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  • View in gallery

    Spatial distribution of the calibrated Firrig at the provincial scale. The variable Firrig is a weighted factor varying from 0 to 1, corresponding to the soil moisture target, which is just enough to result in no water stress for crops or up to full soil saturation.

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    Spatial distributions of annual irrigation amounts (km3 yr−1) simulated by (a) CLM4 after calibration and (b) observation dataset in 2000 (observation data provided online at www.data.ac.cn/zrzy/ntBA35.asp?Page=6).

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    Simulated irrigation amounts by the CLM4 after calibration vs observation dataset at the provincial level.

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    Simulated irrigation amounts vs observation dataset from 1995 to 1999 for the 16 irrigated provinces with available observations in (a)–(n) northern and (o),(p) southern China.

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    (a)–(g) NS statistics comparing monthly IWD computed using CLM_CTRL with monthly IWD computed using the sensitivity experiments for 10 river basins during 1960–99 and (h) the spatial distribution of 10 river basins over China. A high NS value close to 1 indicates the significantly independent role of specific climatic factor in governing the IWD interannual variability.

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    Spatial distribution of annual mean IWD (mm yr−1) by CLM_DC and CLM_BC for the (a),(b) current scenarios for 1960–99 and (c),(d) relative changes for 2060–99 minus 1960–99 [unit for (c) and (d) is percent changes (%)].

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    As in Fig. 6, but for spatial distribution of STDs of annual mean IWD.

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    CDF for summer IWD (mm month−1) for the current and future scenarios by CLM_DC and CLM_BC.

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    Spatial distributions of relative changes of the 95th-percentile IWD for 2060–99 minus 1960–99 predicted by (a) CLM_DC and (b) CLM_BC.

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    Seasonal IWD for the current and future scenarios by CLM_DC and CLM_BC.

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    CDF for summer (a) ET (mm month−1) and (b) runoff (mm month−1) for the current and future scenarios by CLM_DC and CLM_BC. The mean of the distribution was also shown in the parentheses for each scenario. Note that this is for the results after IWD was met by withdrawing water from local rivers (Leng et al. 2013).

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    CDF for summer (a) precipitation (mm month−1) and (b) dry days (<1 mm day−1) for the current and future scenarios by CLM_DC and CLM_BC. The mean and CV of the distribution was also shown in the parentheses for each scenario.

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Modeling the Impacts of Future Climate Change on Irrigation over China: Sensitivity to Adjusted Projections

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  • 1 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China
  • | 2 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
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Abstract

Because of the limitations of coarse-resolution general circulation models (GCMs), delta change (DC) methods are generally used to derive scenarios of future climate as inputs into impact models. In this paper, the impact of future climate change on irrigation was investigated over China using the Community Land Model, version 4 (CLM4), which was calibrated against observed irrigation water demand (IWD) at the provincial level. The results show large differences in projected changes of IWD variability, extremes, timing, and regional responses between the DC and bias-corrected (BC) methods. For example, 95th-percentile IWD increased by 62% in the BC method compared to only a 28% increase in the DC method. In addition, a shift of seasonal IWD peaks (averaged over the country) to one month later in the year was projected when using the BC method, whereas no evident changes were predicted when using the DC method. Furthermore, low-percentile runoff has larger impacts in the BC method compared with proportional changes in the DC method, indicating that hydrological droughts seem to be exacerbated by increased climate variability. The discrepancies between the two methods were potentially due to the inability of the DC method to capture the changes in precipitation variability. Therefore, the authors highlight the potential effects of climate variability and the sensitivity to the choice of particular strategy-adjusting climate projection in assessing climate change impacts on irrigation. Some caveats, however, should be placed around interpretation of simulated percentage changes for all of China since a large model bias was found in southern China.

Corresponding author address: Guoyong Leng, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China. E-mail: lenggy.11b@igsnrr.ac.cn

Abstract

Because of the limitations of coarse-resolution general circulation models (GCMs), delta change (DC) methods are generally used to derive scenarios of future climate as inputs into impact models. In this paper, the impact of future climate change on irrigation was investigated over China using the Community Land Model, version 4 (CLM4), which was calibrated against observed irrigation water demand (IWD) at the provincial level. The results show large differences in projected changes of IWD variability, extremes, timing, and regional responses between the DC and bias-corrected (BC) methods. For example, 95th-percentile IWD increased by 62% in the BC method compared to only a 28% increase in the DC method. In addition, a shift of seasonal IWD peaks (averaged over the country) to one month later in the year was projected when using the BC method, whereas no evident changes were predicted when using the DC method. Furthermore, low-percentile runoff has larger impacts in the BC method compared with proportional changes in the DC method, indicating that hydrological droughts seem to be exacerbated by increased climate variability. The discrepancies between the two methods were potentially due to the inability of the DC method to capture the changes in precipitation variability. Therefore, the authors highlight the potential effects of climate variability and the sensitivity to the choice of particular strategy-adjusting climate projection in assessing climate change impacts on irrigation. Some caveats, however, should be placed around interpretation of simulated percentage changes for all of China since a large model bias was found in southern China.

Corresponding author address: Guoyong Leng, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China. E-mail: lenggy.11b@igsnrr.ac.cn

1. Introduction

Globally, about 70% of water withdrawals and 90% of water consumption is used for irrigation purposes (Shiklomanov 2000; Döll 2009), which contributes to approximately 40% of crop production and spans 16% of agricultural land (Postel et al. 1996; Tilman et al. 2002). These large-scale water withdrawals from local rivers, reservoirs, lakes, and groundwater to irrigated areas have greatly affected the local and regional water balance (e.g., Haddeland et al. 2006; Tang et al. 2007; Ozdogan et al. 2010) while also causing modifications to surface energy balances and subsequent changes to local and regional climate (e.g., Kueppers et al. 2007; Lobell et al. 2009; Sacks et al. 2009). These alterations are particularly large for heavily irrigated areas where groundwater pumping is a dominant source of irrigation water because of limited surface water availability, which results in consistent depletion of aquifer storage (e.g., Famiglietti et al. 2011; Wada et al. 2012; Leng et al. 2014a), subsidence, and saltwater intrusion (e.g., Narasimhan 2009), as well as the possible increase of sea level at the global scale (e.g., Pokhrel et al. 2012; Wada et al. 2012). The problem is likely to become progressively more acute with growing world population and a rapid increase of global food demand, as explained in the Shared Socioeconomic Pathways (SSPs) for social–economic scenarios in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report on climate change (Tilman et al. 2011; van Vuuren et al. 2012).

