Different Rates of Soil Drying after Rainfall Are Observed by the SMOS Satellite and the South Fork in situ Soil Moisture Network

Rondinelli, Wesley; Hornbuckle, B. K.; Patton, Jason C.; Cosh, Michael H.; Walker, Victoria A.; Carr, Benjamin; Logsdon, S. D.

doi:10.1175/jhm-d-14-0137.1

Cited by 66 publications

(53 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We expect thicker surface layer dynamics to be dampened when compared to a thinner layer (e.g., Rondinelli et al, 2015). However, changing Noah's layer one soil depth from 0-10 to 0-5 cm only increases soil drying rates by 6 % (Fig.…”

Section: Discussionmentioning

confidence: 88%

See 1 more Smart Citation

Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

Shellito

Small

Livneh

2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Drydown periods that follow precipitation events provide an opportunity to assess controls on soil evaporation on a continental scale. We use SMAP (Soil Moisture Active Passive) observations and Noah simulations from drydown periods to quantify the role of soil moisture, potential evaporation, vegetation cover, and soil texture on soil drying rates. Rates are determined using finite differences over intervals of 1 to 3 days. In the Noah model, the drying rates are a good approximation of direct soil evaporation rates, and our work suggests that SMAP-observed drying is also predominantly affected by direct soil evaporation. Data cover the domain of the North American Land Data Assimilation System Phase 2 and span the first 1.8 years of SMAP's operation.Drying of surface soil moisture observed by SMAP is faster than that simulated by Noah. SMAP drying is fastest when surface soil moisture levels are high, potential evaporation is high, and when vegetation cover is low. Soil texture plays a minor role in SMAP drying rates. Noah simulations show similar responses to soil moisture and potential evaporation, but vegetation has a minimal effect and soil texture has a much larger effect compared to SMAP. When drying rates are normalized by potential evaporation, SMAP observations and Noah simulations both show that increases in vegetation cover lead to decreases in evaporative efficiency from the surface soil. However, the magnitude of this effect simulated by Noah is much weaker than that determined from SMAP observations.

show abstract

Section: Discussionmentioning

confidence: 88%

“…Drying dynamics may be affected by the depth of soil being sensed or modeled (Rondinelli et al, 2015;Shellito et al, 2016b). We use the six supplementary simulations described in Sect.…”

Section: Noah Simulation Depthmentioning

confidence: 99%

Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

Shellito

Small

Livneh

2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…We expect thicker surface layer dynamics to be dampened when compared to a thinner layer (e.g., 25 Rondinelli et al, 2015). However, changing Noah's layer 1 soil depth from 0-10 cm to 0-5 cm only increases soil drying rates by 6 % (Figure 12), implying that the model structure itself prevents Noah from accurately reproducing the surface soil moisture dynamics observed by SMAP.…”

Section: Discussionmentioning

confidence: 96%

“…Drying dynamics may be affected by the depth of soil being sensed or modeled (Rondinelli et al, 2015;Shellito et al, 2016b). We use the six supplementary simulations described in Sect.…”

Section: Noah Simulation Depthmentioning

confidence: 99%

Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

Shellito¹,

Small²

2017

Preprint

View full text Add to dashboard Cite

Abstract. Drydown periods that follow precipitation events provide an opportunity to assess the mechanisms by which soil moisture dissipates from the land surface. We use SMAP (Soil Moisture Active Passive) observations and Noah simulations from drydown periods to quantify the role of soil moisture, potential evaporation, vegetation cover, and soil texture on soil drying rates. Rates are determined using finite differences over intervals of 1 to 3 days. In the Noah model, the drying rates are a good approximation of direct soil evaporation rates. Data cover the domain of the North American Land Data 10 Assimilation System phase 2 and span the first 1.8 years of SMAP's operation.Drying of surface soil moisture observed by SMAP is faster than that simulated by Noah. SMAP drying is fastest when surface soil moisture levels are high, potential evaporation is high, and when vegetation cover is low. Soil texture plays a minor role in SMAP drying rates. Noah simulations show similar responses to soil moisture and potential evaporation, but vegetation has a minimal effect and soil texture has a much larger effect compared to SMAP. When drying rates are 15 normalized by potential evaporation, SMAP observations and Noah simulations both show that increases in vegetation cover lead to decreases in evaporative efficiency from the surface soil. However, the magnitude of this effect simulated by Noah is much weaker than that determined from SMAP observations.

show abstract

“…Burgin et al, 2017;Chan et al, 2016;Das et al, 2014;Kerr et al, 2016;Pathe et al, 2009). Earth observations in the microwave spectrum, which are most often used for the estimation of soil moisture by earth observations (Kornelsen and Coulibaly, 2013;Petropoulos et al, 2015), originate from the soil surface to 0.01-0.05 m depth Kornelsen and Coulibaly, 2013;Nolan and Fatland, 2003;Rondinelli et al, 2015;Ulaby et al, 1996). Measurement depth is controlled by the microwave wavelength, sensor type (active or passive) and moisture conditions Nolan and Fatland, 2003;Rondinelli et al, 2015;Ulaby et al, 1996).…”

Section: Introductionmentioning

confidence: 99%

The Raam regional soil moisture monitoring network in the Netherlands

Benninga

Carranza

Pezij

et al. 2018

Earth Syst. Sci. Data

View full text Add to dashboard Cite

Abstract.We have established a soil moisture profile monitoring network in the Raam region in the Netherlands. This region faces water shortages during summers and excess of water during winters and after extreme precipitation events. Water management can benefit from reliable information on the soil water availability and water storing capacity in the unsaturated zone. In situ measurements provide a direct source of information on which water managers can base their decisions. Moreover, these measurements are commonly used as a reference for the calibration and validation of soil moisture content products derived from earth observations or obtained by model simulations. Distributed over the Raam region, we have equipped 14 agricultural fields and 1 natural grass field with soil moisture and soil temperature monitoring instrumentation, consisting of Decagon 5TM sensors installed at depths of 5, 10, 20, 40 and 80 cm. In total, 12 stations are located within the Raam catchment (catchment area of 223 km 2 ), and 5 of these stations are located within the closed sub-catchment Hooge Raam (catchment area of 41 km 2 ). Soil-specific calibration functions that have been developed for the 5TM sensors under laboratory conditions lead to an accuracy of 0.02 m 3 m −3 . The first set of measurements has been retrieved for the period 5 April 2016-4 April 2017. In this paper, we describe the Raam monitoring network and instrumentation, the soil-specific calibration of the sensors, the first year of measurements, and additional measurements (soil temperature, phreatic groundwater levels and meteorological data) and information (elevation, soil physical characteristics, land cover and a geohydrological model) available for performing scientific research. The data are available at https://doi.org/10.4121/uuid:dc364e97-d44a-403f-82a7-121902deeb56.

show abstract

Different Rates of Soil Drying after Rainfall Are Observed by the SMOS Satellite and the South Fork in situ Soil Moisture Network

Cited by 66 publications

References 42 publications

Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

The Raam regional soil moisture monitoring network in the Netherlands

Contact Info

Product

Resources

About