scholarly journals Evaluation of the soil moisture model with data assimilation by the triple collocation method

2020 ◽  
pp. 35-38
Author(s):  
Olena Kozhushko ◽  
Mykhailo Boiko ◽  
Mykola Kovbasa ◽  
Petro Martyniuk ◽  
Olha Stepanchenko ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Collocation Method ◽  
Moisture Transport ◽  
Data Sources ◽  
Climatic Data ◽  
Independent Data ◽  
Method Evaluation ◽  
Ground Station ◽  
Soil Moisture Model ◽  
Nudging Method

This paper deals with a nonlinear soil moisture transport problem, solved with addition of satellite observed soil moisture. The satellite data are assimilated into the model using Newtonian nudging method.  Evaluation is done by the triple collocation method, which involves three independent data sources: model results, ground stations and ERA5 climatic data. The results testify that model results are nearly as accurate as the ground station measurements.

CALIBRATION AND VERIFICATION OF THE SACRAMENTO SOIL MOISTURE MODEL ON THE SAN FRANCISQUITO CREEK WATERSHED

2002 ◽  
Vol 4 (1-2) ◽  
pp. 22
Author(s):  
William B. Mills ◽  
Katherine H. Hancock
Keyword(s):  
Soil Moisture ◽  
Creek Watershed ◽  
Soil Moisture Model ◽  
Calibration And Verification

scholarly journals CAS-LSM Datasets for the CMIP6 Land Surface Snow and Soil Moisture Model Intercomparison Project

2021 ◽  
Author(s):  
Binghao Jia ◽  
Longhuan Wang ◽  
Yan Wang ◽  
Ruichao Li ◽  
Xin Luo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Grid Point ◽  
Global Ocean ◽  
Surface Model ◽  
Past Century ◽  
Model Intercomparison ◽  
Soil Moisture Model ◽  
Surface Snow ◽  
Intercomparison Project

AbstractThe datasets of the five Land-offline Model Intercomparison Project (LMIP) experiments using the Chinese Academy of Sciences Land Surface Model (CAS-LSM) of CAS Flexible Global-Ocean-Atmosphere-Land System Model Grid-point version 3 (CAS FGOALS-g3) are presented in this study. These experiments were forced by five global meteorological forcing datasets, which contributed to the framework of the Land Surface Snow and Soil Moisture Model Intercomparison Project (LS3MIP) of CMIP6. These datasets have been released on the Earth System Grid Federation node. In this paper, the basic descriptions of the CAS-LSM and the five LMIP experiments are shown. The performance of the soil moisture, snow, and land-atmosphere energy fluxes was preliminarily validated using satellite-based observations. Results show that their mean states, spatial patterns, and seasonal variations can be reproduced well by the five LMIP simulations. It suggests that these datasets can be used to investigate the evolutionary mechanisms of the global water and energy cycles during the past century.

Understanding hydrological trends by combining the Budyko hypothesis and a stochastic soil moisture model

2014 ◽  
Vol 60 (1) ◽  
pp. 145-155 ◽  
Author(s):  
Zhentao Cong ◽  
Xiaoying Zhang ◽  
Dan Li ◽  
Hanbo Yang ◽  
Dawen Yang
Keyword(s):  
Soil Moisture ◽  
Soil Moisture Model

scholarly journals Multi-source hydrological soil moisture state estimation using data fusion optimisation

2017 ◽  
Vol 21 (7) ◽  
pp. 3267-3285 ◽  
Author(s):  
Lu Zhuo ◽  
Dawei Han
Keyword(s):  
Soil Moisture ◽  
Data Fusion ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Local Linear Regression ◽  
Data Sources ◽  
Superior Performance ◽  
Flood Prediction ◽  
Time To Build ◽  
Moisture State

