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

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.

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

10.5194/hess-2020-303 ◽

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.

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Improving soil moisture predictions from a land surface model by optimisation of pedotransfer functions.

10.5194/ismc2021-81 ◽

2021 ◽

Author(s):

Elizabeth Cooper ◽

Eleanor Blyth ◽

Hollie Cooper ◽

Richard Ellis ◽

Ewan Pinnington ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Cosmic Ray ◽

Pedotransfer Functions ◽

Small Scale ◽

Surface Model ◽

Land Surface Models ◽

Field Scale ◽

Surface Models

Accurate soil moisture predictions from land surface models are important in hydrological, ecological and agricultural applications. Despite increasing availability of wide area soil moisture measurements, few studies have combined soil moisture predictions from models with in-situ observations beyond the point scale. This work uses the LAVENDAR data assimilation framework to markedly improve soil moisture estimates from the JULES land surface model using field scale Cosmic Ray Neutron sensor observations from the UKCEH COSMOS-UK network. Rather than directly updating modelled soil moisture estimates towards measured values, we optimize constants in the underlying pedotransfer functions (PTF) which relate soil texture to soil hydraulics parameters. In this way we generate a single set of newly calibrated PTFs based on field scale observations from a number of UK sites with different soil types. We demonstrate that calibrating PTFs in this way can improve the performance of JULES. Further, we suggest that calibrating PTFs for the soils on which they are to be used and at the scales at which land surface models are applied (rather than on small-scale soil samples) will ultimately improve the performance of land surface models, potentially leading to improvements in flood, drought and climate projections.

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

Water ◽

10.3390/w11071362 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1362 ◽

Cited By ~ 2

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.

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Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

10.5194/hess-2020-359 ◽

2020 ◽

Author(s):

Elizabeth Cooper ◽

Eleanor Blyth ◽

Hollie Cooper ◽

Rich Ellis ◽

Ewan Pinnington ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Large Scale ◽

Pedotransfer Functions ◽

Surface Model ◽

Climate Projections ◽

Land Surface Models ◽

Surface Models

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological and meteorological applications. In recent years the availability of wide-area soil-moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in-situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the JULES land surface model using field scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

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Improving estimates of UK soil moisture using the JULES land surface model with COSMOS-UK measurements in the LaVEnDAR data assimilation framework

10.5194/egusphere-egu2020-11566 ◽

2020 ◽

Author(s):

Elizabeth Cooper ◽

Ewan Pinnington ◽

Richard Ellis ◽

Eleanor Blyth ◽

Simon Dadson ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Soil Texture ◽

Land Surface ◽

Land Surface Model ◽

Cosmic Ray ◽

Surface Model ◽

Soil Physics ◽

Soil Moisture Sensors ◽

The Uk

Soil moisture predictions are increasingly important in hydrological, ecological and agricultural applications. In recent years the availability of wide-area assessments of current and future soil-moisture states has grown, yet few studies have combined model-based assessments with observations beyond the point scale. Here we use the JULES land surface model together with COSMOS-UK data to evaluate the extent to which data assimilation can improve predictions of soil moisture across the United Kingdom.COSMOS-UK is a network of soil moisture sensors run by UKCEH. The network provides soil moisture measurements at around 50 sites throughout the UK using innovative Cosmic Ray Neutron Sensors (CRNS). Half hourly measurements of the meteorological variables that the Joint UK Land Environment Simulator (JULES) requires as driving data are also recorded at COSMOS-UK sites, allowing us to run JULES at observation locations. This provides a unique opportunity to compare soil moisture outputs from JULES with CRNS observations; these measurements have a footprint of up to 12 ha (approx 30 acres) and are therefore better scale matched with JULES outputs than those from point sensors.We have used the Land Variational Ensemble Data Assimilation Framework (LaVEnDAR) to combine soil moisture estimates from JULES with daily CRNS observations from one year at a number of COSMOS-UK sites. We show that this results in improved soil moisture predictions from JULES over several years. This has been achieved by optimising parameters in the pedo-transfer function used to derive JULES soil physics parameters from soil texture information. Using data assimilation with LaVEnDAR in this way allows us to explore the relationships between soil moisture estimates, soil physics parameters and soil texture, as well as improving the agreement between JULES model outputs and observations.

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Using data assimilation to optimize pedotransfer functions using field-scale in situ soil moisture observations

Hydrology and Earth System Sciences ◽

10.5194/hess-25-2445-2021 ◽

2021 ◽

Vol 25 (5) ◽

pp. 2445-2458

Author(s):

Elizabeth Cooper ◽

Eleanor Blyth ◽

Hollie Cooper ◽

Rich Ellis ◽

Ewan Pinnington ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Land Surface Model ◽

Pedotransfer Functions ◽

Surface Model ◽

Climate Projections ◽

Soil Physics ◽

Field Scale

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological, and meteorological applications. In recent years, the availability of wide-area soil moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the Joint UK Land Environment Simulator (JULES) land surface model using field-scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way, we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way improves the soil moisture predictions of a land surface model at 16 UK sites, leading to the potential for better flood, drought, and climate projections.

