A Spaceborne Multisensor Approach to Monitor the Desiccation of Lake Urmia in Iran

Remote Sensing of Environment 156 (2015) 349–360

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Remote Sensing of Environment

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A spaceborne multisensor approach to monitor the desiccation of Lake Urmia in Iran

M.J. Tourian ⁎, O. Elmi, Q. Chen, B. Devaraju, Sh. Roohi, N. Sneeuw

University of Stuttgart, Institute of Geodesy (GIS), Geschwister-Scholl-Str. 24, 70174 Stuttgart, Germany article info abstract

Article history: Lake Urmia, a hypersaline lake in northwestern Iran, is under threat of drying up. The high importance of the Received 22 May 2014 lake's watershed for human life demands a comprehensive monitoring of the watershed's behavior. Spaceborne Received in revised form 23 September 2014 sensors provide a number of novel ways to monitor the hydrological cycle and its interannual changes. The use of Accepted 4 October 2014 GRACE gravity data makes it possible to determine continental water storage changes and to assess the water Available online xxxx budget on monthly to multi-annual time scales. We use satellite altimetry data from ENVISAT and CryoSat-2 to Keywords: monitor the lake water level. Moreover, we employ optical satellite imagery to determine the surface water ex- Satellite altimetry tent of the lake repeatedly and at an appropriate time interval. Satellite imagery Our altimetry results indicate that, on average, the lake has lost 34 ± 1 cm of its water level every year from 2002 GRACE to 2014. The results from satellite imagery reveal a loss of water extent at an average rate of 220 ± 6 km2/yr, Lake desiccation which indicates that the lake has lost about 70% of its surface area over the last 14 years. By combining water level from altimetry, surface water extent from satellite imagery and local bathymetry, we ascertain the changes in lake volume. Results indicate that the lake volume has been decreasing at an alarming rate of 1.03 ± 0.02 km3/yr. The water volume of the lake behaves differently from the water storage of the whole basin captured by GRACE. Our results show that the onset of a drought in 2007 over this region together with an increase in the rate of groundwater depletion caused a new equilibrium level for water storage of the whole basin. Comparing the results from GRACE and the obtained water volume in the lake with in situ groundwater level data reveals the anthropogenic inﬂuences on an accelerated lake desiccation. In fact, our monitoring approach raises critical issues regarding water use in the basin and highlights the important role of spaceborne sensors for any urgent or long-term treatment plan. © 2014 Elsevier Inc. All rights reserved.

1. Introduction social catastrophe, not only in Iran, but also in neighboring countries such as Turkey, Iraq and Azerbaijan (Pengra, 2012). The drastic decline The Urmia Lake is located in the northwest of Iran between the two of lake surface area has been acknowledged and quantified by many re- Iranian provinces of East and West Azerbaijan. It was the largest inland search groups reporting a decrease of about 60% in less than a decade body of salt water in the Middle East and the largest permanent hyper- (Eimanifar & Mohebbi, 2007). saline lake in the world with an area varying from 5200 to 6000 km2 in Hassanzadeh et al. (2012) stated that changes in inflows due to the the twentieth century (Zarghami, 2011; Hassanzadeh, Zarghami, & climate change and overuse of surface water resources are responsible Hassanzadeh, 2012). The lake and its basin is home to many species in- for 65% of the desiccation, the construction of four dams on upstream cluding a unique brine shrimp species Artemia urmiana (Karbassi, rivers is responsible for 25%, and reduced precipitation accounts for Bidhendi, Pejman, & Bidhendi, 2010). However, in the recent past the 10%. The overuse of surface water resources and especially groundwater lake and its corresponding basin have suffered from an environmental extraction was the subject of a study by Voss et al. (2013) over a broader disaster. It has been reported that the surface area and the lake's region, the Tigris–Euphrates–western Iran region, including the Urmia water level have been declining since 1995 due to various climatic and Lake basin. They quantified an alarming rate of decrease in total water anthropogenic reasons (Delju, Ceylan, Piguet, & Rebetez, 2013; storage of approx. −27.2 ± 0.6 mm/yr between 2003 and 2009. Karbassi et al., 2010). The lake contains an estimated 8 billion tons of Forootan et al. (2014) proposed a statistically-based signal separation salt. In the case of a complete desiccation, releasing this amount of salt procedure to separate terrestrial and surface water storage changes in by windborne transport could lead to an ecological, agricultural, and the GRACE signal over a broad region encompassing Iran. They obtained a groundwater loss of 11.2 mm/yr over the Lake Urmia region, which is actually the highest rate among the six regions studied in Iran. In a sim- ⁎ Corresponding author. ilar study, Joodaki, Wahr, and Swenson (2014) stated that most of the E-mail address: [email protected] (M.J. Tourian). long-term water loss in Iran is due to a decline in groundwater storage,

