1. Introduction
Global warming is expected to continue in the future according to outcomes of atmospheric general circulation models (AGCMs; IPCC 2013). Within AGCMs, land surface models (LSMs) provide the lower boundary conditions in the form of moisture and energy exchanges at the land–atmosphere interface. To make credible predictions of climate change, LSMs evolved from a simple bucket model (Manabe 1969) to more sophisticated soil–vegetation–atmosphere transfer (SVAT) schemes (Dai et al. 2003; Ek et al. 2003; Lawrence et al. 2011; Niu et al. 2011; Sellers et al. 1986) to better represent the complex interplay of processes linked to vegetation, soil, and snow. However, state-of-the-art LSMs still have difficulties with reliably simulating the states that drive heat (Decker et al. 2012; Jiménez et al. 2011) as well as water (Dirmeyer et al. 2004, 2006b) fluxes. Further improvement of LSMs and validation against observations remain, therefore, imperative.
Soil moisture is a key state variable controlling the partitioning of available energy at the land surface between sensible and latent heat, as well as determining the partitioning of rainfall between subsoil drainage, surface runoff Rsurf, and evaporation. Thus, it is crucial to reliably simulate soil water flow processes to provide realistic initial states to the models that are used to simulate climate change and its impact on the terrestrial water cycle. However, large differences and biases exist among the soil moisture products generated by various LSMs driven with the same meteorological forcing (Dirmeyer et al. 2006a; Xia et al. 2014) because of different model physics, structure, and parameter choices. For instance, several vertical root distribution schemes (i.e., root depth and density) are implemented by the current LSMs (Zeng 2001), and the diversity of soil hydraulic functions as well as hydraulic parameters also pose a high uncertainty (Decharme et al. 2011; Shao and Irannejad 1999). Moreover, the LSMs have originally been developed for large-scale applications and numerical efficiency. Therefore, the presence of irrigation and groundwater processes is often ignored (e.g., Xia et al. 2014), and the number of soil layers is limited and extends down to a few meters (e.g., Gulden et al. 2007). Besides, the soil profile is assumed homogeneous, and either the diffusivity form of Richards’ equation (e.g., Balsamo et al. 2009; Liang et al. 1996) or a force–restore approach (Decharme et al. 2006) is employed for the soil water flow simulation by LSMs that does not accommodate for transport across layers with different hydraulic properties.
Recently, Chen et al. (2013), Su et al. (2013), and Xue et al. (2013) have reported on the inability of LSMs to reproduce the soil moisture and temperature profiles measured by newly developed in situ monitoring networks across the central and eastern parts of the Tibetan Plateau (Su et al. 2011; Yang et al. 2013). A major reason for the weak performance of LSMs in this region is the absence of vertical soil heterogeneity within model structures (Chen et al. 2013; Yang et al. 2005). Particularly, many roots are present in the upper soil layer of Tibetan ecosystems as an adaptation to the harsh Tibetan environment (Yang et al. 2009a). This leads to the accumulation of organic matter in the topsoil (Yang et al. 2009b, hereafter Y09b) and causes a soil stratification (Chen et al. 2012). Organic matter and living and decayed root systems share a large volume of the topsoil, affecting the soil structure as well as its hydraulic and thermal properties (Chen et al. 2012). Organic matter generally has a higher porosity, hydraulic conductivity, and thermal heat capacity, while a lower thermal heat conductivity and less suction needs to be applied to release water as compared to mineral soils (de Vries 1963; Lawrence and Slater 2008; Letts et al. 2000).
Previous studies have shown that the vertical soil heterogeneity as well as the effect of organic matter and root systems not only affects the thermal and moisture regimes at the ground (Beringer et al. 2001; Letts et al. 2000; Yang et al. 2005), but also the dynamic interactions with the overlaying atmosphere (Lawrence and Slater 2008; Rinke et al. 2008). It is, therefore, indispensable for the applicability of AGCMs to polar and boreal ecosystems that the organic matter as well as the vertical soil heterogeneity is considered by LSMs. However, parameterizations for organic soil types are typically not implemented by the state-of-the-art LSMs. In the past, Beringer et al. (2001) and Letts et al. (2000) have studied the soil water flow through a column of pure organic material overlaying mineral soil layers, while Lawrence and Slater (2008) conceptualized the soil water transport through a mixture of coexisting organic and mineral components whose physical properties are additive. These two approaches have been developed specifically for the Arctic and boreal organic soils and its applicability to the Third Pole Environment, as the Tibetan Plateau is also referred to, is yet to be proven. Although Chen et al. (2012) have recently investigated the effect of organic soil on soil thermal parameterization for grasslands in central Tibet, additional work is needed to assess the impact of organic soil on soil hydraulic parameterization as well as the simulated surface energy and water budgets.
