Influence of wavephasedifferencebetweensurfacesoilheatflux and soilsurfacetemperature on landsurfaceenergybalanceclosure
The sensitivity of climate simulations to the diurnal variation in surfaceenergy budget encourages enhanced inspection into the energybalanceclosure failure encountered in micrometeorological experiments. The diurnal wave phases of soilsurfaceheatflux and temperature are theoretically characterized and compared for both moist soil and absolute dry soil surfaces, indicating that the diurnal wavephasedifferencebetweensoilsurfaceheatflux and temperature ranges from 0 to π/4 for natural soils. Assuming net radiation and turbulent heat fluxes have identical phase with soilsurface temper- ature, we evaluate potential contributions of the wavephasedifference on the surfaceenergybalanceclosure. Results show that the sum of sensible heatflux (H ) and latent heatflux (LE ) is always less than surface available energy (Rn − G0) even if all energy components are accurately measured, their footprints are strictly matched, and all cor- rections are made. The energybalanceclosure ratio (ε) is extremely sensitive to the ratio of soilsurfaceheatflux amplitude (A4) to net radiation flux amplitude (A1), and large value of A4/A1 causes a significant failure in surfaceenergybalanceclosure. An experimental case study confirms the theoretical analysis.
The surface, through which the exchange betweenenergy and matter takes place, is called an active surface. Hence, both the surface of the bare soil and that of a field with well-developed plant cover can be qualified as an active surface. A surface of this kind absorbs short-wave sun radiation and emits long-wave radiation whose intensity depends on the temperature of the surface (Kêdziora, 1995). The exchange of vapour between the soil and atmosphere, as well as the exchange of energy during the evaporation process connected with it, also takes place through an active surface. One of the ways of the micrometeorological description of our environment is the presentation of the heatbalance structure of the active surface. It is assumed that the fluxes coming to the surface have positive values and the outgoing have negative values. In literature it is usually described as an equation (Boyen et al., 1976; Kêdziora, 1995; Monteith, 1977; Oke, 1978; Paszyñski, 1972):
Generally, surface evapotranspiration (i.e. latent heatflux LE) is estimated as the residual term of surfaceenergy bal- ance equation. Remotely sensed data have been used suc- cessfully over the past years to estimate the surface net radia- tion and the soilheatflux (hence available energy) from com- bined visible, near infrared and thermal infrared data (Nor- man et al., 1995; Liang et al., 2000; Jacobs et al., 2000; Ma et al., 2002; Ma, 2003). Therefore, the primary focus has been the determination of the sensible heatflux based on the spatially distributed surfacetemperature fields. The turbulent heat fluxes models to estimate the sensible heatflux can be categorized into two groups, single-source models and dual- source models, according to whether or not the model sepa- rates the foliage and the substrate soil. In the single-source models, a so called “excess” resistance or parameter kB −1 is used to account for the differencebetween the remotely sensed radiative surfacetemperature T r and the aerodynamic
Recently, there have been keen attempts to include ground- water systems in landsurface models (i.e. models that sim- ulate the interactions betweensoil, vegetation and the atmo- sphere). York et al. (2002), the earliest to include aquifers within coupled landsurface models, triggered a series of in- vestigations that approached the coupling between ground- water and landsurface models using different schemes and techniques (Liang and Xie, 2003; Chen and Hu, 2004; Maxwell and Miller, 2005; Gulden et al., 2007; Fan et al., 2007; Niu et al., 2007; Jiang et al., 2009). Careful inspec- tion of these works shows that their focal point was the mass aspect of the linkage between the surface and the subsur- face domains via moisture flux. In this way, the main interest was the influence of groundwater, as an extra source of wa- ter for evaporation, on water budget at landsurface. Specif- ically, Niu et al. (2007) developed a simple groundwater model (SIMGM) which considers unsaturated soil water and evaluated the model against the Gravity Recovery and Cli- mate Experiment (GRACE) terrestrial water storage change data. Therefore, these studies could not provide a complete prospective of shallow groundwater effect. The temporal pat- terns of that effect on surfacetemperature, net radiation, and surfaceheat fluxes (latent, sensible and ground heat fluxes) have not been featured. More importantly, utilizing thermal remote sensing in these efforts or reversely, utilizing their findings in detecting shallow groundwater via thermal remote sensing has been beyond the scope of these studies.
2. EXPERIMENTAL SET UP
Soiltemperature is governed by two kinds of processes: (a) energy exchange/transfer at earth-atmosphere boundary (b) heat propagation within the soil which can vary substantially with moisture content. The former one determines the energy quantity stored in the soil, the latter one control how energy is distributed once inside the soil (Saito et al., 2009; Smits et al., 2010). For this, continuous measurements of parameters needed to fully evaluate energy and moisture balance were carried out in situ at the geothermal energy test facility to investigate energy transfer betweensoil and atmosphere and study the thermal and moisture regimes. Figure 1 shows the test facility where these measurements have been processed.
