An eco-hydrological approach to predicting regional vegetation and groundwater response to ecological water conveyance in dryland riparian ecosystems

Quaternary International xxx (2015) 1e13

Contents lists available at ScienceDirect

Quaternary International journal homepage: www.elsevier.com/locate/quaint

An eco-hydrological approach to predicting regional vegetation and groundwater response to ecological water conveyance in dryland riparian ecosystems Ming Han a, b, Chengyi Zhao a, *, Gary Feng a, 1, Markus Disse c, Fengzhi Shi a, Juyan Li a a b c

State Key Laboratory of Oasis Desert and Ecology, Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China University of Chinese Academy of Sciences, Beijing 100049, China Technical University Munich, Munich 80333, Germany

a r t i c l e i n f o

a b s t r a c t

Article history: Available online xxx

The ecosystem along the lower reach of Tarim River has been undergoing remarkable changes due to the ecological water conveyance activity since 2000. To improve the scientific basis of riparian restoration, the clearing understanding of the dynamic of eco-hydrological system and the feedback between hydrological and ecological components are needed. A fully distributed eco-hydrological model combines a hydrology component based on the modified MODFLOW, with a vegetation dynamic component described by Verhulst logistic equation. The validation of the eco-hydrological model using groundwater well observation, river stage observation and remote sensing data demonstrated that the good capability of the model in simulating the dynamic of the eco-hydrological system in lower reach of Tarim River. The interaction between the hydrological component and ecological component has been revealed through water balance analysis and scenario studies. Vegetation has a significant impact on groundwater evapotranspiration, groundwater storage change and groundwater depth, while groundwater depth is a critical feedback variable that determines the vegetation dynamics, which is shown by observation and simulation result. The eco-hydrological model represented in this study could help to groundwater dependent ecosystem restoration and protection, particularly for riparian ecosystem. © 2015 Elsevier Ltd and INQUA. All rights reserved.

Keywords: MODFLOW Eco-hydrological model Vegetation dynamic Groundwater dependent ecosystem Riparian Tarim River

1. Introduction The Tarim River, with a length of 1321 km, is located in the arid zone of northwestern China. Due to the severe abuse of water resources in the upper and middle reaches, the lower reach of the Tarim River has dried up gradually, the groundwater table has dropped sharply, and riparian vegetation has degraded severely since the 1970s (Chen et al., 2010). The central and local governments of China have invested RMB 10.7 billion to restore and reconstruct the riparian ecosystem in the lower reach of the Tarim River. One of the key actions was water conveyance from Daxihaizi reservoir to the lower reach of the Tarim River. Until 2007, water delivery was carried out 9 times (Table 1). Research showed that the riparian ecosystem in the lower reach is a groundwater dependent

* Corresponding author. E-mail addresses: [email protected] (C. Zhao), [email protected] (G. Feng). 1 Current address: Genetics and Precision Agriculture Research Unit, USDA-ARS, P. O. BOX 5367, 810 Highway 12 East, Mississippi State, MS 39762, USA.

system, and the water conveyance considerably lifted the groundwater table and restored the riparian forest (Zhao et al., 2004; Chen et al., 2008, 2010; Hao et al., 2009; Yu et al., 2012). In order to improve scientific basis for riparian restoration and provide a better appreciation of the eco-hydrological consequences of restoration, the understanding of the response of the eco-hydrological system to the water conveyance activity is needed. Many of the world's ecosystems are water-controlled in that water availability is the most important driver of vegetation structure and organization (Loheide and Gorelick, 2007). The understanding of the systems and their response to anthropogenic and natural disturbances are of foremost importance for management and restoration of these ecosystems (Wright and Chambers, 2002; Ridolfi et al., 2006). To understand such systems, integration of hydrology and ecology is needed, and a new interdisciplinary research focus called eco-hydrology has been created (Crawford, 2000; Zalewski, 2002; Hannah et al., 2004). Eco-hydrological models provide a potentially useful tool in characterizing interactions in eco-hydrological systems, and

http://dx.doi.org/10.1016/j.quaint.2015.02.032 1040-6182/© 2015 Elsevier Ltd and INQUA. All rights reserved.

Please cite this article in press as: Han, M., et al., An eco-hydrological approach to predicting regional vegetation and groundwater response to ecological water conveyance in dryland riparian ecosystems, Quaternary International (2015), http://dx.doi.org/10.1016/j.quaint.2015.02.032

2

M. Han et al. / Quaternary International xxx (2015) 1e13

Table 1 Water delivery duration and volume from Daxihaizi Reservoir. Time/phase

Duration

Total water conveyance ( 108 m3)

Transection for water reach

1 2 3 4 5 6

14/05/2000e13/07/2000 03/11/2000e14/02/2001 01/04/2001e06/07/2001 12/09/2001e17/11/2001 20/07/2002e10/11/2002 03/03/2003e11/97/2003 12/09/2003e07/11/2003 25/03/2004e25/06/2004 01/08/2004e15/09/2004 07/05/2005e07/06/2005 30/08/2005e02/11/2005 25/09/2006e30/11/2006

0.99 2.27 1.84 1.98 3.31 6.20

Kardayi Kaogan Kaogan Taitema Lake Taitema Lake Taitema Lake Taitema Lake Taitema Lake Taitema Lake Taitema Lake Taitema Lake Kaoergan

7 8 9

1.12 2.82 2.01

Note: Transection for water reach means the furthest section in which the river flow larger than zero during each water convergence period.

quantifying the responses of eco-hydrological system to anthropogenic and natural disturbances. The prior coupling and simulating feedback between hydrological and ecological components has been realized in several recently developed eco-hydrological models. A probabilistic-mechanistic model framework with combination of climate, soil, and vegetation has been introduced to study the inter-annual climate fluctuation's impact on conditions of water stress of vegetation (Porporato et al., 2001; Rodriguez-Iturbe et al., 2001; Laio et al., 2001a, 2001b; Porporato et al., 2002). The vegetation dynamic model was not coupled in this kind of framework, but based on an assumption such as: ecosystems strive to minimize the vegetation stress, this approach is widely used to predict the vegetation pattern under climate change and human activities (Caylor et al., 2005; Ursino, 2005; Baudena et al., 2007; Franz et al., 2010; Liu, 2011). Then, the vegetation dynamic model is incorporated into the hydrology model through the concept of ecosystem carrying capacity or vegetation colonization rate and local extinction rate, which are highly influenced by the environment variables such as soil water content and groundwater depth (Zhao et al., 2005; Ridolfi et al., 2006; Borgogno et al., 2007; Muneepeerakul et al., 2008; Sun et al., 2011; Shi et al., 2012). This kind of model has been used to study properties, and potential steady state of eco-hydrological systems (Baudena et al., 2007; Chui et al., 2011; Borgogno et al., 2012; Liu et al., 2012; Karunasingha et al., 2013). Most of the eco-hydrological models established in previous studies have their limitations when applied to groundwater dependent ecosystem restoration studies at river basin scale. For example, the probabilistic-mechanistic model framework of Laio et al. (2001a) is constrained in a rainfall controlled ecosystem and is difficult to calibrate with short term observations such as vegetation coverage or soil moisture. Because this model framework mainly focuses on the stable state of an eco-hydrological system under anthropogenic and natural disturbance, it could not quantify the variation processes. The eco-hydrological model developed by Chui et al. (2011) simulated the hydrological component with a variably saturated groundwater model. This approach faces the problem of the huge computation effort and difficulty of parameterization when applied to a large river basin. Given these recent advances in understanding and modeling the interaction between ecology and hydrology system, we are now embarking on an era when we can hope to establish a spatial distributed eco-hydrological model, which is suitable for a groundwater dependent ecosystem and could quantify responses of the eco-hydrological system to hydrologic regime alterations at a large river basin scale. Furthermore, the water transfers enhanced riparian groundwater and vegetation development at a much faster