http://dx.doi.org/10.1016/j.rse.2014.10.006 0034-4257/© 2014 Elsevier Inc. All rights reserved. 350 M.J. Tourian et al. / Remote Sensing of Environment 156 (2015) 349–360 where they quantified 25 ± 3Gt/yr as the rate of mass loss due to far below its spatial resolution but with a strong seasonal signal. This, groundwater depletion. However, one cannot compare the aforemen- we believe, characterizes our current study. tioned values as they are estimated over different regions. Further, The paper starts with a description of complementary hydrological Delju et al. (2013) mainly focused on climatic change over the basin. data sets in Section 2. Section 3 describes our approach to analyzing They quantified a decrease of 9.2% for mean precipitation and an in- the surface water extent of the lake using satellite imagery. In crease of 0.8 °C for maximum temperature over the past four decades. Section 4 we explain the data sets and our method to derive the lake's Of course, the decrease in precipitation and increase in temperature water level from satellite altimetry. The results from satellite altimetry caused a drought in this region (Trigo, Gouveia, & Barriopedro, 2010), and satellite imagery, together with local bathymetry data, are used in which, together with the increasing demands of irrigation, accelerate Section 5 for computing total water volume in the lake. Sections 3–5 the rise of salinity in the lake to more than 300 g/L i.e. 8 times saltier deal with monitoring the quantities over the lake itself, while than typical seawater during recent years (Asem, Mohebbi, & Ahmadi, Section 6 discusses estimation of equivalent water height over the 2012). whole basin using GRACE. We extensively analyze and quantify the des- Moreover, a causeway was built across the lake (middle of the lake) iccation of the lake in Section 7, where the estimated time series of to connect the two cities of Tabriz and Urmia starting from 1979 to 2008 water volume over the lake and over the whole basin are investigated with only a 1500-m gap for water to move between the northern and toward desiccation monitoring. Section 8 will summarize and conclude southern halves of the lake. This causeway was also identified by previ- the results of this study. ous studies as one of the reasons as the desiccation initially started from southern part of the lake (Eimanifar & Mohebbi, 2007). From these pre- 2. Data vious studies (Delju et al., 2013; Eimanifar & Mohebbi, 2007; Golabian, 2011; Hassanzadeh et al., 2012; Karbassi et al., 2010; Voss et al., 2013; Apart from the spaceborne data needed to monitor the lake's desic- Zarghami, 2011), we summarize the different reasons for the drying cation, we also need precipitation, evapotranspiration and groundwater up of the lake (1) increased extraction of groundwater for irrigated ag- data for a better analysis and interpretation of hydrological behavior riculture within the lake's watershed, (2) reduced precipitation, (3) the over Lake Urmia and its basin (Fig. 1). Table 1 summarizes all datasets construction of a causeway across the lake, (4) dam construction and and sensors that are used in this study. (5) climate change. This study uses a spaceborne multisensor approach to monitor and quantify the desiccation of Lake Urmia. Here we analyze and combine, 2.1. Precipitation for the first time over Lake Urmia and its wider basin area, the water storage change time series from GRACE, surface water level time series Among available precipitation data, the Global Precipitation Clima- over different parts of the lake from ENVISAT and CryoSat-2 altimetry, tology Center (GPCC) seems to deliver better datasets around the surface water extent time series from optical imagery (MODIS) together world (Lorenz & Kunstmann, 2012; Riegger et al., 2012). However, our with in situ data of groundwater and hydrological fluxes to reconstruct pre-analysis over this region showed that the data from the Global the lake's hydrological cycle. Precipitation Climatology Project (GPCP) provides a slightly higher Satellite altimetry and satellite imagery for the analysis of water vol- compatibility with other hydrological data. Moreover, the GPCC data is ume variation have been combined by several studies. Duan and only available up to 2010, which impedes our hydrological analysis Bastiaanssen (2013) estimated water volume variations in Lake Mead after 2011. Therefore, in this study we use the monthly 2.5° × 2.5° (U.S.A.), Lake Tana (Ethiopia) and Lake IJssel (The Netherlands) from data sets of the GPCP data, version 2.2. Our confidence in using a global four operational satellite altimetry databases and satellite imagery data set has increased after comparing it with in situ precipitation data data. Michailovsky and Bauer-Gottwein (2013) employed satellite al- of 1996–2003 over the lake basin and obtaining a good agreement (not timetry and imagery to develop a rainfall runoff model of the Zambezi shown here). River basin for inflow forecasting. Recently, Baup, Frappart, and Maubant (2014) combined high-resolution satellite images and altime- 2.2. Evapotranspiration try to estimate the volume changes of small lakes in France, which are mainly used for irrigation purposes. Evapotranspiration is typically available from models or remote Singh, Seitz, and Schwatke (2012) included mass changes from sensing only. The models are available from different sources, which GRACE in addition to the geometric variables from satellite altimetry range from simple empirical to complex ones including radiative energy and optical imagery, demonstrating the combination of physical and balance models. Among the data from the Global Land-surface Evapora- geometrical observations. The application of the GRACE data for hydro- tion from Amsterdam Methodology (GLEAM) (Miralles et al., 2011), logical purposes has been growing since 2002 e.g. Döll et al. (2014), ECMWF (Berrisford, & for Medium Range Weather Forecasts, 2009; Longuevergne, Scanlon and Wilson (2010), Riegger, Tourian, Devaraju Simmons, Uppala, Dee, & Kobayashi, 2006) and MOD16 from MODIS and Sneeuw (2012) and Schmidt et al. (2008). It captures water storage (Mu, Heinsch, Zhao, & Running, 2007) the ECMWF data delivers better change over landmasses, which is a useful indicator of climate variabil- time series in terms of compatibility with other hydrological data ity and human impact on the environment. However, GRACE can re- (Lorenz et al., 2014). Therefore, in this study we use the monthly solve water storage changes if the signal variability is larger than its 0.75° × 0.75° data sets of the ECMWF data. uncertainty level of 7–10 mm (Riegger et al., 2012). Here, we use the same sensors as Singh et al. (2012), but differ from their methodology. Instead of concentrating only on the lake surface 2.3. Groundwater data area, we take a holistic view of the lake and its basin, thereby enabling us to study the hydrological cycle and its balance. Further, we also In this study, we use in situ groundwater observations of 19 piezo- make full use of the complementary hydrological data that is available metric stations of the Iranian Water-resource Research Center (WRC) for the Urmia basin: precipitation, evapotranspiration and in situ over the Urmia basin (Fig. 1). The observations of different stations groundwater data. The use of such complementary data allows us to cover different time periods, but all the 19 stations provide observation study the anthropogenic influences, as will be shown later. Our results for the time period of 2003–2011. Apart from two stations of Salmas and from spaceborne sensors would be of great importance for mitigation Oshnavieh that show peculiar behavior after 2008 containing ~8 m of planning of the ongoing environmental disaster over the Urmia Lake sudden change in groundwater level, we use all the other stations for basin. On top of this, we also demonstrate the utility of GRACE in regions our analysis. M.J. Tourian et al. / Remote Sensing of Environment 156 (2015) 349–360 351

Armenia Azerbaijan Turkey Turkmenistan

Iran 50 km Iraq

Fig. 1. Urmia Lake basin, sub-basins, rivers and the black circles represent the location of precipitation gauges and the orange triangles show the location of piezometric stations. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)