As a part of the trend in LSM development, there have been ongoing efforts to improve the representation of the landsurface processes in the Interactions between the soil– biosphere–atmosphere (ISBA) LSM within the EXternalized SURFace (SURFEX; Masson et al.,2013) model platform. The original two-layer ISBA force–restore model (Noilhan and Planton, 1989) consists in a single bulk soil layer (gen- erally having a thickness on the order of 50 cm to sev- eral meters) coupled to a superficially thin surface compos- ite soil–vegetation–snow layer. Thus, the model simulates fast processes that occur at sub-diurnal timescales, which are pertinent to short-term numerical weather prediction, and it provides a longer-term water storage reservoir, which provides a source for transpiration, a time filter for water reaching a hydro-graphic network, and a certain degree of soil moisture memory in the ground amenable to longer- term forecasts and climate modeling. Additional modifica- tions were made to this scheme over the last decade to in- clude soil freezing (Boone et al., 2000; Giard and Bazile, 2000), which improved hydrological processes (Mahfouf and Noilhan, 1996; Boone et al., 1999; Decharme and Douville, 2006). This scheme was based on the pioneering work of Deardorff (1977) and it has proven its value for coupled land–atmosphere research and applications since its incep- tion. For example, it is currently used for research within the mesoscale non-hydrostatic research model (Meso-NH) (Lafore et al., 1998). It is also used within the operational high-resolution short-term numerical weather prediction at Météo-France within the limited area model AROME (Seity et al., 2011) and by HIRLAM countries within the ALADIN– HIRLAM system as the HARMONIE–AROME model con- figuration (Bengtsson et al., 2017). Finally, it is used for cli- mate research within the global climate model (GCM) Ac-
Highway drainage is an essential part of highway design and construction which remove the surplus water with in the highway limits and satisfactory dispose it. Road way drainage is mainly due to surface runoff from adjacent area, precipitation of rain and moisture rising by capillarity from the ground water table. Removal and diversion of surface water from road way and adjoining land is known as surface drainage. Removal of excess sub soil water from the sub grade is termed as sub surface drainage. The importance of drainage is one of the most important aspects for location and design of highway because of following reasons:
As mentioned before, most channels measure data in the 8 to 14 μm region. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) was launched on the first of NASA’s Earth Observing System polar- orbiting spacecrafts, EOS-1. This sensor consists of fourteen channels, five of which are devoted to the thermal-infrared (TIR) region of the spectrum from 8 to 14μm (Table 1) for the purpose of land-surface "kinetic" temperatures and emissivities retrieval, especially where none of these parameters are known as a priori. Because of their high spatial resolution (90 m), ASTER T and ε data can be verified through field experiments and to some extent by spectral libraries if pure pixels can be identified. In order to equalize the number of unknowns and measurements so that the set of Planck equations for the measured thermal radiances can be inverted to temperature and emissivities, a Temperature and Emissivity Separation (TES) algorithm is developed. This algorithm relies on an empirical relationship between spectral contrast and minimum emissivity determined from laboratory and field emissivity spectra. It is designed to produce the surfacetemperature in one band and emissivity in 5 bands.
In situ field testing enables larger volumes of soil to be tested and so tends to be more representative of the soil mass compared with laboratory testing. In situ field tests have an advantage as samples do not need to be retrieved. For very soft clays, sands and gravels, sampling is a major problem because these materials easily change their soil structure and, as a result, produce disturbed samples. Good correlations have been produced between field tests and laboratory tests, which has led to acceptance of field techniques (Charles and Watt, 2002). Of the range of in situ tests, penetration testing, dynamic probing, pressuremeter testing, field vane shear testing, plate loading testing and geophysical testing are used for site investigations. Cost and time constraint factors are the main reasons why it is not easy to investigate the subsurface completely. Hence, site investigation may only involve the laboratory testing of samples collected by site personnel or field testing for limited areas. This may lead to either an underestimate or overestimate of the strength of the existing subsurface. Therefore, to achieve greater certainty of the site investigation, a robust approach is needed to adopt. Geophysical methods can provide excellent resolution of spatial variability across a site. The main advantages with such an approach are their non- destructive, non-invasive nature and relative speed of assessment. If calibrated, details of stiffness with depth can be relatively easily obtained.
