Irrigation is a significant human activity that affects surface water fluxes in the Tarim River Basin. To quantitatively assess the irrigation impact of this activity on surface water fluxes in the Tarim River, a land surface hydrologic model was coupled with a modified irrigation scheme and a reservoir module and applied to simulate these fluxes. Modeling results indicate that the combined effect of the irrigation process and reservoir operation is prominent in the study area, from which 70–75% of the surface water is extracted and used for irrigation. This scenario can primarily be attributed to the significant amount of water losses as a result of evaporation and the seepage of canals and aqueducts. The effective utilization coefficient of the extracted surface water is only approximately 0.40. The irrigation water withdrawals increased with the recent rapid expansion of cultivated land. Therefore, the water flowing into the main stem of the Tarim River still shows a downward trend, despite the significant increase in the total discharge of headwater basins since the 1960s.
INTRODUCTION
The problem of over-abstraction in surface water has been well documented (Shiklomanov & Rodda 2003). It is commonly exacerbated in combination with extended natural dry periods. Substantive reductions in major river flows have been noted worldwide, along with significant reductions in the size and volume of lakes and inland sea areas (Postel 2000; Vörösmarty et al. 2000). These problems are typically tied closely to upstream diversions and reservoirs in some large river basins, especially those in parts of Asia and in the western United States, where humans directly alter the dynamics of the water cycle through dams constructed for water storage and through water withdrawals for agricultural purposes (Haddeland et al. 2013). Moreover, water-intensive farming and irrigation enhance evapotranspiration and reduce runoff. Awareness regarding the effect of irrigation on the surface water balance is increasingly important in arid and semi-arid regions in northwestern China (Hao et al. 2008; Tao et al. 2008). The most prominent example of an affected region is the Tarim River Basin (TRB), which is the largest inland river basin in China. Many of the causes of this effect persist despite years of over-use changes in both water and related ecosystem conditions. These causes include highly inefficient water supply provision practices to expand oasis agriculture use and a basic lack of control over water source exploitation. The inappropriate development of reservoirs and diversions has complicated and enhanced the impacts on existing water resources and ecosystems in the TRB.
A number of studies have focused on the impacts of climate change and human activities on water resources in the TRB. Xu et al. (2010) detected the long-term trend of the hydrological time series, including air temperature, precipitation, and streamflow, using both parametric and nonparametric techniques. They concluded that although the precipitation and streamflow from the headwaters of the Tarim River increased significantly as a result of climate change, the streamflow displays a decreasing trend along the main stem. This observation may be attributed to the effect of human activities. Liu & Chen (2006) collected time series data on population change, economic development, climate change, water volume and quality, and changes in oasis land use to study the interactions between these factors in the TRB. The results imply that human activity, rather than climate change, dominates the recent environmental changes in the basin. Tao et al. (2011) revealed that the local human activities starting from the 1970s have reduced the water volume diverted into the main stem. This reduction was aggravated in the 2000s based on a comparison of the total discharge of the four headstreams in the mountains and of the discharge flowing into the main stem of the Tarim River. Most of the previous studies attempted to examine the trends in hydro-climatic variables and to explain the relationship between these variables using statistical techniques. However, the methods used lack a physical basis in human activities such as irrigation withdrawal and related reservoir operation, both of which dominate the surface water fluxes in the TRB. Thus, we still know very little about their impacts on surface water fluxes in space and time, as well as their background mechanisms.
Some recent studies have analyzed the effects of irrigation on surface water and energy fluxes using large-scale hydrological models and land surface schemes. Döll & Siebert (2002) used a model of global water resources and water use, to compute global irrigation requirements under present-day climate. Boucher et al. (2004) represented the effect of irrigation in a general circulation model by incorporating the increase in evapotranspiration caused by irrigation in idealized climate simulations. de Rosnay et al. (2003) developed an irrigation scheme for a land surface model to estimate the increase in latent heat flux over the Indian Peninsula. Fekete et al. (2010) used a water balance model to compare the impacts of human water consumption and those of the expected climate changes on annual runoff over the next decades. An irrigation scheme based on simulated soil moisture deficit and available water was developed in the land surface hydrologic model to predict irrigation water demands and actual water withdrawals (Haddeland et al. 2006a). Unlike other approaches, this model considers the effects of dams and reservoirs using a reservoir module. Hence, water can be stored for later use. This model has been successfully implemented to reproduce irrigation effects on surface water fluxes at the regional, continental, and global scale (Haddeland et al. 2006a, b, 2013). However, the direct use of the model is problematic given that its irrigation scheme does not represent water supply efficiency, which is very low in the TRB (Zhou et al. 2000). To address this issue, the current paper presents an improved irrigation system. It mainly differs from the original irrigation scheme in that it also estimates irrigation water demands, actual water withdrawals, and water losses using a generalization algorithm and based on a prescribed effective utilization coefficient.
