scholarly journals Estimation of ecosystem evapotranspiration in a Robinia pseudoacacia L. plantation with the use of the eddy covariance technique and modeling approaches

2021 ◽  
Author(s):  
Nikos Markos ◽  
Kalliopi Radoglou
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Eddy Covariance ◽  
Land Surface ◽  
Robinia Pseudoacacia ◽  
Environmental Drivers ◽  
Net Radiation ◽  
Eddy Covariance Technique ◽  
Modeling Approaches

Abstract Τhe eddy covariance technique provides reliable ecosystem-level ET measurements. These measurements, when combined with models and satellite products, could offer high spatiotemporal coverage and valuable mechanistic interpretation of the underlying processes. This study address one-year eddy covariance measurements from a Robinia pseudoacacia site in Northern Greece and remote sensing products, we (a) provide a medium-term description of daily ET fluxes for a R. pseudoacacia plantation in a degraded land, (b) assess the contribution of environmental drivers (e.g., net radiation, temperature etc) on ET and (c) evaluate a simple satellite and meteorological driven model for larger-scale applications, based on the land surface water index (LSWI) and the FAO approach. R. pseudoacacia was found to have quite high water consumption, especially during leaf expansion. Net radiation and soil water content had the greatest effect on ecosystem evapotranspiration. LSWI was found to be correlated with both soil water content and evapotranspiration. Its use as an index for water limitation in models lead to high accuracy when compared to ET measurements. Our results (a) provide a significant contribution to the assessment of R. pseudoacacia ecophysiology and (b) highlight the potential of accurate ecosystem ET estimation with simple modeling approaches.

Using satellite data in the Land Surface Model to estimate water availability of arid agricultural region

2020 ◽  
Author(s):  
Eugene Muzylev ◽  
Zoya Startseva ◽  
Elena Volkova ◽  
Eugene Vasilenko
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Water Availability ◽  
Land Surface ◽  
Land Surface Model ◽  
Soil Surface ◽  
Surface Model ◽  
Heat Regime ◽  
Vegetation Characteristics

<p>The water availability of agricultural arid regions can be assessed at presence using the physical-mathematical model of water and heat exchange between land surface and atmosphere LSM (Land Surface Model) adapted to satellite-derived estimates of meteorological and vegetation characteristics. The LSM is designed to calculate soil water content W, evapotranspiration Ev, vertical heat fluxes and other water and heat regime elements. Soil and vegetation characteristics were used in the LSM as parameters and meteorological characteristics were utilized as input variables.</p><p>The case study was carried out for the territory of the Saratov and Volgograd Trans-Volga region (the left-bank part of the Saratov and Volgograd regions) of 66600 km<sup>2</sup> for the vegetation seasons 2016-2018.</p><p>The satellite measurement data from radiometers AVHRR/NOAA, SEVIRI/Meteosat-10, -11, -8, and MSU-MR/Meteor-M No. 2 in visible and IR ranges were thematic processed to built estimates of vegetation index NDVI, emissivity E, vegetation cover fraction B, leaf index LAI, land surface temperature LST and precipitation.</p><p>LAI and B estimates were obtained using empirical dependencies on NDVI. The adequacy of the LAI and B estimates obtained from all sensor data was verified when comparing the LAI time behavior built for named vegetation seasons. Errors of determining B and LAI were 15 and 20%, respectively.</p><p>Satellite-derived estimates of daily, decadal and monthly precipitation sums for each pixel were obtained using the Multi Threshold Method (MTM) for detecting clouds, identifying its types allocating precipitation zones and determining their maximum intensity. The MTM is based on the developed algorithm of the transition from the assessment of precipitation intensity to the assessment of their daily amounts. Testing of the method was carried out when comparing these amounts with observed at meteorological stations. The probability of satellite-detected precipitation zones corresponded to the actual ones was ~ 80% for all radiometers.</p><p>Based on the MTM, computational algorithm to evaluate the LST was developed and verified on the study region data. Comparison of ground-measured and satellite-derived LST showed that the latter estimates for the overwhelming number of observation turned out to be comparable in accuracy with each other and with the ground-based data.</p><p>Calculations of water and heat regime elements (being the final products of the simulation) were carried out when replacing ground-based estimates of precipitation, LST, LAI and B in the LSM by satellite-derived ones at each time step in all nodes of the computational grid. The efficiency of such replacement procedures was confirmed by comparing measured and calculated values of W and Ev (the difference between them didn’t exceed 15% for W and 25% for Ev).</p><p>The possibility of using soil surface moisture estimates obtained from all-weather measurements by the scatterometer ASCAT/MetOp in the microwave range when simulating soil water content was also revealed. These estimates may use to set initial conditions for the vertical soil water transfer equation, as well as for calculating evaporation from the soil surface and the subsequent formation of the upper boundary condition for this equation.</p><p>As a summary, the described approach can be considered as a method for assessing the water availability for agricultural arid region.</p>

