Evaluating the impact of climate change on groundwater resources in a small Mediterranean watershed

STOTEN-16558; No of Pages 11 Science of the Total Environment xxx (2014) xxx–xxx

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Evaluating the impact of climate change on groundwater resources in a small Mediterranean watershed Ali Ertürk a, Alpaslan Ekdal b,⁎, Melike Gürel b, Nusret Karakaya c, Cigdem Guzel d, Ethem Gönenç d a

Istanbul University, Faculty of Fisheries, Division of Freshwater Biology, 34470 Laleli, Istanbul, Turkey Istanbul Technical University, Environmental Engineering Department, 34469 Maslak, Istanbul, Turkey Abant İzzet Baysal University, Environmental Engineering Department, Gölköy Campus, 14280 Bolu, Turkey d IGEM Research & Consulting Co., Kadıköy, Istanbul, Turkey b c

H I G H L I G H T S • • • • •

Impacts of future changes in climate on groundwater resources were examined. Future conditions for four scenarios were simulated by SWAT model. The results indicated a significant decrease in groundwater recharge and storage. Agriculture sector is the main consumer of water. Switching to efficient irrigation and less water demanding crops should be studied.

a r t i c l e

i n f o

Article history: Received 15 March 2014 Received in revised form 29 June 2014 Accepted 1 July 2014 Available online xxxx Keywords: Köyceğiz–Dalyan Watershed Groundwater dependent ecosystems Regional climate models SWAT Hydrological modeling

a b s t r a c t Western Mediterranean Region of Turkey is subject to considerable impacts of climate change that may adversely affect the water resources. Decrease in annual precipitation and winter precipitation as well as increase in temperatures are observed since 1960s. In this study, the impact of climate change on groundwater resources in part of Köyceğiz–Dalyan Watershed was evaluated. Evaluation was done by quantifying the impacts of climate change on the water budget components. Hydrological modeling was conducted with SWAT model which was calibrated and validated successfully. Climate change and land use scenarios were used to calculate the present and future climate change impacts on water budgets. According to the simulation results, almost all water budget components have decreased. SWAT was able to allocate less irrigation water because of the decrease of overall water due to the climate change. This resulted in an increase of water stressed days and temperature stressed days whereas crop yields have decreased according to the simulation results. The results indicated that lack of water is expected to be a problem in the future. In this manner, investigations on switching to more efficient irrigation methods and to crops with less water consumption are recommended as adaptation measures to climate change impacts. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Climate change occurs naturally, but human population growth and associated land-cover conversion (e.g., deforestation) and burning of fossil fuel have substantially accelerated the increase of greenhouse gases (Wu et al., 2012). Water is a prime natural resource, a basic human need; in the absence of which, no socio-economic development activity can be sustained (Narula and Gosain, 2013). Therefore, water resource managers must consider the potential impacts of climate ⁎ Corresponding author. Tel.: +90 212 285 65 40. E-mail addresses: [email protected] (A. Ertürk), [email protected] (A. Ekdal), [email protected] (M. Gürel), [email protected] (N. Karakaya), [email protected] (C. Guzel), [email protected] (E. Gönenç).

change that could further stress water availability for human use and natural ecosystems (Kim et al., 2013), and plan the appropriate measures accordingly. This is of utmost important for arid and semi-arid areas such as the Mediterranean Basin (Giorgi and Lionello, 2008; Calbó, 2010; García-Ruiz et al., 2011; Ludwig et al., 2011; Boithias et al., 2014). Turkey's southern region is a part of the Mediterranean Basin. There are several recent studies conducted in Turkey analyzing the trends on precipitation and temperature. In the study of Toros (2012) daily precipitation data of 271 stations in Turkey for the period between 1961 and 2008 were evaluated. It is seen that annual precipitation and winter precipitation have a significant decreasing trend in southwest of Turkey. In another study Sen (2013) investigated the meteorological data between the period 1961 and 1990. According to his analyses average of summer temperatures in Turkey has increased 1.5 °C from

http://dx.doi.org/10.1016/j.scitotenv.2014.07.001 0048-9697/© 2014 Elsevier B.V. All rights reserved.

