dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018)  is used to simulate SM over China. Besides land surface hydrology, ORCHIDEE-MICT simulates energy budgets and vegetation dynamics (mechanistic phenology, photosynthesis, and ecosystem carbon cycling) which interact with the water cycle and climate  (Guimberteau et al. , 2012). Moreover, the evaluation of simulated SM controlled by natural processes is useful to identify human effects (e. g. , crops, irrigation, dam operation) on the water budget in regions where there is a large misfit between the model and observations. Four global atmospheric forcing datasets are chosen to drive the simulations in China, including GSWP3 (Global Soil Wetness Project Phase 3), PGF (Princeton Global meteorological Forcing), CRU-NCEP (Climatic Research Unit-National Center for Environmental Prediction), and WFDEI (WATCH Forcing Data methodology applied to ERA-Interim reanalysis data), due to their wide application in numerous hydrological studies  (Getirana et al.
, 2014; Guimberteau et al. , 2014 Guimberteau et al. , , 2017 Guimberteau et al. , , 2018;; Hirschi et al. , 2014; Van Den Hurk et al. , 2016; Polcher et al. , 2016; Müller Schmied et al. , 2016; Tangdamrongsub et al. , 2018; Yang et al. , 2015; Zhao et al. , 2017; Zhou et al. , 2018). Although they provide gridded surface climate variables at the global scale, their uncertainties of representing regional climate are not clear. Through comparison of simulated SM to various datasets, our study also addresses which forcing has the best performance in SM simulation in China. Our SM simulations are evaluated with different SM datasets including in situ data, remote sensing measurements, and reanalysis. In situ measurements including ISMN  (International Soil Moisture Network; Dorigo et al. , 2011)  and PKU (in situ SM from Peking University;  Piao et al.
, 2009)   et al. , 2012)  is applied in the comparison. Note that both in situ and satellite SM datasets represent the truth to some extent. This implies that real-world SM is influenced by processes that are not modeled such as irrigation and wetlands. Thus mismatches between measured and simulated SM may exist in some regions strongly affected by anthropogenic factors. Moreover, satellite instruments do not measure SM directly; it is derived via a complex modelization of the radiative transfer at the soil-vegetation interface calibrated with in situ data. Finally, GLEAM SM data (Global Land Evaporation Amsterdam Model;  Martens et al. , 2017)  are compared to the simulated SM. Different from other SM datasets, GLEAM SM results from a land surface model constrained by a number of satellite and in situ observations.
This reanalysis product was shown to reproduce reasonable long-term SM dynamics at the global scale  (Martens et al. , 2017) , which is valuable to evaluate ORCHIDEE-MICT simulations for both surface and root-zone SM. Furthermore, GLEAM assimilates CCI SM data, so that evaluation of our model against rootzone SM from GLEAM is consistent with evaluation against surface SM from CCI. Through the simulations and comparisons, three questions will be addressed: -Is the model able to provide a reasonable estimation of SM dynamics in China, as a prerequisite for further hydrological studies? -Which atmospheric forcing gives the best SM simulation according to the comparisons with available observations? -Which meteorological variable drives the differences of SM among the simulations? The study area, atmospheric forcing, and SM datasets used in this study are described in Sect.
Atmospheric forcing Four gridded atmospheric forcing datasets are used to force the model over China: GSWP3, PGF, CRU-NCEP, and WFDEI. All input variables needed are the air temperature at 2 m (T a ), rainfall and snowfall rates, atmospheric specific humidity at 2 m (Q a ), surface pressure, downward short-/longwave radiation (R s and R l ), and wind speed (W ). The four forcing datasets are combinations of reanalysis and observation data. These datasets, although built by different methods, are not independent from each other since they share some common inputs. Detailed descriptions are listed below and general information is summarized in Table  1. Preprocessing of the datasets for ORCHIDEE-MICT is described in Sect. 3. 2.
CRU-NCEP The CRU-NCEP v6. 1 (ftp://nacp. ornl. gov/synthesis/2009/ frescati/model_driver/cru_ncep/analysis/readme. htm, last access: 16 October 2018) provides 6-hourly 0. 5 • data. It combines the coarse temporal resolution (monthly) CRU TS dataset with the NCEP reanalysis, which has a higher time interval (6-hourly) but is only available at 2. 5 •. Monthly climate (except for precipitation) is identical to CRU TS, and NCEP is used only to reconstruct the 6-hourly variability within each month after bi-linearly interpolated to 0. 5 •. For precipitation the original NCEP values are used for temporal linear interpolation in those CRU grid cells (0. 5 • ) covered by the specific NCEP grid cell (2. 5 • ) in each month. CRU-NCEP dataset is available from 1901 to 2015 at the global scale and it is updated every year.