In this investigation, we seek to further improve a state-of-the-art Noah LSM (Ek et al. 2003) in its ability to simultaneously produce soil moisture and temperature profiles measured in the source region of the Yellow River (SRYR) on the northeastern Tibetan Plateau. In this two-part series, we study the model physics associated with the soil water flow simulation through comparisons of the soil parameterization with hydraulic properties measured in the laboratory and through comparisons of simulations with in situ soil moisture profile measurements. Three augmentations are made. First, the effect of organic matter on soil hydraulic properties is considered via the “additivity” hypothesis (Zeiliguer et al. 2000). Second, the saturated hydraulic conductivity 
The structure of this paper is as follows. Section 2 describes the in situ micrometeorological measurements and the soil moisture and temperature profile measurements as well as the soil properties measured in the laboratory. Section 3 introduces the soil water flow and root water uptake components of Noah. Section 4 provides a description of the augmentations made to the Noah model physics. Section 5 presents comparisons of the modified hydraulic parameterization and measured soil properties. Section 6 provides a performance assessment of the soil moisture profiles simulated by Noah with different options. The impact of the improved soil moisture simulations on the calculated surface energy and water budgets is evaluated in section 7, and section 8 summarizes the findings of this study.
2. Observations and experiments
a. Maqu observation station
Maqu Climate and Environment Observation Station (Fig. 1) is located in the SRYR in a landscape dominated by alpine meadows (e.g., Cyperaceae and Gramineae) at elevations varying from 3200 to 4200 m. Cold dry winters and rainy summers are characteristic for its climate, with a mean annual air temperature of 1.2°C, −10°C for the coldest month (January) and 11.7°C for the warmest month (July). The groundwater level is about 8.5–10.0 m below the soil surface.
(top) Location of Maqu station in the SRYR in China and (bottom) the field observation system and experimental sites.
Citation: Journal of Hydrometeorology 16, 6; 10.1175/JHM-D-14-0198.1
Maqu is a micrometeorological station equipped with a 20-m planetary boundary layer (PBL) tower providing wind speed and direction, air humidity, and temperature at five levels; instrumentation for measuring four radiation components; and an eddy covariance (EC) system installed at a height of 3.2 m. Table 1 lists the equipment deployed and the measured hydrometeorological variables. Across an area of 40 km × 80 km centered on Maqu station, a network of 20 soil moisture and soil temperature (SMST) monitoring sites has been operational since 2008 (Dente et al. 2012). This regional-scale SMST network is part of the Tibetan Plateau Observatory (Tibet-Obs; Su et al. 2011) and has been designed to contribute to the calibration–validation of satellite-based soil moisture products as well as to an improved understanding of land processes on the plateau. Two SMST sites (CST01 and NST01) of the Tibet-Obs are situated in the vicinity of Maqu station and are used for the presented analyses.
Equipment deployed and the hydrometeorological variables measured at Maqu station.
The time period under investigation covers the majority of the monsoon season starting on 8 June and ending on 30 September 2010. This episode is selected to avoid the impact of the cold season (e.g., snowpack and frozen soil) on the assessment of Noah’s soil water flow and heat transport model physics. All the data were processed to a value for every 30-min interval within this episode, whereby the ground surface temperature is computed from measured upward and downward longwave radiations as in Zheng et al. (2014). Additional details for the measurements and data processing can be found in Dente et al. (2012) and Zheng et al. (2014).
b. Field and laboratory experiments
Soil samples were collected at two SMST sites (CST01 and NST01) near the micrometeorological station, as well as two sites (NST04 and NST11) located in a wetland environment (shown in Fig. 1) to quantify the soil texture and hydraulic properties through laboratory analyses in July 2013. Two or three soil profiles were obtained from each site, with samples taken at depths of 0.1, 0.3, and 0.6 m. Duplicates of undisturbed soil samples were collected by soil-cutting ring augers, whereby the hydraulic characterization was complemented by field measurement of the saturated hydraulic conductivity with the Guelph Permeameter manufactured by Soilmoisture Equipment Corp.