Please cite this article as: Ertürk A, et al, Evaluating the impact of climate change on groundwater resources in a small Mediterranean watershed, Sci Total Environ (2014), http://dx.doi.org/10.1016/j.scitotenv.2014.07.001

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1960s–1970s to 2000s. Temperatures in spring and fall seasons have also increased within the last decade. However, this increase is not as high as in summer. The results of future projections in the study indicated that the temperature increase for the region including western Mediterranean part of Turkey will be higher than the average of the country. Consequently, this part of Turkey will be subjected to considerable impacts of climate change that may adversely affect the water budget. The components of water cycle such as evaporation, precipitation and evapotranspiration are affected in large-scale by climate change. The changes in recharge and discharge rates, and changes in quantity and quality of water in aquifers are related to climate change affecting groundwater (Panwar and Chakrapani, 2013). Response of groundwater resources to climate change is slower with respect to surface waters (Kløve et al., 2011). If the recharge to groundwater decreases and its quality deteriorates due to climate change impacts, when the measures are not taken in time, it will be too late to prevent the adverse effects in the future on these resources. Effects of climate change on the hydrologic cycle and water quality of a watershed are associated with large uncertainty from both the climate projections and the hydrologic modeling approaches (Luo et al., 2013). We must consider that the climate projections should always be considered as scenarios but not as forecast, because: • There is no way to predict the greenhouse gas emissions that are considered as the main driving force behind the climate change. • The land use change responses to climate change are not clear. There are feedback loops between the climate change and land use, so that climate change may affect the socio-economic decisions and change the land use and the land use changes may affect the greenhouse gas emissions resulting in a positive or negative feedback loop in climate change. • Different climate models produce different outputs (Kjellstrom et al., 2011; Nikulin et al., 2011). Despite the advances in climate change modeling approaches, the uncertainty within climate scenarios affects the prediction of impacts (Zhou et al., 2010). • There is no consensus among climate modelers about the acceptable level of uncertainty and how far the current estimates of uncertainty can be reduced (Zhou et al., 2010). Therefore, meteorological time series produced by regional climate models (RCM's) should be considered as scenarios rather than as carbon emissions themselves. However, RCM's are forced by the boundary conditions generated by global circulation models (GCMs) that are based on carbon emission scenarios. Therefore, the scenarios are based on two main components; the presumed carbon emission and the prediction success of climate models where the uncertainties from each component cannot be separated. In short, the scenarios are defined such as “if the meterological time series produced by the response of the model to the greenhouse gas emissions are realized…” rather than “if the greenhouse gas emissions occur as presumed”. Watershed models have been widely used by researchers and decision makers to understand hydrologic, ecological, and biogeochemical processes as well as to examine effects of human activities and climate change/variability on water quantity and quality (Zhang et al., 2013). With the rapid development of computer technology, hydrological and environmental models are widely used to support decision making in the environmental field because these mathematical models are useful for investigating natural processes and predicting potential impacts of global changes such as climate and land cover changes. Accompanied by the rising knowledge about the causal mechanism within environmental systems and the increase in computing power, the use of large and complex models is a common practice and their further growth seems to be inevitable (Beck, 1999; Brun et al., 2001; Fujihara et al., 2008; Pisinaras et al., 2010; Yilmaz and Harmancioglu, 2010; Baloch et al., 2014).

