Soil Moisture Content: Statistical Estimation of Its Probability Distribution

Faragó, Tibor

doi:10.1175/1520-0450(1985)024<0371:smcseo>2.0.co;2

Cited by 6 publications

(3 citation statements)

References 13 publications

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“… Farago [1985] derived a stochastic model for the estimation of soil moisture distribution based on daily rainfall and an initial value of the soil moisture. However, similar to the API method, the requirement of initial information on soil moisture condition makes Farago's method less generally applicable.…”

Section: Introductionmentioning

confidence: 99%

An analytical method for predicting surface soil moisture from rainfall observations

Pan

Peters‐Lidard

Salé

2003

Water Resources Research

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[1] A simple analytical method for estimating surface soil moisture directly from rainfall data is proposed and studied. Soil moisture dynamics are represented by a linear stochastic partial differential equation [Entekhabi and Rodriguez-Iturbe, 1994]. A diagnostic equation is derived from the soil moisture dynamics equation by eliminating the diffusion term. The derived daily soil moisture function is a time-weighted average of previous cumulative rainfall over a given period (e.g., >14 days). The advantage of this method is that information on the initial condition of soil moisture, which is often not available at all times and locations, is not needed. The loss coefficient in the diagnostic equation for soil moisture can be estimated from land surface characteristics and soil properties. The method for determining the averaging window size, the loss coefficient, and the infiltration coefficient are described and demonstrated. The soil moisture data observed during three field experiments, i.e., Monsoon'90, Washita'92, and SGP'97, are compared to the calculated soil moisture. The results indicate that the proposed method is robust and has the potential for useful soil moisture predictions.

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Section: Introductionmentioning

confidence: 99%

An analytical method for predicting surface soil moisture from rainfall observations

Pan

Peters‐Lidard

Salé

2003

Water Resources Research

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“…Firstly, antecedent soil moisture conditions cannot be required, as these, by definition, would be unavailable if we intend to predict backwards in time. Though precipitation variability is generally accepted to be the primary driver of wetting and drying [12], the antecedent precipitation index (API) approach used to estimate soil moisture [23] or even the stochastic tool designed to estimate soil moisture distributions [14], necessitate an initial condition at the model's location. Even if a soil water balance approach were deployed with the intention of remedying this issue, one would need to generate an initial soil moisture condition at the beginning of the time period historically for which the record is to be extended and then incur cumulative errors for the duration of the extension period [18].…”

Section: Introductionmentioning

confidence: 99%

Extending the soil moisture data record of the U.S. Climate Reference Network (USCRN) and Soil Climate Analysis Network (SCAN)

Coopersmith

Bell

Cosh

2015

Advances in Water Resources

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a b s t r a c tSoil moisture estimates are valuable for hydrologic modeling, drought prediction and management, climate change analysis, and agricultural decision support. However, in situ measurements of soil moisture have only become available within the past few decades with additional sensors being installed each year. Comparing newer in situ resources with older resources, previously required a period of crosscalibration, often requiring several years of data collection. One new technique to improve this issue is to develop a methodology to extend the in situ record backwards in time using a soil moisture model and ancillary available data sets. This study will extend the soil moisture record of the U.S. Climate Reference Network (USCRN) by calibrating a precipitation-driven model during the most recent few years when soil moisture data are available and applying that model backwards temporally in years where precipitation data are available and soil moisture data are not. This approach is validated by applying the technique to the Soil Climate Analysis Network (SCAN) where the same model is calibrated in recent years and validated during preceding years at locations with a sufficiently long soil moisture record. Results suggest that if two or three years of concurrent precipitation and soil moisture time series data are available, the calibrated model's parameters can be applied historically to produce RMSE values less than 0.033 m 3 /m 3 . With this approach, in locations characterized by in situ sensors with short or intermittent data records, a model can now be used to fill the relevant gaps and improve the historical record as well.Published by Elsevier Ltd.

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“…This index is then used to estimate current levels of soil moisture (Saxton and Lenz, 1967) and has been implemented with recession modeling for soil water in agriculture (Choudhury and Blanchard, 1983) and also in weather prediction (Wetzel and Chang, 1988). Other precipitation-focused approaches utilize stochastic models to estimate the distributions of soil moisture values using an initialization of daily rainfall (Farago, 1985). Both the stochastic and API approaches require some initial condition for soil moisture at the forecast location -requiring either professional judgment or a sensor.…”

Section: Introductionmentioning

confidence: 99%

Using similarity of soil texture and hydroclimate to enhance soil moisture estimation

Coopersmith

Minsker

Sivapalan

2014

Hydrol. Earth Syst. Sci.

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Abstract. Estimating soil moisture typically involves calibrating models to sparse networks of in situ sensors, which introduces considerable error in locations where sensors are not available. We address this issue by calibrating parameters of a parsimonious soil moisture model, which requires only antecedent precipitation information, at gauged locations and then extrapolating these values to ungauged locations via a hydroclimatic classification system. Fifteen sites within the Soil Climate Analysis Network (SCAN) containing multiyear time series data for precipitation and soil moisture are used to calibrate the model. By calibrating at 1 of these 15 sites and validating at another, we observe that the best results are obtained where calibration and validation occur within the same hydroclimatic class. Additionally, soil texture data are tested for their importance in improving predictions between calibration and validation sites. Results have the largest errors when calibration-validation pairs differ hydroclimatically and edaphically, improve when one of these two characteristics are aligned, and are strongest when the calibration and validation sites are hydroclimatically and edaphically similar. These findings indicate considerable promise for improving soil moisture estimation in ungauged locations by considering these similarities.

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Soil Moisture Content: Statistical Estimation of Its Probability Distribution

Cited by 6 publications

References 13 publications

An analytical method for predicting surface soil moisture from rainfall observations

An analytical method for predicting surface soil moisture from rainfall observations

Extending the soil moisture data record of the U.S. Climate Reference Network (USCRN) and Soil Climate Analysis Network (SCAN)

Using similarity of soil texture and hydroclimate to enhance soil moisture estimation

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