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Article

Water Budget, Biological Water Use, and the Soil Hydrological Cycle across Typical Ecosystems of the Heihe River Basin

State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
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Author to whom correspondence should be addressed.
Water 2022, 14(18), 2895; https://doi.org/10.3390/w14182895
Submission received: 26 July 2022 / Revised: 31 August 2022 / Accepted: 8 September 2022 / Published: 16 September 2022
(This article belongs to the Special Issue Plant-Water Interactions under a Changing Environment)

Abstract

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Quantification of the water budget of an arid inland river ecosystem is essential but still a challenge for the sustainable development of water resources. In situ observed data were used to analyze the monthly and annual water budgets and the soil hydrological cycle for six typical ecosystems in the Heihe River Basin (HRB). The two-source model was used to partition evapotranspiration (ET) into transpiration (T) and evaporation, after which the validated model was applied to quantitatively analyze the biological water use fraction [T/Ecosystem Water Supply (WS)] for different ecosystems. There were differences in the water budgets of the different ecosystems due to differences in climate, vegetation, soil, and external inputs. Precipitation in the HRB decreased from upstream to downstream, whereas there was a gradual increase in ET. External sources of water (e.g., natural runoff from upstream, irrigation in the middle reaches, and groundwater recharge in the lower reaches) to soil layers played an important role in regulating the water budgets of HRB ecosystems. Cropland obtained the maximum biological water use fraction (0.50), followed by Populus euphratica (0.49), alpine meadow (0.49), alpine swamp meadow (0.44), Tamarix ramosissima (0.42), and Kalidium foliatum (0.4). The soil water residence time (at a depth of 40 cm) varied from 14 d to 97 d (average of 60 d). The order of plant species in terms of soil water residence time was: K. foliatum (88 d) > T. ramosissima (72 d) > alpine meadow (68 d) > alpine swamp meadow (63 d) > cropland (53 d) > P. euphratica forest (20 d). Differences in the biological water use fraction and soil water residence time could be attributed to the characteristics of the water budget for each ecosystem. This study quantified the water budget, biological water use, and soil hydrological cycle across typical ecosystems in HRB, and can act as a reference for ecosystem management of the arid inland river basin.

1. Introduction

Inland river basins in arid and semi-arid regions account for 13.3% of the Earth’s land area [1]. Terrestrial ecosystems comprising inland rivers and terminal lakes are considered to be highly sensitive to climate change and anthropogenic activities [2]. The water budget is a quantitative description of elements of the water cycle, such as precipitation, evapotranspiration (ET), runoff, and soil water storage. Therefore, characterization of the water budget is essential for water resources planning and management, particularly in arid inland river basins. There has been increasing attention focused on many watersheds in recent decades within the context of global climate change, population growth, water shortages, and ecosystem degradation [3,4]. Estimating the water budgets of arid inland river ecosystems is essential for the sustainable development of water resources.
The Heihe River Basin (HRB) is in the inland arid region of northwestern China and is characterized by a complex natural and geographical environment with a unique water system from upper to downstream [5]. Although there have been many studies on hydrological processes in the HRB, most simulated the water balance at a watershed scale or focused on individual elements of the water balance [6,7,8,9]. The Variable Infiltration Capacity (VIC) model was used to estimate the water balance of different landscape types in the upper reaches of the HRB [10]. The water budget of the HRB was studied by using the modified Budyko hypothesis and a validated monthly water balance model to divide the HRB into six different regions according to differences in hydrothermal conditions [11]. Gao et al. [3] used the distributed eco-hydrological model to estimate hydrological processes in the upper reaches of the Heihe River and analyzed the relationships between the water balance characteristics of the river basin and vegetation patterns. Li et al. [12] combined an integrated ecohydrology model with systematic observations to analyze the hydrology cycle in the HRB. Although the above studies allowed a rigorous analysis of the water balance at the basin scale, there remains a lack of verification of water budgets at the ecosystem scale. In addition, vegetation transpiration is an important component of the water budget of terrestrial ecosystems and is directly related to ecosystem water use efficiency. The ratio of direct biological water use (T) to total water supply (including precipitation, irrigation, and groundwater supply; WS) at the ecosystem scale remains poorly characterized [13]. A study by Good et al. [13] applying field and remote sensing data to an ecohydrological model showed that the maximum biological water use fraction (T/P) (P was the only supply of water) occurred under a mesic condition. T/WS is an important indicator for understanding the mechanism and efficiency of water use. There has been increasing attention on partitioning ET in recent years, with most related studies in the HRB focusing on individual ecosystems [7,14]. However, there remains a lack of understanding of the role of vegetation transpiration in the water budget and how biological water use varies under differences in the hydroclimate. The water budget describes various components of the hydrological cycle. While the residence time of soil water reflects the hydrological cycle when precipitation is the main water source [15], ecosystem hydrological processes are complex, and soil responses to precipitation events are also affected by irrigation, groundwater, lateral runoff, and other sources of water. Quantifying the residence time of soil water can reflect the water-flow path, water storage, and water sources, and is also a comprehensive indicator describing the hydrological function of an ecosystem [5]. Many studies have attempted to characterize catchments based on soil water retention time. Since most studies have also focused on small watersheds due to the difficulties in field sampling and observations, there have been few studies on the HRB.
Linking vegetation water demands with ecosystem water supplies can be achieved though quantification of the fraction of total water supply used by vegetation. Transpiration is broadly understood to comprise most global surface-to-atmosphere vapor flux. However, the interactions between soil moisture availability, vegetation structure, and plant water use remain poorly understood across typical ecosystems in the HRB. The aim of this study was to: (1) quantify the characteristics of water budgets of typical ecosystems at monthly and annual scales based on first-hand observation; (2) explore characteristics of biological water use fraction (BWF) and soil residence time among ecosystems of the HRB; (3) analyse the main factors and process on the water budgets of each ecosystem and possible impacts under future climate change.

