scholarly journals Assimilation of surface soil moisture into a multilayer soil model: design and evaluation at local scale

2014 ◽  
Vol 18 (2) ◽  
pp. 673-689 ◽  
Author(s):  
M. Parrens ◽  
J.-F. Mahfouf ◽  
A. L. Barbu ◽  
J.-C. Calvet
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Soil Column ◽  
Cumulative Distribution ◽  
Surface Soil Moisture ◽  
Deep Soil ◽  
Background Error ◽  
Soil Layers ◽  
Diffusion Scheme

Abstract. Land surface models (LSM) have improved considerably in the last two decades. In this study, the Interactions between Surface, Biosphere, and Atmosphere (ISBA) LSM soil diffusion scheme is used (with 11 soil layers represented). A simplified extended Kalman filter (SEKF) allows ground observations of surface soil moisture (SSM) to be assimilated in the multilayer LSM in order to constrain deep soil moisture. In parallel, the same simulations are performed using the ISBA LSM with 2 soil layers (a thin surface layer and a bulk reservoir). Simulations are performed over a 3 yr period (2003–2005) for a bare soil field in southwestern France, at the SMOSREX (Surface Monitoring Of the Soil Reservoir Experiment) site. Analyzed soil moisture values correlate better with soil moisture observations when the ISBA LSM soil diffusion scheme is used. The Kalman gain is greater from the surface to 45 cm than below this limit. For dry periods, corrections introduced by the assimilation scheme mainly affect the first 15 cm of soil whereas weaker corrections impact the total soil column for wet periods. Such seasonal corrections cannot be described by the two-layer ISBA LSM. Sensitivity studies performed with the multilayer LSM show improved results when SSM (0–6 cm) is assimilated into the second layer (1–5 cm) than into the first layer (0–1 cm). The introduction of vertical correlations in the background error covariance matrix is also encouraging. Using a yearly cumulative distribution function (CDF)-matching scheme for bias correction instead of matching over the three years permits the seasonal variability of the soil moisture content to be better transcribed. An assimilation experiment has also been performed by forcing ISBA-DF (diffusion scheme) with a local forcing, setting precipitation to zero. This experiment shows the benefit of the SSM assimilation for correcting inaccurate atmospheric forcing.

scholarly journals Assimilation of surface soil moisture into a multilayer soil model: design and evaluation at local scale

2013 ◽  
Vol 10 (7) ◽  
pp. 9645-9688 ◽  
Author(s):  
M. Parrens ◽  
J.-F. Mahfouf ◽  
A. Barbu ◽  
J.-C. Calvet
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Soil Column ◽  
Bare Soil ◽  
Surface Soil Moisture ◽  
Deep Soil ◽  
Background Error ◽  
Soil Layers ◽  
Diffusion Scheme

Abstract. Land surface models (LSM) have improved considerably in the last two decades. In this study, the ISBA LSM soil diffusion scheme is used (with 11 soil layers represented). A Simplified Extended Kalman Filter (SEKF) allows surface soil moisture (SSM) to be assimilated in the multi-layer LSM in order to constrain deep soil moisture. In parallel, the same simulations are performed using the ISBA LSM with 2 soil layers (a thin surface layer and a bulk reservoir). Simulations are performed over a 3 yr period (2003–2005) for a bare soil field in southwestern France, at the SMOSREX experimental site. Analyzed soil moisture values correlate better with soil moisture observations when the ISBA LSM soil diffusion scheme is used. The Kalman gain is greater from the surface to 45 cm than below this limit. For dry periods, corrections introduced by the assimilation scheme mainly affect the first 25 cm of soil whereas weaker corrections impact the total soil column for wet periods. Such seasonal corrections cannot be described by the two-layer ISBA LSM. Sensitivity studies performed with the multi-layer LSM show improved results when SSM (0–6 cm) is assimilated into the second layer (1–5 cm) than into the first layer (0–1 cm). The introduction of vertical correlations in the background error covariance matrix is also encouraging. Using a yearly CDF-matching scheme for bias correction instead of matching over the three years permits the seasonal variability of the soil moisture content to be better transcribed. An assimilation experiment has also been performed by forcing ISBA-DF with a local forcing setting precipitation to zero. This experiment shows the benefit of the SSM assimilation for correcting inaccurate atmospheric forcing.

scholarly journals Analysis of the linearised observation operator in a soil moisture and temperature analysis scheme

2015 ◽  
Vol 2 (1) ◽  
pp. 505-535 ◽  
Author(s):  
I. Dharssi ◽  
B. Candy ◽  
P. Steinle
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Land Surface Model ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Soil Layers ◽  
Model Soil ◽  
Screen Level