As irrigation is the major purpose of water use in arid and subarid agricultural regions, an estimation of the irrigation water demand (IWD) in these changing environments is crucial for long-term water resource development and decision making. During the past decades, a number of studies have been carried out to investigate the impact of future climate change on irrigation at the regional and global scale to enhance our ability to adapt to anthropogenic climate change (e.g., Döll 2002; Fischer et al. 2007; Elgaali et al. 2007; Yano et al. 2007; Rodríguez Díaz et al. 2007; Pfister et al. 2011; Konzmann et al. 2013). Overall, an increase in long-term mean IWD was estimated in a warming climate in most of these studies because of enhanced evaporative demand. For example, Döll (2002) predicted an increase of global IWD by ~5% by the 2020s and by ~10% by the 2070s based on the year 1995 irrigated area. Meanwhile, Fischer et al. (2007) predicted an increase of IWD by ~30% globally by the 2080s. Rodríguez Díaz et al. (2007) estimated an increase of IWD by ~15%–20% by the 2050s in the Guadalquivir River basin in Spain, and de Silva et al. (2007) predicted an increase of 13%–23% of IWD depending on climate change scenarios in the paddy fields of Sri Lanka. All of these studies show significant increases in IWD over time on account of a warming climate, although the specific magnitudes of these increases are dependent on local factors. Because of the limitations of coarse-resolution general circulation model (GCM) climate data (used in all of these predictions), calculations of IWD changes usually adopt the delta change (DC) method to assist them in deriving scenarios of future climate (e.g., Döll 2002; Rodríguez Díaz et al. 2007; Fischer et al. 2007; de Silva et al. 2007; Yano et al. 2007; Shahid 2011; Chung and Nkomozepi 2012). In this method, climate inputs into the impact models for future time periods are derived by scaling the observed climate data (for the historical record) by the climate change as computed by the GCMs. This results in a new time series for the future scaled according to the GCMs but based on actual historical observations. The obtained future time series is then used as input into the impact models (e.g., hydrologic models) to determine changes in long-term averages of simulation fields. A key feature of this approach is that, because it uses historical data as its basis and GCM data only to change the magnitude of the historical data, it fails to account for changes in climatic variability predicted by these same GCMs.

Most studies suggest that increased climatic variability is a key component of climate change. However, despite our understanding that increased variability will be a facet of future climate changes (Benestad 2006; O’Gorman and Schneider 2009; Wetherald 2010), the effect of variability has rarely been accounted for in assessing climate change impacts on IWD and their resulting effects on future water balances. In this paper, our primary goal was to compare the results obtained using the DC method, which ignores changes in variability, to an alternative method designed to include these changes [the bias-corrected (BC) method] to illustrate what issues adopting a particular method could occur in assessing future changes in IWD in China. Possible reasons for the differences in simulated changes between the two methods were also illustrated and discussed.

2. Data and methodology

a. Model description

The Community Land Model (CLM), the land surface model for the Community Earth System Model (CESM), has a subgrid hierarchy with multiple soil columns existing in each grid cell (Lawrence et al. 2011). Each soil column could allow multiple plant functional types (PFTs) to exist and has separate dynamics for soil water, soil organic carbon, litter, etc. (Lawrence et al. 2011). The full water and energy balances are simulated in CLM for a mosaic of rain-fed vegetation classes. Climate inputs for the CLM are hourly precipitation, temperature, air pressure, humidity, wind speed, and radiations. CLM can be used in offline or coupled modes within the framework of the CESM (Gent et al. 2010; Lawrence et al. 2011) or a regional earth system model based on the Weather Research and Forecasting (WRF) Model (Ke et al. 2012; Leung et al. 2006). The CLM has been widely used for investigating the spatial–temporal variance of the water cycle over China (e.g., Wang and Zeng 2011; Liu and Xie 2013). In this study, we used the CLM, version 4 (CLM4), which includes several advances over prior versions such as improved canopy treatment, surface hydrology and runoff, and more realistic treatment of evapotranspiration by plant transpiration, evaporation from soils, and canopy evaporation (Oleson et al. 2010). CLM4 can be coupled to a water resource management model (Voisin et al. 2013) and a physically based routing model (Li et al. 2013b) using the subbasin-based representation (Li et al. 2013a). In CLM4, several improvements allowing for consideration of water management, crop growth, and agricultural activities have recently been incorporated as well (Levis et al. 2012; Drewniak et al. 2013; Leng et al. 2013, 2014a).

In CLM4, the irrigation scheme that is implemented for the C3 generic crop only works dynamically with the soil water balance in existing hydrologic schemes. That is, irrigation amounts that are dependent on the soil moisture state modify water cycle and energy budgets (e.g., soil moisture, evapotranspiration, and runoff) in the simulations after being applied to the soil column. The irrigation scheme has successfully been validated and applied for studying the effects of irrigation on land surface and subsurface hydrology over the conterminous United States by Leng et al. (2013, 2014a). Details for the irrigation scheme can be found in Leng et al. (2013). Briefly, the cropland area of each grid cell is divided into an irrigated and an unirrigated fraction based on a dataset of areas equipped for irrigation (e.g., Siebert et al. 2005; Ozdogan and Gutman 2008). For irrigated croplands, irrigation is triggered when the crop leaf area >0 and βt < 1 (i.e., water is limiting photosynthesis) at 0600 local time (LT) each day. Variable βt is a function that decreases the carboxylation rate through soil water stress. It varies between 0 and 1, corresponding to when the soil water is low or high. The IWD is then defined by the deficit between the current soil moisture content and the target soil moisture content aggregated from each soil layer. The target soil moisture content of each soil layer Wtarget,i (kg m−2) is calculated as
e1
where Wo,i is the minimum soil moisture content for soil layer i, resulting in no water stress in that layer; Wsat,i is the soil moisture content at saturation in that layer; and Firrig is a weighted factor varying from 0 to 1, corresponding to the soil moisture target, which is just enough to result in no water stress for crops or up to full soil saturation. The IWD was then met by withdrawing water from local rivers (i.e., total runoff) or groundwater (Leng et al. 2014a) and applying this water evenly to the soil beneath the irrigated crops between 0600 and 1000 LT.

