Assimilation of SMAP-enhanced and SMAP/Sentinel-1A/B soil moisture data into land surface models

2020 ◽  
Author(s):  
Hyunglok Kim ◽  
Venkataraman Lakshmi ◽  
Sujay Kumar ◽  
Yonghwan Kwon
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Regional Scale ◽  
Land Surface Model ◽  
The United States ◽  
Heat Fluxes ◽  
Surface Model ◽  
Data Sets ◽  
Surface Models ◽  
Band Radiometer

<p>Prediction of water-related natural disasters such as droughts, floods, wildfires, landslides, and dust outbreaks on a regional-scale can benefit from the high-spatial-resolution soil moisture (SM) data of both satellite and modeled products. The reason is that the amount of surface SM controls in the partitioning of outgoing energy fluxes into latent and sensible heat fluxes.</p><p>Recently, NASA’s SMAP mission has been implemented, in order to provide 3-km and 1-km SM data from a combination of SMAP and Sentinel-1A/B observations along with 9- and 36-km SM data retrieved from an L-band radiometer brightness temperature (TB). The 3-km and 1-km SM products were produced by combining the Sentinel-1A/B C-band radar backscatter and SMAP radiometer TB observations.</p><p>In the present study, we assimilated SMAP-enhanced (9-km) and SMAP/Sentinel-1A/B SM (3-km and 1-km) products into a land surface model (LSM): SMAP-enhanced and SMAP/Sentinel-1A/B SM data were assimilated into Noah-MP3.6 LSM. Then, these products were evaluated against ground observations in the United States. Three DA products’ error characteristics were intercompared: (1) SMAP-enhanced 9-km DA, (2) SMAP/Sentinel-1A/B 3-km DA and (3) SMAP/Sentinel-1A/B 1-km DA.</p><p>When SMAP and SMAP/Sentinel SM data sets were assimilated into LSM, the R- and ubRMSE values for 9-, 3-, and 1-km SM data were greatly improved.</p>

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

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

<p>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<sup>-2</sup> 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.</p>

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

2021 ◽  
Vol 25 (1) ◽  
pp. 447-471
Author(s):  
Mengyuan Mu ◽  
Martin G. De Kauwe ◽  
Anna M. Ukkola ◽  
Andy J. Pitman ◽  
Teresa E. 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

Abstract. 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, using a more realistic initialisation of the groundwater aquifer state and 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.

Response of the surface climate to different land surface models: WRF sensitivity to surface model options

2021 ◽  
Author(s):  
Daniela C.A. Lima ◽  
Rita M. Cardoso ◽  
Pedro M.M. Soares
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
Heat Fluxes ◽  
Surface Processes ◽  
Surface Model ◽  
Land Surface Models ◽  
Land Surface Processes ◽  
Surface Models

<p>The Weather Research and Forecasting (WRF) model version 4.2 includes different land surface schemes, allowing a better representation of the land surface processes. Four simulations with the WRF model differing in land surface models and options were investigated as a sensitivity study over the European domain. These experiments span from 2004-2006 with a one-month spin-up and were performed at 0.11<sup>o</sup> horizontal resolution with 50 vertical levels, following the CORDEX guidelines. The lateral boundary conditions were driven by ERA5 reanalysis from European Centre for Medium-Range Weather Forecasts. For the first experiment, the Noah land surface model was used. For the remaining simulations, the Noah-MP (multi-physics) land surface model was used with different runoff and groundwater options: (1) original surface and subsurface runoff (free drainage), (2) TOPMODEL with groundwater and (3) Miguez-Macho & Fan groundwater scheme. The physical parameterizations options are the same for all simulations. These experiments allow the analysis of the sensitivity of different land surface options and to understand how the representation of land surface processes impacts on the atmosphere properties. This study focusses on the investigation of land-atmosphere feedbacks trough the analysis of the soil moisture – temperature and soil moisture – precipitation interactions, latent and sensible heat fluxes, and moisture fluxes. The influence of different surface model options on atmospheric boundary layer is also explored.</p><p>Acknowledgements. The authors wish to acknowledge the LEADING (PTDC/CTA-MET/28914/2017) project funded by FCT. The authors would like to acknowledge the financial support FCT through project UIDB/50019/2020 – Instituto Dom Luiz.</p>

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

2020 ◽  
Author(s):  
Mengyuan Mu ◽  
Martin G. De Kauwe ◽  
Anna M. Ukkola ◽  
Andy J. Pitman ◽  
Teresa E. 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

Abstract. 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 4.5 m depth to evaluate the Community Atmosphere-Biosphere Land Exchange (CABLE) land surface model. We demonstrated 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 simulated water fluxes remained important: using a site calibrated function reduced the median level of water stress 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. Alternative parameterisations 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.

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.