3. Surface water extent from satellite imagery Crétaux et al., 2011; Klein et al., 2014; Töyrä et al., 2001). These studies mainly rely on the fact that water bodies appear dark in middle- and In this study, we use MODIS surface reflectance 8-day composites near-infrared bands, as almost the whole radiant flux is absorbed in with 250 m spatial resolution (MOD09Q1) to monitor the area of the this part of the spectrum (740–2500 nm) (Jensen, 2009). In this study, Urmia Lake within a 14 year time period (2000–2014). This level 3 we follow a post-classification comparison procedure to monitor the product provides two bands (620–670 nm, 841–876 nm) with change in the lake water extent. For this, we apply the iterative self- 250 meter resolution in an 8-day gridded product using the sinusoidal organizing data analysis technique (ISODATA) unsupervised classifica- map projection. Precise geo-location information is given in the prod- tion algorithm to each image separately, out of which changes in water uct, which is free from clouds, cloud shadow, aerosol loading and atmo- area are determined by comparing multi-temporal maps pixel by pixel. spheric effect (Vermote & Kotchenova, 2008)(Fig. 2). Applying a threshold to image values of an infrared band is a simple In order to analyze the surface water extent, we apply a classification and effective way to detect water in an image but determining this method to each image to distinguish between water and land. The time threshold is the most critical part of classification (Klein et al., 2014). series of classified images are then used to monitor the changes of water In our case, this problem is more crucial, as we are aiming to derive extent. time series of surface water extent and we deal with a multitude of im- A number of studies have focused on applying different change detec- ages acquired at different times under different environmental condi- tion methods for monitoring of inland water bodies (Alsdorf et al., 2000; tions. This is, in fact, crucial, as an appropriate threshold should be Bjerklie, Lawrence Dingman, Vörösmarty, Bolster, & Congalton, 2003; determined for every image separately.

Table 1 Summary of the datasets and sensors used in this study.

Variable Dataset Version Resolution Time period Spatial Temporal

Precipitation P GPCP 2.2 2.5° × 2.5° 1 mo 2000–2014 Evapotranspiration ET ECMWF – 0.75° × 0.75° 1 mo, 1 d, 6 h 2000–2012 Groundwater level WRC –– 1 mo 2003–2011 Lake area S MODIS – 250.0 m × 250.0 m 8 d 2000–2014

Water level Halt ENVISAT –– 35 d 2002–2010 ENVISAT XT –– 30 d 2010–2012 CryoSat-2 –– 3–50 d 2012–2014 Water storage ΔM GRACE GFZ R5 – 1 mo 2003–2014 WGHM IRR70 05° × 0.5° 1 mo 2002–2009 352 M.J. Tourian et al. / Remote Sensing of Environment 156 (2015) 349–360

The ISODATA procedure is introduced by Ball and Hall (1965). Similar to most unsupervised classification methods, it first defines an arbitrary initial cluster vector. Then, each pixel is assigned to the closest cluster. In the last step, the new cluster mean vectors are calculated based on the previous clustering. The last two steps will continue until the chosen termination criteria, like the number of iterations, the per- centage of pixels that have changed between two iterations or the change in mean clusters, are satisfied. Moreover, ISODATA has the abil- 2000 2002 2004 2006 ity to merge or split clusters. If the number of elements in a cluster is less than a certain threshold or if the difference between means of two clusters is smaller than a certain threshold, then clusters are merged. Also, a cluster is split into two clusters if its standard deviation exceeds a defined value. Fig. 3 shows the computed water extent within the studied time frame over Lake Urmia. Before 2008, the main shrinkage occurs around the lake boundary at northeastern, eastern and especially southeastern parts. After 2008, a clear shoreline recession is seen everywhere, except 2008 2010 2012 2014 for the western bulge. The area of obtained surface water extent is computed at each epoch Fig. 3. Snapshot surface water extent obtained from MODIS imagery from 2000 to 2014. leading to a time series with seasonal behavior and a declining trend especially after 2006 (Fig. 4). The small error envelope provided in Fig. 4 is March 2002–October 2010, it was placed in a sun-synchronous orbit the classification error varying between 9 and 17 km2 (not clearly visi- at an altitude of 790 km. The orbit has an inclination of 98.5 ° that ble in the figure, as it is small compared to the estimated area). In leads to a global coverage of the Earth between latitudes of ± 81.5 ° order to quantify the classification error, we assume that each pixel with a 35-day repeat period (da Silva et al., 2010). In October 2010, appointed as water can be a wrong decision. Hence, the error of surface the satellite was placed in a lower partly drifting-phase orbit with a water extent can be quantified by pffiffiffi σ ¼ σ ; ð Þ S p s 1 Table 2 The defined virtual stations (VS) along the ground tracks of ENVISAT and ENVISAT extended mission (XT) over Lake Urmia. The box length refers to the length of defined rectangu- where p is the number of water pixels involved in the classification pro- lar box for virtual station. 2 cedure and σs is the MODIS resolution i.e. 0.0625 km . The estimated time series and its error envelope are used for our Virtual station Track Lat. Lon Box length desiccation monitoring, which will be discussed in detail in Section 7. [°] [°] [km]

VS1 371 38.139 45.315 6 4. Water level from satellite altimetry VS2 178 37.641 45.352 6 VS3 371 37.619 45.470 6 Due to the good performance of the ENVIromental SATellite VS4 70XT 37.688 45.338 4 (ENVISAT) over inland water bodies, which was demonstrated by previous studies e.g. da Silva et al. (2010), Frappart, Calmant, Cauhopé, Seyler and Cazenave (2006) and Tourian, Sneeuw and Bárdossy (2013),we 30-day repeat cycle. The ground tracks of both primary and extended have used ENVISAT data from 2002 to 2011 for Lake Urmia's water ENVISAT missions are shown in Fig. 5. level monitoring. ENVISAT was launched in March 2002 as a successor It was strongly emphasized, in previous studies, that a ﬁne selection to ERS-1 and ERS-2 by the European Space Agency (ESA). During of the region where an altimetry track crosses a hydrological object, the

Fig. 2. A false-color RGB combination of bands 2, 2, and 1 over Lake Urmia at two different epochs: (Left) May 2000. (Right) May 2013. M.J. Tourian et al. / Remote Sensing of Environment 156 (2015) 349–360 353