The soil samples were transported to the laboratory for precise measurement of the soil texture (sand, clay, and silt), organic carbon mass content 
Table 2 lists the mean values for the soil texture 
Average feature of soil properties measured by field and laboratory experiments in this study. From each depth we take three samples, and at some sites we find that at the same depth different soil properties, such as texture, are found, so we use a and b to highlight this.
3. Noah LSM
The Noah LSM (Ek et al. 2003) is designed to form the land component of deterministic climate models, for example, the Weather Research and Forecasting (WRF) Model of the National Center for Atmospheric Research (NCAR), and is the LSM for which NASA makes the most extensive set of simulations available as part of the Global Land Data Assimilation System (GLDAS; Rodell et al. 2004). The model structure consists of a modestly complex canopy resistance scheme (Chen et al. 1996) linked to the diurnal Penman approach (Mahrt and Ek 1984) for simulating the latent heat flux and a surface energy balance approach whereby the entire soil–vegetation system is represented as a single heat and water vapor source. A four-layer soil scheme is implemented with the thermal diffusion equation for simulating heat transport and the diffusivity form of Richards’ equation for water flow (Mahrt and Pan 1984; Pan and Mahrt 1987). A simple water balance approach (Schaake et al. 1996) is adopted to simulate the surface runoff, and the cold season physics are implemented as described in Koren et al. (1999).
The model physics of Noah, version 3.4.1, associated with soil water flow are provided below, and the processes related to the soil heat transport and surface heat fluxes exchange are described in Part II. The readers are also referred to existing literature (e.g., Ek et al. 2003; Niu et al. 2011; van der Velde et al. 2009) for more additional information on the Noah LSM.
a. Soil water flow
b. Soil hydraulic parameterization
c. Root water uptake
4. Augmentations to the Noah LSM
a. Consideration of organic matter
Soil organic content (SOC) affects the structure as well as the physical properties of the soil and, thus, the effects of organic matter on the hydraulic properties need to be understood and taken into consideration. The approach utilized in this study is based on the additivity hypothesis (Federer et al. 1993; Lawrence and Slater 2008; Zeiliguer et al. 2000) that considers 1) each soil layer as a mixture of organic and mineral masses, 2) the bulk densities of soil organic matter 
The hydraulic parameters of the mineral soil 
b. Decrease of 
Organic matter and living and decayed root systems affect the soil saturated hydraulic conductivity (Decharme et al. 2006), which can be very high near the surface and enlarge the hydraulic conductivity of the soil. On the other hand, the absence of organic material can reduce 
The exponential profile decay factor in Eq. (7) can be estimated indirectly through calibration against measured streamflow recession curves or directly from in situ measurements that capture the 
c. Root distribution
d. Modified soil water flow scheme implementation
The current implementation of the diffusivity form of Richards’ equation in Noah is not able to simulate water flow across a vertically heterogeneous soil profile. As such, the model code is first revised to enable the assignment of different hydraulic parameters for each soil layer. Specifically, the soil hydraulic parameters 
5. Estimation of soil hydraulic properties
a. Bulk density and porosity
From section 4 three methods can be deduced for calculating 
In this study, we apply the methods for estimating the 
Values of R2, ME, and RMSE between measured and estimated 
The error statistics associated with the 
Correlation coefficients calculated among 
b. Soil water retention curve
Figure 2 shows the soil water retention measurements for the three soil types (organic soil, silt loam, and sandy loam) as well as estimates of the retention curve with Campbell’s soil hydraulic model [Eq. (3a)]. Specifically, the soil data given in Table 2 are regrouped by soil texture. Comparable to the previous section, the hydraulic parameters 
Comparison of the observed and estimated soil water retention curves for (a) organic soil, (b) silt loam, and (c) sandy loam deduced from Cosby’s class or continuous PTF without and with consideration of the SOC.
