PIAHS | Articles | Volume 383

**Post-conference publication**
16 Sep 2020

**Post-conference publication** | 16 Sep 2020

Changing climate and implications for water use in the Hetao Basin, Yellow River, China

^{1}Fenner School of Environment and Society, Australian National University, Canberra, ACT, 0200, Australia^{2}State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430068, China^{3}Water Resources Research Institute of Inner Mongolia, No. 11, Genghis Khan East Road, New Town, Hohhot, Inner Mongolia, 010020, China^{4}Centre for Agricultural Water Research in China, China Agricultural University, No. 17, East Rd, Haidian, Beijing, 100083, China

^{1}Fenner School of Environment and Society, Australian National University, Canberra, ACT, 0200, Australia^{2}State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430068, China^{3}Water Resources Research Institute of Inner Mongolia, No. 11, Genghis Khan East Road, New Town, Hohhot, Inner Mongolia, 010020, China^{4}Centre for Agricultural Water Research in China, China Agricultural University, No. 17, East Rd, Haidian, Beijing, 100083, China

**Correspondence**: Ian White (ian.white@anu.edu.au)

**Correspondence**: Ian White (ian.white@anu.edu.au)

Abstract

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Balancing water allocations in river basins between upstream irrigated agriculture and downstream cities, industry and environments is a global challenge. The effects of changing allocations are exemplified in the arid Hetao Irrigation District on the Yellow River, one of China's three largest irrigation districts. Amongst the many challenges there, the impact of changing climate on future irrigation water demand is an underlying concern. In this paper we analyse trends in local climate data from the late 1950s and consider the implications for irrigation in the Basin. Since 1958, daily minimum temperatures, *T*_{min} in the Basin have
increased at three times the rate of daily maximum temperatures, *T*_{max}. Despite this, there has been no significant increases in annual precipitation, *P* or pan evaporation, *E*_{pan}. The difference between the increasing trends in *T*_{max} and *T*_{min} means that the average annual diurnal temperature range, DTR, has decreased very significantly, part of a global phenomenon. Hargreaves empirical approach is used to estimate changes in both incoming solar radiation, *R*_{s}, and potential evaporation, ET_{0}. Changes in estimated ET_{0} correlated well with changes in measured pan evaporation, *E*_{pan}. Paradoxically, the estimated decreasing trend in *R*_{s} does not correspond to a significant decreasing trend in *E*_{pan}. Implications of changing climate on water use and soil salinity in the Basin are discussed.

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White, I., Xu, T., Zeng, J., Yu, J., Ma, X., Yang, J., Huo, Z., and Chen, H.: Changing climate and implications for water use in the Hetao Basin, Yellow River, China, Proc. IAHS, 383, 51–59, https://doi.org/10.5194/piahs-383-51-2020, 2020.

1 Introduction

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The world-wide challenge of allocating water fairly between competing
upstream and downstream users and the environment is exemplified in the
Yellow River, the so-called “cradle of civilization” in China (Xu et al.,
2005). The challenge is compounded when upstream uses include vital
irrigated food production systems. The 10 000 km^{2} Hetao Irrigation
District on the northern side of the Ordos loop of the Yellow River is one
of China's three largest irrigation areas. This arid, cold region (Peel et
al., 2007) in Inner Mongolia has been farmed for 2300 years, since the Qin
dynasty. It is an important source of grains, oil seeds, sugar, fruits,
vegetables and poultry.

Increasing water abstractions from the Yellow River for irrigation,
downstream demands from rapidly expanding industrial and urban developments,
obligatory environmental flows, coupled to channel elevation changes due to
sediment deposition have all severely stressed the river system (Xu et al.,
2005; Tang et al., 2008; Lehane, 2015). In response the Yellow River Water Conservancy Commission mandated a 15 % reduction in irrigation supply to Hetao to be phased in over time with a reduced supply target of 4.0 km^{3} yr^{−1} for the Hetao District.