In this paper, it was aimed to quantify the impacts of climate change on the water budget components in a small Mediterranean Basin, by using SWAT model. 2. Material and methods 2.1. The case study area The case study area is situated within the Köyceğiz–Dalyan Coastal Lagoon Watershed (KDCLW) in the southwestern Mediterranean Coast of Turkey (Fig. 1). KDCLW is part of the region, which was declared as a Special Protection Area in 1988, as it is a unique and important ecosystem with a high diversity of species. The area incorporates many geographical features listed below: • Alluvial plains with fertile soil suitable for agriculture • A brackish coastal lagoon suitable for fisheries and aquaculture • A long sandy beach parallel to Mediterranean Sea, popular for recreation and tourism as well as one of the few breeding sites for marine turtles • Cold springs forming groundwater dependent ecosystems as well as hot springs suitable for health tourism. The case study area is under the Mediterranean climate with hot and dry summers and mild winters in terms of temperature but high precipitation intensities during storms that may cause floods, even though there are no perennial streams, but only dry channels in the area. Since the lagoon's water is brackish to saline, it is not used for irrigation so that groundwater is the main source for water supply and irrigation in the case study area. 2.2. The hydrological modeling process The Soil and Water Assessment Tool (SWAT) model is one of the most widely used water quality and watershed model worldwide, applied extensively for a broad range of hydrologic and/or environmental problems (Gassman et al., 2014). It was developed by the U.S. Department of Agriculture (USDA) Agricultural Research Service (ARS) to aid the evaluation of land management practices on water supplies and nonpoint source pollution loading (Wilson and Weng, 2011), to explore the effects of climate and land management practices on water, sediment, and agricultural chemical yields on a daily basis (Arnold et al., 1998; Neitsch et al., 2005; Gassman et al., 2007; Douglas-Mankin et al., 2010; Wu and Chen, 2012). SWAT is a physically based, computationally efficient, continuous time model with spatially explicit parameterization (Narula and Gosain, 2013). It is operated on daily time steps, but it can aggregate the results to monthly or yearly output. SWAT simulations can be separated into two major divisions of “land phase” for water and pollutant loadings to streams, and “routing phase” for in-stream and reservoir water quantity and quality (Luo et al., 2013). The hydrological component of SWAT is the most important part of the model for this study. It is based on the water-balance equation in the soil profile, with terms representing processes of precipitation, surface runoff, infiltration, evapotranspiration, lateral flow, percolation, and groundwater flow (Arnold et al., 1998; Neitsch et al., 2005; Wu et al., 2012). The model divides watershed into subbasins connected by a stream network. Subbasins are discretized into Hydrologic Response Units (HRUs) consisting of unique soil, slope, and land use combinations. More details on SWAT model are provided in Supplementary material, and the hydrological processes and their interaction with the storage volumes are illustrated in Fig. S1. Many hydrological models contain parameters that cannot be determined directly from field measurements (Beven, 2001). Therefore, model calibration is used to adjust such parameters to optimize the agreement between observed and simulated values (Tolson and

Please cite this article as: Ertürk A, et al, Evaluating the impact of climate change on groundwater resources in a small Mediterranean watershed, Sci Total Environ (2014), http://dx.doi.org/10.1016/j.scitotenv.2014.07.001

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TURKEY

Mediterranean Sea

Koycegiz Lake

L ca oca se tio st n o ud f y the ar ea

Location of the case study area Dalyan Lagoon

The K yce iz-Dalyan Lagoon Watershed

Mediterranean Sea

Fig. 1. Location of Köyceğiz–Dalyan Lagoon Watershed.

Shoemaker, 2007; Zhang et al., 2009, Wu et al., 2012; Kim et al., 2013). The original design objective of the SWAT model was to operate in large-scale, ungaged basins with little or no calibration efforts (Arnold et al., 1998, 2000). Narula and Gosain (2013) state that the studies, where SWAT was used, demonstrated that input parameter values can be successfully estimated without or with minimum calibration in a wide variety of hydrologic systems and geographic locations using readily available GIS databases developed based on prior knowledge, and supports this statement with literature such as Srinivasan et al. (1998), Arnold and Allen (1999), Gosain et al. (2005), Zhang et al. (2008) and Shi et al. (2011). On the other hand, according to Arnold et al. (2009), GIS interfaces that incorporate databases for SWAT parameterization, are designed for large-scale modeling and the default parameter values assigned by the interface are highly generic. The purpose of the GIS interfaces is to quickly built datasets so that the user is not spending a large amount of time manually creating input files for the model. Therefore, many assumptions, which may or may not be valid for a given watershed, were incorporated into the map processing. Considering that the

case study area in this study is too small to be considered for large scale modeling, we decided that updates to GIS interface databases and model calibration are necessary. ArcSWAT, which is the GIS interface to SWAT, was used to construct the model input datasets. All of the meteorological time series needed by SWAT were available from States Meteorological Service of Turkey as daily time series. A 3-arc second digital elevation model (DEM) from Space Radar Topographic Mission (SRTM) datasets produced by NASA, was used as topographical dataset. Land use data that includes the basic crop patterns, and the soil data that includes soil depth, soil structure, information related to erodibility were obtained from the Republic of Turkey Ministry of Food, Agriculture and Livestock. Soil data was supplemented by the field survey based data conducted by Yuceil et al. (2007), where soil samples from several soil layers were analyzed. These surveys provided us with a more detailed data on soil structure including the nutrients and organic matter content of the soil when compared with regionally available data published by the Republic of Turkey Ministry of Food, Agriculture and Livestock. SWAT soil parameters HYDGRP (soil hydrologic group), SOL_ZMX

Please cite this article as: Ertürk A, et al, Evaluating the impact of climate change on groundwater resources in a small Mediterranean watershed, Sci Total Environ (2014), http://dx.doi.org/10.1016/j.scitotenv.2014.07.001

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Larger basin area 958 km2 SWAT subbasins corresponding to case study area (ungaged )

SWAT reaches SWAT subbasins corresponding to ungaged area

Case study area 69 km 2

Stream gage locations

SWAT subbasins corresponding to gaged area

Waterbodies

Fig. 2. Basic layout of the SWAT model.

