Effects of changes in alpine grassland vegetation cover on hillslope hydrological processes in a permafrost watershed

Journal of Hydrology 444–445 (2012) 22–33

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Effects of changes in alpine grassland vegetation cover on hillslope hydrological processes in a permafrost watershed Wang Genxu ⇑, Liu Guangsheng, Li Chunjie Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, PR China

a r t i c l e

i n f o

Article history: Received 25 June 2010 Received in revised form 6 February 2012 Accepted 24 March 2012 Available online 7 April 2012 This manuscript was handled by Konstantine P. Georgakakos, Editor-in-Chief, with the assistance of Ana P. Barros, Associate Editor Keywords: Alpine grassland Vegetation degradation Hillslope water cycle Influence Permafrost watershed

s u m m a r y Two main types of grasslands on the Qinghai–Tibet Plateau, alpine swamp and alpine meadow, were selected for this study. Monitoring plots were constructed on each type of grassland with varying degrees of vegetation degradation. The impacts of alpine grassland cover changes on the hillslope water cycle were analyzed in terms of runoff generation, precipitation interception, dew water formation, and soil water dynamics of the active layer, etc. The results showed that different types of grasslands led to different runoff generation regimes; namely, runoff varied linearly with precipitation in alpine swamp, whereas in alpine meadow, runoff exhibited an exponential precipitation-dependence. The decrease in vegetation cover in alpine swamp leads to a decrease in soil moisture content in the top 20 cm of the soil, a delay in the thawing start time in the spring, and a decrease in both surface runoff and subsurface interflow. In alpine meadow, however, the decrease in vegetation cover led to a significant increase in the depth of topsoil moisture content during the thawing period, earlier occurrence of thawing, and an increase in the runoff generation ratio. The alpine meadow vegetation canopy had a higher maximum interception ratio and saturation precipitation than alpine swamp vegetation. With the decrease in vegetation cover, the rainfall interception ratios decreased by almost an identical range in both the alpine meadow and alpine swamp grasslands. Dew water commonly occurs on alpine grassland, accounting for about 12.5–16.5% of precipitation in the same period, and thus, is an important component of the water cycle. With the degradation of vegetation, surface dew water decreased; however, the ratio of dew water formed in the air to the total amount of dew water rose significantly. At the hillslope scale, the changes of alpine vegetation cover had a great influence on the water cycle, which were partly attributed to that the changes of alpine vegetation cover directly altered the surface energy balance, surface water cycle processes, and the thermal and hydraulic properties of active soil. Ó 2012 Elsevier B.V. All rights reserved.

1. Introduction On a catch scale, the impacts of land cover and land use on hydrological processes are reflected in fluctuations in the rainfall–runoff relationship and runoff dynamics, which in turn, will significantly affect the ecosystem, environment, and economy. Therefore, a better understanding of how land-cover and landuse changes affect watershed hydrological processes is a crucial issue for the planning, management, and sustainable development of water resources (DeFries and Eshleman, 2004; Potter, 1991; Vorosmarty et al., 2000). Although scientists have long recognized that changes in land use and land cover are important factors affecting water circulation and the spatial–temporal variations in the distribution of water resources, little is known about the quantitative relationship between land use/coverage characteristics and runoff generation or processes (Kokkonen and Jakeman, 2002; ⇑ Corresponding author. Tel.: +86 28 85233420. E-mail address: [email protected] (W. Genxu). 0022-1694/$ - see front matter Ó 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jhydrol.2012.03.033

Hundecha and Bardossy, 2004). Although some quantitative research methods and numerical models have been developed to address this dearth of data, no widely accepted theories or models have been established to elucidate the mechanism underlying the affect of land use/coverage changes on hydrological processes (Vorosmarty et al., 2000; DeFries and Eshleman, 2004). Therefore, there is a need to conduct case studies in representative regions to explore and establish a theoretical system elucidating the effects of land use and land coverage differences on the hydrological processes in a watershed (Legesse et al., 2003; DeFries and Eshleman, 2004; Bewket and Sterk, 2004). Permafrost regions are some of the most sensitive to global climatic change. Increasing air temperature has resulted in a rise in soil temperature, a deeper migration of the active layer, and the degradation of permafrost. These factors have led to significant alterations in regional ecosystem and water cycling (McGuire, 2002; Walker et al., 2003). The permafrost regions cover approximately one quarter of the land surface of the world, but the peculiarities of permafrost hydrology have seldom been

W. Genxu et al. / Journal of Hydrology 444–445 (2012) 22–33

investigated (Kuchment et al., 2000; Hayashi et al., 2003). Studies in the permafrost regions of Alaska and Siberia have shown that permafrost plays an important role in the coupling of regional heat-water processes (Ishikawa et al., 2006; Yamazaki et al., 2006). Changes in the depth of the active layer lead to alterations in water cycle components such as the soil water-retention capacity, groundwater dynamics, and surface infiltration. In turn, these alterations can lead to changes in vegetative cover and affect watershed hydrological processes (Jorgenson et al., 2001; Ishikawa et al., 2006; Yamazaki et al., 2006). An understanding of soil water and thermal coupling and water yield properties within the frost vegetation-active soil system is critical to accurately forecasting the influence of global climate change on the water cycle in the permafrost regions (McGuire, 2002; Mölders and Romanovsky, 2006; Dekker et al., 2005). Alpine meadows, which constitute the main land type in the permafrost regions on the Qinghai–Tibet Plateau, are located in the source areas of several large rivers, including the Yangtze, Yellow, and Lancang Rivers, and therefore, play an important role in regulating river flow and the productivity of local grazing grasslands (Zhou, 2001; Wang et al., 2001). Over the last 50 years, the permafrost has undergone degradation: the active layer has deepened and the ground temperature has increased (Wu and Liu, 2004). For the last 40 years, there has been a 24% and 32% decline in the high-cover (greater than 85% vegetative cover) alpine meadows and alpine swamp, respectively (Wang et al., 2001; Yang et al., 2007). Although the air temperature has risen by 0.34 °C per decade on average, no notable changes have been observed in regional precipitation. Although there has been enhancement of glacier melting, the stream flow in the headwaters of these rivers has decreased by 15–23.7% (Lan et al., 2003; Shen et al., 2009; Wang et al., 2007). Thus, there are some critical issues required an urgent resolution, such as to identifying the driving factors behind the decreases in surface stream flow in the headwater regions on the Qinghai–Tibet Plateau, to determining how the degradation of terrestrial alpine ecosystems has affected the water cycle. In fact, these have become general problems of concern in Arctic research, where recently, researchers found that changes in vegetation cover

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resulted in subtler, persistent changes in total runoff by 10% (Vano et al., 2008). However, few field observations have been reported on the vegetation cover–water cycle relationship at the hillslope or watershed scale before this study. Also, little has been published on the mechanism of the impacts of the frost soil thawing–freezing cycle and vegetation cover coupling on the hydrological processes in the permafrost region. Therefore, the objectives of this study were to: (1) investigate the variation in land surface hydrological processes at different levels of vegetative cover in alpine grassland; and (2) determine the effects of alpine meadow and alpine swamp grassland degradation caused by climatic changes on the water cycle in terms of runoff generation, active soil thermal and moisture dynamics, vegetation interception and condensation, using observational plots established on alpine meadow and alpine swamp grassland hillslopes in the permafrost areas on the Qinghai–Tibet Plateau.

