Local evaluation of the Interaction between Soil ... - Wiley Online Library

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116, D20126, doi:10.1029/2011JD016002, 2011

Local evaluation of the Interaction between Soil Biosphere Atmosphere soil multilayer diffusion scheme using four pedotransfer functions B. Decharme,1 A. Boone,1 C. Delire,1 and J. Noilhan1 Received 24 March 2011; revised 13 July 2011; accepted 8 August 2011; published 27 October 2011.

[1] The Surface Monitoring Of the Soil Reservoir Experiment (SMOSREX) site is used in this study to evaluate the performance of the Interaction between Soil Biosphere Atmosphere soil multilayer diffusion scheme (ISBA‐DF) in reproducing short‐term and long‐term evolution of soil moisture and temperature profiles, surface energy fluxes, and drainage rate using both the Brooks and Corey and the van Genuchten models describing soil‐water retention and conductivity curves. The site consists of a fallow field in southwestern France where intensive measurements were made during the 2001–2007 period. Two sets of four simulations describing homogeneous and heterogeneous soil properties are performed using four continuous pedotransfer functions. ISBA‐DF is also compared with the ISBA “force‐restore” soil scheme (ISBA‐FR) since this version is currently used in several meteorological, hydrological, and/or climate applications. ISBA‐DF exhibits a good performance in terms of simulating the soil moisture profile and the surface energy fluxes, especially when heterogeneous soil properties are considered. Its soil moisture dynamic does not depend on the field capacity, which is a clear advantage compared with ISBA‐FR. However, it shows some drawbacks in simulating the surface temperature. The Brooks and Corey model exhibits the best skill scores in simulating the soil moisture profile and the surface fluxes compared with the van Genuchten model. Nevertheless, the differences are not significant, and the results on a single site reduce the generality of this intercomparison. Finally, two additional sets of experiments are performed to assess the scheme sensitivity to increasing soil depth and to the soil vertical discretization. Citation: Decharme, B., A. Boone, C. Delire, and J. Noilhan (2011), Local evaluation of the Interaction between Soil Biosphere Atmosphere soil multilayer diffusion scheme using four pedotransfer functions, J. Geophys. Res., 116, D20126, doi:10.1029/2011JD016002.

1. Introduction [2] Since Charney et al. [1977] demonstrated the importance of land surface processes on the atmospheric general circulation, Land Surface Models (LSMs) have been introduced in atmospheric models to provide realistic lower boundary conditions for temperature and moisture. The problem of how to compute the soil moisture and heat profiles was recognized as being of primary importance to properly simulate the water and energy budgets at the surface/atmosphere interface, as well as the water fluxes to rivers. These profiles are physically related to Darcy’s law for mass transfer and Fourier’s law for heat conduction. However, in the past, the computational computer time to numerically solve these diffusive equations was considered too expensive, and some simpler approaches based on physical approximations were first favored. Bhumralkar [1975] and Blackadar [1976] proposed the “force‐restore” 1

CNRS, Météo‐France, GAME‐CNRM, Toulouse, France.

Copyright 2011 by the American Geophysical Union. 0148‐0227/11/2011JD016002

method to quickly solve the soil heat diffusion: the time tendency of the soil surface temperature is forced by the sum of all the surface/atmospheric energy fluxes (evaporative plus sensible heat minus the net downward radiation) and restored toward a bulk soil temperature parameterized as the mean surface temperature over a diurnal period. As noted by Deardorff [1977], the surface energy budget is closely related to the evolution of the superficial soil moisture. He generalized the force‐restore method to quickly solve the soil moisture diffusion: the surface moisture content is forced by the surface/atmospheric water fluxes (evaporation minus precipitation) and restored toward the total moisture content of a bucket model. [3] In this simplified framework, Noilhan and Planton [1989] developed the Interaction between Soil Biosphere Atmosphere (ISBA) model. ISBA contains the basic physics of the land surface and needs only a limited number of parameters that depend on the type of soil and vegetation. It has been implemented in the Météo‐France forecast models [Manzi and Planton, 1994; Mahfouf et al., 1995] and more recently in the Canadian Regional Weather Forecast Model [Bélair et al., 2003]. Through the use of the SURFace EXternalized (SURFEX) modeling platform, ISBA is now-

D20126

1 of 29

D20126

DECHARME ET AL.: ISBA SOIL MULTILAYER DIFFUSION SCHEME

adays implemented in all mesoscale, forecast, and climate models of Météo‐France [Noilhan et al., 2011; Seity et al., 2011; A. Voldoire et al., The CNRM‐CM5 global climate model: Description and basic evaluation, submitted to Climate Dynamics, 2011], as well as in hydrological forecasting [Habets et al., 2004] and large‐scale hydrological systems [Alkama et al., 2010]. [4] More recently, soil multilayer diffusion schemes where introduced in LSMs to explicitly solve mass and heat‐diffusive equations [Braud et al., 1995; Wetzel and Boone, 1995; Viterbo and Beljaars, 1995; Thompson and Pollard, 1995; Chen et al., 1997; Cox et al., 1999; Boone et al., 2000; de Rosnay et al., 2000; Dai et al., 2003]. The total soil depth is discretized with the use of several layers, and both moisture and temperature profiles can be explicitly computed according to homogeneous or heterogeneous vertical soil characteristics. For the soil hydrology, two closed‐ form equations are commonly used for predicting the dependence to volumetric water content of the soil hydraulic conductivity and matric potential (or water pressure head). Brooks and Corey [1966] (BC66) and/or Campbell [1974] proposed simple analytical power soil‐water retention and conductivity functions based on North American soil observations, while van Genuchten [1980] (VG80) derived more complex analytical forms from European soils. The BC66 model is mainly used by the atmospheric community [Wetzel and Boone, 1995; Viterbo and Beljaars, 1995; Thompson and Pollard, 1995; Chen et al., 1997; Cox et al., 1999; Dai et al., 2003], while hydrologists generally prefer VG80 [de Rosnay et al., 2000; Balsamo et al., 2009]. Some models use a combined form with VG80 for the soil‐water retention curve and BC66 for the soil‐water conductivity curve [Braud et al., 1995]. These two models are significantly modulated by the soil hydrodynamic parameters, such as, for example, the soil porosity, the saturated matric potential, and the saturated hydraulic conductivity. Fortunately, the prediction of these parameters from data recorded in soil surveys, such as the soil fraction of sand, silt, and clay, the soil bulk density, and the soil organic matter content, can be done by using a variety of existing PedoTransfer Function (PTF) approaches [e.g., Clapp and Hornberger, 1978; Cosby et al., 1984; Carsel and Parrish, 1988; Wösten et al., 1999]. [5] These multilayer diffusion schemes are theoretically superior to previous simple schemes. They allow explicit representation of many processes that are more difficult to parameterize in bucket models: the vertical distribution of the root profile in the soil [Feddes et al., 2001; Braud et al., 2005], the surface/groundwater capillarity exchanges [Maxwell and Miller, 2005; Miguez‐Macho et al., 2007; Anyah et al., 2008], the interaction between cold processes (snow and soil freezing) and the soil processes (heat exchange, infiltration, runoff) [Slater et al., 2001; Luo et al., 2003]. Nowadays, the representation of these processes remains a challenge in various fields of model application such as meteorology, hydrology, environmental risk analyses, and climate change. As a result of this pursuit, a multilayer diffusion scheme using BC66 was first introduced in ISBA (ISBA‐DF) by Boone et al. [2000] to calibrate the soil superficial freezing/thawing processes in the ISBA force‐restore model (ISBA‐FR). During the PILPS‐Phase‐2(e) experiment [Bowling et al., 2003], ISBA‐DF was applied over a Scandinavian catchments in order to improve the simulation of the frozen soil

D20126

compared with ISBA‐FR and has shown fairly good results in terms of soil ice, temperature, and runoff [Habets et al., 2003]. More recently, during the African Monsoon Multidisciplinary Analysis (AMMA) LSM Intercomparaison Project (ALMIP), ISBA‐DF was used over West Africa and demonstrated its relative ability to reproduce surface brightness temperature over the dry Sahelian area [Boone et al., 2009; de Rosnay et al., 2009]. [6] The goal of this study is to evaluate the performance of an updated version of ISBA‐DF that uses both the BC66 and VG80 approaches to simulate soil moisture and temperature profiles under temperate midlatitude conditions. This evaluation is done over the Surface Monitoring Of the Soil Reservoir Experiment (SMOSREX) site located in southwestern France where intensive field measurements during the 2001–2007 period provide soil moisture and temperature profiles, as well as surface energy fluxes at a 30 min time step [de Rosnay et al., 2006; Albergel et al., 2010]. Besides the direct comparison with these observations, ISBA‐DF is put into perspective by the comparison of its results with ISBA‐FR, since ISBA‐FR is currently used in various meteorological, climate, and/or hydrological applications. In addition, no runoff/drainage observations are available in the SMOSREX database, and the runoff/ drainage from ISBA‐FR serves, therefore, as a reference to understand the ISBA‐DF behaviors. Two sets of four simulations describing homogeneous and heterogeneous soil properties are performed by the use of four continuous PTFs: Clapp and Hornberger [1978] and Cosby et al. [1984] for the BC66 model, and Carsel and Parrish [1988] and Wösten et al. [1999] for the VG80 model. Several additional experiments are also done with different configurations of ISBA‐DF in order to assess the model sensitivities to increasing the total soil depth and to the vertical resolution through the number of soil layers. An overview of ISBA‐FR and ISBA‐DF is presented in section 2. For more clarity, and because snow and soil freezing are fairly rare events over the SMOREX site, the representation of snow and soil ice processes are not described in this study. The SMOSREX data set and the experimental design are described in section 3. The results of each experiment are shown in section 4. Sensitivity studies to the ISBA‐DF configuration are presented in section 5. Finally, section 6 gives the main discussion and conclusions of this study.

2. Review of the ISBA Model 2.1. ISBA‐FR [7] A single surface temperature is used to represent the surface energy balance of the land/cover system [Noilhan and Planton, 1989]. This surface soil/vegetation temperature evolves according to the surface heat flux rate, G (W m2) (see Appendix A). Ts is approximated as the temperature of a thin superficial layer, d1 (m), fixed to 0.01 m depth. Ts is restored toward its mean value T2 (K) over one day, t (s), as proposed by Bhumralkar [1975] and Blackadar [1976]:

2 of 29

@Ts 2 @t ¼ CT G ðTs T2 Þ @T 2 1 ¼ ðTs T2 Þ @t

ð1Þ

D20126


where CT (K m−2 J−1) is the surface composite thermal inertia coefficient parameterized as the harmonic mean between the soil and the vegetation thermal inertia coefficients weighted by the veg fraction [Noilhan and Planton, 1989]. [8] The soil hydrology is represented by three layers with the use of the force‐restore method proposed by Deardorff [1977]. The superficial volumetric water content, w1 (m3 m−3), of the superficial layer is restored toward the volumetric water content, w2 (m3 m−3), of the rooting layer (including the surface layer), while a third layer, w 3 (m 3 m −3 ), was added to distinguish between the rooting depth, d2 (m), and the total soil depth, d3 (m) [Boone et al., 1999]. The volumetric water content is constrained to be less than the soil porosity or saturation water content, wsat (m3 m−3): @w1 C2 C1 @t ¼ w d1 Pg Eg w1 weq 8w1 wsat @w 1 2 ¼ Pg Eg Etr K2 D2 8w2 wsat @t w d2 @w3 d2 8w3 wsat @t ¼ ðd3 d2 Þ ðK2 þ D2 Þ K3

ð2Þ

where rw (kg m−3) is the density of liquid water, Pg (kg m−2 s−1) is the flux of water reaching the soil surface, C1 is the dimensionless surface force‐restore soil transfer coefficient for moisture, C2 is the dimensionless diffusion restore coefficients, and wgeq (m3 m−3) is the surface volumetric water content at the balance of gravity and capillary forces computed according to w2 and soil textural properties. Note that, plant transpiration stops when w2 is below the wilting point volumetric water content, wwilt (m3 m−3), corresponding to a matric potential of −150 m. [9] D2 (s−1) is the vertical soil moisture diffusion between the rooting layer and the deep layer, while K2 (s−1) and K3 (s−1) are the gravitational drainages from the rooting layer toward the deep layer and draining out the deep layer, respectively: D2 ¼ C4 ðw2 w3 Þ d 3 C3 max 0; w2 wfc K2 ¼ d2 d3 C3 K3 ¼ max 0; w3 wfc ðd3 d2 Þ

ð3Þ

where C4 is the dimensionless diffusion restore coefficients between the rooting layer and the deep layer [Boone et al., 1999], and C3 is the dimensionless drainage coefficient [Mahfouf and Noilhan, 1996] that characterizes the rate at which the water profile is restored toward the field capacity, wfc (m3 m−3). In addition, if the soil moisture of the root and/or deep layers exceeds the soil porosity, a saturation excess surface and/or subsurface runoff are generated. [10] All force‐restore coefficients (C1, C2, C3, and C4) and soil hydrological parameters (wsat, wwilt, and wfc) are related to soil textural properties and moisture using the Noilhan and Lacarrère [1995] continuous relationships derived from the BC66 model and the Clapp and Hornberger [1978] parameters. For more details, see Noilhan and Planton [1989] and Boone et al. [1999].