(maximum rooting depth of soil profile), SOL_Z (depth from soil surface to bottom of the layer), SOL_CBN (organic matter content of soil) and SOL_AWC (available water capacity of soil layer) were directly determined from the soil data gathered and surveyed whereas,

SOL_BD (soil bulk density) and SOL_K (saturated hydraulic conductivity) were estimated using pedotransfer functions (Wösten et al., 1999) using the same soil data. In the next step, the soil database in ArcSWAT was updated for the local conditions.

Yearly averaged minimum temperature

20

36

Yearly averaged maximum temperature

16

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2800

1970

0

0

Scenarios Fig. 3. The meteorological time series from climate simulations.

Please cite this article as: Ertürk A, et al, Evaluating the impact of climate change on groundwater resources in a small Mediterranean watershed, Sci Total Environ (2014), http://dx.doi.org/10.1016/j.scitotenv.2014.07.001

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Once the model for the large basin is calibrated, a new and smaller SWAT model for the case study area was developed. The model consists of 49 subbasins and 717 HRUs (Fig. S3). The calibrated SWAT model parameters from the larger basin model were refined and overlapped to the case study area model. To reduce the uncertain issues in basic hydrological inputs related to land use more field data was collected during the model input set generation. During the years 2010, 2011 and 2012, seasonal surveys with 236 farms were conducted and land use data were checked on site. These surveys allowed a more precise crop pattern (Fig. S4) and further refinements of SWAT crop database.

2.3. Climate change scenarios

Fig. 4. Model prediction vs stream gage data.

The model was set up from 1970 to 2010, where the first 11 years were considered as the warm-up period. Mainly stream gage data are needed for model calibration. However, there are no perennial streams in the case study area, but only a few small ephemeral channels that only appear during storms and a few hours to days after the storms. Therefore, no stream gages were installed on these natural channels due to their intermittent nature by the water authorities and no stream flow data were available. This is a problem for modeling, since stream flow data, which is crucial to hydrological model calibration, is missing; thus, a direct model calibration for the case study area is impossible. However, the case study area is embedded in a larger basin, which incorporates two perennial gaged streams (Fig. 2). Consequently, the authors had to set up SWAT for the larger basin and calibrate and validate it using the available gage data from two perennial streams. The modeling chain for the model is given in Fig. S2, and further details on the setup and input data organization for the larger basin model are given by Guzel (2010).

Climate change scenarios, which were based on the Global Climate Model (GCM) results, were dynamically downscaled using RCA3 Regional Climate Model (RCM), where lateral boundary data to force the RCM has been extracted from a global scenario produced by a specific GCM. The GCM models used for the simulations are ECHAM5-r3, IPSL, CNRM and HADCM3-Q0. This study is one of the 16 case studies in the GENESIS Project which deals with groundwater and dependent ecosystems. For all case studies, the same dynamic downscaling method was applied by the GENESIS Project Partner Swedish Meteorological and Hydrological Institute (SMHI) that provided meteorological forcing time series for the climate change scenarios (Kjellstrom et al., 2011; Nikulin et al., 2011). All scenarios were based on the SRES A1B greenhouse gas emission scenario (Nakicenovic and Swart, 2000). The meteorological time series from climate simulations are summarized in Fig. 3. As seen in Fig. 3, the yearly averaged minimum and maximum temperatures are increasing. There is a decreasing trend in annual precipitation and no clear trend in relative humidity. Solar radiation and wind velocity did not show any trend as well. In order to provide a base case to compare with climate change scenarios, the current meteorological time series were used to create weather generator files for SWAT assuming that those time series would represent the period between 2030 and 2100. SWAT's built-in weather generator, WGEN, automatically generated the daily time series representing the

Table 1 Model performances related to stream flows. Criterion

Namnam gage calibration

Namnam gage validation

Kargıcak gage calibration

Kargıcak gage validation

R2 Nash–Sutcliffe Efficiency (NSE) Hydrological years

0.85 0.65 1981–1992a

0.83 0.65 1993–1999

0.87 0.80 1999–2004b

0.88 0.85 2005–2008

a b

Stream gage records from hydrological years 1986–1990 were missing in the datasets. Stream gage records from hydrological years 2002–2003 were missing in the datasets.