In order to obtain the lake level time series for the time period of

] 5000 2 January 2012–October 2013, we use CryoSat-2 data. The CryoSat-2 mission is part of ESA's Earth Explorer series, which focuses on the 4000 science and research elements of the Living Planet program. The focus of the CryoSat-2 mission lies on monitoring of Earth's land and sea ice 3000 surfaces. However, its potential to monitor inland water bodies is also acknowledged by Bercher and Calment (2013). It is placed in a Low Earth Orbit (LEO) at an altitude of about 717 km and an inclination of 2000 92°. The repeat cycle of CryoSat-2 is 369 days with 30-day sub-cycles surface water extent [km leading to different ground track patterns over Lake Urmia (Fig. 6). 1000 2000 2002 2004 2006 2008 2010 2012 2014 Such a varying ground track pattern allows us to monitor the lake water level in a better temporal resolution. Therefore, to achieve a con- Fig. 4. Time series of surface water extent obtained from MODIS imagery. sistent time series the CryoSat-2 Low Resolution Mode (LRM) observations within the MODIS-derived boundary of the lake in February 2014 so-called virtual station, is as important as the processing of altimetry is used (Fig. 6). We convert the water level values of each track into the data (Berry, Garlick, Freeman, & Mathers, 2005; da Silva et al., 2010). EGM2008-corrected orthometric heights and apply the range bias cor- Therefore, in order to avoid uncertain measurements around the rection of −244 mm according to Bosch et al. (2014). Although we con- coast, we have deﬁned our virtual stations within the boundary of the ﬁne the lake boundary to its smallest possible shape, the water values lake in February 2014 (See Table 2), which we obtained from satellite near the shoreline seem to be erroneous and deteriorated by neighbor- imagery (red curve in Fig. 5). ing topography. Therefore, over each track we select the median value Our algorithm to achieve the water level time series for the virtual of the track, which appears at different locations (brown dots in stations conforms to a large extent to the standard processing of altim- Fig. 6). The error bar, shown as a light blue band in Fig. 7, is then obtain- etry data in hydrological applications (Sneeuw et al., 2014). This algo- ed by computing the standard deviation of water level measurements rithm mainly relies on the Ice-1 retracker, which is known to provide over each track. the best estimate of inland water level variations (da Silva et al., 2010; Frappart et al., 2006). However, the water level measurements are opti- 5. Water volume of the lake mized by employing information from the other retrackers as well (Sneeuw et al., 2014). The water level is then converted to the In order to assess the total water storage in the lake, we need to com- orthometric heights using the EGM2008 geoid model (Pavlis, Holmes, bine either the water extent or the water level with the bathymetry of Kenyon, & Factor, 2012). We also apply range biases of 450.8 mm and the lake. To this end, we rely on the bathymetry provided by 441.2 mm according to Bosch, Dettmering and Schwatke (2014) on Abbaspour, Javid, Mirbagheri, Givi, and Moghimi (2012) shown in ENVISAT and ENVISAT extended mission data, respectively. The obtain- Fig. 8. The variation of the surface water extent within the last ed time series with their corresponding error bar are shown in Fig. 7. 12 years agrees somewhat with lake bed topography (Fig. 8). The mis- matches between water extent and bathymetry give a visual impression

Fig. 5. Ground tracks of ENVISAT (white lines) and ENVISAT extended (dark blue lines) Fig. 6. CryoSat-2 ground tracks (yellow lines) within the time period of January 2012– and the defined virtual stations. Background image: the band 7 image of MODIS of October October 2013 plotted over the band 7 image of MODIS for October 24, 2002. The red curve 24, 2002. The red curve indicates the obtained boundary of the lake in February 2014. (For indicates the boundary of the lake in February 2014. The brown dots indicate the location interpretation of the references to color in this figure legend, the reader is referred to the of median values over each track. (For interpretation of the references to color in this fig- web version of this article.) ure legend, the reader is referred to the web version of this article.) 354 M.J. Tourian et al. / Remote Sensing of Environment 156 (2015) 349–360

1275 between 0 and 0.16 m. We then estimate the error of computed water

1274 volume in the lake by ENVISAT 1273 σ 2 ¼ σ 2Δ 2 þ σ 2 2; ð Þ ENVISAT extended V S H ΔHS 3 1272

1271 in which σS is the error level of surface water extent from satellite imag- lake level [m] Δ 1270 ery (Fig. 4)and H is the average water depth at each epoch and

1269 CryoSat−2 2 2 2 σ Δ ¼ σ þ σ ; ð4Þ H Halt Hb 1268 2002 2004 2006 2008 2010 2012 2014 σ where H is obtained by computing the standard deviation of water Fig. 7. Lake level time series derived from ENVISAT, ENVISAT extended mission and alt level measurements over each track (Fig. 7). Fig. 9 shows the estimated CryoSat-2. water volume time series and its error envelope over Lake Urmia. The behavior of time series and its alarming trend will be discussed in details of the quality of the bathymetry, which is indeed unsatisfactory. This in Section 7. might be due to low sampling density of bathymetry and/or poor inter- polation, or the poor measurement accuracy. However, as we do not 6. Water storage analysis using GRACE have access to the original survey data, we cannot identify the reason for the poor accuracy of the bathymetry. We have used 11 years (2003–2014) of GRACE release 5 datasets For volume computation, we use both the water level from satellite from the GeoForschungsZentrum (GFZ) (Dahle et al., 2013). The data altimetry and the surface water extent from imagery in combination are given as monthly snapshots in terms of spherical harmonic coeffi- with the bathymetry to obtain an accurate estimation for the lake's cients, which, after removing a long-term mean, describe the monthly water volume. To this end, within the water surface that is obtained anomalies in the geoid. The monthly solutions are contaminated by by satellite imagery the bathymetry data H is subtracted from b noise from different sources: aliasing of residual tidal signal (Seo, altimetric water level H leading to the height of the water column alt Wilson, Han, & Waliser, 2008; Tourian, 2013) and other signals, and ΔH ⋅ s . All water columns are then summed up to provide volume V i high-frequency noise in the spherical harmonic coefficients due to the orbit geometry and sensor noise. We modeled the effect of the main XN ¼ − : ð Þ tidal constituents and removed them from the GRACE monthly solu- V Halt Hb;i si 2 i¼1 tions to a large extent (Tourian, 2013). In order to reduce the high- frequency noise, we have used a 300 km Gaussian filter. The filtered monthly solutions are converted to equivalent water heights ΔM on The ∑N s is equal to the area of the lake obtained from satellite i =1 i 0.5 ° × 0.5 ° grids using (Wahr, Molenaar, & Bryan, 1998): imagery S. N is the number of grid cells with the bathymetry data Hb. Due to the inaccuracy of the bathymetry data at each time epoch, the re- − ρ lXmax þ Xl sults of Halt Hb for some grid cells may lead to negative values. For the Δ ðÞ¼θ; λ; R ave 2l 1 ðÞθ; λ Δ ðÞ; ð Þ M t ρ þ Ylm Klm t 5 volume computation, we turn these values to zero and we use the stan- 3 w ¼ 1 kl ¼− σ l 0 m l dard deviation of differences as the bathymetry error Hb varying 3 where ρave is the average density of the Earth (5515 kg/m ), ρw is the 3 average density of water (ρw =1000kg/m), R is the radius of the Earth (6378.137 km), kl is the load Love number of degree l, Ylm 2002 are the normalized spherical harmonic functions of degree l and order Δ 2008 m with lmax =90and Klm are the normalized complex spherical har- 2014 monic coefficients after subtracting the temporal mean. The estimated equivalent water heights are aggregated over the Urmia basin via area weighted averaging:

