The impact of using area-averaged land surface properties —topography, vegetation condition, soil wetness—in calculations of intermediate scale (approximately 10 km) surface-atmosphere heat and moisture fluxes

It is commonly assumed that biophysically based soil-vegetation-atmosphere transfer (SVAT) models are scale-invariant with respect to the initial boundary conditions of topography, vegetation condition and soil moisture. In practice, SVAT models that have been developed and tested at the local scale (a few meters or a few tens of meters) are applied almost unmodified within general circulation models (GCMs) of the atmosphere, which have grid areas of 50–500 km². This study, which draws much of its substantive material from the papers of Sellers et al. (1992c, J. Geophys. Res., 97(D17): 19033–19060) and Sellers et al. (1995, J. Geophys. Res., 100(D12): 25607–25629), explores the validity of doing this. The work makes use of the FIFE-89 data set which was collected over a 2 km × 15 km grassland area in Kansas. The site was characterized by high variability in soil moisture and vegetation condition during the late growing season of 1989. The area also has moderate topography.

The 2 km × 15 km ‘testbed’ area was divided into 68 × 501 pixels of 30 m × 30 m spatial resolution, each of which could be assigned topographic, vegetation condition and soil moisture parameters from satellite and in situ observations gathered in FIFE-89. One or more of these surface fields was area-averaged in a series of simulation runs to determine the impact of using large-area means of these initial or boundary conditions on the area-integrated (aggregated) surface fluxes. The results of the study can be summarized as follows:

1. 1. analyses and some of the simulations indicated that the relationships describing the effects of moderate topography on the surface radiation budget are near-linear and thus largely scale-invariant. The relationships linking the simple ratio vegetation index (SR), the canopy conductance parameter (_F) and the canopy transpiration flux are also near-linear and similarly scale-invariant to first order. Because of this, it appears that simple area-averaging operations can be applied to these fields with relatively little impact on the calculated surface heat flux.

2. 2. The relationships linking surface and root-zone soil wetness to the soil surface and canopy transpiration rates are non-linear. However, simulation results and observations indicate that soil moisture variability decreases significantly as an area dries out, which partially cancels out the effects of these non-linear functions.In conclusion, it appears that simple averages of topographic slope and vegetation parameters can be used to calculate surface energy and heat fluxes over a wide range of spatial scales, from a few meters up to many kilometers at least for grassland sites and areas with moderate topography. Although the relationships between soil moisture and evapotranspiration are non-linear for intermediate soil wetnesses, the dynamics of soil drying act to progressively reduce soil moisture variability and thus the impacts of these non-linearities on the area-averaged surface fluxes. These findings indicate that we may be able to use mean values of topography, vegetation condition and soil moisture to calculate the surface-atmosphere fluxes of energy, heat and moisture at larger length scales, to within an acceptable accuracy for climate modeling work. However, further tests over areas with different vegetation types, soils and more extreme topography are required to improve our confidence in this approach.

     

A forest floor model for heat and moisture including a litter layer

Forest soils are often covered with a litter that influences the rate of mass and energy transfer between the soil and the air above, thereby modifying the temperature and moisture fields in the soil. The presence of a litter should therefore be accounted for in forest SVAT models, especially when long-term simulations are to be performed. A heat and moisture litter model has been developed by adding two dynamical equations to a force-restore type soil model. The experimental data used for the model validation was collected in a pine forest canopy in the South-West of France, that was part of the Euroflux network. The model is tested and validated over a two-year period. It is shown to provide a fairly good simulation of soil and litter moisture, soil and litter temperature and turbulent fluxes measured above the forest floor. It is also shown that simulations without the litter layer are unable to reproduce all these variables simultaneously. We then perform a sensitivity analysis to the parameters whose values are either uncertain or likely to be variable in time and space, such as the litter thickness, the rainfall fraction intercepted by the litter or the maximum value of the surface resistance. A threshold value of the litter moisture used in the surface resistance parameterisation turns out to be the most critical parameter. Further work is needed to investigate the possible relationships between the various parameters describing the litter, but the present litter model can already be used in combination with other forest SVAT models.     