rate than under natural conditions in the lower reach of the Tarim River (Liu et al., 2012). Therefore, water transfer in the Tarim River serves as a good basin scale experiment for eco-hydrological system evolution. The objective of this work is to link models of groundwater flow and vegetation dynamics, so that responses of eco-hydrological system during the ecological restoration could be predicted and the interaction inside the eco-hydrological system could be quantified. This eco-hydrological model could be applied and calibrated for large river basins, to support vegetation restoration activities.

2. Materials and methods 2.1. Eco-hydrological model structure The simulation domain of the eco-hydrological model was discretized into MODFLOW grids, and then MODFLOW grids were reclassified into different vegetation zones. Grid cells in each vegetation zone were supposed to have identical vegetation type, similar vegetation growth status, and similar groundwater depth. During the simulation, grid cells in the same vegetation zone had the same vegetation dynamic process, potential evaporation rate, and potential transpiration rate. The structure of the eco-hydrological model was shown in Fig. 1. A vegetation dynamic model in a yearly time step was used. Instead of simulating vegetation dynamic of each MODFLOW grids, the model was applied to each MODFLOW zone. The input of this vegetation dynamic model was the yearly seasonal averaged groundwater depth of each MODFLOW vegetation zone covering the growing season. Several vegetation indexes have been chosen to represent vegetation dynamics such as: biomass, vegetation coverage, and Normalized Difference Vegetation Index (NDVI) (Liu et al., 2012; Jeevarathinam et al., 2013). In ecological research, the NDVI derived from satellite images was widely used as a tool to reflect the ecosystem status. For example, it has been used to study the woody plant response to drought (Lloret et al., 2007), and used to evaluate the post fire vegetation recovery processes (White et al., 1996). Recent work has shown that the response of NDVI to the water conveyance was very significant and NDVI could be used as an index to represent the riparian vegetation recovery process in the Tarim River (Wu and Tang, 2010; Sun et al., 2011). In this study, the yearly maximum NDVI was selected as the vegetation index that was modeled by the vegetation dynamic model. The yearly maximum NDVI(NDVImax) of each vegetation zone simulated by vegetation dynamic model was used to determine the yearly maximum leaf area index (LAImax) and the yearly maximum vegetation coverage (fcmax) for each vegetation zone through two empirical functions. The vegetation coverage was estimated using the method proposed by Gutman and Ignatov (1998), which is popular because of its feasibility of implementation (Montandon and Small, 2008). The formula is as follows:

fc

max;i


 NDVImax;i  NDVI0;i  ¼
NDVIg;i  NDVI0;i

(1)

Where: NDVI0,i and NDVIg,i are the bare soil NDVI and dense vegetation NDVI for vegetation zone i, respectively. NDVImax,i is the yearly maximum NDVI simulated by vegetation dynamic model for vegetation zone i. fc max,i is the yearly maximum vegetation coverage for vegetation zone i. The relationship between NDVI and the leaf area index has been studied by many researchers (Gamon et al., 1995; Wang et al., 2005; Liu et al., 2010). According to the research of Liu et al. (2010), the yearly maximum LAI was estimated by:

Please cite this article in press as: Han, M., et al., An eco-hydrological approach to predicting regional vegetation and groundwater response to ecological water conveyance in dryland riparian ecosystems, Quaternary International (2015), http://dx.doi.org/10.1016/j.quaint.2015.02.032

M. Han et al. / Quaternary International xxx (2015) 1e13

3

Fig. 1. Schematic diagram of the coupled eco-hydrological processes in the present study.

LAImax;i ¼

   NDVI 0:9385 ln 1  0:8max;i 0:3619

(2)

Where: LAImax,i is the yearly maximum LAI for vegetation zone i. The daily LAI and the vegetation coverage for each vegetation zone were estimated based on the vegetation phenology. A vegetation phenology function was defined for each vegetation zone. The phenology of the vegetation was divided into three periods shown in Fig. 2. The first period included the vegetation bud swelling stage, and early leaf stage. In this period, the leaf area index (LAI) and vegetation coverage (fc) were linearly increased from zero to its yearly maximum value. The second period included the vegetation early flowering stage, nutrition growing period stage, fruit ripening stage, and early fruit drop stage. At this period the LAI and fc are constant with yearly maximum values. The third period included the vegetation leaf color changing stage. At this period, the LAI and the fc linearly decreased from yearly maximum values to zero. In all, four parameters were used to define this

phenology function, D1 the starting day of period 1; D2 the end day of period 1 (the starting day of period 2); D3 the end day of period 2 (the starting day of period 3), D4 the end day of period 3. As rainfall is relatively small in this region, the main impact of the climate system on the eco-hydrological process is through the potential evapotranspiration. The Penman-Monteith equation was used to estimate the potential evapotranspiration for each MODFLOW zone:


 D$ðHnet  GÞ þ rair $cp $ e0z  ez ra   lE ¼ D þ g$ 1 þ rrac

where: lE is the latent heat flux density (MJ m2 d1), E is the depth rate evaporation (mm d1), D is the slope of the saturation vapor pressure-temperature curve, de/dT (kPa  C1), Hnet is the net radiation (MJ m2 d1), G is the heat flux density to the ground (MJ kg1  C1), e0z is the saturation vapor pressure of air at height z (kPa), ez is the water vapor pressure of air at height z (kPa),g is the psychrometric constant (kPa  C1), rc is the plant canopy resistance (s m1), and ra is the diffusion resistance of the air layer (aerodynamic resistance) (s m1). Many methods have been applied to divide the potential evapotranspiration into potential evaporation and potential transpiration (Allen et al., 2005; Neitsch et al., 2005). Here, we adopt the method of Neitsch et al. (2005) to calculated the potential transpiration and potential evaporation. The daily potential vegetation transpiration was calculated by Neitsch et al. (2005):

Et;i ¼

Ei $LAIday;i 3:0

Et;i ¼ Ei

Fig. 2. Leaf area index and vegetation coverage development based on the vegetation phenology information.

(3)

0  LAIday;i  3:0

LAIday;i > 3:0

(4) (5)

Where Et,i is the potential transpiration on a given day (mm) for vegetation zone i, Ei is the reference evapotranspiration calculated by Penman-Monteith equation (mm) for vegetation zone i. The LAIday,i is the daily leaf area index on a given day estimated from the phenology function for vegetation zone i.