The detection of irrigation through remote sensing soil moisture and a land surface model: a case study in Spain

2020 ◽  
Author(s):  
Jacopo Dari ◽  
Pere Quintana-Seguí ◽  
María José Escorihuela ◽  
Luca Brocca ◽  
Renato Morbidelli ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Land Surface Model ◽  
Surface Model ◽  
Data Sets ◽  
Data Set ◽  
Agricultural Areas ◽  
Irrigation Practices

<p>Irrigation practices introduce imbalances in the natural hydrological cycle at different spatial scales and put pressure on water resources, especially under climate changing and population increasing scenarios. Despite the implications of irrigation on food production and on the rational management of the available freshwater, detailed information about the areas where irrigation actually occurs is still lacking. For this reason, the comprehensive knowledge of the dynamics of the hydrological cycle over agricultural areas is often tricky.</p><p>The first aim of this study is to evaluate the capability of five remote sensing soil moisture data sets to detect the irrigation signal over an intensely irrigated area located within the Ebro river basin, in the North of Spain, during the biennium 2016-2017. As a second objective, a methodology to map the irrigated areas through the K-means clustering algorithm is proposed. The remotely sensed soil moisture products used in this study are: SMOS (Soil Moisture and Ocean Salinity) at 1 km, SMAP (Soil Moisture Active Passive) at 1 km and 9 km, Sentinel-1 at 1 km and ASCAT (Advanced SCATterometer) at 12.5 km. The 1 km versions of SMOS and SMAP are DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled versions of the corresponding coarser resolution products. An additional data set of soil moisture simulated by the SURFEX-ISBA (<em>Surface Externalisée - Interaction Sol Biosphère Atmosphère</em>) land surface model is used as a support for the performed analyses.</p><p>The capability of soil moisture products to detect irrigation has been investigated by exploiting indices representing the spatial and temporal dynamics of soil moisture. The L-band passive microwave downscaled products, especially SMAP at 1 km, result the best performing ones in detecting the irrigation signal over the pilot area; on the basis of these data sets, the K-means algorithm has been employed to classify three kinds of surfaces within the study area: the dryland, the forest or natural areas, and the actually irrigated areas. The resulting maps have been validated by exploiting maps of crops in Catalonia as ground truth data set. The percentage of irrigated areas well classified by the proposed method reaches the value of 78%; this result is obtained for the period May - September 2017. In addition, the method performs well in distinguishing the irrigated areas from rainfed agricultural areas, which are dry during summer, thus representing a useful tool to obtain explicit spatial information about where irrigation practices actually occur over agricultural areas equipped for this purpose.</p>

scholarly journals Towards a public, standardized, diagnostic benchmarking system for land surface models

2012 ◽  
Vol 5 (3) ◽  
pp. 819-827 ◽  
Author(s):  
G. Abramowitz
Keyword(s):  
Land Surface ◽  
A Priori ◽  
Model Performance ◽  
Land Surface Model ◽  
Surface Model ◽  
Data Sets ◽  
Land Surface Models ◽  
Surface Models ◽  
Definition Of ◽  
Benchmarking System

Abstract. This work examines different conceptions of land surface model benchmarking and the importance of internationally standardized evaluation experiments that specify data sets, variables, metrics and model resolutions. It additionally demonstrates how essential the definition of a priori expectations of model performance can be, based on the complexity of a model and the amount of information being provided to it, and gives an example of how these expectations might be quantified. Finally, the Protocol for the Analysis of Land Surface models (PALS) is introduced – a free, online land surface model benchmarking application that is structured to meet both of these goals.

scholarly journals On the Nature of Soil Moisture in Land Surface Models

2009 ◽  
Vol 22 (16) ◽  
pp. 4322-4335 ◽  
Author(s):  
Randal D. Koster ◽  
Zhichang Guo ◽  
Rongqian Yang ◽  
Paul A. Dirmeyer ◽  
Kenneth Mitchell ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Global Scale ◽  
Surface Model ◽  
Land Surface Models ◽  
Common Information ◽  
Surface Models ◽  
Moisture State ◽  
Environmental Prediction

Abstract The soil moisture state simulated by a land surface model is a highly model-dependent quantity, meaning that the direct transfer of one model’s soil moisture into another can lead to a fundamental, and potentially detrimental, inconsistency. This is first illustrated with two recent examples, one from the National Centers for Environmental Prediction (NCEP) involving seasonal precipitation forecasting and another from the realm of ecological modeling. The issue is then further addressed through a quantitative analysis of soil moisture contents produced as part of a global offline simulation experiment in which a number of land surface models were driven with the same atmospheric forcing fields. These latter comparisons clearly demonstrate, on a global scale, the degree to which model-simulated soil moisture variables differ from each other and that these differences extend beyond those associated with model-specific layer thicknesses or soil texture. The offline comparisons also show, however, that once the climatological statistics of each model’s soil moisture variable are accounted for (here, through a simple scaling using the first two moments), the different land models tend to produce very similar information on temporal soil moisture variability in most parts of the world. This common information can perhaps be used as the basis for successful mappings between the soil moisture variables in different land models.

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.

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