Another important observations from Fig. 8, is that day- time observations from both satellites become of higher qual- ity when the vegetation density increases compared to the night-time observations over the same areas. Several stud- ies (Loew and Schlenz, 2011; Brocca et al., 2011) indicated this already, but none of them explained this phenomenon. One possible explanation is that the vegetation water con- tent during the day decreases due to transpiration induced by photosynthesis, making the vegetation more transparent to microwave emission, and consequently increasing the sen- sitivity to the underlying soil moisture signal. In general, for the majority of vegetation species the dry wood density is smaller than the density of water leading to a decrease in vegetation bulk densities when vegetation water content de- creases. Also, higher canopy temperatures during the day could lead to decreased vegetation optical depth values, re- sulting in the same higher penetration through the overlying canopy. In any case, these findings show that the traditional view, which expects a higher quality of night-time observa- tions since the environmental state is closer to equilibrium at these times (de Jeu et al., 2008) might be incomplete. 4.3 MERRA scenarios
One of the most profound impacts of draining Carolina bays for agriculture is sinking of the landsurface via primary and secondary subsidence. The loss of buoyant force after drainage leads to sinking, i.e., primary subsidence. Secondary subsidence decreases landsurface elevations due to microbial decomposition of soil organic matter, loss by fire, and shrinking (Terzaghi, 1943). Of primary importance to C budgeters is the loss of C due to organic matter decomposition. Ewing and Vepraskas (2006) evaluated the rates of subsidence in a Carolina bay 15, 20, and 30 years after drainage. They estimated that primary and secondary subsidence decreased the landsurface by approximately 121 cm after 30 years of drainage. Secondary subsidence was responsible for one- third of the total subsidence and occurred at a rate between 1.7 and 2.8 cm yr -1 . While secondary subsidence cannot be easily separated into its main components of oxidation and shrinkage (Ewing and Vepraskas, 2006), it can be assumed that the losses due to C oxidation are significant. These researchers suggest that subsidence, and therefore oxidative C loss, could be decreased by restoring the water table to near-surface levels outside the growing season. Efforts to further reduce C loss could entail restoring the wetland and keeping the water table high indefinitely.
Abstract. Evapotranspiration (ET) is the main link between the natural water cycle and the landsurfaceenergy bud- get. Therefore water-balance and energy-balance approaches are two of the main methodologies for modelling this pro- cess. The water-balance approach is usually implemented as a complex, distributed hydrological model, while the energy- balance approach is often used with remotely sensed obser- vations of, for example, the landsurfacetemperature (LST) and the state of the vegetation. In this study we compare the catchment-scale output of two remote sensing models based on the two-source energy-balance (TSEB) scheme, against a hydrological model, MIKE SHE, calibrated over the Skjern river catchment in western Denmark. The three models uti- lize different primary inputs to estimate ET (LST from differ- ent satellites in the case of remote sensing models and mod- elled soil moisture and heatflux in the case of the MIKE SHE ET module). However, all three of them use the same ancil- lary data (meteorological measurements, land cover type and leaf area index, etc.) and produce output at similar spatial res- olution (1 km for the TSEB models, 500 m for MIKE SHE). The comparison is performed on the spatial patterns of the fluxes present within the catchment area as well as on tem- poral patterns on the whole catchment scale in 8-year long time series. The results show that the spatial patterns of la- tent heatflux produced by the remote sensing models are more similar to each other than to the fluxes produced by MIKE SHE. The temporal patterns produced by the remote sensing and hydrological models are quite highly correlated (r ≈ 0.8). This indicates potential benefits to the hydrological
Soil moisture affects soiltemperature in different and con- tradicting aspects. These effects fall into five categories: soilsurface albedo, soil emissivity, evaporation, soil thermal con- ductivity and volumetric heat capacity. Firstly, increasing soil moisture decreases albedo which in turn increases the ab- sorbed radiation during day time; hence, this increases day- time temperature. Secondly, soil emissivity increases with increasing soil moisture, which in turn increases the upward emission thus decreases surfacetemperature. Thirdly, the in- crease of soil moisture increases actual evaporation which accordingly decreases temperature. Fourthly, the increase of soil moisture increases soil thermal conductivity which eases heat transfer down and up within the surfacesoil and thereby decreases the absolute values of daily maximum and min- imum temperatures. Finally, the increase of soil moisture increases soil volumetric heat capacity which increases the energy required for raising or lowering soiltemperature, and in a similar manner to that of the third effect, it decreases the absolute values of the daily maximum and minimum temper- atures.
Our approach, of course, only represents a general descrip- tion of the full dynamics of surface–atmosphere exchange. Notable effects not considered in our approach that could al- ter the results and potentially modulate the outcome of the maximum power limit include a more detailed representa- tion of radiative transfer, a distinction between the sensible and latent heat fluxes which result in different forms of stor- age changes in the atmosphere, entrainment effects at the top of the boundary layer, advection and coupling to large-scale atmospheric processes, and a better representation of night- time processes, particularly regarding the formation of stable conditions at night that prevent convection to occur. These aspects can be explored further in future extensions. Yet even at this highly simplified level, the agreement of the estimated flux partitioning with observations is rather remarkable, indi- cating that the dominant forcing and the dominant constraints are captured by our approach.