Xp Δ ðÞ¼χ; Δ ðÞθ ; λ ; Ai ; ð Þ M t M i i t 6 Aχ i¼1

14 ] 3 12

2 total water volume of lake [km 0 2002 2004 2006 2008 2010 2012 2014 1266 1268 1270 1272 1274 Fig. 9. Time series of lake water volume obtained by combination of lake bathymetry, sur- Fig. 8. Bathymetry of the lake varying between 1265 and 1274 m together with the surface face water extent from satellite imagery and water level from satellite altimetry. The non- water extent of January 2002, 2008 and 2014. linear trend is obtained from singular spectrum analysis (Section 7). M.J. Tourian et al. / Remote Sensing of Environment 156 (2015) 349–360 355 where χ is the basin index, p are the number of pixels associated with χ, χ ° Ai is the area of the grid cell i (0.5 ° × 0.5 °) in ,andAχ is the total area 38 N of χ. We concluded from our filter analysis (not shown here) that apply- Jan. 2007 ing the decorrelation filter suggested by Swenson and Wahr (2006) ° 36 N might lead to deterioration the long-term behavior of time series for such a small basin. Therefore, in this study we further smooth the estimated time series in time domain using a three-month moving average ° filter. The aggregated equivalent water height over the whole Urmia 38 N basin after smoothing with the moving average of three months is Apr. 2007 shown in Fig. 11 (black curve) indicating a clear seasonal behavior vary- ° 36 N ing between ±200 mm and a clear negative trend. The error envelope is obtained by propagating the calibrated standard deviations provided by the GFZ. ° It is clear that the Urmia basin, with an area of 51,931 km2 is far too 38 N small for GRACE resolution. According to Longuevergne et al. (2010), Jul. 2007 due to the effects of the filtering, leakage and poor spatial resolution, ° 36 N the utility of the GRACE data is only limited to catchments with an area of ≈200,000 km2. However, the results from Lorenz et al. (2014) support the fact that the spatial resolution of GRACE alone is ° not the determinant for studying catchments. Apart from spatial resolu- 38 N tion, the signal strength is also a determinant, and this we will term as Oct. 2007 ° the gravimetric resolution. In our case, the total volume change of 36 N 8km3 (Fig. 9) is certainly resolvable by GRACE gravimetry. In other ° ° ° ° ° ° ° ° ° words, if a basin represents a significant water storage variation, one 44 E 46 E 48 E 44 E 46 E 48 E 44 E 46 E 48 E can derive meaningful results if all caveats are considered in advance. The main caveats that one must take into consideration over such −100 0 100 small basin are 1) leakage and 2) signal attenuation. Fig. 10. Left column: WGHM equivalent water height signal over the Urmia basin and neighboring area, Middle column: WGHM field, in which the Urmia basin is set to zero 6.1. Leakage for convolution, Right column: the leaked signal into the Urmia basin.

It can be concluded from, for example Baur, Kuhn, and Featherstone estimated so that (2009) or Longuevergne et al. (2010) that leakage due to filtering cannot be undone perfectly, as the noise field of GRACE is fundamentally ∥ ∥⇒ ; ¼ Δ − Δ filtered: e min e Mmod k Mmod unknown. However, one can approximately quantify the leakage effect using corresponding models. To assess the amplitude and phase of the A re-scaling factor of k = 1.4 is estimated over the Urmia basin when leaked signal into the Urmia basin, we rely here on the WGHM model WGHM is used, from which the time series of filtered GRACE data is (Döll et al., 2014), which provides comparable water storage time series re-scaled. over Urmia basin (blue curve in Fig. 11). Fig. 12 shows the re-scaled time series of equivalent water height In order to quantify the leaked signal, we convolve a 300 km over the Urmia basin, in which the 22 mm error of leakage is added to Gaussian filter function over the WGHM field, in which the basin is set the propagated error of GRACE. to zero. In fact, the result of convolution for each grid cell inside the Urmia basin is nothing other than the leakage from outside the basin 7. Desiccation monitoring (see right column in Fig. 10). Indeed, if the neighboring basins have hydrological cycles that are out-of-phase with the Urmia basin, the phase 7.1. Alarming trends of leaked and original time series should be different. However, this is not the case here as the leaked (brown curve in Fig. 11) and original The results obtained from satellite imagery, altimetry and GRACE time series show a correlation coefficient of 0.92. allow us to monitor the behavior of the lake and its catchment in the re- Although such a high correlation coefficient increases our con- cent past. Clear but different negative trends are seen in the time series fidence about the phase of the time series from filtered field, the ampli- of surface water extent (Fig. 4), lake level (Fig. 7), total water volume of tude of the leaked-in signal with root mean square of 22 mm highlights the lake (Fig. 9) and equivalent water height over the whole basin the fact that the neighboring basins could significantly contribute to the GRACE signal on the basin. It should be noted that we cannot per- 300 GRACE filtered with Gaussian 300 km form such an analysis over a non-filtered GRACE field as the outcome WGHM would be strongly influenced by noise. Therefore, to account for leakage, leaked−in WGHM signal 200 WGHM filtered with Gaussian 300 km we assume an additional error level of 22 mm on top of the propagated re−scaled WGHM error derived from calibrated standard deviations (Fig. 12). This, of 100 course, does not solve the leakage problem, but rather serves as a pre- caution for any further analysis using GRACE time series. 0