2. Materials and Methods

2.1. Study Location

The Heihe River is the second largest inland river in China, and originates from the glacial snow belt in the Qilian Mountains in Qinghai Province. The HRB lies between 97.1° E–102° E and 37.7° N–42.7° N, and has an area of 143,000 km2 (Figure 1). The ecosystems of the HRB are complex and comprise high mountain glaciers, forest grasslands, plain oasis, and Gobi Desert [16,17]. The upper reaches of Heihe River are located at 1674–5564 m above sea level in the Qilian Mountains, with a cold and dry climate and mean annual precipitation of 300–600 mm. Vegetation in this area has an obvious vertical distribution due to the numerous mountains and significant elevation differences. The vegetation types mainly include frozen desert zone, forest zone, alpine meadow zone, mountain steppe zone, steppe desert zone, glacier zone, etc. The middle reaches are 1352 to 1700 m above sea level, and there is a temperate continental arid climate, an average annual precipitation of 90–160 mm, and annual evaporation of 2000–2500 mm. Vegetation in this middle area were natural desert vegetation (e.g., Reaumurica soongorica, Kalidium foliatum and Nitria sphaerocarpa) and artificial oases vegetation types (e.g., Maize, wheat, rape, potato). The middle reaches experienced the most intense anthropogenic activities, leading to centralized agricultural consumption of water resources in the basin. The lower reaches of the HRB with elevation are less than 1352 m, which have relatively lower precipitation with average annual values of 36.6 mm and higher ET. The vegetation in this region is mainly desert and riparian forest. The depth of groundwater is heterogeneous. In the upper mountainous area and middle natural desert vegetation area, previous studies have shown that the depth was more than 100 m, and were much decreased due to flood irrigation recharged in the farmland area [18]. The groundwater depth of lower reaches was about 2~5 m [18].
The present study selected six typical ecosystems of the HRB separated by environment gradients (altitude, precipitation, and temperature). The land surface of those selected six sites was wide and flat with enough footprints. The characteristics of the selected ecosystems, including soil, vegetation type, and climate, were summarized in Table 1. Briefly, two upstream alpine meadows at different elevations, named the A’rou (ARC) and Dashalong (DSL) sites, were selected. The Apline meadow is the main vegetation type in the ARC site, which has a rich variety of plants including Kobresia humilis, Stellera chamaejasme L., Poa calliopsis, Potentilla multifida, Medicago Sativa L., Elymus nutans et al. Different to the ARC, the DSL site belongs to the Apline swamp meadow; Kobresia pygmaea was the dominant species at the DSL site, and other herbs include Carex atrofusca, Schkuhr subsp, Kobresia tibetica Maxim, et al. The cropland at the Daman site (DMC) and nature desert at the Huazhaizi site (HZZ) in the middle reaches were chosen. Maize was planted at DMC in late April and harvested during mid-to-late September. During the growing season, flood irrigation was generally performed four times (e.g., May, June, July, and August). Kalidium foliatum is the dominate and key plant species in the HZZ site, whereas the other plants were fewer. In the lower reaches, two different types of desert riparian forest ecosystems which serve as important wildlife habitat were selected. At the sidaoqiao (SDQ) site, T. ramosissima were dominated in this site by a few herbs such as Phragmites communis, Sophora alopecuroides L., Achnatherum splendens, and Agropyron cristatum. For the Hunhelin (HHL) site, P. euphratica and T. ramosissima are key species, accompanied by herbs similar to those in the SDQ.

2.2. Description of Datasets

The present study used a hydrometeorological observation network dataset provided by the National Tibetan Plateau Data Center (TPDC) (https://data.tpdc.ac.cn/, accessed on 27 December 2019), which includes eddy covariance (EC) and automatic weather station (AWS) data. The data collection process by the TPDC is described in detail in previous studies [20,21]. EC post-processing calculations were performed using EdiRe, as described by Xu et al. [21]. In-filling of missing data was performed using the “REddyProc” R-package [22]. Module-simulated data were used to fill data gaps extending beyond a month. After data gap filling, energy budget closure was used to elevate the EC data using linear regression statistics between available energy flux (Rn-G) and the sum (LE + H) of latent heat (LE) and sensible heat flux (H) [23]. The annual mean energy budget closure values during the study period were 0.95 for the alpine meadow (ARC site), 0.83 for the alpine swamp meadow (DSL site), 0.84 for cropland (DMC site), 0.81 for Kalidium foliatum (HZZ site), 0.78 for Populus euphratica (HHL site), and 0.89 for Tamarix ramosissima (SDQ site). The closures of the energy budgets indicated that EC data for the HRB were seasonably accurate. Precipitation data measured by a tipping bucket rain gauge and other meteorology data were used for input into the model. Transpiration data for P. euphratica measured using a Thermal Dissipation Probe (TDP) [24,25] at the Hunhelin site were obtained from TPDC (https://data.tpdc.ac.cn/, accessed on 6 May 2019) and were used to test model performance. The GLASS leaf area index (LAI) products (http://glassproduct.bnu.edu.cn/,accessed on 1 January 2020) were obtained from Beijing Normal University [26]. These data had an 8-day interval and were interpolated into a daily interval, following which they were used to represent vegetation dynamics in the present study.