Table 2 Calibrated values of model coefficients. Parameter

Description

Unit

Calibrated values

SOL_ZMX SOL_BD SOL_AWC SOL_K SOL_CBN SOL_ALB CN2 GW_DELAY GW_REVAP GWQMN ALFA_BF

Maximum rooting depth Bulk density of soil Available water capacity of soil Saturated hydraulic conductivity of the soil Organic matter content of the soil Moist soil albedo Initial soil conservation service runoff curve number for moist condition The lag between the time that water exits the soil profile and enters the shallow aquifer Groundwater “revap” coefficient Threshold water depth in the shallow aquifer for base flow to occur Base flow recession coefficient

mm H2O g·cm−3 mm H2O/mm H2O mm·h−1 % soil weight

200–1200a 1.15–1.44a 0.03–0.14a 3.5–6.5a 0.9–1.8a 0.01–0.2a 25–85b 60 0.05 700 0.1

a b

day mm H2O day

Different values were taken for different soils. Different values were taken for different soils and land use/land cover.

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prolongation of the current status in terms of meteorology without any climate change. 3. Results and discussion 3.1. Model calibration and validation The results of the calibration and validation process of the model for the stream gage at the Namnam Stream are illustrated in Fig. 4. The

calibration and validation process was conducted using monthly time series. Model performances for stream flow related to both gaged streams are given in Table 1. The Nash–Sutcliffe Efficiency (NSE) values for the calibration and validation of Namnam gage seem on the lower range of the recent studies compiled by Gassman et al. (2014). Detailed information on the values is given in Fig. S5. In an extensive literature review conducted by Moriasi et al. (2007) values over 0.5 for NSE for stream flow calibration are suggested acceptable, which is also found as satisfactory for our study.

Fig. 5. Relative distribution of the main components of water budget for the period between (a) 1981 and 2010 hydrological year (b) 2001 and 2010 hydrological years.

Please cite this article as: Ertürk A, et al, Evaluating the impact of climate change on groundwater resources in a small Mediterranean watershed, Sci Total Environ (2014), http://dx.doi.org/10.1016/j.scitotenv.2014.07.001

1000 900 800 700

(a) 600 560 520 480 440 400

200 170 140 110 80 50

300 260

y = -6.97x + 549.23 R2 = 0.76

y = -7.67x + 165.17 R2 = 0.66

y = -12.14x + 260.79 R2 = 0.68

GW recharge (mm/yr)

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y = -26.45x + 886.75 R2 = 0.84

y = 0.06x + 25.65 R2 = 0.12

y = -3.62x + 74.93 R2 = 0.67 220

350 290 230 170

50

110

30 26 22 18 14 10

80 70 60 50 40

y = -11.98x + 206.45 R2 = 0.81

y = -0.93x + 22.47 R2 = 0.86

y = -2.66x + 67.97 R2 = 0.87

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Revap

2090's

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2060s 2060s

Please cite this article as: Ertürk A, et al, Evaluating the impact of climate change on groundwater resources in a small Mediterranean watershed, Sci Total Environ (2014), http://dx.doi.org/10.1016/j.scitotenv.2014.07.001

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(b) 0 -20 -40 -60 -80

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Fig. 6. Simulation results for climate change scenarios (a) trends (b) comparison of different scenarios with current status.

2080's

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2070's 2080's

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Change (%) Change (%)