Calibration of an integrated land surface process and radiobrightness (LSP/R) model during summertime

In this study, a soil vegetation and atmosphere transfer (SVAT) model was linked with a microwave emission model to simulate microwave signatures for different terrain during summertime, when the energy and moisture fluxes at the land surface are strong. The integrated model, land surface process/radiobrightness (LSP/R), was forced with weather and initial conditions observed during a field experiment. It simulated the fluxes and brightness temperatures for bare soil and brome grass in the Northern Great Plains. The model estimates of soil temperature and moisture profiles and terrain brightness temperatures were compared with the observed values. Overall, the LSP model provides realistic estimates of soil moisture and temperature profiles to be used with a microwave model. The maximum mean differences and standard deviations between the modeled and the observed temperatures (canopy and soil) were 2.6 K and 6.8 K, respectively; those for the volumetric soil moisture were 0.9% and 1.5%, respectively. Brightness temperatures at 19 GHz matched well with the observations for bare soil, when a rough surface model was incorporated indicating reduced dielectric sensitivity to soil moisture by surface roughness. The brightness temperatures of the brome grass matched well with the observations indicating that a simple emission model was sufficient to simulate accurate brightness temperatures for grass typical of that region and surface roughness was not a significant issue for grass-covered soil at 19 GHz. Such integrated SVAT-microwave models allow for direct assimilation of microwave observations and can also be used to understand sensitivity of microwave signatures to changes in weather forcings and soil conditions for different terrain types.     

Impact of spatial variations of land surface parameters on regional evaporation: a case study with remote sensing data

Most precipitation in watersheds is consumed by evaporation, thus techniques to appraise regional evaporation are important to assess the availability of water resources. Many algorithms to estimate evaporation from remotely sensed spectral data have been developed in the recent past. In addition to differences in the physical parameterization of surface fluxes, these algorithms have different solutions for describing spatial variations of the parameters in the soil–vegetation–atmosphere–transfer (SVAT) continuum. In this study, the necessity to spatially distinguish SVAT parameters for computing surface heat fluxes is analysed for the Naivasha watershed in the Kenyan Rift Valley. Landsat Thematic Mapper (TM) spectral data have been used to first delineate the watershed into 15 hydrological units using surface temperature, normalized difference vegetation index and surface albedo as attributes. Thereafter, semi‐empirical relationships between these TM‐based parameters and other SVAT parameters have been applied to compute the spatial variation of SVAT parameters and the associated evaporation from the different hydrological units. The impact of using watershed‐constant or watershed‐distributed SVAT parameters on the fluxes is analysed. The determination of watershed averaged evaporation with area‐aggregated SVAT parameters is feasible without significant loss of accuracy. Distributed evaporation in heterogeneous watersheds, however, can be investigated only with remote sensing flux algorithms that can account for spatially variable air temperature, surface roughness, surface albedo and the stability correction of the temperature profile due to buoyancy. Erroneous results can be expected if area‐aggregated SVAT parameters are used to calculate local evaporation. As most of the recently developed remote sensing flux algorithms are based on areal constant SVAT parameters, direct applications in watersheds are still limited. Copyright © 2001 John Wiley & Sons, Ltd.     

Comparative analysis of relationships between NLDAS‐2 forcings and model outputs

This study reports results from an analysis of the relationship between atmospheric forcing and model‐simulated water and energy fluxes for the North American Land Data Assimilation System Project Phase 2 (NLDAS‐2). The relationships between mean monthly precipitation and total runoff are stronger in the Sacramento (SAC) and variable infiltration capacity (VIC) models, which grew out of the hydrological community, than in the Noah and Mosaic models, which grew out of the soil‐vegetation‐atmosphere transfer (SVAT) community. The reverse is true for the relationship between mean monthly precipitation and evapotranspiration. In addition, surface energy fluxes in VIC are less sensitive to model forcing (except for air temperature) than those in the Noah and Mosaic model. Notwithstanding these general conclusions, the relationships between forcings and model‐simulated water and energy fluxes for all models vary for different seasons, variables, and regions. These findings will ultimately inspire a combination of SVAT‐type model energy components with hydrological model water components to develop a SVAT‐hydrology model to improve both evapotranspiration and total runoff simulations. Copyright © 2011 John Wiley & Sons, Ltd.     