2.3. Water Balance of the Terrestrial Ecosystem

The basic equation of the terrestrial ecosystem water budget [27,28,29] can be defined as:
P + G W + I + Δ R = E T + D + Δ Q ,
where P is precipitation, GW is groundwater recharge, I is irrigation, ΔR is lateral inflow/outflow of the ecosystem, ET is evapotranspiration, which can be partitioned into evaporation and transpiration, D is soil drainage (since the depths of observations of soil moisture ranged from 0 cm to 160 and 320 cm, D was ignored in calculation), and ΔQ is the change in soil water storage.
The present study simplified the water budget equation for different ecosystems according to their characteristics. The formula for the water budget can be simplified for the alpine meadow area of the upper HRB as P + ΔR = ET + ΔQ. The water supply (WS) is equal to P + ΔR. The formula for the ecosystem water balance can be simplified to P + I = ET + ΔQ in cropland ecosystems due to the influence of anthropogenic activities, thereby stressing the importance of irrigation as a water source. WS is equal to P + I. The water budget formula can be simplified to P + GW = ET + ΔQ in desert scrub and riparian forest ecosystem. WS is equal to P + GW.

2.4. Estimation of Potential ET

The Food and Health Organization (FAO) Penman-Monteith method is recommended to estimate potential ET which can be derived by:
ET o = 0.048 Δ ( R n G ) + γ 900 T + 273 u 2 ( e s e a ) Δ + γ ( 1 + 0.34 u 2 ) ,
where ETo is potential ET (mm/day), Rn is net radiation at the crop surface [MJ m−2 day−1], G represents soil heat flux density [MJ m−2 day−1], u2 is wind speed at a height of 2 m [m s−1], es and ea represent saturation and actual vapor pressure [kPa], respectively, ∆ is the slope of the vapor pressure curve [kPa °C−1], and γ is the psychrometric constant [kPa °C−1].

2.5. Estimation of Biological Water Use Fraction

Biological water use fraction (BWF) in a terrestrial ecosystem can be defined as:
BWF = T WS ,  
where T is transpiration, WS is the water supply (WS varied across the different ecosystems), and the main source of WS was precipitation (P), irrigation (I) in agricultural ecosystems, groundwater recharge (GW) for desert ecosystems, or lateral runoff supply (ΔR) in alpine ecosystems. T was estimated by the two-source model by partitioning the ET flux in the ecosystems. The Appendix A describes the partitioning of ET of typical ecosystems in the HRB, whereas more details are available in the literature (Tong et al., 2019). The Appendix A provides more information on the two-source model.

2.6. Estimation of Soil Water Residence Time

Water storage in a soil column with a unit ground-surface area Q (mm) can be evaluated by summing the water storage in each sub-layer. If all water from precipitation returns to the atmosphere by ET, the mean residence (or turnover) time of water in the column, τ (day), can be evaluated as:
τ = Q P + I / GW / Δ R ,
where Q (mm) is the mean water storage in the soil column for a given period, P (mm/day) is the mean precipitation for the same period, and I / GW / Δ R   ( mm ) represents other sources of water (e.g., irrigation, groundwater recharge, lateral runoff) within the ecosystem. The parameter τ provides a measure of the timescale of the soil–hydrological cycle within the study region [27].

3. Results

3.1. Seasonal Variations in the Observed Soil Water Profile and Water Budget Components in the HRB

Figure 2 shows the observed spatial distribution of daily average soil moisture of different ecosystems in the HRB at different soil depths. In the alpine meadow ecosystem, soil moisture was mainly distributed in the surface soil layer (0–40 cm), whereas soil moisture showed an inverse relationship with soil depth in the 40–320 cm soil layer and soil water was stable throughout growing season. In the alpine swamp meadow ecosystem, soil moisture first decreased with increasing depth in the 0–80 cm soil layer, following which it gradually increased with increasing depth in the 80–160 cm soil layer. The change in water storage of deep soil showed a time lag compared with that in the surface soil. The soil moisture content of the cropland ecosystem was affected by irrigation, characterized by four peaks a year from 2014 to 2016. Soil moisture content increased sharply at the beginning of the growing season, but then decreased sharply at the end of the growing season with a relatively uniform vertical distribution. There was relatively high evaporation of surface soil water in the K. foliatum ecosystem. In addition, the water content of surface soil was less than that of the deep soil. Soil moisture increased with increasing depth, and there were no obvious inter-annual changes. In the P. euphratica ecosystem, the 0–100 cm soil layer showed a low soil moisture, with a minimum occurring in the 20–40 cm soil layer from December to February of the next year. This could be attributed to P. euphratica mainly using deeper soil water. The distribution of soil water in the T. ramosissima ecosystem varied sharply among different layers, and soil moisture in the 20–160 cm soil layer from March to July exceeded that in other soil layers and was relatively stable. The soil water regime in the HRB showed both consistencies and variations among the different ecosystems, and was mainly regulated by the water budget and the trade-off between water loss (transpiration and soil evaporation) and supply (e.g., P, I, GW, or ΔR).
As shown in Figure 3, the present study analyzed the monthly variation ΔR in the water budget of each ecosystem over two typical years. Precipitation decreased from upstream to downstream, whereas there was a gradual increase in ET, mainly between April–September. The variations in soil water storage could be attributed to differences growing between the ecosystems. The maximum upstream ΔQ occurred in April in the alpine meadow ecosystem, whereas that of the high-altitude alpine swamp meadow occurred in May and June. These results could be attributed to soil layers experiencing a maximum water content after recharge by the thawing of snow and frozen soil during the rising temperature. P exceeded the sum of ET and ΔQ in the alpine meadow at the end of the season during rainy years when the ecosystem showed a water surplus, whereas the alpine swamp meadow ecosystem showed a water deficit during other periods. ET was sustained by water storage to a greater degree than by direct precipitation. This result indicated that an absence of snow melt water and stream inflow would lead to considerable stress amongst the upstream vegetation in the growing season. In the cropland ecosystem, the maximum ΔQ occurred in March, whereas ΔQ remained constant and positive in May–August and was hardly affected by irrigation. The decrease in ΔQ between September and October was due to the halting of irrigation after crop maturity. There was small monthly variation in the ΔQ of the natural desert K. foliatum ecosystem, with changes mainly occurring between June and August, which indicated that soil storage water in K. foliatum was related to precipitation in the growing season. In the riparian forest ecosystem, ΔQ was positive in February–April, with the maximum reached in March due to diversion of ecological water. Water originating from the upper and middle reaches of the river replenished soil moisture, following which soil water storage gradually decreased under high ET.