b. Observed climate

The Water and Global Change (WATCH) Forcing Data (WFD; Weedon et al. 2011) for the period from 1960 to 1999 were used as an observation-based reference dataset. These data are produced by combining the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) daily data and the Climatic Research Unit (CRU) Time Series, version 2.1 (TS2.1), dataset for the overlapping periods of 1960–99. The ERA-40 dataset provides daily climate at a low resolution of 2.5°, whereas the CRU dataset provides observed monthly time series of climate over the last century at a high resolution of 0.5°. Additionally, the interpolated data were corrected for elevation differences between the grids of ERA-40 and CRU. The systematic bias of precipitation in the WFD was accounted for by correcting the monthly mean precipitation with the Global Precipitation Climatology Centre (GPCC) dataset. The WFD are created by combining the daily variability of ERA-40 with the monthly mean characteristics derived from the CRU and GPCC datasets. The WFD comprise all of the climatic variables for driving land surface models such as precipitation, temperature, wind speed, humidity, air pressure, and radiation and are available on the 0.5° grid over land based on the land–sea mask from the CRU.

c. Bias-corrected climate model projections and temporal disaggregation

Within the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) (Warszawski et al. 2014), only five GCMs from phase 5 of the Coupled Model Intercomparison Project (CMIP5) archive driven by multiple representative concentration pathway (RCP) scenarios were bias corrected to provide climate change forcing for our impact models. After correcting to the reference dataset of the WFD for the overlapping period 1960–99, the dynamics of the climate data, such as interannual variability, mean, variability, frequency, and intensity, were well preserved (Piani et al. 2010; Hempel et al. 2013). Thus, this dataset represents a complete climate change picture in that it includes both the mean properties and variation of future climates. A detailed description of the bias-correction method can be found in Hempel et al. (2013). A short summary of the bias-correction method will be provided in the next section.

Several previous studies have demonstrated the value of this bias-correction approach by quantifying the impacts of climate change and climate variability on water (e.g., Portmann et al. 2013; Wada et al. 2013; Piontek et al. 2014; Elliott et al. 2014; Schewe et al. 2014). Unlike most previous studies that only used data from one or two GCM in climate change impact studies on IWD (e.g., Döll 2002; Fischer et al. 2007; Rodríguez Díaz et al. 2007), we used the bias-corrected climate data from all five GCMs (HadGEM2-ES, GFDL-ESM2M, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M) with the RCP8.5 scenario provided by ISI-MIP for our analysis. Of the four RCPs, RCP8.5 exhibits the highest levels of forcing and covers the largest possible temperature change at the end of the twenty-first century (Moss et al. 2010; Rogelj et al. 2012). We selected RCP8.5 to highlight the impact of climate change under the highest greenhouse gas emission scenario on the spatial–temporal pattern of China’s irrigation. The details for the five GCMs can be found in Table 1.

Table 1.

Description of the five GCMs used in this study.

Table 1.

The climate data, including precipitation, air temperature, wind speed, surface radiation, air pressure, and specific humidity, were provided at a 0.5° × 0.5° spatial resolution and at a daily time step from 1951 to 2099 (Hagemann et al. 2013). To drive the CLM4, which requires hourly climate data, temporal disaggregation techniques were used to regenerate the hourly climate data. We follow the method of Vormoor and Skaugen (2013) to accomplish this, in which daily gridded climate data for Norway were disaggregated to the hourly scale by analogizing to the observed daily weather sequence. We disaggregate the bias-corrected GCM daily means using historic weather sequences from the observation-constrained 0.5° CRU–National Centers for Environmental Prediction (CRUNCEP) dataset, which includes temperature, precipitation, wind speed, pressure, specific humidity, shortwave radiation, and longwave radiation at 6-h intervals. That is, the observed hourly values (i.e., CRUNCEP) in a selected analog day at each grid cell are adjusted to the bias-corrected GCM daily mean to produce the disaggregated downscaled hourly time series. Taking precipitation, for example, as in the following Eq. (2), the hourly values from the analog day are divided by the analog daily mean and then multiplied by the bias-corrected GCM daily mean to produce hourly downscaled precipitation such that the shifted hourly mean precipitation averaged over the day equals the bias-corrected GCM daily mean precipitation. Therefore, the diurnal pattern is derived from the analog day with the daily mean preserved. This diurnal pattern is not subject to climate change by assuming that the daily weather patterns remain essentially the same in the future:
e2
where is the temporal disaggregated GCM hourly data for analog day i, is the observed hourly values from analog day i, is the daily mean of the observed hourly values from the analog day i, and is the daily mean value of GCM data for the analog day i.

d. Delta change and bias-correction methods

The DC method consists of simply scaling the observed climate data using monthly change factors calculated from the differences in climatology predicted by GCMs for the current and future periods. In this study, the observed climate data, that is, WFD, from 1960 to 1999 are scaled to derive the 40-yr future climate scenario for the period 2060–99. Relative change factors are applied to observed flux variables (e.g., precipitation) to calculate future climate variables. The DC method future flux variables are formulated as
e3
where is the scaled flux variable using the DC method and is the observed flux variable in the historic period. The suffixes i and j stand for the day and the month, respectively, and is the monthly DC factor, which is calculated as follows:
e4
where and are the mean values of the time series for month j for the future and current time periods by GCMs. For the state variable (e.g., temperature), absolute change factors are applied to derive the DC future climate scenario as follows:
e5
e6

The current climate for the DC method is by definition the WFD. The relative/absolute changes of climate variables predicted by the GCMs were applied to the current climate for each grid in the model domain. By applying the DC method, the changes in monthly climate factors were preserved while the information on changes in variability was ignored.