Abstract. Several weather forecasting agencies have developed advanced land data assimilation systems that, in principle, can analyse any model land variable. Such systems can make use of a wide variety of observation types, such as screen level (2 m above the surface) observations and satellite based measurements of surface soil moisture and skin temperature. Indirect measurements can be used and information propagated from the surface into the deeper soil layers. A key component of the system is the calculation of the linearised observation operator matrix (Jacobian matrix) which describes the link between the observations and the land surface model variables. The elements of the Jacobian matrix (Jacobians) are estimated using finite difference by performing short model forecasts with perturbed initial conditions. The calculated Jacobians show that there can be strong coupling between the screen level and the soil. The coupling between the screen level and surface soil moisture is found to be due to a number of processes including bare soil evaporation, soil thermal conductivity as well as transpiration by plants. Therefore, there is significant coupling both during the day and at night. The coupling between the screen level and root-zone soil moisture is primarily through transpiration by plants. Therefore the coupling is only significant during the day and the vertical variation of the coupling is modulated by the vegetation root depths. The calculated Jacobians that link screen level temperature to model soil temperature are found to be largest for the topmost model soil layer and become very small for the lower soil layers. These values are largest during the night and generally positive in value. It is found that the Jacobians that link observations of surface soil moisture to model soil moisture are strongly affected by the soil hydraulic conductivity. Generally, for the Joint UK Land Environment Simulator (JULES) land surface model, the coupling between the surface and root zone soil moisture is weak. Finally, the Jacobians linking observations of skin temperature to model soil temperature and moisture are calculated. These Jacobians are found to have a similar spatial pattern to the Jacobians for observations of screen level temperature. Analysis is also performed of the sensitivity of the calculated Jacobians to the magnitude of the perturbations used.

scholarly journals Contributions of climate change and groundwater extraction to soil moisture trends

2019 ◽  
Vol 10 (3) ◽  
pp. 599-615 ◽  
Author(s):  
Longhuan Wang ◽  
Zhenghui Xie ◽  
Binghao Jia ◽  
Jinbo Xie ◽  
Yan Wang ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Water Cycle ◽  
Supply And Demand ◽  
Surface Model ◽  
Groundwater Extraction ◽  
Surface Soil Moisture ◽  
Deep Soil

Abstract. Climate change affects water availability for soil, and groundwater extraction influences water redistribution by altering water demand, both of which significantly affect soil moisture. Quantifying their relative contribution to the changes in soil moisture will further our understanding of the mechanisms underlying the global water cycle. In this study, two groups of simulations were conducted with and without groundwater (GW) extraction (estimated based on local water supply and demand) from 1979 to 2010 using the Chinese Academy of Sciences land surface model, CAS-LSM, with four global meteorological forcing datasets (GSWP3, PRINCETON, CRU-NCEP, and WFDEI). To investigate the contribution of climate change and GW extraction, a trajectory-based method was used. Comparing the simulated results with the in situ dataset of the International Soil Moisture Network (ISMN) and the satellite-based soil moisture product of the European Space Agency's Climate Change Initiative (ESA-CCI) indicated that the CAS-LSM reasonably reproduced the distribution of soil moisture and matched the temporal changes well. Globally, our results suggested a significant decreasing trend in surface soil moisture (0–10 cm, 0.98×10-4 mm3 mm−3 yr−1) over the 32-year period tested. The drying trends were mainly observed in arid regions such as the tropical desert regions in North Africa and the Arabian Peninsula, while the wetting trends were primarily in tropical forested areas in South America and northeastern Asia. Climate change contributed 101.2 % and 90.7 % to global drying and wetting trends of surface soil moisture, respectively, while GW extraction accounted for −1.2 % and 9.3 %, respectively. In deep soil, GW extraction contributed 1.37 % and −3.21 % to the drying and wetting trends, respectively. The weak influence of GW extraction may be because this activity occurs in limited areas. GW extraction contributed more than 35 % to the change in surface soil moisture in wetting areas where GW overexploitation occurs. GW is mainly extracted for irrigation to alleviate soil water stress in semiarid regions that receive limited precipitation, thereby slowing the drying trend and accelerating the wetting trend of surface soil. However, GW exploitation weakens the hydraulic connection between the soil and aquifer, leading to deeper soils drying up. Overall, climate change dominated the soil moisture trends, but the effect of GW extraction cannot be ignored.

scholarly journals Contributions of climate change and groundwater extraction to soil moisture trends

2019 ◽  
Author(s):  
Longhuan Wang ◽  
Zhenghui Xie ◽  
Binghao Jia ◽  
Jinbo Xie ◽  
Yan Wang ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Water Cycle ◽  
Supply And Demand ◽  
Surface Model ◽  
Groundwater Extraction ◽  
Surface Soil Moisture ◽  
Deep Soil