Abstract. Reliable estimation of hydrological soil moisture state is of critical importance in operational hydrology to improve the flood prediction and hydrological cycle description. Although there have been a number of soil moisture products, they cannot be directly used in hydrological modelling. This paper attempts for the first time to build a soil moisture product directly applicable to hydrology using multiple data sources retrieved from SAC-SMA (soil moisture), MODIS (land surface temperature), and SMOS (multi-angle brightness temperatures in H–V polarisations). The simple yet effective local linear regression model is applied for the data fusion purpose in the Pontiac catchment. Four schemes according to temporal availabilities of the data sources are developed, which are pre-assessed and best selected by using the well-proven feature selection algorithm gamma test. The hydrological accuracy of the produced soil moisture data is evaluated against the Xinanjiang hydrological model's soil moisture deficit simulation. The result shows that a superior performance is obtained from the scheme with the data inputs from all sources (NSE = 0.912, r = 0.960, RMSE = 0.007 m). Additionally, the final daily-available hydrological soil moisture product significantly increases the Nash–Sutcliffe efficiency by almost 50 % in comparison with the two most popular soil moisture products. The proposed method could be easily applied to other catchments and fields with high confidence. The misconception between the hydrological soil moisture state variable and the real-world soil moisture content, and the potential to build a global routine hydrological soil moisture product are discussed.

scholarly journals U.S. Climate Reference Network Soil Moisture and Temperature Observations

2013 ◽  
Vol 14 (3) ◽  
pp. 977-988 ◽  
Author(s):  
Jesse E. Bell ◽  
Michael A. Palecki ◽  
C. Bruce Baker ◽  
William G. Collins ◽  
Jay H. Lawrimore ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Performance Studies ◽  
Climate Science ◽  
Reference Network ◽  
Climatic Data ◽  
Soil Sensors ◽  
National Climatic Data Center ◽  
Temperature And Precipitation ◽  
And Performance ◽  
Soil Climate

Abstract The U.S. Climate Reference Network (USCRN) is a network of climate-monitoring stations maintained and operated by the National Oceanic and Atmospheric Administration (NOAA) to provide climate-science-quality measurements of air temperature and precipitation. The stations in the network were designed to be extensible to other missions, and the National Integrated Drought Information System program determined that the USCRN could be augmented to provide observations that are more drought relevant. To increase the network’s capability of monitoring soil processes and drought, soil observations were added to USCRN instrumentation. In 2011, the USCRN team completed at each USCRN station in the conterminous United States the installation of triplicate-configuration soil moisture and soil temperature probes at five standards depths (5, 10, 20, 50, and 100 cm) as prescribed by the World Meteorological Organization; in addition, the project included the installation of a relative humidity sensor at each of the stations. Work is also under way to eventually install soil sensors at the expanding USCRN stations in Alaska. USCRN data are stewarded by the NOAA National Climatic Data Center, and instrument engineering and performance studies, installation, and maintenance are performed by the NOAA Atmospheric Turbulence and Diffusion Division. This article provides a technical description of the USCRN soil observations in the context of U.S. soil-climate–measurement efforts and discusses the advantage of the triple-redundancy approach applied by the USCRN.

scholarly journals Spreading of chemical substance after its accidental spillage onto the soil surface under unsaturated conditions and variable porosity

2019 ◽  
pp. 41-44
Author(s):  
Olena Kozhushko ◽  
Petro Martyniuk
Keyword(s):  
Mathematical Model ◽  
Soil Moisture ◽  
Soil Profile ◽  
Moisture Transport ◽  
Soil Surface ◽  
Chemical Substance ◽  
High Concentration ◽  
Solute Transfer ◽  
Variable Porosity ◽  
The One

In this paper we study a mathematical model of soil moisture transport with variable porosity. The problem is set for the case of highly concentrated solute spilled onto soil surface. We investigate the way solute transfer, adsorption of contaminant by soil particles and variable porosity influence infiltration of solute into the soil profile. For that purpose, two models are used: a classical one and the one with consideration of mentioned factors. By comparing the results of both models, we established that high concentration of solute causes moisture transport to transpire more slowly, and the pollutant to remain on the soil surface for longer time. Numerical results indicate that porosity can vary considerably under the conditions of intensive contamination with salts.

