A simple model to predict soil moisture: Bridging Event and Continuous Hydrological (BEACH) modelling

Sheikh, Vahedberdi; Visser, Saskia; Stroosnijder, L.

doi:10.1016/j.envsoft.2008.10.005

Cited by 93 publications

(64 citation statements)

References 52 publications

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“…If the soil moisture content is lower, then the actual evapotranspiration is lower than its potential rate. This reflects the two evapotranspirative stages: an energy limiting stage (K r = 1) and a soil moisture limiting stage (K r < 1) (Sheikh et al, 2009, and references therein).…”

Section: Hydrological Model Structure and Inputsmentioning

confidence: 99%

“…A spatially distributed soil moisture model, inspired by the hydrological BEACH model of Sheikh et al (2009), was used here to estimate the soil moisture contents of the topsoil. This particular model was selected because the soil moisture model needed to (1) run on a limited amount of readily available input data, (2) explicitly account for soil texture, (3) be spatially distributed, and (4) provide soil moisture estimates on a daily basis.…”

Section: Hydrological Model Structure and Inputsmentioning

confidence: 99%

“…The basic processes incorporated in the model are precipitation, infiltration, transpiration and evaporation. The soil water balance at location i is calculated as: Runoff was estimated by a bucket model assuming that infiltration (I ) proceeds until the infiltration capacity of the topsoil has been reached (Sheikh et al, 2009):…”

Section: Hydrological Model Structure and Inputsmentioning

confidence: 99%

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Accounting for seasonality in a soil moisture change detection algorithm for ASAR Wide Swath time series

doninck

Peters

Lievens

et al. 2012

Hydrol. Earth Syst. Sci.

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Abstract.A change detection algorithm is applied on a three year time series of ASAR Wide Swath images in VV polarization over Calabria, Italy, in order to derive information on temporal soil moisture dynamics. The algorithm, adapted from an algorithm originally developed for ERS scatterometer, was validated using a simple hydrological model incorporating meteorological and pedological data. Strong positive correlations between modelled soil moisture and ASAR soil moisture were observed over arable land, while the correlation became much weaker over more vegetated areas. In a second phase, an attempt was made to incorporate seasonality in the different model parameters. It was observed that seasonally changing surface properties mainly affected the multitemporal incidence angle normalization. When applying a seasonal angular normalization, correlation coefficients between modelled soil moisture and retrieved soil moisture increased overall. Attempts to account for seasonality in the other model parameters did not result in an improved performance.

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Section: Hydrological Model Structure and Inputsmentioning

confidence: 99%

Section: Hydrological Model Structure and Inputsmentioning

confidence: 99%

Section: Hydrological Model Structure and Inputsmentioning

confidence: 99%

See 1 more Smart Citation

Accounting for seasonality in a soil moisture change detection algorithm for ASAR Wide Swath time series

doninck

Peters

Lievens

et al. 2012

Hydrol. Earth Syst. Sci.

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“…The statistical characteristics of soil moisture fields and their variability were analyzed relating to the heterogeneity of precipitation, soil properties, vegetation, and topography (Kim and Barros, 2002;Rodriguez-Iturbe et al, 1995). Soil moisture prediction models were proposed using physical approaches (Sheikha et al, 2009) and statistical approaches (Laio, 2006). Time series analysis is an active research issue in machine learning and data engineering (Van Gestel et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

Soil moisture prediction using a support vector regression

Lee¹,

Kim²,

Lee³

2013

Journal of the Korean Data and Information Science Society

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Soil moisture is a very important variable in various area of hydrological processes. We predict the soil moisture using a support vector regression. The model is trained and tested using the soil moisture data observed in five sites in the Yongdam dam basin. With respect to soil moisture data of of four sites-Jucheon, Bugui, Sangieon and Ahncheon which are used to train the model, the correlation coefficient between the esimtates and the observed values is about 0.976. As the result of the application to Cheoncheon2 for validating the model, the correlation coefficient between the estimates and the observed values of soil moisture is about 0.835. We compare those results with those of artificial neural network models.

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“…Specific monitoring programs (Merz and Bardossy, 1998;Aubert et al, 2003;Castillo et al, 2003;Brocca et al, 2009a;Matgen et al, 2012;Morbidelli et al, 2012) and modelling studies (Gautam et al, 2000;Anctil et al, 2004;Komma et al, 2008;Berthet et al, 2009;Sheikh et al, 2009;Brocca et al, 2012;Tramblay et al, 2012;Van Steenbergen and Willems, 2013) were carried out to investigate the influence of soil moisture on producing runoff hydrographs.…”

Section: Introductionmentioning

confidence: 99%

Coupling soil moisture and precipitation observations for predicting hourly runoff at small catchment scale

Tayfur

Zucco

Brocca

et al. 2014

Journal of Hydrology

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The importance of soil moisture is recognized in rainfall-runoff processes. This study quantitatively investigates the use of soil moisture measured at 10, 20, and 40cm soil depths along with rainfall in predicting runoff. For this purpose, two small sub-catchments of Tiber River Basin, in Italy, were instrumented during periods of October 2002-March 2003 and January-April 2004. Colorso Basin is about 13km2 and Niccone basin 137km2. Rainfall plus soil moisture at 10, 20, and 40cm formed the input vector while the discharge was the target output in the model of generalized regression neural network (GRNN). The model for each basin was calibrated and tested using October 2002-March 2003 data. The calibrated and tested GRNN was then employed to predict runoff for each basin for the period of January-April 2004. The model performance was found to be satisfactory with determination coefficient, R2, equal to 0.87 and Nash-Sutcliffe efficiency, NS, equal to 0.86 in the validation phase for both catchments. The investigation of effects of soil moisture on runoff prediction revealed that the addition of soil moisture data, along with rainfall, tremendously improves the performance of the model. The sensitivity analysis indicated that the use of soil moisture data at different depths allows to preserve the memory of the system thus having a similar effect of employing the past values of rainfall, but with improved GRNN performance.CNR-IRPI Office in Perugia, Ital

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A simple model to predict soil moisture: Bridging Event and Continuous Hydrological (BEACH) modelling

Cited by 93 publications

References 52 publications

Accounting for seasonality in a soil moisture change detection algorithm for ASAR Wide Swath time series

Accounting for seasonality in a soil moisture change detection algorithm for ASAR Wide Swath time series

Soil moisture prediction using a support vector regression

Coupling soil moisture and precipitation observations for predicting hourly runoff at small catchment scale

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