D20126

2.2. ISBA‐DF [11] To be consistent with the ISBA surface energy budget, the surface temperature evolves according to the heat storage in the soil/vegetation composite (see Appendix A) and to the thermal gradient between the surface (the same fine superficial layer than for ISBA‐FR) and the second layer [Wetzel and Boone, 1995; Boone et al., 2000]. Accordingly, Ts equals T1 for a soil discretized in N soil layers. Below, the heat transfer within the soil is described by the use of the classical one‐dimensional Fourier law. The governing equations for heat transfer at the surface and within the soil are then written as follows: @Ts 1 @t ¼ CT G D~z ðTs T2 Þ 1 @Ti 1 1 i i1 ¼ ð T T Þ ð T T Þ 8i ¼ 2; N ; i1 i i iþ1 @t cgi Dzi D~zi1 D~zi ð4Þ

where Dzi (m) is the thickness of the layer i, D~zi (m) is the thickness between two consecutive layer midpoints or nodes, cgi (J m−3 K−1) is the total soil heat capacity, and i (W m−1 K−1) is the inverse‐weighted arithmetic mean of the soil thermal conductivity at the interface between two consecutive nodes. The total soil heat capacity is computed as the sum of the water heat capacity and the heat capacity of the soil matrix computed according to Peters‐Lidard et al. [1998]. The soil thermal conductivity is expressed as a function of volumetric water content, soil porosity, and dry soil conductivity following the method of Johansen [1975] with modifications by Farouki [1986], as configured for LSM applications by Peters‐Lidard et al. [1998]. The numerical solutions of equation (4) are solved by using a backward‐difference implicit time scheme where the linear set of diffusion equations can be cast in a tridiagonal form. [12] The ISBA‐DF soil hydrology uses the “mixed” form of the Richards equation to describe the water mass transfer within the soil via Darcy’s law. The tendency is solved in terms of volumetric water, and the hydraulic gradient is solved in terms of water pressure head. This mixed form is generally considered superior to the pressure‐based or the moisture‐based forms because of robustness with respect to mass balance. In addition, the mixed form is applicable to homogeneous or heterogeneous soils and to saturated or unsaturated soils [Milly, 1985; Allen and Murphy, 1985; Celia et al., 1990; Johnsen et al., 1995; Mansell et al., 2002]. The pressure‐based form (time tendency and hydraulic gradient solved in terms of water pressure head) generally exhibits very poor preservation of mass balance from numerical solutions [Allen and Murphy, 1985; Celia et al., 1990], unacceptable time‐step limitations [Milly, 1985], and relatively slow convergence [Baca et al., 1997], which undermines its physical basis in meteorological or climate applications. Commonly used by the atmospheric community, the moisture‐ based form or Fokker‐Planck equation (time tendency and hydraulic gradient solved in terms of volumetric water content) have significantly improved mass balance, and numerical solutions converge more quickly. Unfortunately, this form is limited to strictly unsaturated conditions and homogeneous media because the soil moisture is discontinuous at

3 of 29

D20126


the soil layer interfaces [Hills et al., 1989, Guarracino and Quintana, 2004], which limits its applicability in hydrological studies. [13] Using the same N layers as for temperature, the ISBA‐DF governing equation for water and vapor‐phase

point where the slope of the VG80 soil‐water retention curve (dw/dy) reaches its maximum value, n is a dimensionless coefficient that characterizes the rate at which the symmetrical‐shaped VG80 retention curve turns toward the ordinate for large negative value of the matric potential, and l is the

@w1 1 y y2 y y2 S1 ¼ þ k 1 1 þ 1 1 1 Dz1 @t D~z1 D~z1 w @w y y y y iþ1 1 S i i iþ1 ¼ with Fi ¼ k i i ; Fii Fi þ þ 1 þ i i @t w D~zi D~zi Dzi

transfers within the soil is written as follows: where Si (kg m−2 s−1) is the soil‐water source/sink term and y i (m) is the soil matric potential. k i (m s−1) and i (m s−1) are the geometric means over two nodes of the ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi qconsecutive soil hydraulic conductivity (k i = ki ðy i Þ kiþ1 y iþ1 ) and isothermal vapor conductivity values, respectively. The choice of an appropriate intrablock approximation for unsaturated hydraulic conductivity has been pointed out as critical in the numerical solution of unsaturated flow by many studies [Haverkamp et al., 1977; Haverkamp and Vauclin, 1979; Hornung and Messing, 1983; Schnabel and Richie, 1984; Warrick, 1991; Zaidel and Russo, 1992; Feng et al., 2007]. As demonstrated by the majority of these studies, the geometric mean generates little weighting error, improves the simulated infiltration front, and is generally applicable in all situations. Equations (5) are solved numerically using a Crank‐Nicholson implicit time scheme where the flux term, Fi, is linearized via a one‐order Taylor series expansion. The resulting linear set of diffusion equations can be cast in a tridiagonal form and solved quickly. Such algorithm is less expensive than an iterative solution method, and it is more suitable for regional to global large‐scale applications. For large time steps, such as in global climate model, a time‐ splitting option is also activated. [14] The vapor conductivity is function of soil texture, water content, and temperature according to Braud et al. [1993]. The relationship between the soil moisture, the soil matric potential, and the hydraulic conductivity is determined (using the BC66 from equation (6) or the VG80 from equation (7)): y ðwÞ ¼ y sat

w wsat

b

and k ðy Þ ¼ ksat

y y sat

2bþ3 b

ð6Þ

31 2 n n 1 6 w wr n 1 17 y ðwÞ ¼ 4 5 wsat wr h and k ðy Þ ¼ ksat

ð1 þ jy jn Þ

11=n

ð1 þ jy jn Þ

jy jn1

ð11=nÞðlþ2Þ

i2 ð7Þ

where b represents the dimensionless shape parameter of the BC66 soil‐water retention curve, wr (m3 m−3) is the residual water content (assumed zero for BC66), y sat (m), and ksat (m s−1) are the soil matric potential and hydraulic conductivity at saturation, respectively, a (m−1) is the inflection

D20126

ð5Þ

Mualem [1976] dimensionless parameter that determines the shape of the hydraulic conductivity curve. [15] Finally, the water‐soil source/sink terms (Si in equation (5)) is related to the land surface infiltration and evapotranspiration processes. Water for soil evaporation is drawn from the superficial layer. The water used for transpiration is removed throughout the root zone in which the roots are asymptotically distributed according to Jackson et al. [1996]. The soil‐water source is the soil infiltration parameterized via a Green and Ampt [1911] approach (see Appendix B).

3. Experimental Design 3.1. SMOSREX Data Set [16] The SMOSREX field site (43°23′N, 1°17′E) is located at 188 m altitude, near Toulouse in southwestern France, and is described in detail by de Rosnay et al. [2006]. It consists of a 180 m by 180 m square covered by grassland or natural fallow in which the vegetation characteristics, such as the Leaf Area Index (LAI), are observed. Soil texture, soil organic matter, and bulk density profiles are measured near the surface (∼0–6 cm) and every 10 cm over a 90 cm depth (Table 1). The set of atmospheric forcing variables (atmospheric pressure, air humidity, air temperature, long‐wave and short‐wave incident radiation, rain rate, wind speed) used to run ISBA has been compiled at a 30 min time step over the 2001–2007 period from the measurements. [17] The SMOSREX data set includes soil moisture and temperature profiles observed since 2001 at a 30 min time step. The soil moisture measurements are performed with a ThetaProbe installed at the same 10 depths as the soil characteristics (0–6, 10, 20, 30, 40, 50, 60, 70, 80, and 90 cm). Soil temperatures are observed at different depths (1, 5, 20, 50, and 90 cm), while the surface radiative skin temperature is measured by the use of a Heitronics infrared thermometer. Surface energy fluxes (Rn, H, LE) have also been observed at a 30 min time step since 2005: LE and H are derived from the eddy covariance method. In this study, only daytime flux measurements are used (short‐wave incident radiation superior to 10 W m−2) because of the poor quality of these measurements at nighttime, which is a common problem for eddy covariance measurements [Aubinet et al., 2002]. 3.2. Model Configuration [18] Both the ISBA‐FR and ISBA‐DF schemes are applied for the SMOSREX field site at a 30 min time step between 2001 and 2007. A spin‐up of 10 years is done using

4 of 29

D20126

D20126


Table 1. Observed Soil Properties and Model Parametersa Soil Properties Soil depth measurement (cm) Model layer depth (cm) Root fraction (%) Sand (%) Clay (%) Organic matter (%) Bulk density (kg m−3)

0–6 1 5.6

5 19.5 37.2 15.6 2.01 1487

Homogeneous Soil

10

20

30

40

50

60

70

80

90

15 32.9

25 18.7

35 10.7

45 6.3

55 3.8

65 2.5

75 0

85 0

95 0

40.8 14.5 1.28 1487

38.7 15.4 1.08 1648

39.6 15.6 1.01 1591

28.4 26.0 0.78 1509

30.1 25.3 0.78 1587

27.2 28.4 0.73 1832

24.0 29.5 0.72 1734

27.7 28.7 0.70 1738

26.6 28.7 0.68 1735

Model Parameters 3

−3

31.7 23.1 0.92 1642 Homogeneous Soil

wsat (m m )

CH78 CO84 CP88 WO99

0.454 0.446 0.431 0.405

0.450 0.441 0.431 0.414

0.452 0.444 0.431 0.366

0.451 0.443 0.430 0.383

0.463 0.455 0.417 0.416

0.462 0.453 0.417 0.391

0.465 0.456 0.413 0.316

0.468 0.460 0.413 0.348

0.464 0.455 0.412 0.346

0.465 0.456 0.413 0.347

0.460 0.451 0.420 0.364–0.372

ksat (10−6 m s−1)

CH78 CO84 CP88 WO99 CH78 CO84 CH78 CO84 CP88 WO99 CP88 WO99 CP88 WO99

9.38 4.14 4.33 2.19 5.64 5.44 −0.33 −0.30 4.04 2.54 1.57 1.23 0.06 −2.32

11.22 4.67 5.48 3.21 5.49 5.25 −0.31 −0.27 4.61 3.02 1.61 1.22 0.06 −1.93

9.84 4.34 4.62 1.84 5.61 5.40 −0.32 −0.29 4.25 2.14 1.59 1.20 0.06 −2.22

9.82 4.44 4.59 2.29 5.64 5.43 −0.32 −0.28 4.33 2.57 1.59 1.21 0.06 −2.13

3.42 2.75 1.20 2.03 7.06 7.09 −0.40 −0.36 2.37 2.84 1.36 1.15 0.08 −3.11

3.68 2.92 1.35 1.61 6.97 6.98 −0.38 −0.34 2.55 2.61 1.38 1.14 0.08 −3.31

2.86 2.56 0.99 0.55 7.39 7.48 −0.41 −0.36 2.15 1.20 1.33 1.09 0.09 −4.45

2.57 2.30 0.79 0.75 7.54 7.66 −0.43 −0.40 1.87 1.57 1.29 1.10 0.09 −4.17

2.83 2.59 0.99 0.79 7.43 7.52 −0.40 −0.36 2.17 1.77 1.32 1.10 0.09 −4.14

2.79 2.51 0.95 0.79 7.43 7.53 −0.41 −0.37 2.09 1.71 1.32 1.10 0.09 −4.09

4.45 3.16 1.68 1.60–1.41 6.66 6.63 −0.37 −0.33 2.83 2.19–2.27 1.42 1.16–1.14 0.08 −3.33

b y sat (m) a (m−1) n wr (m3 m−3) l a

Observed soil properties and model parameters are given for homogeneous (layer‐average textural properties) and heterogeneous soils. The root fraction is used in both cases according to Jackson et al. [1996]. The soil hydrodynamic parameters are computed according to the four pedotransfer functions given in Appendix 2. Note that for homogeneous soil, two values are given for WO99 corresponding to topsoil and subsoil (see Appendix C).

the 2001‐year forcing data in order to ensure an adequate numerical equilibrium for soil water and temperature profiles whatever the configuration of the scheme. The soil properties observed at the SMOSREX field site are used to derive all ISBA parameters (Table 1). The observed LAI extrapolated at a daily time step is used to force the model, while other vegetation parameters are specified according to the standard ISBA values such as 2 × 10−5 K m−2 J−1 for the vegetation thermal inertia, 40 s m−1 for the minimum stomatal resistance or 0.3, 0.1 and 0.08 for the near‐infrared, visible, and ultraviolet vegetation albedo, respectively [Noilhan and Planton, 1989; Masson et al., 2003]. Because the site is entirely covered by natural fallow, the veg fraction is fixed to one. [19] For ISBA‐FR, because the majority of the roots are observed in the uppermost 0.3–0.4 m depth, but can reach 0.6–0.7 m, the rooting depth (d2) is therefore fixed to 0.65 m and the total soil depth (d3) extents to 0.95m. For ISBA‐DF, the same surface, root zone, and total soil depths are used. The soil is discretized by 11 soil layers (0.01, 0.05, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85 and 0.95 m) in order to match the soil moisture profile measurements. As shown in Table 1, the root distribution profile computed according to Jackson et al. [1996] is also in agreement with the root profile estimates. [20] For both schemes, and according to soil moisture measurements (Figure 1), the volumetric water content at field capacity (wfc) and at wilting point is fixed to 0.3 m3 m−3 and 0.1 m3 m−3, respectively [Albergel et al., 2010]. The field capacity is especially important to ensure acceptable

simulations with the ISBA‐FR scheme because of the strong dependence of the soil water fluxes on this parameter (equation (3)). As already mentioned, the ISBA‐DF results must be put into perspective in comparison with the ISBA‐FR performances, since ISBA‐FR is currently used in meteorological, climate, and/or hydrological applications. In addition, no drainage observations are available in the SMOSREX database, so that the deep drainage from ISBA‐FR can serve as a reference to understand the ISBA‐DF deep drainage behavior. 3.3. Experiments and PTFs [21] In this study, nine experiments are done first: the control experiment with ISBA‐FR, and two sets of four ISBA‐DF simulations with homogeneous and heterogeneous soil conditions, denoted by Hom and Het, respectively. These simulations are related to the four continuous PTFs (Appendix C) used to compute the main hydrodynamic parameters (wsat, y sat, ksat, b, a, l, n) given in Table 1: [22] 1. CH78: as for ISBA‐FR, Clapp and Hornberger [1978] hydrodynamic parameters and soil textures are used to describe the BC66 model. These parameters were observed at 1446 soil samples from 34 localities throughout the United States. The continuous relationships were derived by Noilhan and Lacarrère [1995]. Note that the ISBA‐FR porosity (wsat) is therefore the same as for ISBA‐DF with CH78 and homogeneous soil conditions (Table 1). [23] 2. CO84: the continuous relationships for the BC66 model are given by Cosby et al. [1984], by whom an exten-

