many other things) time-varying estimates of soil moisture content in discrete layers of soil defined within the vertical soil profile. These parameters are calibrated using multi-parameter optimization techniques against MCNPX calculations for a suite of hypothetical soil moisture profiles
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