Modeling of a forested study site with the Community Land Model version 5 using climate projections for the 21st century.

2020 ◽  
Author(s):  
Lukas Strebel ◽  
Klaus Goergen ◽  
Bibi S. Naz ◽  
Heye Bogena ◽  
Harry Vereecken ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Land Surface ◽  
Model Parameters ◽  
Climate Projections ◽  
Modeling Framework ◽  
Area Index ◽  
Community Land Model

<p>Modeling forest ecosystems is important to facilitate adaptations in forest management approaches necessary to address the challenges of climate change, particularly of interest are ecohydrological states and fluxes such as soil water content, biomass, leaf area index, and evapotranspiration.</p><p>The community land model in its current version 5 (CLM5) simulates a broad collection of important land-surface processes; from moisture and energy partitioning, through biogeophysical processes, to surface and subsurface runoff. Additionally, CLM5 contains a biogeochemistry model (CLM5-BGC) which includes prognostic computation of vegetation states and carbon and nitrogen pools. However, CLM5 predictions are affected by uncertainty related to uncertain model forcings and parameters. Here, we use data assimilation methods to improve model performance by assimilating soil water content observations into CLM5 using the parallel data assimilation framework (PDAF).</p><p> </p><p>The coupled modeling framework was applied to the small (38.5 ha) forested catchment Wüstebach located in the Eifel National Park near the German-Belgian border. As part of the terrestrial environmental observatories (TERENO) network, the SoilNet sensors at the study site provide soil water content and soil temperature measurements since 2009.</p><p>CLM5 simulations for the period 2009-2100 were made, using local atmospheric observations for the period of 2009-2018 and an ensemble of regional climate model projections for 2019-2100. Simulations illustrate that data assimilation of soil water content improves the characterization of past model states, and that estimated model parameters and default model parameters result in different trajectories of ecohydrological states for 2019-2100. The simulations also illustrate that this site is hardly affected by increased water stress in the future.</p><p>The developed framework will be extended and applied for both ecosystem reanalysis as well as further simulations using climate projections across forested sites over Europe.</p>

Evaluating Soil Water Content in a WRF-Noah Downscaling Experiment

2013 ◽  
Vol 52 (10) ◽  
pp. 2312-2327 ◽  
Author(s):  
Peter Greve ◽  
Kirsten Warrach-Sagi ◽  
Volker Wulfmeyer
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Land Surface ◽  
Root Zone ◽  
Regional Scale ◽  
Land Surface Model ◽  
Surface Model ◽  
Flux Partitioning

AbstractSoil water content (SWC) depends on and affects the energy flux partitioning at the land–atmosphere interface. Above all, the latent heat flux is limited by the SWC of the root zone on one hand and radiation on the other. Therefore, SWC is a key variable in the climate system. In this study, the performance of the Weather Research and Forecasting model coupled with the Noah land surface model (WRF-Noah) system in a climate hindcast simulation from 1990 to 2008 is evaluated with respect to SWC versus two reanalysis datasets for Europe during 2007 and 2008 with in situ soil moisture observations from southern France. When compared with the in situ observations, WRF-Noah generally reproduces the SWC annual cycle while the reanalysis SWCs do not. The biases in areal mean WRF-Noah SWCs relate to biases in precipitation and evapotranspiration in a cropland environment. The spatial patterns and temporal variability of the seasonal mean SWCs from the WRF-Noah simulations and from the two reanalyses correspond well, while absolute values differ significantly, especially at the regional scale.