The BC method for correcting the GCMs used in this study is based on a distribution-based, bias-correction algorithm also used in the Water Model Intercomparison Project (WaterMIP) and WATCH to correct temperature and precipitation values (Piani et al. 2010; Hagemann et al. 2011). The details of this bias-correction method are described in Hempel et al. (2013). A short summary is given here. First, the GCM datasets are interpolated to the spatial resolution of the WFD. Second, an additive approach preserving the absolute changes (e.g., temperature) and a multiplicative one preserving the relative changes (e.g., precipitation) over the reference period 1960–99 were developed and applied to GCM data to match the climatology of the WFD. A parametric quantile mapping was then applied to the residual or normalized data to adjust the daily variability of GCM data to match that of the WFD. Subsequently, the derived monthly correction factors were interpolated toward daily ones and applied to the projected GCM data for future periods. The BC algorithm was also applied to radiation, humidity, pressure, and wind data. For example, for temperature the main equations and procedures were as follows: for each month, the monthly mean temperature values over a given grid point were first corrected using an additive approach as defined:
e7
where and are the uncorrected and corrected GCM temperature value for year y and day i, respectively, and and denote the monthly value for the WFD and the GCM for year y. Then, the daily variability of the GCM data was adjusted to that of the WFD based on a linear fit as follows:
e8
where S is the slope of a linear regression on the rank-ordered residual temperature from WFD and GCM data for all days in a specific month during the 40-yr reference period and f(⋅) indicates a function. A linear fit is considered an appropriate approximation in most cases because temperature values are considered to generally follow a normal distribution. The residual temperature was defined as
e9
By this, the residual distribution of the GCM was corrected to that of the WFD using a parametric quantile mapping.

The DC and BC methods were both based on the observed data (i.e., WFD). Thus, the DC and BC methods should have the same underlying climatology as the current climate. In the DC method, delta change factors calculated from GCMs were applied to the WFD to derive the DC method future climate. In the BC method, the transfer factors constructed between GCMs and the WFD for the period of 1960–99 were applied to future periods to obtain the BC future climate. For the future period, the DC method incorporated the changes in climatology predicted by GCMs whereas BC method incorporated both the changes in climatology and variance well.

e. Experimental design

First, we calibrate the model following the method by Leng et al. (2013) by perturbing Firrig between (0, 1) at a regular interval of 0.05 since accurate estimation of irrigation water amounts is crucial for irrigation modeling studies (Sacks et al. 2009; Sorooshian et al. 2012; Leng et al. 2013, 2014a). Specifically, 20 1-yr simulations using the WFD for the year 1999 were conducted with Firrig varying between 0 and 1 with an increment of 0.05. Then, by comparing the simulated irrigation amounts aggregated into provincial level with the corresponding census data in the year 1999, the best value of Firrig was selected for each province such that the simulated irrigation amounts could match the observations at the provincial level. This process was iterated at the provincial level to obtain the spatial distribution of Firrig over China. Agricultural water use for the year 1999 for each province in China was collected (www.data.ac.cn/zrzy/ntBA35.asp?Page=6) and was used as the target for model tuning, assuming that agricultural water was all used for irrigation (as no separate irrigation water use only data were available). All simulations for the analysis that follows adopted the calibrated parameters (i.e., Firrig) in modeling IWD.

Before investigating the role of climate variability in IWD, sensitivity experiments were conducted to quantify the independent role of each climate factor. In these simulations, we used the same subset of observed climate forcing data (i.e., WFD) and retained one of the seven factors while removing the interannual variability of the other six (i.e., precipitation, temperature, wind speed, shortwave radiation, longwave radiation, air pressure, and humidity are varied in CLMprecip, CLMtemp, CLMwind, CLMfsds, CLMflds, CLMpress, and CLMhumu simulations, respectively). For example, in simulation CLMprecip, the 12 values of long-term mean monthly climate factors (except for precipitation) are calculated for 1960–99 and are used to scale climatic factors (except for precipitation) at the original hourly resolution to match the long-term monthly mean climatology each year; therefore, only interannual variability and long-term trends in precipitation can affect the temporal variability of IWD. The independent role of each individual climate factor is then analyzed by calculating the Nash–Sutcliffe (NS) statistic (Nash and Sutcliffe 1970) and comparing the monthly time series by CLM_CTRL (i.e., the control simulation with original WFD) with that by other models. The NS calculation was formulated as
e10
where N is the number of observations, is the measured value for the year i, is the model-estimated value for the year i, and is the long-term mean of the measurements. The value ranges from −∞ to 1, with a value approaching 1 indicating good agreement between measures and estimates.

Then, two simulations were conducted. In simulation CLM_DC, future IWD changes were modeled using the DC approach, which is a common approach in climate change impact assessment. In simulation CLM_BC, we used the bias-corrected climate data by ISI-MIP to model future changes of IWD over China. The results obtained using the DC method–derived future climate scenario data (i.e., CLM_ DC future) were compared to the results obtained using the observed climate data (i.e., CLM_DC current). Likewise, the results for the future scenario obtained using the BC method–derived future climate scenario data (i.e., CLM_BC future) were compared to the current results also using the BC method–derived current climate data (i.e., CLM_BC current). By comparing the difference of changes in modeled fields between CLM_DC (climate variability information is not incorporated) and CLM_BC (climate variability information is incorporated), the sensitivity to the choice of particular strategy-adjusting climate projection in assessing climate change impacts on irrigation were investigated. The potential effects of climate variability on China’s irrigation were also discussed. In this study, the “future” scenario refers to the far future period 2060–99, while the current scenario refers to the period 1960–99. The far future period 2060–99 was selected to highlight the impact of the largest possible temperature increase on irrigation at the end of the twenty-first century (Moss et al. 2010; Rogelj et al. 2012). The “changes” in this study indicate the relative difference between the future period and the current period. The multimodel ensemble mean results were used for analysis in this study.

Land cover composition and land surface parameters such as plant functional types (PFTs), leaf area index (LAI), and stem area index (SAI) of the grid cells were derived from the 0.05° remote sensing–based CLM4 input dataset developed by Ke et al. (2012). The irrigation fraction map was obtained from the widely used dataset representing the fraction of irrigated area around the year 2000 by Siebert et al. (2005). All gridded input datasets were mapped to 0.125° resolution for use in CLM4. Before the simulations, an offline spinup was performed by cycling the meteorological forcing for 20 cycles using the CLM_CRTL setup until equilibrium conditions were achieved for all state variables including soil moisture, temperature, and water table depth. The resulting state variables were saved and used to initialize other simulations.

