scholarly journals Big root approximation of site-scale vegtation water uptake

2019 ◽  
Author(s):  
Martin Bouda
Keyword(s):  
Soil Moisture ◽  
Water Uptake ◽  
Land Surface ◽  
Land Surface Model ◽  
Root Water Uptake ◽  
Surface Model ◽  
Summer Drought ◽  
Ohm’S Law ◽  
Ohm's Law ◽  
Vegetation Water

AbstractLand surface model (LSM) predictions of soil moisture and transpiration under water-limited conditions suffer from biases due to a lack of mechanistic process description of vegetation water uptake. Here, I derive a ‘big root’ approach from the porous pipe equation for root water uptake and compare its predictions of soil moistures during the 2010 summer drought at the Wind River Crane site to two previously used Ohm’s law analogue plant hydraulic models. Structural error due to inadequate representation of root system architecture (RSA) in both Ohm’s law analogue models yields significant and predictable moisture biases. The big root model greatly reduces these as it better represents RSA effects on pressure gradients and flows within the roots. It represents a major theoretical advance in understanding vegetation water limitation at site scale with potential to improve LSM predictions of soil moisture, temperature and surface heat, water, and carbon fluxes.

scholarly journals Evaluation of root water uptake in the ISBA-A-gs land surface model using agricultural yield statistics over France

2014 ◽  
Vol 11 (5) ◽  
pp. 5421-5461
Author(s):  
N. Canal ◽  
J.-C. Calvet ◽  
B. Decharme ◽  
D. Carrer ◽  
S. Lafont ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Water Uptake ◽  
Land Surface ◽  
Land Surface Model ◽  
Root Water Uptake ◽  
Surface Model ◽  
Above Ground Biomass ◽  
Soil Layers ◽  
Ground Biomass ◽  
Cereal Grain

Abstract. The interannual variability of cereal grain yield and permanent grassland dry matter yield is simulated over French sites by the Interactions between Soil, Biosphere and Atmosphere, CO2-reactive (ISBA-A-gs) generic Land Surface Model (LSM). The two soil profile schemes available in the model are used to simulate the above-ground biomass (Bag) of cereals and grasslands: a 2-layer force-restore (FR-2L) bulk reservoir model and a multi-layer diffusion (DIF) model. The DIF model is implemented with or without deep soil layers below the root-zone. The evaluation of the various root water uptake models is achieved by using the French agricultural statistics of Agreste over the 1994–2010 period at 45 cropland and 48 grassland sites, for a range of rooting depths. The number of sites where the simulated annual maximum Bag presents a significant correlation with the yield observations is used as a metric to benchmark the root water uptake models. Significant correlations (p value < 0.01) are found for up to 29% of the cereal sites and 77% of the grassland sites. It is found that modelling additional subroot zone base flow soil layers does not improve (and may even degrade) the representation of the interannual variability of the vegetation above-ground biomass. These results are particularly robust for grasslands as calibrated simulations are able to represent the extreme 2003 and 2007 years corresponding to unfavourable and favourable fodder production, respectively.

scholarly journals Assessing various drought indicators in representing summer drought in boreal forests in Finland

2016 ◽  
Vol 20 (1) ◽  
pp. 175-191 ◽  
Author(s):  
Y. Gao ◽  
T. Markkanen ◽  
T. Thum ◽  
M. Aurela ◽  
A. Lohila ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Boreal Forests ◽  
Forest Health ◽  
Land Surface Model ◽  
Extreme Drought ◽  
Forest Damage ◽  
Surface Model ◽  
Summer Drought ◽  
Observation Data

Abstract. Droughts can have an impact on forest functioning and production, and even lead to tree mortality. However, drought is an elusive phenomenon that is difficult to quantify and define universally. In this study, we assessed the performance of a set of indicators that have been used to describe drought conditions in the summer months (June, July, August) over a 30-year period (1981–2010) in Finland. Those indicators include the Standardized Precipitation Index (SPI), the Standardized Precipitation–Evapotranspiration Index (SPEI), the Soil Moisture Index (SMI), and the Soil Moisture Anomaly (SMA). Herein, regional soil moisture was produced by the land surface model JSBACH of the Max Planck Institute for Meteorology Earth System Model (MPI-ESM). Results show that the buffering effect of soil moisture and the associated soil moisture memory can impact on the onset and duration of drought as indicated by the SMI and SMA, while the SPI and SPEI are directly controlled by meteorological conditions. In particular, we investigated whether the SMI, SMA and SPEI are able to indicate the Extreme Drought affecting Forest health (EDF), which we defined according to the extreme drought that caused severe forest damages in Finland in 2006. The EDF thresholds for the aforementioned indicators are suggested, based on the reported statistics of forest damages in Finland in 2006. SMI was found to be the best indicator in capturing the spatial extent of forest damage induced by the extreme drought in 2006. In addition, through the application of the EDF thresholds over the summer months of the 30-year study period, the SPEI and SMA tended to show more frequent EDF events and a higher fraction of influenced area than SMI. This is because the SPEI and SMA are standardized indicators that show the degree of anomalies from statistical means over the aggregation period of climate conditions and soil moisture, respectively. However, in boreal forests in Finland, the high initial soil moisture or existence of peat often prevent the EDFs indicated by the SPEI and SMA to produce very low soil moisture that could be indicated as EDFs by the SMI. Therefore, we consider SMI is more appropriate for indicating EDFs in boreal forests. The selected EDF thresholds for those indicators could be calibrated when there are more forest health observation data available. Furthermore, in the context of future climate scenarios, assessments of EDF risks in northern areas should, in addition to climate data, rely on a land surface model capable of reliable prediction of soil moisture.