Recent research in Hetao has focussed on reducing water use while maintaining or increasing productivity (e.g. IWC-IM, 1999; Xu et al., 2010; Xue and Ren, 2017). Air temperatures have increased at a significant rate in northern and western China (Tang et al., 2010) and the impacts of climate change on irrigation water demand is a continuing concern for both regulators and irrigators. In this work we analyse trends in local measured climate parameters across Hetao, relate them to physical mechanisms and consider the implications of the already observed climate changes on irrigation water demand and salinity management.

2 Materials and methods

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The general features of the flat, bow-shaped, approximately 200 km long by 50 km wide Hetao Irrigation District are in Fig. 1 and listed in Table 1. The District is bounded to the west by the Gobi Desert, to the north by the western portion of the Yinshan Mountains and by the Yellow River in the south. This region of northern China has experienced significant increasing trends in maximum and temperatures over the past 60 years (Fig. 2). From April until early November, water is diverted from the Yellow River at Dengkou on the southwestern edge of the region and distributed throughout the District by a series of multi-level canals. Drainage discharges into Wulansuhai Lake in the eastern edge of the region.

The Basin is divided into five sub-districts: Yigan (previously called
Wulanbuhe), Jiefangzha, Yongji, Yichang and Wulate comprising 19.2 %,
20.7 %, 16.2 %, 30 % and 13.9 % of the total area of the District.
Annual pan evaporation, *E*_{pan} across the District, far exceeds annual precipitation and the variability of annual rainfall decreases with
increasing rainfall across the District from west to east as does *E*_{pan} (Table 1).

This study used data drawn from a range of disparate and scattered sources collected by different organisations over diverse time periods (Table 2).

Monthly precipitation data was available from five available stations, each
within one of the five irrigation sub-districts (Table 3). Monthly *E*_{pan} data was available for this study from the Yongii and Wulate stations in Table 3 for the period 1957 to 2013 together with daily maximum and minimum temperatures, *T*_{max} and *T*_{min}, from 1957
and 1954, respectively, to 2013. Mean monthly precipitation peaks in July
and August. Because of this, mean pan evaporation peaks earlier in May but
mean temperature peaks in July. On average mean atmospheric temperatures are
at or below zero for 5 months of the year.

Trends in annual temperature, DTR, precipitation, and pan evaporation,
irrigation supply, specific irrigation drainage, and groundwater and
drainage salinity were determined using linear regression (LR). Trends were
considered not significant when the probability, *p*, was less than 0.9.
Because of the sensitivity of linear regression trends to start and end
values of a series, weaker trends (*p*<0.95) were also tested using the nonparametric (NP) Theil–Sen slope estimator (Theil, 1950). The significance of nonparametric trends was determined using the Mann–Kendall test (Kendall, 1970).

The daily DTR (^{∘}C) is defined as:

$$\begin{array}{}\text{(1)}& \mathrm{DTR}={T}_{\mathrm{max}}-{T}_{\mathrm{min}}.\end{array}$$

FAO-56 (Allen et al., 1998) pointed out that DTR can be used as an indicator of the fraction of extra-terrestrial radiation, *R*_{a} (MJ m^{−2} d^{−1}), that reaches the earth's surface. Hargreaves and Samani (1982) (see also Hargreaves and Allen, 2003) proposed that the ratio of incoming solar radiation incident on the earth's surface, *R*_{s} (MJ m^{−2} d^{−1}), to *R*_{a} can be approximated by the empirical relation:

$$\begin{array}{}\text{(2)}& {R}_{\mathrm{s}}/{R}_{\mathrm{a}}={k}_{{R}_{\mathrm{s}}}{\mathrm{DTR}}^{\mathrm{1}/\mathrm{2}}\end{array}$$

where ${k}_{{R}_{\mathrm{s}}}$ (^{∘}C^{−0.5}) is an empirical adjustment
coefficient (0.16 for interior regions, 0.19 for coastal regions).

Using estimates of mid-monthly *R*_{a} from FAO-56 and the latitude of the stations in Table 3, together with ${k}_{{R}_{\mathrm{s}}}=\mathrm{0.16}$ (^{∘}C^{−0.5}) in Eq. (2) and DTR data, temporal changes in the annual incoming solar radiation to Hetao can be estimated.