Effects of soil moisture aggregation on surface evaporative fluxes

The effects of small-scale heterogeneity in land surface characteristics on the large-scale fluxes of water and energy in the land-atmosphere system has become a central focus of many climatology research experiments. The acquisition of high resolution land surface data through remote sensing and intensive land-climatology field experiments (like HAPEX, FIFE, and BOREAS) has provided data to investigate the interactions between microscale land-atmosphere interactions and macroscale models. To determine the effect of small scale heterogeneities, the spatially averaged evaporative fraction is analytically derived for spatially variable soil moisture and soil-atmospheric controls on evaporation at low soil moisture. This average evaporative fraction is compared with the evaporative fraction determined using the spatially averaged soil moisture, as if from a lumped, or aggregated, land surface model. Results show that the lumped-model based evaporation will over estimate evaporation during periods of low atmospheric demands (early morning/late afternoon, Winter periods, etc.) and under estimate evaporation during periods of high demand (midday Summer periods.) The accuracy of using ‘effective’ parameters in lumped macroscale models depends on the variability of soil moisture and the sensitivity of the soil-vegetation system to low soil moisture.     

Understanding of the Common Land Model performance for water and energy fluxes in a farmland during the growing season in Korea

The Common Land Model (CLM) is one of the most widely used land surface models (LSMs) due to the practicality of its simple parameterization scheme and its versatility in embracing a variety of field datasets. The improved assessment of land surface water and energy fluxes using CLM can be an alternative approach for understanding the complex land–atmosphere interactions in data‐limited regions. The understanding of water and energy cycles in a farmland is crucial because it is a dominant land feature in Korea and Asia. However, the applications of CLM to farmland in Korea are in paucity. The simulations of water and energy fluxes by CLM were conducted against those from the tower‐based measurements during the growing season of 2006 at the Haenam site (a farmland site) in Korea without optimization. According to the International Geosphere–Biosphere Programme (IGBP) land cover classification, a homogeneous cropland was selected initially for this study. Although the simulated soil moisture had a similar pattern to that of the observed, the former was relatively drier (at 0·1 m³ m^?3) than the latter. The simulated net radiation showed good agreement with the observed, with a root mean squared error (RMSE) of 41 W m^?2, whereas relatively large discrepancies between the simulation and observation were found in sensible (RMSE of 66 W m^?2) and latent (RMSE of 60 W m^?2) heat fluxes. On the basis of the sensitivity analysis, soil moisture was more receptive to land cover and soil texture parameterizations when compared to soil temperature and turbulent fluxes. Despite the uncertainty in the predictive capability of CLM employed without optimization, the initial performance of CLM suggests usefulness in a data‐limited heterogeneous farmland in Korea. Further studies are required to identify the controls on water and energy fluxes with an improved parameterization. Copyright © 2010 John Wiley & Sons, Ltd.     

Intercomparison of the JULES and CABLE land surface models through assimilation of remotely sensed soil moisture in southeast Australia

Numerous land surface models exist for predicting water and energy fluxes in the terrestrial environment. These land surface models have different conceptualizations (i.e., process or physics based), together with structural differences in representing spatial variability, alternate empirical methods, mathematical formulations and computational approach. These inherent differences in modeling approach, and associated variations in outputs make it difficult to compare and contrast land surface models in a straight-forward manner. While model intercomparison studies have been undertaken in the past, leading to significant progress on the improvement of land surface models, additional framework towards identification of model weakness is needed. Given that land surface models are increasingly being integrated with satellite based estimates to improve their prediction skill, it is practical to undertake model intercomparison on the basis of soil moisture data assimilation. Consequently, this study compares two land surface models: the Joint UK Land Environment Simulator (JULES) and the Community Atmosphere Biosphere Land Exchange (CABLE) for soil moisture estimation and associated assessment of model uncertainty. A retrieved soil moisture data set from the Soil Moisture and Ocean Salinity (SMOS) mission was assimilated into both models, with their updated estimates validated against in-situ soil moisture in the Yanco area, Australia. The findings show that the updated estimates from both models generally provided a more accurate estimate of soil moisture than the open loop estimate based on calibration alone. Moreover, the JULES output was found to provide a slightly better estimate of soil moisture than the CABLE output at both near-surface and deeper soil layers. An assessment of the updated membership in decision space also showed that the JULES model had a relatively stable, less sensitive, and more highly convergent internal dynamics than the CABLE model.     