5 of 29

D20126


D20126

Figure 1. Time series of the observed and simulated soil moisture profile from 2002 to 2007 at a 30 min time step. Observations are in black, while ISBA‐FR, ISBA‐FR‐Wfc, CH78‐Hom, and CH78‐Het are in red, dotted red, blue, and green respectively. The profile is divided into three horizons: the surface layer (0–6 cm), the root zone layer (0–65 cm), and the deep soil layer (65 to 95 cm). The related skill scores are given in Table 2. sion of the CH78 study was proposed. They have derived a multiple linear regression analysis between the hydrodynamic parameters and the soil textures using the U.S. Department of Agriculture (USDA) textural classification and a soil property data set similar to CH78. [24] 3. CP88: the continuous relationships for the VG80 model are derived in this study (Appendix B) using multiple linear or polynomial regressions between the soil textures and the hydrodynamic parameters given by Carsel and Parrish [1988]. CP88 used a large soil database compiled by Carsel et al. [1988] using the 12 Soil Conservation Service (SCS) textural classification. These data were obtained from measurements for all soils reported in the SCS Soil Survey Information Reports established through-

out 42 countries in the world. Note that l is fixed to 0.5 in equation (7) by CP88. [25] 4. WO99: the continuous relationships are given by Wösten et al. [1999] for the VG80 model using the HYdraulic PRoperties of European Soil (HYPRES) database that consists of 4030 soil samples. WO99 have derived multiple linear or polynomial regression analysis between the soil hydrodynamic parameters, the soil textures, the soil bulk density, and the soil carbon content. Note that wr is set to 0 in equation (7) by WO99. [26] Finally, two additional sets of experiments with homogeneous soil properties are performed to assess the scheme sensitivity to different soil depth configurations (from 0.95 m to 3.95 m with a step of 0.5 m with layer

6 of 29

D20126

D20126


Table 2. Simulated 30 min Soil Moisture Scores Over 2002–2007: Mean Annual Bias (10−2 m3 m−3), the Square Correlation (r2), the Root Mean Square Error of the Anomalies (RMSE‐A; m3 m−3), and the Efficiency Defined as Eff = 1 − ∑ni=1 (SIMi − OBSi)2/∑ni=1 (OBSi − OBS)2, Where SIMi and OBSi are the Simulated and Observed Values and OBS Is the Observed Average Over the Number of Point Measurement, n, Given for Each Soil Layera CH78 Soil Total (92,120)

Surface 0–6 cm (92,131)

Root 0–65 cm (92,120)

Deep 65–95 cm (92,120)

Criteria −2

3

m r2 RMSE‐A (m3 Eff Bias (10−2 m3 r2 RMSE‐A (m3 Eff Bias (10−2 m3 r2 RMSE‐A (m3 Eff Bias (10−2 m3 r2 RMSE‐A (m3 Eff Bias (10

−3

m ) m−3) m−3) m−3) m−3) m−3) m−3) m−3)

CO84

CP88

WO99

FR

FR‐Wfc

Hom

Het

Hom

Het

Hom

Het

Hom

Het

−0.59 0.91 0.019 0.90 −0.89 0.68 0.064 0.65 −0.85 0.93 0.020 0.92 −0.04 0.79 0.020 0.78

−1.91 0.88 0.024 0.76 −1.62 0.62 0.069 0.58 −2.28 0.90 0.025 0.80 −1.11 0.75 0.024 0.61

−1.01 0.90 0.020 0.88 −4.01 0.83 0.051 0.64 −0.44 0.92 0.022 0.92 −2.21 0.80 0.023 0.44

−0.003 0.93 0.017 0.93 −5.70 0.83 0.055 0.46 −0.33 0.93 0.021 0.92 0.72 0.83 0.022 0.70

−0.90 0.90 0.020 0.88 −3.81 0.83 0.050 0.66 −0.41 0.92 0.021 0.92 −1.97 0.79 0.023 0.49

−0.13 0.93 0.017 0.93 −5.34 0.81 0.056 0.49 −0.52 0.94 0.021 0.92 0.70 0.84 0.022 0.72

−1.01 0.89 0.021 0.86 −2.79 0.76 0.054 0.68 −0.73 0.92 0.022 0.91 −1.64 0.64 0.028 0.42

0.42 0.92 0.018 0.91 −5.83 0.71 0.064 0.37 −0.79 0.93 0.022 0.90 3.06 0.83 0.020 0.28

−0.17 0.89 0.022 0.88 −4.00 0.86 0.044 0.70 0.43 0.93 0.021 0.92 −1.48 0.72 0.030 0.40

0.93 0.91 0.019 0.89 −3.72 0.85 0.044 0.72 0.79 0.93 0.021 0.92 1.22 0.77 0.021 0.68

a The homogeneous (Hom) and heterogeneous (Het) experiments are shown for all simulations. The two ISBA “Force‐restore” simulations (FR and FR‐ Wfc) are also given.

thicknesses of 0.1 m) and to the soil vertical discretization by decreasing the number of soil layers (from 95 to 5 layers) over a 0.95 m depth.

4. Results 4.1. Soil Moisture Profile [27] Figure 1 shows the time series of the observed and simulated soil moisture profiles from 2002 to 2007 at a 30 min time step. Only ISBA‐FR, CH78‐Hom, and CH78‐ Het are compared since the hydrodynamic parameters are derived from the same PTF, and, therefore, only the soil physics differ. The profile is divided into three horizons: the surface with the direct measurement between 0 and 6 cm, the root zone layer average (0–65 cm), and the deep soil layer average (65–95 cm). The surface measurements are compared with the first layer of ISBA‐FR (1 cm) and with the average of the two first layers over 5 cm for ISBA‐DF. Table 2 presents the related skill scores such as the mean bias, the square correlation (r2), the efficiency (Eff), and the root mean square error of the anomalies (RMSE‐A). For observations and simulations, the anomalies are computed as the actual signal less its mean value. [28] At the surface, a general underestimation is found during the wet season whatever the model version (Figure 1). Over the SMOSREX site, dead plant material accumulated on the ground and formed a dense mulch layer that absorbed a large amount of water [de Rosnay et al., 2006]. Such a process, not represented in ISBA, is known to enable the soil surface to remain wetter [Gonzalez‐Sosa et al., 1999, 2001] and can partly explain this general bias. This surface soil moisture underestimation is more pronounced for heterogeneous (CH78‐Het) than homogeneous (CH78‐Hom) soils, which impacts the CH78‐Het simulated efficiency score (Table 2). CH78‐Het exhibits coarser textural properties than CH78‐Hom at the surface (Table 1), resulting in larger drainage and therefore a lower surface moisture equilibrium.

[29] Compared with ISBA‐FR, the bias is larger for the diffusion scheme. In contrast, the correlations and the RMSE‐A are improved with ISBA‐DF (Table 2) for at least two reasons. (1) the surface soil moisture decreases more rapidly than ISBA‐FR following a precipitation event and is therefore more in phase with the observations. (2) ISBA‐DF represents correctly the diurnal cycle of the surface soil moisture that is strongly modulated by the transpiration over this site (not shown). This process is neglected in ISBA‐FR at the surface and the transpiration is directly withdrawn from the entire root zone (equation (4)). However, to confront the first layer of 1 cm thick of ISBA‐FR against the observed 5 cm thick soil moisture can distort this comparison. [30] Evaluating LSMs against soil moisture data requires that the root zone water content must be well reproduced in comparison with observations because it is closely related to the simulation of the transpiration flux and therefore of the surface/atmosphere energy budget. Here, all ISBA versions perform well, even if the model somewhat overestimates the dry season (2003–2004) or underestimates the beginning of the wet season (2006–2007). These biases are more pronounced for ISBA‐DF, although the skill scores remain similar to ISBA‐FR. [31] In the deep soil, ISBA‐FR performs relatively well. CH78‐Hom acceptably simulates the wet season but underestimates the dry season. Conversely, the deep soil in the heterogeneous case exhibits finer textural properties than in the homogeneous case (Table 1) that enables wetter soil moisture equilibrium for CH78‐Het than for CH78‐Hom. This fact improves the deep soil simulation, although it is still overestimated during the wet season. [32] According to equation (7), the good performance in simulating the soil moisture profile with ISBA‐FR is mainly due to the field capacity tuning at 0.3 m3 m−3. In general, the field capacity is computed according to Noilhan and Lacarrère [1995] giving a value of 0.27 m3 m−3 over the SMOSREX site (see Appendix C). An additional simulation, named ISBA‐FR‐Wfc, is also shown with this standard

7 of 29

D20126


D20126

Figure 2. Soil moisture profile evaluation for all experiments from 2002 to 2007. (a) The ISBA‐DF simulated bias and efficiency scores at each 10 soil measurements for the homogeneous (thin line) and heterogeneous (dashed line) cases. (b) The monthly mean annual cycles observed and simulated by each experiments (including ISBA‐FR) over the averaged surface, root zone, and deep soil layers as defined in Figure 1. The related skill scores are given in Table 2. value of the field capacity (Figure 1 and Table 2). ISBA‐ FR‐Wfc cannot reproduce the performance of ISBA‐FR. The degradation of the scores is especially due to larger drainage fluxes (equation (3)) involving a strong underestimation of the simulated root zone and deep soil moisture content during winter and spring compared with the observations. As it can be seen, the soil moisture is clearly better represented with ISBA‐DF that does not depend on field capacity. This fact represents a strong advantage for regional and/or global scale applications where the value of the field capacity is generally uncertain. [33] The soil moisture profile evaluation for all model experiments is shown in Figure 2. Figure 2a shows the ISBA‐DF simulated bias and efficiency scores computed at the depths of the soil moisture measurements (i.e., section 3.2) for the homogeneous (thin line) and heterogeneous (dashed line) cases. Figure 2b presents the monthly mean annual cycles observed and simulated by each experiment (including ISBA‐FR) over the averaged surface, root zone, and deep

soil layers as defined in Figure 1. The related skill score values are given in Table 2. These figures confirm the general soil moisture underestimation at the surface whatever the experiments, which is generally larger for heterogeneous cases. The main exception is for WO99, where the difference between WO99‐Hom and WO99‐Het is less important. It is due to a lower deviation in hydrodynamic parameters at the surface from homogeneous to heterogeneous soil than for other PTFs (Table 1). The surface monthly mean annual cycles simulated by ISBA‐DF (at least with homogeneous soils) can be considered as superior to ISBA‐FR because of the better surface drying during summer (Figure 2b). CP88 exhibits the wettest surface soil moisture (Table 3), especially from the end of summer toward the end of winter (Figure 2b). [34] Moving to the root zone, the soil moisture is relatively well reproduced for each of the experiments, and the skill scores in Table 2 confirm this result. The heterogeneous experiment exhibits drier soil than the homogeneous

8 of 29

D20126

D20126


Table 3. Simulated 30 min Soil Temperature Skill Scores Over 2002–2007a CH78

Tskin (63,324)

T‐5

cm

(92,429)

T‐20

cm

(92,429)

T‐50

cm

(92,429)

T‐90

cm

(92,429)

CO84

CP88

WO99

Criteria

FR

Hom

Het

Hom

Het

Hom

Het

Hom

Het

Bias r2 RMSE‐A Eff Bias r2 RMSE‐A Eff Bias r2 RMSE‐A Eff Bias r2 RMSE‐A Eff Bias r2 RMSE‐A Eff

0.56 0.94 2.36 0.94 /

1.11 0.93 2.60 0.92 0.29 0.94 2.45 0.85 0.16 0.94 2.37 0.83 0.11 0.95 2.63 0.72 0.08 0.89 3.54 0.27

1.16 0.94 2.58 0.92 0.33 0.94 2.46 0.85 0.21 0.94 2.39 0.82 0.14 0.95 2.62 0.72 0.11 0.89 3.53 0.28

1.12 0.93 2.61 0.92 0.30 0.94 2.45 0.85 0.18 0.94 2.38 0.82 0.13 0.95 2.63 0.72 0.10 0.89 3.55 0.27

1.17 0.93 2.59 0.92 0.35 0.94 2.47 0.85 0.22 0.94 2.41 0.82 0.16 0.95 2.63 0.72 0.13 0.89 3.54 0.27

1.18 0.93 2.65 0.92 0.37 0.94 2.49 0.85 0.25 0.94 2.44 0.81 0.21 0.95 2.68 0.71 0.18 0.89 3.60 0.25

1.22 0.93 2.58 0.92 0.40 0.94 2.51 0.84 0.27 0.94 2.42 0.82 0.21 0.95 2.64 0.72 0.18 0.89 3.55 0.27

1.20 0.93 2.74 0.91 0.38 0.94 2.43 0.85 0.26 0.94 2.43 0.81 0.20 0.94 2.70 0.70 0.17 0.88 3.63 0.24

1.18 0.93 2.68 0.91 0.36 0.94 2.41 0.86 0.24 0.94 2.39 0.82 0.19 0.95 2.64 0.72 0.16 0.89 3.56 0.26

/

/

/

a

The bias and the RMSE‐A are expressed in K. Notations are the same as in Table 2.

soil during winter and spring. Heterogeneous soils preferentially store the water in the deepest soil layers, while the water remains closer to the surface for the homogeneous case (Figure 2a). The SMOREX heterogeneous soil is related to an “exponential” soil type with surface coarse and deep fine textural properties that favors water movement into the soil, water storage in the deep soil, and deep drainage at the bottom of the soil column [Montaldo and Albertson, 2001; Decharme et al., 2006]. During summer, heterogeneous experiments, therefore, appear wetterthan homogeneous soils because of the larger water storage in the deepest part of the soil in spring that can back up to the overlaying layers by capillarity during the summer. In terms of PTFs, WO99 induces wetter root zone soil moisture than the others. WO99 leads to an overestimation of the soil moisture from the end of winter to the end of spring, whereas the other model versions show an underestimation of a varying severity (Figure 2b). Conversely, CP88 induces the driest root zone compared with the other PTFs. [35] In the deep soil, all homogeneous cases underestimate the soil moisture measurements in the same way, whereas the spread between the layer skill scores (Figure 2a) or the mean annual cycles (Figure 2b) are larger for heterogeneous simulations than for homogeneous cases. The heterogeneous hydrodynamic properties involve wetter deep soil simulations related to finer soil textural properties in depth compared with homogeneous cases, which explains why the heterogeneous cases better match the observations. The main exception is for CP88‐Het, which is drastically too wet and then degrades the simulated scores. [36] To sum up, Figure 2 and Table 2 point out that the CH78 and CO84 simulated scores are closely similar and give the best results for simulating the total soil moisture, especially with heterogeneous soil properties. Layer by layer, the difference with the WO99 simulated scores, however, is not significant, whereas CP88 exhibits the poorest scores. The root zone wetness is well reproduced by all the model versions even if CP88 experiments generally appear too dry. The deep soil is better described with heterogeneous soil

properties, except for CP88, which generally simulates drastically wetter soil moisture conditions compared with observations and other PFTs. 4.2. Water Budget [37] The soil moisture results confirm that the calibration of the field capacity for ISBA‐FR induces an accurate soil moisture simulation (especially compared with ISBA‐FR‐ Wfc) and therefore can serve as a reference for ISBA‐DF in terms of water budget and drainage flux. Note that no surface runoff occurs during the entire period for each experiment. Figure 3a presents the annual water budget simulated for all experiments. The soil moisture tendencies from 1 January 2002 to 31 December 2007, the total evapotranspiration, and the drainage ratios to total precipitation are expressed in percentage. The soil moisture tendency shows a negligible decrease from −0.5% to −0.9%, especially because of a simulated drier winter in 2007 than in 2002. For all experiments, the total evapotranspiration is the major component, while the drainage represents only around 11% of the total precipitation for ISBA‐FR and ISBA‐DF homogeneous cases. The main exception is found when using CP88, for which the drainage rate can reach 14% of the total precipitation. This result suggests that the CP88 PTF drains the most (at least over SMOSREX). This is confirmed by the driest soil moisture conditions in both the root zone and deep soil during the rainy season (February to May) shown in Figure 2b. [38] Compared with homogeneous cases, heterogeneous soils involve larger drainage rates that are still in agreement with the previous soil moisture results (wetter deep soil). As mentioned earlier, the texture at SMOSREX is coarse at the surface and finer in depth, favoring water storage in the deep soil and deep drainage at the soil bottom. However, while the drainage rate increases by a factor of 1.2 to 1.3 from CH78 (1.21) and CO84 (1.25) to CP88 (1.3), it remains closer to 1 for WO99 (1.05). These results seem to confirm that WO99 induces less diffusive soil than other PTFs, whereas CP88 simulates the most soil drainage.

