Abstract
Abstract. At very high resolution scale (i.e. grid cells of 1 km2), land surface model parameters can be calibrated with eddy-covariance flux data and point-scale soil moisture data. However, measurement scales of eddy-covariance and point-scale data differ substantially. In our study, we investigated the impact of reducing the scale mismatch between surface energy flux and soil moisture observations by replacing point-scale soil moisture data with observations derived from Cosmic-Ray Neutron Sensors (CRNSs) made at larger spatial scales. Five soil and evapotranspiration parameters of the Joint UK Land Environment Simulator (JULES) were calibrated against point-scale and Cosmic-Ray Neutron Sensor soil moisture data separately. We calibrated the model for 12 sites in the USA representing a range of climatic, soil, and vegetation conditions. The improvement in latent heat flux estimation for the two calibration solutions was assessed by comparison to eddy-covariance flux data and to JULES simulations with default parameter values. Calibrations against the two soil moisture products alone did show an advantage for the cosmic-ray technique. However, further analyses of two-objective calibrations with soil moisture and latent heat flux showed no substantial differences between both calibration strategies. This was mainly caused by the limited effect of calibrating soil parameters on soil moisture dynamics and surface energy fluxes. Other factors that played a role were limited spatial variability in surface fluxes implied by soil moisture spatio-temporal stability, and data quality issues.
Highlights
The land surface water and energy balances are coupled through the process of evapotranspiration
The Cosmic-Ray Neutron Sensors (CRNSs) data appear noisier than the PS data, which is an effect of inherent randomness in neutron radiation reaching the CRNS sensor element (Zreda et al, 2012)
We investigated whether the spatial-scale mismatch between the surface energy flux data and soil moisture data could be reduced through the use of Cosmic-Ray Neutron Sensors (CRNSs)
Summary
The land surface water and energy balances are coupled through the process of evapotranspiration. Soil moisture provides a first-order (i.e. direct) control on evapotranspiration when there is insufficient water to meet the evaporative demand (Manabe, 1969; Budyko, 1956; Seneviratne et al, 2010). An indirect effect of soil moisture on surface energy flux partitioning is for instance the damping effect on soil and air temperature, which in its turn affects humidity, evapotranspiration, boundary-layer stability, and in some cases precipitation (Seneviratne et al, 2010). The control of soil moisture on temperature at seasonal scales is especially strong in transitional climate regions (Koster et al, 2004). Land surface models (LSMs) solve the surface mass (including water), energy, and momentum balances to provide the weather and climate prediction models with lower boundary conditions. Because the soil moisture state and surface fluxes are so closely connected, it is important to accurately simulate these simultaneously (Henderson-Sellers et al, 1996; Richter et al, 2004; Seneviratne et al, 2010; Dirmeyer, 2011; Dirmeyer et al, 2013)
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