3. Results

a. Model calibration and validation

Province-specific tuning of the CLM4 against census data helps to compute reasonable estimates of IWD. Figure 1 shows the spatial distribution of the calibrated Firrig values at the provincial scale. Low values of Firrig are distributed over northern China while high values of Firrig up to 1 are found in most provinces over southern China. Comparison of yearly IWD (km3 yr−1) simulated after calibration and census data indicates that the spatial pattern of irrigation amounts was captured reasonably well after calibration at the provincial scale (Fig. 2). In this study, there tends to be a high target irrigation amount in southern China partly because it was assumed that agricultural water from census statistics was used entirely to supply irrigation demand because of the limitations of data availability on irrigation water use directly. However, in the real world, there are places for which water used for forestry, livestock watering, and fishery is high. In addition, the agricultural census data are actually for agricultural water use, which means much of the water withdrawals would become drainage, especially in southern China, where paddy rice is grown by flooding a field above soil moisture saturation (Frolking et al. 2002). However, irrigation water in the model is water withdrawal minus drainage. Therefore, given a high target irrigation amount, a high Firrig will be expected because of the limitations in data availability (Leng et al. 2013) and simulated IWD could still be underestimated compared to the unrealistically high target, especially in southern China, as shown in Fig. 2. Statistics in Fig. 3 show that the mean bias and RSME were −3.61 and 6.52 km3 yr−1, respectively. Relatively high irrigation amounts in southern China, such as in the Human, Hubei, Guangxi, and Guangdong provinces, contributed to the overall underestimation for the country as a whole.

Fig. 1.
Fig. 1.

Spatial distribution of the calibrated Firrig at the provincial scale. The variable Firrig is a weighted factor varying from 0 to 1, corresponding to the soil moisture target, which is just enough to result in no water stress for crops or up to full soil saturation.

Citation: Journal of Hydrometeorology 15, 5; 10.1175/JHM-D-13-0182.1

Fig. 2.
Fig. 2.

Spatial distributions of annual irrigation amounts (km3 yr−1) simulated by (a) CLM4 after calibration and (b) observation dataset in 2000 (observation data provided online at www.data.ac.cn/zrzy/ntBA35.asp?Page=6).

Citation: Journal of Hydrometeorology 15, 5; 10.1175/JHM-D-13-0182.1

Fig. 3.
Fig. 3.

Simulated irrigation amounts by the CLM4 after calibration vs observation dataset at the provincial level.

Citation: Journal of Hydrometeorology 15, 5; 10.1175/JHM-D-13-0182.1

Figure 4 shows the comparison of modeled IWD with available census data for the consecutive period 1995–99 to test the predicted IWD changes in response to interannual climate variability. Only 16 provinces have complete data for the consecutive period 1995–99, including two (i.e., Anhui and Qinghai provinces) in southern China. It was found that CLM4 after calibration captured the magnitude and interannual variability of IWD when compared to observations for provinces in northern China. More importantly, the response of modeled IWD to interannual climate variations was also captured for the provinces in southern China, even though the modeled IWD was underestimated against census data. These results confirm the capability of the model after calibration in responding to the climate change/variability that was the focus of this study.

Fig. 4.
Fig. 4.

Simulated irrigation amounts vs observation dataset from 1995 to 1999 for the 16 irrigated provinces with available observations in (a)–(n) northern and (o),(p) southern China.

Citation: Journal of Hydrometeorology 15, 5; 10.1175/JHM-D-13-0182.1

b. Independent role of climatic factors

Figure 5 shows the NS values by comparing the monthly time series by CLM_CTRL with that of other simulations for each month for each river basin. Comparisons of IWD from CLM_CTRL with IWD computed using the CLMprecip show NS statistics above 0.7 for most months and water resources regions (Fig. 5a). This indicates that the CLMprecip model can explain most of the variability in IWD for most months and most river basins. Additionally, NS values up to 0.7 were also found between CLMtemp and CLM_CTRL for winter and early spring months in river basins over northeastern and northwestern China (Fig. 5b), indicating the effects of temperature on IWD variations during these months for these river basins. Rather than snow during winter, these results may suggest the effects of temperature on the occurrence of rain and/or the effects on snowmelt runoff (Barnett et al. 2005). However, the NS statistics for the comparisons of IWD by CLM_CTRL and IWD by other models (Figs. 5c–g) are mostly near or below zero, suggesting minor effects of these climate factors on IWD variations. Depending on the method adopted in calculating potential evaporation (e.g., Penman–Monteith, Priestley–Taylor, or Hamon), some studies take into account the variations of limited meteorological forcing (e.g., temperature and precipitation) with or without explicit consideration of variations of other meteorological forcings [e.g., PCRaster Global Water Balance (PCR-GLOBWB; Wada et al. 2011a,b), Water—A Global Assessment and Prognosis (WaterGAP; Döll and Siebert 2002), Water Balance Model plus (WBMplus; Wisser et al. 2008), and Lund–Potsdam–Jena managed Land (LPJmL; Rost et al. 2008; Konzmann et al. 2013)]. Our results using CLM4 show minor effects on IWD variations in most river basins over China if ignoring the variability of other climatic factors except for precipitation and temperature.

Fig. 5.
Fig. 5.

(a)–(g) NS statistics comparing monthly IWD computed using CLM_CTRL with monthly IWD computed using the sensitivity experiments for 10 river basins during 1960–99 and (h) the spatial distribution of 10 river basins over China. A high NS value close to 1 indicates the significantly independent role of specific climatic factor in governing the IWD interannual variability.

Citation: Journal of Hydrometeorology 15, 5; 10.1175/JHM-D-13-0182.1

c. Projected changes in IWD using DC and BC methods

Figure 6 shows the spatial distribution of IWD for the current and future scenarios by CLM_DC and CLM_BC. Similar spatial structure was captured in the CLM_BC current scenario, compared to the corresponding IWD estimates in the CLM_DC current scenario. This indicates that historical climate projections by GCM match the observed climate data well in terms of spatial structure after the GCM climate data were bias corrected to the WFD for 1960–99. The annual IWD for the whole country for 1999 by CLM_DC and CLM_BC was 357.18 and 346.27 km3 yr−1, respectively, which was relatively low compared to reports (~400 km3 yr−1) by Wada et al. (2011a) for the year 2000. In a warming climate, future IWD was projected to increase across China by both CLM_DC and CLM_BC because of enhanced evaporative demand over irrigated areas, which tends to outweigh increasing precipitation amounts (Wada et al. 2013). Over northern China, an increase of IWD lower than 50% was found over major parts of irrigated areas by both the CLM_DC and the CLM_BC. Over southern China, a much larger increase in IWD by 100% was projected by both the CLM_DC and the CLM_BC across major parts of irrigated regions. Compared to the changes of IWD predicted by the CLM_DC, however, the changes of IWD by the CLM_BC exhibited a more widespread and pronounced increase over southern China.