In this study, we address the question of whether and how irrigation impacts surface water fluxes by using a coupled version of the land surface hydrologic model (Liang et al. 1994; Haddeland et al. 2006a). A related question is regarding how the impacts have changed in recent decades. The following section describes the selected study site and datasets. The third section explains the model construction in detail and focuses on the modification to the model. The fourth section presents the results, including model performance, irrigation impacts on surface water fluxes, and their changing trends. The fifth section discusses the results with respect to the applied method and provides conclusions and recommendations for further research.
STUDY SITE AND DATASETS
Study site
Location of the Tarim River Basin.
Datasets
Contours of annual average temperature and precipitation in the Tarim River Basin.
Contours of annual average temperature and precipitation in the Tarim River Basin.
As indicated above, most of the streamflow originates from the snow melt, glacier melt, and orographic rainfall in the mountainous areas around the TRB. To effectively estimate the surface water fluxes over the irrigated area, the discharge data from 1961 to 2007 observed at the five hydrometric gauges monitored by the Xinjiang TRB Management Bureau were selected as part of the routing model inputs. The hydrometric gauges are located upstream of the reservoir system and the irrigated area (Figure 1). In addition, information on a series of major reservoirs operated by a local agency is collected to parameterize the reservoir module, which is included in the routing model. Detailed information on the hydrometric gauges and reservoirs is listed in Tables 1 and 2.
Detailed information on the observing hydrometric gauges in the headstreams of the Tarim River Basin
| Water system | Hydrometric gauges | Drainage area (km2) | Latitude | Longitude |
|---|---|---|---|---|
| Aksu River | Xiehela | 12,816 | 41° 34′ N | 79° 37′ E |
| Shaliguilanke | 17,898 | 40° 57′ N | 78° 36′ E | |
| Hotan River | Tongguziluoke | 14,575 | 36° 49′ N | 79° 55′ E |
| Wuluwati | 15,557 | 36° 57′ N | 79° 41′ E | |
| Yarkant River | Kaqun | 48,100 | 37° 59′ N | 76° 54′ E |
| Water system | Hydrometric gauges | Drainage area (km2) | Latitude | Longitude |
|---|---|---|---|---|
| Aksu River | Xiehela | 12,816 | 41° 34′ N | 79° 37′ E |
| Shaliguilanke | 17,898 | 40° 57′ N | 78° 36′ E | |
| Hotan River | Tongguziluoke | 14,575 | 36° 49′ N | 79° 55′ E |
| Wuluwati | 15,557 | 36° 57′ N | 79° 41′ E | |
| Yarkant River | Kaqun | 48,100 | 37° 59′ N | 76° 54′ E |
Detailed information on major reservoirs in the Tarim River Basin
| Reservoir | Year of construction | Height of dam (m) | Storage of reservoir (106m3) | Surface area (km2) | Latitude | Longitude |
|---|---|---|---|---|---|---|
| Shengli | 1970 | 5 | 108 | 51.6 | 40° 15′ N | 80° 45′ E |
| Shangyou | 1961 | 5 | 180 | 136.5 | 40° 15′ N | 80° 45′ E |
| Duolang | 1965 | 5 | 128 | 48.0 | 40° 45′ N | 80° 45′ E |
| Xiaohaizi | 1960 | 16 | 872 | 145.0 | 39° 45′ N | 78° 45′ E |
| Sukuqiake | 1985 | 5 | 281 | 133.6 | 38° 45′ N | 77° 15′ E |
| Yiganqi | 1956 | 6 | 166 | 47.2 | 38° 15′ N | 77° 15′ E |
| Qianjin | 1969 | 6 | 127 | 45.0 | 38° 45′ N | 77° 15′ E |
| Wuluwati | 2001 | 132 | 347 | 13.0 | 37° 15′ N | 79° 45′ E |
| Reservoir | Year of construction | Height of dam (m) | Storage of reservoir (106m3) | Surface area (km2) | Latitude | Longitude |
|---|---|---|---|---|---|---|
| Shengli | 1970 | 5 | 108 | 51.6 | 40° 15′ N | 80° 45′ E |
| Shangyou | 1961 | 5 | 180 | 136.5 | 40° 15′ N | 80° 45′ E |
| Duolang | 1965 | 5 | 128 | 48.0 | 40° 45′ N | 80° 45′ E |
| Xiaohaizi | 1960 | 16 | 872 | 145.0 | 39° 45′ N | 78° 45′ E |
| Sukuqiake | 1985 | 5 | 281 | 133.6 | 38° 45′ N | 77° 15′ E |
| Yiganqi | 1956 | 6 | 166 | 47.2 | 38° 15′ N | 77° 15′ E |
| Qianjin | 1969 | 6 | 127 | 45.0 | 38° 45′ N | 77° 15′ E |
| Wuluwati | 2001 | 132 | 347 | 13.0 | 37° 15′ N | 79° 45′ E |
MODEL CONSTRUCTION
Land surface hydrologic model