Please cite this article in press as: Han, M., et al., An eco-hydrological approach to predicting regional vegetation and groundwater response to ecological water conveyance in dryland riparian ecosystems, Quaternary International (2015), http://dx.doi.org/10.1016/j.quaint.2015.02.032

4

M. Han et al. / Quaternary International xxx (2015) 1e13

The potential evaporation is defined by (Neitsch et al., 2005):





Es;i ¼ 1  fcday;i $Ei

(6)

Where: Es,i is the maximum soil evaporation on a given day (mm) for vegetation zone i, fcday,i is the daily vegetation coverage estimated by phenology function for vegetation zone i. The daily potential transpiration and potential evaporation for each vegetation zone were used as MODFLOW input for a modified EVT package, which could consider the transpiration and evaporation separately. Then, the MODFLOW model with new EVT package was used to simulate the groundwater movement. The seasonal averaged groundwater depth of each vegetation zone from the MODFLOW simulation result were used as new inputs in the vegetation dynamic model, and the yearly maximum NDVI of each vegetation zone was updated. This modeling cycle was ended when the differences of yearly maximum NDVI of each vegetation zone between two iterations were smaller than 0.001.

2.2. Vegetation dynamic model The Verhulst logistic model has been widely used as a basic equation to describe vegetation dynamics in various ecohydrological models (Ridolfi et al., 2006; Muneepeerakul et al., 2008). Mostly, two variables are inside the model: intrinsic growth rate b which reflects the temporal response of the system, and the environment carrying capacity Vcc, which is the maximum of vegetation index with the available resource, the key variable that relates the environment factors such as groundwater depth to the vegetation dynamics. The increase or decrease of the vegetation is defined by the difference between environment capacity and vegetation index status at a certain time. The vegetation dynamic model is defined as:

dNDVImax;i ¼ bi  NDVImax;i  dt

NDVImax;i 1 Vcc;i

! (7)

Where: NDVImax,i is the yearly maximum NDVI for vegetation zone i, i is the vegetation zone number. The impact of the groundwater table on vegetation dynamics is difficult to quantify. Vegetation can suffer from water stress, because of an insufficient aeration of the root zone, when the groundwater table is too shallow; if the groundwater table is too deep, vegetation can suffer as well. In most eco-hydrological models, these effects were taken into account by a quadratic dependence of the carrying capacity, Vcc, on the water table depth, h (Ridolfi et al., 2006; Scarsoglio et al., 2012). Due to the quadratic function properties, there is only one unique groundwater depth, at which the environment carrying capacity Vcc would reach its maximum value A. However, conceptually the suitable groundwater depth should be a range, as roots influence water uptake. So instead of defining the carrying capacity, Vcc as a quadratic function of h, it is defined as a piecewise function of h, as below:

Vcc;i ðhi Þ ¼

8 > > > > > > > > > > <

Ai 

hi vha;i

Ai
 > Ai  hi  vhb;i > > > 
> > > vhd;i  vhb;i > > > : 0

hi  vha;i vhb;i < hi < vha;i vhd;i  hi  vhb;i hi > vhd;i

(8)

Where i is the vegetation zone number, vha,i is the groundwater depth, above which the water stress of environment carrying capacity would increase because of insufficient aeration of the root zone; vhb,i is the groundwater depth, below which the water stress of environment carrying capacity would increase due to the insufficient water in soil for vegetation. When groundwater depth is smaller than vhb,i and larger than vha,i, there is no water stress for environment carrying capacity. While vhd,i is the groundwater depth below which there is no vegetation that could survive, and the environment carry capacity is linearly decreased from its maximum value Ai. hi is the yearly average groundwater depth during vegetation growing season of vegetation zone i, hi was obtained from MODFLOW simulation results.

2.3. Modification of MODFLOW In order to calculate the actual vegetation transpiration and evaporation for each MODFLOW grid, the origin MODFLOW EVT package was modified (Harbaugh et al., 2000). For each grid, the actual evaporation and transpiration were calculated in following steps: (1) calculating the water stress for soil evaporation and vegetation transpiration separately, based on grid's surface elevation, simulated groundwater head in the MODFLOW grids, grid's water stress function. The water stress function for bare soil and the vegetation was defined as:

aðmhÞ ¼

8 > > > > > > > > > > <

mh tha;i

1
 > mh  thb;i > > > 
> > thd;i  thb;i > > > > : 0

mh  tha;i thb;i < mh < tha;i

(9)

thd;i  mh  thb;i mh > thd;i

where: i is the vegetation zone number, the mh is grid's simulated the groundwater depth on a given day(m), the tha,i, thb,i, thd,i are the threshold parameters of vegetation zone i where the grid located(m), the conceptual shape of this stress function for bare soil and vegetation is shown in Fig. 3. (2) Then, the actual evaporation and transpiration in each time step for each MODFLOW grid is calculated as:

ETact ¼ aðmhÞveg;i  Et;i

(10)

ESact ¼ aðmhÞsoil  Es;i

(11)

Where: i is the vegetation zone number, a( )veg,i is the water stress function of grid's vegetation type on a given day, a( )soil is the water stress function for bare soil on a given day. ETact is the grid's actual transpiration (mm) on a given day, and the ESact is the grid's actual evaporation (mm) on a given day, mh is the grid's groundwater depth calculated by the difference between cell's surface elevation(m) and simulated groundwater table elevation(m). For the non-vegetation region, the actual evaporation is calculated by:

ESactnon ¼ aðmhÞsoil  Ei

(12)

Where ESactnon is the actual evaporation for the non-vegetation region on a given day (mm).

Please cite this article in press as: Han, M., et al., An eco-hydrological approach to predicting regional vegetation and groundwater response to ecological water conveyance in dryland riparian ecosystems, Quaternary International (2015), http://dx.doi.org/10.1016/j.quaint.2015.02.032

M. Han et al. / Quaternary International xxx (2015) 1e13

Fig. 3. Conceptual water stress function for different vegetation zone.

