Interactive comment on “ Modelling irrigated maize with a combination of coupled-model simulation and ensemble forecasting , in the west of China ”

General Comments: This manuscript analyzes the performance of a coupled hydrologic-crop growth model (Hydrus-1D and WOFOST) against the field observations while evaluating it further through uncertainty and sensitivity analyses. In addition to the model simulations, an ensemble approach was used for predicting the probability of crop production, with special concern on the impact of reduced irrigation. The manuscript is related to a timely, important study. However, there are major flaws in the writing and organization of the manuscript. It has too much information of the methods used, and the methods have been given even under Introduction and Results sections. It could be greatly shortened by avoiding unnecessary details. Also, certain mistakes


Introduction
In semi-arid and arid regions, there is an increasing competition between the limited water resources and the increasing demand for crop irrigation (Molden, 1997;Seckler et al., 1998).The efficient utilization of water in agriculture and tackling the issue of optimal water use are needed to balance water supply and demand (Tuong and Bhuiyan, 1999;Ines et al., 2002).In the last 20 yr, irrigation planning methods have switched from the allocation approach, e.g. based on socio-political considerations, to Figures technological ones (Paudyal and Das Gupta, 1990;Raman et al., 1992).The development of mathematical models allows fundamental progress to guide irrigation quantitatively.The accurate estimation of soil moisture change, evaporation, and transpiration is important for determining availability of water resources (Scanlon et al., 2002) and the sustainable management of limited water resources, especially in arid and semiarid regions (e.g., Gartuza-Pay án et al., 1998).Variation in available soil moisture is one of the main causes of variation in crop yields (Rodriguez-Iturbe et al., 2001;Shepherd et al., 2002;Anwar et al., 2003;Patil and Sheelavantar, 2004).Meanwhile, actual evapotranspiration is the main variable for water loss in the soil-plant system and determines soil moisture status (Burman and Pochop, 1994;Monteith and Unsworth, 1990).
Crops can only absorb the soil moisture that is present within the reach of their roots.Therefore, the root growth algorithm and plant water uptake modules are critical to estimate soil moisture and crop production in crop and ecological models.However, these processes are represented in hydrologic models, the coupling of hydrologic and crop growth models are useful for both hydrology and agronomy.
In the last few years numerous scientists have oriented their research towards enhancing the knowledge of the complex interactions between ecological systems and the hydrological cycle, contributing to the development of eco-hydrologic models and soil-plant-atmosphere models (Smettem, 2008;De Willigen, 1991;Engel and Priesack, 1993;Diekkr üger et al., 1995;Smith et al., 1997;Shaffer et al., 2001;van Ittersum and Donatelli, 2003).Kendy et al. (2003) evaluated recharge specifically for irrigated cropland using a model in which soil water flow was governed by a tipping-bucket-type mechanism, and actual transpiration was computed based on the soil water condition using a method introduced by Campbell and Norman (1998).By coupling of hydrologic and crop growth models, Eitzinger et al. (2004) studied soil water movement during crop growth processes and concluded that the coupled modeling approach is better than a single model method.A few studies have been conducted to investigate the effects of soil moisture distribution along the vertical soil profile on crop transpiration (Varado et al., 2006).Introduction

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Full Complex mathematical models could help to understand interactions between water and energy cycle in soil-plant-atmosphere systems.However, models have many degrees of freedom (with many parameters, state-variables and non linear relations) and can be made to produce virtually any desired behavior (Hornberger and Spear, 1981).Debates on the reliability of environmental models have emerged both in the academy and among practitioners (Veld, 2000;Lomborg, 2001;Van der Sluijs, 2002).The United States Environmental Protection Agency (EPA)'s science panel found that quantitative evidence must be characterized as having high uncertainties (David, 2008).The International Food Policy Research Institute (IFPRI) had raised about $460 000 for the modeling, which would have provided insights to help policymakers compare the outcomes of four broad policy scenarios, such as futures with more free trade or green technologies.But Greenpeace's Haerlin and others objected that the models were not "transparent".(Stokstad, 2008).Columbia University published the book titled "Useless Arithmetic: why Environmental Scientists Can't Predict the Future" (Pilkey and Pilkey-Jarvis, 2007) presented "Quantitative mathematical models used by policymakers and government administrators to form environmental policies are seriously flawed".The main problem is that models are often asked to answer specific questions about the present or future behaviour of the system under uncertainty conditions (e.g. is climate change, different environmental scenarios and presumptive boundary conditions of the dynamics).However, the model only can be confirmed or corroborated by demonstrating agreement between observations and predictions.So, we need a combination of model simulation and ensemble forecasting to analyse and predict the scientific problem from a probabilistic viewpoint.In this view, uncertainty and sensitivity analysis (UA/SA) can help investigating the propagation of different sources of uncertainties to the output variables through ensemble sampling.Sensitivity analysis was used to identify the effect of model parameters and structure on the output estimation.Uncertainty analysis quantifies the variability of the output caused by the incomplete knowledge or misspecification of the modeller.Introduction