The physically based, semi-distributed variable infiltration capacity (VIC) model was used to derive soil moisture over the study area. Liang et al. (1994) described the formulation of the VIC model with two soil layers in detail; thus, we provide only a brief introduction in this section. A third soil layer was added by Liang et al. (1996) to improve the evapotranspiration predictions in arid climates, and the VIC model solves the water and energy balance equations given the land surface over each grid cell (half degree in this case). Subgrid variability in topography, vegetation, and precipitation is represented in the form of a mosaic, wherein each grid cell is partitioned into elevation bands. Each band contains a number of land cover tiles. The soil column is divided into multiple layers (typically three). The saturation excess mechanism, which produces surface runoff, is parameterized through the Xinanjiang variable infiltration curve (Zhao 1992). Drainage from the lowest soil layer is controlled through a non-linear recession curve (Nijssen et al. 2001).
Irrigation scheme and reservoir module
Schematic of the VIC irrigation scheme. (a) Original (Haddeland et al. 2006a); (b) modified.
Schematic of the VIC irrigation scheme. (a) Original (Haddeland et al. 2006a); (b) modified.
In order to carry out water withdrawals correctly, this coupled version of VIC model was rewritten to run from upstream to downstream locations in a basin. In this way, the routing model is allowed to interact with the VIC model, and is no longer simply a post-processing step. In the original version of the irrigation scheme (Haddeland et al. 2006a), the canals and aqueducts built for irrigation water transport were excluded from the modeling scheme. As a result, actual irrigation water withdrawals are underestimated because of possible irrigation water loss from these structures. According to an observation-based study on the surface water supply efficiency in the TRB (Zhou et al. 2000), the effective utilization coefficient of surface water is low because of intense evaporation and the seepage from canals, aqueducts, and reservoirs. Directly using the original irrigation scheme in the TRB is problematic. To address this issue and to consider these considerable water losses, we present an improved irrigation scheme. This scheme not only estimates irrigation water demands, actual water withdrawals, and actual water losses based on simulated soil moisture deficit and available water, but also through an effective utilization coefficient (Cu), which is prescribed by a generalization algorithm. This scenario indicates that the withdrawn irrigation water returns to the soil column of the irrigated area only partially to sustain the crops, while the rest is assigned to the non-irrigated area as a loss term (Figure 3(b)). Specifically, irrigation water demand is defined as the amount of water demanded by the crops in the irrigated area, whereas irrigation water withdrawal is defined as the actual amount of water withdrawn from rivers for irrigation use. Irrigation water loss is part of irrigation water withdrawal and is defined as the amount of water lost through evaporation or seepage during transportation.
As indicated in the previous section, most of the streamflow originates from snow melt, glacier melt, and orographic rainfall in the mountainous area around the TRB. Zhang et al. (2013) established a modeling scheme across the Tibetan Plateau (TP) by linking the VIC model with a degree-day glacier-melt scheme. The glacier area vs. volume relationship used was mostly derived from the field measurements in TP. It is not clear whether this relationship is suitable to our study area. Besides, meteorological data uncertainties and its impact on land surface hydrology model simulations including melt water estimations are probably the primary limitation because of the scarcity of meteorological stations over the mountainous area in the TRB. Thus, we applied the observed discharge as part of the routing model inputs instead of the runoff simulated by the VIC model from the headwater basins. Hence, we can focus on the area between the observation gauges in the upstream (Table 1) and the Alaer gauge. Furthermore, we can effectively estimate and understand the impact of irrigation on surface water fluxes in these areas.
RESULTS
Model performance
Contour plots for the calibrated model parameters: (a) NSE; (b) RMSE.