2.4. Case study in lower reaches of Tarim River and input The eco-hydrological model was applied to the lower reach of the Tarim River to simulate the groundwater movement and vegetation dynamics from 2000 to 2006. The study area is located between Dxihaizi reservoir and Taitema lake, covering an area of 3521.75 km2 between 39 000 and 41000 N, 87 200 and 88 600 W (Fig. 4). The Daxihaizi reservoir is located in the upper part of the study region. The total capacity of the reservoir is 1.68  108 m3 and average depth is 2e3 m. Vegetation is distributed sparsely along the river. Common flora consists of trees, shrubs, and herbs. Trees include Populus euphratica, shrubs include Tamarix spp, and Lycium ruthenicum, and herbs include Phragmites communis (Chen et al., 2010). The Tarim River is divided into two branches starting from Daxihaizi reservoir. The west is the old Tarim River and the east is the Qiwenkuoer river. The two branches meet at Alagan (Fig. 4). Total length of the river system is 492 km. There are two deserts on both sides of the lower Tarim River, the Taklimakan desert on the west and the Kuluk desert on the east. It is one of the most arid

5

areas in China, with an average annual precipitation of less than 50 mm, and the annual potential evaporation ranging between 2500 mm and 3000 mm (Chen et al., 2010). The lower Tarim River is located in an alluvial plain and its hydrogeological condition is simple. The multi-layered aquifer with homogenous lithology is dominated by fluvial and lacustrine facies of fine sand, silt fine sand, and locally by eolian sand (Deng, 2009; Huang and Pang, 2010). The typical porous aquifer can be divided into phreatic and confined zones. The phreatic aquifer mainly consist of fine sand and silty fine sand and has relatively low permeability and poor water yield with a thickness of 30e40 m, and is underlain by a clay bed (Deng, 2009; Huang and Pang, 2010). The regional flow direction of groundwater is south-southeast, as the general direction of river flow. The slope of the water table is very small, and the low hydraulic conductivity makes the movement of the groundwater system very slow. Recharge from the upper-left boundary can be neglected (Yang, 2005). The main recharge of the phreatic aquifer is leakage from the lower reach of Tarim River. When there is water in the river channel, the flow direction of groundwater near the river channel would be 90 to the river flow direction. However, the area impacted by the river is very limited. The main groundwater discharge is evapotranspiration, and Taitema Lake is the final groundwater discharge area. Our study focused on the phreatic aquifer. The groundwater system of the lower reach of Tarim River could be conceptually described as a single layer, unconfined, heterogeneous, horizontal isotropy, two-dimensional and transient flow system. The boundary of the groundwater system was simplified as a no flow boundary, because there is little exchange at the upper-left boundary and lower right boundary, and the remainder of the boundary was at 90 to the groundwater flow direction (Fig. 5). The reservoir and the Taitema lake were characterized as general head boundary and lake boundary respectively. The interaction between river and phreatic aquifer was simulated with the MODFLOW SFR1 package (Prudic et al., 2004). The land use map (1:100,000) obtained from the Geographic institute of CAS revealed 5 land cover classes found in the study region, reclassified into 10 classes based on vegetation type and maximum NDVI during 2000e2006 (Fig. 5 and Table 2). Vegetation zones G4 and T4 (Table 2) were treated as bare soil and not simulated by the vegetation dynamic model, as the six year maximum

Fig. 4. A is Location of study area in Tarim river basin, B is the study area and monitoring sections along lower reach of Tarim.

Please cite this article in press as: Han, M., et al., An eco-hydrological approach to predicting regional vegetation and groundwater response to ecological water conveyance in dryland riparian ecosystems, Quaternary International (2015), http://dx.doi.org/10.1016/j.quaint.2015.02.032

6

M. Han et al. / Quaternary International xxx (2015) 1e13

Fig. 5. Eco-hydrological model input. A) is the initial groundwater table distribution, B) is the vegetation zones divided base on the land use map and six year maximum NDVI from 2000 to 2006 (Table 2). C) is the boundary conditions of the eco-hydrological model. D) is the surface elevation from STRM and resampled to 250 m resolution.

NDVI of these two zones were smaller than 0.1. Observations from the climate station at Alagan were used to estimate the potential evapotranspiration for all zones in the study area. In order to evaluate the eco-hydrological model, nine observation sites were established for monitoring groundwater levels and vegetation dynamics (Fig. 5). The distance between the two neighboring sections of the first 6 sections was about 20 km, and the distance between the last three sections was about 40 km. All sections were at 90 to the channel. Three to eight groundwater wells were installed along the centerline of each transaction at

distances of 50 m, 150 m, 350 m, 450 m, 700 m, and 1000 m respectively. The groundwater table was measured using the conductance method, and we generally took measurements every 1 or 2 months, but every 10 days during the water conveyance (Chen et al., 2010). The vegetation dynamic model and groundwater model were calibrated separately. First, the groundwater model was calibrated with the observed yearly maximum NDVI from MODIS observation. Then, the averaged seasonal groundwater depth from calibrated groundwater model was used as input to calibrate the vegetation

Please cite this article in press as: Han, M., et al., An eco-hydrological approach to predicting regional vegetation and groundwater response to ecological water conveyance in dryland riparian ecosystems, Quaternary International (2015), http://dx.doi.org/10.1016/j.quaint.2015.02.032

M. Han et al. / Quaternary International xxx (2015) 1e13

2

Table 2 Vegetation zone classes in the study area. Original vegetation type

New type

Six year maximum NDVI

Area (km2)

Desert Tree or shrub

Desert T1 T2 T3 T4 G1 G2 G3 G4 Cotton field Reservoir Lake

NDVI ＜ 0.1 NDVI  0.2 0.2 ＞ NDVI  0.15 0.2 ＞ NDVI  0.1 <0.1 >0.2 0.2 ＞ NDVI  0.15 0.2 ＞ NDVI  0.1 <0.1 >0.7 <0.1 <0.1

1436.5 83.1875 149.625 340.125 77.125 68.1875 79.4375 449.625 664.3125 10.375 56.6875 106.5625

Grass

Cotton field Reservoir Lake

dynamic model. Finally, the combined model cycling shown in Fig. 1 was set up, and the result from combined model cycling was used to evaluate the model performance. Initial value and the calibrated of model parameters are listed in Table 3. The observation of 60 wells with total 3072 observations from 2000 to 2006, the river head observations of each water conveyance event (Table 1), and the observed yearly maximum NDVI of vegetation zones from MODIS were used to calibrated the parameters in the vegetation dynamic package, groundwater package, and MODFLOW SFR1 package (Table 3). The parameters in the EVT package, NDVI-fc package and Phenology package were obtained from the literature. NDVIo and NDVIg used the results of Guli et al. (2009) and Niu et al. (2005), who studied the relationship between the vegetation coverage and NDVI in Yahepu section and Yinsu section. Parameters for vegetation phenology were obtained from Chang et al. (2009), who studied the 42 types of vegetation phenology based on observation data since 1974. The phenology of Populus enphratica was used to represent the forest phenology in the study area, while the phenology of Phragmites communis was used to represent the grass (herb) phenology in the study area. Agreement between simulated results and observed data was evaluated by correlation coefficient R2 and Root Mean Standard error (RMSE).

" RMSE ¼

n 1X ðCsi  Cob Þ2 n

7

#1=2 (13)

i¼1

32 

 Pn

6 7 Cob  Cob 6 7 i¼1 Csi  Csi 7 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi r R2 ¼ 6 6 P

2 P

2 7 4 5 n n i¼1 Csi  Csi i¼1 Cob  Cob

(14)

Where: Csi is the simulated data, Cob is the observed data, n is the total number of data, Csi and Cob are the mean values of simulated and observed data set, respectively.

2.5. Scenarios study 2.5.1. Scenario 1(S1): impact of vegetation dynamics on evapotranspiration To study the impact of vegetation dynamics on the simulated evapotranspiration during 2000e2006, the case study of the lower Tarim River (base case) was modified. Instead of using the vegetation dynamic model to simulate yearly maximum NDVI for each MODFLOW vegetation zone, the observed yearly maximum NDVI of 2000 from MODIS was used as a constant value for each MODFLOW zone from 2000 to 2006.