2. Study area The study was conducted in the watershed of the Zuomaokong River, a tributary of the Tongtian River (the upper Yangtze River) in northeastern Qinghai–Tibet Plateau (92°500 –93°30 E, 34°400 – 34°480 N) (Fig. 1). The total area of the watershed was 128 km2 with elevations ranging from 4610 to 5323 m a.s.l. Alpine meadow and alpine swamp were the two grassland types in the basin. Kobresia pygmaea C. B. Clarke and Kobresia humilis Serg were the dominant vegetation in alpine meadow, whereas alpine swamp was dominated by Kobresis tibetica and Carex moorcoroftii. Between 2005 and 2007, the daily mean, maximum, and minimum air temperatures were 5.2 °C, 24.7 °C, and 45.2 °C, respectively. Precipitation during this period ranged from 250 to 367 mm yr1, and the average relative humidity was 53–59%. Based on the degree of degradation, vegetation cover in the region’s grasslands was divided into three categories: non-degraded, moderately degraded, and severely degraded, corresponding to 93%, 65%, and 30% coverage, respectively. In severely degraded grassland, Kobresia sp. was replaced by Festuca sp. and Poa sp.

Fig. 1. Schematic view of the location of the study area.

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W. Genxu et al. / Journal of Hydrology 444–445 (2012) 22–33

Table 1 Characteristics of the vegetation, soil, and permafrost in the study area. Vegetation type

Permafrost

Type

Cover (%)

Soil profile (m)

Bulk density (mg m3)

Sand gravel (0.5 mm) (%)

Organic matter (g kg1)

0–0.10 0.10–0.20 0.20–0.40 0–0.10 0.10–0.20 0.20–0.40 0–0.10 0.10–0.20 0.20–0.40 0–0.10 0.10–0.20 0.20–0.40 0–0.10 0.10–0.20 0.20–0.40

1.26 1.34 1.51 1.10 1.21 1.35 0.95 1.09 1.29 0.67 0.84 1.12 0.93 1.02 1.22

5 11 16 1 3 15 2 2 12 – 3 6 3 7 11

15.7 10.1 12.5 47.3 36.0 21.3 67.1 44.5 23.7 76.2 52.1 33.2 53.6 42.7 27.4

Active layer (m) 1980s

Present

Festuca sp., Poa sp.

30

1.0–1.5

1.5–2.3

Roegneria nutans, Kobresia humilis

67

0.8–1.5

1.2–2.0

Kobresia humilis

93

0.8–1.5

1.2–2.0

Kobresia tibetica, Festuca sp.

97%

0.8–1.2

1.2–1.8

Carex atrofusca, Leontopodium

65%

0.8–1.2

1.2–2.0

(Table 1, Wang et al., 2001; Zhou, 2001). The single vegetation type in these areas with different degrees of degradation was favorable to the aims of this study. Table 1 lists the main physical properties and nutrient contents of the region’s alpine meadow and alpine swamp soils under different levels of vegetative cover. In severely degraded alpine meadows, the amount of coarse sand and gravel in the topsoil was significantly higher than that in non-degraded areas. Soil organic matter content (SOM) in the 0–0.2 m soil layer (root zone) declined significantly with increasing degradation of vegetation cover: when cover decreased from 93% to 30%, SOM fell from 55.8 to 13.0 g kg–1, along with a corresponding 44% reduction in total N content. The situation was same in alpine swamp grassland, when cover decreased from 97% to 65%, SOM in topsoil dropped from 64.1 to 48.2 g kg1 (Table 1). To eliminate the influences of heterogeneity in geological and microtopography factors, two observation locations (location A and B, which were located on two slopes with similar soil types, bedrock, and geohydrological conditions) were selected to investigate slope-scale water cycle within the Zuomaokong watershed. Location A represents an alpine meadow on an east-facing slope, and location B represents an alpine swamp on a south-facing slope (Fig. 1). At location A (alpine meadow), which is located at approximately 4692 m a.s.l. and on an east-facing slope, the soils were classified as mattic cryic cambisols (i.e., alpine meadow soils) by the Chinese taxonomy (NSSO, 1998) or as cambisols by the FAO/ UNESCO taxonomy. These soils were characterized by the presence of a mattic epipedon (Oo), and the mean organic matter content of the topsoil (0–20 cm depth) ranged from 15.7 to 67.1 g kg1 (Table 1). At location B (i.e., alpine swamp), which is located at approximately 4710 m a.s.l. and on a slope facing south by east, two sites were chosen: one showed no degradation and had a vegetation cover of 97%, and the other represented a degraded alpine swampland with vegetation cover that ranged from 61% to 65%. The soils were also mattic cryic cambisols (alpine meadow soil), and the mean organic matter content in the topsoil ranged from 53.6 to 76.2 g kg1 and varied with the level of vegetation cover

(Table 1). In alpine meadow and swamp, the coarse sand and gravel contents of the topsoil were significantly increased with the vegetation cover decrease. The greater the vegetation cover, the larger the soil organic matter content and the lesser the soil Bulk density (Table 1). Permafrost was well developed in the study area, averaging between 50 and 120 m in depth. Under climatic warming, the mean annual permafrost surface temperature increased with a mean ratio of 0.06 °C per year from 1990 to 2001 (Wu and Liu, 2004), and the thickness of the active layer increased from an average of 0.8–1.5 m in 1980s to 1.2–2.3 m at the present. The ground generally begins to thaw in mid-April and thaws to a maximum depth (about 150–200 cm) in late August. The surface begins to freeze around mid-October and the entire active layer is frozen by about late November. The snow cover was irregular, filmy, and discontinuously distributed over the ground surface, even in the middle of the winter (Sato, 2001; Zhou et al., 2000). Therefore, the role of snow cover in the soil water–temperature coupling relationship and its changes under variable vegetative cover were not like those reported in other permafrost regions, such as those in North America and Siberia (Christensen et al., 2004), and was ignored in this study. In the study region, permafrost features are controlled by the altitude and latitude (Zhou et al., 2000). At the two observation sites, the different slope directions and gradients have caused differences in vegetation type (alpine meadow and alpine swamp) under similar permafrost conditions. Two portable micro-meteorological stations were established in the experimental fields to measure the climatic factors of air temperature (at 1.2 m in height), precipitation, wind velocity and direction, and solar radiation. The meteorological indexes such as air temperature (Air T), precipitation, and solar radiation (Solar R) in the two stations are listed in Table 2. Due to the little differences in altitude (only 18 m), latitude and slope direction between two sites, the meteorological heterogeneity between the monitoring sites is little and could be ignored.

Table 2 The meteorological indexes in the alpine swamp and meadow sites.