3.2. Statistics of the Water Budget and Its Components across Ecosystems in the HRB

The upstream alpine and swamp meadow ecosystems constituted the main areas for water conservation and ecosystem water yield. As summarized in Table 2, the mean precipitation of the alpine meadow ecosystem varied from 392 to 520 mm during 2013 to 2016, with a mean annual precipitation of 444 mm, and mean ET varied from 471 to 551 mm, with a mean of 508 mm. Water storage in the soil layer showed a small interannual change. ET was the main form of water loss from the ecosystem, and mainly comprised vegetation transpiration, particularly during the growing season in the alpine meadow ecosystem. The mean precipitation of the alpine swamp meadow ecosystem at high altitude varied from 310 to 388 mm during 2014 to 2016, with a mean annual precipitation of 341 mm, whereas ET varied from 202 to 232 mm, with a mean of 215 mm. Water storage in the soil layer remained constant. Annual ET was largely attributed to soil evaporation (66%), even though its contribution was less than that of T during the growing season. The calculation of the water budget showed deficits in both the alpine and swamp meadow ecosystems (water loss > water supply), and no surface runoff occurred for most of the time, besides for the peak rainfall season between June and September.
Table 3 summarizes the annual water budget and its components in the middle and downstream ecosystems. Precipitation of the cropland ecosystem varied from 101 to 161 mm during 2013–2016, with a mean annual precipitation of 133 mm. ET varied from 638 to 703 mm during 2013–2016, with a mean of 662 mm, of which T accounted for ~50% and ~82% of the average annual and growing season ET, respectively. The calculation of the water balance indicated an average irrigation of 556 mm, close to that calculated by Yang et al. [30] of the irrigation depth of the DMC in 2012 of 540 mm. The average annual precipitation of the desert K. foliatum ecosystem was 149 mm for three years, accounting for 59.4% of water consumed by the ecosystem. The average annual ET was 251 mm, with T for K. foliatum of ~111 mm accounting for 56% of the total water supply for the three years. The ecosystem showed a water deficit, and the average local irrigation recharge of 129 mm was a possible water source, similar to the conclusion of the study by Zhao et al. [31] of the desert shrub Calligonum mongolicum in the HRB.
ET far exceeded precipitation in the downstream region. Mean P was ~33 mm at two riparian forest ecosystems, whereas there were significant differences in mean ET and BWF (Table 3). The ET of P. euphratica of 694 mm exceeded that of T. ramosissima of 604 mm. The deep root system of P. euphratica allows it to use deeper groundwater, resulting in a higher transpiration flux compared with T. ramosissima. The calculated changes in soil water storage and water budget indicated that groundwater supplies most water in desert riparian ecosystems in an extremely dry climate (P < 50 mm).

3.3. Biological Water Uses and Soil Water Residence among Ecosystems

Most water assimilated by plants is lost through transpiration. Therefore, BWF is an index reflecting the real water demand of plants during photosynthesis [13]. BWF can be quantified by dividing plant transpiration by WS for each ecosystem. The BWF values of the ecosystems from upstream and downstream in the HRB ranged from 0.3 to 0.52, with an average of 0.47. As shown in Table 4, among the six ecosystems, the rank of the different ecosystems in terms of mean annual BWF was cropland (0.50) > P. euphratica (0.49) > alpine meadow (0.49) > alpine swamp meadow (0.44) > T. ramosissima (0.42) > K. foliatum (0.4). The BWF of the cropland ecosystem exceeded that of other ecosystems in the middle and lower reaches of the HRB when evaporative demand (potential ET) slightly exceeded supplied precipitation.
The differences in the water budget and BWF among different ecosystems can be associated with soil water residence time (τ, day). The observations of soil water storage (Q) in each sub-layer were used to calculate the total soil water residence time of typical ecosystems in the HRB. The present study compared the soil water residence times (days) of different ecosystems at shallow layers (0–40 cm) to allow a comparison at different soil depths in the HRB (Table 3). The τ values of shallow soil layers (0–40 cm) ranged from 14 to 97 days from upstream and downstream in the HRB, with an average of 60.3 days. The rank of the six ecosystems in terms of τ was: K. foliatum (88) > T. ramosissima (72) > alpine meadow (68) > alpine swamp meadow (63) > cropland (53) > P. euphratica (20).