At the Finnish Meteorological Institute’s Arctic Research Center (FMI-ARC) in Sodankylä, northern Finland, the ex- ploration of hydrological processes is one of the multidis- ciplinary key research topics. On this site there are sev- eral projects dealing with the characterization of moisture content in organic-rich soil surfaces as well as freeze-thaw characteristics using different remote sensing techniques as well as landsurface modeling (e.g., Rautiainen et al., 2012, 2014; European Space Agency: ESA SMOS + Innovation Permafrost, ESA CCI Soil Moisture, ESA SMOSHiLat; Na- tional Aeronautics and Space Administration: NASA SMAP cal/val). In the Skjern River catchment in western Denmark related actions are ongoing coordinated by the Danish Center for Hydrology (HOBE). Therefore, a joined effort aimed at calibrating the used soil moisture sensors, namely, the capac- itance Decagon ECH2O 5TE sensor (Decagon 5TE) 1 and the impedance Delta-T ThetaProbe ML2X (ThetaProbe) 1 , for or- ganic substrate. At both sites, the Decagon 5TE sensors are installed at permanent network stations (Bircher et al., 2012a; Ikonen et al., 2016) providing data to the International Soil Moisture Network (ISMN, Dorigo et al., 2011) – a global in situ soil moisture database to support validation and im- provement of satellite observations and landsurface mod- els. Meanwhile, ThetaProbes are used for hand-held mea- surement campaigns (e.g., Bircher et al., 2012b), a current method for spatial variation studies of soil water content at different scales (e.g., Baggaley et al., 2009; Lopez-Vicente et al., 2009) and thus, frequently applied in the scope of satellite validation (e.g., Cosh et al., 2005; Kurum et al., 2012).
In the present study, we focused on the SECHIBA mod- ule (Ducoudré et al., 1993), which is part of the ORCHIDEE landsurface model dedicated to the resolution of the surfaceenergy and water budgets. Our objective was to test the abil- ity of 4D-VAR to estimate a set of its inner parameters. A dedicated software (called SECHIBA-YAO) was developed by using the adjoint semi-generator software called YAO de- veloped at LOCEAN-IPSL (Nardi et al., 2009). YAO serves as a framework to design and implement dynamical mod- els, helping to generate the adjoint of the model, which per- mits one to compute the model gradients. SECHIBA-YAO provides an opportunity to control the most influent inter- nal parameters of SECHIBA by assimilating LST (land sur- face temperature) observations. At a given location and for specific soil and climate conditions, twin experiments of as- similation have been executed. These twin experiments con- ducted on actual sites were used to demonstrate the accuracy and usefulness of the code and the potential of 4D-VAR when dealing with LST assimilation.
of early snow melting and, probably, water transfer from the slightly warmer wet region below. Liquid water content is low (< 0.1) and ice content is high (> 0.3) when the wet re- gion is frozen, and temperature is significantly low (around − 2 ◦ C). When the wet region thaws in the summer it gets even wetter as a result of infiltration. However, thawing takes place at a relatively slow rate in this region, as a result of the high ice content and, therefore, the high energy content re- quired to melt it. The dry region has slowly lost the initial wa- ter content over many years due to the lateral drainage during the summer. When the thawing front reaches this region in late summer, the thawed soil depth sharply increases because the energy required to thaw the soil is relatively low. At the same time a large amount of liquid water is drained from the wet region above, which virtually disappears. This amount of liquid water is gradually drained horizontally, and it even- tually accumulates in a thin layer in the lower part of the region. This layer gets thinner as the season progresses, but it does not immediately disappear when the region freezes in early winter, as a result of relatively high (though nega- tive) temperature. The undisturbed region below was never affected by summer thawing, and the total water content is still dependent on the initial condition. Even though a spin- up simulation has been performed repeating for 100 years the meteorological data corresponding to the hydrological year 2001–2002, it is not guaranteed that an equilibrium state has been reached, and, therefore, a longer spin-up simulation could entail different conditions.
Figure 2 shows the downscaled SMAP soil moisture with PALS soil moisture. The maps indicate that most of the soil moisture patterns observed with PALS are successfully replicated with the downscaling process. In some cases, the magnitude of soil moisture is notably different while the patterns are still clearly identifiable (such as on August 2). Some artifacts can be identified as well. For example, on August 13 in the southeastern corner the wet areas do not correspond to PALS soil moisture. This may be the result of poor quality LST data because some of the quality flags are raised around this area. The NDVI map in Figure 1 shows the riparian area in the middle of the pixel. The downscaled soil moisture on August 13 and 16 appear to have a systematic difference in the soil moisture between the east and west sides of the river, but this does not correspond to the PALS soil moisture. The different sides of the river (with opposite elevation gradients) may experience different temperature dynamics, which is the likely cause for this effect.