scholarly journals An Intercomparison of ERS-Scat and AMSR-E Soil Moisture Observations with Model Simulations over France

2009 ◽  
Vol 10 (2) ◽  
pp. 431-447 ◽  
Author(s):  
Christoph Rüdiger ◽  
Jean-Christophe Calvet ◽  
Claire Gruhier ◽  
Thomas R. H. Holmes ◽  
Richard A. M. de Jeu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Coupled Model ◽  
Surface Model ◽  
Monitoring Site ◽  
Remotely Sensed Data ◽  
Mountainous Regions ◽  
Vegetation Water Content ◽  
Vegetation Water

Abstract This paper presents a study undertaken in preparation of the work leading up to the assimilation of Soil Moisture and Ocean Salinity (SMOS) observations into the land surface model (LSM) Interaction Soil Biosphere Atmosphere (ISBA) at Météo-France. This study consists of an intercomparison experiment of different space-borne platforms providing surface soil moisture information [Advanced Microwave Scanning Radiometer for Earth Observing (AMSR-E) and European Remote Sensing Satellite Scatterometer (ERS-Scat)] with the reanalysis soil moisture predictions over France from the model suite of Système d’analyse fournissant des renseignements atmosphériques à la neige (SAFRAN), ISBA, and coupled model (MODCOU; SIM) of Météo-France for the years of 2003–05. Both modeled and remotely sensed data are initially validated against in situ observations obtained at the experimental soil moisture monitoring site Surface Monitoring of the Soil Reservoir Experiment (SMOSREX) in southwestern France. Two different AMSR-E soil moisture products are compared in the course of this study—the official AMSR-E product from the National Snow and Ice Data Center (NSIDC) and a new product developed at the Vrije Universiteit Amsterdam and NASA (VUA–NASA)—which were obtained using two different retrieval algorithms. This allows for an additional assessment of the different algorithms while using identical brightness temperature datasets. This study shows that a good correlation generally exists between AMSR-E (VUA–NASA), ERS-Scat, and SIM for low altitudes and low-to-moderate vegetation covers (1.5–3 kg m−2 vegetation water content), with a reduction in the correlation in mountainous regions. It also shows that the AMSR-E (NSIDC) soil moisture product has significant differences when compared to the other datasets.

scholarly journals Evaluation of root water uptake in the ISBA-A-gs land surface model using agricultural yield statistics over France

2014 ◽  
Vol 18 (12) ◽  
pp. 4979-4999 ◽  
Author(s):  
N. Canal ◽  
J.-C. Calvet ◽  
B. Decharme ◽  
D. Carrer ◽  
S. Lafont ◽  
...  
Keyword(s):  
Water Uptake ◽  
Land Surface ◽  
Land Surface Model ◽  
Root Water Uptake ◽  
Surface Model ◽  
Above Ground Biomass ◽  
Soil Layers ◽  
Annual Variability ◽  
Ground Biomass ◽  
Cereal Grain

Abstract. The simulation of root water uptake in land surface models is affected by large uncertainties. The difficulty in mapping soil depth and in describing the capacity of plants to develop a rooting system is a major obstacle to the simulation of the terrestrial water cycle and to the representation of the impacts of drought. In this study, long time series of agricultural statistics are used to evaluate and constrain root water uptake models. The inter-annual variability of cereal grain yield and permanent grassland dry matter yield is simulated over France by the Interactions between Soil, Biosphere and Atmosphere, CO2-reactive (ISBA-A-gs) generic land surface model (LSM). The two soil profile schemes available in the model are used to simulate the above-ground biomass (Bag) of cereals and grasslands: a two-layer force–restore (FR-2L) bulk reservoir model and a multi-layer diffusion (DIF) model. The DIF model is implemented with or without deep soil layers below the root zone. The evaluation of the various root water uptake models is achieved by using the French agricultural statistics of Agreste over the 1994–2010 period at 45 cropland and 48 grassland départements, for a range of rooting depths. The number of départements where the simulated annual maximum Bag presents a significant correlation with the yield observations is used as a metric to benchmark the root water uptake models. Significant correlations (p value < 0.01) are found for up to 29 and 77% of the départements for cereals and grasslands, respectively. A rather neutral impact of the most refined versions of the model is found with respect to the simplified soil hydrology scheme. This shows that efforts should be made in future studies to reduce other sources of uncertainty, e.g. by using a more detailed soil and root density profile description together with satellite vegetation products. It is found that modelling additional subroot-zone base flow soil layers does not improve (and may even degrade) the representation of the inter-annual variability of the vegetation above-ground biomass. These results are particularly robust for grasslands, as calibrated simulations are able to represent the extreme 2003 and 2007 years corresponding to unfavourable and favourable fodder production, respectively.

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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