Air T (°C) P (mm) Solar R (W m2)

Swamp site Meadow site Swamp site Meadow site Swamp site Meadow site

March

April

May

June

July

August

September

October

November

Annual

9.5 9.8 6.0 5.2 245.1 240.8

4.5 4.3 0.2 0.2 272.7 286.6

0.3 0.1 41.6 40.7 313.8 309.5

1.2 1.3 71.9 70.5 299.8 260.5

5.3 5.3 51.1 50.8 298.4 304.4

6.3 6.2 108.0 106.2 237.5 236.6

2.4 2.1 70.2 69.7 250.8 198.7

4.5 5.1 13.5 13.7 245.5 245.0

12.9 12.7 2.4 2.8 190.0 197.7

5.2 5.3 372.8 367.5 238.9 234.7

W. Genxu et al. / Journal of Hydrology 444–445 (2012) 22–33

3. Data and methods 3.1. Data collection 3.1.1. Precipitation-runoff monitoring The hillslope runoff processes and water balance observing plot method was applied (Zhang, 1992; Zhang et al., 2002). Three linear-type hillslopes with consistent aspect and contrasting vegetation covers of 30%, 67%, and 93% were selected as observational plots in alpine meadow. Each plot was 20  5 m2 (slope length  width) with the longer sides parallel to the slope gradients, which were at an angle of 21° with similar microtopography conditions. In alpine swamp, two linear hillslope plots with vegetation covers of 65% and 97% and plot sizes of 20  5 m2 with slopes of 12°, were selected to monitor rainfall-runoff processes. The runoff-monitoring plot was arranged on the regular and straight slope surface run-off (Zhang, 1992; Loerup et al., 1998; Li et al., 2004). Fiberglass tile block borders (water resistant materials) were used to construct cutoff walls to stop the interaction of lateral flow between the inside of the plots and the surrounding terrain. A bucket that was 60 cm in diameter and 95 cm high with a lid and a HOBO rainfall gauge inside was placed in the middle of the lower edge of each plot, to automatically record runoff during each precipitation event. 3.1.2. Soil temperature and moisture In each of the above-mentioned runoff-monitoring plots, two 1.5-m deep wells were constructed to monitor soil moisture and temperature synchronously. Soil moisture and soil temperature sensors were installed in adjacent wells at depths of 0.20, 0.40, 0.70, 1.20, and 1.60 m. Soil moisture was determined by a frequency domain reflectometer (FDR) using a calibrated soil moisture sensor equipped with a Theta-probe (Holland Eijkelamp Co.). Volumetric soil moisture was derived from changes in the soil’s dielectric constant, converted to a millivolt signal, with an accuracy of ±2%. The soil moisture that was monitored was the liquid water content, and during the soil-freezing period, the measured soil moisture represented the unfrozen liquid water content. Soil temperature was monitored using a thermal resistance sensor sensitive to temperature changes in the range of 40 to 50 °C, with an overall system precision of ±0.02 °C. The thermal resistance sensors were developed by the State Key Laboratory of Frozen Soil Engineering in Lanzhou, China, using Fluke 180 series digital multimeters (Fluke Co., USA). The sensors had been successfully used in other projects on the Qinghai–Tibet Plateau over the past 20 years (Wu et al., 2002, 2004). All of the soil temperature and moisture data were collected automatically once every 30 min by a CR1000 data logger (Campbell Scientific, Inc., USA). At the two observation sites, which have similar permafrost conditions, the different slope directions and gradients have caused differences in the vegetation type (i.e., alpine meadow and alpine swamp). After decreases in vegetation cover occurred, the chemical and physical properties of the soil changed differently in the two types of alpine grassland (Table 1). We assumed that the influences of geology, geohydrology, and bedrock could be ignored because these conditions were similar among the monitoring and sampling plots in same sites. Therefore, the observed indexes, including soil moisture and soil temperature, resulted from changes in vegetation cover and soil properties. 3.1.3. Canopy interception of precipitation Compared to forests, there are relatively few approaches to measuring interception by grassland vegetation. However, the two most frequently used methods, water absorption and an infusion-weighing approach, suffer from a number of problems, includ-

25

ing difficulties in precisely measuring time, and measurements that are easily disturbed by manual operation of the device, etc (Jin et al., 2003; Wang et al., 2004). Therefore, the precision and reliability of the measurements are suspect. In this study, a weighing lysimeter-based device was constructed to monitor interception by the alpine grassland canopy. The weighing lysimeter was 40 cm deep and 30.5 cm in diameter with screened fine sands on the surface to form a regular slope with a gradient of 5–10%. After it was wetted with water, the surface of the sand layer was sprayed with Chloroprene rubber latex, and then the original litter layer that had been stripped off was placed on top of it again. The interception amount was determined by calculating the difference between the precipitation and the outflow from the rainspout. The advantage of this approach was that manual operation disturbances were absent, and it enabled continuous monitoring of the interception process. The weighing scales had a capacity of 30 kg and could detect a 1.0 g change in mass, which is equivalent to ±0.013 mm of water on the surface (Zhang et al., 2003; Li et al., 2009). The interception process was monitored at three different levels of vegetation cover under varying precipitation intensities from August to September when the vegetation was at its production peak. 3.1.4. Dew water observation Dew water or fog drip water is considered an important hydrological and chemical input factor in alpine and coastal zone ecosystems (Bruijnzeel, 2001; Neil and Ingraham, 2000). In this study, the dew water in the alpine vegetation-soil system was observed by using the methods as follows: A weighing lysimeter was employed to monitor dew water (Jacobs et al., 2000; Fang and Ding, 2005; Agam and Berliner, 2006). For each level of vegetation cover, two weighing lysimeters, one with its bottom covered with a 400-mesh nylon wire; the other with its bottom sealed to separate moisture flux from deep soil, were constructed at three thicknesses: 5, 10, and 20 cm. Each treatment had three replicates. These lysimeters were measured every 2 h from August to September. The weighing scales had a capacity of 10 kg, and could detect a 0.1 g change in mass. The amount of dew water was determined by the differences in its weights, and the formula was as follows: h

h ¼ m=pqr 2

ð1Þ

where h is the condensation amount (mm); m is the change in lysimeter weight (g); r is the inner radius of the lysimeter (mm); and q is water density (g mm3). 4. Analysis methods The rational method based on statistical analysis was most widely used for analyzing variation in the soil moisture and temperature relationship, while the effects of vegetation on the water cycle were obtained based on field observational data (Jansson and Karlberg, 2001; Kang et al., 2005; Wang et al., 2007). We analyzed the relationship between hydrological processes and vegetation cover at the hillslope scale using two different approaches. (1) The significance of differences in soil water dynamic, rainfall-runoff processes, canopy interception and dew water under the various vegetation fractions was assessed by analysis of variance (ANOVA), using the Tukey-HSD test. (2) The Levenberg-Marquardt (LM) algorithm (Nocedal and Wright, 1999; Hagan et al., 1996) and the Universal Global Optimization (UGO, Price et al, 2005) method were used in this study to obtain the optimal ascertain model to interpret the canopy interception and rainfall-runoff relationship under different vegetation cover in permafrost region. Using statistical methods available in SAS 8.1 (SAS Institute 2000), we generated regression models for linking precipitation