Spatio-temporal variability behavior of land surface soil water content in shrub- and grass-land

Geoderma ◽  
2011 ◽  
Vol 162 (3-4) ◽  
pp. 260-272 ◽  
Author(s):  
Wei Hu ◽  
Mingan Shao ◽  
Fengpeng Han ◽  
Klaus Reichardt
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Temporal Variability ◽  
Land Surface ◽  
Surface Soil ◽  
Grass Land ◽  
Spatio Temporal

scholarly journals Coupling the Community Land Model version 5.0 to the parallel data assimilation framework PDAF: Description and applications

2021 ◽  
Author(s):  
Lukas Strebel ◽  
Heye Bogena ◽  
Harry Vereecken ◽  
Harrie-Jan Hendricks Franssen
Keyword(s):  
Data Assimilation ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Land Surface ◽  
Land Surface Models ◽  
Community Land Model ◽  
Model Version ◽  
Parallel Data ◽  
Surface Models

Abstract. Land surface models are important for improving our understanding of the earth system. They are continuously improving and becoming more accurate in describing the varied surface processes, e.g. the Community Land Model version 5 (CLM5). Similarly, observational networks and remote sensing operations are increasingly providing more and higher quality data. For the optimal combination of land surface models and observation data, data assimilation techniques have been developed in the past decades that incorporate observations to update modeled states and parameters. The Parallel Data Assimilation Framework (PDAF) is a software environment that enables ensemble data assimilation and simplifies the implementation of data assimilation systems in numerical models. In this paper, we present the further development of the PDAF to enable its application in combination with CLM5. This novel coupling adapts the optional CLM5 ensemble mode to enable integration of PDAF filter routines while keeping changes to the pre-existing parallel communication infrastructure to a minimum. Soil water content observations from an extensive in-situ measurement network in the Wüstebach catchment in Germany are used to illustrate the application of the coupled CLM5+PDAF system. The results show overall reductions in root mean square error of soil water content from 7 % up to 35 % compared to simulations without data assimilation. We expect the coupled CLM5+PDAF system to provide a basis for improved regional to global land surface modelling by enabling the assimilation of globally available observational data.

scholarly journals Energy balance closure on a winter wheat stand: comparing the eddy covariance technique with the soil water balance method

2015 ◽  
Vol 12 (9) ◽  
pp. 6783-6820 ◽  
Author(s):  
K. Imukova ◽  
J. Ingwersen ◽  
M. Hevart ◽  
T. Streck
Keyword(s):  
Energy Balance ◽  
Water Balance ◽  
Winter Wheat ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Eddy Covariance ◽  
Soil Water Balance ◽  
Flux Data ◽  
Closure Method

Abstract. The energy balance of eddy covariance (EC) flux data is typically not closed. The nature of the gap is usually not known, which hampers using EC data to parameterize and test models. The present study elucidates the nature of the energy gap of EC flux data from winter wheat stands in southwest Germany. During the vegetation periods 2012 and 2013, we continuously measured, in a half-hourly resolution, latent (LE) and sensible (H) heat fluxes using the EC technique. Measured fluxes were adjusted with either the Bowen-ratio (BR), H or LE post-closure method. The adjusted LE fluxes were tested against evapotranspiration data (ETWB) calculated using the soil water balance (WB) method. At sixteen locations within the footprint of an EC station, the soil water storage term was determined by measuring the soil water content down to a soil depth of 1.5 m. In the second year, the volumetric soil water content was also continuously measured in 15 min resolution in 10 cm intervals down to 90 cm depth with sixteen capacitance soil moisture sensors. During the 2012 vegetation period, the H post-closed LE flux data (ETEC = 3.4 ± 0.6 mm day−1) corresponded closest with the result of the WB method (3.3 ± 0.3 mm day−1). ETEC adjusted by the BR (4.1 ± 0.6 mm day−1) or LE (4.9 ± 0.9 mm day−1) post-closure method were higher than the ETWB by 20 and 33%, respectively. In 2013, ETWB was in best agreement with ETEC adjusted with the H post-closure method during the periods with low amount of rain and seepage. During these periods the BR and LE post-closure methods overestimated ET by about 30 and 40%, respectively. During a period with high and frequent rainfalls, ETWB was in-between ETEC adjusted by H and BR post-closure methods. We conclude that, at most vegetation periods on our site, LE is not a~major component of the energy balance gap. Our results indicate that the energy balance gap other energy fluxes and unconsidered or biased energy storage terms.