6.2. Signal attenuation −100 equivalent water height [mm] A re-scaling factor is determined using the water storage time series −200 2002 2004 2006 2008 2010 2012 2014 obtained from WGHM model ΔMmod following Swenson (2011) and Landerer and Swenson (2012), to account for the signal attenuation Fig. 11. Equivalent water height time series from GRACE and WGHM together with the that occurs due to ﬁltering of GRACE data. The re-scaling factor is leaked-in WGHM signal. 356 M.J. Tourian et al. / Remote Sensing of Environment 156 (2015) 349–360

400 20 strong seasonal variation is, in fact, due to the moderate slope of the

] lake bed, over which the extent changes tremendously with small vari- 3 200 10 ations in lake level. nonlinear trend of equivalent water height Fig. 14 depicts three phases of desiccation over Lake Urmia, where the surface water extent (area of the lake) is plotted versus water 0 0 level. The colored dots (deliberately ordered in traffic light colors) in the scatter plot reflect three different patterns in the relationship of −200 −10 the surface water extent and water level. These patterns represent the water storage change [km equivalent water height [mm] different lake bed behaviors over different parts of the lake. For instance, the red dots in Fig. 14 describe the mild slope for the lake bed toward −400 −20 2002 2004 2006 2008 2010 2012 2014 the center. However, according to the bathymetry map that is provided by Alipour (2006) and Abbaspour et al. (2012),incaseofmoredesicca- Fig. 12. Equivalent water height derived from GRACE over Lake Urmia basin, corrected for the signal attenuation; its error envelope takes leakage into account. tion for the lake, one can expect that the relationship of water level and area must change. The depth of the lake in the southern part is shallower, deepening toward the north and the deepest point at the (Fig. 12). Instead of a direct simple line fitting, we use singular spectrum time of sampling was approx. 9 m (with height of 1265 m) located in analysis (SSA) to separate the nonlinear trend and time-variable sea- the northern part. Thus, in the case of more desiccation, the scatter sonal signals and estimate the trend afterwards. This way, the annual plot of surface water extent versus water level would show a milder signals will not affect the trend estimation (Blewitt & Lavallée, 2002). slope than the red dots in Fig. 14. SSA is a data-driven approach to extract information from short and The above analysis, however, does not describe the behavior of the noisy time series without prior knowledge of the dynamics affecting total water volume in the lake. The time series of the water volume of the time series (Broomhead & King, 1986). An important feature of the lake show an average negative trend of 1.03 ± 0.02 km3/yr. The be- SSA is that the obtained trends, shown in Figs. 12 and 13, are not neces- havior of our time series is comparable with the water volume time se- sarily linear and oscillations can be modulated in both amplitude and ries modeled by Sima and Tajrishy (2013) using a power model and a phase (Ghil et al., 2002). In our implementation of SSA, a 4-year window truncated pyramid model. However, the time series are only similar in size is applied for all the datasets we used in this study (Chen et al., terms of behavior and not concerning the value of water volume. The 2013). modeled water volume by Sima and Tajrishy (2013) seems to be Alinefitting on a full cycle of nonlinear trend that is derived from overestimated. As we combine the results from two spaceborne sensors SSA time series indicates a negative trend of 220 ± 6 km2/yr, 34 ± with local bathymetry, we believe that our estimate of water volume is 1 cm/yr and 26.9 ± 1.8 mm/yr for surface water extent, lake level and closer to its true value. A truth that is indeed bitter as the time series in- equivalent water height, respectively. By multiplying the trend of equiv- dicates a water loss of approx. 8 km3 from 2002 to 2014. Such an alent water height with the area of the basin 51,931 km2, we obtain amount of water corresponds to the daily consumption of approx. about 1.4 ± 0.09 km3/yr as the rate of water volume loss over the 53 billion people with an average daily consumption of 150 l which cor- whole basin. Of course, each of these values should be interpreted dif- responds to the weekly consumption of the whole world. This is indeed ferently. The equivalent water height time series refers to the whole alarming as the total remaining water volume indicates a value of only Urmia Lake basin, while the other time series represent the behavior 1.8 km3, which tells us that, at the current rate, (0.44 ± 0.02 km3/yr), of the lake itself. the lake will be empty by 2020. This disaster has been occurring with The trends in the water extent and water level time series indicate different rates over the last 12 years. However, one can clearly distin- that the lake is losing water at an accelerated rate of approx. guish between the negative trend before 2008, 0.76 ± 0.05 km3/yr 0.07 km3/yr2 (34 ⋅ 10−5 × 220). Such acceleration is not seen in the and after 2008, 0.83 ± 0.04 km3/yr (Fig. 9). time series of equivalent water height from GRACE. However, the lake's area and water level have not remained constant over the years. The 7.2. Drought and groundwater extraction: interpretation lake has been suffering from a clear water loss, which reflects on area and water level of the lake. Before 2006, the lake has lost on average In fact, what we observe in our estimated time series conforms to 43 ± 8 km2/yr of its area and 8 ± 1 cm/yr of its water level. The area our knowledge about the onset of a drought in 2007 (Trigo et al., loss rate increased strongly after 2006, where the lake lost more than 2 309 ± 14 km /yr of its area and 49 ± 2 cm/yr of its water level up to 4500 2010 (Fig. 13). After 2010, the area decreased at a lower rate of 232 ± 11 km2/yr and with far more seasonal variation (Fig. 3). The ex- treme change in seasonal variation is not, however, seen in the water 4000 ] level time series after 2010 with decrease rate of 29 ± 0.5 cm/yr. This 2 3500

1274 water level

5000 ] 3000 non linear trend of 2 surface water extent 4000 2500 1272 surface water extent [km surface water extent 3000 2000 2002−2006 water level [m] 2006−2010 2000 1270 nonlinear trend of water level 2010−2014 surface water extent [km 1500 1269 1270 1271 1272 1273 1274 1275 1000 2000 2002 2004 2006 2008 2010 2012 2014 water level [m]

Fig. 13. Comparison of time series of water level and surface water extent over Lake Urmia. Fig. 14. Scatter plot of surface water extent versus water level of Lake Urmia for three dif- The nonlinear trends of the time series are obtained from the singular spectrum analysis. ferent time periods: 2002–2006, 2006–2010 and 2010–2014. M.J. Tourian et al. / Remote Sensing of Environment 156 (2015) 349–360 357