4. Discussion

4.1. Water Budget across Different Ecosystems and Uncertainties

While accurate estimation of the water budget of ecosystems is vital for promoting sustainable development, this remains a challenge amongst diverse ecosystems. The present study used the observed dataset and water balance equation to identify clear differences in the water budget amongst the six typical ecosystems in the HRB. Previous studies have indicated the clear water conservation and water yield functions of grassland meadow ecosystems [32]. Soil evaporation and transpiration of the alpine and swamp meadow ecosystems investigated in the present study not only consumed all precipitation, but also soil water. Therefore, these two ecosystems play an important role in regulating the water budget. Although ET was similar to precipitation in the alpine swamp meadow ecosystem, soil water storage increased greatly, indicating that changes in soil water storage were due to external supplies, such as glacier and snow melt water or subsurface flow. There was no net runoff at an annual scale in the alpine and swamp meadow ecosystems, except for during the growing season of wet years during which there were higher levels of precipitation. The relatively small runoff of the alpine and swamp meadow ecosystems with runoff coefficients of ~0.08 and ~0.13, respectively, could be attributed to their relatively lower elevation. A study by Yang et al. [29] which used a distributed hydrological model to study the water balance in the upper reaches of the HRB found that groundwater runoff and subsurface flow accounted for ~80% and 20% of total runoff, respectively. In addition, the small contribution of surface runoff in the study by Yang et al. of 2–3% was consistent with the results of the present study. Chen et al. [33] showed that the main runoff areas of alpine cold desert occur in the mountain basin, whereas the alpine meadow/grassland areas showed little contribution to runoff, with their ecological function exceeding their hydrological function. Alpine grass/meadow with a flat terrain typically showed greater water conservation functions and less contribution to runoff. Stream inflow in swamp meadow ecosystems exceed that of alpine meadows ecosystem, which can be attributed to higher altitudes and greater external sources of water (e.g., snowmelt). Lateral inflow, which may originate from snow melt and subsurface flow, is an important source of water in upstream ecosystems in the HRB. Precipitation decreased from upstream to downstream in the cropland ecosystems of the middle reaches, whereas ET increased gradually, mainly from April to September. Annual ET was significantly greater than precipitation, resulting in ecosystem water stress, consistent with the results of the study by Chen [34] on the effects of irrigation on ET of the cropland ecosystem. ET due to crop growth is mainly supplied by irrigation. The calculation of the water budget showed that irrigation varied from 512 to 597 mm, with a mean of 529 mm [35], consistent to the recorded irrigation depth of 540 mm in 2012 [36]. The estimates of irrigation depth in the present study are reasonable considering the uncertainties in both actual measurements of water components (e.g., P, ET, and soil water) and statistics. Smart irrigation techniques are currently applied within agricultural irrigation management in arid and semi-arid areas to improve water use efficiency [37,38]. Accurate management of irrigation is needed to avoid waste of valuable water resources in deserted oases regions. Rainfall was insufficient to meet vegetation water demand in the dry downstream ecosystems, such as the HHL and SDQ sites. Hence, natural vegetation at these sites is mainly dependent on groundwater [39]. Groundwater is the main source of recharge to shallow soil water and sustains the ET process driven by strong vapor pressure deficiencies (VPDs). Although P. euphratica and T. ramosissima showed similar ET characteristics, the water consumption capacity of P. euphratica exceeded that of T. ramosissima. Riparian forest can regulate stream-flow dynamics and water budgets by assimilating large volumes of water from groundwater compartments [40]. Yuan et al. [41] also found that water consumption of P. euphratica exceeds that of T. ramosissima in the lower reaches of the Tarim River. A study by Yu et al. [42] that analyzed the variation in daily ET of P. euphratica and T. ramosissima in the lower reaches of the HRB found that the daily average ET of P. euphratica exceeded that of T. ramosissima. The studies mentioned above obtained results that were consistent with those of the present study for the downstream reaches of the HRB.
Climate change will directly affect water budget by changes in water input such as glacier melting and precipitation. The ecosystem at the upper stream was easily influenced because of the higher elevation and more glaciers, which possibly increases melting runoff under future warming and a wet climate. At the same time, the permafrost will depredate, which may impair the ecosystem structure and function such as water conservation and yield in this area. For the middle stream, irrigated water sustains the oasis transpiration, which relies heavily on the incoming water resource from the upper stream. For the lower stream, priority should be given to ecological water requirements to sustain the ecosystem function which needs to control the level of agricultural water use in the middle stream to solve the water use conflicts. Overall, it is necessary to carry out climate impact assessment and comprehensive water resource management in the whole river basin.
However, many uncertainties in the estimations of the current study exist. While precipitation is a critical input variable for calculation of the water balance, it is difficult to measure accurately [43], especially in alpine areas. Additionally, winter snowfall in the alpine region may be underestimated, and it is difficult to discriminate between snow melt water and subsurface flow within the water consumption of the ecosystems. Therefore, the present study collectively referred to these elements as external stream inflow recharge. The present study assumed simple water budgets for the upper, middle, and lower reaches based on observational data. However, it is recognized that although this assumption is reasonable, it may lead to uncertainties in the calculation of the water budget components.

4.2. Differences in the Biological Water Use Fraction among Different Ecosystems

A study by Good et al. [13] on the global BWF (T/P, in which P was the only water source) identified a maximum aridity index (ET0/P) between 1.3 and 1.9, following which BWF decreased with increases or decreases in aridity. The results of the present study indicated similar changes in BWF in the six ecosystems, although they exhibited divergences in the water environment (e.g., irrigation, widely developed runoff, and groundwater). The rank of the six typical ecosystems of the HRB in the growing season according to mean annual water supply (WS) was: alpine swamp meadow (664 mm) > P. euphratica (557 mm) > cropland (500 mm) > T. ramosissima (480 mm) > alpine meadow (425 mm) > K. foliatum (258 mm) (Figure 4 and Table 4). There were significant differences in aridity (ET0/WS) between the different ecosystems in the HRB. Figure 5 shows the relationship between BWF and ecosystem aridity. The rank of the six ecosystems in terms of mean annual BWF was: cropland (0.50) > P. euphratica (0.49) > alpine meadow (0.49) > alpine swamp meadow (0.44) > T. ramosissima (0.42) > K. foliatum (0.4). The cropland ecosystem achieved the maximum BWF due to moderate dryness and irrigation. However, the lowest BWF of the K. foliatum ecosystem could be attributed to its high aridity (2.31) and water limitation. The BWF of the alpine swamp meadow ecosystem was lower than that of the alpine meadow due to the former being limited by available sunlight energy at higher elevation. The higher BWF of P. euphratica compared to T. ramosissima could be attributed to its larger leaf area index and deeper roots, consistent with the results of a study by Zhang et al. [44] which determined that the root system of P. euphratica has a large extension range (reaching 7.0 m).