Spatio‐temporal distribution of near‐surface and root zone soil moisture at the catchment scale

Soil moisture is highly variable both spatially and temporally. It is widely recognized that improving the knowledge and understanding of soil moisture and the processes underpinning its spatial and temporal distribution is critical. This paper addresses the relationship between near‐surface and root zone soil moisture, the way in which they vary spatially and temporally, and the effect of sampling design for determining catchment scale soil moisture dynamics. In this study, catchment scale near‐surface (0–50 mm) and root zone (0–300 mm) soil moisture were monitored over a four‐week period. Measurements of near‐surface soil moisture were recorded at various resolutions, and near‐surface and root zone soil moisture data were also monitored continuously within a network of recording sensors. Catchment average near‐surface soil moisture derived from detailed spatial measurements and continuous observations at fixed points were found to be significantly correlated (r² = 0·96; P = 0·0063; n = 4). Root zone soil moisture was also found to be highly correlated with catchment average near‐surface, continuously monitored (r² = 0·81; P < 0·0001; n = 26) and with detailed spatial measurements of near‐surface soil moisture (r² = 0·84). The weaker relationship observed between near‐surface and root zone soil moisture is considered to be caused by the different responses to rainfall and the different factors controlling soil moisture for the soil depths of 0–50 mm and 0–300 mm. Aspect is considered to be the main factor influencing the spatial and temporal distribution of near‐surface soil moisture, while topography and soil type are considered important for root zone soil moisture. The ability of a limited number of monitoring stations to provide accurate estimates of catchment scale average soil moisture for both near‐surface and root zone is thus demonstrated, as opposed to high resolution spatial measurements. Similarly, the use of near‐surface soil moisture measurements to obtain a reliable estimate of deeper soil moisture levels at the small catchment scale was demonstrated. Copyright © 2007 John Wiley & Sons, Ltd.     

A modified ISBA surface scheme for modeling the hydrology of Athabasca River Basin with GCM-scale data

A soil–vegetation–atmosphere transfer model (SVAT), interactions between the soil–biosphere–atmosphere (ISBA) of Météo France, is modified and applied to the Athabasca River Basin (ARB) to model its water and energy fluxes. Two meteorological datasets are used: the archived forecasts from the Meteorological Survey of Canada’s Global Environmental Multiscale Model (GEM) and the European Centre for Mid-range Weather Forecasts global re-analysis (ERA-40), representing spatial scales typical of a weather forecasting model and a global circulation model (GCM), respectively. The original treatment of soil moisture and rainfall in ISBA (OISBA) is modified to statistically account for sub-grid heterogeneity of soil moisture and rainfall to produce new, highly non-linear formulations for surface and sub-surface runoff (MISBA). These new formulations can be readily applied to most existing SVATs. Stand alone mode simulations using the GEM data demonstrate that MISBA significantly improves streamflow predictions despite requiring two fewer parameters than OISBA. Simulations using the ERA-40 data show that it is possible to reproduce the annual variation in monthly, mean annual, and annual minimum flows at GCM scales without using downscaling techniques. Finally, simulations using a simple downscaling scheme show that the better performance of higher resolution datasets can be primarily attributed to improved representation of local variation of land cover, topography, and climate.     

Modelling spatio-temporal soil moisture dynamics in mountain tundra

Soil moisture has a fundamental influence on the processes and functions of tundra ecosystems. Yet, the local dynamics of soil moisture are often ignored, due to the lack of fine resolution, spatially extensive data. In this study, we modelled soil moisture with two mechanistic models, SpaFHy (a catchment-scale hydrological model) and JSBACH (a global land surface model), and examined the results in comparison with extensive growing-season field measurements over a mountain tundra area in northwestern Finland. Our results show that soil moisture varies considerably in the study area and this variation creates a mosaic of moisture conditions, ranging from dry ridges (growing season average 12 VWC%, Volumetric Water Content) to water-logged mires (65 VWC%). The models, particularly SpaFHy, simulated temporal soil moisture dynamics reasonably well in parts of the landscape, but both underestimated the range of variation spatially and temporally. Soil properties and topography were important drivers of spatial variation in soil moisture dynamics. By testing the applicability of two mechanistic models to predict fine-scale spatial and temporal variability in soil moisture, this study paves the way towards understanding the functioning of tundra ecosystems under climate change.     

