Upscaling of field-scale soil moisture measurements using distributed land surface modeling

Abstract This paper introduces a new method to improve land surface model skill by merging different available precipitation datasets, given that an accurate land surface parameter ground truth is available. Precipitation datasets are merged with the objective of improving terrestrial water and energy cycle simulation skill, unlike most common methods in which the merging skills are evaluated by comparing the results with gauge data or selected reference data. The optimal merging method developed in this study minimizes the simulated land surface parameter (soil moisture, temperature, etc.) errors using the Noah land surface model with the Nelder–Mead (downhill simplex) method. In addition to improving the simulation skills, this method also impedes the adverse impacts of single-source precipitation data errors. Analysis has indicated that the results from the optimally merged precipitation product have fewer errors in other land surface states and fluxes such as evapotranspiration (ET), discharge R, and skin temperature T than do simulation results obtained by forcing the model using the precipitation products individually. It is also found that, using this method, the true knowledge of soil moisture information minimized land surface modeling errors better than the knowledge of other land surface parameters (ET, R, and T). Results have also shown that, although it does not have the true precipitation information, the method has associated heavier weights with the precipitation product that has intensity, amount, and frequency that are similar to those of the true precipitation.

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A Novel Method for Quantifying Value in Spaceborne Soil Moisture Retrievals

Journal of Hydrometeorology ◽

10.1175/jhm553.1 ◽

2007 ◽

Vol 8 (1) ◽

pp. 56-67 ◽

Cited By ~ 44

Author(s):

Wade T. Crow

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Remotely Sensed ◽

Rain Gauge ◽

Surface Modeling ◽

Added Value ◽

Balance Model ◽

Land Surface Modeling ◽

Antecedent Precipitation ◽

Retrieval Strategies

Abstract A novel methodology is introduced for quantifying the added value of remotely sensed soil moisture products for global land surface modeling applications. The approach is based on the assimilation of soil moisture retrievals into a simple surface water balance model driven by satellite-based precipitation products. Filter increments (i.e., discrete additions or subtractions of water suggested by the filter) are then compared to antecedent precipitation errors determined using higher-quality rain gauge observations. A synthetic twin experiment demonstrates that the correlation coefficient between antecedent precipitation errors and filter increments provides an effective proxy for the accuracy of the soil moisture retrievals themselves. Given the inherent difficulty of directly validating remotely sensed soil moisture products using ground-based observations, this assimilation-based proxy provides a valuable tool for efforts to improve soil moisture retrieval strategies and quantify the novel information content of remotely sensed soil moisture retrievals for land surface modeling applications. Using real spaceborne data, the approach is demonstrated for four different remotely sensed soil moisture datasets along two separate transects in the southern United States. Results suggest that the relative superiority of various retrieval strategies varies geographically.

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The Importance of Subsurface Processes in Land Surface Modeling over a Temperate Region: An Analysis with SMAP, Cosmic Ray Neutron Sensing and Triple Collocation Analysis

Remote Sensing ◽

10.3390/rs13163068 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3068

Author(s):

Haojin Zhao ◽

Carsten Montzka ◽

Roland Baatz ◽

Harry Vereecken ◽

Harrie-Jan Hendricks Franssen

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Water Movement ◽

Land Surface Model ◽

Cosmic Ray ◽

Surface Modeling ◽

Temperate Climate ◽

Surface Model ◽

Model Simulations ◽

Land Surface Modeling

Land surface models (LSMs) simulate water and energy cycles at the atmosphere–soil interface, however, the physical processes in the subsurface are typically oversimplified and lateral water movement is neglected. Here, a cross-evaluation of land surface model results (with and without lateral flow processes), the National Aeronautics and Space Administration (NASA) Soil Moisture Active/Passive (SMAP) mission soil moisture product, and cosmic-ray neutron sensor (CRNS) measurements is carried out over a temperate climate region with cropland and forests over western Germany. Besides a traditional land surface model (the Community Land Model (CLM) version 3.5), a coupled land surface-subsurface model (CLM-ParFlow) is applied. Compared to CLM stand-alone simulations, the coupled CLM-ParFlow model considered both vertical and lateral water movement. In addition to standard validation metrics, a triple collocation (TC) analysis has been performed to help understanding the random error variances of different soil moisture datasets. In this study, it is found that the three soil moisture datasets are consistent. The coupled and uncoupled model simulations were evaluated at CRNS sites and the coupled model simulations showed less bias than the CLM-standalone model (−0.02 cm3 cm−3 vs. 0.07 cm3 cm−3), similar random errors, but a slightly smaller correlation with the measurements (0.67 vs. 0.71). The TC-analysis showed that CLM-ParFlow reproduced better soil moisture dynamics than CLM stand alone and with a higher signal-to-noise ratio. This suggests that the representation of subsurface physics is of major importance in land surface modeling and that coupled land surface-subsurface modeling is of high interest.

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