4.3. The Soil Water Residence Time and Soil Hydrological Cycle

Soil water residence time varies with ecosystem topography, land cover type, climate, and soil texture, thereby reflecting the characteristics of the soil hydrological cycle for each unique ecosystem. The present study selected three soil depths (10 cm, 20 cm, and 40 cm) of measured soil water to calculate the total soil water storage (Q) for easy comparison among the six ecosystems. The soil water residence time of ecosystems in the HRB varied from 14 d to 97 d, with an average of 60 d. The rank of the six ecosystems in terms of soil water residence time was: K. foliatum (88 d) > T. ramosissima (72 d) > alpine meadow (68 d) > alpine swamp meadow (63 d) > cropland (53 d) > P. euphratica forest (20 d). The soil water residence time of P. euphratica was the lowest among all the ecosystems. The soil water storage (Q) of P. euphratica was lower than that of T. ramosissima and the other ecosystems in the HRB, which may be related to its higher BWF and more rapid soil hydrological cycle compared with those of the other ecosystems. For cropland, τ varied from 48 d to 59 d, with an average of 53 d, which may be related to its high evapotranspiration and highest BWF. The higher Q and lower ET of the alpine meadow resulted in a τ ranging from 63 d to 75 d, with an average of 68 d, exceeding that of the alpine swamp meadow of 59 d to 67 d, with an average of 63 d. K. foliatum achieved the highest τ among the ecosystems, varying from 72 d to 97 d, with an average of 88 d, which may be related to its smaller evapotranspiration characterized by the lowest BWF among the ecosystems.

5. Conclusions

The present study analyzed and modelled the monthly and annual water budget of the ecosystems of the HRB using meteorological data. The ecosystems showed increasing water deficit from upstream to downstream, i.e., precipitation entering the ecosystems was insufficient to meet ET demand. Snowmelt water and stream inflow were important sources of ecosystem water in the upper reaches, and the volume of replenishment increased with increasing altitude. Irrigation accounted for ~80% of ecosystem water supply in the cropland ecosystem in the middle reaches, thereby maintaining a high ET. Groundwater is a key factor maintaining vegetation growth in desert and desert riparian forest ecosystems, and groundwater consumed by desert riparian forests exceeded that of natural desert ecosystems by a factor of ~5. Water consumption of ecosystems in the riparian zone was related to the vegetation type. Water consumption of P. euphratica exceeded that of T. ramosissima due to its larger LAI and deeper roots. The maximum BWF of the cropland ecosystem could be attributed to its moderate dryness and irrigation, whereas the small BWF of the alpine swamp meadow ecosystem and K. foliatum ecosystem could be attributed to limited available energy and limited water, respectively. The soil water residence time of ecosystems in the HRB varied from 14 d to 97 d, with an average of 60 d. The rank of the six ecosystems in terms of soil water residence time was: K. foliatum (88 d) > T. ramosissima (72 d) > alpine meadow (68 d) > alpine swamp meadow (63 d) > cropland (53 d) > P. euphratica forest (20 d). The characteristics of the water budget for each ecosystem could be explained by their differentiates in biological water use fraction and soil water residence time.

Author Contributions

Conceptualization, P.W.; methodology, Q.C.; software, Q.C.; validation, Y.X. and D.Z.; formal analysis, H.S.; investigation, P.W.; resources, P.W.; data curation, Q.C.; writing—original draft preparation, Q.C.; writing—review and editing, P.W.; visualization, Q.C.; supervision, P.W.; project administration, P.W.; funding acquisition, P.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number U21A2001 and 42071034.

Data Availability Statement

All hydrometeorological datasets used in this paper are available on request from the National Qinghai-Tibet Plateau Data Center (TPDC) (https://data.tpdc.ac.cn, accessed on 6 May 2019).

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Appendix A.1. Model ET Partitioning and Estimation of Transpiration

The two-source model is based on the energy balance between the soil surface and the vegetation canopy and considers the energy interaction between the canopy and the soil. The model was used to divide ET into soil evaporation and plant transpiration for the selected ecosystems and among different spatial and temporal scales. The results were used to assess vegetation productivity and the water budget in the context of climate change.
The formula of the model is as follows:
RnV = (1 − fV) [(1 − αV)Sd + Ld + σTG4 − 2σTL4]
= HV + lT,            
RnG = fV [(1 − αG)Sd + Ld] + (1 − fV)σTL4 − σTG4
= G + HG + lE,              
where RnV is the net radiation of the vegetation canopy(W m−2), HV is the sensible heat flux from the vegetation canopy (Wm−2), T is the transpiration flux (kg m−2 s−1), fv is the permittivity of the vegetation canopy, αV is the albedo of the vegetation canopy, Sd is the downward shortwave radiation (W m−2), Ld is the downward longwave radiation (W m−2), σ is the Stefan–Boltzmann constant (=5.67 × 10−8 W m−2 K−4), TG is the ground surface temperature (°C), TL is the leaf temperature (°C), RnG is the net radiation at the ground surface (W m−2), G is the ground heat flux (W m−2), HG is the sensible heat flux from the ground surface (W m−2), E is the evaporation flux (kg m−2 s−1), and αG is the albedo of the ground surface. αG and αV can be approximated to constants 0.1 and 0.2.
The HV and HG can be given by the following bulk equations:
HV = cpρa (TL − Ta)/raV,
HG = cpρa (TG − Ta)/raG,
where cp is the heat capacity of dry air, raV is the aerodynamic impedance of the vegetation canopy, and raG is the aerodynamic impedance of the surface (s/m). These parameters are mainly related to wind speed, vegetation height, and the height of anemometer. A more detailed description is provided by Wang and Yamanaka [45].