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Full An overview of UA/SA methodologies can be found in Saltelli et al. (2000Saltelli et al. ( , 2004Saltelli et al. ( , 2005)).Some applications of SA techniques relevant to ecological and environmental science include, e.g.atmospheric chemistry (Campolongo et al., 1999a, b), transport emissions (Kioutsioukis et al., 2004), geographic information systems (Crosetto andTarantola, 2001), environmental management (EPA, 2003) and population dynamics (Zaldìvar and Campolongo, 2000;Fieberg and Jenkins, 2005).Some effort has been put into understanding the correct role of SA from an environmental regulatory point of view.Both the report on Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (EPA, 2003) and the Guidance on the Development, Evaluation, and Application of Regulatory Environmental Models (IPCC, 2000) provide the information about application of UA/SA on environmental prediction.EPA (2003) also contains recommendations on good practices for UA/SA methods.In Europe, sensitivity analysis is mentioned in the guidelines for impact assessment (EC, 2005).
This paper aims to efficiently manage water resources in agriculture and improve the prediction of crop production under various environmental and water resources management conditions.For this purpose, an eco-hydrological model is developed by coupling a HYDRUS model with a WOFOST model.Based on the coupled modeling, we used UA/SA methods to evaluate the coupled model, predict the risk of a crop production loss as irrigation decreases and quantitatively study impact of coupled model parameters and environmental factors change on maize production.This method could be used as reference for predicting the crop production in regions with no or reduced data availability.

Study region and experimental field description
The Heihe river basin, located in semi-arid and arid region, is the second largest inland river basin in China.The region has a typical temperate continental climate, with the mean annual precipitation and evaporation ranging from 60 to 280 mm and 1000 Introduction

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Full to 2000 mm, respectively.The main crops of this region are maize and wheat, and water use efficiency is low.The key to solve water scarcity and ecological problems of this region is effective management of agricultural water resource and optimization irrigation.So, an agricultural experimental field (latitude 38 • 51 N, longitude 100 • 25 E, altitude 1519 m), which is shown in Fig. 1, is operated by CAS (Chinese Academy of Science) to study the impact of quantitative irrigation on maize growth.The station is managed according to agricultural practices in the Heihe river basin region, including crop rotations (maize and spring wheat) and flood irrigation.

Characterization of the soil properties
The experimental field was established on a clay loam soil (USDA classification system).To characterize the soil physical properties, five root zone soil samples were extracted from the ground to a depth of 85 cm.The samples were analyzed in the laboratory to determine soil bulk density (Grossman and Reinsch, 2002), volumetric water content (Topp and Ferr é, 2002), and percentages of sand, silt, and clay (Gee and Or, 2002).The analysis' results are shown in Table 1.

Field experiment
The field was instrumented to monitor soil water dynamics in the root zone and the groundwater table .The instrumentation consisted of time-domain reflectometers (TDR) (CS616, Cambell Scientific, USA) for soil moisture measurements and groundwater observation wells.The depth of soil moisture measurements was 10 cm, 20 cm, 40 cm, 60 cm, 80 cm, respectively and the data were collected every hour.The agricultural field was intensively monitored throughout the study period, which lasted from 20 April through 22 September 2009.The field was cultivated with maize and quantitatively irrigated.The field was irrigated 9 times throughout the period of crop growth.The water amount of irrigation is approximately 100 mm each time.The respectively.Meanwhile, the data of Leaf area index (LAI) were measured once every 15 days by LAI-2200 instrument.Dry weight of storage organs, dry weight of total above-ground biomass and crop height were measured every 15 days by samples during crop growth.
Half-hourly meteorological data were recorded by the meteorological station Vaisala Co,Finland), located in the experimental field.Available data were net radiation, solar radiation, maximum air temperature, minimum air temperature, precipitation, wind speed, atmospheric pressure, and relative humidity.We measured actual evapotranspiration during crop growth using eddy covariance systems (EC) (Li7500 and CSAT3, Cambell Scientific, USA), which have been widely applied to measure the exchange of water vapor, energy and carbon between the earth's surface and atmosphere (Aubinet et al., 2000;Baldocchi et al., 2003).