W. Genxu et al. / Journal of Hydrology 444–445 (2012) 22–33

DW s ¼ ðhm  h0 Þ=h0

ð2Þ

where hm and h0 are the soil moisture contents at the thaw-rise peak value and at its initial value, respectively. For the freezing period, similar indexes describing the dynamics of soil water were selected: the freeze-fall time of soil water td and the soil water freeze-fall amplitude DWd. These were used to describe distribution changes in the water profile during freezing periods. 5. Results 5.1. Effects of changes in grassland vegetation cover on soil hydrologic dynamics in the active layer Soil hydrologic dynamics of the active layer is a key factor driving the water cycle, as well as an important manifestation of the soil water cycle itself. For a deeper understanding of the relationship between runoff generation and vegetation cover, it is crucial

100

Thawing periods

90 80

8

70 60

6

50 40 30 20

4 2 0

-2 10 -4 0 20-Apr 30-Apr 10-May 20-May 30-May 26-Jun

5.1.1. Response of soil moisture to changes in alpine swamp cover This study focuses on soil moisture dynamics at depths of 5 and 20 cm, which are subject to the influences of vegetation. The results are illustrated in Fig. 2. From the thawing start time in the spring, to the total active soil layer thawed period (TIP stage), there were significant differences in the soil moisture dynamics in the non-degraded swamp meadow with a high vegetation cover (97%) and the degraded swamp meadow with a low vegetation cover (65%; p < 0.01). The active soil moisture was always higher in the non-degraded alpine swamp than that in the degraded one (Fig. 2a). During the early thawing period, from April to May, when vegetation cover decreased from an average of 97% to 65%, the soil liquid water content at the 5 cm depth decreased by 37.5–85.1%, whereas the 20 cm depth exhibited a decrease of 12.3–85.2%. From June to August, when the active layer was completely thawed (ETP stage), soil moisture at the 5 cm depth decreased an average of 51– 58.5% when vegetation cover was degraded, whereas the 20 cm depth exhibited an average decrease of 29.5–40.6%. When the active layer was completely thawed (ETP), soil moisture content at the 20 cm depth was almost the same as that at the 5 cm depth in non-degraded alpine swamp. However, the situation was the opposite in the degraded alpine swamp. The soil moisture content at the 20 cm depth was larger than that at the 5 cm depth by 21– 37% (Fig. 2a). In contrast, higher vegetation cover was associated with lower soil moisture in the surface soil during the freezing period (Fig. 2b). The greater the vegetative cover, the earlier the freeze-drop time td and the thaw-rise time ts for the active layer of the soil profile (Fig. 2a and b). 5.1.2. Response of soil moisture to change in alpine meadow cover The soil moisture dynamics of the 20 cm layers were selected to analyze the effects of vegetation cover. Fig. 3 illustrates the notable difference between the dynamics of soil moisture under different vegetation covers during the thawing and freezing periods in alpine meadow. During the thawing period (TIP), the unfrozen soil water content decreased with an increase in vegetation cover (Fig. 3a). In May, the average soil moisture of the 20 cm depth layer with 30% cover was larger by 22.7% and 36.8% than when cover was 67% and 93%, respectively. Concerning the thaw-rise time ts at 20 cm soil layer, the situation was adverse to the alpine swamp. The higher the vegetation cover, the later the thaw-rise time. In TIP, the annual variation in the soil moisture content corresponded

b

10

4 3 2 60 1 50 0 40 -1 30 -2 20 -3 10 -4 -5 0 10-Oct 16-Oct 22-Oct 28-Oct 3-Nov 9-Nov 15-Nov 80

Freezing periods

70

Soil temperature ( )

Soil moisture (%)

a

to indicate the soil water dynamics under the coupled effects of vegetation and the soil thawing–freezing cycle. Based on field observation data, there were different manifestations of soil moisture dynamics with changes in vegetation cover in alpine meadow and swamp meadow grasslands.

97%-5 cm 65%-20 cm

97%-20 cm 97%-5cm

Soil temperature ( )

and water yield, storm runoff coefficients and precipitation, runoff coefficients and the soil thawing or freezing processes from mean daily data collected in the hillslope runoff plots with different levels of vegetation cover. Daily and event runoff coefficients were used to estimate the variation in runoff under vegetation effects and the soil thawing-freezing cycle. Event runoff coefficients are usually estimated as the water yield ratio in the hillslope runoff plots by using the ratio of event runoff volume and event rainfall, which is straightforward if all the events are clearly separated and direct runoff between the events is small (Merz et al., 2006). Because the runoff data in this study were collected in hillslope plots from observations of events in the field, and the direct surface runoff and subsurface runoff were recorded separately, the problem was straight forwardly overcome. Wang et al. (2008) pointed out that the coupled changes in soil moisture content and soil temperature occurring over the year in the active layer of permafrost soils could be clearly divided into four periods: freeze initiation period (FIP), entirely frozen period (EFP), thaw initiation period (TIP), and entirely thawed period (ETP). To investigate the quantitative and spatial responses of soil water to changes in vegetation cover objectively during the permafrost region’s freeze–thaw cycles, the FIP (from October 20 to November 10) and TIP stages (from May 25 to June 15) were selected, while and a number of indexes were selected to analyze the soil water dynamics (Wang et al., 2008). The thaw-rise time ts (represents the time at the inflexion point in the plot of soil water versus time, i.e., where soil water content begins to increase following the initiation of thawing.) and the soil water thaw-rise amplitude DWs is calculated as follows:

Soil moisture (%)

26

65%-5 cm 65%-5cm

Fig. 2. The dynamics of the soil moisture (line) and temperature (circles and cross) at different depth in non-degraded and degraded alpine swamp during thawing periods (a) and freezing periods (b). The percent value is indicated the amounts of vegetation cover in the legend (it is same in all the figures in fellow).

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W. Genxu et al. / Journal of Hydrology 444–445 (2012) 22–33

25

35 30

20

25

15

20

10

15

5

10

Precipitation (mm)

5

0

0

-5

45 40

30.0 Freezing periods 25.0

35 30

20.0

25

15.0

20

10.0

15 10

5.0

5

0.0

0 2005

2006

2007

Precipitation in September θ-67% Air temperature in September

2008

Soil moisture variation (%)

40

d Soil moisture variation (%)

30

AT( )/Soil moisture (%)

Precipitation (mm)

45

b

c

35

Thawing processes

50

AT( )/Soil moisture (%)

a

35

Thawing processes

30 25 20 15 10 5 0 25

Freezing processes

20 15 10

Δ W-93%

5

Δ W-30%

Δ W-67%

2009

θ-93% θ-30%

0

2005

2006

2007

2008

2009

Fig. 3. The average soil moisture and its annual variation under different vegetation cover in alpine meadow during thawing periods (a) and freezing periods (b) comparing with annual variation of air temperature and precipitation. While, the soil water thaw-rise amplitude DWs (c), soil water freeze-fall amplitude DWd (d), and their annual variation under different vegetation cover in alpine meadow.