9 of 29

D20126


D20126

Figure 3. Simulated water budget and drainage rate by all experiments from 2002 to 2007. (a) The soil moisture tendencies from 1 January 2002 to 31 December 2007, the total evapotranspiration, and the drainage ratios to total precipitation expressed in percentage. (b) Composites for the 2002–2007 period of daily drainage events during winter and spring simulated by all experiments. [39] Figure 3b shows the composites of the daily drainage events simulated by all experiments. ISBA‐FR exhibits reactive events that can rapidly reach zero when the soil moisture is below the field capacity according to the linear expression of the drainage rate (equation (3)). The drainage rate with ISBA‐DF does not depend on the field capacity and logically shows a smoother behavior, while the use of heterogeneous soil properties increases the reactivity of the model. There is a negligible difference between CH78 and CO84, whereas WO99 exhibits the most reactive drainage and CP88 the most diffuse, confirming the previous results from Figure 3a. 4.3. Soil Temperature Profile [40] Figure 4 compares the surface and soil temperatures simulated by ISBA‐FR and ISBA‐DF (CH78‐Hom) with the corresponding observations. The related skill scores computed at a 30 min time step are given in Table 3. The ISBA‐DF and the ISBA‐FR surface temperatures are compared with the observed radiative skin temperature. The

ISBA‐DF temperature at 5 cm depth is computed as the weighted arithmetic mean of the temperatures between the second and the third layers (Table 1), whereas other deep soil temperature nodes correspond to the depth of each observation (20, 50, 90 cm). The soil temperature observed at 1 cm depth is similar to 5 cm depth and therefore not shown. [41] Figure 4a compares the seasonally averaged diurnal cycle of the simulated surface temperatures with the observed skin temperature. The seasonal cycle is well reproduced by each model version. The diurnal cycle is better represented with ISBA‐FR, while the ISBA‐DF simulated nighttime surface temperatures are constantly warmer than the observations. The ISBA‐FR surface temperature is only controlled by the thermal properties of the vegetation at this site because the vegetation fraction, veg, equals 1, so that the surface composite thermal inertia coefficient (CT) is equal to the thermal inertia of the vegetation (equation (1)). However, equation (4) shows that the ISBA‐DF surface temperature is controlled by the thermal properties of both the

10 of 29


D20126

Figure 4

D20126

11 of 29

D20126


vegetation and the soil via CT and the soil thermal conductivity of the first layer (1), respectively. The time tendency of the surface temperature is dominated by the surface energy budget and therefore by the surface heat storage rate (G in equations (1) and (4)). However, the restore term (toward the deep temperature, T2) is nonnegligible, especially during the night, and larger for ISBA‐DF than ISBA‐ FR (because, according to equations (5) and (8), CT 1/D~z1 2p/t with CT equal to 2.10−5 K m−2 J−1, D~z1 equal to 0.025 m, and 1 ranging from 0.2 to 1.5 W m−1 K−1 from dry to saturated soil). This fact explains the ISBA‐DF colder daytime and warmer nighttime surface temperatures compared with ISBA‐FR. [42] This nearest deep soil temperature is represented in Figure 4a as the temperature simulated and observed at 5 cm depth. ISBA‐DF reproduces fairly well both the seasonal and the diurnal cycles of this soil temperature, although the daily amplitude can be overestimated mainly during summer and, to a lesser extent, during autumn. Besides the uncertainties in model parameters, such as the soil thermal conductivity and heat capacity, the presence of a plant‐dead mulch layer, which is not represented in ISBA‐DF and which insulates the soil surface, can still partly explain this general bias. Over the MUREX site, a fallow site close to and similar to SMOSREX, Gonzalez‐Sosa et al. [1999] showed that without an explicit parameterization of the mulch thermal processes, the simulated daily soil shallow temperature is consistently overestimated by more than 5 K especially during summer and autumn. [43] The ISBA‐DF temperature simulated at 20 cm depth is compared with soil measurements at a 20 cm depth in Figure 4b in terms of monthly mean annual cycles. The annual cycle is poorly reproduced because of a drastic overestimation during summer and underestimation in winter. The same weaknesses are found for deeper temperatures at 50 and 90 cm depth as shown in Figures 4c and 4d, in which the monthly mean annual cycles simulated by ISBA‐DF are compared with observations. It will be shown in the following sensitivity study that the rather shallow soil depth of 0.95 m is the main cause of these deep temperature weaknesses. [44] Figure 5 presents the same comparison as in Figure 4a between the ISBA‐DF surface temperature and the observed skin temperature, but for all PTFs. The related skill scores are given in Table 3. There is no difference between CH78 and CO84, according to similar water budget behavior and soil properties. During the entire season, WO99 exhibits the simulated coldest daytime and the warmest nighttime surface temperatures. These nighttime temperatures explain why, in Table 3, WO99 appears to be the warmest soil. Two factors influence this difference with other PTFs. The WO99 surface porosity is significantly lower than other PTFs which induces larger dry soil conductivity (0.2 W m−1 K−1 for CH78 and CO84, 0.22 for CP88, and 0.28 for WO99)

D20126

and then larger surface thermal conductivity according to Peters‐Lidard et al. [1998]. Additionally, WO99 simulates the wettest soil, which contributes to increase the surface thermal conductivity and therefore to warm and to cool the surface temperature more than the other PTFs during the night and the day, respectively. The same processes explain the warmer (or colder) nighttime surface temperature for CP88 (or WO99) compared with CH78 and CO84. Conversely, CP88 exhibits the warmest daytime temperature during spring and summer because of the driest soil moisture conditions (Figure 2). [45] Table 3 reveals that heterogeneous soils induce globally warmer surface temperatures than homogeneous soils. This is mainly due to drier surface soil conditions (Figure 2). However, in summer heterogeneous soils are wetter than homogeneous soils and the surface temperature is therefore colder. To sum up, the hydrodynamic processes alone explain these differences, because the negligible differences in surface porosity from homogeneous to heterogeneous soils are not sufficient to generate different dry soil conductivities (Table 1). The main exception is found for WO99 in which heterogeneous soils appear colder than homogeneous soils while the soil moisture is similar. In fact, WO99‐Het daytime surface temperatures are constantly warmer than WO99‐Hom, whereas the nighttime temperatures are colder because of the larger surface porosity in WO99‐Het (Table 1) inducing lower soil thermal conductivities near the surface. This is simply the inverse of the mechanism described previously. 4.4. Surface Energy Fluxes [46] Figure 6 compares seasonally averaged diurnal cycle of the observed and simulated surface energy fluxes from ISBA‐FR and ISBA‐DF (CH78‐Hom and CH78‐Het). Table 4 presents the related skill scores computed during 2002–2007 at a 30 min time step. The surface net radiation (Rn) is well reproduced by the three experiments, although there is a slight overestimation during winter and spring, and an underestimation during summer and autumn. Compared with ISBA‐FR, the bias is reduced with ISBA‐DF, and the RMSE‐A decreases independently of the soil configuration, while other skill scores remain similar (Table 4). The larger ISBA‐DF surface net radiation is related to colder daytime surface temperatures than ISBA‐FR (Figure 4). [47] The sensible heat flux (H) is overestimated by ISBA‐ FR from winter to summer, while the agreement is better during autumn. ISBA‐DF simulates a lower sensible heat flux because of the difference in daytime surface temperatures, and accordingly this is also the case for the surface net radiation. The ISBA‐DF sensible heat flux matches the spring and summer observations, but it underestimates the value in the autumn and, to a lesser extent, the winter rates. The skill scores are generally improved with ISBA‐DF, except for the square correlation. The degradation of the

Figure 4. The soil temperature profile observed in situ and simulated by ISBA‐FR and ISBA‐DF (CH78‐Hom) from 2002 to 2007. (a) The seasonally average diurnal cycle of the simulated surface temperatures is compared with the observed skin temperature (plain lines), whereas the ISBA‐DF temperature at 5 cm depth computed as the arithmetic mean of the temperatures between the second and the third layers is compared with the soil measurement at 5 cm depth (dashed lines). (b) The monthly mean annual cycles of both the observation and the simulation at 20 cm depth. (c, d) Similar to Figure 4b, but at 50 cm and 90 cm depth. The related skill scores are given in Table 3. 12 of 29


D20126

Figure 5

D20126

13 of 29

D20126


correlation is related to a slower decrease of the sensible heat flux rate toward the end of the afternoon because of the higher surface temperature for ISBA‐DF than for ISBA‐FR (Figure 4). [48] The latent heat flux (LE) simulated by ISBA‐FR is underestimated during winter and summer, whereas a drastic overestimation is found in spring, and the rate is also reproduced in autumn. ISBA‐DF simulates a lower evaporation than ISBA‐FR in winter and spring that contributes to a reduction of the drastic spring bias, a larger latent heat flux in summer, and a similar rate in autumn. Two reasons explain these differences: the colder ISBA‐DF daytime surface temperature contributes to decrease the evaporation rate compared with ISBA‐FR, as well as the ISBA‐DF root profile. This root profile favors the storage of a part of the water infiltrated in spring in the deepest layers of the root zone where the root density is the lowest (Table 1). During summer toward the beginning of the autumn, this water rises up to the overlaying layers by capillarity and is therefore available to be evaporated. Note that all LE skill scores are improved with ISBA‐DF in comparison with ISBA‐FR. [49] CH78‐Hom and CH78‐Het exhibit some slight differences (Figure 6 and Table 4) that can be explained by the same thermal and hydrological processes previously described. In winter, spring, and autumn, the surface net radiation fluxes are lower and the sensible heat fluxes are larger with heterogeneous soils corresponding to warmer surface temperature and lower evaporation rates compared with homogeneous soil conditions. Conversely, the summer evaporation is favored with heterogeneous soil according to wetter deep soil conditions (Figure 2). Note that most of the skill scores are improved with heterogeneous soils compared with homogeneous conditions (Table 4). [50] Moving to the comparison between all PTFs used within ISBA‐DF, Figure 7 shows the H/LE scatterplot of the seasonal biases simulated by all experiments. Figure 7 confirms that ISBA‐FR exhibits a significantly larger sensible heat flux than the observations, whereas all ISBA‐DF experiments reduce this bias. According to Figure 6, all ISBA‐DF versions simulate a larger latent heat flux in summer and, to a lesser extent, in autumn, while they evaporate at a lower rate during winter and spring. For all PTFs, the seasonal energy flux biases seem to be reduced, while other skill scores are generally improved (Table 4). This improvement is more pronounced when using heterogeneous soils. According to warmer surface temperatures and drier root zone conditions for heterogeneous than homogeneous soil, the sensible heat flux is larger, while the latent heat flux and the surface net radiations are lower during winter, spring, and autumn, and conversely in summer. [51] However, WO99‐Het exhibit a larger summer sensible heat flux than WO99‐Hom, because of the warmer surface temperature as already discussed. Figure 7 confirms that homogeneous or heterogeneous soil conditions also have less impact on WO99 than on other PTFs. According to previous results, the CH78 and CO84 simulated energy fluxes are very close in terms of skill scores, whatever the season, and give globally the best results, especially for

D20126

heterogeneous soil conditions. CP88 simulates the lowest latent heat flux and the largest sensible heat flux during spring and summer, because of the driest and warmest soil conditions (Figures 2 and 5). Conversely, CP88 has a slightly larger evaporation in winter and autumn balanced by lower sensible heat fluxes than CH78 and CO84 according to the wetter and colder near‐surface conditions (Figures 2 and 5), which also induce larger surface net radiation (Table 4). Over the entire season, WO99 exhibits the lowest sensible heat flux and the largest surface net radiation (Table 4), because of the coldest surface soil temperature (Figure 5), as well as the wettest soil conditions (Figure 2), that favor the latent heat flux during spring and especially summer. [52] Finally, despite these results, all experiments show a drastic overestimation of the latent heat flux during spring. At least two reasons can explain this bias. Over midlatitudes and for fallows, the evaporation is at a maximum during spring. Some local comparisons between ISBA‐FR simulations and flux observations have already shown a similar bias at the end of spring, but drastically less intense [Calvet et al., 1999; Boone et al., 1999; Habets et al., 1999a]. This fact can be related to uncertainties in LAI measurements or in input standard vegetation parameters, as well as in the simple Jarvis [1976] stomatal resistance approach. Calvet et al. [1998] have shown that using an interactive vegetation scheme, such as ISBA‐Ags, could help to reduce this bias. In addition, the surface energy composite approach (equation (1) and Appendix A) does not allow discriminating energy budgets between the vegetation and the soil that can still contribute to this general evaporation weakness. [53] However, this statement must be balanced in the present study according to the realistic simulation of the surface net radiation, sensible heat flux, and both the surface and the shallow soil temperatures during spring (Figures 4a and 6). These results suggest that the surface heat storage as well as the heat transfer into the soil is correctly simulated by ISBA‐DF. Uncertainties in latent heat flux measurement seem therefore to exaggerate this simulated evaporative bias. Considering the energy balance (equation (A1)), the surface heat storage can appear suspicious during spring in the SMOSREX field measurements. The monthly mean surface heat storage would be around 105 W m−2 in spring against 68 W m−2 in summer, 55 W m−2 in autumn, and 47 W m−2 in winter. Even if the surface heat storage can be at a maximum during spring over midlatitudes, it does not seem physical that the spring heat storage is largely superior to the summer rate. During the MUREX experiment (43°24′N, 1°10′E), a fallow site similar to SMOSREX, Calvet et al. [1999] showed that the spring heat storage is not so different than the summer rate.