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The performance of the model calibration and validation of this study is provided in Fig. S6. Calibrated values of model coefficients are given in Table 2. 3.2. Results for the current status SWAT simulations were run from 1970 to 2010, where the first 11 years were used as warm up period to filter out the effect of initial conditions for which no spatial data exist. The discussions with the local experts revealed that agricultural practices and land use did not considerably change from early 1980s. That's why we decided to report the results as of year 1981. Year 1981 also coincided with the beginning of the stream gage flow time series. SWAT simulation results showing the relative distribution of the main components of water budget for 30 years (1981–2010 hydrological years) and for 10 years (2001–2010 hydrological years) in our case study are given in Fig. 5a and b, respectively. The last ten years of the simulation represent the time period that is after the model calibration and validation since the stream gaging was not conducted by the authorities after the year 2000. Averaging over both periods generated similar results, which indicated that there was no considerable trend related to hydrological process rates (water stocks and flows) from the year 2000 towards the first decade of the 21st century, in our case study area. As seen clearly, almost 60% of precipitation is lost via evapotranspiration. The results presented in this paper are the first published outcomes of a study conducted in the case study area on this subject. 3.3. Discussion on land use distribution change Land use change is another important factor that could directly influence the watershed hydrology apart from climate change (Li et al., 2009). This is the reason why the combined effect of the climate and land use change was considered in many hydrological modeling studies such as Bathurst et al. (2004), Ewen and Parkin (1996), Lahmer et al. (2001), Legesse et al. (2003), Li et al. (2009), Montenegro and Ragab (2012), Parkin et al. (1996), Ranjan et al. (2006), Sen et al. (2013), and Tomer and Schilling (2009). In several other studies, the effect of climate change on the vegetation dynamics as a land use/land cover (LULC) change is dynamically coupled into the modeling framework (Bonan et al., 2003; Foley et al., 1998; Matthews et al., 2004; Woodward and Lomas, 2004). The only LULC change study in our case study area was conducted by Erturk et al. (2012), where Landsat 5 TM images with 30 m spatial resolution (1984, 2000, 2003 and 2010) were utilized to analyze the temporal changes in land use. Considering the development plans, the land properties and the land use changes in the last three decades, we concluded that no considerable increase in agricultural areas is expected. This expectation is justified by following: • Most of the suitable areas were already allocated for agriculture. The soil in unused areas that are on the development plan are is of land class VII, not suitable for agriculture. • The land use change analysis study by Erturk et al. (2012) did not indicate any considerable increase of agriculture in the last two decades.

• According to the development plans, the main focus of regional development is tourism rather than agriculture. The case study area is located in a special environmental protection area. Therefore, even if tourism is the development focus, extensive construction of hotels and summer residential sites will not be allowed; therefore, will not cause any serious land use change that would increase the impervious areas considerably. Taking this fact together with the expectation of no considerable increase of agricultural areas, the future scenarios in the model are based on the same land use distribution of 2010. 3.4. Results for the simulations for climate change scenarios Simulations were conducted for current status and four climate change scenarios. The current status simulation is simply a prolongation of the simulation period as described in Section 2.3. Simulation results for each climate change scenario explained in Section 2.3, are given in Fig. 6. These results indicate the averages of a decade. For example 2030s means the average of the yearly water budget components from the first day of 2030 until the last day of 2039. The error bars in Fig. 6a indicate minimum and maximum values of the relevant hydrological processes from simulation results of different scenarios whereas the dots are the averaged simulation results over scenarios and the solid line is the superimposed linear regression which indicates a decrease/increase according to negative/positive slope. Fig. 6b illustrates the predicted differences for hydrological process rates of each scenario from the current status. The water yield given in Fig. 6 is calculated as follows: Water yield ¼ surface runoff þ subsurface runoff þ base flow–losses through streambeds:

As seen in Fig. 6, all the water budget components indicate that there is a clear decline except for revap which was slightly increased. Revap is the rise of groundwater to supplement the soil water deficit. Thus, an increase of revap indicates that more water from groundwater storage has risen to supplement the deficiency. In other words, the model estimated that the soil became drier. SWAT estimated real evapotranspiration decreased as well indicating that less water is available for evapotranspiration. SWAT incorporates algorithms that estimate how much water the crops need and allocate that amount of water. If enough water is available at the source required amount is withdrawn; otherwise only available water is withdrawn. As seen in Fig. 6, SWAT was able to allocate less irrigation water because of the decrease of overall water due to the climate change. This also resulted in increase of water stressed days and temperature stressed days (Table 3), whereas crop yields have decreased according to the simulation results given in Fig. 7, except for wheat and corn that require relatively less water. 3.5. Changes in groundwater storage and projections of groundwater dependent ecosystems Groundwater quantity decreased for all climate change scenarios because of the decrease in groundwater recharge, as expected (Fig. 6).

Table 3 Number of water and temperature stressed days for plants averaged over space and time. Scenario

Number of water stressed days in a year

Number of temperature stressed days in a year

Current status ECHAM5-r3 IPSL CNRM HADCM3-Q0

5 10 12 10 8

14 32 38 19 35

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Fig. 7. The changes in the yield of major crops in the case study area.

