scholarly journals Using satellite observations of precipitation and soil moisture to constrain the water budget of a land surface model

2017 ◽  
Author(s):  
Ewan Pinnington ◽  
Tristan Quaife ◽  
Emily Black
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Mean Squared Error ◽  
Land Surface Model ◽  
Remotely Sensed ◽  
Agricultural Drought ◽  
Sub Saharan Africa ◽  
Surface Model ◽  
Root Mean Squared Error ◽  
Squared Error

Abstract. Early warning of agricultural drought can enable decision makers to act to improve food security. Land-surface models are useful tools to inform such monitoring systems, but model errors are problematic. We show that satellite-derived estimates of shallow soil moisture can be used to calibrate a land-surface model at the regional scale in Ghana, using data assimilation techniques. The modified calibration significantly improves model estimation of soil moisture. Specifically, we find a 44 % reduction in root-mean-squared error for a 5-year hindcast after assimilating a single year of soil moisture observations to update model parameters. The use of an improved remotely-sensed rainfall dataset contributes to 10 % of this reduction in error. Improved rainfall data has the greatest impact on model estimates during the seasonal wetting-up of soil, with the assimilation of remotely sensed soil moisture having greatest impact during drying down. The significant reduction in root-mean-squared error we find after assimilating a single year of observations bodes well for the production of improved soil moisture forecasts over sub-Saharan Africa where subsistence farming remains prevalent.

scholarly journals Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data

2021 ◽  
Vol 25 (3) ◽  
pp. 1617-1641
Author(s):  
Ewan Pinnington ◽  
Javier Amezcua ◽  
Elizabeth Cooper ◽  
Simon Dadson ◽  
Rich Ellis ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Mean Squared Error ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Pedotransfer Functions ◽  
Surface Model ◽  
Squared Error ◽  
Surface Models

Abstract. Pedotransfer functions are used to relate gridded databases of soil texture information to the soil hydraulic and thermal parameters of land surface models. The parameters within these pedotransfer functions are uncertain and calibrated through analyses of point soil samples. How these calibrations relate to the soil parameters at the spatial scale of modern land surface models is unclear because gridded databases of soil texture represent an area average. We present a novel approach for calibrating such pedotransfer functions to improve land surface model soil moisture prediction by using observations from the Soil Moisture Active Passive (SMAP) satellite mission within a data assimilation framework. Unlike traditional calibration procedures, data assimilation always takes into account the relative uncertainties given to both model and observed estimates to find a maximum likelihood estimate. After performing the calibration procedure, we find improved estimates of soil moisture and heat flux for the Joint UK Land Environment Simulator (JULES) land surface model (run at a 1 km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK) and three flux tower sites. The spatial resolution of the COSMOS probes is much more representative of the 1 km model grid than traditional point-based soil moisture sensors. For 11 cosmic-ray neutron soil moisture probes located across the modelled domain, we find an average 22 % reduction in root mean squared error, a 16 % reduction in unbiased root mean squared error and a 16 % increase in correlation after using data assimilation techniques to retrieve new pedotransfer function parameters.

scholarly journals Improving Soil Moisture Prediction of a High–Resolution Land Surface Model by Parameterising Pedotransfer Functions through Assimilation of SMAP Satellite Data

2020 ◽  
Author(s):  
Ewan Pinnington ◽  
Javier Amezcua ◽  
Elizabeth Cooper ◽  
Simon Dadson ◽  
Rich Ellis ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Mean Squared Error ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Pedotransfer Functions ◽  
Surface Model ◽  
Squared Error ◽  
Surface Models

Abstract. Pedotransfer functions are used to relate gridded databases of soil texture information to the soil hydraulic and thermal parameters of land surface models. The parameters within these pedotransfer functions are uncertain and calibrated through analyses of point soil samples. How these calibrations relate to the soil parameters at the spatial scale of modern land surface models is unclear, because gridded databases of soil texture represent an area average. We present a novel approach for calibrating such pedotransfer functions to improve land surface model soil moisture prediction by using observations from the Soil Moisture Active Passive (SMAP) satellite mission within a data assimilation framework. Unlike traditional calibration procedures data assimilation always takes into account the relative uncertainties given to both model and observed estimates to find a maximum likelihood estimate. After performing the calibration procedure we find improved estimates of soil moisture for the JULES land surface model (run at a 1 km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK). The spatial resolution of these COSMOS probes is much more representative of the 1 km model grid than traditional point based soil moisture sensors. For 11 cosmic-ray neutron soil moisture probes located across the modelled domain we find an average 22 % reduction in root-mean squared error, a 16 % reduction in unbiased root-mean squared error and a 16 % increase in correlation after using data assimilation techniques to retrieve new pedotransfer function parameters.

scholarly journals Impact of remotely sensed soil moisture and precipitation on soil moisture prediction in a data assimilation system with the JULES land surface model

2018 ◽  
Vol 22 (4) ◽  
pp. 2575-2588 ◽  
Author(s):  
Ewan Pinnington ◽  
Tristan Quaife ◽  
Emily Black
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Land Surface Model ◽  
Remotely Sensed ◽  
Sub Saharan Africa ◽  
Surface Model ◽  
Model Parameters ◽  
The South ◽  
The North