scholarly journals Model representation of the coupling between evapotranspiration and soil water content at different depths

2020 ◽  
Vol 24 (2) ◽  
pp. 581-594 ◽  
Author(s):  
Jianxiu Qiu ◽  
Wade T. Crow ◽  
Jianzhi Dong ◽  
Grey S. Nearing
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Coupling Strength ◽  
Land Surface ◽  
Potential Evapotranspiration ◽  
Physical Models ◽  
Retrieval Algorithm ◽  
Surface Model ◽  
Surface Atmosphere

Abstract. Soil water content (θ) influences the climate system by controlling the fraction of incoming solar and longwave energy that is converted into evapotranspiration (ET). Therefore, investigating the coupling strength between θ and ET is important for the study of land surface–atmosphere interactions. Physical models are commonly tasked with representing the coupling between θ and ET; however, few studies have evaluated the accuracy of model-based estimates of θ ∕ ET coupling (especially at multiple soil depths). To address this issue, we use in situ AmeriFlux observations to evaluate θ ∕ ET coupling strength estimates acquired from multiple land surface models (LSMs) and an ET retrieval algorithm – the Global Land Evaporation Amsterdam Model (GLEAM). For maximum robustness, coupling strength is represented using the sampled normalized mutual information (NMI) between θ estimates acquired at various vertical depths and surface evaporation flux expressed as a fraction of potential evapotranspiration (fPET, the ratio of ET to potential ET). Results indicate that LSMs and GLEAM are generally in agreement with AmeriFlux measurements in that surface soil water content (θs) contains slightly more NMI with fPET than vertically integrated soil water content (θv). Overall, LSMs and GLEAM adequately capture variations in NMI between fPET and θ estimates acquired at various vertical depths. However, GLEAM significantly overestimates the NMI between θ and ET, and the relative contribution of θs to total ET. This bias appears attributable to differences in GLEAM's ET estimation scheme relative to the other two LSMs considered here (i.e., the Noah model with multi-parameterization options and the Catchment Land Surface Model, CLSM). These results provide insight into improved LSM model structure and parameter optimization for land surface–atmosphere coupling analyses.

scholarly journals Soil Water Content Diachronic Mapping: An FFT Frequency Analysis of a Temperature–Vegetation Index

Geosciences ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 23 ◽  
Author(s):  
Fulvio Capodici ◽  
Carmelo Cammalleri ◽  
Antonio Francipane ◽  
Giuseppe Ciraolo ◽  
Goffredo La Loggia ◽  
...  
Keyword(s):  
Time Series ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Land Surface ◽  
Vegetation Index ◽  
Root Zone ◽  
Simple Relationship ◽  
Meteorological Forcing ◽  
Triangle Method

Among the indirect estimation approaches of soil water content in the upper layer of the soil, the “triangle method” is one of the most common that relies on the simple relationship between the optical and thermal features sensed via Earth Observation. These features are controlled by water content at the surface and within the root zone but also by meteorological forcing including air temperature and humidity, as well as solar radiation. Night- and day-time MODIS composites of land-surface temperature (LST) allowed applying a version of the triangle method that takes into account the temporal admittance of the soil. In this study, it has been applied to a long time-series of pair images to analyze the seasonal influence of the meteorological forcing on a triangle method index (or temperature–vegetation index, TVX), as well as to discuss extra challenges of the diachronic approach including seasonality effects and the variability of environmental forcing. The Imera Meridionale basin (Sicily, Italy) has been chosen to analyze the method over a time-series of 12 years. The analysis reveals that, under these specific environmental and climatic conditions (strong seasonality and rainfall out of phase with vegetation growth), Normalized Difference Vegetation Index (NDVI) and LST pairs move circularly in time within the optical vs. thermal feature space. Concordantly, the boundaries of the triangle move during the seasons. Results showed a strong correlation between TVX and rainfall normalized amplitudes of the power spectra (r2 ~0.8) over the range of frequencies of the main harmonics.

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