Citation: Journal of Hydrometeorology 16, 6; 10.1175/JHM-D-14-0198.1
Table 5 lists for each soil type the mean 
Average feature of soil properties regrouped by soil type, as well as RMSE between measured and estimated soil water contents associated with different pressure heads.
In contrast to the 
c. Saturated hydraulic conductivity
Figure 3 shows the in situ saturated hydraulic conductivity measurements as a function of soil depth carried out around the four SMST sites. In addition, 
Comparison of the observed and estimated saturated hydraulic conductivity as an exponential function of soil depth carried out for four SMST sites. The legend indicates laboratory measurements (Obs) and estimates obtained with the Kozeny–Carman equation (KC), Cosby’s class (class), and the continuous PTF (continuous).
Citation: Journal of Hydrometeorology 16, 6; 10.1175/JHM-D-14-0198.1
The measurements indicate that, in general, the 
The implementation of Beven’s approach for describing Ks is a function of the soil depth whereby the reference saturated hydraulic conductivity is estimated by the Kozeny–Carman equation. The most notable differences are obtained near the soil surface where 
6. Simulation of soil moisture with the Noah LSM
a. Design of numerical experiments
Three experiments are designed to assess the impact of the augmentations to the default Noah LSM described in section 4. A control experiment (Ctrl) is performed first by running the Noah LSM with the default soil hydraulic and root uptake scheme as described in section 3. Second, the default hydraulic scheme is replaced with the soil organic scheme (see section 5) and the modified diffusivity form of Richards’ equation [Eq. (11)] that resolves the soil moisture discontinuity at the interface of two layers (EXP1; see section 4d). Third, the distribution of roots in the soil profile is implemented as a function of depth following Eqs. (10a) and (10b) instead of the default uniform distribution (EXP2). It should be noted that the augmentations to the surface heat fluxes exchange and soil heat transport described in Part II are implemented by the three experiments.
All the experiments are forced by the meteorological measurements collected at the PBL tower from 8 June to 30 September 2010 and include air temperature, relative humidity, wind speed, air pressure, upward and downward shortwave radiations, downward longwave radiation, and precipitation (see Table 1). The observation height of the air temperature and wind speed is 2.35 m. The prescribed vegetation type is grassland, and the monthly values of green vegetation fraction (GVF) and leaf area index (LAI) are derived from the Système Pour l’Observation de la Terre (SPOT) 10 daily synthesis NDVI product as in Zheng et al. (2014). The other vegetation parameters (e.g., number of root layers) are obtained from Noah’s default land-cover database. The β for EXP2 is taken as the value (0.900) for the alpine meadow reported in Yang et al. (2009a), and then the number of root layers is computed using the method described in section 4c.
The silt loam is adopted as soil texture according to measured properties (see Table 2) found at the upper layers of the two SMST sites (CST01 and NST01) near the PBL tower. Corresponding hydraulic parameters for Ctrl are obtained using Cosby’s class PTF. The 
Soil moisture and temperature measurements are used to initialize each model run as well as to validate Noah simulations. For both, the measurements collected at sites CST01 and NST01 are averaged for each soil depth (e.g., 0.05, 0.10, 0.20, 0.40, and 0.80 m) and subsequently interpolated to the midpoints of the upper three model layers (i.e., 0.05, 0.25, and 0.70 m). Then, the soil moisture and temperature of the fourth layer is taken for initialization equal to the states of the third layer. The Noah simulations are further validated through comparisons of the simulated sensible and latent heat fluxes with measurements collected by an EC system.
b. Noah soil moisture simulations
Figure 4 shows time series with a 30-min interval of the measured soil moisture and the simulations produced by the previously described three numerical experiments along with the measured rainfall. Figures 4a–c provide the measurements and simulations for soil depths of 5, 25, and 70 cm, respectively. Three distinct dry-down episodes (periods in which soil moisture gradually depletes) can be deduced from Fig. 4a, for example, 1) days of year (DOY) 159–179, 2) DOY 204–224, and 3) DOY 244–264 with corresponding wetting periods (i.e., DOY 179–204, 224–244, and 264 onward). The default configuration of the Noah LSM (Ctrl) tends to underestimate the soil moisture, especially for the two upper soil layers. For the top layer the soil moisture underestimation is most notable under wet conditions, while below a soil moisture content of 0.25 m3 m−3 the underestimation changes into an overestimation.
