Global characterization of surface soil moisture drydowns

McColl, Kaighin A.; Wang, Wei; Peng, Bin; Akbar, Ruzbeh; Gianotti, Daniel J. Short; Lu, Hui; Pan, Ming; Entekhabi, Dara

doi:10.1002/2017gl072819

Cited by 119 publications

(168 citation statements)

References 65 publications

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“…We automate this selection process for all pixels according to the following logic: (1) the event precipitation depth must surpass 5 mm in a 24 hour period; (2) the dry period must begin after the event precipitation stops and end a day before 3 mm or more additional precipitation 5 accumulates; and (3) the dry period must be at least 3 days long (Figure 2). We note this differs from McColl et al (2017a) that based the identification of dry down periods on the soil moisture time series alone, and did not use any precipitation data. …”

Section: Drydown Periodsmentioning

confidence: 85%

“…Our study builds upon McColl et al (2017a) in two ways. First, we examine the effects of dynamic controls on soil drying, rather than static covariates.…”

mentioning

confidence: 99%

“…Soil drying rates determined from satellite-based observations can provide an estimate of surface evaporation rates on a larger scale (McColl et al, 2017a). This requires that the depth supplying evaporation is sampled by the sensor and that vertical redistribution within the soil is negligible.…”

mentioning

confidence: 99%

“…Thus, fluxes in and out of this surface soil layer control the drying rate determined from SMAP soil moisture time series (McColl et al, 2017a).…”

mentioning

confidence: 99%

“…Only recently has satellite remote sensing of soil moisture advanced sufficiently to make it possible to monitor drying rates at large scales (Entekhabi et al, 2010;Kerr, 2006), thus allowing scientists to evaluate the physical controls on soil drying across a wider range of 15 conditions. McColl et al (2017a) studied global soil drying dynamics by fitting SMAP surface soil moisture observations to an exponential decay function. They found shorter drying timescales in areas that have higher aridity indexes and higher soil sand content, although the effects of soil texture were relatively minor.…”

mentioning

confidence: 99%

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

Shellito¹,

Small²

2017

Preprint

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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.

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Section: Drydown Periodsmentioning

confidence: 85%

“…Our study builds upon McColl et al (2017a) in two ways. First, we examine the effects of dynamic controls on soil drying, rather than static covariates.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

“…Thus, fluxes in and out of this surface soil layer control the drying rate determined from SMAP soil moisture time series (McColl et al, 2017a).…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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

Shellito¹,

Small²

2017

Preprint

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Irrigation Signals Detected From SMAP Soil Moisture Retrievals

Lawston

Santanello

Kumar

2017

Geophysical Research Letters

130

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Irrigation can influence weather and climate, but the magnitude, timing, and spatial extent of irrigation are poorly represented in models, as are the resulting impacts of irrigation on the coupled land‐atmosphere system. One way to improve irrigation representation in models is to assimilate soil moisture observations that reflect an irrigation signal to improve model states. Satellite remote sensing is a promising avenue for obtaining these needed observations on a routine basis, but to date, irrigation detection in passive microwave satellites has proven difficult. In this study, results show that the new enhanced soil moisture product from the Soil Moisture Active Passive satellite is able to capture irrigation signals over three semiarid regions in the western United States. This marks an advancement in Earth‐observing satellite skill and the ability to monitor human impacts on the water cycle.

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Hydrological Storage Length Scales Represented by Remote Sensing Estimates of Soil Moisture and Precipitation

Akbar

Gianotti

McColl

et al. 2018

Water Resources Research

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The soil water content profile is often well correlated with the soil moisture state near the surface. They share mutual information such that analysis of surface‐only soil moisture is, at times and in conjunction with precipitation information, reflective of deeper soil fluxes and dynamics. This study examines the characteristic length scale, or effective depth Δz, of a simple active hydrological control volume. The volume is described only by precipitation inputs and soil water dynamics evident in surface‐only soil moisture observations. To proceed, first an observation‐based technique is presented to estimate the soil moisture loss function based on analysis of soil moisture dry‐downs and its successive negative increments. Then, the length scale Δz is obtained via an optimization process wherein the root‐mean‐squared (RMS) differences between surface soil moisture observations and its predictions based on water balance are minimized. The process is entirely observation‐driven. The surface soil moisture estimates are obtained from the NASA Soil Moisture Active Passive (SMAP) mission and precipitation from the gauge‐corrected Climate Prediction Center daily global precipitation product. The length scale Δz exhibits a clear east‐west gradient across the contiguous United States (CONUS), such that large Δz depths (>200 mm) are estimated in wetter regions with larger mean precipitation. The median Δz across CONUS is 135 mm. The spatial variance of Δz is predominantly explained and influenced by precipitation characteristics. Soil properties, especially texture in the form of sand fraction, as well as the mean soil moisture state have a lesser influence on the length scale.

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Global characterization of surface soil moisture drydowns

Cited by 119 publications

References 65 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

Irrigation Signals Detected From SMAP Soil Moisture Retrievals

Hydrological Storage Length Scales Represented by Remote Sensing Estimates of Soil Moisture and Precipitation

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