Fig. 6.
Fig. 6.

Spatial distribution of annual mean IWD (mm yr−1) by CLM_DC and CLM_BC for the (a),(b) current scenarios for 1960–99 and (c),(d) relative changes for 2060–99 minus 1960–99 [unit for (c) and (d) is percent changes (%)].

Citation: Journal of Hydrometeorology 15, 5; 10.1175/JHM-D-13-0182.1

When looking at the standard deviations (STDs) changes of annual mean IWD as shown in Fig. 7, an increase of STD of annual mean IWD was projected over irrigated pixels across China. In some regions, the magnitude of the STD of IWD was as large as the IWD itself, indicating a very high interannual variability of IWD. It was also found that there was a more pronounced increase of the STD of annual mean IWD over southern China by the CLM_BC than the CLM_DC. The contrast in regional response between northern and southern China was consistent with the research results of Leng et al. (2014b), who found a much larger increase of climate variability and a larger decrease of surface water amounts in southern China than northern China with climate change. The difference in changes in IWD spatial structure obtained using the CLM_DC and the CLM_BC should indicate that changes in climate variability could exert a large influence on estimation of IWD. Therefore, it is important to account for changes in climate variability as they can significantly affect future predictions.

Fig. 7.
Fig. 7.

As in Fig. 6, but for spatial distribution of STDs of annual mean IWD.

Citation: Journal of Hydrometeorology 15, 5; 10.1175/JHM-D-13-0182.1

For an assessment of water scarcity, it is necessary to consider that the conditions in typical dry years as water scarcity caused by high interannual climate variability could be more severe than water scarcity for a normal climate (Döll 2002). Figure 8 shows the cumulative frequency distributions (CDFs) of summer mean IWD for the current and future scenarios obtained from the CLM_DC and the CLM_BC averaged over all irrigated grids across China. The statistics for northern China, southern China, and China as a whole are summarized in Table 2. It was found that IWD increased throughout the CDF curve using both the DC and BC methods. For the country as a whole, future IWD increased from 8.88 to 11.33 mm month−1 and from 8.32 to 10.86 mm month−1, corresponding to a 28% and 31% increase using the CLM_DC and the CLM_BC, respectively. This suggests a comparable magnitude of change is obtained using these two methods under long-term average climatic conditions when averaged over the country. However, there tends to be a disproportional increase of IWD with a larger increase of high-percentile IWD in the future when using the CLM_BC (similar findings were also found for the spring season; see Table 2). For example, an increase of the 95th-percentile IWD by 7.07 mm month−1 (62%) was found using the CLM_BC method compared to only an increase of 3.50 mm month−1 (28%) for the same percentile when using the CLM_DC. This disproportional response of IWD throughout the CDF curve by the two methods (i.e., DC and BC) indicates that the high-percentile IWD changes would be underestimated if climate variability is not accounted for, although the long-term mean changes could be similar. From the spatial distribution of changes in the 95th-percentile IWD by the CLM_DC and the CLM_BC as shown in Fig. 9, we found that the increase of the 95th-percentile IWD occurred mainly in irrigated areas over northeastern and southern China as projected by both the CLM_DC and the CLM_BC. Furthermore, a much larger increase of the 95th-percentile IWD was predicted by the CLM_BC than the CLM_DC. This discrepancy was even more pronounced in irrigated areas over southern China (Table 2), as the climate variability here is expected to increase the most compared to other regions in China (Leng et al. 2014b).

Fig. 8.
Fig. 8.

CDF for summer IWD (mm month−1) for the current and future scenarios by CLM_DC and CLM_BC.

Citation: Journal of Hydrometeorology 15, 5; 10.1175/JHM-D-13-0182.1

Table 2.

Comparison of summer [June–August (JJA)] and spring [March–May (MAM)] irrigation statistics between CLM_DC and CLM_BC. Northern China includes river basins 1–5 while southern China includes river basins 6–10. The spatial distributions of river basins 1–10 are shown in Fig. 5h.

Table 2.
Fig. 9.
Fig. 9.

Spatial distributions of relative changes of the 95th-percentile IWD for 2060–99 minus 1960–99 predicted by (a) CLM_DC and (b) CLM_BC.

Citation: Journal of Hydrometeorology 15, 5; 10.1175/JHM-D-13-0182.1

Figure 10 shows the seasonal IWD for the current and future scenario obtained using the CLM_DC and the CLM_BC, respectively. The changes in seasonal IWD obtained using the CLM_DC match well with those obtained from using the CLM_BC. However, the peak IWD was projected to shift by approximately one month later (from May to June) when averaged over irrigated pixels for the whole country using the CLM_BC, while no evident shift of peak IWD was found when using the CLM_DC. Unfortunately, our ability to physically explain this phenomenon is rather limited, although we suspect this to be a consequence of the difference in the consideration of the changes in climate variability between the two methods (suggesting that this shift is driven by changes in climate variability rather than mean climatic conditions), which will be explored in the next section. This supposition is supported by the findings of Wada et al. (2013), who showed that the peak IWD is projected to shift by approximately a month later from June to July over eastern Asia by the 2080s using the same bias-corrected climate data under the same emissions scenario (i.e., RCP8.5).

Fig. 10.
Fig. 10.

Seasonal IWD for the current and future scenarios by CLM_DC and CLM_BC.