Comparison of the observed and simulated soil moisture produced by three Noah numerical experiments for each soil layer from 8 Jun to 30 Sep 2010: (a) 5 cm, (b) 25 cm, and (c) 70 cm.
Citation: Journal of Hydrometeorology 16, 6; 10.1175/JHM-D-14-0198.1
The inability to accurately simulate the surface soil moisture over the Tibetan Plateau has also been recently reported for other LSMs (Chen et al. 2013; Su et al. 2013; Xue et al. 2013). Yang et al. (2005) and Chen et al. (2013) attributed this to the absence of a soil stratification linked specifically to organic matter within model structures. Indeed, the underestimation of surface soil moisture significantly improved during the wetting periods after implementing the soil organic scheme (EXP1). The explanation for this is that the relative abundance of organic matter in the upper layers leads to a larger soil porosity and higher water holding capacity. Consequently, the EXP1 simulation tends to retain water in the upper layers, which causes an overestimation of soil moisture in the upper layers during dry-downs. This is further enhanced by the exponential 
As such, the augmentations implemented for EXP1 enable the Noah model to better simulate the soil moisture content during wet episodes, but it does not lead to an improvement for the dry-downs. The soil moisture results following from the EXP2 simulation show that this can be associated with the default uniform root distribution implemented in Noah. This assumed that the root fraction in each layer is proportional to the layer thickness, which leads to more root uptake from the deeper (lower) soil layers. In reality, however, the majority of the plant roots are located in the upper soil layer of Tibetan ecosystems (Yang et al. 2009a). Hence, both Ctrl and EXP1 overestimate the soil moisture content of the upper soil layers during dry-downs (i.e., 0.05 and 0.25 m) and underestimate it for the lower soil layers (i.e., 0.70 m). Implementation of the root distribution as an asymptotic function of depth [Eqs. (10a), (10b)] allows Noah (EXP2) to take up more water for transpiration from the upper soil layers. This modification to the model structure also enables the soil moisture simulations to better capture the dynamics measured at each soil depth under dry-down conditions, as can be seen in Fig. 4.
Table 6 gives the error statistics (i.e., R2, ME, and RMSE) between the measured and simulated soil moisture produced by the three experiments. The error statistics confirm improvement in the soil moisture simulation achieved by EXP1 and EXP2 model runs in comparison to Ctrl. The R2 calculated for the difference between the measurements and simulations increased on average for the three soil depths (0.05, 0.25, and 0.70 m) from 0.73 for Ctrl to 0.8 for EXP1 to 0.93 for EXP2. This clearly highlights that the EXP2 simulations are superior in capturing the soil moisture dynamics. This is further supported by reductions in the ME by about 39%, 77%, and 61% and RMSE by 49%, 70%, and 56% in comparison to the Ctrl error statistics for the three soil depths, respectively.
Error statistics computed between measured and simulated soil moisture produced by three Noah numerical experiments from 8 Jun to 30 Sep 2010.
7. Discussion
a. Impact on surface energy budget simulations
Soil moisture plays an important role in the energy balance by controlling the partition of the surface energy budget into the sensible and latent heat fluxes. Below critical soil moisture levels, evaporation is suboptimal and the radiation excess is converted into heat. Soil moisture also affects the soil heat conductivity and the soil heat capacity that influences the transport of heat G0 into the soil. The accuracy of the simulated soil moisture will, therefore, inevitably have an impact on the simulations of the heat fluxes and soil temperatures.