closely with precipitation changes, which decreased from 2005 to 2006 and increased from 2006 to 2009 (Fig. 3a). Greater vegetation cover was associated with the higher soil water thaw-rise amplitude DWs. The annual variation of soil water thaw-rise amplitude DWs, however, corresponded with the synergistic effect of air temperature and precipitation. Lower air temperature or lower precipitation was associated with less DWs (Fig. 3c). The degradation of the vegetation cover resulted in a larger standard deviation Cv of soil moisture, indicating that the soil thawing–freezing cycle has a greater impact on soil moisture dynamics than climate change during the TIP and FIP seasons and that the degradation of vegetation cover accelerated the shifts of soil moisture between years. During the freezing period (FIP), the unfrozen soil water content increased with an increase in vegetation cover at the 20 cm depth (Fig. 3b). From October 20 to November 5, the average soil moisture at the 20 cm depth with a 93% vegetation cover was higher than when vegetation cover was 67% and 30% by 17.3% and 37.8%, respectively. At the 20 cm depth, the freeze-drop time (td) was later as vegetation cover increased. This was similar to that in the thawing process. The freeze-drop time (td) when vegetation cover was 30%proceeded an average of 14 days and 20 days earlier than when vegetation cover was 67% and 93%, respectively. The annual variation of the soil moisture content also corresponded closely with precipitation changes, which approximately increased from 2005 to 2009 (Fig. 3b). It is contrast with soil water content; greater vegetation cover was associated with the lower soil water freeze-fall amplitude (DWd). Similar to the thawing processes, the annual variation of DWd was synergistically effected by the precipitation and air temperature changes in September (Fig. 3d). Thus, the active soil moisture dynamics were controlled by the synergic influences of climate and vegetation cover variation. In permafrost regions, the soil moisture distribution in profile and its dynamics in alpine grassland are controlled by the coupling effects of the soil thawing–freezing cycle and vegetation cover.

During the thawing period, soil moisture decreased with a decrease in vegetation cover in alpine swamp; in contrast, soil moisture increased in alpine meadow. During the freezing period, soil moisture content decreased with a decrease in vegetation cover in alpine meadow, but increased in alpine swamp. Vegetation cover changes were associated with the variation of soil properties. The topsoil layer (<20 cm) in the alpine swamp and the highly vegetation-covered alpine meadow contained dense grass roots that resulted in a higher soil organic content (SOM) (Table 1) than in the sparsely vegetated areas, which contained more clay and had lower average Bulk density. Consequently, the variation of soil properties caused the changes in soil water capacity and saturated hydraulic conductivity. Thus, under the same soil thawing– freezing processes, changes in vegetation cover associated with the variation of soil properties reshaped the pattern of the soil moisture profile distribution and its dynamics, which is one of the most important factors that controls runoff generation. 5.2. Responses of runoff generation to changes in vegetation cover 5.2.1. Variation in the rainfall-runoff relationship under different vegetation covers During the 2006–2007 observation period, 37 effective precipitation events were recorded at in the alpine meadow runoff plot, and 44 effective precipitation events were recorded in the alpine swamp runoff plot. Fig. 4 shows the observed relationship between daily rainfall and runoff in the hillslope plots with different vegetation covers. In alpine meadow, the relationship between daily runoff and daily rainfall fit an exponential function that explained more than 86% of the variance and exhibited a statistically significant relationship (p < 0.001) under different vegetation covers. However, for the alpine swamp, a significant linear relationship explained more than 88% of the variance (p < 0.001). When vegetation cover varied, the rainfall-runoff relationships for both

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W. Genxu et al. / Journal of Hydrology 444–445 (2012) 22–33

b

8 7

67

6

92

cover

5

30

cover

Alpine swample

6

R2 = 0.873

4 3 R2 = 0.908

2

2

R = 0.899

R2 = 0.867

cover

Run off (mm/day)

Runoff (mm/day)

a

5

97% cover

4

65% cover

3 2 2

R = 0.886

1

1

0

0 0

10

20

30

40

0

5

Precipitation (mm)

10

15

20

25

Precipitation (mm)

Fig. 4. The rainfall-runoff relationship at different vegetation covers for (a) alpine meadow and (b) alpine swamp.

Table 3 Comparison of the runoff ratios for alpine meadow grassland and alpine swamp grassland under different vegetation covers (mean value, %).

Alpine meadow

Alpine swamp

93% 67% 30% 97% 65%

0–5

5–10

10–15

15–20

>20

1.68 2.33 2.35 25.92 20.80

3.68 3.71 4.21 24.42 20.92

2.34 3.38 7.20 24.29 15.34

3.69 4.73 11.89 20.62 15.37

10.14 11.68 21.01 21.53 15.69

alpine grassland types changed. In alpine meadow, the lower the vegetation cover, the more runoff generated with the same amount of rainfall. When precipitation was greater than 10 mm, the runoff rapidly increased by over 80% if the vegetation cover dropped from 92% to 30% (Fig. 4a). In contrast, runoff tended to decrease in the degraded vegetation in alpine swamp (Fig. 4b). When precipitation was greater than 10 mm, the runoff decreased by 20–46% if the vegetation cover declined from 97% to 65%. Those natural characteristics of the rainfall-runoff relationship and its variation with changes in vegetation cover indicated that there were different runoff generation mechanisms in alpine meadow and swamp on a hillslope scale. As illustrated in Table 3, the 44 recorded effective precipitation events were categorized into five classes and the average runoff coefficients were calculated for each class. The runoff coefficient for alpine swamp was far greater than that in alpine meadow grassland at the same precipitation level. The greater the vegetation covers in alpine meadow, the lower its runoff coefficient. When precipitation increased from 10 mm to more than 20 mm, the runoff coefficient increased from 28.5% to 51.7% when cover increased from 30% to 93%. The influence of decreasing vegetation cover on runoff generation was enhanced when precipitation increased, and the runoff coefficient reached its maximum when the event precipitation ranged from 20 mm to 30 mm. In contrast, in alpine swamp, the greater the vegetation cover, the higher the

a Runoff coefficient (%)