Citation: Journal of Hydrometeorology 15, 5; 10.1175/JHM-D-13-0182.1

d. The resulting effects of irrigation using DC and BC methods

Given the discrepancies in changes of IWD variability and extremes obtained when using the two methods, we provided a further investigation on the difference of the resulting effects of irrigation on summer (i.e., JJA) mean evapotranspiration (ET) and summer mean total runoff after IWD was met by withdrawing water from local rivers (Leng et al. 2013). Possible mechanisms that might be responsible for these observed differences were also considered. For ET changes, a similar percent increase by 15% from 71.81 to 82.90 mm month−1 and 15% from 69.73 to 80.10 mm month−1 was found when using the CLM_DC and the CLM_BC, respectively, when averaged over the country. In addition, ET changes obtained when using the CLM_DC and the CLM_BC both showed a proportional increase throughout the CDF curve (Fig. 11a). This suggests a small bias when simulating ET changes induced by irrigation between the CLM_DC and the CLM_BC. When considering future runoff predicted by the CLM_DC, a proportional decrease in the runoff was found throughout the CDF curve (Fig. 11b; meaning that the shape of the curve does not change much, but the curve shifts to the left, indicating less runoff). These results are consistent with the proportional increase of IWD (Fig. 8) and the proportional increase of ET predicted by this model (Fig. 11a). In contrast, the fast and slow components of total runoff are subject to higher climate variability, which suggests that changes in high- and low-percentile runoff events might be picked up by the CLM_BC. This is indeed the case, with a large decrease of low-percentile runoff and a small decrease in high-percentile runoff. These results indicate the potential exacerbation of hydrological droughts by irrigation with changes in climate variability. Moreover, these results illustrate that climate variability tends to have large control on the extreme events of runoff, which also tends to be among the more important elements of the runoff curve for water resource managers. By failing to include a consideration of climate variability, the CLM_DC is then unable to adequately predict changes in these important parts of the runoff curve while the CLM_BC captures these changes.

Fig. 11.
Fig. 11.

CDF for summer (a) ET (mm month−1) and (b) runoff (mm month−1) for the current and future scenarios by CLM_DC and CLM_BC. The mean of the distribution was also shown in the parentheses for each scenario. Note that this is for the results after IWD was met by withdrawing water from local rivers (Leng et al. 2013).

Citation: Journal of Hydrometeorology 15, 5; 10.1175/JHM-D-13-0182.1

To illustrate the mechanism governing the differences between the two methods, Fig. 12 shows the CDF of summer mean precipitation for the current and future scenario as inputs into the CLM_DC and the CLM_BC since precipitation is the dominant climatic factor in governing the IWD variance, as discussed in section 3b. Both methods predict a similar magnitude of increase (7%) for summer precipitation when averaged over the country. This is not surprising as the advantage of the DC method is its ability to preserve the changes in mean climatology from GCMs. However, the coefficient of variation (CV; i.e., standard deviations normalized by the mean) of summer precipitation obtained using the BC method increased by 25%, whereas little change (2%) was found when using the DC method. This suggests that the DC method is incapable of capturing the increase in interannual variability of future climate. Moreover, obvious differences between predicted summer mean precipitations were found throughout the CDF curve between the two models. For example, the BC method predicted a 10% drier summer in comparison with the DC method, which predicted all summers becoming wetter. In addition, the BC method predicts an increase in dry days per summer of 12% from 25.42 to 28.48 days while the DC method predicts a decrease in dry days per summer of 3% from 25.71to 24.83 days (Fig. 12b). Significant differences are especially apparent between the two methods when comparing high-percentile summer dry days. Here, the BC method predicts an increase of up to 28% for the 95th-percentile dry days while the DC method predicted negligible change in such days. Combined, these differences suggest that any studies on irrigation that use the DC method will necessarily differ in their interpretations and results from those that use the BC approach. Therefore, much caution should be taken as to the underlying assumptions when adopting a particular approach for adjusting climate projections in assessing climate change impacts on irrigation.

Fig. 12.
Fig. 12.

CDF for summer (a) precipitation (mm month−1) and (b) dry days (<1 mm day−1) for the current and future scenarios by CLM_DC and CLM_BC. The mean and CV of the distribution was also shown in the parentheses for each scenario.

Citation: Journal of Hydrometeorology 15, 5; 10.1175/JHM-D-13-0182.1

4. Summary and discussion

Accounting for the effects of irrigation on the water cycle is very important in the assessment of past, current, and future water resources (Döll 2002). With climate change, not only the mean but also the variability of climate is expected to change in the future (Benestad 2006; O’Gorman and Schneider 2009; Wetherald 2010); however, the extent to which climate variability will impact IWD and subsequently the water cycle in the future has rarely been explored. In this paper, CLM4 was calibrated in order to match the simulated IWD with agricultural census data over China. Based on the calibrated model, the changes in IWD and resulting effects on water balance components with climate change/variability were investigated using the DC and BC methods.

Our results show that among other climatic factors with potential effects on IWD variance, precipitation exerts the most distinct influence on IWD variations, with NS values up to 0.7 for most months in most river basins (Fig. 5a). The impact of temperature variability on IWD is as significant as that of precipitation for river basins over northeast and northwest China in the winter season (Fig. 5b) because of the larger impact of temperature on rainfall rather than snowfall or timing of snowmelt (Barnett et al. 2005) but was comparatively less important in other parts of China. Compared to precipitation and temperature, other climatic factors (e.g., humidity, radiation, wind speed, and air pressure) showed relatively minor effects on IWD variations for most months in most river basins.

Comparison of future changes of IWD using the DC and BC methods indicate a comparable magnitude of increase in the mean and variability of IWD in northern China. However, discrepancies between the two methods in IWD estimates were evident in southern China (Table 2), where climate variability is expected to increase more than northern China (Leng et al. 2014b). When averaged over China, the 95th-percentile IWD increased by 62% when using the BC method compared to 28% when using the DC method (Table 2). Moreover, a shift of IWD peaks by one month later was found when using the BC method while no evident changes were found when using the DC method (Fig. 10). The CLM_DC also predicted a proportional increase of ET and a proportional decrease of runoff throughout the CDF curve. In contrast, a larger decrease of low-percentile runoff was found when using the CLM_BC method (Fig. 11b), indicating the potential exacerbation of hydrological droughts. These differences in predicted IWD and the resulting effects of these predictions on runoff were potentially due to the inability of the DC method to capture the increased variability of precipitation such as drier summers and more dry days per summer (Fig. 12). However, it should be noted that at the national level, important caveats should be placed around interpretation of simulated changes since large model bias was found in southern China.

The significant role of climate variability in modulating IWD variations and regional water balance has many implications. First, it demonstrates the importance of developing a climate dataset that captures climate variability. Also, our results suggest that, as climate variability does seem to be a key factor in determining the magnitude and variability of many important hydrological parameters, a range of climate models should be considered that can account for the full range of uncertainty in future predictions of climate variability since a single GCM can differ significantly in its treatment of precipitation variability (Reichler and Kim 2008; Pierce et al. 2009). Second, it suggests that the choice of method for deriving the changes in climate variability can play a significant role in irrigation modeling. Thus, studies that rely on numerical tools such as CLM to assess the impact of climate change on irrigation and the water cycle need to give careful consideration to the choice of downscaling method.