Figure 5 shows the partitioning of the surface energy budget into LE, H, and G0 produced by the three Noah runs (Ctrl, EXP1, and EXP2) presented as a ratio of the net radiation Rn, whereby the heat fluxes are accumulated over the entire study period. In general, LE is the dominant component of the surface energy budget for the selected simulation period and little difference is noted in the energy partitioning between the three Noah runs. To support the analysis, Fig. 6 shows the mean diurnal cycle of the measured and simulated sensible and latent heat fluxes for June–September, which confirms the minor impact of soil water flow physics on the simulated surface heat flux. The reason for this is that the LE in the selected study area is primarily driven by the available energy during the wet monsoon season. Hence, all three numerical experiments generate soil moisture profiles that sustain the production of non-water-limited LE, while performing significantly differently in redistributing the total transpiration and soil water extraction across the soil profile (see Fig. 4). Nevertheless, it is noted that the measured H is overestimated by ~20–40 W m−2 during midday for each of the three Noah runs, whereas fairly small systematic differences are observed between the simulated and measured LE.
Partitioning of the surface energy budget into LE, H, and G0 produced by three Noah numerical experiments (Ctrl, EXP1, and EXP2) presented as a ratio of the net radiation accumulated from 8 Jun to 30 Sep 2010.
Citation: Journal of Hydrometeorology 16, 6; 10.1175/JHM-D-14-0198.1
Average diurnal cycles for June–September of the measured and simulated (a) LE and (b) H produced by three Noah numerical experiments (Ctrl, EXP1, and EXP2).
Citation: Journal of Hydrometeorology 16, 6; 10.1175/JHM-D-14-0198.1
Table 7 gives the error statistics (i.e., RMSE and ME) computed between the measured and simulated heat fluxes (e.g., LE and H) and soil temperature (e.g., surface and 25 cm) for the study period at a 30-min time step. Overall, the statistics indicate that the heat fluxes produced by Noah are reasonable (also shown in Fig. 6) with RMSE values for LE on the order of 30 W m−2 and confirm small differences among the three numerical experiments. It should, however, be noted that Noah somewhat overestimates heat fluxes and soil temperature, which can be associated with the energy closure problem of EC observations as described in Zheng et al. (2014). Moreover, the RMSE computed for the heat fluxes (i.e., H and LE) increased upon implementation of each set of augmentations, from Ctrl to EXP1 to EXP2, because less LE and more H and G0 is produced (see Fig. 5). This can be attributed to the definitions of wilting and critical soil moisture as well as the vegetation parameters that control the soil water stress imposed on the soil evaporation and vegetation transpiration within Noah. Indeed, van der Velde et al. (2009) have shown that through modification of the vegetation parameterization, large improvements can be obtained in the simulation of the heat fluxes and particularly LE. However, further work is needed to assess the suitability of Noah’s LE parameterization for the Tibetan Plateau ecosystems.
Error statistics computed between measured and simulated LE and H as well as temperature at the surface Tsfc and at 25-cm soil depth Ts25cm produced by three Noah numerical experiments from 8 Jun to 30 Sep 2010.
b. Impact on surface water budget simulations
Apart from its influence on the surface energy balance, soil moisture also has an effect on the simulated water budget by 1) determining the rainfall–runoff response through partitioning of precipitation into surface runoff and infiltration, 2) defining the drainage through the soil column toward deeper layers, and 3) limiting the evapotranspiration in cases of soil water stress (see the previous section). The impact of the augmentations made to the Noah model structure on the simulated water budget is illustrated by Fig. 7, in which the ratios of the different water budget components are shown. These ratios are calculated by dividing the total simulated ET, Rsurf, drainage, and change in soil water storage (ΔSM) by the total rainfall measured from 8 June to 30 September 2010.
Comparison of the ratios of different water budget components produced by three Noah numerical experiments (Ctrl, EXP1, and EXP2) presented as a ratio of the rainfall accumulated from 8 Jun to 30 Sep 2010.