5. Sensitivity Experiments 5.1. Soil Depth [54] A sensitivity experiment to the total soil depth is performed in order to access the robustness of the model to soil depth uncertainties. Six additional experiments are done for each PTF with homogeneous soil properties. The pre-

Figure 5. The 2002–2007 seasonally average diurnal cycle of surface temperatures simulated using the four pedotransfer functions and homogeneous soil properties compared with the observed skin temperature. The related skill scores are given in Table 3. 14 of 29


D20126

Figure 6

D20126

15 of 29

D20126

D20126


Table 4. Daytime Simulated 30 min Surface Fluxes Skill Scores Over 2005–2007a CH78

CO84

CP88

WO99

Flux

Criteria

FR

Hom

Het

Hom

Het

Hom

Het

Hom

Het

Rn (22,411)

Bias r2 RMSE‐A Eff Bias r2 RMSE‐A Eff Bias r2 RMSE‐A Eff

−2.38 0.97 28.25 0.97 9.21 0.62 63.93 0.13 17.10 0.82 48.10 0.73

0.29 0.97 27.98 0.97 8.91 0.62 59.45 0.24 −2.40 0.77 50.23 0.74

0.02 0.97 27.78 0.97 7.49 0.64 56.59 0.32 −1.72 0.77 48.62 0.76

0.38 0.97 27.99 0.97 8.72 0.62 59.42 0.25 −2.99 0.76 50.53 0.74

0.08 0.97 27.75 0.97 6.96 0.64 56.09 0.33 −2.02 0.77 49.31 0.75

0.53 0.97 28.01 0.97 6.13 0.60 59.65 0.25 −3.49 0.75 53.04 0.71

−0.35 0.97 27.65 0.97 3.00 0.62 54.65 0.38 0.97 0.75 51.28 0.73

1.37 0.97 28.00 0.97 9.35 0.63 58.96 0.25 −10.68 0.74 51.26 0.72

0.88 0.97 28.00 0.97 9.03 0.63 58.50 0.27 −7.38 0.76 49.84 0.74

LE (17,258)

H (14,224)

The surface net radiation (Rn), the latent heat flux (LE), and the sensible heat flux (H) are in W m−2. Notations are the same as in Table 2.

a

vious experiments with a 0.95 m soil depth represent the references. The soil depth is extended to 1.45, 1.95, 2.45, 2.95, 3.45, and 3.95 m for each ISBA‐DF experiment by adding 0.5 m at each depth with a thickness discretization of 0.1 m, whereas for ISBA‐FR the thickness of the deep layer alone is increased keeping the rooting depth at 0.65 m depth. [55] Figure 8 shows the vertical profile of the ISBA‐DF soil moisture skill scores computed at the depths of the soil moisture measurements (i.e., section 3.2). The heterogeneous soil simulations with a 0.95 m soil depth are also shown. For each PTF compared with its own reference (0.95 m), the annual biases (Figure 8a) reveal that increasing soil depth moistens the soil, especially from the bottom of the layer where the roots are the densest (0.35 m) to the deep soil. Therefore, the bias in the deep soil is reduced (especially during the dry season) with increasing soil depth, and the 1.45 m simulations give the best results, even compared with the 0.95 m heterogeneous soil experiments. In the first 0.35 m depth, the heterogeneous experiments remain drier, however. The reduction of the deep soil biases induces a significant increase in efficiency scores (Figure 8b) that allows the majority of the PTFs to simulate similar scores as the 0.95 m heterogeneous cases, whereas the 1.45 m simulations give the best scores. In contrast to the previous results (Figure 2 and Table 2) showing that consideration of heterogeneous soil properties in ISBA‐DF could be useful to simulate realistic soil moisture conditions in the deep soil during the dry season, the use of an appropriate soil depth can balance the moisture weakness found with homogeneous soil conditions. [56] Comparing all experiments, CP88 and WO99 using the VG80 soil‐water retention function are less sensitive than CH78 and CO84 using BC66 in terms of soil moisture simulation when increasing soil depth. This result suggests that the VG80 experiments store a lower quantity of water in the soil column than with BC66, which is consistent with the CP88 and the WO99 lower soil porosities (Table 1). Note that, with ISBA‐FR, the root and deep layers are also wetter with increasing soil depth (not shown). For a soil depth of 0.95 m to 3.95 m, the ISBA‐FR root zone moisture bias increases from −0.85 × 10−2 m3 m−3 to 0.33 × 10−2 m3 m−3,

respectively, and the deep soil moisture bias from −0.04 × 10−2 m3 m−3 to 1.67 × 10−2 m3 m−3. [57] Figure 9 presents the soil depth sensitivity of ISBA‐ FR and ISBA‐DF in terms of drainage rate. For all of the schemes, the deeper the soil, the less reactive is the daily drainage (Figure 9a), while the annual drainage rate decreases (Figure 9b). This annual decrease is much larger with ISBA‐FR than with ISBA‐DF pointing out that, even if the daily drainage rate reactivity decreases in the same way whatever the scheme used, the multilayer diffusion approach is less sensitive than the force‐restore approach to increasing soil depth. Another difference compared with ISBA‐FR can be observed from June to August in which ISBA‐DF allows a significant residual drainage for all soil depth superior to 0.95 m. However, if the simulations performed with 1.45 m depth are considered as the best with respect to the soil moisture scores (Figure 8), Figure 9a points out that a too‐ deep soil depth can drastically inhibit the drainage response to a precipitation event, at least in the experimental conditions of this study. Comparing all PTFs, Figure 9b reveals that the annual drainage rates simulated by WO99 and especially CP88 increase for very deep soils compared with CH78 and CO88. According to the difference in soil porosity, this result explains why the VG80 simulations are more robust in terms of soil moisture compared with BC66 as discussed in Figure 8. [58] Figure 10 compares the monthly mean annual cycles of the deep soil temperatures simulated by ISBA‐DF with the soil measurements. The efficiency scores given for each soil depth are computed at a 30 min time step. Only CH78 simulations are shown because the results are similar to those with the other PTFs. For the deepest soil temperature measurements (50 and 90 cm), the use of a very deep soil (more than 3 m) drastically improves the simulation, which is mainly due to the increase in total soil thermal inertia. This improvement is slight but still nonnegligible for near‐ soil temperatures (−5 and −20 cm). These results point out that the soil must be very deep to simulate a realistic soil temperature profile. [59] In terms of energy fluxes, Figure 11 shows the simulated annual biases and RMSE‐A computed at a 30 min

Figure 6. The seasonally average diurnal cycle of the daytime surface fluxes observed and simulated by ISBA‐FR and ISBA‐DF (CH78‐Hom and CH78‐Het) from 2005 to 2007. The surface net radiation (Rn), the latent heat flux (LE), and the sensible heat flux (H) are shown. The related skill scores are given in Table 4. 16 of 29

D20126


D20126

Figure 7. Scatterplot of the seasonal latent (LE) and sensible (H) heat flux biases simulated by all experiments from 2005 to 2007. The other skill scores are given in Table 4. time step for all PTFs and all soil depth conditions. The surface net radiation remains slightly impacted by increasing soil depth for all of the schemes. In contrast to the drainage rate and consistent with the wetter soil moisture conditions, the latent heat flux increases with soil depth (especially in spring and summer). This increase is more pronounced with ISBA‐FR than with ISBA‐DF. However, the RMSE‐As are slightly impacted when using the diffusion scheme. This increase in evaporation is balanced by a decrease in sensible heat flux, although the sensible heat flux is less impacted than the latent heat flux when the soil depth increases. The wetter soil conditions, as well as the larger soil thermal inertia when increasing soil depth, induce slightly lower surface temperatures, which therefore contribute to increasing the surface net radiation and decreasing the sensible heat flux. In terms of RMSE‐A, only the sensible heat flux simulated by ISBA‐FR is impacted, and the decrease seen in Figure 11 is primarily due to a significant reduction of the summer bias (Figure 6) that is balanced by an increase in evapotranspiration. Finally, this figure confirms that the simulated energy fluxes are globally better simulated with ISBA‐DF than ISBA‐FR.

5.2. Vertical Resolution [60] Many LSMs that use a soil diffusion approach exhibit a coarse resolution with a first layer ranging from 1 cm to 10 cm thick and, at most, five soil layers because of the limitations of the computational time [Wetzel and Boone, 1995; Viterbo and Beljaars, 1995; Thompson and Pollard, 1995; Chen et al., 1997; Cox et al., 1999, Balsamo et al., 2009]. However, a resolution that is too coarse impacts the LSMs skill to simulate realistic soil moisture profiles and energy fluxes [Blyth and Daamen, 1997; de Rosnay et al., 2000]. Other modelers prefer therefore to use a fine resolution with a first layer inferior or equal to 1 cm thick and more than 10 soil layers [McCumber and Pielke, 1981; Braud et al., 1995; de Rosnay et al., 2000; Dai et al., 2003], as in this study. A sensitivity experiment to the soil vertical discretization by decreasing the number of soil layers is thus performed in order to study the sensitivity of ISBA‐DF to the vertical resolution. Six additional experiments are again done for each PTF with homogeneous soil conditions and a total soil depth of 0.95 m. The previous experiments with 11 soil layers represent the reference. Table 5 presents the vertical discretization for 95, 32, 21, 11, 9, 7, and 5 soil

17 of 29

D20126


D20126

Figure 8. Sensitivity to increasing soil depth of the ISBA‐DF simulated soil moisture profile scores from 2002 to 2007. The (a) annual bias expressed in percent and the (b) efficiency are computed at the depths of the soil moisture measurements (Table 1). The heterogeneous soil case simulations with a 0.95 m soil depth are also shown (black dotted line). layer cases with a medium and deep soil thickness ranging from 1 cm to 30 cm, respectively, whereas the first layer is fixed to 1 cm for all cases. [61] Figure 12 shows the soil moisture profile scores for each simulation in terms of annual bias and efficiency. The simulated surface soil moisture (0–5 cm) appears robust for all PTFs for any number of soil layers, although the five‐ layer experiment appears wetter (drier) than the other configurations with CP88 (or WO99). The soil moisture in the deep soil (65–95 cm) is not affected until a nine‐layer soil configuration for which the vertical discretization in the deep soil decreases from several layers to only one (Table 5). This configuration favors nonphysical downward water fluxes at the expense of capillarity rise and partly contributes to

moisten the deep soil. This limits artificially the dry bias found with homogeneous soil (section 4.1). The use of a seven‐layer soil configuration with even a fewer number of layers in the deepest part of the root zone (below 35 cm) enhances this nonphysical downward water flux and artificially dries up the root zone. To sum up, Figure 12 points out that the number of soil layers has a limited impact on the ISBA‐DF simulated soil moisture, at least when the first layer is kept fixed for all cases. The root zone must be sufficiently discretized in both the upper and the deeper parts to ensure an acceptable root zone and especially deep soil moisture simulation as shown by the seven‐layer soil configuration, while the discretization of the deep soil should not be neglected as shown by the nine‐layer soil configuration.

18 of 29

D20126


D20126

Figure 9. Soil depth sensitivity of ISBA‐FR and ISBA‐DF in simulating the drainage rate. (a) Composites of daily drainage rates from 2002 to 2007 for each experiment where the gray‐shaded region corresponds to the simulated drainage rate with a 0.95 m soil depth. (b) Cumulated annual drainage rate simulated by all experiments. [62] Figure 13 presents the sensitivity of ISBA‐DF to the number of soil layers in terms of drainage rate. Figure 13a shows that the daily drainage rate is not affected by the soil discretization, but the drainage reactivity during some rain events decreases with coarser resolution, especially for seven and five layers. This result is confirmed by Figure 13b, which shows that the annual drainage rate simulated with five layers is lower than those in the other experiments. According to these soil moisture results, a coarse resolution in the deeper part of the soil could have a nonnegligible impact on the ability of the scheme to simulate the drainage rate. [63] Finally, Figure 14 shows the annual biases and the RMSE‐A scores of the simulated energy fluxes by all PTFs and all resolution configurations. For each panel, the y axis range is the same as in Figure 11 in order to compare the ISBA‐DF discretization and soil depth sensitivities. As was the case for the previous results, these fluxes are only slightly impacted by the resolution up to five layers. A resolution that is too coarse near the surface with five layers

(Table 5) induces a lower thermal gradient between the second and the third layers (D~z2 = 0.17 m for five layers against 0.07 for others configurations in equation (4)) which insulates the surface from the deep soil compared with a finer resolution. This numerical effect leads to a warmer daytime surface temperature and to a decrease of the surface net radiation and an increase in both the sensible and the latent heat fluxes. Note that the same drawback, but much less noticeable, is observed from 21 layers to 11 layers.