International Soil Moisture Network (ISMN) The ISMN is an international cooperative project providing a global gauged SM database  (Dorigo et al. , 2011). It is based on in situ measurements from multiple monitoring regional subprojects. Here only data from the CHINA subproject are used  (Robock et al. , 2000)  with in situ volumetric water content (depth of water column over depth of soil in m 3 m -3 ) from 40 stations between 1981 and 1999. SM profiles on 11 vertical layers were collected three times per month (on 8th, 18th, and 28th of each month). The 11 sampled soil layers are 0-5, 5-10, and then every 10 cm layers until 1 m. Most stations are located in cropland or grasslands, but information about land use types and soil texture of each site is not provided. Moreover, there is no information about management practices affecting SM, such as irrigation or tillage.
In spite of the long length of this dataset, the data availability and monitoring period among stations vary widely. Some stations only recorded SM during the growing season, while others have a full year record. Furthermore, the measurements including the five deep layers (below 50 cm) are fewer in number than those including the top six layers. Only stations with more than 15 years of data were selected, which at least cover the same period  (1984) (1985) (1986) (1987) (1988) (1989) (1990) (1991) (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999). To make sure that at least half of the data are available in the 15-year time series, stations with fewer than 270 measurement points in the top six layers are removed. This leads to selecting a subset of 20 stations, and given the sparseness of data below 50 cm, only SM in the top six layers is used for model evaluation.
In situ SM from Peking University The SM was measured over 778 stations of agrometeorological stations over China by the Chinese Meteorological Administration and collected and harmonized by the research team in Peking University (PKU;  Piao et al. , 2009). The dataset provides 10-day SM variation during the growing season (mainly between May and September) from 1991 to 2007. It provides SM profiles in seven soil layers  (0-10, 10-20, 0-30, 30-40, 40-50, 50-70, and 70-100 cm) , but the bottom four layers often have missing records. This dataset concerns exclusively croplands but there is no explicit information of soil texture and irrigation. Similar to the ISMN, the monitoring durations among gauging stations are different. A total of 203 stations that cover the period of 1992-2006 are chosen.
0A are from ERA-Interim, and precipitation is from MSWEP (Multi-Source Weighted-Ensemble Precipitation;  Beck et al. , 2017). Both surface and root-zone SM from GLEAM, which has been validated by  Martens et al. (2017) , are used for comparison. The surface SM in the top 0-10 cm is a combination of simulated SM from the GLEAM soil module, SMOS (the Soil Moisture Ocean Salinity satellite mission;  Kerr et al. , 2001) , and ESA CCI SM (ESA Climate Change Initiative Soil Moisture;  Liu et al. , 2011 Liu et al. , , 2012;; Wagner et al. , 2012)  through the data assimilation system developed by  Martens et al. (2016). The Community Noah land surface model SM fields in GLDAS (Global Land Data Assimilation System;  Rodell et al. , 2004)  were used to estimate the errors of these SM products. Root-zone SM is derived from the GLEAM soil module based on mass balance.
Simulation protocol Four simulations were performed driven by different forcing datasets described in Sect. 2. 2. In the simulations, CO 2 rise and land use change are taken into account but without human processes like irrigation. The 13-PFT map is from LUH2 (http://luh. umd. edu) and the soil texture map is from  Zobler (1986). For the 3-soil texture scheme of  Zobler (1986) , the minimum residual and maximum saturated SM are 0. 065 and 0. 43 m 3 m -3 , respectively. The model domain covers the main part of China ([85-124 • E] × [20-44 • N]). The spatial resolution is the same as the atmospheric forcing (Table  1 ). The simulation period covers 39 years, from 1971 to 2009, except for the one driven by WFDEI, which is from 1979 to 2009. To make sure that carbon (LAI and biomass) and water cycle variables can reach equilibrium, a 100-year spin-up was performed by repeating the forcing of the period 1971-1980 10 times (for WFDEI, 50 times the period 1979-1980).