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Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction

Hydrology and Earth System Sciences ◽

10.5194/hess-21-2509-2017 ◽

2017 ◽

Vol 21 (5) ◽

pp. 2509-2530 ◽

Cited By ~ 13

Author(s):

Roland Baatz ◽

Harrie-Jan Hendricks Franssen ◽

Xujun Han ◽

Tim Hoar ◽

Heye Reemt Bogena ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Land Surface Model ◽

Cosmic Ray ◽

Surface Model ◽

Hydraulic Parameters ◽

Soil Map ◽

Soil Hydraulic Parameters ◽

Soil Hydraulic

Abstract. In situ soil moisture sensors provide highly accurate but very local soil moisture measurements, while remotely sensed soil moisture is strongly affected by vegetation and surface roughness. In contrast, cosmic-ray neutron sensors (CRNSs) allow highly accurate soil moisture estimation on the field scale which could be valuable to improve land surface model predictions. In this study, the potential of a network of CRNSs installed in the 2354 km2 Rur catchment (Germany) for estimating soil hydraulic parameters and improving soil moisture states was tested. Data measured by the CRNSs were assimilated with the local ensemble transform Kalman filter in the Community Land Model version 4.5. Data of four, eight and nine CRNSs were assimilated for the years 2011 and 2012 (with and without soil hydraulic parameter estimation), followed by a verification year 2013 without data assimilation. This was done using (i) a regional high-resolution soil map, (ii) the FAO soil map and (iii) an erroneous, biased soil map as input information for the simulations. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the FAO soil map and the biased soil map, soil moisture predictions improved strongly to a root mean square error of 0.03 cm3 cm−3 for the assimilation period and 0.05 cm3 cm−3 for the evaluation period. Improvements were limited by the measurement error of CRNSs (0.03 cm3 cm−3). The positive results obtained with data assimilation of nine CRNSs were confirmed by the jackknife experiments with four and eight CRNSs used for assimilation. The results demonstrate that assimilated data of a CRNS network can improve the characterization of soil moisture content on the catchment scale by updating spatially distributed soil hydraulic parameters of a land surface model.

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

10.5194/hess-2017-705 ◽

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.

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SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

10.20944/preprints201904.0216.v1 ◽

2019 ◽

Author(s):

Nemesio Rodriguez-Fernandez ◽

Patricia de Rosnay ◽

Clement Albergel ◽

Philippe Richaume ◽

Filipe Aires ◽

...

Keyword(s):

Neural Network ◽

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Positive Impact ◽

Land Surface Model ◽

Surface Model ◽

Northern Asia ◽

The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.

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Evaluating a land surface model at a water-limited site: implications for land surface contributions to droughts and heatwaves

10.5194/egusphere-egu21-4083 ◽

2021 ◽

Author(s):

Mengyuan Mu ◽

Martin De Kauwe ◽

Anna Ukkola ◽

Andy Pitman ◽

Teresa Gimeno ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Heat Fluxes ◽

Surface Model ◽

Strong Impact ◽

Water Fluxes ◽

Land Surface Models ◽

Groundwater Aquifer ◽

Surface Models

Land surface models underpin coupled climate model projections of droughts and heatwaves. However, the lack of simultaneous observations of individual components of evapotranspiration, concurrent with root-zone soil moisture, has limited previous model evaluations. Here, we use a comprehensive set of observations from a water-limited site in southeastern Australia including both evapotranspiration and soil moisture to a depth of 4.5 m to evaluate the Community Atmosphere-Biosphere Land Exchange (CABLE) land surface model. We demonstrate that alternative process representations within CABLE had the capacity to improve simulated evapotranspiration, but not necessarily soil moisture dynamics - highlighting problems of model evaluations against water fluxes alone. Our best simulation was achieved by resolving a soil evaporation bias; a more realistic initialisation of the groundwater aquifer state; higher vertical soil resolution informed by observed soil properties; and further calibrating soil hydraulic conductivity. Despite these improvements, the role of the empirical soil moisture stress function in influencing the simulated water fluxes remained important: using a site calibrated function reduced the soil water stress on plants by 36 % during drought and 23 % at other times. These changes in CABLE not only improve the seasonal cycle of evapotranspiration, but also affect the latent and sensible heat fluxes during droughts and heatwaves. The range of parameterisations tested led to differences of ~150 W m-2 in the simulated latent heat flux during a heatwave, implying a strong impact of parameterisations on the capacity for evaporative cooling and feedbacks to the boundary layer (when coupled). Overall, our results highlight the opportunity to advance the capability of land surface models to capture water cycle processes, particularly during meteorological extremes, when sufficient observations of both evapotranspiration fluxes and soil moisture profiles are available.

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