Estimation of near surface soil moisture in a sloping terrain of a Himalayan watershed using ENVISAT ASAR multi‐incidence angle alternate polarisation data

Soil moisture is one of the important input variables in hydrological and water erosion models. The extraction of information on near surface soil moisture from synthetic aperture radar (SAR) is well established mostly for flat terrain and using low incidence angle single polarisation data. The ENVISAT advanced SAR (ASAR) data available in multiple incidence angles and alternate polarisation modes were investigated in this study for soil moisture estimation in sloping terrain. The test site was Sitla Rao watershed in the Lesser Himalayas of northern India. Empirical models were developed to estimate near surface soil moisture in bare agricultural fields using alternate polarisation ASAR data. Both soil moisture and surface roughness field measurements were performed during the satellite passes. Backscatter from medium incidence angle (IS‐4) and vertical‐vertical (VV) polarisation signal is correlated better with volumetric soil moisture content compared to other incidence angles. The model parameters were further improved, and soil moisture estimation was refined by combining medium incidence angle (IS4) vertical‐horizontal polarisation response as another variable along with VV polarisation response. The effect of slope on the radar backscatter was minimized by incorporating local incidence angles derived from an ASTER DEM. Copyright © 2012 John Wiley & Sons, Ltd.     

Some aspects of the HAPEX-MOBILHY programme: The data base and the modelling strategy

The main thrust of the HAPEX-MOBILHY experiment was towards investigating techniques involved in integrating the turbulent surface fluxes measured at local sites to a larger scale approaching that used in general circulation models.Some aspects of the field data collected at various times and spatial scales are presented. Annual cycle of the soil moisture at many sites is discussed in relation with outputs of a large scale hydrological model. At shorter time scales, the spatial variability of surface energy partition is examined with regard to spatial contrasts in albedo, surface roughness and plant properties related to the two main vegetation classes found in the HAPEX square: A pine forest and the nearby agricultural area.Finally, examples of daily spatial integration with an atmospheric mesoscale model including a comprehensive treatment of land surface processes are presented.     

Diurnal cycle of surface energy fluxes in high mountain terrain: High-resolution fully coupled atmosphere-hydrology modelling and impact of lateral flow

Water and energy fluxes are inextricably interlinked within the interface of the land surface and the atmosphere. In the regional earth system models, the lower boundary parameterization of land surface neglects lateral hydrological processes, which may inadequately depict the surface water and energy fluxes variations, thus affecting the simulated atmospheric system through land-atmosphere feedbacks. Therefore, the main objective of this study is to evaluate the hydrologically enhanced regional climate modelling in order to represent the diurnal cycle of surface energy fluxes in high spatial and temporal resolution. In this study, the Weather Research and Forecasting model (WRF) and coupled WRF Hydrological modelling system (WRF-Hydro) are applied in a high alpine catchment in Northeastern Tibetan Plateau, the headwater area of the Heihe River. By evaluating and intercomparing model results by both models, the role of lateral flow processes on the surface energy fluxes dynamics is investigated. The model evaluations suggest that both WRF and coupled WRF-Hydro reasonably represent the diurnal variations of the near-surface meteorological fields, surface energy fluxes and hourly partitioning of available energy. By incorporating additional lateral flow processes, the coupled WRF-Hydro simulates higher surface soil moisture over the mountainous area, resulting in increased latent heat flux and decreased sensible heat flux of around 20–50 W/m² in their diurnal peak values during summertime, although the net radiation and ground heat fluxes remain almost unchanged. The simulation results show that the diurnal cycle of surface energy fluxes follows the local terrain and vegetation features. This highlights the importance of consideration of lateral flow processes over areas with heterogeneous terrain and land surfaces.     

Numerical Simulation of the Sensitivity of Summer Monsoon Circulation and Rainfall over India to Land Surface Processes

—The influence of soil moisture and vegetation variation on simulation of monsoon circulation and rainfall is investigated. For this purpose a simple land surface parameterization scheme is incorporated in a three-dimensional regional high resolution nested grid atmospheric model. Based on the land surface parameterization scheme, latent heat and sensible heat fluxes are explicitly estimated over the entire domain of the model. Two sensitivity studies are conducted; one with bare dry soil conditions (no latent heat flux from land surface) and the other with realistic representation of the land surface parameters such as soil moisture, vegetation cover and landuse patterns in the numerical simulation. The sensitivity of main monsoon features such as Somali jet, monsoon trough and tropical easterly jet to land surface processes are discussed.¶Results suggest the necessity of including a detailed land surface parameterization in the realistic short-range weather numerical predictions. An enhanced short-range prediction of hydrological cycle including precipitation was produced by the model, with land surface processes parameterized. This parameterization appears to simulate all the main circulation features associated with the summer monsoon in a realistic manner.     