2.5.2. Scenario 2(S2): groundwater and vegetation dynamics prediction under certain water conveyance To investigate the groundwater and vegetation dynamics under certain water conveyance scenarios, a scenario study has been carried out. Under this scenario, the water conveyance was carried out at constant release rate to both the Qiwenkuoer River and old Tarim River during the vegetation growing season from April to December. The daily flow rate was 423621.46 m3, total 2.07  108 m3 per year. The model was run for 20 years.

2.5.3. Scenario 3(S3): impact of vegetation dynamic on groundwater prediction To test the significance of accounting for vegetation dynamics on groundwater hydrology, the simulation with a hypothetical water conveyance (S2) was repeated with a revised model. In this model, the vegetation dynamic model has been disabled, and the yearly maximum NDVI of 2000 was used as a constant value for each vegetation zone during the 20 years simulation.

Table 3 Initial and calibrated parameter for eco-hydrology model. Package

Parameter

Tree

Grass

Desert

Cotton

Source

EVT

Tha(m) Thb(m) Thd(m) NDVImax NDVImin D1 (day) D2 (day) D3 (day) D4 (day)

2.00 5.50 8.00 0.57 0.05 98 124 249 292 T1 0.4 0.42 2.00 4.41 9.69 4 0.15

0.10 3.50 5.00 0.57 0.05 102 117 268 293 T2 0.2 0.758 2.00 4.00 14.10

0.00 2.00 4.00 0.57 0.05 e e e e T3 0.19 0.141 2.00 6.00 13.10

0.1 0.5 2 0.57 0.05 163 194 234 291 G1 0.4 0.423 2.00 5.74 7.41

(Deng, 2009)

NDVI-fc Phenology

Vegetation dynamic

Groundwater

A b vha(m) vhb(m) vhd(m) Ks(m/day)

m

(Niu et al., 2005; Guli et al., 2009) (Chang et al., 2009)

G2 0.19 0.6 2.00 4.50 12.00

G3 0.166 0.173 2.00 5.3 15.00

Calibrated

Calibrated

Note: Parameters in EVT, NDVI-fc, Phenology packages are defined for each vegetation type. Vegetation zones belongs to the same vegetation type use the same parameter value. The parameters in Vegetation dynamic package is defined for each vegetation zones, parameters between vegetation zones are different.

Please cite this article in press as: Han, M., et al., An eco-hydrological approach to predicting regional vegetation and groundwater response to ecological water conveyance in dryland riparian ecosystems, Quaternary International (2015), http://dx.doi.org/10.1016/j.quaint.2015.02.032

8

M. Han et al. / Quaternary International xxx (2015) 1e13

3. Results and discussion 3.1. Model calibration 3.1.1. Calibration of groundwater model The scatter plot between simulated and observed groundwater head was shown in Fig. 6a, and the simulated groundwater head of three observation wells at Yinsu, Alagan and Yiganbujima sections were shown in Figs. 6b, c, d, respectively. The RMSE and the R2 between simulated groundwater head and groundwater table observations were labeled on Fig. 6a. During the calibration period, wells with an RMSE less than 0.7 m accounted for 83% of the total observation wells. The result shown in Fig. 6 indicated that good agreement between simulated and observed groundwater head was achieved. The groundwater distribution after the 1st, 3rd, 4th, 6th, 8th and 9th water conveyance were shown in Fig. 7. A rise of the groundwater table was observed between Daxihaizi reservoir and Kaerdayi section after 1st water conveyance (Nov 2, 2000), and after 3rd water conveyance (Sep 11, 2001) a rise of the groundwater table was observed between Daxihaizi reservoir and Kaogan section. After the 4th water conveyance (July 19, 2002) a rise of groundwater head was found between Daxihaizi reservoir and Taitema Lake. At the same time, the discharge from Tarim River to Taitema lake was larger than zero in the 4th water conveyance period. These processes were consistent with river flow observations shown in Table 1. Overall, the model result showed reasonably good agreement with the observed values, which indicated that the mathematical model could describe the hydrogeological conceptual model of the study area. 3.1.2. Calibration of vegetation dynamic model The calibrated simulation results of yearly maximum NDVI at each vegetation zone are shown in Fig. 8 and Fig. 9. The observed

yearly maximum NDVI of each vegetation zone calculated by the 16-day MODIS NDVI composite, also shown in Figs. 8 and 9, showed a general increasing trend from 2000 to 2006. The simulated yearly maximum NDVI presented a similar trend with observed yearly maximum NDVI in all vegetation zones. The simulated and observed yearly maximum NDVI both showed decreasing trends from vegetation zone T1 to T3, and G1 to G3. The R2 and the RMSE between the observed yearly maximum NDVI and simulated yearly maximum NDVI were calculated for tree and grass vegetation zones separately. The R2 was larger than 0.98 for both tree and grass zones. RMSE for both tree and grass vegetation zones was smaller than 0.01. Overall, the eco-hydrological model could generally describe the vegetation dynamic during the water conveyance from 2000 to 2006. 3.2. Analysis of water balance The water balance components of groundwater during 9 water conveyance periods are shown in Table 4. The results showed that the total groundwater recharge from river was 1.97  109 m3, accounting for 87.4% of the total amount of water conveyance, water consumed by evapotranspiration (bare soil evaporation and vegetation transpiration) was 1.04  109 m3, taking 46.1% of the total amount of water conveyance. The reservoir seepage, flow into Taitema lake, and Taitema lake leakage were very little compared with river leakage. The variations in groundwater storage and evapotranspiration during 2000e2006 are also shown in Table 4. Evapotranspiration was increasing during the 9 water conveyances. For example, the total amount of evapotranspiration during the 1st, 2nd and 3rd water conveyance (period 1) was 1.48  108 m3, the sum of evapotranspiration during the 4th and 5th water conveyance (period 2) was 1.93  108 m3, and the evapotranspiration amount in the 6th water conveyance (period 3) was 3.02  108 m3. Together, the three periods cover almost 1 year. Compared to the

Fig. 6. Comparison of simulated and observed groundwater elevation at observation sections. A) is the observed and simulated groundwater elevation for all observations. Line in B), C), and D) is the simulated groundwater elevation at observation well CT1, G4, and I2. The dot in B), C), and D) is the observations in corresponding observation wells.