Good daily *T*_{max} and *T*_{min} data are available from 1958 to 2013 for two sites, Yongii and Wulate in the Hetao Basin listed in Table 3.

Temperature is not the main driver of ET_{0}. In the Penman–Monteith
approach, ET_{0} can be expressed as (FAO-56, Allen et al., 1998):

$$\begin{array}{}\text{(3)}& {\mathrm{ET}}_{\mathrm{0}}={C}_{\mathrm{1}}\left[(\mathrm{1}-\mathit{\alpha})\cdot {R}_{\mathrm{S}}-{R}_{\mathrm{nl}}-G\right]+{C}_{\mathrm{2}}f(u,\mathrm{\Delta}e).\end{array}$$

In Eq. (3), *C*_{1}, *C*_{2} are factors which depend weakly on temperature,
pressure and crop characteristics, *α* the surface albedo, *R*_{nl} the net outgoing long wave radiation, *G* the soil heat flux, *u* the wind speed and Δ*e* the water vapour pressure deficit. The first term in Eq. (3) represents the key contribution of *R*_{s} to ET_{0}, the second term represents the “drying power of the air”. For arid locations, such as Hetao, the “drying power of the air” is expected to contribute significantly to ET_{0}, although evaporation of surrounding irrigation water may confound this locally.

For Hetao, available long-term data necessary to fully evaluate trends in
ET_{0} via the Penman–Monteith approach is not readily available. FAO-56 suggest that the approach developed by Hargreaves (Hargreaves and Samani, 1982, 1985; Hargreaves, 1994), based on daily *T*_{max} and *T*_{min} give useful estimates of ET_{0}, particularly over periods longer than 5 d:

$$\begin{array}{}\text{(4)}& {\mathrm{ET}}_{\mathrm{0}}=\mathrm{0.0023}\cdot {R}_{\mathrm{a}}(\stackrel{\mathrm{\u203e}}{T}+\mathrm{17.8}){\mathrm{DTR}}^{\mathrm{0.5}}\end{array}$$

with $\stackrel{\mathrm{\u203e}}{T}=({T}_{\mathrm{max}}-{T}_{\mathrm{min}})\mathrm{2}$.

Using Eq. (2), Eq. (4) can be written in terms of *R*_{s} as:

$$\begin{array}{}\text{(5)}& {\mathrm{ET}}_{\mathrm{0}}=\mathrm{0.0023}\cdot (\stackrel{\mathrm{\u203e}}{T}+\mathrm{17.8}){R}_{\mathrm{s}}/{k}_{{R}_{\mathrm{s}}}.\end{array}$$

FAO-56 noted that the 1985 Hargreaves' method, Eq. (4), “has shown
reasonable ET_{0} results with global validity”. Hargreaves and Allen (2003) did an extensive evaluation of Eq. (4) over a very wide range of
global locations against the FAO-56 Penman Monteith method and concluded
that: “Where equipment cost is a consideration, where data quality is
questionable, or where historical data are missing, both the reduced set
FAO-PM or the 1985 Hargreaves are recommended, since the two methods are
surprisingly equivalent over a wide range of climates.” Use over 5 d or
longer is recommended because of the influence of moving weather fronts, and
large variations of wind speed and cloud cover at daily time steps.

Equation (4) appears to compensate for the lack of *R*_{n}, humidity and wind speed data required by the Penman–Monteith method. The diurnal
temperature range in the equation implicitly accounts for the influence of
cloudiness because DTR decreases with increasing cloudiness. As well, it has been shown that DTR correlates with relative humidity and vapor pressure deficit and is inversely influenced by wind run (Hargreaves and Allen, 2003). Hargreaves and Allen (2003) recommend that Eq. (4) should be calibrated against Penman–Monteith combination equations to produce a regionally calibrated relationship.