Data assimilation methods in the Earth sciences

Although remote sensing data are often plentiful, they do not usually satisfy the users’ needs directly. Data assimilation is required to extract information about geophysical fields of interest from the remote sensing observations and to make the data more accessible to users. Remote sensing may provide, for example, measurements of surface soil moisture, snow water equivalent, snow cover, or land surface (skin) temperature. Data assimilation can then be used to estimate variables that are not directly observed from space but are needed for applications, for instance root zone soil moisture or land surface fluxes. The paper provides a brief introduction to modern data assimilation methods in the Earth sciences, their applications, and pertinent research questions. Our general overview is readily accessible to hydrologic remote sensing scientists. Within the general context of Earth science data assimilation, we point to examples of the assimilation of remotely sensed observations in land surface hydrology.     

Estimation of mean water transit time on a steep hillslope in South Korea using soil moisture measurements and deuterium excess

A method for estimating daily mean transit time (DMTT) within a soil layer was proposed using field measurements of soil moisture. Vertical profiles of soil moisture time series were used for storage estimation. Water fluxes were evaluated through matrix and bypass flow. Variations in soil moisture and soil thickness were used to evaluate matrix flow. Exponential decay in depth of macropores was also used for bypass flow approximation. DMTT evaluation was compared to results obtained from a stable water isotope model using two years of data acquired on a steep granite hillslope in the Sulmachun watershed, South Korea. Various uncertainties in transit time evaluation such as model structure, non‐stationary assumption and data acquisition of existing approaches can be accounted for in the proposed methodology, and the flowpath contribution can be further configured in conjunction with hydrometric measurements. Probability density functions of isotope analyses were partially explained by transit time distributions that were based on soil moisture measurements. Supplementary sensitivity analyses for uncertainty configurations indicate that matrix flow is the primary process in determining transit time distribution while the impact of bypass flow is minor. The feasibility of a DMTT approach over isotope‐based methodologies highlights not only the strength of this proposed method, both in cost and time, but also its further application potential for existing soil moisture measurements. Copyright © 2013 John Wiley & Sons, Ltd.     

Is the soil moisture precipitation feedback enhanced by heterogeneity and dry soils? A comparative study

The interaction between the land surface and the atmosphere is a crucial driver of atmospheric processes. Soil moisture and precipitation are key components in this feedback. Both variables are intertwined in a cycle, that is, the soil moisture – precipitation feedback for which involved processes and interactions are still discussed. In this study the soil moisture – precipitation feedback is compared for the sempiternal humid Ammer catchment in Southern Germany and for the semiarid to subhumid Sissili catchment in West Africa during the warm season, using precipitation datasets from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), from the German Weather Service (REGNIE) and simulation datasets from the Weather Research and Forecasting (WRF) model and the hydrologically enhanced WRF-Hydro model. WRF and WRF-Hydro differ by their representation of terrestrial water flow. With this setup we want to investigate the strength, sign and variables involved in the soil moisture – precipitation feedback for these two regions. The normalized model spread between the two simulation results shows linkages between precipitation variability and diagnostic variables surface fluxes, moisture flux convergence above the surface and convective available potential energy in both study regions. The soil moisture – precipitation feedback is evaluated with a classification of soil moisture spatial heterogeneity based on the strength of the soil moisture gradients. This allows us to assess the impact of soil moisture anomalies on surface fluxes, moisture flux convergence, convective available potential energy and precipitation. In both regions the amount of precipitation generally increases with soil moisture spatial heterogeneity. For the Ammer region the soil moisture – precipitation feedback has a weak negative sign with more rain near drier patches while it has a positive signal for the Sissili region with more rain over wetter patches. At least for the observed moderate soil moisture values and the spatial scale of the Ammer region, the spatial variability of soil moisture is more important for surface-atmosphere interactions than the actual soil moisture content. Overall, we found that soil moisture heterogeneity can greatly affect the soil moisture – precipitation feedback.     