Starting from the end of the spin-up, simulations were run from 1981 to 2009. The output driven by PGF forcing was re-gridded at 0. 5 • × 0. 5 • to match the resolution of other simulation outputs. The temporal resolution of forcing datasets is either 3hourly or 6-hourly (CRU-NCEP), which is larger than the simulation time step of SECHIBA (30 min). To have a reasonable precipitation intensity, and thus a good infiltration of water in the soil, the default precipitation splitting algorithm of ORCHIDEE is applied in our simulations. At the beginning of each forcing time step, if precipitation occurred, the precipitation amount (precipitation rate multiplied by the time interval of specific forcing) will be uniformly distributed to the first half of the forcing time step.
Comparison protocol As the soil depths, periods, and spatiotemporal resolutions are different in the four SM datasets (Sect. 2. 3), we have to choose corresponding ORCHIDEE outputs for each comparison. For the comparison with the in situ data of ISMN and PKU, we first extracted the modeled daily SM profile (θ p ) from the nearest grid cell for each station. Then the SM above a certain soil depth was chosen (50 cm for ISMN and 20 cm for PKU). PKU SM is provided in degrees of saturation, defined as the volume ratio of actual water content to its maximum value when the soil is saturated. As the soil porosity is unknown, the PKU SM dataset cannot be directly compared with simulated SM from ORCHIDEE, which is defined from modeled porosity. To overcome this problem, normalization was applied in both datasets before comparison.
The normalized data at each station and in the corresponding grid cell of Hydrol. Earth Syst. Sci. , 22, 5463-5484, 2018  www. hydrol-earth-syst-sci. net/22/5463/2018/  the model are the ratio of the difference between the original value and its mean (during the observation period) to its standard deviation. According to the sampled depth of the ESA CCI SM, the daily top four-layer (2. 2 cm) averaged SM from ORCHIDEE is used. Regarding the definition of GLEAM SM (Sect. 2. 3), we used the daily top six-layer (approximately 9. 2 cm depth) averaged SM and the total SM of ORCHIDEE for the comparison with GLEAM surface and root-zone SM, respectively. The period length and soil depth of each comparison are shown in Table  2. In addition, the timing of all SM datasets is made uniform according to coordinated universal time (UTC).
Metrics Pearson's correlation coefficient (r) is calculated to estimate the correlation between simulated and observed SM. Daily SM corresponding to the measurement date reported in ISMN was collected to calculate r. As there is no date information from the 10-day PKU dataset, we used the 10-day averaged SM from ORCHIDEE for comparison. The root mean square error (RMSE) is applied in order to estimate the temporal differences between simulation and observation. The same data pairs are used for RMSE calculation as the correlation coefficient except for PKU due to the normalization. Note that RMSE is related to the magnitude of SM, which varies significantly in China. To make it comparable in space, the relative RMSE is calculated by dividing the mean of the simulated and observed SM. According to  Kobayashi and Salam (2000) , the mean squared deviation (MSD), which is RMSE 2 , can be decom-posed into the squared bias (SB), the squared difference between standard deviation (SDSD), and the lack of correlation weighted by the standard deviation (LCS), as MSD = RMSE 2 = SB + SDSD + LCS.
The NLD difference is used to compare the overall characteristic timescales between datasets. The difference of R k profiles gives additional information on the autocorrelations for lag. The R k comparison was implemented between GLEAM and ORCHIDEE because other datasets do not have complete daily records. The linear trend of SM change in the 29 years is of interest as well. The Mann-Kendall test  (Kendall, 1975; Mann, 1945)  is applied to test if simulations capture observed trends of SM, with a p value < 0. 05 indicating a significant trend.
3 ). The correlation coefficients of θ t between simulations and PKU dataset are shown in Fig. 2  as circles. Modeled θ t has a better performance in the Loess Plateau and the North China Plain than other regions, suggesting that ORCHIDEE is able to capture the variations of SM in semi-arid and temperate zones. In comparison to ISMN, r between ORCHIDEE and PKU θ t is lower. This may be caused by the shallower depth of the PKU data (20 cm), with a stronger influence from fast infiltration and transpiration processes than in the ISMN records (1 m). Moreover, the PKU dataset only records θ t during the growing season, leading to lower r in absence of full seasonal variations. Negative correlations are found in several sites located along river networks. The negative r (-0. 4 < r < -0. 2) coincides with the coupling of wetness anomaly and irrigation: when droughts occur (reflected by low simulated SM), more water will be withdrawn from the river and irrigated on the croplands (reflected by high observed SM).