170 150

2090s

130

2080s

2090's

2080's

190

2070s

-80

210

2060s

-60

y = -4.88x + 202.57 R2 = 0.80

2050s

-40

2070's

2060's

2050's

2040's

0 -20

(b) 230

2040s

Change (%)

2030's

Aquifer storage

2030s

(a)

amount of the soil water is important to maintain the natural vegetation. The decrease in precipitation and increase in evaporation, especially in summer, can quickly reduce soil moisture, and lower soil moisture can have adverse effects on plants and may also decrease the supply of groundwater (Garssen et al., 2014). The projections related to soil water content is provided in Fig. 8b. A decrease in pine forests biomass can be seen clearly in Fig. 8c.

Soil water storage (mm)

Changes in other hydrological variables such as the base flow are the results of a considerable decrease of the groundwater storage (Fig. 8a). In this study, soil water content was considered to be a relevant variable for the groundwater dependent ecosystems, since the area is partly covered by pine forests and shrubs that are an important part of the natural vegetation and form habitats for the terrestrial wild life. As the summers in the region are dry with high rate of evapotranspiration,

(c)

Fig. 8. (a) Change of aquifer storage for different scenarios (b) Trend of soil water storage (c) Pine biomass.

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4. Conclusions and recommendations In this study, SWAT hydrological model was set up to calculate the present and future climate change impacts on water budget in a small Mediterranean Basin. SWAT was successfully calibrated and validated to be used as a tool for the given purpose. For climate change scenarios, SWAT was run in auto-irrigation mode, where the model was provided with water sources (in our case study, the only water source for irrigation was groundwater) for each HRU with agriculture. The model results showed clearly a decrease in irrigation water, because less groundwater was available at the designated water resource for irrigation. Lack of water is expected to be a pressure in the case study area. Under these circumstances it is clear that water conservation should be considered as the primary climate change adaptation strategy. Since agriculture is the main water demanding sector in the area, further investigations about the possibilities related to switching to more efficient irrigation methods and to less water demanding crops is recommended. Such studies should be supported with comprehensive model applications that can test these scenarios in terms of adaptations to the social and environmental context. The hydrological results from our study could form the first tier of such an integrated approach. Secondary measures, such as artificial recharge during the wet months when no irrigation is required, harvesting rainwater or transferring water from perennial streams in the northern part of the large basin that are approximately 10 km far from the case study area, could be only advised after more detailed field investigations and modeling efforts, which were kept out of the scope of this study. Such water management practices could have unintended side effects on the adjacent ecosystems; so suggesting them as mitigations would be premature depending only on the results of this study. Therefore, initiation of comprehensive further studies addressing possible secondary measures is recommended. Conflict of interest The authors declare no conflict of interest. Acknowledgments The study was supported by the European Community 7th Framework Project GENESIS (226536) on groundwater systems. Authors would also like to thank Istanbul Technical University, Scientific Research Fund, for supporting the study through project “Estimation of Nutrient Loads by SWAT in Köyceğiz-Dalyan System (Grant No: 33622)”, and Gökhan Cüceloğlu for his efforts Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2014.07.001. References Arnold JG, Allen PM. Automated methods for estimating baseflow and groundwater recharge from streamflow records. J Am Water Resour Assoc 1999;35(2):411–24. Arnold JG, Srinivasan R, Muttiah RS, Williams JR. Large area hydrologic modeling and assessment — part 1: model development. J Am Water Resour Assoc 1998;34:73–89. Arnold JG, Kiniry JR, Srinivasan R, Williams JR, Haney EB, Neitsch SL. Soil and water assessment tool input/output file documentation, version 2009. Texas Water Resources Institute Technical Report No. 365; 2009. Arnold JG, Muttiah RS, Srinivasan R, Allen PM. Regional estimation of baseflow and groundwater recharge inthe Upper Mississippi River basin. J Hydrol 2000;227(1– 4):21–40. Baloch MA, Ames DP, Tanik A. Hydrologic impacts of climate and land-use change on Namnam Stream in Koycegiz Watershed, Turkey. Int J Environ Sci Technol 2014. http://dx.doi.org/10.1007/s13762-014-0527-x. Bathurst JC, Ewen J, Parkin G, O'Connell PE, Cooper JD. Validation of catchment models for predicting land-use and climate change impacts. 3. Blind validation for internal and outlet responses. J Hydrol 2004;287:74–94.

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