The scaling characteristics of soil parameters: From plot scale heterogeneity to subgrid parameterization

The variation in soil texture, surface moisture or vertical soil moisture gradient in larger scale atmospheric models may lead to significant variations in simulated surface fluxes of water and heat. The parameterization of soil moisture fluxes at spatial scales compatible with the grid size of distributed hydrological models and mesoscale atmospheric models ( 100 km²) faces principal problems which relate to the underlying microscopic or field scale heterogeneity in soil characteristics.

The most widely used parameterization in soil hydrology, the Darcy-Richards (DR) equation, is gaining increasing importance in mesoscale and climate modelling. This is mainly due to the need to introduce plant-interactive soil water depletion and stomatal conductance parameterizations and to improve the calculation of deep percolation and runoff. Covering a grid of several hundreds of square kilometres, the DR parameterization in soil-vegetation-atmosphere-transfer schemes (SVATs) is assumed to be scale-invariant. The parameters describing the non-linear, area-average soil hydraulic functions in this scale-invariant DR-equation should be treated as calibration-parameters, which do not necessarily have a physical meaning. The saturated hydraulic conductivity is one of the soil parameters to which the models show very high sensitivity. It is shown that saturated hydraulic conductivity can be scaled in both vertical and horizontal directions for large flow domains.

In this paper, a distinction is made between effective and aggregated soil parameters. Effective parameters are defined as area-average values or distributions over a domain with a single, distinct textural soil type. They can be obtained by scaling or inverse modelling. Aggregated soil parameters represent grid-domains with several textural soil types. In soil science dimensional methods have been developed to scale up soil hydraulic characteristics. With some specific assumptions, these techniques can be extrapolated from classical field-scale problems in soil heterogeneity to larger domains, compatible with the grid-size of large scale models. Particularly promising is the estimation of effective soil hydraulic parameters from area averaging measurements through inverse modelling of the unsaturated flow.

Techniques to scale and aggregate the soil characteristics presented in this paper qualify for direct or indirect use in large scale meteorological models. One of the interesting results is the effective behaviour of the reference curve, which can be obtained from similar media scaling. If the conclusions of this paper survive further studies, a relatively simple method will become available to parameterize soil variability at large scales. The inverse technique is found to provide effective soil parameters which perform well in predicting both the area-average evaporation and the area-average soil moisture fluxes, such as subsurface runoff. This is not the case for aggregated soil parameters. Obtained from regression relationships between soil textural composition and hydraulic characteristics, these aggregated parameters predict evaporation fluxes well, but fail to predict water balance terms such as percolation and runoff. This is a serious drawback which could eventually hamper the improvement of the representation of the hydrological cycle in mesoscale atmospheric models and in GCMs.     

Evaluation of the hydrology of the IBIS land surface model in a semi‐arid catchment

This paper evaluates the Integrated BIosphere Simulator (IBIS) land surface model using daily soil moisture data over a 3‐year period (2005–2007) at a semi‐arid site in southeastern Australia, the Stanley catchment, using the Monte Carlo generalized likelihood uncertainty estimation (GLUE) approach. The model was satisfactorily calibrated for both the surface 30 cm and full profile 90 cm. However, full‐profile calibration was not as good as that for the surface, which results from some deficiencies in the evapotranspiration component in IBIS. Relatively small differences in simulated soil moisture were associated with large discrepancies in the predictions of surface runoff, drainage and evapotranspiration. We conclude that while land surface schemes may be effective at simulating heat fluxes, they may be ineffective for prediction of hydrology unless the soil moisture is accurately estimated. Sensitivity analyses indicated that the soil moisture simulations were most sensitive to soil parameters, and the wilting point was the most identifiable parameter. Significant interactions existed between three soils parameters: porosity, saturated hydraulic conductivity and Campbell ‘b’ exponent, so they could not be identified independent of each other. There were no significant differences in parameter sensitivity and interaction for different hydroclimatic years. Even though the data record contained a very dry year and another year with a very large rainfall event, this indicated that the soil model could be calibrated without the data needing to explore the extreme range of dry and wet conditions. IBIS was much less sensitive to vegetation parameters. The leaf area index (LAI) could affect the mean of daily soil moisture time series when LAI < 1, while the variance of the soil moisture time series was sensitive to LAI > 1. IBIS was insensitive to the Jackson rooting parameter, suggesting that the effect of the rooting depth distribution on predictions of hydrology was insignificant. Copyright © 2014 John Wiley & Sons, Ltd.