57) due to the lower variability of θ r , which smoothes out misfits related to differences in individual rainfall events between ORCHIDEE and GLEAM for θ s. Compared to CCI θ s , the r between ORCHIDEE and GLEAM θ s is much higher. It is probably due to the shallower depth of the CCI θ s , which is more sensitive to surface processes and forcing data errors. Moreover, CCI θ s is a purely satellite product while GLEAM θ s (v3. 0A) is a combination of modeled, in situ, and satellite SM. The latter is not totally independent of the forcing datasets and therefore more comparable to our simulations. Figure  S6  and the right panel of Fig. 5  show the relative RMSE and the MSD decomposition of θ s between GLEAM and ORCHIDEE. A low relative RMSE (< 0. 3) covers most regions except for the North China Plain (> 0.
The trend of ORCHIDEE θ s (Fig. S9 ) is less significant than that of GLEAM θ s (Fig. S9 ). In northwestern China, increasing θ s is found in simulations (< 0. 2 × 10 -3 m 3 m -3 yr -1 ) and GLEAM (0. 2-0. 4 × 10 -3 m 3 m -3 yr -1 ). The trend may be due to increasing P (Fig. S10 ). GLEAM θ s decreased dramatically in eastern China ([103-122 • E] × [20-35 • N]), while the trends of ORCHIDEE θ s are not homogeneous in this region. In addition, all forcing datasets show an increasing P in the North China Plain, which leads to a slight increase of simulated θ s. However, GLEAM shows decreasing θ s in most areas of the North China Plain. The mismatch of θ s and P trends suggests that the change of precipitation amount is not the only driver of the trend of SM.
Comparison of the four forcing datasets To find the most realistic forcing dataset for SM performance given the ORCHIDEE model, several metrics were calculated and are shown in Fig. 7. Radar charts show the correlation coefficients (r) and RMSE of simulated SM in comparison to different datasets. Histograms show MSD and its three components. The median of specific metrics is listed in Table  3. GSWP3 has the best performance in estimating the magnitude of SM (lowest MSD), while WFDEI shows the best score in simulating SM variation (highest r). PGF provides as good an estimation as GSWP3 in the YZRB, but performs more poorly in capturing SM variation in the YLRB, which is also reflected in the components of MSD. The largest MSD is found in CRU-NCEP in most of comparisons, which is mainly contributed by SB.
S11b ), suggesting that the difference of simulated θ t is not caused by the difference of precipitation amount of forcing data. Similarly, in southwestern China, no high D is found in meteorological variables except for the number of precipitation days (N p ) and air humidity (Q a ), although the patterns of D N p and D Q a overlap with D θ t but extend to zones with low D θ t as well (Fig. S11c  and g ). To look for clearer links between input and SM D, we decompose N p and θ t by scales of P and soil layers, respectively (Sect. 3. 4). The r values of D between simulated SM and meteorological variables are shown in Fig. 8. D Q a , D N p , and D N 2 p are highly correlated with D θ t , implying that the difference of simulated θ t can be explained by the differences of Q a and N p among the four forcing datasets.
Performance of the model to simulate SM Due to the spatiotemporal complexity of SM and its vertical profile, four datasets were selected to drive the simulations, and modeled SM at different depths was validated against multiple datasets. The results showed that ORCHIDEE SM coincides well with CCI (median r = 0. 48; median RMSE = 0. 06) and GLEAM SM (median r = 0. 55; median RMSE = 0. 07) in comparison to other model studies  (Lai et al. , 2016). Higher r values were systematically found in southern China, the Loess Plateau, and the North China Plain; lower r values were found in northwestern China, the western Tibetan Plateau, the eastern Sichuan Basin, and downstream of the YZRB. SM is underestimated significantly in the Loess Plateau and the North China Plain, with modeled values being 20% and 30% less than in CCI and GLEAM, respectively (Figs.