Crop growth model
WOFOST has been used to (Van Keulen and Wolf, 1986) simulate crop growth and production under various environments (soil, climate and fertilization), crop characteristics, and irrigation schemes.In WOFOST, the crop growth simulator (SUCROS) (Van Laar et al., 1997) approach for potential production conditions is used to simulate gross CO 2 assimilation, maintenance growth respiration.The CO 2 assimilation is obtained on a daily basis with Gaussian integration of the instantaneous CO 2 assimilation rates computed at three moments of a day and for three canopy depths.Maintenance respiration is assumed to be proportional to the dry weight of plant organs, considering that leaf biomass using a development-dependent specific leaf area (SLA).In the waterlimited situation, the soil water balance was calculated using a tipping bucket approach with three compartments, i.e., a root zone, a transmission zone, and a groundwater zone.The potential evapotranspiration was estimated with the Penman-Monteith equation (Monteith, 1965;Monteith and Unsworth, 1990).The actual crop uptake from soil was calculated as the product of the potential evapotranspiration, a crop factor and a water stress factor.A detailed model description can be found in van Ittersum et al. (2003).
The numerical software, WOFOST, is a very useful code for determining the production potential, optimizing crop management and quantifying yield gaps of various crops (e.g., wheat, maize, potatoes) (Van Laar et al., 1997;Bouman et al., 2001;Wolf, 2002).The code can also be used to study the effects of environmental variability and climatic change on crop production (Kropff et al., 1996;Berge et al., 1997;Tsuji et al., 1998;Matthews and Stephens, 2002).

Hydrologic model
HYDRUS-1D ( Šim ǔnek et al., 2005) has an advantage in simulating water flow and root water uptake.The simulation is based on the following assumptions: (i) The soil is homogeneous and isotropic, (ii) The air phase does not affect liquid flow processes, and (iii) Moisture movement due to thermal gradients is negligible.So, the governing equation for water flow is the 1-D Richards equation: where h is soil water pressure head (L); θ represents volumetric water content (L 3 L −3 ); t is time (T ); x is the vertical space coordinate (L); K is the unsaturated hydraulic conductivity (L T −1 ); and S represents a sink term (L 3 L −3 T −1 ), defined as the volume of water removed from a unit volume of soil per unit time due to plant water uptake.Introduction

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Full The sink term is specified in terms of a potential water uptake rate and a stress factor (Feddes et al., 1978): where S is the root water uptake rate (L 3 L −3 T −1 ); R(z) is the distribution function of the root; l r is the depth of root (L); T P is potential transpiration (L); the dimensionless water stress response function α(h) (0 ≤ α(h) ≤ 1) prescribes the reduction in uptake that occurs due to drought stress.For α(h), we used the functional form introduced by Feddes et al. (1978): where h 1 ,h 2 ,h 3 , and h 4 are threshold parameters.The uptake is at the potential rate when the pressure head is between h 2 and h 3 .It drops off linearly when h > h 2 or h < h 3 .The uptake rate becomes zero when h < h 4 or h > h 1 .Crop-specific values for these parameters were chosen from the database contained in HYDRUS-1D ( Šim ůnek et al., 2005).An atmospheric boundary condition was implemented at the soil surface.The atmospheric boundary condition required specifying daily irrigation and precipitation rates, as well as the potential evaporation and transpiration rates.To determine evaporation and transpiration, we calculated a reference evapotranspiration ET 0 (t) using the Penman-Monteith method (e.g., Kashyap and Panda, 2001).The potential evapotranspiration ET p (t) was then given by (Allen et al., 1998): where ET 0 (t) was estimated in daily time steps and K c (t) is a crop-specific coefficient that characterizes plant water uptake and evaporation relative to the reference crop.Introduction