6. Discussion and Conclusions [64] This study evaluates the ability of the ISBA‐DF multilayer diffusion scheme to simulate the soil moisture and temperature profiles and the surface energy fluxes over the SMOSREX field experiment where many observations over the 2001–2007 period are available [de Rosnay et al., 2006]. ISBA‐DF reproduces the evolution of the soil moisture profile reasonably well, and it improves the simulation

19 of 29


D20126

Figure 10

D20126

20 of 29

D20126


D20126

Figure 11. Soil depth sensitivity in simulating the surface energy fluxes. The ISBA‐FR and ISBA‐DF simulated annual bias and RMSE‐A skill scores are shown. These scores are computed at a 30 min time step from 2005 to 2007. of the surface energy fluxes compared with ISBA‐FR, especially using heterogeneous soil properties. The ISBA‐ DF soil moisture dynamic does not depend on the field capacity value, which is a clear advantage compared with ISBA‐FR for regional and/or global applications. The use of ISBA‐DF leads to many differences compared with ISBA‐ FR in solving the diurnal cycle of the surface temperature, in partitioning latent and sensible heat fluxes at the daily to interannual timescales, and in simulating the drainage rate response after a precipitation event. Such processes are expected to have local, regional, and global impacts on numerical weather forecasting, climate projections, and/or hydrological applications. [65] The comparison of the diverse PTFs shows that BC66 (CH78 and CO84) give the best results for simulating the soil moisture profile even if VG80 with WO99 displays the best score at the surface. In addition, the BC66 experiments

(CH78 and CO84) exhibit the best skill scores in simulating surface fluxes compared with the VG80 experiments (CP88 and WO99). However, it must be recognized that the results between BC66 and VG80 are not significantly different. The slight superiority of BC66 is only valid for the SMOSREX site and should not be generalized. Apart from these differences, the possibility to switch between several pedotransfer functions will increase the flexibility of ISBA‐DF, especially for hydrological applications. [66] The sensitivity experiments show that the use of an appropriate soil depth can balance the deep soil moisture deficit found with homogeneous soil properties. Nevertheless, a soil that is too deep can inhibit the daily drainage reactivity simulated by ISBA‐DF. The soil discretization has a limited impact on the simulated drainage rate compared with increasing soil depth, even if too coarse a resolution in the deep soil can also contribute to inhibiting the daily

Figure 10. ISBA‐DF soil depth sensitivity in simulating the monthly mean annual cycles of the deep soil temperatures at 5, 20, 50, and 90 cm depth from 2002 to 2007. The efficiency scores given on each panel are computed at a 30 min time step. Only CH78 values are shown because the results are similar to other PTFs. 21 of 29

D20126


Table 5. Grid Geometries Used for the Vertical Soil Discretization Sensitivity Experimenta 5 Layers 7 Layers 9 Layers 11 Layers 21 Layers 32 Layers 95 Layers 0.01 0.05 0.35 0.65 0.95

0.01 0.05 0.15 0.25 0.35 0.65 0.95

0.01 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.95

0.01 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95

0.01 0.03 0.05 0.10 0.15 0.20 0.25 0.30 : : 0.95

0.01 0.02 0.03 0.04 0.05 0.0833 0.1166 0.15 : : 0.95

0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 : : 0.95

a

Units are meters.

drainage reactivity. The simulation of the surface fluxes is more sensitive to the fine discretization of the soil surface than to increasing soil depth. This result confirms the conclusion of Blyth and Daamen [1997] and de Rosnay et al. [2000] which showed that excessively coarse resolutions drastically affect the ability of multilayer diffusion schemes

D20126

to give realistic estimates of moisture and energy fluxes. Conversely, the evaporation is more sensitive to the soil depth configuration, at least when transpiration alone is considered. Note that similar tests have been performed for bare soil conditions and the same drawbacks have been observed. [67] In addition, an important conclusion of this study is that the soil must be very deep to simulate a realistic soil temperature profile, whereas, in terms of hydrology, the soil column should not be extended below a certain limit (1.5 m in this study for a fallow field). This problem will be addressed in the future by extending the soil temperature computations below the hydrological soil column maximum depth and by extrapolating the soil water content to each underlying temperature node assuming an equilibrium soil moisture profile. Concerning global hydrological applications and/or climate studies, extending the soil temperature profile up to a hundred meters will be particularly relevant for reproducing the evolution of the permafrost processes from the seasonal to the century time scales [Alexeev et al., 2007; Riseborough et al., 2008].

Figure 12. ISBA‐DF simulated soil moisture profile sensitivity to the vertical soil resolution. The annual bias and efficiency skill scores are computed at a 30 min time step from 2002 to 2007. 22 of 29

D20126


D20126

Figure 13. As in Figure 10, but for the ISBA‐DF sensitivity to vertical soil discretization. The gray‐ shaded region corresponds to the simulated daily drainage rate with 95 soil layers. [68] ISBA‐DF exhibits some weaknesses throughout this study in simulating surface and near‐surface soil temperatures. These drawbacks are related to the soil‐vegetation composite approach used to solve the surface energy budget that does not permit the discrimination between soil and vegetation thermal effects. The future implementation in ISBA‐DF of a multienergy balance approach solving a thin upper soil‐low vegetation layer (or mulch), a single vegetation canopy layer, and explicit snow layers should permit exploration of the limits of the composite‐surface concept. It must be recognized that this multienergy balance approach could have a greater impact on soil moisture, surface temperature, and surface fluxes than only the differences between ISBA‐DF versus ISBA‐FR, homogeneous versus heterogeneous soils, or BC66 versus VG80 pedotransfer functions. [69] In addition, these differences can appear relatively small compared with the uncertainties in some basic surface parameters (e.g., LAI, vegetation fraction, root zone profile), and with the imperfect atmospheric forcing used in regional or global offline applications or currently simulated by

atmospheric models. For example, many studies have shown that the quality of the simulated hydrology was significantly correlated with the quality of the precipitation [Oki et al., 1999; Chapelon et al., 2002; Decharme and Douville, 2006] and that uncertainty in precipitation generally was translated to at least the same and typically much greater uncertainty in total runoff (surface runoff plus deep drainage) [Fekete et al., 2004; Decharme and Douville, 2006]. [70] Nevertheless, the lower drainage rate reactivity induced by ISBA‐DF compared with ISBA‐FR could change some specific model calibrations for streamflow forecasting [Habets et al., 2004; Thirel et al., 2010]. The use of heterogeneous soil conditions that are found to improve the ISBA‐DF drainage reactivity could partly temper this remark. Soil heterogeneous properties could be taken into account with the use of the Harmonized World Soil Database of the Food and Agricultural Organization [International Institute for Applied Systems Analysis, 2009]. This global scale database contains soil properties, such as soil textures, reference bulk density, and organic matter, discretized over two horizons (0–0.3 m and 0.3–1 m) at a 1 km resolution. In

23 of 29

D20126


D20126

Figure 14. As in Figure 11, but for the ISBA‐DF sensitivity to vertical soil discretization. addition, ISBA‐DF presents the advantage of simulating a residual drainage during the dry season, whereas a tuned linear residual drainage below the field capacity had to be introduced into ISBA‐FR [Habets et al., 1999b; Etchevers et al., 2001] to simulate realistic summer base flow in order to improve streamflow forecasting [Quintana Seguí et al., 2009]. The diffusion scheme could thus eliminate this specific tuning. [71] Finally, the evaluation of ISBA‐DF over a small Scandinavian catchment [Habets et al., 2003], for freezing conditions over the American Midwest [Boone et al., 2000], over the dry Sahelian area [Boone et al., 2009; de Rosnay et al., 2009], and, in this study, over the SMOSREX field experiment increase the confidence that the model will be able to consistently represent a variety of environmental conditions in different climate regimes, and therefore will be able to be used for these diverse applications. To reach this objective, the model will need to be further evaluated, especially on regional or global scales. At these spatial scales, the use of ISBA‐DF in hydrological systems with a river‐routing model such as SAFRAN‐ISBA‐MODCOU over France [Habets et al., 2004; Quintana Seguí et al., 2009] and/or ISBA‐TRIP over the globe [Alkama et al.,

2010; Decharme et al., 2010] would permit the comparison between simulated runoff patterns and river discharge measurements in order to properly evaluate the impact on the simulated large‐scale water budget.

Appendix A: Review of the ISBA Surface Energy Budget [72] The surface energy budget is computed in the same way for ISBA‐FR and ISBA‐DF. The surface heat flux rate, G (W m−2), into this soil‐vegetation composite is equal to the sum of all the surface/atmosphere energy fluxes: G ¼ Rn H LE

ðA1Þ

where Rn (W m−2) is the net radiation, H (W m−2) is the sensible heat flux, and LE (W m−2) is the latent heat flux. [73] Rn is the sum of the absorbed fraction of the incoming short‐wave solar radiation, RG (W m−2), and of the atmospheric long‐wave radiation, RA (W m−2), reduced by the emitted infrared radiation at the surface:

24 of 29

Rn ¼ RG ð1 Þ þ " RA Ts4

ðA2Þ

D20126

D20126


where the albedo, a, and the emissivity, ", are a linear combination of the soil and vegetation reflectivities, while s (W m−2 K−4) is the Stefan‐Boltzmann constant and Ts (K) is the surface soil/vegetation composite temperature. [74] H is calculated by means of the classical aerodynamics formula according to: H ¼ a cp CH Va ðTs Ta Þ

ðA4Þ

where Lv (J kg−1) is the latent heat of vaporization, veg is the fraction of the vegetation cover, qsat(Ts) (kg kg−1) is the saturated specific humidity at the surface, qa (kg kg−1) is the atmospheric specific humidity, hu is the dimensionless relative humidity at the ground surface related to the superficial soil moisture content, and hv is the dimensionless Halstead coefficient partitioning the direct evaporation from the canopy intercepted water, Ec, and the transpiration of the leaves, Etr. Surface resistance in the formulation of transpiration is proportional to the soil water stress and the stomatal resistance according to Jarvis [1976]. More details can be found in the study by Noilhan and Planton [1989].

Appendix B: ISBA‐DF Soil Infiltration Capacity [76] In general, infiltration in a soil multilayer diffusion scheme is computed as the difference between Pg (flux of water reaching the soil surface) and the saturated hydraulic conductivity of the superficial soil layer. This kind of approach is viable only if one considers a very small time step or a coarse thickness for the first layer from 0.1 to 0.2 m depending on frozen soil conditions [Johnsson and Lundin, 1991]. However, the first layer must be very fine in order to properly simulate the Darcy flux and the water and energy budgets [de Rosnay et al., 2000]. In ISBA‐DF, the first layer is fixed to 0.01 m, and a coarse time step from 300 s (for hydrological simulations) to 1800 s (for climate applications) is used. [77] Given this model configuration, the soil infiltration can be computed via a Green and Ampt [1911] approach. This approach, based on Darcy’s law, includes the hydrodynamic parameters of the soil and determines the infiltration capacity of soil over the time, I(t) (m.s−1). It presents the soil infiltration as a wetting front in which the hydraulic gradient is uniform: y0 yf I ðt Þ ¼ ksat þ1 zf

Ic ¼

ðA3Þ

where ra (kg m−3), cp (J kg−1 K−1), Va (m s−1), and Ta (K) are the density, the specific heat, the wind speed, and the temperature of the air, respectively, while CH is the dimensionless drag coefficient depending upon the thermal stability of the atmosphere. [75] Finally, LE is related to the sum of the evaporation from the bare soil surface, Eg (kg m−2 s−1), and of the evapotranspiration from the vegetation, Ev (kg m−2 s−1): LE ¼ Lv Eg þ Ev Eg ¼ ð1 vegÞa CH Va ½hu qsat ðTs Þ qa Ev ¼ Ec þ Etr ¼ vega CH Va hv ½qsat ðTs Þ qa ;

[78] In ISBA‐DF, the Green‐Ampt approach is used to determine the maximum amount of water that infiltrates the soil to a depth close to 0.2 m, the thickness generally used for the coarse time step. The infiltration capacity, Ic (kg m−2 s−1), is therefore parameterized as follows: ðB2Þ

where zn (m) represents the depth of the n layer that is the nearest 0.2 m, and Dzi (m) is the thickness of each i layer. y 0 is zero for VG80 (equation (7)) and is related to the saturated matric potential of each layer for BC66 (equation (6)). Finally, the soil‐water infiltration in ISBA‐DF can be computed by comparing this infiltration capacity with the flux of water reaching the soil, e.g., I = min(Pg, Ic). All water in excess is treated as a “Horton” surface runoff flux, whereas the soil infiltration is treated as a moisture source terms in equation (5). The soil‐water infiltration is put preferentially in the first layer. If this first layer cannot contain this amount of water, the remaining is forced into the next layer and so forth.