Please cite this article in press as: Han, M., et al., An eco-hydrological approach to predicting regional vegetation and groundwater response to ecological water conveyance in dryland riparian ecosystems, Quaternary International (2015), http://dx.doi.org/10.1016/j.quaint.2015.02.032

M. Han et al. / Quaternary International xxx (2015) 1e13

9

Fig. 7. Groundwater table distribution after 1st, 3rd, 4th, 6th, 8th, and 9th water conveyances.

evapotranspiration in period 1, the evapotranspiration in period 2 and period 3 increased by 30.9% and 100.2% respectively. This could be explained by the rise of the groundwater table and the increasing of the yearly maximum NDVI. The rise of groundwater table would reduce the water stress, while the increasing of the vegetation yearly maximum NDVI would increase the leaf area index and vegetation coverage which would further result in increasing of the potential transpiration. To study the impact of vegetation dynamic on the increase of evapotranspiration from period 1 to period 2 and period 3, a scenario study that remain the yearly maximum NDVI of 2000 has been carried out (S1). The results showed that evapotranspiration could increase by 3.8% from 2000 to 2006 with vegetation dynamic considered. Comparison of the evapotranspiration between S1 and base case in period 1, period 2, and period 3 indicates that the coupling of vegetation

dynamic model could contribute 1.76% of evapotranspiration increase from period 1 to period 2, and 4.41% of evapotranspiration increase from period 1 to period 3. The remainder of the increase in evapotranspiration was mainly due to the rise of the groundwater table. The ratios of groundwater storage change (net recharge) to the amount of water conveyance were 70.8%, 48.3%, and 35.4%, in period 1, period 2, and period 3 respectively (Table 4, base case study). The decreasing trend of this ratio showed that the rise of groundwater table and recovery of vegetation resulted in more water lost by evapotranspiration. Evapotranspiration and the ratio of net recharge to water conveyance amount were not constant from 2000 to 2006: they were influenced by both vegetation recovery process and the rise of the groundwater table. In order to make a sound schedule of water conveyance, the coupling model of the groundwater and ecology is required.

Please cite this article in press as: Han, M., et al., An eco-hydrological approach to predicting regional vegetation and groundwater response to ecological water conveyance in dryland riparian ecosystems, Quaternary International (2015), http://dx.doi.org/10.1016/j.quaint.2015.02.032

10

M. Han et al. / Quaternary International xxx (2015) 1e13

Fig. 8. Comparison of simulated and observed yearly maximum NDVI for vegetation zone Tree. A) is the simulated yearly maximum NDVI and observed yearly maximum NDVI for all Tree vegetation zones. B), C), D) are the time series of simulated yearly maximum NDVI and observed yearly maximum NDVI of vegetation zone T1, T2, and T3, respectively.

3.3. Prediction of groundwater depth and vegetation dynamic under water conveyance scenario The groundwater depth and the vegetation dynamic during the 20 years simulation of S3 are shown in Fig. 10 and Fig. 11. Figs. 10a

and b present the dynamic of averaged groundwater depth in each forest zone and each grass zone, respectively. Figs. 11a, and Fig. 11b show the yearly maximum NDVI dynamic in each zone. The groundwater table and yearly maximum NDVI increased year by year, and the dynamic of groundwater depth reached stable status

Fig. 9. Comparison of simulated and observed yearly maximum NDVI for grass vegetation zones. A) is the simulated yearly maximum NDVI and observed yearly maximum NDVI for all zones. B), C), D) are the time series of simulated yearly maximum NDVI and observed NDVI of vegetation zone G1, G2, and G3, respectively.

Please cite this article in press as: Han, M., et al., An eco-hydrological approach to predicting regional vegetation and groundwater response to ecological water conveyance in dryland riparian ecosystems, Quaternary International (2015), http://dx.doi.org/10.1016/j.quaint.2015.02.032

M. Han et al. / Quaternary International xxx (2015) 1e13

11

Table 4 Water balance of the groundwater system during each water conveyance period (  108 m3). Start date

End date

2000-5-14 2000-11-3 2001-3-32 2001-9-12 2002-7-20 2003-3-3 2004-3-25 2005-5-7 2006-9-25 2000-5-14

2000-11-2 0.99 2001-3-31 2.27 2001-9-11 1.84 2002-7-19 1.98 2003-3-2 3.31 2004-3-24 6.20 2005-5-6 1.12 2006-9-24 2.82 2006-12-31 2.01 2006-12-31 22.55

Total water conveyance To Taitema lake River leakage Reservoir leakage Lake leakage Evapotraspiration Groundwater storage change 0.00 0.00 0.00 0.00 0.08 0.37 0.00 0.12 0.00 0.57

0.96 2.16 1.79 1.75 2.53 4.96 1.01 2.56 1.99 19.71

quicker than did the vegetation dynamic. For example, the groundwater depth of T1 reached stable status approximately 6 years after water conveyance, while the vegetation dynamic of T1 reached stable status around 11 years after water conveyance. After 20 years of water conveyance, vegetation zone T1 and G1 had yearly maximum NDVI larger than 0.25, and seasonal averaged groundwater depth less than 6 m. The total area of this region was 151.375 km2, 4.3% of the total area of study region and 13% of the vegetation zone area. The area of yearly maximum NDVI larger than 0.12 including the vegetation zones (T1, T2, T3, G1, and G2) was 720.56 km2, 20% of the total area of this study region, and accounting for 62% of the vegetation zone. This kind of information would be helpful for management strategies of water conveyance. 3.4. Comparisons between considering vegetation dynamic and using pre-specified yearly maximum NDVI Fig. 12 shows the variation in averaged groundwater depth in each vegetation zone during the 20 years simulation period. Groundwater depth obtained in the simulation in which vegetation dynamic was considered (mean: 7.4 ± 0.42 m standard error) was found to be significantly higher than in the simulation without

0.07 0.06 0.06 0.12 0.08 0.14 0.15 0.19 0.04 0.91

0.00 0.00 0.00 0.06 0.07 0.10 0.00 0.03 0.00 0.28

0.26 0.14 1.08 0.86 0.75 3.02 1.93 2.17 0.18 10.39

0.76 2.08 0.78 1.07 1.93 2.19 0.77 0.61 1.85 10.50

consideration of the vegetation dynamic (mean: 7.26 ± 0.48 m standard error). The actual evapotranspiration in the simulation where vegetation dynamic was considered was 3770880 m3 (2% of the evapotranspiration in the 20th year), larger than that simulated without consideration of the vegetation dynamic. The change in groundwater storage obtained in the simulation with vegetation dynamic was 3230336 m3 (20.4% of groundwater storage change in the 20th year), larger than in the simulation without the vegetation dynamic. By explicitly modeling for the time-dependent vegetation dynamic with changes in hydrologic and weather conditions, the eco-hydrological model directly accounts for this vegetation physiological response to environmental conditions, and its feedback to the groundwater hydrology. This study clearly revealed that predicted seasonal groundwater depth, evapotranspiration, and change in groundwater storage between the two scenarios were significantly different. 4. Conclusion The eco-hydrological model has attracted substantial research attention recently. To our knowledge, eco-hydrological models established in previous studies have their limitations as applied to

Fig. 10. Groundwater depth of vegetation zones during 20 years simulation with scenario 3. Scenario 3 is modified from base case study, with constant amount of water conveyance during the vegetation growing season in 20 years. A) is the averaged groundwater depth variation of Tree vegetation zones in 20 years, B) is the averaged groundwater depth variation of grass vegetation zones in 20 years.