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Full The potential evaporation E p (t) can be calculated according to (e.g., Kroes and Van Damm, 2003;Pachepsky et al., 2004): where β is the radiation extinction coefficient and LAI (t) is the leaf area index.
With ET p an dE p given by Eqs. ( 4) and ( 5), the potential transpiration T p (t) was specified by: The soil hydraulic properties were modeled using the van Genuchten-Mualem constitutive relationships (Mualem, 1976;van Genuchten, 1980): where S e is effective saturation and θ s is saturated water content (L 3 L −3 ); θ r is residual water content (L 3 L −3 ); K s is saturated hydraulic conductivity (L T −1 ); α is the air entry parameter; n is the pore size distribution parameter; and l is the pore connectivity parameter.The parameters α,n, and l are empirical coefficients that determine the shape of the hydraulic functions.To reduce the number of free parameters, we took l = 1, a common assumption which is based on Mualem's study result (1976).
Running the model required specifying the hydraulic parameters θ r ,θ s ,α,n,K s , and l .The soil profile is divided into three layers in vertical direction according to the soil Introduction

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Full physical properties.The fist layer is from the ground to a depth of 30 cm.The second layer and the third layer are from a depth of 30 cm to a depth of 60 cm and from a depth of 60 cm to a depth of 100 cm, respectively.Meanwhile, a deep drainage condition was used at the bottom.The condition needs given initial reference groundwater depth ( Šim ůnek et al., 2005).We estimated the parameters of the three layers using SCE-UA algorithm (shuffled complex evolution algorithm) (Duan et al., 1993).The NSE (Nash-Sutcliffe coefficient) is chosen as the objective function,

Coupling of the model
The coupling has been performed at a daily scale.Coupling process is shown in the parameter values are reasonable for local maize characteristics and soil properties in the study field.The coupled model can be used to quantitatively predict agricultural production under water-limited conditions.The dry matter accumulation and partition between the various plant organs, the final yield and harvest index are simulated by the coupled model, as shown in Table 4.The simulated evapotranspiration also match as well the evapotranspiration observed by eddy covariance systems (EC), which are shown in Fig. 7.The simulated evapotranspiration is divided into actual transpiration and actual evaporation.The cumulative simulated actual transpiration is 364 mm.The cumulative simulated actual evaporation is 203 mm.The result reveals that the crop's effective transpiration is approximately 1.79 times the soil evaporation during maize growth under realistic irrigation conditions.

Sensitivity analysis
Sensitivity analysis determines the contribution of each input factor to the uncertainty of the outputs.Sensitivity analysis was evaluated using a two-step method: the screening method proposed by Morris (1991) and a variance-based technique proposed by Sobol' (1993).The Morris method provides a qualitative assessment of the importance of each input factor, while the Sobol' method performs a quantitative analysis of sensitivity and uncertainty.This two-step methodology has been used in recent studies of inputoutput relationship and model evaluation (Fox et al., 2010;Jawitz et al., 2008;Mu ñoz-Carpena et al., 2010).
The one-factor-at-a-time Morris (Morris, 1991) method is particularly effective to screen a subset of relevant parameters among those contained in models with a large number of parameters or with time consuming simulations.The method calculate a set of incremental ratios (∆output/∆parameter) at various points of the parameters space and to obtain means (µ * ; calculated on absolute values) and standard deviations (σ) of these ratios.A large value of µ * belongs to a parameter with an important overall influence (total effect), whilst a large value of σ indicates nonlinearities in model response or interactions with other parameters.Introduction