Appendix C: The Continuous PTFs [79] Noilhan and Lacarrère [1995] derived continuous relationships using the dimensionless fractions of clay, Xclay, and sand, Xsand, to estimate the BC66 set of parameters according to the 11 soil textural types and parameters values from the Clapp and Hornberger [1978]. These continuous equations and the related square correlation, r2, calculated over the 11 soil values are given by: b ¼ 13:7 Xclay þ 3:501 wsat ¼ 0:108 Xsand þ 0:494305 wfc ¼ 0:0890467 100 Xclay 0:3496 Log10 jy sat j ¼ 0:88 Xsand 0:15 Log10 ðksat Þ ¼ 5:82 Xclay 0:091 Xsand þ5:29 X 2 1:203 X 2 4:38 clay sand

ðr2 ¼ 0:964Þ ðr2 ¼ 0:935Þ ðr2 ¼ 0:938Þ ðr2 ¼ 0:366Þ

ðC1Þ

ðr2 ¼ 0:960Þ

[80] Cosby et al. [1984] derived the fallowing continuous relationships using sand, clay and silt, Xsilt, dimensionless fractions to estimate the BC66 parameters according to the 11 soil textural types and parameters values from Clapp and Hornberger [1978]: b ¼ 15:7 Xclay 0:3 Xsand þ 3:1 wsat ¼ 0:037 Xclay 0:142 Xsand þ 0:505 Log jy j ¼ 0:63 Xsilt 0:95 Xsand 0:46 10 sat Log ðksat Þ ¼ 0:64 Xclay þ 1:26 Xsand 5:75 10

ðr2 ¼ 0:966Þ ðr2 ¼ 0:785Þ ðr2 ¼ 0:850Þ ðr2 ¼ 0:872Þ ðC2Þ

ðB1Þ

where zf (m) is the depth of the wetting front, and y 0 (m) and y f (m) are the soil matric potential at the surface and at the bottom of this wetting front, respectively.

n w X y yi ksat;i 0 þ 1 Dzi ; zn i¼1 Dzi

[81] For this study, the following continuous equations are derived using multiple linear or polynomial regressions toestimate the VG80 parameters according to the 12

25 of 29

D20126


D20126

soil textural types and parameters values from Carsel and Parrish [1988]: 2 2 Log10 ðn 1Þ ¼ 0:712 Xsand 0:764 Xclay 0:108 Xsand 1:457 Xclay 0:339 2 Log10 ðwr Þ ¼ 3:227 Xclay þ 0:309 Xsand 4:325 Xclay 1:707 wsat ¼ 0:197 Xclay 0:067 Xsand þ 0:48744 Log10 ðÞ ¼ 1:9495 Xsand 1:089 Xclay 0:9193 X 2 þ 0:179 sand Log10 ðksat Þ ¼ 4:287 Xsand þ 7:504 Xclay þ 6:577 X 1=2 10:512 X 1=2 4:797826 sand clay

ðr2 ¼ 0:987Þ ðr2 ¼ 0:956Þ ðr2 ¼ 0:796Þ

ðC3Þ

ðr2 ¼ 0:987Þ ðr2 ¼ 0:942Þ

[82] Finally, Wösten et al. [1999] analyzed the HYPRES all‐Europe database and derived complex continuous PTFs to estimate the VG80 parameters using multiple linear or polynomial regressions between measured soil textures, soil bulk density, rb (g m−3), soil organic matter, OM (%), and soil hydrodynamic parameter. The following continuous equations are computed according to the 4030 soil samples and not with the mean value of a textural classification:

2 lnðn 1Þ ¼ 25:23 2:195 Xclay þ 0:74 Xsilt 0:1940 OM þ 45:5 b 7:24 2b þ 3:658 Xclay 1 1 þ0:002885 OM 2 12:81 1 0:2876 lnð100 Xsilt Þ b 0:001524 Xsilt 0:01958 OM

ðr2 ¼ 0:54Þ

0:0709 lnðOM Þ 44:6 lnðb Þ 2:264 b Xclay þ 0:0896 b OM þ 0:718 Xclay Gsoil 2 lnðl*Þ ¼ 0:0202 þ 6:193 Xclay 0:001136 OM 2 0:2316 lnðOM Þ 3:544 b Xclay þ 0:283 b Xsilt þ 0:0488 b OM

ðr2 ¼ 0:12Þ

2 wsat ¼ 0:7919 þ 0:1691 Xclay 0:29619 b 0:01491 Xsilt þ 0:0000821 OM 2 1 1 þ0:0002427 Xclay þ 0:0001113 Xsilt þ 0:01472 lnð100 Xsilt Þ

0:00733 OM Xclay 0:0619 b Xclay 0:001183 b OM 0:01664 Xsilt Gsoil

ðr2 ¼ 0:76Þ

ðC4Þ

lnðÞ ¼ 10:355 þ 3:135 Xclay þ 3:51 Xsilt þ 0:646 OM þ 15:29 b 0:192 Gsoil 4:671 2b 2 7:81 Xclay 0:00687 OM 2 þ 0:0449 OM 1 þ 0:0663 lnð100 Xsilt Þ þ 0:1482 lnðOM Þ

4:546 b Xsilt 0:4852 b OM þ 0:673 Xclay Gsoil

ðr2 ¼ 0:20Þ

2 2 1 lnðksat Þ ¼ 8:217 þ 3:52 Xsilt þ 0:93 Gsoil 0:967 2b 4:84 Xclay 3:22 Xsilt þ 1:105 Xsilt

0:0748*OM 1 0:643 lnð100 Xsilt Þ 1:398 b Xclay 0:1673 b OM þ 2:986 Xclay Gsoil 3:305 Xsilt Gsoil

where l* = (10 + l)/(10 − l), and Гsoil is a topsoil or subsoil qualitative variable having the value of 1 or 0, respectively. [83] Acknowledgments. This study was initiated with the complicity of Joël Noilhan who passed away on 31 October 2010; the authors want to pay tribute to the memory of their friend and mentor. This work is supported by the “Centre National de Recherches Météorologiques” (CNRM) of Méteo‐France, and the “Centre National de la Recherche Scientifique” (CNRS) of the French research ministry. The authors would like to thank Jean‐Christophe Calvet (CNRM) and Clément Albergel (CNRM) for their useful comments on the SMOSREX data set. Thanks are also due to Marianne Saillard (LMTG) and Eric Martin (CNRM) as well as to anonymous reviewers for their useful comments.

ðr2 ¼ 0:19Þ

References Albergel, C., J.‐C. Calvet, A.‐L. Gibelin, S. Lafont, J.‐L. Roujean, C. Berne, O. Traullé, and N. Fritz (2010), Observed and modelled ecosystem respiration and gross primary production of a grassland in southwestern France, Biogeosciences, 7, 1657–1668, doi:10.5194/bg-7-1657-2010. Alexeev, V. A., D. J. Nicolsky, V. E. Romanovsky, and D. M. Lawrence (2007), An evaluation of deep soil configuration in the CLM3 for improved representation of permafrost, Geophys. Res. Lett., 34, L09502, doi:10.1029/2007GL029536. Alkama, R., B. Decharme, E. Douville, M. Becker, A. Cazenave, J. Sheffield, A. Voldoire, S. Titeca, and P. Lemoigne (2010), Global evaluation of the ISBA‐TRIP continental hydrological system. Part 1: A twofold constraint

26 of 29

D20126


using GRACE Terrestrial Water Storage estimates and in‐situ river discharges, J. Hydrometeorol., 11, 583–600, doi:10.1175/2010JHM1211.1. Allen, M. B., and C. L. Murphy (1985), A finite element collocation method for variably saturated flows in porous media, Numer. Methods Partial Differential Equations, 1(3), 229–239, doi:10.1002/ num.1690010306. Anyah, R. O., C.‐P. Weaver, G. Miguez‐Macho, Y. Fan, and A. Robock (2008), Incorporating water table dynamics in climate modeling: 3. Simulated groundwater influence on coupled land‐atmosphere variability, J. Geophys. Res., 113, D07103, doi:10.1029/2007JD009087. Aubinet, M., B. Heinesch, and B. Longdoz (2002), Estimation of the carbon sequestration by a heterogeneous forest: Night flux corrections, heterogeneity of the site and inter‐annual variability, Global Change Biol., 8(11), 1053–1071, doi:10.1046/j.1365-2486.2002.00529.x. Baca, R. G., J. N. Chung, and D. J. Mulla (1997), Mixed transform finite element method for solving he non‐linear equation for flow in variably saturated porous media, Int. J. Numer. Methods Fluids, 24, 441–455. Balsamo, G., A. Beljaars, K. Scipal, P. Viterbo, B. van den Hurk, M. Hirschi, and A. K. Betts (2009), A revised hydrology for the ECMWF model: Verification from field site to terrestrial water storage and impact in the integrated forecast system, J. Hydrometeorol., 10, 623–643, doi:10.1175/ 2008JHM1068.1. Bélair, S., L.‐F. Crevier, J. Mailhot, B. Bilodeau, and Y. Delage (2003), Operational implementation of the ISBA land surface scheme in the Canadian regional weather forecast model. Part I: Warm season results, J. Hydrometeorol., 4, 352–370, doi:10.1175/1525-7541(2003)42.0.CO;2. Bhumralkar, C.‐M. (1975), Numerical experiments on the computation of ground surface temperature in an atmospheric general circulation model, J. Appl. Meteorol., 14, l, doi:10.1175/1520-0450(1975)0142.0.CO;2. Blackadar, A.‐K. (1976), Modeling the nocturnal boundary layer, Proceedings of the Third Symposium on Atmospheric Turbulence, Diffusion and Air Quality, pp. 46–49, Am. Meteor. Soc., Boston. Blyth, E. M., and C. C. Daamen (1997), The accuracy of simple soil water models in climate forecasting, Hydrol. Earth Syst. Sci., 1(2), 241–248, doi:10.5194/hess-1-241-1997. Boone, A., J. C. Calvet, and J. Noilhan (1999), Inclusion of a third soil layer in a land surface scheme using the force‐restore method, J. Appl. Meteorol., 38, 1611–1630, doi:10.1175/1520-0450(1999)0382.0.CO;2. Boone, A., V. Masson, T. Meyers, and J. Noilhan (2000), The influence of the inclusion of soil freezing on simulation by a soil‐atmosphere‐transfer scheme, J. Appl. Meteorol., 39, 1544–1569, doi:10.1175/1520-0450 (2000)0392.0.CO;2. Boone, A., et al. (2009), The AMMA Land Surface Model Intercomparison Project (ALMIP), Bull. Am. Meteorol. Soc., 90(12), 1865–1880, doi:10.1175/2009BAMS2786.1. Bowling, L., et al. (2003), Simulation of high latitude hydrological processes in the Torne–Kalix basin: PILPS phase 2(e) 1: Experiment description and summary intercomparisons, Global Planet. Change, 38, 1–30, doi:10.1016/S0921-8181(03)00003-1. Braud, I., J. Noilhan, P. Bessemoulin, P. Mascart, R. Haverkamp, and M. Vauclin (1993), Bare ground surface heat and water exchanges under dry conditions: Observation and parameterization, Boundary Layer Meteorol., 66, 173–200, doi:10.1007/BF00705465. Braud, I., A. C. Dantas‐Antonino, M. Vauclin, J. L. Thony, and P. Ruelle (1995), A simple soil‐plant‐atmosphere transfer model (SiSPAT) development and field verification, J. Hydrol., 166, 213–250, doi:10.1016/ 0022-1694(94)05085-C. Braud, I., N. Varado, and A. Olioso (2005), Comparison of root water uptake modules using either the surface energy balance or potential transpiration, J. Hydrol., 301, 267–286, doi:10.1016/j.jhydrol.2004.06.033. Brooks, R. H., and A. T. Corey (1966), Properties of porous media affecting fluid flow, J. Irrig. Drain. Am. Soc. Civil Eng., 17, 187–208. Calvet, J. C., J. Noilhan, J.‐L. Roujean, P. Bessemoulin, M. Cabelguenne, A. Olioso, and J.‐P. Wigneron (1998), An interactive vegetation SVAT model tested against data from six contrasting sites, Agric. For. Meteorol., 92, 73–95, doi:10.1016/S0168-1923(98)00091-4. Calvet, J.‐C., et al. (1999), MUREX: A land‐surface field experiment to study the annual cycle of the energy and water budgets, Ann. Geophys., 17, 838–854, doi:10.1007/s00585-999-0838-2. Campbell, G. S. (1974), A simple method for determining unsaturated conductivity from moisture retention data, Soil Sci., 117, 311–314, doi:10.1097/00010694-197406000-00001. Carsel, R. F., and R. S. Parrish (1988), Developing joint probability distributions of soil water retention characteristics, Water Resour. Res., 24(5), 755–770, doi:10.1029/WR024i005p00755.

D20126

Carsel, R. F., R. S. Parrish, R. L. Jones, J. L. Hansen, and R. L. Lamb (1988), Characterizing the uncertainty of pesticide movement in agricultural soils, J. Contam. Hydrol., 2(2), 125–138, doi:10.1016/0169-7722 (88)90003-4. Celia, M. A., E. T. Bouloutas, and R. L. Zarba (1990), A General mass‐ conservative numerical solution for the unsaturated flow equation, Water Resour. Res., 26(7), 1483–1496, doi:10.1029/WR026i007p01483. Chapelon, N., H. Douville, P. Kosuth, and T. Oki (2002), Off‐line simulation of the Amazon water balance: A sensitivity study with implications for GSWP, Clim. Dyn., 19, 141–154, doi:10.1007/s00382-001-0213-9. Charney, J., W. J. Quirk, S.‐H. Chow, and J. Kornfield (1977), A comparative study of the effects of albedo change on drought in semi‐arid regions, J. Atmos. Sci., 34, 1366–1385, doi:10.1175/1520-0469(1977) 0342.0.CO;2. Chen, F., Z. Janjic, and K. Mitchell (1997), Impact of atmospheric surface‐ layer parameterizations in the new land‐surface scheme of the NCEP mesoscale Eta model, Boundary Layer Meteorol., 85(3), 391–421, doi:10.1023/A:1000531001463. Clapp, R., and G. Hornberger (1978), Empirical equations for some soil hydraulic properties, Water Resour. Res., 14, 601–604, doi:10.1029/ WR014i004p00601. Cosby, B. J., G. M. Hornberger, R. B. Clapp, and T. R. Ginn (1984), A statistical exploration of the relationship of soil moisture characteristics to the physical properties of soils, Water Resour. Res., 20(6), 682–690, doi:10.1029/WR020i006p00682. Cox, P. M., R. A. Betts, C. B. Bunton, R. L. H. Essery, P. R. Rowntree, and J. Smith (1999), The impact of a new land surface physics on the GCM simulation of climate and climate sensitivity, Clim. Dyn., 15, 183–203, doi:10.1007/s003820050276. Dai, Y., et al. (2003), The Common Land model, Bull. Am. Meteorol. Soc., 84(8), 1013–1023, doi:10.1175/BAMS-84-8-1013. Deardorff, J. W. (1977), A parametrization of ground‐surface moisture content for use in atmospheric prediction model, J. Appl. Meteorol., 16, 1182–1185, doi:10.1175/1520-0450(1977)0162.0. CO;2. Decharme, B., and H. Douville (2006), Uncertainties in the GSWP‐2 precipitation forcing and their impacts on regional and global hydrological simulations, Clim. Dyn., 27, 695–713, doi:10.1007/s00382-006-0160-6. Decharme, B., H. Douville, A. Boone, F. Habets, and J. Noilhan (2006), Impact of an exponential profile of saturated hydraulic conductivity within the ISBA LSM: Simulations over the Rhone basin, J. Hydrometeorol., 7, 61–80, doi:10.1175/JHM469.1. Decharme, B., R. Alkama, E. Douville, M. Becker, and A. Cazenave (2010), Global evaluation of the ISBA‐TRIP continental hydrologic system. Part 2: Uncertainties in river routing simulation related to flow velocity and groundwater storage, J. Hydrometeorol., 11, 601–617, doi:10.1175/2010JHM1212.1. de Rosnay, P., M. Bruen, and J. Polcher (2000), Sensitivity of surface fluxes to the number of layers in the soil model used in GCMs, Geophys. Res. Lett., 27(20), 3329–3332, doi:10.1029/2000GL011574. de Rosnay, P., et al. (2006), SMOSREX: A long term field campaign experiment for soil moisture and land surface processes remote sensing, Remote Sens. Environ., 102, 377–389, doi:10.1016/j.rse.2006.02.021. de Rosnay, P., et al. (2009), AMMA Land Surface Model Intercomparison Experiment coupled to the Community Microwave Emission Model: ALMIP‐MEM, J. Geophys. Res., 114, D05108, doi:10.1029/ 2008JD010724. Etchevers, P., C. Colaz, and F. Habets (2001), Simulation of the water budget and the rivers flows of the Rhône basin from 1981 to 1994, J. Hydrol., 244, 60–85, doi:10.1016/S0022-1694(01)00332-8. Farouki, O. T. (1986), Thermal Properties of Soils, Ser. on Rock and Soil Mech., vol. 11, 136 pp., Trans. Tech. Publ., Clausthal‐Zellerfled, Germany. Feddes, R. A., et al. (2001), Modeling root water uptake in hydrological and climate models, Bull. Am. Meteorol. Soc., 82(12), 2797–2809, doi:10.1175/1520-0477(2001)0822.3.CO;2. Fekete, B. M., C. J. Vörösmarty, J. O. Road, and C. J. Willmott (2004), Uncertainties in Precipitation and their impacts on runoff estimates, J. Clim., 17, 294–304, doi:10.1175/1520-0442(2004)0172.0.CO;2. Feng, C. Y., T. H. Lee, W. S. Lee, and C.‐H. Chen (2007), Estimating finite difference block equivalent hydraulic conductivity for numerically solving the Richards’ equation, Hydrol. Process., 21, 3587–3600, doi:10.1002/hyp.6585. Gonzalez‐Sosa, E., I. Braud, J. L. Thony, M. Vauclin, P. Bessemoulin, and J. C. Calvet (1999), Modelling heat and water exchanges of fallow land covered with plant‐residue mulch, Agric. For. Meteorol., 97, 151–169, doi:10.1016/S0168-1923(99)00081-7.

