Analysis of spatial variability of near-surface soil moisture to increase rainfall-runoff modelling accuracy in SW Hungary

AbstractBetween September 5, 2008 and September 5, 2009, near-surface soil moisture time series were collected in the northern part of a 1.7 km2 watershed in SWHungary at 14 monitoring locations using a portable TDR-300 soil moisture sensor. The objectives of this study are to increase the accuracy of soil moisture measurement at watershed scale, to improve flood forecasting accuracy, and to optimize soil moisture sensor density.According to our results, in 10 of 13 cases, a strong correlation exists between the measured soil moisture data of Station 5 and all other monitoring stations; Station 5 is considered representative for the entire watershed. Logically, the selection of the location of the representative measurement point(s) is essential for obtaining representative and accurate soil moisture values for the given watershed. This could be done by (i) employing monitoring stations of higher number at the exploratory phase of the monitoring, (ii) mapping soil physical properties at watershed scale, and (iii) running cross-relational statistical analyses on the obtained data.Our findings indicate that increasing the number of soil moisture data points available for interpolation increases the accuracy of watershed-scale soil moisture estimation. The data set used for interpolation (and estimation of mean antecedent soil moisture values) could be improved (thus, having a higher number of data points) by selecting points of similar properties to the measurement points from the DEM and soil databases. By using a higher number of data points for interpolation, both interpolation accuracy and spatial resolution have increased for the measured soil moisture values for the Pósa Valley.

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Implementation of surface soil moisture data assimilation with watershed scale distributed hydrological model

Journal of Hydrology ◽

10.1016/j.jhydrol.2011.11.039 ◽

2012 ◽

Vol 416-417 ◽

pp. 98-117 ◽

Cited By ~ 63

Author(s):

Eunjin Han ◽

Venkatesh Merwade ◽

Gary C. Heathman

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Hydrological Model ◽

Surface Soil ◽

Watershed Scale ◽

Distributed Hydrological Model ◽

Surface Soil Moisture ◽

Soil Moisture Data

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Evaluation of near-surface soil moisture data from an AAFC monitoring network in Manitoba, Canada: Implications for L-band satellite validation

Journal of Hydrology ◽

10.1016/j.jhydrol.2014.10.024 ◽

2015 ◽

Vol 521 ◽

pp. 582-592 ◽

Cited By ~ 26

Author(s):

Justin R. Adams ◽

Heather McNairn ◽

Aaron A. Berg ◽

Catherine Champagne

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Monitoring Network ◽

Surface Soil Moisture ◽

Near Surface ◽

Soil Moisture Data ◽

Satellite Validation ◽

L Band

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Characterizing coarse-resolution watershed soil moisture heterogeneity using fine-scale simulations

Hydrology and Earth System Sciences ◽

10.5194/hess-18-2463-2014 ◽

2014 ◽

Vol 18 (7) ◽

pp. 2463-2483 ◽

Cited By ~ 32

Author(s):

W. J. Riley ◽

C. Shen

Keyword(s):

Soil Moisture ◽

Hydrological Model ◽

Surface Soil ◽

Reduced Order Models ◽

Watershed Scale ◽

Surface Soil Moisture ◽

Near Surface ◽

Reduced Order ◽

Coarse Resolution ◽

Soil Moisture Heterogeneity

Abstract. Watershed-scale hydrological and biogeochemical models are usually discretized at resolutions coarser than where significant heterogeneities in topography, abiotic factors (e.g., soil properties), and biotic (e.g., vegetation) factors exist. Here we report on a method to use fine-scale (220 m grid cells) hydrological model predictions to build reduced-order models of the statistical properties of near-surface soil moisture at coarse resolution (25 times coarser, ~7 km). We applied a watershed-scale hydrological model (PAWS-CLM4) that has been previously tested in several watersheds. Using these simulations, we developed simple, relatively accurate (R2 ~0.7–0.8), reduced-order models for the relationship between mean and higher-order moments of near-surface soil moisture during the nonfrozen periods over five years. When applied to transient predictions, soil moisture variance and skewness were relatively accurately predicted (R2 0.7–0.8), while the kurtosis was less accurately predicted (R2 ~0.5). We also tested 16 system attributes hypothesized to explain the negative relationship between soil moisture mean and variance toward the wetter end of the distribution and found that, in the model, 59% of the variance of this relationship can be explained by the elevation gradient convolved with mean evapotranspiration. We did not find significant relationships between the time rate of change of soil moisture variance and covariances between mean moisture and evapotranspiration, drainage, or soil properties, as has been reported in other modeling studies. As seen in previous observational studies, the predicted soil moisture skewness was predominantly positive and negative in drier and wetter regions, respectively. In individual coarse-resolution grid cells, the transition between positive and negative skewness occurred at a mean soil moisture of ~0.25–0.3. The type of numerical modeling experiments presented here can improve understanding of the causes of soil moisture heterogeneity across scales, and inform the types of observations required to more accurately represent what is often unresolved spatial heterogeneity in regional and global hydrological models.

