How well does Noah-MP simulate the regional mean and spatial variability of topsoil water content in two agricultural landscapes in southwest Germany?

Publikations-Art

Zeitschriftenbeitrag (peer-reviewed)

Autoren

Poltoradnev, M., Ingwersen, J., Imukova, K., Högy, P., Wizemann, H.-D., Streck, T.

Erscheinungsjahr

2018

Veröffentlicht in

Journal of Hydrometeorology

Band/Volume

19/

DOI

10.1175/JHM-D-17-0169.1

Seite (von - bis)

555-573

The spatial variability of topsoil water content (SWC) is often expressed through the relationship between its spatial mean 〈θ〉 and standard deviation σ_θ. The present study tests the concept that a reasonably performing land surface model (LSM) should be able to produce σ_θ–〈θ〉 data pairs that fall into a polygon, spanned by the cloud of observed data and two anchor points: σ_θ at the permanent wilting point σ_θ–〈θ_wp〉 and σ_θ at saturation σ_θ–〈θ_s〉. A state-of-the-art LSM, Noah-MP, was driven by atmospheric forcing data obtained from eddy covariance field measurements in two regions of southwestern Germany, Kraichgau (KR) and Swabian Alb (SA). KR is characterized with deep loess soils, whereas the soils in SA are shallow, clayey, and stony. The simulations series were compared with SWC data from soil moisture networks operating in the two study regions. The results demonstrate that Noah-MP matches temporal 〈θ〉 dynamics fairly well in KR, but performs poorly in SA. The best match is achieved with the van Genuchten–Mualem representation of soil hydraulic functions and site-specific rainfall, soil texture, green vegetation fraction (GVF) and leaf area index (LAI) input data. Nevertheless, most of the simulated σ_θ–〈θ〉 pairs are located outside the envelope of measurements and below the lower bound, which shows that the model smooths spatial SWC variability. This can be mainly attributed to missing topography and terrain information and inadequate representation of spatial variability of soil texture and hydraulic parameters, as well as the model assumption of a uniform root distribution.