scholarly journals Modelling soil moisture for a grassland and a woodland site in south-east England

2002 ◽  
Vol 6 (1) ◽  
pp. 39-48 ◽  
Author(s):  
E. Blyth
Keyword(s):  
Soil Moisture ◽  
Prediction Models ◽  
Meteorological Data ◽  
Field Capacity ◽  
Runoff Generation ◽  
Surface Exchange ◽  
Soil Moisture Model ◽  
Meteorological Observations ◽  
The Uk ◽  
Grassland Site

Abstract. This paper describes a comparison between two soil moisture prediction models. One is MORECS (Met Office Rainfall and Evaporation Calculation Scheme), the Met Office soil moisture model that is used by agriculture, flood modellers and weather forecasters to initialise their models. The other is MOSES (Met Office Surface Exchange Scheme), modified with a runoff generation module. The models are made compatible by increasing the vegetation information available to MOSES. Both models were run with standard parameters and were driven using meteorological observations at Wallingford (1995-1997). Detailed soil moisture measurements were available at a grassland site and a woodland site in this area. The comparison between the models and the observed soil moisture indicated that, for the grassland site, MORECS dried out too quickly in the spring and, for the woodland site, was too wet. Overall, the performance of MOSES was superior. The soil moisture predicted by the new, modified MOSES will be included as a product of Nimrod - the 5 km x 5km gridded network of observed meteorological data across the UK. Keywords: Soil moisture, model, observation, field capacity

scholarly journals A multi-collocation method for coastal zone observations with applications to Sentinel-3A altimeter wave height data

Ocean Science ◽  
2019 ◽  
Vol 15 (2) ◽  
pp. 249-268 ◽  
Author(s):  
Johannes Schulz-Stellenfleth ◽  
Joanna Staneva
Keyword(s):  
Collocation Method ◽  
Wave Height ◽  
Numerical Models ◽  
Coastal Areas ◽  
Data Sources ◽  
Data Sets ◽  
Model Data ◽  
Data Set ◽  
Stochastic Errors

Abstract. In many coastal areas there is an increasing number and variety of observation data available, which are often very heterogeneous in their temporal and spatial sampling characteristics. With the advent of new systems, like the radar altimeter on board the Sentinel-3A satellite, a lot of questions arise concerning the accuracy and added value of different instruments and numerical models. Quantification of errors is a key factor for applications, like data assimilation and forecast improvement. In the past, the triple collocation method to estimate systematic and stochastic errors of measurements and numerical models was successfully applied to different data sets. This method relies on the assumption that three independent data sets provide estimates of the same quantity. In coastal areas with strong gradients even small distances between measurements can lead to larger differences and this assumption can become critical. In this study the triple collocation method is extended in different ways with the specific problems of the coast in mind. In addition to nearest-neighbour approximations considered so far, the presented method allows for use of a large variety of interpolation approaches to take spatial variations in the observed area into account. Observation and numerical model errors can therefore be estimated, even if the distance between the different data sources is too large to assume that they measure the same quantity. If the number of observations is sufficient, the method can also be used to estimate error correlations between certain data source components. As a second novelty, an estimator for the uncertainty in the derived observation errors is derived as a function of the covariance matrices of the input data and the number of available samples. In the first step, the method is assessed using synthetic observations and Monte Carlo simulations. The technique is then applied to a data set of Sentinel-3A altimeter measurements, in situ wave observations, and numerical wave model data with a focus on the North Sea. Stochastic observation errors for the significant wave height, as well as bias and calibration errors, are derived for the model and the altimeter. The analysis indicates a slight overestimation of altimeter wave heights, which become more pronounced at higher sea states. The smallest stochastic errors are found for the in situ measurements. Different observation geometries of in situ data and altimeter tracks are furthermore analysed, considering 1-D and 2-D interpolation approaches. For example, the geometry of an altimeter track passing between two in situ wave instruments is considered with model data being available at the in situ locations. It is shown that for a sufficiently large sample, the errors of all data sources, as well as the error correlations of the model, can be estimated with the new method.

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