Please cite this article in press as: Han, M., et al., An eco-hydrological approach to predicting regional vegetation and groundwater response to ecological water conveyance in dryland riparian ecosystems, Quaternary International (2015), http://dx.doi.org/10.1016/j.quaint.2015.02.032

12

M. Han et al. / Quaternary International xxx (2015) 1e13

Fig. 11. Yearly maximum NDVI of vegetation zones during 20 years simulation with scenario 3. A is the yearly maximum NDVI variation of Tree vegetation zones in 20 years, B is the yearly maximum NDVI variation of grass vegetation zones in 20 years.

ecological restoration studies and validation at a river basin scale. This is the first report regarding development and application of an eco-hydrological model in a large river basin scale. The eco-hydrological model coupled with groundwater movement and the vegetation dynamic was developed in this study. The evaluation of the model by groundwater table and yearly maximum NDVI from 2000 to 2006 indicated that the model can be used at acceptable accuracy. After 6 years of water conveyance, the total amount of groundwater recharge from the river was 1.97  109 m3, 87.4% of the total amount of water conveyance. Water consumed by evapotranspiration (bare soil evaporation and vegetation transpiration) was 1.04  109 m3, 46.1% of the amount of total water conveyance. The evapotranspiration and the ratio of groundwater net recharge to water conveyance amount varied significantly from 2000 to 2006. They were influenced by both vegetation recovery and the rise of the groundwater table. Therefore, a coupling model of the groundwater and vegetation dynamic is required for best management practices and schedules of water conveyance. After 20 years of water conveyance, the vegetation zone which has yearly maximum NDVI larger than 0.25 was 151.375 km2, 4.3% of the total area of this study region and 13% of the vegetation zone area. The area of yearly maximum NDVI larger than 0.12 was 720.56 km2, 20% of the total area of the study region, and 62% of the vegetation zone. Such information would be helpful for the regulation agency to make better strategies of water conveyance. The interaction between hydrology and vegetation components has been revealed by analysis of water balance and studies of

Fig. 12. Average groundwater depth in the 20th year simulation under S2 and S3. S2 is the 20 years simulation under a water conveyance scenario with vegetation dynamic model. S3 is the result from simulation that revised from scenario 2 by removing vegetation dynamic, and using constant yearly maximum NDVI of 2000 during 20 years simulation.

scenarios. The vegetation dynamic has significant impact on groundwater evapotranspiration, groundwater storage change and groundwater depth, while groundwater depth is a critical feedback variable that determines the vegetation dynamics. The Tarim River is experiencing severe shortage of water resources and there is great conflict between ecological and economical water requirements in the area. This situation is rather common for many other river basins in both arid and semiarid areas. The eco-hydrological model developed in this study can be used to help to recover vegetation and protect ecological systems in these regions.

Acknowledgements The study was supported by the National Natural Science Foundation (4117095, U14003281), National Project (2013BAC10B01) and National 973 Project (2013CB429905) of China. The authors are grateful to Dr Pengnian Yang of Xinjiang Agriculture University, China and Keilholz Patrick of Technical University Munich, Germany, for their help in obtaining the groundwater observations.

References Allen, R.G., Pereira, L.S., Smith, M., Raes, D., Wright, J.L., 2005. FAO-56 dual crop coefficient method for estimating evaporation from soil and application extensions. Journal of Irrigation and Drainage Engineering 131, 2e13. Baudena, M., Boni, G., Ferraris, L., Von Hardenberg, J., Provenzale, A., 2007. Vegetation response to rainfall intermittency in drylands: results from a simple ecohydrological box model. Advances in Water Resources 30, 1320e1328. Borgogno, F., D'Odorico, P., Laio, F., Ridolfi, L., 2007. Effect of rainfall interannual variability on the stability and resilience of dryland plant ecosystems. Water Resources Research 43. Borgogno, F., D'Odorico, P., Laio, F., Ridolfi, L., 2012. Stochastic resonance and coherence resonance in groundwater-dependent plant ecosystems. Journal of Theoretical Biology 293, 65e73. Caylor, K.K., Manfreda, S., Rodriguez-Iturbe, I., 2005. On the coupled geomorphological and ecohydrological organization of river basins. Advances in Water Resources 28, 69e86. Chang, Z., Han, F., Zhong, S., 2009. Cluster analysis on Phenological type of 42 kinds of plants in Minqin desert area. Chinese Agricultural Science Bulletin 25, 228e234. Chen, Y.N., Chen, Y.P., Xu, C.C., Ye, Z.X., Li, Z.Q., Zhu, C.G., Ma, X.D., 2010. Effects of ecological water conveyance on groundwater dynamics and riparian vegetation in the lower reaches of Tarim River, China. Hydrological Processes 24, 170e177. Chen, Y.N., Pang, Z.H., Chen, Y.P., Li, W.H., Xu, C.C., Hao, X.M., Huang, X., Huang, T.M., Ye, Z.X., 2008. Response of riparian vegetation to water-table changes in the lower reaches of Tarim River, Xinjiang Uygur, China. Hydrogeology Journal 16, 1371e1379. Chui, T.F.M., Low, S.Y., Liong, S.Y., 2011. An ecohydrological model for studying groundwaterevegetation interactions in wetlands. Journal of Hydrology 409, 291e304. Crawford, R., 2000. Eco-hydrology: Plants and water in Terrestrial and Aquatic environments. Journal of Ecology 88, 1095e1096. Deng, M., 2009. Theory and Practice of Water Management in the Tarim River, China. Science Press, Beijing.

Please cite this article in press as: Han, M., et al., An eco-hydrological approach to predicting regional vegetation and groundwater response to ecological water conveyance in dryland riparian ecosystems, Quaternary International (2015), http://dx.doi.org/10.1016/j.quaint.2015.02.032

