Abstract
Considerable advancement in spatiotemporal resolution of remote sensing and ground-based measurements has enabled refinement of parameters used in land surface models for simulating surface water fluxes. However, land surface modeling capabilities are still inadequate for accurate representation of subsurface properties and processes, which continue to limit the accuracy of land surface model simulation. Our objective in this study was to examine the performance of the variously parameterized Noah land surface model with multiphysics option (Noah-MP) in simulating evapotranspiration (ET) and soil moisture dynamics in stony soils using verification from eddy covariance ET and in situ soil moisture data during the growing season of year 2015, obtained from the Lower Sheep subcatchment within the Reynolds Creek Experimental Watershed in southwestern Idaho. We evaluated the performance of Noah-MP considering four different scenarios with 1) a one-layer soil profile with Noah-MP default soil hydraulic parameters and three more five-layer soil profiles using 2) Noah-MP default soil hydraulic parameters; 3) soil hydraulic parameters derived from a pedotransfer function using field observations; and 4) hydraulic parameters from scenario 3, which also accounted for stone content in each layer. Each modeling experiment was forced with the same set of initial conditions, atmospheric input, and vegetation parameters. Our results indicate that enhanced representation of soil profile properties and stone content information noticeably improve the Noah-MP land surface model simulation of soil moisture content and evapotranspiration.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1889-1904 |
| Number of pages | 16 |
| Journal | Journal of Hydrometeorology |
| Volume | 21 |
| Issue number | 8 |
| DOIs | |
| State | Published - Aug 2020 |
Bibliographical note
Funding Information:Acknowledgments. This research was supported by the iUTAH project funded through an NSF EPSCoR Grant EPS 1208732 awarded to Utah State University, as part of the State of Utah Research Infrastructure Improvement Award. This research was also supported by the Utah Agricultural Experiment Station (Seed Grant, Project UTA01189), Utah State University, and approved as journal paper number 9188. The Reynolds Creek Critical Zone Observatory is supported by the National Science Foundation under Award NSF EAR 1331872. Any opinions, findings, and conclusions or recommendations expressed are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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