However, there are some uncertainties associated with this research. First, the census data provided the total irrigation, but the model estimates the net irrigation requirement. Specifically, large amounts of paddy rice were grown in southern China (Frolking et al. 2002; Xiao et al. 2003; Liu et al. 2005). Rice is a crop that requires a certain depth of water in the paddy field during growing seasons. According to the field data, the paddy field was kept as a pool constantly or intermittently with water on the surface in order to achieve more rice production. In this case, the value of Firrig for paddy rice would have to be >1 in order to represent irrigation water used to maintain a flooded field. However, paddy rice was not represented in CLM4. Irrigation is implemented for the C3 generic crop only (Leng et al. 2013, 2014a) as it is the only crop type available in CLM4. With this specific irrigation practice in southern China, a tremendous amount of the water withdrawals that are attributed to irrigation may subsequently become drainage. Hence, the drainage ratio (drainage to total irrigation) is high in the paddy fields of southern China while the drainage ratio is low in the dry areas of northern China (Frolking et al. 2002; Liu et al. 2005). The irrigation withdrawals in agricultural water use census data include drainage that is not consumed. However, irrigation water in the model is water withdrawal minus drainage (i.e., net irrigation amounts). The inability of current irrigation schemes to represent different irrigation schedules should be addressed in the future.

Second, an assumption has to be made when using the agricultural census data as a model calibration target. That is, all agriculture water use (including irrigation and livestock) was assumed to be for irrigation because of the limitation of data availability on the irrigation water use itself. The exact amounts of water use for irrigation cannot be separated without additional information, which was not available. Therefore, it is almost certain that the amount allocated as irrigation water is higher than the actual irrigation, as some water use in every agricultural environment will be used for other purposes (domestic use, stock watering, etc.). Therefore, the model tends to underestimate the yearly irrigation, even though we set soil moisture to saturation (Firrig = 1).

Third, the data is provided only at provincial scale, but finer spatial resolution is essential. The calibration could be better constrained by observations (i.e., census) at a higher resolution (Leng et al. 2014a). More efforts should be made to develop a high spatial–temporal dataset and a better irrigation module in China to eliminate this limitation. Like most previous studies on climate change impacts on IWD (e.g., Döll 2002; Fischer et al. 2007; Rodríguez Díaz et al. 2007; Elgaali et al. 2007; de Silva et al. 2007), the impact of climate change on crop phenology was ignored. Thus, changes in IWD in this study were driven only by climate change/variability. Despite these limitations, this work is novel in that it investigated the difference of simulated changes in IWD and the water balance between the DC method, which is the most common method in assessing climate change impact on irrigation, and the BC method, which is based on the newly available CMIP5 climate dataset. Also, the potential effects of climate variability on IWD and the water balance with emphasis on the extremes were clarified.

We acknowledge that to attribute the difference of changes in IWD and runoff extremes between the DC and BC methods to the climate variability effects (from mean climatic condition effects) is not perfect, since the unavoidable difference in distribution properties between the DC and the BC methods’ current climate could propagate into the future scenario and impact the results. However, our results show that the magnitude of the modeled IWD (Figs. 68, 10) for the current period is comparable between the CLM_DC and CLM_BC methods. The major difference of the methods for deriving the future climate scenario lies in the ability of the CLM_BC method to account for changes in climate variability. That is, information on changes in climate variability is not incorporated in the CLM_DC method, whereas climate variability information is incorporated in the CLM_BC method. Thus, we expect that the differences in the modeled changes in IWD and the resulting effects on runoff extremes between CLM_DC and CLM_BC may be due to the effects of changes in climate variability captured by the latter method.

5. Concluding remarks

In the past decade, methods of different complexity have been used for adjusting various moments of the climate projections, ranging from relatively simple linear methods (Hay et al. 2000; Lenderink et al. 2007) to distribution-mapping-based algorithms (Piani et al. 2010; Themeßl et al. 2011). The existing approaches in climate change studies can generally be grouped into two distinct strategies: delta change factor and bias correction (Ho et al. 2012). The delta change factor strategy does not adjust the climate projections but assumes that the signal or changes are reasonably projected by climate models, even though the models are biased. Alternatively, the bias-correction strategy supposes that different levels of climate variability could be preserved after the model biases are corrected (Maraun et al. 2010). However, a fundamental assumption has to be made in the bias-correction strategy. That is, bias-correction algorithms are stationary, which means that the transfer function constructed for current climate conditions is assumed to also be valid for the changed future climate conditions (Maraun et al. 2010; Chen et al. 2011; Teutschbein and Seibert 2012). This is the main weak point of any bias-correction strategy and emphasizes the need to either explore new approaches of better accounting for GCM and regional climate model (RCM) biases or improve the performance of GCMs and RCMs to reduce biases (Chen et al. 2011; Teutschbein and Seibert 2012). Recently, based on differential split sample testing (Klemeš 1986), Teutschbein and Seibert (2013) showed that distribution mapping, which is similar to the correction method used in this study, was best able to cope with nonstationary conditions.

This article has investigated the sensitivity of climate change impacts on irrigation to the choice of the two distinct strategies for adjusting climate projections: change factor and bias correction (Ho et al. 2012). In an example of China’s irrigation and water balance by a land surface model (i.e., CLM4), we have shown that the two strategies could give substantially different spatial–temporal patterns of extreme IWD and runoff. Therefore, much caution should be taken as to the underlying assumptions when adopting a particular strategy for adjusting climate projections in assessing climate change impacts on irrigation.

Acknowledgments

We thank the editor and reviewers for their pertinent and professional comments and suggestions that were greatly helpful for further improvement of the quality of this manuscript. This work was supported by the National Natural Science Foundation of China (Grant 41171031), the National Basic Research Program of China (Grant 2012CB955403), and the Hundred Talents Program of the Chinese Academy of Sciences. The authors would like to acknowledge Yin Tang for her help in preparing the agricultural census data and Dr. Scott Rayburg for kind help in English editing.

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