Abstract. Climate change affects water availability for soil, and groundwater extraction influences water redistribution by altering water demand, both of which significantly affect soil moisture. Quantifying their relative contribution to the changes in soil moisture will further our understanding of the mechanisms underlying the global water cycle. In this study, two groups of simulations were conducted with and without groundwater (GW) extraction (estimated based on local water supply and demand) from 1979–2010 using the land surface model CAS-LSM with four global meteorological forcing datasets (GSWP3, PRINCETON, CRU-NCEP, and WFDEI). To investigate the contribution of climate change and GW extraction, a trajectory-based method was used. Comparing the simulated results with the in-situ dataset of the International Soil Moisture Network (ISMN) and the satellite-based soil moisture product of the European Space Agency’s Climate Change Initiative (ESA-CCI) indicated that the CAS-LSM reasonably reproduced the distribution of soil moisture, and well matched the temporal changes. Globally, our results suggested a significant decreasing trend in surface soil moisture (0.98 e−4 mm3 mm−3 yr−1) over the 32-year period tested. The drying trends were mainly observed in arid regions such as the tropical desert regions in North Africa and the Arabian Peninsula. While the wetting trends were primarily in tropical forested areas in South America and Northeast Asia. Climate change contributed 101.2 % and 90.7 % to global drying and wetting trends of surface soil moisture, respectively, while GW extraction accounted for −1.2 % and 9.3 %, respectively. In deep soil, GW extraction contributed 1.37 % and −3.21 % to the drying and wetting trends, respectively. The weak influence of GW extraction may be because this activity occurs in limited areas. GW extraction contributed more than 35 % to the change in surface soil moisture in wetting areas where GW overexploitation occurs. GW is mainly extracted for irrigation to alleviate soil water stress in semiarid regions that receive limited precipitation, thereby slowing the drying trend and accelerating the wetting trend of surface soil. However, GW exploitation weakens the hydraulic connection between soil and aquifer, leading to deeper soils drying up. Overall, climate change dominated soil moisture trends, but the effect of GW extraction cannot be ignored.

scholarly journals Calibration of Noah Soil Hydraulic Property Parameters Using Surface Soil Moisture from SMOS and Basinwide In Situ Observations

2016 ◽  
Vol 17 (8) ◽  
pp. 2275-2292 ◽  
Author(s):  
Peter J. Shellito ◽  
Eric E. Small ◽  
Michael H. Cosh
Keyword(s):  
Soil Moisture ◽  
Soil Texture ◽  
Land Surface ◽  
Surface Soil ◽  
Soil Column ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Remotely Sensed Data ◽  
Soil Hydraulic

Abstract Soil hydraulic properties (SHPs) control infiltration and redistribution of moisture in a soil column. The Noah land surface model (LSM) default simulation selects SHPs according to a location’s mapped soil texture class. SHPs are instead estimated at seven sites in North America through calibration. A single-objective algorithm minimizes the root-mean-square difference (RMSD) between simulated surface soil moisture and observations from 1) a dense network of in situ probes, 2) Soil Moisture Ocean Salinity (SMOS) satellite retrievals, and 3) SMOS retrievals adjusted such that their mean equals that of the in situ network. Parameters are optimized in 2012 and validated in 2013 against the in situ network. RMSD and unbiased RMSD (ubRMSD) assess resulting surface soil moisture behavior. At all sites, assigning SHP parameters from a different soil texture than the one that is mapped decreases the RMSD by an average of 0.029 cm3 cm−3. Similar improvements result from calibrating parameters using in situ network data (0.031 cm3 cm−3). Calibrations using remotely sensed data show comparable success (0.029 cm3 cm−3) if the SMOS product has no bias. Calibrated simulations are superior to texture-based simulations in their ability to decrease ubRMSD at times of year when the default simulation is worst. Changes to both RMSD and ubRMSD are small when the default simulation is already good. Most calibrated simulations have higher runoff ratios than do texture-based simulations, a change that warrants further evaluation. Overall, parameter selection using SMOS data shows good potential where biases are low.

The Computation of the Effective Temperature of the Land Surface Covered with Canopy

2014 ◽  
Vol 607 ◽  
pp. 830-834
Author(s):  
Hong Zhang Ma ◽  
Su Mei Liu
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Effective Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Passive Microwave ◽  
Vegetation Canopy ◽  
Surface Soil Moisture ◽  
Remote Sensors ◽  
Energy Exchanges

—Surface soil moisture is an important parameter in describing the water and energy exchanges at the land surface/atmosphere interface. Passive microwave remote sensors have great potential for monitoring surface soil moisture over land surface. The objective of this study is going to establish a model for estimating the effective temperature of land surface covered with vegetation canopy and to investigate how to compute the microwave radiative brightness temperature of land surface covered with vegetation canopy in considering of the canopy scatter effect.

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.

scholarly journals An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data

2019 ◽  
Author(s):  
Bouchra Ait Hssaine ◽  
Olivier Merlin ◽  
Jamal Ezzahar ◽  
Nitu Ojha ◽  
Salah Er-raki ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Energy Balance ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Wide Range ◽  
The Mean

Abstract. Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (LST) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1 km resolution MODIS (Moderate resolution imaging spectroradiometer) LST and fc data and the 1 km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014–2018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014–2018). The field was seeded for the 2014–2015 (S1), 2016–2017 (S2) and 2017–2018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015–2016 (B1) agricultural season. The mean retrieved values of (arss, brss) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated αPT ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved αPT remains at a mostly constant value (∼ 0.7) throughout the study period, because of the lack of clear sky disaggregated SM and LST observations during this season. Compared to eddy covariance measurements, TSEB driven only by LST and fc data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181 W/m2 for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and LST combined data) with the mean bias values estimated as 39, 4, 7 and 62 W/m2 for S1, S2, S3 and B1 respectively.

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