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Full Sobol's method (Sobol, 1993) is a variance-based method.The method is modified by Saltelli (2002) by decomposing the output variance into terms of increasing dimensions (i.e., partial variances), representing the contribution of single parameters, and of groups of parameters to the overall uncertainty of the model output.This method allows the simultaneous exploration of the parameter space via a Monte Carlo method.
Statistical estimators of partial variances are provided by quantifying the relevance of parameters and parameter groups through multi-dimensional integrals.The advantage of Sobol's method is that it allows the simultaneous computation of the first order and total order effect indices for a given parameter.A main sensitivity index (S x ) quantifies the first order effect of a parameter.A total sensitivity index (S T x ) quantifies the overall effect of a parameter (i.e., including all the possible interactions).
Prior to performing sensitivity analysis, the ranges of the 34 input factors were defined (Table 5) based on values from literature review, experience, research objectives and default, minimum and maximum values of WOFOST and HYDRUS databases.Uniform distributions were assigned to input factors when only the base value was known, the range was considered finite, and no explicit knowledge of the distribution was available (McKay, 1995).This conservative assumption allows an equal probability of occurrence of the input factors along the probability range (Mu ñoz-Carpena et al., 2010).We divided the parameters into 13 groups according to physical properties and functions.The groups of parameters and the value ranges of all parameters are shown in Table 5.
One model output for weight of storage organs at physiological maturity (WSO) was considered in this analysis.WSO was selected as it determines the productity of maize and a synthetic representation of the culmination of numerous physical processes.The variation of WSO in response to variations of the crop and environment parameters were investigated using Morris and Sobol's sensitivity study methods, based on Sim-Lab Dynamic Link Library (http://simlab.jrc.ec.europa.eu/),integrated in the coupled HYDRUS and WOFOST models.Introduction

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Full For Morris method, the means and standard deviations of the sensitivity parameters (µ * , σ) for each factor are obtained from 320 samples using the total range of trajectories (10) and levels (4) (Saltelli et al., 2004).For Sobol' method, Monte Carlo sample size was set to 5000 for each factor.The guided irrigation scheme (Each time 100 mm of water is applied to maize, in total 9 times) was explored in this study.Figure 8 displays graphically the average strength (µ * ) and spread (σ) of model response (change of yield) to the variation of parameters according to their various functions of crop growth (phenology, assimilation, respiration, conversion, etc.) and environment factors (sowing date, groundwater depth, soil characteristics, etc.).The parameters were ranked in descending order of the µ * values, which are shown Table 6.The screening carried out with the Morris method allowed identifying 13 out of 33 parameters (40%) as not relevant.Each parameter cause a yield change less than 500 kg ha −1 , which approximately accounting for 5% of the total output 10 777 kg ha −1 .The 12 out of 33 parameters (36%) are identified with an effect between 500 and 2000 kg ha −1 .The 8 out of 33 parameters (24%) have an effect greater than 2000 kg ha −1 (including HYDRUS parameters, ZIT, SLATB1, IDSOW, EFFTB, RDMCR, KDIFIB, CFET).Further, σ indicates that interaction, correlation and non-linearity are relevant for coupled model.We also analyzed the distribution of simulated yields with Monte Carlo methods to gain information about the reaction of maize production to the variations of the parameters under various irrigation schemes.The Monte Carlo sample size was set to 5000.Four scenarios were proposed.In the four scenarios the single application of irrigation-water is respectively assumed to be 40 mm, 60 mm, 80 mm and 100 mm for a total of 9 irrigation times.The uncertainty analysis was performed.The results are shown in Fig. 9, which reveal the risk of crop production loss with decrease of irrigation.The average crop production increases from 4204.2 kg ha −1 in the case where each irrigation-water is 40 mm to 7781.2 kg ha −1 in the case where each irrigationwater is 100 mm.When each irrigation-water is more than 80 mm, the distribution of simulated yields is mainly between 5500 kg ha −1 and 11 000 kg ha −1 , which account for Introduction

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Full 85% realizations.This method can predict probability of crop production in uncertain range of crop parameters and environment parameters.The Sobol' method is used to improve our understanding of the effect of parameter groups on crop production under various irrigation schemes.The results are shown in Table 7.In the above mentioned irrigation-water scenarios, summations of firstorder indices of parameters are always close to 1, which suggests that the coupled model has not over-parameterization.Total-order indices of parameters were not significantly different in the coupled model, which may be attributed to the coupled model as being balance.Summation of total-order indices leads to values between 2.65¬3.8,suggesting that the simulated yield is always affected by more parameters acting in conjunction with each other.Table 7 reveals that the crop outputs were mainly influenced by physiological parameters (including CO 2 assimilation, green area, correction factor transpiration rate, the conversion of assimilates into the various organs compounds) and environment parameters (including sowing date, groundwater depth, soil hydraulic characteristic).Table 7 further shows that the effect of groundwater, soil hydraulic characteristic and correction factor transpiration rate on output increases as irrigation-water decreases.The effect of most physiological parameters on output decreases as irrigation-water decreases, owing to the fact that a shortage of transpiration supplied water uptake from the soil causes stomata closure and reduces assimilation and respiration of crops.