Abstract. We show that satellite-derived estimates of shallow soil moisture can be used to calibrate a land surface model at the regional scale in Ghana, using data assimilation techniques. The modified calibration significantly improves model estimation of soil moisture. Specifically, we find an 18 % reduction in unbiased root-mean-squared differences in the north of Ghana and a 21 % reduction in the south of Ghana for a 5-year hindcast after assimilating a single year of soil moisture observations to update model parameters. The use of an improved remotely sensed rainfall dataset contributes to 6 % of this reduction in deviation for northern Ghana and 10 % for southern Ghana. Improved rainfall data have the greatest impact on model estimates during the seasonal wetting-up of soil, with the assimilation of remotely sensed soil moisture having greatest impact during drying-down. In the north of Ghana we are able to recover improved estimates of soil texture after data assimilation. However, we are unable to do so for the south. The significant reduction in unbiased root-mean-squared difference we find after assimilating a single year of observations bodes well for the production of improved land surface model soil moisture estimates over sub-Saharan Africa.

scholarly journals A comparison between remotely-sensed and modelled surface soil moisture (and frozen status) at high latitudes

2013 ◽  
Vol 10 (8) ◽  
pp. 11241-11291 ◽  
Author(s):  
I. Gouttevin ◽  
A. Bartsch ◽  
G. Krinner ◽  
V. Naeimi
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Land Surface Model ◽  
Remotely Sensed ◽  
Hydrological Modelling ◽  
Northern Eurasia ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
North Eastern

Abstract. In this study, the combined surface status and surface soil moisture products retrieved by the ASCAT sensor within the ESA-DUE Permafrost project are compared to the hydrological outputs of the land surface model ORCHIDEE over Northern Eurasia. The objective is to derive broad conclusions as to the strengths and weaknesses of hydrological modelling and, to a minor extent, remote sensing of soil moisture over an area where data is rare and hydrological modelling is though crucial for climate and ecological applications. The spatial and temporal resolutions of the ASCAT products make them suitable for comparison with model outputs. Modelled and remotely-sensed surface frozen and unfrozen statuses agree reasonably well, which allows for a seasonal comparison between modelled and observed (liquid) surface soil moisture. The atmospheric forcing and the snow scheme of the land surface model are identified as causes of moderate model-to-data divergence in terms of surface status. For unfrozen soils, the modelled and remotely-sensed surface soil moisture signals are positively correlated over most of the study area. The correlation deteriorates in the North-Eastern Siberian regions, which is consistent with the lack of accurate model parameters and the scarcity of meteorological data. The model shows a reduced ability to capture the main seasonal dynamics and spatial patterns of observed surface soil moisture in Northern Eurasia, namely a characteristic spring surface moistening resulting from snow melt and flooding. We hypothesize that these weak performances mainly originate from the non-representation of flooding and surface ponding in the model. Further identified limitations proceed from the coarse treatment of the hydrological specificities of mountainous areas and spatial inaccuracies in the meteorological forcing in remote, North-Eastern Siberian areas. Investigations are currently underway to determine to which extent plausible inaccuracies in the satellite data could also contribute to the diagnosed model-to-data discrepancies.

scholarly journals Using Cosmic-Ray Neutron Probes in Validating Satellite Soil Moisture Products and Land Surface Models

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1362 ◽  
Author(s):  
Mustafa Berk Duygu ◽  
Zuhal Akyürek
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Surface Model ◽  
High Temporal Resolution ◽  
Land Surface Models ◽  
Statistical Measures ◽  
Surface Models ◽  
Global Land

Soil moisture content is one of the most important parameters of hydrological studies. Cosmic-ray neutron sensing is a promising proximal soil moisture sensing technique at intermediate scale and high temporal resolution. In this study, we validate satellite soil moisture products for the period of March 2015 and December 2018 by using several existing Cosmic Ray Neutron Probe (CRNP) stations of the COSMOS database and a CRNP station that was installed in the south part of Turkey in October 2016. Soil moisture values, which were inferred from the CRNP station in Turkey, are also validated using a time domain reflectometer (TDR) installed at the same location and soil water content values obtained from a land surface model (Noah LSM) at various depths (0.1 m, 0.3 m, 0.6 m and 1.0 m). The CRNP has a very good correlation with TDR where both measurements show consistent changes in soil moisture due to storm events. Satellite soil moisture products obtained from the Soil Moisture and Ocean Salinity (SMOS), the METOP-A/B Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer 2 (AMSR2), Climate Change Initiative (CCI) and a global land surface model Global Land Data Assimilation System (GLDAS) are compared with the soil moisture values obtained from CRNP stations. Coefficient of determination ( r 2 ) and unbiased root mean square error (ubRMSE) are used as the statistical measures. Triple Collocation (TC) was also performed by considering soil moisture values obtained from different soil moisture products and the CRNPs. The validation results are mainly influenced by the location of the sensor and the soil moisture retrieval algorithm of satellite products. The SMAP surface product produces the highest correlations and lowest errors especially in semi-arid areas whereas the ASCAT product provides better results in vegetated areas. Both global and local land surface models’ outputs are highly compatible with the CRNP soil moisture values.

scholarly journals Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model

2016 ◽  
Vol 20 (12) ◽  
pp. 4895-4911 ◽  
Author(s):  
Gabriëlle J. M. De Lannoy ◽  
Rolf H. Reichle
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Incidence Angle ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Errors ◽  
Surface Soil Moisture ◽  
Brightness Temperatures

Abstract. Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40° incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.

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