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Characterizing coarse-resolution watershed soil moisture heterogeneity using fine-scale simulations

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-11-1967-2014 ◽

2014 ◽

Vol 11 (2) ◽

pp. 1967-2009 ◽

Cited By ~ 4

Author(s):

W. J. Riley ◽

C. Shen

Keyword(s):

Soil Moisture ◽

Hydrological Model ◽

Surface Soil ◽

Reduced Order Models ◽

Watershed Scale ◽

Surface Soil Moisture ◽

Near Surface ◽

Reduced Order ◽

Coarse Resolution ◽

Soil Moisture Heterogeneity

Abstract. Watershed-scale hydrological and biogeochemical models are usually discretized at resolutions coarser than where significant heterogeneities in topographic, subsurface abiotic and biotic, and surface vegetation exist. Here we report on a method to use fine-resolution (220 m gridcells) hydrological model predictions to build reduced order models of the statistical properties of near-surface soil moisture at coarse-resolution (25 times coarser; ~7 km). We applied a watershed-scale hydrological model (PAWS+CLM) that has been previously tested in several watersheds and developed simple, relatively accurate (R2 ~ 0.7–0.8) reduced order models for the relationship between mean and higher-order moments of near-surface soil moisture during the non-frozen periods over five years. When applied to transient predictions, soil moisture variance and skewness were relatively accurately predicted (R2 ~ 0.7–0.8), while the kurtosis was less accurately predicted (R2 ~ 0.5). We tested sixteen system attributes hypothesized to explain the negative relationship between soil moisture mean and variance toward the wetter end of the distribution and found that, in the model, 59% of the variance of this relationship can be explained by the elevation gradient convolved with mean evapotranspiration. We did not find significant relationships between the time rate of change of soil moisture variance and covariances between mean moisture and evapotranspiration, drainage, or soil properties, as has been reported in other modeling studies. As seen in previous observational studies, the predicted soil moisture skewness was predominantly positive and negative in drier and wetter regions, respectively. In individual coarse-resolution gridcells, the transition between positive and negative skewness occurred at a mean soil moisture of ~0.25–0.3. The type of numerical modeling experiments presented here can improve understanding of the causes of soil moisture heterogeneity across scales, and inform the types of observations required to more accurately represent unresolved spatial heterogeneity in regional and global hydrological models.

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Calibrating an evapotranspiration model using radiometric surface temperature, vegetation cover fraction and near-surface soil moisture data

Agricultural and Forest Meteorology ◽

10.1016/j.agrformet.2018.02.033 ◽

2018 ◽

Vol 256-257 ◽

pp. 104-115 ◽

Cited By ~ 18

Author(s):

Bouchra Ait Hssaine ◽

Olivier Merlin ◽

Zoubair Rafi ◽

Jamal Ezzahar ◽

Lionel Jarlan ◽

...

Keyword(s):

Soil Moisture ◽

Surface Temperature ◽

Vegetation Cover ◽

Surface Soil ◽

Surface Soil Moisture ◽

Near Surface ◽

Soil Moisture Data

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Improving Near-Surface Short-Range Weather Forecasts Using Strongly Coupled Land–Atmosphere Data Assimilation with GSI-EnKF

Monthly Weather Review ◽

10.1175/mwr-d-19-0370.1 ◽

2020 ◽

Vol 148 (7) ◽

pp. 2863-2888

Author(s):

Liao-Fan Lin ◽

Zhaoxia Pu

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Surface Soil ◽

Surface Soil Moisture ◽

Near Surface ◽

Strongly Coupled ◽

Soil Moisture Data ◽

Control State

Abstract Strongly coupled land–atmosphere data assimilation has not yet been implemented into operational numerical weather prediction (NWP) systems. Up to now, upper-air measurements have been assimilated mainly in atmospheric analyses, while land and near-surface data have been assimilated mainly into land surface models. Thus, this study aims to explore the benefits of assimilating atmospheric and land surface observations within the framework of strongly coupled data assimilation. Specifically, we added soil moisture as a control state within the ensemble Kalman filter (EnKF)-based Gridpoint Statistical Interpolation (GSI) and conducted a series of numerical experiments through the assimilation of 2-m temperature/humidity and in situ surface soil moisture data along with conventional atmospheric measurements such as radiosondes into the Weather Research and Forecasting (WRF) Model with the Noah land surface model. The verification against in situ measurements and analyses show that compared to the assimilation of conventional data, adding soil moisture as a control state and assimilating 2-m humidity can bring additional benefits to analyses and forecasts. The impact of assimilating 2-m temperature (surface soil moisture) data is positive mainly on the temperature (soil moisture) analyses but on average marginal for other variables. On average, below 750 hPa, verification against the NCEP analysis indicates that the respective RMSE reduction in the forecasts of temperature and humidity is 5% and 2% for assimilating conventional data; 10% and 5% for including soil moisture as a control state; and 16% and 11% for simultaneously adding soil moisture as a control state and assimilating 2-m humidity data.

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