M. Han et al. / Quaternary International xxx (2015) 1e13 Franz, T.E., Caylor, K.K., Nordbotten, J.M., Rodríguez-Iturbe, I., Celia, M.A., 2010. An ecohydrological approach to predicting regional woody species distribution patterns in dryland ecosystems. Advances in Water Resources 33, 215e230. Gamon, J.A., Field, C.B., Goulden, M.L., Griffin, K.L., Hartley, A.E., Joel, G., Penuelas, J., Valentini, R., 1995. Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecological Applications 5, 28e41. Guli, J., Chen, X., Bao, a., 2009. Coverage extraction and up-scaling of sparse desertvegetation in arid area. Chinese Journal of Applied Ecology 20, 2925e2934. Gutman, G., Ignatov, A., 1998. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. International Journal of Remote Sensing 19, 1533e1543. Hannah, D.M., Wood, P.J., Sadler, J.P., 2004. Ecohydrology and hydroecology: a ‘new paradigm’? Hydrological processes 18, 3439e3445. Hao, X.M., Chen, Y.N., Li, W.H., 2009. Indicating appropriate groundwater tables for desert river-bank forest at the Tarim River, Xinjiang, China. Environmental Monitoring and Assessment 152, 167e177. Harbaugh, A.W., Banta, E.R., Hill, M.C., McDonald, M.G., 2000. MODFLOW-2000, the US Geological Survey Modular Ground-water Model: User Guide to Modularization Concepts and the Ground-water Flow Process. US Geological Survey Reston, VA, USA. Huang, T., Pang, Z., 2010. Changes in groundwater induced by water diversion in the Lower Tarim River, Xinjiang Uygur, NW China: evidence from environmental isotopes and water chemistry. Journal of Hydrology 387, 188e201. Jeevarathinam, C., Rajasekar, S., Sanjuan, M.A., 2013. Vibrational resonance in groundwater-dependent plant ecosystems. Ecological Complexity 15, 33e42. Karunasingha, D.S., Chui, T.F.M., Liong, S.-Y., 2013. An approach for modelling the effects of changes in hydrological environmental variables on tropical primary forest vegetation. Journal of Hydrology 505, 102e112. Laio, F., Porporato, A., Fernandez-Illescas, C., Rodriguez-Iturbe, I., 2001a. Plants in water-controlled ecosystems: active role in hydrologic processes and response to water stress: IV. Discussion of real cases. Advances in Water Resources 24, 745e762. Laio, F., Porporato, A., Ridolfi, L., Rodriguez-Iturbe, I., 2001b. Plants in watercontrolled ecosystems: active role in hydrologic processes and response to water stress: II. Probabilistic soil moisture dynamics. Advances in Water Resources 24, 707e723. Liu, D., Tian, F., Hu, H., Lin, M., Cong, Z., 2012. Ecohydrological evolution model on riparian vegetation in hyperarid regions and its validation in the lower reach of Tarim River. Hydrological processes 26, 2049e2060. Liu, H.-H., 2011. Impact of climate change on groundwater recharge in dry areas: an ecohydrology approach. Journal of Hydrology 407, 175e183. Liu, Y., Wang, J., Zhou, H., Xue, H.z., 2010. LAI measuring data Processing,Analysis and spatial scaling in the middle reaches of Heihe Experimental research region. Remote Sensing Technology and Application 25, 805e813. Lloret, F., Lobo, A., Estevan, H., Maisongrande, P., Vayreda, J., Terradas, J., 2007. Woody plant richness and NDVI response to drought events in Catalonian (northeastern Spain) forests. Ecology 88, 2270e2279. Loheide, S.P., Gorelick, S.M., 2007. Riparian hydroecology: a coupled model of the observed interactions between groundwater flow and meadow vegetation patterning. Water Resources research 43, W07414. Montandon, L., Small, E., 2008. The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI. Remote Sensing of Environment 112, 1835e1845. Muneepeerakul, C.P., Miralles-Wilhelm, F., Tamea, S., Rinaldo, A., RodriguezIturbe, I., 2008. Coupled hydrologic and vegetation dynamics in wetland ecosystems. Water Resources Research 44. Neitsch, S., Arnold, J., Kiniry, J., Williams, J., King, K., 2005. Soil and Water Assessment Tool Theoretical Documentation Version 2005 (Texas, USA).

13

Niu, B., Liu, J., Wang, Z., 2005. Remote sensing information extraction based vegetation fraction in semiarid area. Journal of Geo-information Science 7, 97e131. Porporato, A., D’odorico, P., Laio, F., Ridolfi, L., Rodriguez-Iturbe, I., 2002. Ecohydrology of water-controlled ecosystems. Advances in Water Resources 25, 1335e1348. Porporato, A., Laio, F., Ridolfi, L., Rodriguez-Iturbe, I., 2001. Plants in watercontrolled ecosystems: active role in hydrologic processes and response to water stress: III. Vegetation water stress. Advances in Water Resources 24, 725e744. Prudic, D.E., Konikow, L.F., Banta, E.R., 2004. A New Streamflow-routing (SFR1) Package to Simulate Stream-aquifer Interaction with MODFLOW-2000. US Department of the Interior, United States Geological Survey. Ridolfi, L., D'Odorico, P., Laio, F., 2006. Effect of vegetationewater table feedbacks on the stability and resilience of plant ecosystems. Water resources research 42. Rodriguez-Iturbe, I., Porporato, A., Laio, F., Ridolfi, L., 2001. Plants in watercontrolled ecosystems: active role in hydrologic processes and response to water stress: I. Scope and general outline. Advances in Water Resources 24, 695e705. Scarsoglio, S., D'Odorico, P., Laio, F., Ridolfi, L., 2012. Spatio-temporal stochastic resonance induces patterns in wetland vegetation dynamics. Ecological Complexity 10, 93e101. Shi, F., Zhao, C., Sun, D., Peng, D., Han, M., 2012. Conjunctive use of surface and groundwater in central Asia area: a case study of the Tailan River Basin. Stochastic Environmental Research and Risk Assessment 26, 961e970. Sun, Z., Chang, N.B., Opp, C., Hennig, T., 2011. Evaluation of ecological restoration through vegetation patterns in the lower Tarim River, China with MODIS NDVI data. Ecological Information 6, 156e163. Sun, D., Zhao, C., Wei, H., Peng, D., 2011. Simulation of the relationship between land use and groundwater level in Tailan River basin, Xinjiang, China. Quaternary International 244, 254e263. Ursino, N., 2005. The influence of soil properties on the formation of unstable vegetation patterns on hillsides of semiarid catchments. Advances in Water Resources 28, 956e963. Wang, Q., Adiku, S., Tenhunen, J., Granier, A., 2005. On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sensing of Environment 94, 244e255. White, J.D., Ryan, K.C., Key, C.C., Running, S.W., 1996. Remote sensing of forest fire severity and vegetation recovery. International Journal of Wildland Fire 6, 125e136. Wright, J.M., Chambers, J.C., 2002. Restoring riparian meadows currently dominated by Artemisa using alternative state concepts-above-ground vegetation response. Applied Vegetation Science 5, 237e246. Wu, J., Tang, D., 2010. The influence of water conveyances on restoration of vegetation to the lower reaches of Tarim River. Environmental Earth Sciences 59, 967e975. Yang, P., 2005. Study on Restoration of Groundwater and Vegetation in Lower Reaches of Tarim River after Intermittent Water Transportation. Xinjiang Agriculture University, Urumchi. Yu, P.J., Xu, H.L., Ye, M., Liu, S.W., Gong, J.J., An, H.Y., Fu, J.Y., 2012. Effects of ecological water conveyance on the ring increments of Populus euphratica in the lower reaches of Tarim River. Journal of Forest Research-Japan 17, 413e420. Zalewski, M., 2002. EcohydrologydThe use of ecological and hydrological processes for sustainable management of water resources. Hydrological Sciences Journal 47, 823e832. Zhao, C., Wang, Y., Chen, X., Li, B., 2005. Simulation of the effects of groundwater level on vegetation change by combining FEFLOW software. Ecological Modelling 187, 341e351. Zhao, C., Wang, Y., Hu, S., Li, Y., 2004. Effects of spatial variability on estimation of evapotranspiration in the continental river basin. Journal of Arid Environments 56, 373e382.

Please cite this article in press as: Han, M., et al., An eco-hydrological approach to predicting regional vegetation and groundwater response to ecological water conveyance in dryland riparian ecosystems, Quaternary International (2015), http://dx.doi.org/10.1016/j.quaint.2015.02.032