The temporal trend in ET_{0} with time follows from Eq. (5),

$$\begin{array}{}\text{(6)}& {\displaystyle \frac{\partial {\mathrm{ET}}_{\mathrm{0}}}{\partial t}}={\displaystyle \frac{\mathrm{0.0023}}{{k}_{{R}_{\mathrm{s}}}}}\left({R}_{\mathrm{s}}\cdot {\displaystyle \frac{\partial \stackrel{\mathrm{\u203e}}{T}}{\partial t}}+[\stackrel{\mathrm{\u203e}}{T}+\mathrm{17.8}].{\displaystyle \frac{\partial {R}_{\mathrm{s}}}{\partial t}}\right).\end{array}$$

Whether estimated ET_{0} will increase or decrease with time will depend
on the relative magnitude of the two terms ${R}_{\mathrm{s}}\frac{\partial \stackrel{\mathrm{\u203e}}{T}}{\partial t}$ and $[\stackrel{\mathrm{\u203e}}{T}+\mathrm{17.8}]\frac{\partial {R}_{\mathrm{s}}}{\partial t}$ in Eq. (6) which may be of opposite sign.

3 Climate trends in the basin

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The trends in annual mean daily *T*_{max} and *T*_{min} at Yongii and Wulate over the period 1958 to 2013 were identical. As a result, the area-averaged mean temperature is used here as representative of the Hetao Basin. Figure 3 plots the average annual mean daily *T*_{max} and *T*_{min} average and annual mean daily precipitation, pan
evaporation and temperature as a function of time. Table 4 shows that both increasing temperature trends were highly significant with *T*_{min} increasing almost three times the rate of *T*_{max}.

Analysis of monthly mean daily temperature data in Hetao shows that
*T*_{min} in all months of the year have increased very significantly and are also increasing at a greater rate than monthly *T*_{max}.
Figure 4 shows the change in the distribution of measured monthly minimum temperatures with time in Hetao as well as the projected temperature distribution for 2050 using the highly significant monthly increasing trends. Because the distribution of monthly temperatures depends on precipitation, the years chosen in Fig. 4 were selected because they had similar annual precipitations, (1960: *P*=155 mm; 1991: *P*=151 mm; 2006; *P*=156 mm; with the projected precipitation for 2050 of 157 mm).

Figure 4 demonstrates that by 2006, the period over which the minimum temperature in Hetao was below zero had decreased by almost 2 months relative to that for 1960 so that the length of the growing season in Hetao is expanding due to increasing minimum temperatures. This also suggests that the depth to which the soil is frozen and the length of time it is frozen for has decreased. This may have implications for salt flushing from topsoil.

Area-weighted average annual precipitation for the Basin between 1957 and 2013 is plotted in Fig. 5. The large variations in annual rainfall over the period 1957 to 2013, from 64 to 288 mm yr^{−1}, are apparent. The small trend, 0.3±4.3 mm per decade, however, is not significant (*p*<0.9).

The area-averaged *E*_{pan} for Hetao, between 1957 and 2013 is plotted in Fig. 6. An increasing linear regression trend of $+\mathrm{13}\pm \mathrm{9}$ mm per decade can be seen. NP analysis gave a similar trend of +15 mm/ per decade. For both analyses, however, the trends were not significant (*p*<0.9). So, the highly significant increasing annual maximum and minimum temperatures in Hetao are not associated with any significant increases in annual precipitation or annual pan evaporation between 1957 and 2013.

Because *T*_{min} increased at three times the rate of *T*_{max}, DTR decreased significantly (Fig. 3) at −0.49 ^{∘}C per decade over the period 1958–2013. This is the same
absolute rate as annual mean daily temperature is increasing (Table 4).

Analyse of the data revealed that between 1958 and 1985, DTR decreased at faster rate, $-\mathrm{0.85}\pm \mathrm{0.10}$ ^{∘}C per decade (*p*>0.9999) than the full period 1958 to 2013. Between 1985 to 2013, however, the trend
$-\mathrm{0.03}\pm \mathrm{0.09}$ ^{∘}C per decade was not significant (*p*<0.9). This change in trend is consistent with regionally-averaged northern hemisphere data where DTR was found to decrease significantly from 1950 to the late 1970's. Between 1979 and 2004, however there was no significant trend in DTR (Vose et al., 2005). This appears to coincide with a fundamental shift in the El Niño–Southern Oscillation phenomenon (Easterling et al., 1997).

