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

2012 ◽  
Vol 416-417 ◽  
pp. 98-117 ◽  
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

A new numerical model for simulating top surface soil moisture and runoff

2018 ◽  
Vol 35 (3) ◽  
pp. 1344-1363 ◽  
Author(s):  
Jiongfeng Chen ◽  
Wan-chang Zhang
Keyword(s):  
Soil Moisture ◽  
Stream Flow ◽  
Hydrological Model ◽  
Surface Soil ◽  
Flow Simulation ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Surface Soil Moisture ◽  
Content Type ◽  
Simulation Results

PurposeThis paper aims to construct a simplified distributed hydrological model based on the surveyed watershed soil properties database.Design/methodology/approachThe new established model requires fewer parameters to be adjusted than needed by former hydrological models. However, the achieved stream-flow simulation results are similar and comparable to the classic hydrological models, such as the Xinanjiang model and the TOPMODEL.FindingsGood results show that the discharge and the top surface soil moisture can be simultaneously simulated, and that is the exclusive character of this new model. The stream-flow simulation results from two moderate hydrological watershed models show that the daily stream-flow simulation achieved the classic hydrological results shown in the TOPMODEL and Xinanjiang model. The soil moisture validation results show that the modeled watershed scale surface soil moisture has general agreement with the obtained measurements, with a root-mean-square error (RMSE) value of 0.04 (m3/m3) for one of the one-measurement sites and an averaged RMSE of 0.08 (m3/m3) over all measurements.Originality/valueIn this paper, a new simplified distributed hydrological model was constructed.

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

2014 ◽  
Vol 18 (7) ◽  
pp. 2463-2483 ◽  
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.

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

2014 ◽  
Vol 11 (2) ◽  
pp. 1967-2009 ◽  
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.

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

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.

Data assimilation of SMAP soil moisture into the PCR-GLOBWB hydrological model to improve discharge estimates via A Novel Local Particle Filter

2021 ◽  
Author(s):  
Zhenwu Wang ◽  
Rolf Hut ◽  
Natthachet Tangdamrongsub ◽  
Nick van de Giesen
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Particle Filter ◽  
Particle Filters ◽  
Hydrological Model ◽  
Surface Soil ◽  
Test Theory ◽  
Surface Soil Moisture ◽  
The Impact ◽  
Number Of Particles

<p>Assimilating surface soil moisture data or GRACE data, retrieved from satellite, into hydrological models has been proven to improve the accuracy of hydrological model estimations and predictions. For data assimilation applications in hydrology, the ensemble Kalm filter(EnKF) is the most commonly used data assimilation(DA) method. Particle filters are a type of non-Gaussian filter that doesn’t need the normality assumption that the EnKF needs. Adding localization defeats the curse of dimensionality that is a problem in normal particle filters. In the present study, we investigated our adaption of the local particle filter based on the Gamma test theory(LPF-GT) to improve discharge estimates by assimilating SMAP satellite soil moisture into the PCR-GLOBWB hydrological model. The study area is the Rhine river basin, driven by forcing data from April 2015 to December 2016. The improved discharge estimates are obtained by using DA to adjust the surface soil moisture in the model. The influence of DA to discharge is not direct but works through the dynamics of the hydrological model.  To explore the potential of LPF-GT, serval sensitivity experiments were conducted to figure out the impact of localization scales and the number of particles on DA's performance. The DA estimates were validated against in situ discharge measurements from gauge stations. To demonstrate the benefit of LPF-GT, EnKF was used as a benchmark in this research. Increases in Nash-Sutcliffe (0.05%– 38%) and decreases in normalized RMSE (0.02%–3.4%) validated the capability of LPF-GT. Results showed that localization scales' impact was substantial. The optimal value of the localization scale was obtained by tuning. LPF-GT achieved a satisfactory performance when only using a few particles, even with as little as five particles. The sample errors posed an adverse impact on the open-loop results. Further improvement could be achieved by considering reduce sample errors due to a small number of particles.</p>

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

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
P. Hegedüs ◽  
S. Czigány ◽  
E. Pirkhoffer ◽  
L. Balatonyi ◽  
R. Hickey
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Watershed Scale ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Moisture Sensor ◽  
Soil Moisture Sensor ◽  
Soil Moisture Data ◽  
Data Points ◽  
Monitoring Stations

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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