3 2010) in this region. After 2008, the maximum monthly precipitation P decreased by 50%, which clearly indicates the onset of the drought (Fig. 15). Such water scarcity has brought a serious demand of water 2 for agricultural purposes in this region. This demand has led to a large increase in the rate of groundwater extraction over this region, which 1 has been acknowledged by (Alipour, 2006; Joodaki et al., 2014; Voss et al., 2013). The effects of groundwater depletion on hydrological be- 0 havior are dependent on the aquifer. However, the most common and direct effect of groundwater extraction is the lowering of water tables. −1

Such effects can be observed in time series of groundwater data from mean groundwater level [m] pieziometric stations over the Urmia basin (Fig. 16). The mean ground- −2 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 water level is computed after removing the mean of each station and normalizing it with its standard deviation and multiplying back the Fig. 16. Mean groundwater level of stations over the Urmia basin except Salmas and mean of standard deviation values of all stations. Fig. 16 shows a clear Oshnavieh stations showing a drastic decrease in 2008. The error envelope represents reduction in groundwater level in 2008 starting from summer 2008, the standard deviation of groundwater level of stations. The red ellipse highlights the which interestingly coincides with the drop in equivalent water height slight increase in groundwater level. (For interpretation of the references to color in this ﬁ time series (Fig. 12). In fact, if the entire ~ 200 mm drop in equivalent gure legend, the reader is referred to the web version of this article.) water height were due to a drop in groundwater, then one could conclude an average porosity of 20% over the Urmia basin. by computing annual accumulated precipitation minus annual accumu- The scatter plot of equivalent water height with respect to mean lated evapotranspiration (Fig. 15 middle), which represents negative groundwater level indeed indicates a direct relationship, especially at values after 2007 (except for 2009). This implies a negative recharge the monthly mean level (Fig. 17). The scatter plot represents a real de- into groundwater leading to a secondary effect on lowering the water crease in the groundwater level and equivalent water height during tables. The agreement between P–ET and GRACE can be explicitly the summer period (May–September), when the groundwater extrac- found via the water balance equation tion is accelerated. However, from the GRACE point of view, the water is still in the basin after groundwater extraction, and there should not dM P–ET ¼ ; ð7Þ be any change in the water storage time series. Nevertheless, the dt GRACE water storage estimate shows a clear drop, meaning that the extracted groundwater leaves the basin. where, over a certain time period, the change in the water storage is In essence, if the extracted groundwater is used for irrigation, it indi- equal to the P–ET. Fig. 15 (bottom) shows the GRACE rate of water stor- rectly affects the hydrological cycle of a basin as it leads to increase of age changes d M/d t together with the precipitation minus evapotrans- crop evapotranspiration ET (Aeschbach-Hertig & Gleeson, 2012). This piration (orange curve). P–ET agrees relatively well with d M/d t with a is actually the case over the Urmia basin,especially during the summer correlation coefﬁcient of 0.73. months after 2008 (Fig. 15 top), where maximum evapotranspiration From the year 2008 onward, the average negative input to the basin is higher than maximum precipitation. This can be better analyzed has gradually generated a new equilibrium for the storage level of the basin. The two equilibrium levels for the storage are even distinguish- 200 P able in the nonlinear trend from SSA (Fig. 12). Given such a situation, ET any trend estimation within a time period that includes both of the 150 aforementioned time periods might lead to a wrong interpretation. However, a visual inspection of Fig. 9 reveals that, after 2009, the behav- 100 ior of the total quantity of the water in the lake is different of the time

[mm/month] 50 series of the equivalent water height. After 2009 the lake has continu- ously lost 0.62 ± 0.03 km3/yr of its water volume, while the rate of 0 water loss in the whole basin is less, namely 0.4 ± 0.05 km3/yr. As 100 the lake itself is also part of the basin, these results mean that, in 300 0

[mm] 200 −100

−200 100 Mar.Apr. Jun.May 200 Feb. dM/dt Jul P − ET 0 100 Jan. Aug Dec. 0 Sep. −100 Nov. Oct. [mm/month] −100 equivalent water height [mm] mean monthly −200 −200 2002 2004 2006 2008 2010 2012 2014 monthly