Equation (2) was used to estimate temporal changes in *R*_{s} using the DTR data using the method of Hargreaves and Samani (1982). The results are given in Fig. 7 which suggests the annual incoming solar radiation to Hetao decreased at a rate of $-\mathrm{105.6}\pm \mathrm{10.2}$ MJ m^{−2} per decade
(*p*>0.9999) between 1958 and 2014, or 9 % over 50 years. This is close to the 10 % to 20 % reduction reported in many places over the same period (Stanhill and Cohen, 2001). This global phenomenon is attributed by some to global “dimming” (a decrease in net radiation due to increased cloudiness or aerosols) (Easterling et al., 1997; Vose et al., 2005). It is noted in Fig. 7, however, that from about 2009 onwards, there appears to have been an increase in annual *R*_{s}.

The overall decrease in estimated *R*_{s} cannot be attributed to increasing cloudiness due to increasing annual precipitation, since, as shown above, annual precipitation in Hetao has not increased significantly between 1957 and 2013. The correlation between estimated annual *R*_{s} and annual precipitation, *P* is weakly significant
(*p*>0.9) with $\partial {R}_{\mathrm{S}}/\partial P=-\mathrm{1.0}\pm \mathrm{0.6}$ MJ m^{−2} mm^{−1}. This correlation between *R*_{s} and *P* appears to be mainly due to a
correlation with fluctuations around the decreasing trend in *R*_{s} Some of the fluctuations of *R*_{s} about the trend line in Fig. 7 may be caused by annual changes in cloud cover associated with annual variations in precipitation.

From Eq. (3) it is expected that the decrease in incoming solar radiation in Fig. 7 associated with a decrease in potential evaporation in Hetao. We next examine that proposition.

In discussing Eq. (6) above it was noted that any trend in ET_{0} will depend on the terms ${R}_{\mathrm{s}}\frac{\partial \stackrel{\mathrm{\u203e}}{T}}{\partial t}$ and $[\stackrel{\mathrm{\u203e}}{T}+\mathrm{17.8}]\frac{\partial {R}_{\mathrm{s}}}{\partial t}$. In Hetao, $\frac{\partial \stackrel{\mathrm{\u203e}}{T}}{\partial t}>\mathrm{0}$ (Table 4) but $\frac{\partial {R}_{\mathrm{s}}}{\partial t}<\mathrm{0}$ (Fig. 7), so depending on their relative magnitudes, ET_{0} could increase, decrease or remain constant with time.

Monthly values of ET_{0} were estimated from Eq. (5) which were summed
to give an estimate of annual ET_{0}. The estimated change of ET_{0}
between 1958 and 2013 is plotted in Fig. 8.

The temporal trend in annual ET_{0} estimated from Eq. (5), shown in
Fig. 8, using linear regression, is $\partial {\mathrm{ET}}_{\mathrm{0}}/\partial t=+\mathrm{0.8}\pm \mathrm{2.2}$ mm yr^{−1} per decade. Nonparametric analysis gave a trend of +1.7 mm yr^{−1} per decade. Both trend estimates were not significant (*p*<0.9) over the period 1958 to 2013.

Over this period, the mean annual potential evaporation estimated from Eq. (5) was 1027±26. This is considerably larger than the actual
evapotranspiration evaluated by Hao and Yang (2016) using two approaches
based on a water balance approach and using NDVI and the FAO-56 Penman–Monteith equation. Their water balance for the period 1960 to 2013
gave actual annual evaporation of 601 mm yr^{−1} and the NDVI
Penman–Monteith approach gave 605 mm yr^{−1} for the period 2006–2010. Yang et al. (2012) used remote sensing to estimate evapotranspiration over the cropping season and found a mean value of 605 mm yr^{−1} between the years 2000 and 2010. Over the May to September growing season Eq. (5) predicts that ET_{0} was 742±21 mm. Hargreaves and Allen (2003) recommended that Eq. (6) should be calibrated against Penman–Monteith combination equations to produce a regionally calibrated relationship.