Summary and conclusions
The objective of this study was to develop a fully coupled hydrology-crop growth model which can optimize irrigation-water under different climatic and environmental conditions.A crop growth model (WOFOST) has been coupled to a hydrologic model (HYDRUS) for this purpose.The coupled model considers not only the physiological processes of the crop, but also the water balance during the crop growth process.Inverse modeling methods (SCE-UA algorithm) are used to identify the parameters of soil hydraulic properties and improve simulated accuracy of the soil moisture profile.Introduction

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Full The coupled model was calibrated using field data collected at an experimental field in the middle reaches of northwest China's Heihe River, located in a semi-arid to arid region.The results show the good agreement was achieved between coupled model simulations and field measurements under water limited-conditions.The results show that the coupled model can have a higher precision than the WOFOST model alone owing to HYDRUS model's advantage in simulating soil moisture and root water uptake as a physical process.These applications illustrate the coupled model can be used for analysis of saving-water approach and also for the study on interaction between crop growth and the hydrological cycle.
The uncertainty analysis and the sensitivity analysis methods were used to improve prediction and evaluation of the coupled model beyond the simple quantification of the agreement between measured and simulated data.In conclusion, the study illustrates that the uncertainty method (Monte Carlo method) not only reveals the risk of facing a loss in crop production as irrigation decreases, but also can estimates the probability of crop production in the uncertainty range of crop parameters and environment parameters.The sensitivity analysis not only can test the coupled model behavior but also quantify the impact of the coupled model parameters and environment scenarios on crop output.Synthetically, the method of integrating a coupled hydrologic and crop growth model with uncertainty analysis and sensitivity analysis can be used for guiding agricultural irrigation, saving water resources, predicting agricultural production and researching effects of the climatic and environmental change on agricultural production.Introduction

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Full  Full  Full    Full  Full  Full Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | sowing date, emergence date and harvest date were 20 April, 6 May and 22 September Discussion Paper | Discussion Paper | Discussion Paper | different organs have different respiration to weight ratios.Total dry matter production is partitioned among the different plant organs according to development-dependent coefficients.During early growth stages, leaf area is considered growing exponentially as a function of temperature.After canopy closure, leaf area index (LAI) is derived from Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Fig. 2 :
Fig. 2: (1) The irrigation and precipitation, the daily net radiation, the daily maximum and minimum temperatures, the daily wind speed and the daily relative humidity are the input terms in the HYDRUS model.(2) The potential evaporation and transpiration are calculated by the Penman-Monteith combination method in the HYDRUS model.(3) The water uptake is calculated according to Feddes equation in the HY-DRUS model.(4) The soil water balance, soil moisture and groundwater depth are calculated using the HYDRUS model.(5) The root water uptake and actual transpiration on a daily basis are assumed the same, because the most root water uptake is consumed by crop transpiration.Therefore, the ratio between calculated actual water uptake based on Feddes equation and potential transpiration based on Penman-Monteith method is regarded as an indicator for the degree of water stress.(6) The potential daily total gross CO 2 assimilation of the crop, which is calculated according to the WOFOST model, is multiplied by the water stress ratio to calculate the actual daily CO 2 assimilation.Then, carbohydrate allocation among different crop parts is calculated according to the WOFOST model.(7) The calculated vegetation parameters from Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | David, G.: Hazy Reasoning Behind Clean Air: science alone can't determine how regulations are written, Nature 453, April 3, 519, 2008.De Willigen, P.: Nitrogen turnover in the soil crop system: comparison of fourteen simulation models, Fertilizer Res., 27, 141-149, 1991.Diekkr üger, B., S öndgerath, D., Kersebaum, K. C., and McVoy, C. W.: Validity of agroecosys-Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Table 2. Nash-Sutcliffe coefficient of the fit to observed data for the three layers.Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | rate storage (kg (CH2O)kg −1 d −1 ) U(0.005¬0.015)organs RMR Relative maintenance respiration rate roots (kg (CH2O)kg −1 d −1 ) U(0.01¬0.016)Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Table 1 .
Measured soil textural and bulk density data.

Table 3 .
The estimated parameters of soil hydraulic properties of three layers by SCE-UA algorithm.

Table 6 .
The Morris sensitivity measures µ * and σ for 13 groups of parameters.

Table 7 .
First effect and total effect indices of 13 groups of parameters.