Antecedent Retention Indexes Predict Soil Moisture

Saxton, K. E.; Lenz, Arno T.

doi:10.1061/jyceaj.0001647

Cited by 36 publications

(4 citation statements)

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“…The parameters (K) in the PHI model were calibrated using an objective function to maximize the coefficient of determination between the observation and prediction values of SWS. Table 1 presents the calibrated Ks for SWS at soil depths of 20, 45, and 85 cm, which were 0.83, 0.85, and 0.87 for hillslope B and 0.93, 0.94, and 0.95 for hillslope S. The higher Ks for hillslope S indicates a slower flux for hillslope S. These values correspond to known parameter ranges (between 0.8 and 0.98) reported in other studies [19]. The contribution to evaporation is different between surficial and deeper layers.…”

Section: Relationship Between Sws and Phisupporting

confidence: 72%

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Identification of the Representative Point for Soil Moisture Storage Using a Precipitation History Model

Kim,

Lee

2023

Water

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Soil water storage is an essential variable in hydrological processes at the hillslope scale. This study proposed models for predicting soil water based on the precipitation history. According to the mathematical analysis of soil water storage on the hillslope scale, hydrological fluxes can be effectively expressed in terms of the weighted time series of precipitation and evapotranspiration. Moreover, the impact of evapotranspiration on soil water storage was incorporated into the model structure as an autoregressive process. A new soil water prediction model was developed through the integration of the soil moisture stochastic process into the structure of a precipitation-based model for the hillslope scale. Intensive soil moisture and rainfall data collected over two years were then used to test the performance of the developed models for two different hillslopes. The proposed model exhibited a better ability to find representative points for soil water storage than either existing precipitation-based models or the temporal stability method.

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Section: Relationship Between Sws and Phisupporting

confidence: 72%

“…The soil moisture data collected between 2009 and 2011 for hillslope B and between 2015 and 2016 for hillslope S were used for the analysis, which indicated a very high rate (≈100%) of successful data acquisition. Rainfall, evapotranspiration, and temperature were measured at the flux tower [19].…”

Section: Acquiring Hydrologic Data On the Hillslopementioning

confidence: 99%

Identification of the Representative Point for Soil Moisture Storage Using a Precipitation History Model

Kim,

Lee

2023

Water

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“…Daily accumulated precipitation was calculated based on the hourly precipitation extracted from the images (NetCDF files). API was calculated based on the daily rainfall for the analysis of rainfall effects on soil moisture spatial patterns (Saxton et al, 1967). In this index, the influence of a prior daily precipitation total decreases exponentially as the number of subsequent days increases.…”

Section: Land Surface Characteristics and Atmospheric Datamentioning

confidence: 99%

“…In this index, the influence of a prior daily precipitation total decreases exponentially as the number of subsequent days increases. The API value for each transect pixel on each transect date was calculated using precipitation data for the prior 90 d (Saxton et al, 1967) following the method of Crow and Zhan (2007), which accounts for the seasonality of atmospheric demand.…”

Section: Land Surface Characteristics and Atmospheric Datamentioning

confidence: 99%

Soil Texture Often Exerts a Stronger Influence Than Precipitation on Mesoscale Soil Moisture Patterns

Dong

Ochsner

2018

Water Resources Research

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Soil moisture patterns are commonly thought to be dominated by land surface characteristics, such as soil texture, at small scales and by atmospheric processes, such as precipitation, at larger scales. However, a growing body of evidence challenges this conceptual model. We investigated the structural similarity and spatial correlations between mesoscale (∼1–100 km) soil moisture patterns and land surface and atmospheric factors along a 150 km transect using 4 km multisensor precipitation data and a cosmic‐ray neutron rover, with a 400 m diameter footprint. The rover was used to measure soil moisture along the transect 18 times over 13 months. Spatial structures of soil moisture, soil texture (sand content), and antecedent precipitation index (API) were characterized using autocorrelation functions and fitted with exponential models. Relative importance of land surface characteristics and atmospheric processes were compared using correlation coefficients (r) between soil moisture and sand content or API. The correlation lengths of soil moisture, sand content, and API ranged from 12–32 km, 13–20 km, and 14–45 km, respectively. Soil moisture was more strongly correlated with sand content (r = −0.536 to −0.704) than with API for all but one date. Thus, land surface characteristics exhibit coherent spatial patterns at scales up to 20 km, and those patterns often exert a stronger influence than do precipitation patterns on mesoscale spatial patterns of soil moisture.

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Estimation of Surface Moisture Availability From Remote Temperature Measurements

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Antecedent Retention Indexes Predict Soil Moisture

Cited by 36 publications

References 0 publications

Identification of the Representative Point for Soil Moisture Storage Using a Precipitation History Model

Identification of the Representative Point for Soil Moisture Storage Using a Precipitation History Model

Soil Texture Often Exerts a Stronger Influence Than Precipitation on Mesoscale Soil Moisture Patterns

Estimation of Surface Moisture Availability From Remote Temperature Measurements

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