Irrigation impacts on surface water fluxes
(a) Annual average discharge at the Alaer gauge; (b) monthly mean discharge at the Alaer gauge; (c) annual average water level at the Xiaohaizi reservoir; (d) monthly mean water level at the Xiaohaizi reservoir.
(a) Annual average discharge at the Alaer gauge; (b) monthly mean discharge at the Alaer gauge; (c) annual average water level at the Xiaohaizi reservoir; (d) monthly mean water level at the Xiaohaizi reservoir.
Spatial effects of irrigation on surface water fluxes and states.
Change in irrigation water withdrawals
The total discharge of four headstreams increased significantly since the 1960s. However, the volume of runoff that was diverted into the main stem of the Tarim River has decreased continuously since then (Tao et al. 2011). These detected trends can be explained by climatic changes and, more importantly, by irrigation and related activities. In this section, we evaluate the irrigation withdrawals in the Aksu River, Hotan River, and Yarkant River based on the model simulation.
(a) Annual irrigation water withdrawal from the 1960s to the 2000s; (b) variation in runoff contribution from three headwater streams to the main stem of the Tarim River.
(a) Annual irrigation water withdrawal from the 1960s to the 2000s; (b) variation in runoff contribution from three headwater streams to the main stem of the Tarim River.
DISCUSSION AND CONCLUSIONS
This study developed a grid-based land surface hydrologic model coupled with an irrigation scheme and reservoir module to simulate multiple components of surface water fluxes for the driest region in China. The model was implemented at half degree in the TRB. To factor in the irrigation water losses from the canals and aqueducts as a result of their poor quality, we improved the irrigation scheme through a generalization. In the improved version of irrigation scheme, we estimate irrigation water demands, actual water withdrawals, and water losses simply by prescribing an effective utilization coefficient of the extracted surface water. We consider that the utilization coefficient of surface water should vary over time and space. Therefore, physical canals and aqueducts should be explicitly included, although this inclusion remains somewhat impractical because of poor data availability in the study area.
Although irrigation is the main cause of water loss along the river reach, other possible reasons have not been explored in this study. For example, our present modeling scheme does not include groundwater recharge and groundwater withdrawal, both of which seem to be important in the lower reaches. Moreover, the model cannot simulate the domestic use and grazing demand associated with population growth and economic development. The Kaidu River transports water to the irrigation area of the lower reaches of the Tarim River from Bosten Lake, and the amount of the transported water sometimes could be significant. However, due to data scarcity and security, we could only collect the observed discharge data in the upstream (above the Alaer gauge). Therefore, our modeling scheme is established with focus on the area between the observing hydrometric gauges and the Alaer gauge, without the Bosten Lake included. All of these factors render our results uncertain and deserve additional attention in the design of future modeling and prediction work.
Using the land surface hydrologic model coupled with a modified irrigation scheme and reservoir module, we presented a physically based approach to evaluate the human impacts on the streamflow of the Tarim River. This streamflow at the main stem of this river has decreased in the past few decades. Our key findings are as follows. (a) We conclude that the combined effect of irrigation activities and reservoir operation is prominent upstream of the basin, from which 70–75% of the surface water is extracted for irrigation use. This condition can largely be attributed to the significant amount of water losses as a result of evaporation and the seepage of the canals and aqueducts when the effective utilization coefficient of the extracted surface water drops to approximately 0.40. The waste of extracted surface water severely reduces the water volume flowing to the main stem, therefore jeopardizing the environmental and ecological health in that area. (b) Although the total discharge of the headwater basins in the mountains has increased significantly since the 1960s, the amount of water flowing into the main stem of the Tarim River still displays a downward trend primarily as a result of increasing irrigation water withdrawals associated with the recent rapid expansion of the cultivated land. Moreover, the Aksu River contributes more actively to the runoff (higher than 60%) of the Tarim River than the Yarkant River and Hotan River do. The Tarim River only receives water from its headwater streams; thus, these findings have important implications for water resource management, agricultural development, and environmental protection in this arid region, as well as for similar river basins in which irrigation and hydraulic projects dominate the surface water fluxes.
ACKNOWLEDGEMENTS
The work presented in this paper was financially supported by the National Basic Research Program of China (grant no. 2010CB951101), the National Natural Science Foundation of China (grant no. 41101015 and 41371047), the Special Fund of State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (1069-50985512) and the PhD Jointly Training Program from the China Scholarship Council (201206710030). The authors are very grateful to Professor Dennis Lettenmaier at the University of California, Los Angeles, and Dr Bart Nijssen at the University of Washington for their guidance and assistance.