Appendix A.2. Assessment of the Model Performance

Previous studies have assessed the model performance for estimation of transpiration [46]. Specifically, the root-mean-square error (RMSE), correlation coefficient (R2), and agreement index were used to validate and test the model performance. Agreement between observations and model simulations of total energy flux demonstrated good performance of the model in capturing the radiation transfer and energy partition of ecosystems in the HRB (Table A1). In addition, the two-source model partitioned ET into evaporation and transpiration. The agreement between the results of the present study and those of related previous studies demonstrated the good performance of the model in capturing the transpiration of each ecosystem (Table A2). In addition, a model sensitivity analysis indicated that the model is insensitive to errors in measured input variables and parameters [46]. Therefore, despite some uncertainty in partitioning ET, there was good consistency between simulated and measured T.
Table A1. Model performance statistics with daily mean data in the five typical ecosystems.
Table A1. Model performance statistics with daily mean data in the five typical ecosystems.
SiteIndexIE
ARCR20.91
I0.96
RMSE (W/m2)16.02
n711
DSLR20.8
I0.95
RMSE (W/m2)20.18
n724
DMCR20.84
I0.94
RMSE (W/m2)23.67
n669
HHLR20.84
I0.95
RMSE (W/m2)27.4
n673
SDQR20.78
I0.89
RMSE (W/m2)38.61
n716
Note: LE, latent heat flux; RMSE (W/m2), root-mean-square error; R2, coefficient of determination; n, number of data points.
Table A2. Summary of transpiration fraction (T/ET) of typical ecosystems estimated by different method at daily time interval in the HRB.
Table A2. Summary of transpiration fraction (T/ET) of typical ecosystems estimated by different method at daily time interval in the HRB.
ReferenceApproachDominated VegetationT/ET
Growing SeasonWhole Year
Su et al. [47] LysimetersT. ramosissima0.37–0.50N/A
Zhao et al. [31]Sap flow measurementCalligonum L.0.64N/A
Zhou et al. [48]uWUEAlpine meadow0.550.51
Cropland0.630.52
P. euphratica0.550.53
Tong et al. [46]Two-source modelAlpine meadow0.79 ± 0.120.51 ± 0.26
Alpine swamp meadow0.55 ± 0.230.31 ± 0.28
Cropland0.80 ± 0.130.53 ± 0.26
P. euphratica0.67 ± 0.070.52 ± 0.17
T. ramosissima0.67 ± 0.060.50 ± 0.20
Note: N/A, not available; uWUE, underlying water use efficiency.