27 of 29

D20126


Gonzalez‐Sosa, E., I. Braud, J. L. Thony, M. Vauclin, and J. C. Calvet (2001), Heat and water exchanges of fallow land covered with a plant‐ residue mulch layer: A modelling study using the three year MUREX data set, J. Hydrol., 244, 119–136, doi:10.1016/S0022-1694(00)00423-6. Green, W. H., and G. A. Ampt (1911), Studies on soil physics, 1: The flow of air and water through soils, J. Agric. Sci., 4, 1–24. Guarracino, L., and F. Quintana (2004), A third‐order accurate time scheme for variably saturated groundwater flow modeling, Commun. Numer. Methods Biomed. Eng., 20, 379–389, doi:10.1002/cnm.680. Habets, F., J. Noilhan, C. Golaz, J. P. Goutorbe, P. Lacarrère, E. Leblois, E. Ledoux, E. Martin, C. Ottlé, and D. Vidal‐madjar (1999a), The ISBA surface scheme in a macroscale hydrological model applied to the HAPEX‐MOBILHY area. Part I: Model and database, J. Hydrol., 217, 75–96, doi:10.1016/S0022-1694(99)00019-0. Habets, F., P. Etchevers, C. Golaz, E. Leblois, E. Ledoux, E. Martin, J. Noilhan, and C. Ottlé (1999b), Simulation of the water budget and the river flows of the Rhône basin, J. Geophys. Res., 104(D24), 31,145– 31,172, doi:10.1029/1999JD901008. Habets, F., A. Boone, and J. Noilhan (2003), Simulation of a Scandinavian basin using the diffusion transfer version of ISBA, Global Planet. Change, 38, 137–149, doi:10.1016/S0921-8181(03)00016-X. Habets, F., P. LeMoigne, and J. Noilhan (2004), On the utility of operational precipitation forecasts to served as input for streamflow forecasting, J. Hydrol., 293, 270–288, doi:10.1016/j.jhydrol.2004.02.004. Haverkamp, R., and M. Vauclin (1979), A note on estimating finite difference interblock hydraulic conductivity values for transient unsaturated flow problems, Water Resour. Res., 15, 181–187, doi:10.1029/ WR015i001p00181. Haverkamp, R., M. Vauclin, J. Touma, P. J. Wierenga, and G. Vachaud (1977), A comparison of numerical simulation models for one‐dimensional infiltr ation, Soil Sci. Soc. A m. J., 41(2), 285–294, doi:10.2136/ sssaj1977.03615995004100020024x. Hills, R. G., I. Porro, D. B. Hudson, and P. J. Wierenga (1989), Modelling one‐dimensional infiltration into very dry soil, 1: Model development and evaluation, Water Resour. Res., 25, 1259–1269, doi:10.1029/ WR025i006p01259. Hornung, U., and W. Messing (1983), Truncation errors in the numerical solution of horizontal diffusion in saturated/unsaturated media, Adv. Water Resour., 6, 165–168, doi:10.1016/0309-1708(83)90029-5. International Institute for Applied Systems Analysis (2009), Harmonized World Soil Database (Version 1.1). Food and Agriculture Organization of the United Nations, Rome, Italy, and International Institute for Applied Systems Analysis, Laxenburg, Austria. (Available at http:// www.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/) Jackson, R. B., J. Canadell, J. R. Ehleringer, H. A. Mooney, O. E. Sala, and E. D. Schulze (1996), A global analysis of root distributions for terrestrial biomes, Oecologia, 108, 389–411, doi:10.1007/BF00333714. Jarvis, P. G. (1976), The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field, Philos. Trans. R. Soc. London, Ser. B, 273, 593–610, doi:10.1098/ rstb.1976.0035. Johansen, O. (1975), Thermal conductivity of soils. Ph.D. thesis, 236 pp., Univ. of Trondheim, Trondheim, Norway. Johnsen, K. E., H. H. Liu, J. H. Dane, L. R. Ahuja, and S. R. Workman (1995), Simulating fluctuating water tables and tile drainage with a modified Root Zone Water Quality model and a new model WAFLOWM, Trans. ASAE, 38, 75–83. Johnsson, H., and L. C. Lundin (1991), Surface runoff and soil water percolation as affected by snow and soil frost, J. Hydrol., 122, 141–159, doi:10.1016/0022-1694(91)90177-J. Luo, L., et al. (2003), Effect of frozen soil on soil temperature, spring infiltration, and runoff: Result from the PILPS 2(d) experiment at Valdai, Russia, J. Hydrometeorol., 4, 334–351, doi:10.1175/1525-7541(2003) 42.0.CO;2. Mahfouf, J.‐F., and J. Noilhan (1996), Inclusion of gravitational drainage in a land surface scheme based on the force‐restore method, J. Appl. Meteorol., 35, 987–992, doi:10.1175/1520-0450(1996)0352.0.CO;2. Mahfouf, J.‐F., A. O. Manzi, J. Noilhan, H. Giordani, and M. Déqué (1995), The land surface scheme ISBA within the Météo‐France climate ARPEGE. Part I: Implementation and preliminary results, J. Clim., 8, 2039–2057, doi:10.1175/1520-0442(1995)0082.0. CO;2. Mansell, R. S., M. Liwang, L. R. Ahuja, and S. A. Bloom (2002), Adaptive grid refinement in numerical models for water flow and chemical transport in soil: A review, Vadose Zone J., 1, 222–238. Manzi, A. O., and S. Planton (1994), Implementation of the ISBA parameterization scheme for land surface processes in a GCM: An annual cycle

D20126

experiment, J. Hydrol., 155, 353–387, doi:10.1016/0022-1694(94) 90178-3. Masson, V., J. L. Champeaux, F. Chauvin, C. Mériguet, and R. Lacaze (2003), A global database of land surface parameters at 1 km resolution for use in meteorological and climate models, J. Clim., 16, 1261–1282, doi:10.1175/1520-0442-16.9.1261. Maxwell, R. M., and N. L. Miller (2005), Development of a coupled land surface and groundwater model, J. Hydrometeorol., 6, 233–247, doi:10.1175/JHM422.1. McCumber, M. C., and R. A. Pielke (1981), Simulation of the effects of surface fluxes of heat and moisture in a mesoscale numerical model 1: Soil layer, J. Geophys. Res., 86(C10), 9929–9938, doi:10.1029/ JC086iC10p09929. Miguez‐Macho, G., Y. Fan, C.‐P. Weaver, R. Walko, and A. Robock (2007), Incorporating water table dynamics in climate modeling: 2. Formulation, validation, and soil moisture simulation, J. Geophys. Res., 112, D13108, doi:10.1029/2006JD008112. Milly, P. C. D. (1985), A mass‐conservative procedure for time‐stepping in models of unsaturated flow, Adv. Water Resour., 8, 32–36, doi:10.1016/ 0309-1708(85)90078-8. Montaldo, N., and J. D. Albertson (2001), On the use of the force‐restore SVAT model formulation for stratified soils, J. Hydrolmeteorol., 2, 571– 578, doi:10.1175/1525-7541(2001)0022.0.CO;2. Mualem, Y. (1976), A new model for predicting the hydraulic conductivity of unsaturated porous media, Water Resour. Res., 12, 513–522, doi:10.1029/WR012i003p00513. Noilhan, J., and P. Lacarrère (1995), GCM grid scale evaporation from mesoscale modeling, J. Clim., 8, 206–223, doi:10.1175/1520-0442 (1995)0082.0.CO;2. Noilhan, J., and S. Planton (1989), A simple parameterization of land surface processes for meteorological models, Mon. Weather Rev., 117, 536– 549, doi:10.1175/1520-0493(1989)1172.0.CO;2. Noilhan, J., S. Donier, P. Lacarrère, C. Sarrat, and P. Le Moigne (2011), Regional scale evaluation of a land surface scheme from atmospheric boundary layer observations, J. Geophys. Res., 116, D01104, doi:10.1029/2010JD014671. Oki, T., T. Nishimura, and P. Dirmeyer (1999), Assessment of annual runoff from land surface models using Total Runoff Integrating Pathways (TRIP), J. Meteorol. Soc. Jpn., 77, 235–255. Peters‐Lidard, C. D., E. Blackburn, X. Liang, and E. F. Wood (1998), The effect of soil thermal conductivity parameterization on surface energy fluxes and temperatures, J. Atmos. Sci., 55, 1209–1224, doi:10.1175/ 1520-0469(1998)0552.0.CO;2. Quintana Seguí, P., E. Martin, F. Habets, and J. Noilhan (2009), Improvement, calibration and validation of a distributed hydrological model over France, Hydrol. Earth Syst. Sci., 13(2), 163–181, doi:10.5194/hess-13163-2009. Riseborough, D., N. Shiklomanov, B. Etzelmüller, S. Gruber, and S. Marchenko (2008), Recent advances in permafrost modeling, Permafrost Periglacial Process., 19, 137–156, doi:10.1002/ppp.615. Schnabel, R. R., and E. B. Richie (1984), Calculation of internodal conductances for unsaturated flow simulations: A comparison, Soil Sci. Soc. Am. J., 48, 1006–1010, doi:10.2136/sssaj1984.03615995004800050010x. Seity, Y., P. Brousseau, S. Malardel, G. Hello, P. Bénard, F. Bouttier, C. Lac, V. Masson (2011), The AROME‐France convective scale operational model, Mon. Weather Rev., 139, 976–991, doi:10.1175/ 2010MWR3425.1. Slater, A. G., et al. (2001), The representation of snow in land surface schemes: Results from PILPS 2(d), J. Hydrometeorol., 2, 7–25, doi:10.1175/1525-7541(2001)0022.0.CO;2. Thirel, G., E. Martin, J.‐F. Mahfouf, S. Massart, S. Ricci, and F. Habets (2010), A past discharges assimilation system for ensemble streamflow forecasts over France–Part 1: Description and validation of the assimilation system, Hydrol. Earth Syst. Sci., 14, 1623–1637, doi:10.5194/hess-14-1623-2010. Thompson, S. L., and D. Pollard (1995), A global climate model (GENESIS) with a land‐surface transfer scheme (LSX). Part I: Present climate simulation, J. Clim., 8(4), 732–761, doi:10.1175/1520-0442(1995)0082.0.CO;2. van Genuchten, M. (1980), A closed‐form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Sci. Soc. Am. J., 44, 892–898, doi:10.2136/sssaj1980.03615995004400050002x. Viterbo, P., and A. C. M. Beljaars (1995), An improved land surface parametrization scheme in the ECMWF model and its validation, J. Clim., 8, 2716–2748, doi:10.1175/1520-0442(1995)0082.0.CO;2. Warrick, A. W. (1991), Numerical approximations of Darcian flow through unsaturated soil, Water Resour. Res., 27, 1215–1222, doi:10.1029/ 91WR00093.

28 of 29

D20126


Wetzel, P. J., and A. Boone (1995), A Parameterization for Land–Cloud– Atmosphere Exchange (PLACE): Documentation and testing of a detailed process model of the partly cloudy boundary layer over heterogeneous land, J. Clim., 8, 1810–1837, doi:10.1175/1520-0442(1995) 0082.0.CO;2. Wösten, J. H. M., A. Lilly, A. Nemes, and C. Le Bas (1999), Development and use of a database of hydraulic properties of European soils, Geoderma, 90, 169–185, doi:10.1016/S0016-7061(98)00132-3.

D20126

Zaidel, J., and D. Russo (1992), Estimation of Finite Difference Interblock Conductivities for Simulation of Infiltration Into Initially Dry Soils, Water Resour. Res., 28(9), 2285–2295, doi:10.1029/92WR00914. A. Boone, B. Decharme, C. Delire, and J. Noilhan, CNRS, Météo‐ France, GAME‐CNRM, 42 av. G. Coriolis, F‐31057 Toulouse, France. ([email protected])

29 of 29