Figure 8 shows the comparison between annual ET_{0} estimated from Eq. (8) and area weighted annual *E*_{pan} for Hetao. Between 1958 and 2013, the correlation between ET_{0} and *E*_{pan} was very significant (*p*>0.99999) with correlation, *R*=0.67. As discussed previously, the linear regression trend in pan evaporation, $\partial {E}_{\mathrm{pan}}/\partial t=+\mathrm{13}\pm \mathrm{9}$ mm per decade was not significant (*p*<0.9) as was the trend in estimated ET_{0}. From 1958 to 1975 the correlation between ET_{0} and *E*_{pan} was stronger, *R*=0.83 (*p*>0.9999) but between 1976 and 2013, the correlation was lower, *R*=0.59 but still very significant (*p*>0.9999).

This comparison has demonstrated a reasonably good correlation between
annual ET_{0}estimated from Eq. (8) and area weighted mean pan evaporation, *E*_{pan}, for Hetao. It also reveals that despite the significant increasing maximum and minimum temperatures and the significant decreases in DTR and in estimated *R*_{s}, there are no significant trends in either estimated ET_{0} or in *E*_{pan} over the period 1958 and 2013.

4 Discussion

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The dramatic trends found in annual mean daily *T*_{max} and *T*_{min} between 1958 and 2013 in the Hetao Basin are not accompanied by any significant trends in annual precipitation or pan evaporation. The significant disparity between the rates of change *T*_{max} and *T*_{min} mean that DTR is decreasing. This is accord with world-wide observations (e.g. Stanhill and Cohen, 2001). In Hetao, however, the rate of decline of DTR seems more intense and has persisted longer than elsewhere. Decreases in DTR and in *E*_{pan} (not found here) have been variously attributed to global “dimming” (a decrease in net radiation due to increased cloudiness or aerosols) or global “stilling” (a decrease in wind speed close to the surface) (Easterling et al., 1997; Stanhill and Cohen, 2001; Roderick and Farquhar, 2002; Vose et al., 2005).

Numerous hypotheses have been advanced for the changes occurring globally in
DTR and in *E*_{pan} including changes in cloud cover, aerosol
concentrations, precipitation, soil moisture, atmospheric circulation, land
use and decreasing wind speeds (Peterson et al., 1999; Roderick and Farquhar, 2004; Vose et al. 2005; Roderick et al., 2007; McVicar et al., 2012). The consensus is that
decreasing DTR is a consequence of firstly a general decrease in net radiation, and secondly a decrease in wind speed close to the surface.

It was found here that that between 1958 and 1985, DTR decreased at faster rate than over the full period 1958 to 2013. Between 1985 to 2013, however, the was not significant. This change in trend is consistent with regionally-averaged northern hemisphere data (Vose et al. 2005). This change in trend appears to coincide with a fundamental shift in the El Niño–Southern Oscillation phenomenon (Easterling et al., 1997).

In Hetao, one explanation for the changing trend in DTR could be the impact of evaporation of surrounding irrigation water. Increase in water vapour concentration in the atmosphere decreases *R*_{s} but increases in coming long-wave radiation at night, raising *T*_{min}. If this is the case, one might expect that the area of land being irrigated might impact on
estimated *R*_{s}. Figure 8 compares the decreasing trend in estimated *R*_{s} with the increasing trend in irrigated land area.
There is a strong, highly significant negative correlation, $R=-\mathrm{0.79}$. The
decline in trend in *R*_{s} after 1985 also corresponds approximately to a period of almost no growth in irrigated area and to the use of plastic film on the soil surface to minimise soil evaporation. While correlation does not imply causality, the impact of the extent of irrigation on incoming short
wave and long-wave radiation is worth researching.

Despite the estimated decline in *R*_{s}, there was no significant trend in *E*_{pan} or estimated ET_{0} between 1958 and 2013. This absence of a trend appears to imply that the declining radiative component is balanced by an increasing drying power of the air. This is counter intuitive since the 46 % increase in area irrigated in Hetao between 1960 and 2013 should have decreased the water saturation deficit. In terms of Eq. (5), the absence of a significant annual trend in ET_{0} over the full period indicates ${R}_{\mathrm{s}}\frac{\partial \stackrel{\mathrm{\u203e}}{T}}{\partial t}\approx -[\stackrel{\mathrm{\u203e}}{T}+\mathrm{17.8}]\frac{\partial {R}_{\mathrm{s}}}{\partial t}$.