Fig. 15. Top: Time series of precipitation P (from GPCP) and evapotranspiration ET (from −1.5 −1 −0.5 0 0.5 1 1.5 ECMWF) over the whole basin of Lake Urmia. Middle: Annual accumulated precipitation mean groundwater level [m] minus annual accumulated evapotranspiration over the whole basin. Bottom: GRACE rate of water storage changes together with corresponding precipitation minus evapo- Fig. 17. Scatter plot of equivalent water height with respect to mean groundwater level in- transpiration P–ET. cluding their relationship at monthly mean level. 358 M.J. Tourian et al. / Remote Sensing of Environment 156 (2015) 349–360 principle, the basin outside the lake is gaining water at a rate of 0.22 ± obtained, whereby a rate of 1.03 ± 0.02 km3/yr is obtained for the 0.06 km3/yr. On the other hand, the slight positive trend in the groundwater loss of the lake. The results from GRACE reveal that the basin of water level (red ellipse in Fig. 16) ascertains a positive, though slow, re- Lake Urmia faces a water loss at an average rate of 1.4 ± 0.09 km3/yr. charge into the groundwater of the basin. Here we investigate the However, this rate can be questioned due to two distinct equilibrium possible reasons: levels of storage before and after 2008. This result agrees very well with a drop in the mean groundwater level in 2008 obtained from in 7.2.1. Scenario 1 situ data. In fact, we have identified the impact of human-related activ- As neither P–ET nor the GRACE equivalent water height time series ities on the lake desiccation by comparing the time series of water vol- of the whole basin show this kind of positive trend, one could conclude ume over the lake and the one from GRACE and the time series of in situ that the water is only exchanged within the basin. This inherently groundwater level. means induced leakage from the lake (lateral flow), which occurs as Our results reveal that, since 2009, the lake has dried up faster, at the groundwater level becomes lower than the lake level. This has 0.62 ± 0.03 km3/yr, than the overall basin, at 0.4 ± 0.05 km3/yr. been observed by local people at the bottom of the wells around the Again the in situ groundwater data helped us to interpret this issue. lake (Kardavani, 2013). The induced leakage causes many serious nega- The mean groundwater level data shows a slight positive trend during tive environmental impacts, like an increased soil salinity of agricultural 2009–2014, following a drastic decrease in 2008. We have discussed area and people's lives being endangered. Indeed, a higher salinity ad- two possible scenarios: 1) the induced leakage from the lake and versely influences the efficiency of crop production, especially on leaf 2) blocking the surface streamflow into the lake. In reality, a combina- number and stem length (Alezadeh, Pirzad, Babaei, & Ghanifathi, tion of these scenarios could be responsible for increasing the ground- 2012). Although induced leakage is observed by local people, it might water level after 2008. Both of these scenarios with anthropogenic not be the only reason for the positive recharge into the groundwater. influences would also lead to an acceleration of the lake desiccation. Moreover, the induced leakage could bring a serious concern in this re- 7.2.2. Scenario 2 gion as it raises the salinity in the neighboring basins and it definitely in- The positive trend in the groundwater level could be indirectly relat- fluences the efficiency of crop production. In fact, it accelerates the ed to water management in the basin. In fact, any water management release of salt in the basin leading to ecological and agricultural and activity like dam construction or diversion of surface water for irrigation social stress. purposes would prevent a normal inflow into the lake. This seems to be We conclude, from our results, that the drying up of the lake has probable due to the water scarcity over this region after 2008. Storing been occurring due to a chain of reasons, which are highly influenced water behind dams can explain the water surplus outside the lake and by anthropogenic activities. Traditionally, the groundwater is extracted also can describe the extra acceleration of waterloss inside the lake for agricultural purposes in this region. However, one can imagine that due to a reduction of lake inflow. However, as we do not have any the rate of groundwater extraction and storing water behind the dams streamflow data, we cannot validate this scenario. correlate with water availability over the region. This means that, in In reality, a combination of the aforementioned scenarios is likely re- the event of a drought, more groundwater is needed for people's daily sponsible for increasing the groundwater level after 2008. Both scenar- use. Unfortunately, the drought after 2007 caused an increased extrac- ios have an anthropogenic background with the consequence of tion of groundwater and led to keeping water behind the dams, which accelerating the lake desiccation. definitely accelerated evapotranspiration from the basin. Indeed, having the evapotranspiration sourced from groundwater with no replenish- 7.3. Lake Urmia's future? ment from precipitation or streamflow will lead to a negative balance in the hydrological input, which, in the end, causes a negative trend in Given the current negative trend, remaining water volume and the the storage. bathymetry of the lake, one can predict that the lake will divide into As a final remark, given the current negative trend in water volume two minor lakes (northern and southern) in the near future. The north- loss, at 0.44 ± 0.02 km3/yr over the lake and the alarmingly low value ern sub lake, due to its depth, will probably exist for a longer time period for the remaining water volume (approx. 1.8 km3) in the lake, one can than the southern part. In terms of water volume, by only following the easily predict that the lake will completely disappear in a couple of current rate of water loss 0.44 ± 0.02 km3/yr (2011–present), one can years if no countermeasures are taken. We acknowledge that there are naively predict that the lake will completely dry up in about 5 years uncertainties in our estimations and conclusions, which can be the (around 2020). From our results, we can conclude that if human- scope of future works. However, combining the results from three dif- related activities like groundwater extraction or water diversion are ferent independent spaceborne sensors and comparing them with stopped, the desiccation rate can be reduced. Indeed, we say reduced, available in situ data of hydrological fluxes and groundwater level and not stopped, as the drought, and consequently the hydrological have given us confidence in understanding the hydrological behavior trend, is also responsible for the desiccation. of the basin and arriving at the aforementioned conclusions. According to our results, the only urgent remedy that one can pro- 8. Summary and conclusion pose for the lake is to stop the groundwater extraction in the basin. According to West Azerbaijan authorities, the permission to drill new In this study, we have used a multisensor approach to monitor the wells and groundwater depletion was terminated within the basin. desiccation of Lake Urmia. We have combined, for the first time, the However, this might not lead to a sustainable therapy as there are still equivalent water height from GRACE, surface water level time series about 20,000 illegal wells in the basin (Hamshahri, 2013). In fact, a re- of the lake from ENVISAT and CryoSat-2 altimetry, surface water extent gional environmental monitoring system is required to track water time series from optical imagery (MODIS) together with in situ and par- availability and consumption in this region. In such a monitoring sys- tially model-based hydrological fluxes to monitor the hydrological cycle tem, the spaceborne sensors presented in this study would play an im- of the Urmia basin. Over the obtained time series, we have employed portant role in augmenting the understanding of the water cycle in the SSA with a 4-year window size to capture nonlinear trends. basin. Our results show that, on average, the lake's water level decreases at a rate of 34 ± 1 cm/yr and its area at 220 ± 6 km2/yr. This result indi- Acknowledgment cates that the lake has lost almost 70% of its water extent. By combining the results from satellite imagery and satellite altimetry with the ba- The authors thank the editor Marvine Bauer and two anonymous thymetry, the time series of total water volume over the lake is reviewers for the helpful comments, which improved the manuscript M.J. Tourian et al. / Remote Sensing of Environment 156 (2015) 349–360 359 considerably. The authors are grateful to Christof Lorenz from the Forootan, E., Rietbroek, R., Kusche, J., Sharifi, M., Awange, J., Schmidt, M., Omondi, P., & Famiglietti, J. (2014). Separation of large scale water storage patterns over Iran using Karlsruhe Institute of Technology (KIT) for his help and support. Thanks GRACE, altimetry and hydrological data. Remote Sensing of Environment, 140, 580–595. are also due to Shima Shahintaj for providing the synoptic precipitation Frappart, F., Calmant, S., Cauhopé, M., Seyler, F., & Cazenave, A. (2006). Preliminary results data sets. The authors are grateful to Ehsan Forootan for providing the of ENVISAT RA-2-derived water levels validation over the Amazon basin. Remote Sensing of Environment, 100(2), 252–264. location of the piezometric stations. The authors would like to also Ghil, M., Allen, M.R., Dettinger, M.D., Ide, K., Kondrashov, D., Mann, M.E., Robertson, A.W., thank Amin Eimanifar from the Iranian Artemia Research Center and Saunders, A., Tian, Y., Varadi, F., & Yiou, P. (Sep. 2002). Advanced spectral methods for Samad Alipour from Urmia University for their continuous help and climatic time series. 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