S7  and S3 ). It is not only due to model parameterization but also due to irrigation activities in those agricultural regions (Fig. 1 ), which are not considered in the simulations. Because the in situ SM measurements were only collected for croplands and grasslands  (Piao et al. , 2009; Robock et al. , 2000) , implying potential disturbances from human activities, r was low in the comparison to ISMN and PKU datasets (median r = 0. 37, Fig. 2 ). For instance, drought occurred in northern China during  1987 -1988 (Yang et al. , 2012)) , which is reflected in the variation of measured SM at Xifeng and Xinxian (Fig. 3a-b ). ORCHIDEE successfully reproduced the drought-induced SM decline at the two stations. But SM measured at Xinxian was maintained at a high level. A possible explanation is that the soil at Xinxian was irrigated.
Linkage of discrepancies between meteorological factors and SM through ORCHIDEE In Sect. 4. 3, we showed that the spatial differences of simulated SM among the four forcing datasets were highly correlated with forcing differences in N p and Q a. This suggests that the uncertainty of precipitation frequency is more critical than that of precipitation amount in determining variation of SM patterns, as pointed out by other studies, especially in arid and semi-arid regions  (Baudena and Provenzale, 2008; Cissé et al. , 2016; Piao et al. , 2009). To precise the result, we studied the correlation coefficients between the spatial averaged D of SM in different soil layers and of N p categorized by classes of precipitation intensity (Fig. 8 ). The result showed that differences in small rainfall events N i p with 1 < P < 5 mm d -1 are more important than other precipita-  tion classes in explaining SM differences due to atmospheric datasets.
Differences in Q a were also shown to explain a large fraction of the simulated SM differences across different forcings. Q a determines vapor pressure deficit, which in turn controls transpiration  (Farquhar and Sharkey, 1982)  and evaporation  (Monteith, 1965) , suggesting a strong control by atmospheric dryness of the differences in SM found among the four forcing datasets. Both Q a and N p have positive impacts on SM, which enhances the correlation in Fig. 8. Estimating impacts of meteorological factors on SM dynamics is difficult. First of all, the importance of a meteorological variable on SM may vary with climate regimes. For instance, the importance of precipitation and radiation on SM changes from water-to energy-limited regions. Secondly, impacts of meteorological variables can be nonlinear through interactions with local ecosystems  (Seneviratne et al.
, 2010) , suggesting that even with the same meteorological variables, the simulated SM can be totally different (e. g. , with different soil texture or land cover types). Moreover, SM can be strongly coupled with the atmosphere  (Koster, 2004; Taylor et al. , 2012) , implying that meteorological factors can be influenced by SM as well (such as cloudiness, precipitation, and air humidity), which is not included in this study. However, the logic of our importance analysis is simple. If the model inputs (forcing data) are the same, the outputs (SM) should be the same. In other words, the differences of outputs can only be caused by the difference of inputs in our simulation results. It does not matter whether the quality of atmospheric forcing is good. On the contrary, the more differences that exist among these forcing datasets, the better our analysis is.
To keep the analysis simple, we did not investigate temporal correlations in each pixel, but focused on spatial patterns of D at the continental scale. Therefore, our results provided a general estimation of the importance of meteorological variable uncertainties to SM simulation through ORCHIDEE. Indeed this approach is not able to demonstrate explicit links between meteorological variables and SM. We underlined the impacts of N p and Q a uncertainties, but it does not mean that other factors are unimportant. For instance, assuming that a variable can strongly influence simulated SM, if there was not much difference of the variable among forcing datasets, its importance cannot be detected in this work. Moreover, only one model was used in this study. Although ORCHIDEE performed very well in SM simulation, the lack of unknown mechanisms may weaken the linkage between SM and specific atmospheric variables.
Conclusions Simulations in China were performed in ORCHIDEE-MICT driven by different forcing datasets, GSWP3, PGF, CRU-NCEP, and WFDEI. Simulated soil moisture was compared to several datasets to evaluate the ability of ORCHIDEE-Hydrol. Earth Syst. Sci. , 22, 5463-5484, 2018  www. hydrol-earth-syst-sci. net/22/5463/2018/ MICT in reproducing soil moisture dynamics in China. Results showed that ORCHIDEE soil moisture coincided well with other datasets in wet areas and in non-irrigated areas. It was suggested that ORCHIDEE-MICT is suitable for further hydrological studies in China. However, the abnormal variation of observed SM in North China Plain implied potential impacts of irrigation, which was recommended to be considered in further simulations. Moreover, results showed that bias was mainly from model parameterization and atmospheric forcing.