The annual ET_{0} estimated here using the empirical approach of Hargreaves were higher than other available estimates but correlated well with measured *E*_{pan}. It has been recommended that despite widespread testing of the Hargreaves approach, its estimates of ET_{0} should be
calibrated locally.

The available data in the Hetao Basin shows that between 1958 and 2013, annual mean daily minimum temperatures increased at three times the rate of maximum temperatures. Between 1960 and 2010, the period over which minimum temperatures were below zero decreased by over one month. If these increases continue, by 2050 that period could decrease by two months. That may imply a longer growing season and increased water demand in Hetao.

These increasing temperatures also mean that topsoil in the Basin will be frozen for a shorter period and to a shallower depth than in the past. Soil freezing has at least two important roles. Firstly, it appears to promote formation of soil structure and improves early season infiltration and root growth. Secondly, the propagation of the freezing front downwards may expel salt that accumulated over the growing season deeper into the soil profile and away from the root zone. A decrease in the time and depth the soil is frozen may lead to a lower efficiency of salt removal.

5 Conclusions

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Annual daily temperatures in the Inner Mongolia region of northern China have increased dramatically over the past 60 years. The limited available records in Hetao show that the period that temperatures are below zero has shortened significantly suggesting that the length of the growing season is expanding. Because minimum temperatures are increasing at three times the rate of maximum temperatures, DTR is decreasing. This implies that either incoming solar radiation or wind run is decreasing or both.

Despite the dramatic change in temperatures, annual rainfall, pan evaporation and estimated potential evaporation, have no significant trends over this period. This means that only an expansion of the length of the growing season could cause an increase in future water demand.

The rapid increase in *T*_{min} means that if the trend continues, the soil will be frozen for a shorter period and to a shallower depth. This has
implications for soil structure formation, infiltration and root growth as
well as the efficiency of salt flushing from the soil. All require further
research.

Data availability

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Data availability.

All data sources used in this work are given in Table 2.

Author contributions

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Author contributions.

JYu and JYa initiated, co-hosted and supported the research, IW drafted the paper and analysed trends, TX carried out the spatial interpolation of mean daily maximum and minimum temperatures throughout China, JZ provided critical insights particularly on all aspects of water in the Hetao Basin as well as invaluable translations, XM provided details of irrigation practices in the basin, ZH and HC shared their research results on the Hetao Basin, especially on evapotranspiration.

Competing interests

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Competing interests.

The authors declare that they have no conflict of interest.

Special issue statement

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Special issue statement.

This article is part of the special issue “Hydrological processes and water security in a changing world”. It is a result of the 8th Global FRIEND–Water Conference: Hydrological Processes and Water Security in a Changing World, Beijing, China, 6–9 November 2018.

Acknowledgements

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Acknowledgements.

This work was supported by the Major Special Project of Inner Mongolia Science and Technology: Research and Demonstration of the Key Technologies of Drip Irrigation Using the Multi-source of Water in Yellow River Irrigation Areas of Inner Mongolia (2013–2017).

Financial support

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Financial support.

This research has been supported by the Major Special Project of Inner Mongolia Science and Technology: Research and Demonstration of the Key Technologies of Drip Irrigation Using the Multi-source of Water in Yellow River Irrigation Areas of Inner Mongolia (grant no. 2013-2017).

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Short summary

Balancing water allocations between upstream irrigated agriculture and downstream users and environments is a major challenge in China's Yellow River. Increasing temperatures in the Hetao Irrigation District indicate that solar radiation has decreased over the past 50 years but surprisingly actual evaporation has not changed. Expansion of the length of the irrigation growing season due to increasing temperatures could lead to higher demands for irrigation.

Balancing water allocations between upstream irrigated agriculture and downstream users and...

Proceedings of the International Association of Hydrological Sciences

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