12 67% 10

93%

8

30%

b

Alpine meadow Thawing period 2

R = 0.406

6 2

R = 0.38

4 2 0 0.0

2

R = 0.4307 2.0

4.0

5.2.2. Responses of runoff dynamics to the coupling effects of the soil thawing–freezing process and changes in vegetation cover In alpine meadow grassland, the daily runoff coefficient showed opposite trends during the soil thawing period and the freezing period (Fig. 5). From mid-June to last August, the active soil layer thawed from 20 cm to more than 150 cm in depth (Wang et al., 2008), and the runoff coefficients tended to increase in the alpine meadow grassland with the three vegetation cover types. A power fit of daily runoff coefficient on daily soil temperature at the 20 cm depth explained 38–43% of the variance and was statistically significant (p < 0.01), which indicated that the influence of the shallow soil temperature on the way runoff was related to rainfall in the thawing period (Fig. 5a). From early September to mid-October, the active soil layer underwent a freezing process, and the top 20 cm layer froze when cover was 67% and 30%, whereas when vegetation cover in the alpine meadow was 93%, the surface 5 cm depth layer froze early (Wang et al., 2008). Daily runoff coefficients tended to decrease when soil temperature in the 20 cm layer decreased (Fig. 5b). The relationship between the daily runoff coefficient on daily soil temperature was logarithmic and explained 12–23% of the variance, and there were a moderately significant relationship between the daily runoff coefficient and shallow soil temperatures (p = 0.064–0.014). Clearly, even if there was a general tendency for an increase in daily runoff coefficients with shallow soil temperatures, the influence of soil temperature on runoff generation was more significant in the thawing period than that in the freezing period. The relationship varied under different vegetation covers. For both the thawing and freezing processes, the daily runoff coefficient under 93% cover was less than

6.0

Soil temperature (°C)

8.0

10.0

Runoff coefficient (%)

Precipitation (mm)

runoff coefficient for every precipitation class, and the runoff coefficient reached its maximum when event precipitation was less than 10 mm (Table 3). When precipitation was greater than 15 mm, the runoff coefficient in 97% cover and 65% cover of alpine swamp stabilized at approximately 21% and 15%, respectively.

18 16 14

67%

12 10

30%

Alpine meadow Freezing period 2

93%

R = 0.2245 2

R = 0.1647

8 6 4 2 0 0.0

2

R = 0.1186

1.0

2.0

3.0

4.0

5.0

6.0

Soil temperature (°C)

Fig. 5. Variation in the runoff coefficients during: (a) soil thawing processes and (b) soil freezing processes in alpine meadow grassland.

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W. Genxu et al. / Journal of Hydrology 444–445 (2012) 22–33

Runoff coefficient (%)

30.0

-2.0

b

2

Alpine swamp Thawing period

R = 0.724 2

R = 0.7826

25.0 20.0 15.0

97% 65%

10.0 5.0 0.0 0.0

2.0

4.0

6.0

8.0

10.0

12.0

Runoff coefficient(%)

a

25.0 20.0 15.0

Alpine swamp

2

R = 0.61

97% 65%

10.0

2

R = 0.56 5.0 0.0 0.0

1.0

2.0

Soil temperature (°C)

3.0

4.0

5.0

6.0

Soil temperature (°C)

2

0.1 Alpine swamp

4

0.08

6

0.06

8

0.04

10

0.02

12 28-Apr 3-May 8-May 13-May 18-May 23-May 28-May 2-Jun Precipitation 65% subsurface

97% subsurface 65% surface

Runoff (mm)

0.12

0 7-Jun 97%surface

Fig. 7. The subsurface and surface runoff processes in alpine swamp with different vegetation covers during the thawing period.

65% cover by 8.1–54.3%. When precipitation was greater than 2.0 mm, the difference in subsurface flow between the two levels of vegetation cover increased when precipitation increased. In contrast, when precipitation was greater than 5.0 mm, surface runoff originating from the non-degraded alpine swamp grassland with 97% cover was less than that from alpine swamp grassland with 65% cover. However, when precipitation was greater than

3.5

surface flow

3

Runoff (mm)

5.2.3. Influence of changes in vegetation cover on components of runoff During the 2005–2008 observation period at the 150 cm soil profile depth, no interflow or subsurface runoff was detected in the alpine meadow grassland; however, both surface flow and interflow were observed in the alpine swamp grassland. As shown in Fig. 7, when the surface 10 cm depth soil layer was thawing in late April, subsurface flow was observed in alpine swamp with 97% cover. However, subsurface flow in the alpine swamp with 67% cover was delayed by almost 10 days. After mid-May, when the 30 cm depth soil layer was completely thawed, surface flow occurred in the alpine swamp with 97% cover, and until June 1–2, surface flow was observed when cover was 65%. The higher the vegetation cover in the alpine swamp, the earlier the subsurface and surface runoff occurred during the thawing period. Fig. 8 shows the difference in subsurface and surface flow when cover was 97% versus 65% in the alpine swamp. The subsurface flow originating from non-degraded alpine swamp grassland with 97% cover was larger than that from alpine swamp grassland with

0

2.5

97% cover

2

65% cover

1.5 1 0.5 0 3.5 3

Runoff (mm)

when cover was 67% and 30%, which corresponded to the characteristics of the soil moisture profile distribution and dynamics (Fig. 2). In alpine swamp, the variation tendency of daily runoff coefficients was similar to alpine meadow, runoff coefficients increased with soil temperature at 20 cm depth layer increased (Fig. 6). During the thawing period, the daily runoff coefficient significantly increased from early June to mid-August. In the non-degraded swamp meadow with 97% cover, the linear relationship between the daily runoff coefficient and daily soil temperatures explained 78% of the variance. But in the degraded swamp meadow (65% cover), the relationship between the daily runoff coefficient on soil temperature was an exponential function and explained 72% of the variance (Fig. 6a). The runoff coefficients and shallow soil temperatures were significantly related (p < 0.01). During the freezing period, the linear relationship between the runoff coefficients on soil temperature explained 61% of the variance in degraded swamp meadow with 65% cover, and a significant exponential relationship was found for non-degraded swamp meadow with 97% cover (56% of the variance was explained, Fig. 6b). In both the thawing and freezing periods, the influence of soil temperature on runoff generation was more significant and linear in alpine swamp grassland than that in alpine meadow grassland. Finally, even if there had not been a significant general relationship between the daily runoff coefficient and daily soil temperatures during the freezing period in the alpine meadow, the nature of the coupling impacts on runoff generation by soil temperature and vegetation cover were relevantly identical in the alpine meadow and alpine swamp grasslands. The changes in vegetation cover resulted in variation in the statistical relationship between soil temperature and the runoff coefficients.

Precipitation (mm)

Fig. 6. Variation in the runoff coefficients with soil temperature during: (a) soil thawing processes and (b) soil freezing processes in alpine swamp grassland.

Subsurface flow

2.5 2 1.5 1 0.5 0 <1.0

1.0-2.0

2.0-5.0

5.0-10.0

>10.0

Precipitation (mm) Fig. 8. Variation in the subsurface flow and surface flow with precipitation with different vegetation covers.

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W. Genxu et al. / Journal of Hydrology 444–445 (2012) 22–33

14.0 Alpine meadow

20.0 15.0

Intercept ratio (%)

Interception ratio (%)

25.0 84% cover 61% cover 32% cover

2

R = 0.952

10.0 2

R = 0.979

5.0 R = 0.972

0

20

30

40

87% cover 63% cover 34% cover

8.0 6.0 4.0

2

0.0

10

2

R = 0.916

10.0

R = 0.935

2.0

2

0.0

Alpine swamp meadow

12.0

2

R = 0.914

0

10

Precipitation (mm)

20

30

40

Precipitation (mm)

Fig. 9. Variation in the interception ratio for alpine meadow and alpine swamp with respect to vegetation cover and level of precipitation.