Citation: Journal of Hydrometeorology 16, 6; 10.1175/JHM-D-14-0198.1
Figure 7 shows in analogy with the simulated surface energy budgets (Fig. 5) that all three Noah runs produce comparable water budgets and that ET is the dominant contribution. In comparison with the Ctrl run, Noah partitions less solar energy into ET (or LE) with the EXP2 setup. This leads to less water being extracted from the soil for evapotranspiration (i.e., lower ratio of ET), which explains the higher ΔSM for EXP1 and, to a lesser extent, EXP2. Further, the implementation of the exponentially decaying 
8. Conclusions
This is the first of two papers aimed at diagnosing and enhancing the performance of the Noah land surface model (LSM) in simulating surface water and energy budgets in the high-altitude source region of the Yellow River (SRYR). In this paper, we investigate the ability of the Noah LSM to simulate the soil water flow through comparison with soil moisture profiles measured during the monsoon season. Noah, with its default model structure, underestimates the soil moisture content of the top layer under wet conditions and overestimates it during dry-down episodes, whereas the moisture contents in the deeper soil layers are systematically underestimated. Three augmentations to the model physics are investigated to remediate these deficiencies: 1) the impact of organic matter on the soil water retention curve is considered via the additivity hypothesis, 2) the saturated hydraulic conductivity 
The modified hydraulic parameterization is compared against laboratory measurements of the soil water retention curve and in situ 
Three numerical experiments are designed to assess the impact of the augmentations: 1) a control run with the default model structure (Ctrl), 2) a Noah run with the modified soil hydraulic parameterization (EXP1), and 3) a Noah run with the modified soil hydraulic parameterization and vertical root distribution (EXP2). Through implementation of the modified hydraulic parameterization alone (EXP1), the soil moisture underestimation in the upper soil layer under wet conditions is resolved, whereas the overestimation during dry-downs remains. This somewhat improves the simulations for the deeper soil layers but not to its full extent. By including the modified root distribution in the soil profile, the soil moisture dynamics of the upper layer under dry conditions are better captured, and the simulations of the deeper layers match the measurements better as well. This leads to a reduction in the RMSE computed between the simulated and measured soil moisture by about 49%, 70%, and 56% for the upper three layers.
The impact of the improved soil moisture simulations on the calculated surface energy and water budgets is assessed and shows that Noah retains more water in the soil column with the augmentations, causing a decrease in the other water balance components. On the other hand, the surface heat flux simulation is hardly affected. This is attributed to the fact that LE in the selected study area and period is primarily constrained by the energy rather than the available soil water.
This study shows through comprehensive measurements performed in the laboratory and field that significant improvements can be achieved in the soil water flow simulation by the Noah LSM for a Tibetan Plateau site through a better representation of the hydraulic parameters and the root distribution across the soil profile. This further confirms the necessity to incorporate the impact of vertical soil heterogeneity caused by organic matter and root systems into state-of-the-art LSMs for their application to the Tibetan Plateau (Chen et al. 2013; Xue et al. 2013; Yang et al. 2005). This study can also be seen as an attempt to investigate the transferability of LSM parameterizations developed for the polar and boreal organic soils (Lawrence and Slater 2008; Letts et al. 2000) to the Third Pole Environment (i.e., Tibetan Plateau).
However, additional work is needed to extend the findings of this study to large spatial domains. The soil property datasets (e.g., soil particle size distribution and soil organic carbon) recently developed for China as well as the globe (Shangguan et al. 2012, 2014, 2013) can, for instance, be utilized for such application. The improved simulated soil moisture information across large areas would greatly enhance our understanding of the hydrologic cycle and assist in preservation of high-altitude ecosystems that are vulnerable to climate change, such as the Tibetan Plateau. Among the other imperatives for the performance of LSMs in the high-altitude ecosystems are robust parameterizations of the cold season processes (i.e., freeze–thaw transitions), which require thorough assessment across the globe to confirm its validity.
Acknowledgments
This study was supported by funding from the FP7 CEOP-AEGIS and CORE-CLIMAX projects funded by the European Commission through the FP7 program, the Chinese Academy of Sciences Fellowship for Young International Scientists (Grant 2012Y1ZA0013), the Key Research Program of the Chinese Academy of Sciences (Grant KZZD-EW-13), and the National Natural Science Foundation of China (Grants 41405079 and 41105003).
APPENDIX
Soil Pedotransfer Functions
The soil PTF approach has been widely used to predict hydraulic parameters (e.g., porosity and saturated hydraulic conductivity) from more easily measured soil data, such as texture and organic matter content. The PTFs can be subdivided into class and continuous PTFs: the class PTF predicts the average hydraulic characteristics based on distinct soil texture classes, while the continuous PTF uses measured soil particle size distribution data (e.g., percentages of sand and clay) to calculate these hydraulic parameters.
Average soil hydraulic characteristics predicted by class PTF.
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