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Figure 1. The location of observation sites and landscape of each site on the HRB. (a) The location of six observation sites; (b) P. euphratica at the HHL site; (c) T. ramosissima at the SDQ site; (d) Kalidium foliatum at the HZZ site; (e) Cropland at the DMC site; (f) Apline swamp meadow at the DSL site; (g) Apline meadow at the ARC site.
Figure 1. The location of observation sites and landscape of each site on the HRB. (a) The location of six observation sites; (b) P. euphratica at the HHL site; (c) T. ramosissima at the SDQ site; (d) Kalidium foliatum at the HZZ site; (e) Cropland at the DMC site; (f) Apline swamp meadow at the DSL site; (g) Apline meadow at the ARC site.
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Figure 2. Spatial distribution of average daily soil moisture during study period for (a) alpine meadow; (b) alpine swamp meadow in 2014–2016; (c) cropland in 2013–2014; (d) Kalidium foliatum in 2013–2014; (e) P. euphratica in 2014–2015; (f) T. ramosissima in 2014–2016.
Figure 2. Spatial distribution of average daily soil moisture during study period for (a) alpine meadow; (b) alpine swamp meadow in 2014–2016; (c) cropland in 2013–2014; (d) Kalidium foliatum in 2013–2014; (e) P. euphratica in 2014–2015; (f) T. ramosissima in 2014–2016.
Water 14 02895 g002
Figure 3. Monthly variation of observed water budget components (precipitation, ET and change of soil water storage) of two years in (a,b) alpine swamp meadow (DSL) in 2014–2015 year; (c,d) alpine meadow (ARC) in 2014–2015 year; (e,f) cropland (DMC) in 2014–2015 year; (g,h) Kalidium foliatum (HZZ) in 2013–2014 years, (i,j) T. ramosissima (SDQ) in 2014–2015 year; (k,l) P. euphratica in 2014–2015 year.
Figure 3. Monthly variation of observed water budget components (precipitation, ET and change of soil water storage) of two years in (a,b) alpine swamp meadow (DSL) in 2014–2015 year; (c,d) alpine meadow (ARC) in 2014–2015 year; (e,f) cropland (DMC) in 2014–2015 year; (g,h) Kalidium foliatum (HZZ) in 2013–2014 years, (i,j) T. ramosissima (SDQ) in 2014–2015 year; (k,l) P. euphratica in 2014–2015 year.
Water 14 02895 g003
Figure 4. Water supply and potential ET of growing season for alpine meadow (ARC), alpines swamp meadow (DSL), cropland (DMC), Kalidium foliatum (HZZ), P. euphratica (HHL), T. ramosissima (SDQ) ecosystems of HRB.
Figure 4. Water supply and potential ET of growing season for alpine meadow (ARC), alpines swamp meadow (DSL), cropland (DMC), Kalidium foliatum (HZZ), P. euphratica (HHL), T. ramosissima (SDQ) ecosystems of HRB.
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Figure 5. The biological water use fraction and soil water residence time (for 40 cm depth) plotted with respect aridity in growing season for alpine meadow (ARC), alpines swamp meadow (DSL), cropland (DM), Kalidium foliatum (HZZ), P. euphratica (HHL), T. ramosissima (SDQ). T means transpiration, WS means total water supply in ecosystems, ET0 calculated with FAO Penman Monteith equation.
Figure 5. The biological water use fraction and soil water residence time (for 40 cm depth) plotted with respect aridity in growing season for alpine meadow (ARC), alpines swamp meadow (DSL), cropland (DM), Kalidium foliatum (HZZ), P. euphratica (HHL), T. ramosissima (SDQ). T means transpiration, WS means total water supply in ecosystems, ET0 calculated with FAO Penman Monteith equation.
Water 14 02895 g005
Table 1. Main characteristics of the typical ecosystems in the study region.
Table 1. Main characteristics of the typical ecosystems in the study region.
SiteLonLatAltitude
(m)
Soil TypeClimate TypeVegetation Type/Dominate SpeciesAPP
(mm)
ATP
(°C)
Time Span
ARC100.46° E38.05° N3044ChernozemDwcApline meadow 322.3−0.412013–2016
DSL98.94° E38.84° N3739LeptosolETApline swamp meadow211.87−1.972014–2016
HZZ100.32° E38.77° N1731CalcisolBWkKalidium foliatum143.58.572013/2014–2016
DMC100.37° E38.86° N1556CambisolBWkMaize147.257.462013–2016
HHL101.13° E41.99° N874SolonchakBWkP. euphratica and T. ramosissima33.510.222014–2016
SDQ101.14° E42.00° N873SolonchakBWkT. ramosissima2610.462014–2016
Note: Lon means longitude, LAT means latitude, APP and ATP mean average precipitation and average temperature during observation time, respectively. According to the method of Köppen–Geiger climate classification, Dwc means sub-frigid monsoon climate with cold and dry winter and cold summer, ET means cold tundra climate, BWk means cold arid desert climate [19].
Table 2. Components of the water budget in the alpine meadow ecosystem of the upper stream.
Table 2. Components of the water budget in the alpine meadow ecosystem of the upper stream.
SiteTime ScaleYearPrecipitation
(mm)
ET
(mm)
Change of Soil Water Storage (mm)Runoff (mm)
ETTEΔQ (ΔR) mm
ARCYearly2013 392 492 241 251 −6 −94
2014 520 518 275 243 21 −18
2015 401 551 292 259 −16 −134
2016 464 471 226 245 10 −17
Mean444 508 259 250 2 −66
May–Sep2013 323 330 274 56 10 −17
2014 427 342 287 55 43 42
2015 314 349 293 60 −9 −26
2016 393 314 257 63 46 33
Mean364 334 278 59 23 8
DSLYearly2014 325 472 212 336 −5 −142
2015 310 505 232 340 9 −204
2016 388 470 202 268 1 −83
Mean341 482 215 315 2 −143
May–Sep2014 284 286 215 137 207 −209
2015 260 282 214 118 112 −134
2016 341 316 228 88 189 −164
Mean295 295 219 114 169 −169
Table 3. Components of water budget in ecosystems of midstream and downstream.
Table 3. Components of water budget in ecosystems of midstream and downstream.
SiteYearPrecipitation
(mm)
ET (mm)Change of Soil Water StorageIrrigation/Groundwater
ETTEΔQ (mm)I/GW (mm)
HZZ2013129 247 127 120 3 122
2014137 233 125 108 81 177
2016182 272 82 190 2 88
Mean149 251 111 139 29 129
DMC2013136 703 373 330 30 597
2014134 640 333 307 6 512
2015161 665 366 299 50 554
2016101 638 313 325 23 560
Mean133 662 346 315 27 556
SDQ201425 547 252 295 158 681
201542 650 312 338 98 741
201633 617 284 333 156 523
Mean33 605 283 322 137 648
HHL201417 671 322 349 66 720
201535 691 346 346 −17 633
201649 720 382 338 31 695
Mean34 694 350 344 27 683
Note: HZZ means Kalidium foliatum ecosystem, DMC means cropland ecosystem, SDQ means T. ramosissima, HHL means P. euphratica ecosystem.
Table 4. Fraction of biological water use and residence time of soil water in ecosystems in Heihe River Basin.
Table 4. Fraction of biological water use and residence time of soil water in ecosystems in Heihe River Basin.
SiteYearPrecipitation (mm)Transpiration
(mm)
Irrigation/
Groundwater/Lateral Runoff
Biological
Water Use Fraction (BWF)
Soil Water
Storage
Soil Water
Residence Time, τ
I/GW (mm)T/WS(mm Q)(Day)
Apline meadow2013392 241 940.50 83.24 63
2014520 275 180.51 99.36 67
2016464 226 170.47 98.94 75
mean459 247 430.49 93.85 68
Apline swamp meadow2014325 212 1420.45 80.30 63
2015310 232 2040.45 82.46 59
2016388 202 830.43 86.10 67
mean318 222 1730.44 82.95 63
Kalidium
Foliatum
2013129 127 122 0.51 64.41 94
2014137 125 177 0.40 62.07 72
2016182 82 88 0.30 71.72 97
mean149 111 129 0.40 66.07 88
Cropland2013136 373 597 0.51 95.47 48
2014134 333 512 0.52 94.91 54
2015161 366 554 0.51 103.58 53
2016101 313 560 0.47 106.62 59
mean133 346 556 0.50 100.14 53
T. rmosissima2014252526810.36121.1263
201542 312 741 0.40 133.63 62
201633 284 523 0.51 137.92 91
mean33 283 648 0.42 130.89 72
P. euphratica201417 322 720 0.44 44.10 22
201535 346 633 0.52 43.92 24
201649 382 695 0.51 29.22 14
mean34 350 683 0.49 39.08 20
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Chen, Q.; Xing, Y.; Zhang, D.; Sun, H.; Wang, P. Water Budget, Biological Water Use, and the Soil Hydrological Cycle across Typical Ecosystems of the Heihe River Basin. Water 2022, 14, 2895. https://doi.org/10.3390/w14182895

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Chen Q, Xing Y, Zhang D, Sun H, Wang P. Water Budget, Biological Water Use, and the Soil Hydrological Cycle across Typical Ecosystems of the Heihe River Basin. Water. 2022; 14(18):2895. https://doi.org/10.3390/w14182895

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Chen, Qi, Yuhua Xing, Dapeng Zhang, Haitao Sun, and Pei Wang. 2022. "Water Budget, Biological Water Use, and the Soil Hydrological Cycle across Typical Ecosystems of the Heihe River Basin" Water 14, no. 18: 2895. https://doi.org/10.3390/w14182895

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