Table 4 Statistical models of precipitation–interception for alpine meadow grassland under different vegetation covers. Two factors model for interception ratio on vegetation cover and precipitation

Multivariate model for interception amount on three factors

Formulation

Test

Formulation

Test

Alpine meadow

I = (15.05Fc + 16.85)  P0.77

R2 = 0.93

a = 0.06

I = 0.0025FcRi0.34T0.19

R2 = 0.96 P < 0.01

a = 0.02

Alpine swamp meadow

I = (19.84Fc–0.313)  P0.83

P < 0.01 R2 = 0.94 P < 0.01

a = 0.05

I = 0.001 FcRi0.18T0.33

R2 = 0.82 P < 0.01

a = 0.01

5.0 mm, the higher the vegetation cover, the greater the surface flow. 5.3. Impacts of vegetation cover on canopy interception and condensation in alpine grasslands 5.3.1. Response of grassland canopy interception to changes in vegetation cover The differences in canopy interception in the two major types of alpine meadow grassland has great significance for understanding the water cycle characteristics and parameterization of the interception process for the hydrologic simulation on the Qinghai–Tibet plateau. As illustrated in Fig. 9, the relationship between the interception ratio and precipitation fit a power function, which explained more than 92% of the variance and was statistically significant (p < .001) with different vegetation covers in alpine meadow and swamp grassland. Within the range of the threshold precipitation (when precipitation exceeded the threshold value, the canopy interception ratio will no longer vary significantly.), the interception ratio decreased with an increase in precipitation. The precipitation threshold value was 35 mm and 25 mm for alpine meadow and alpine swamp, respectively. The recorded maximum interception ratio was 21.6–11.0% for alpine meadow and alpine swamp grassland, respectively; the former was 2.2 times that of the latter. Under the same precipitation, the canopy interception ratio in alpine meadow was far greater than that in alpine swamp with the difference ranging from 42.4% to 54.9%. The significant difference in the canopy interception ratios between alpine meadow and swamp was mainly caused by the different leaf structures related to the different dominant vegetation species. Kobresia humilis, the dominant vegetation species in alpine meadow grassland, had an average leaf area index of 4.9–5.1, whereas the average leaf area index for Kobresia tibetica, the dominant vegetation species in alpine swamp grassland, was 1.3–2.5. The larger leaf area index in alpine meadow grassland resulted in a higher canopy interception capacity than in alpine swamp grassland. In the same type of alpine grassland, the change in vegetation cover had a great effect on canopy interception. As illustrated in Fig. 9, the canopy interception ratio of rainfall in alpine meadow grassland decreased when vegetation cover declined. When vege-

tation cover in alpine meadow grassland dropped from above 84% to 61%, and then to 32%, the canopy interception ratio decreased by 22% and 36%, respectively. The same situation occurred in alpine swamp grassland; the same range of vegetation cover changes caused the interception ratio of alpine swamp grassland to decrease by 23% and 39%, respectively. However, the influence of a change in vegetation cover on canopy interception was obviously controlled by rainfall volume, and there were a certain range of threshold rainfall values for which the impacts of vegetation changes on canopy interception were obvious. As shown in Fig. 9, when precipitation was greater than 30 mm, the influence of vegetation cover change on the interception ratio tended to disappear in alpine swamp grassland with the interception ratio was almost the same for the different vegetation covers. In alpine meadow, this threshold value was about 35 mm.The Levenberg–Marquardt (LM) algorithm and the Universal Global Optimization (UGO) method were used to obtain the simulation model of rainfall intercept variation on driving factors. As shown in Table 4, without considering the influences of precipitation intensities and durations, the simulation model of the interception ratio on vegetation cover and precipitation amount showed a negative power relationship with precipitation amount, and a positive linear relationship with vegetation cover. When the three-factor of precipitation intensity, vegetation cover and precipitation duration were considered, the interception rate showed a positive power law relationship with precipitation intensity and duration, and a positive linear relationship with vegetation cover. All the models were significant at the 0.01 level, with R2 P 0.82, and the standard error of the estimate between 0.01 and 0.06. 5.3.2. Variability of dew water under different alpine meadow covers Dew water observations are shown in Table 5. During the entire observation period (from August to September), the average amount of dew water differed significantly in non-degraded, moderately degraded, and severely degraded alpine meadow (p < 0.01). The highest mean amount of dew water was observed in non-degraded alpine meadow (0.32 mm day1), followed by moderately degraded meadow (0.25 mm day1), and severely degraded alpine meadow (0.22 mm day1). The surface dew water mainly occurred in the top 0–5 cm of the soil and was formed from two sources—

31

W. Genxu et al. / Journal of Hydrology 444–445 (2012) 22–33 Table 5 Sources and proportion of 0–5 cm depth soil condensation water with different degrees of vegetation degradation in alpine meadow grassland.

Atmosphere

Vadose zone soil moisture

Degree of degradation

Daily mean dew water (mm d1)

Proportion to total daily mean dew water (%)

Non-degraded(93%) Intermediately degraded(67%) Severely degraded(30%) Non-degraded(93%) Intermediately degraded (67%) Severely degraded (30%)

0.315 0.253 0.221 0.103 0.068 0.033

67.3 73.2 85.3 32.7 26.8 14.7

water vapor in the air and moisture in the vadose zone. The ratio of dew water formed from moisture in the air to total dew water formed increased significantly as vegetation cover decreased (from 67% when cover was 93% to 85.3% when cover was degraded, ie., 30%) (Table 5), whereas the proportion formed by moisture moving upward from the vadose soil zone decreased as vegetation cover decreased. When the alpine meadow vegetation cover dropped from 93% to 67% and then to 30%, the daily surface dew water decreased by 19.7% and 29.8% respectively, of which the condensation formed by water vapor in the air decreased by 12.5% and 11%, respectively. The results indicated that the variation in vegetation cover had a significant effect on dew water formation. Because dew water formation is closely related to such factors as daily differences in air and ground temperatures, ground surface air moisture, and vadose zone soil moisture, the reason for variation in dew water was that changes in vegetation cover resulted in shifts in surface air and soil temperature, topsoil moisture as mentioned as above. In alpine ecosystems, the dew water was not only an important source of water, but may also be an important part of the water cycle as a whole. Ingraham et al. determined that dew water recharged to groundwater by 35.3–69.5% on the Otago Plateau in New Zealand (Ingraham and Matthews, 1988). Scholl et al. suggested that dew water was an important recharge source for groundwater and even exceeded the rainfall recharge in some high elevation locations in Hawaii (Scholl et al., 2002). Whether the dew water formed in al-

Soil temperature ( )

2

10

Thawing process 20cm

R = 0.74

6 65% cover

-12

-9

4 2

0 -3 -2 0

-6

3

9

Air temperature (

-4 2

R = 0.80

6

6. Discussion and conclusions In the permafrost region, heat was the main factor influencing the soil water phase changes and its water distribution profile at active soil layer. In the alpine meadow of permafrost region, the sensible heat flux (H) and soil surface heat flux (Gs) decreased as vegetation cover increased in alpine meadows. The annual average H and Gs values at sites with 30% cover exceeded those with 93% cover by 19% and 41%, respectively (Wang et al., 2008). During the grass growing season (from May to September), the latent heat flux (LE) at sites with 93% cover was 47% higher than at sites with 30% cover. The latent heat flux was several times higher in poorly drained ecosystems (alpine swamp) and wetter growing periods compared to well-drained areas and drier periods (Yoshikawa et al., 2003; Zhang et al., 2005; Yi et al., 2009). The energy flux differences under different vegetation covers led to differences in soil temperature. As shown in Figs. 10, the vegetation cover changes resulted in the variations of the linear relationships between air and soil temperature at certain depths. In alpine swamp, the greater the vegetation cover, the higher the linear intercept, which indicated the greater vegetation cover was associated with higher soil

a

8

97% cover

pine meadow grassland on the Qinghai–Tibet Plateau takes part in regional water cycle processes is an issue worth further research in the future.

12

)

0 -20

-30

8

c

4 2 0 -3

2

2

-2 0 -4

R = 0.64

-2 -4 -6

0

3

6

9

Air temperature ( )

d Soil temperature ( )

Soil temperature ( )

R = 0.74

93% cover

R = 0.79

-10

R = 0.86

6

-6

2

10

Air temperature ( )

-6

2

30% cover

-9

4

65% cover

2

10

67% cover

R = 0.88

6

-6

Thawing process 20 cm

2

8

20 cm 97% cover

-8

-12

10

Freezing process

b Soil temperature ( )

Sources of dew water

-8

Freezing process

6

20cm

2

R = 0.8

4

67% cover 93% cover

2

30% cover

2

R = 0.76

0 -18 -15 -12

-9

-6

-3 0 -2 -4

3

6

9

Air temperature ( )

2

R = 0.79

-6

Fig. 10. Variation of the relationship between air temperature and soil temperature at 20 cm depth in alpine swamp (a and b) and alpine meadow (c and d).

32

W. Genxu et al. / Journal of Hydrology 444–445 (2012) 22–33

temperature under same air temperature both in thawing and freezing periods (Fig. 10 a and b). In alpine meadow, however, the lower vegetation cover was associated with greater soil temperatures at same air temperature during the thawing period. It was contrast during freezing periods; the greater vegetation cover was associated with higher soil temperature (Fig. 10 c and d). In boreal forest covered permafrost areas, it was suggested that, as with organic and fine-grained soils, the surface litter layers have low thermal conductivity generally (Fukui et al., 2008; Shur & Jorgenson, 2007). In the alpine swamp and meadow under high vegetation cover, there was not only high soil organic content in the topsoil layer (Table 1), but a thin surface litter layer (generally between 0.2 and 5 cm depth). The topsoil layer under high vegetation cover, as with the high organic, fine-grained soils and litter layer, reduced the thermal conductivity and increased the water infiltration and water hold capacity, which altered the active soil water-heat relationship (Zhou et al. 2000; Wang et al., 2008). Therefore, differences in surface energy balance and soil properties with vegetation cover variation have a great affects on active soil moisture and the available water distribution pattern. During the thawing period, greater vegetation cover was associated with higher soil moisture content in alpine swamp, but with lower soil moisture in alpine meadow. During the freezing period, however, soil moisture content decreased with a decrease of vegetation cover in alpine meadow, but increased in alpine swamp. The degradation of alpine grassland ecosystems in permafrost areas on the Qinghai–Tibet Plateau exerted a great influence on the water cycle on the hillslope scale. The degradation of vegetation cover in alpine swamp led to decreases in soil moisture content in the top 20 cm of soil, a delay in thawing start time in the spring, and a decrease in both surface runoff and subsurface interflow. At the same amount of precipitation, the decrease in the runoff generation ratio can reach 39%. Conversely, the degradation of alpine meadow with a decrease in vegetation cover led to a significant increase in the soil moisture content of the topsoil during the earlier occurrence of thawing, and an increase in the range of variation in the water content of unfrozen soil. Thus, the lower vegetation cover in alpine meadow was associated with the higher runoff generation ratio. As vegetation cover decreased from 93% to 30%, the surface runoff generation ratio increased and was as high as 52%. Runoff generation in alpine swamp grassland is dominated by the saturation-surface runoff process. While in alpine meadow, however, runoff generation is dominated by the infiltration-excess process. The alteration in soil thermal properties, soil water hold capacity and water distribution pattern, plant canopy interception and surface evaportranspiration with vegetation degradation were the main reason of the variation in runoff generation ratio. Topography dominated the runoff generation processes in different slope sites (Carey and Woo, 2001). The difference of runoff generation processes between alpine swamp and meadow sites was partially attributed to the variation in topography (the different slope directions and gradients). In turn, it is exactly the differences in topography that produced the different vegetation types in the two observation sites. Thus, on slope scale, variations in topography and permafrost condition dominated the surface water cycle pattern in different slope sites, but the changes in vegetation cover and responding alteration in soil properties and energy balance reshaped the slope runoff processes in a certain slope site. Although alpine meadow and alpine swamp were the two types of alpine grassland in Qinghai–Tibet plateau, the alpine meadow canopy has larger maximum interception ratio and saturation intercept threshold rainfall than alpine swamp canopy; and there were different simulation model for rainfall interception in alpine meadow and alpine swamp grassland. As reported in forest region, the differences in vegetation types of community and leaf structures were the driving factor of the interception variation (Bewket

and Sterk, 2004; Cheng et al., 2004). As vegetation degraded and vegetation cover decreased, the rainfall interception ratios decreased by almost an identical range in alpine meadow and alpine swamp grasslands. This demonstrates that the interception loss of precipitation in the permafrost region on the Qinghai–Tibet plateau tended to decrease with degradation of the vegetation. The combined effects of large diurnal temperature variability and the succession of freezing and thawing make the ground surface favorable for the occurrence of dew water. The total dew water that occurred in the observation periods (from August to September) was as high as 12.5–16.5% of the corresponding precipitation during that period, which is vital to alpine grassland ecosystems located in semi-arid areas where yearly precipitation is less than 300 mm. As vegetation became more degraded, surface dew water formed in alpine meadow grassland tended to decrease. The degradation in vegetation cover resulted in the reduction in daily differences of air and ground temperatures and topsoil moisture (Bakalin and Vetrova, 2008; Wang et al., 2008), which was the main reason to lead the dew water shifts with alpine meadow grassland degradation. In terms of rainfall interception, runoff generation, soil water dynamics, and dew water, the degradation of alpine grassland ecosystems, associating with the alteration in surface energy flux and soil properties, had a major influence on all the components of the water cycle at the hillslope scale. How will this influence change when moving up to the watershed scale? This would require research on the responses of the water balance and transfer to the watershed scale, and is an issue needing further exploration.

Acknowledgements This study was funded the Natural Science Foundation of China (Nos. 40925002 and 40730634) and by the National Basic Research Program of China (973, No. 2007CB411504).

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