Quantitative analysis of water balance components in Lake Urmia, Iran using remote sensing technology

Author’s Accepted Manuscript Quantitative analysis of water balance components in Lake Urmia, Iran using remote sensing technology Hamid Mohebzadeh, Mahboobeh Fallah www.elsevier.com/locate/rsase

PII: DOI: Reference:

S2352-9385(18)30172-1 https://doi.org/10.1016/j.rsase.2018.12.009 RSASE205

To appear in: Remote Sensing Applications: Society and Environment Received date: 15 May 2018 Revised date: 21 November 2018 Accepted date: 26 December 2018 Cite this article as: Hamid Mohebzadeh and Mahboobeh Fallah, Quantitative analysis of water balance components in Lake Urmia, Iran using remote sensing technology, Remote Sensing Applications: Society and Environment, https://doi.org/10.1016/j.rsase.2018.12.009 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Quantitative analysis of water balance components in Lake Urmia, Iran using remote sensing technology

Hamid Mohebzadeha,*, Mahboobeh Fallahb

a

b

Department of Civil Engineering, ERI, Gyeongsang National University, 501 Jinju-daero, Jinju, Gyeongnam 660-701, South Korea

Department of Soil Science, Tarbiat Modares University, Tehran, Iran

*

Corresponding author. E-mail: [email protected]; Phone: +98 9193209187; fax: +98 2155225413

Abstract Accurate quantification of water balance components in large lakes is of great importance in assessing the changes in hydrological components, and subsequent variations in lakes water balance. The water balance of Urmia Lake in northwestern Iran was quantitatively analyzed in the year 2005 using remotely-sensed data (e.g. evapotranspiration and precipitation) and meteorological data. The analysis was based on GIS operations. For this purpose, the estimation of pixel-scaled monthly evapotranspiration (ET) was conducted via the Surface Energy Balance Algorithm for Land (SEBAL) using time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images and meteorological data; precipitation was derived from the 1

monthly Tropical Rainfall Measuring Mission (TRMM) satellite observations; surface runoff was estimated by the empirical formula; and finally, the computations of recharge were carried out using the water balance approach for the winter months where estimation of ET and surface runoff were subtracted from precipitation. Water balance components were quantified for the lake and the lake basin separately. Results showed that the water balance components calculated by remote sensing were in conformity with the reported figures in the literature. According to the results of the water balance method, Urmia Lake was suffering from a negative water balance of 3443 MCM yr-1. Findings of this study demonstrated the application of the derived satellite data in estimating lake’s water balance components in order to improve the accuracy of the lake water budget calculation. Graphical abstract

Keywords: MODIS, TRMM, Evapotranspiration, Precipitation, SEBAL, Water balance analysis

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1. Introduction Water balance analysis of the hydrological system is of vital importance for assessing the water resources in different regions of the world, where hydrological measurement stations have been poorly developed. There are three principal schemes for the present implantation of the water balance models as follows: rehabilitation of the catchment hydrology, assessment of the climate change impact, and evaluation of the seasonal as well as geographical schema of the water supply and irrigation demand (Xu and Singh, 1998). Precipitation and evaporation as the most dominant water balance components are dependent on the availability of extensive data for the accurate and reliable estimation, while conventional measurements providing only point data obtained at the weather and gauging stations cannot be appropriate enough for the estimation of these parameters. Hence, the acquisition of high-quality and spatially distributed hydrological information is one of the main goals in hydrological modeling. Recent developments in remote sensing instrumentation and modeling provide distributed spatial data for certain parameters, including ET (Bastiaanssen et al., 1998a; Bastiaanssen et al., 1998b) and precipitation (Herman et al., 1997; Milewski et al., 2009a; Milewski et al., 2009b), which can be transmuted cell by- cell into a deterministic distributed estimation for variables. Even though the absolute values are undetermined, relative data can still bring about more desirable set of modeled estimations, especially for the spatial information (Khalaf and Donoghue, 2012). Remote sensing contribution has been explored in several studies as a method to quantify water balance components. In a study conducted by Khalaf and Donoghue (2012) during the winter of 2004, the water balance components were computed using remote sensing for the estimation of recharge rates in the West Bank, Israel. In this study, the MODIS sensor and TRMM were employed as a source for the 3

regional scale energy and water fluxes such as ET and precipitation distribution, correspondingly. In order to establish and map the main surface indexes of recharge regions, data obtained from satellite images, including ET, precipitation, land cover and DEM derivation were incorporated with the hydrogeological data in a GIS format. Results of quantitative analysis demonstrated the closeness of recharge estimate to the figures of previous estimates. They indicated the possibility of applying further refinements to the proposed model of recharge estimation such as using shorter rainfall intensities (daily or hourly), application of datasets with higher resolution, modifying the SEBAL-derived outputs compared with the field measurements, and calibrate TRMM values according to the long-term average precipitations. In a study performed by Milewski et al. (2009b) in the arid Sinai Peninsula and the Eastern Desert of Egypt, some methodologies were applied to measure rainfall–runoff and groundwater recharge, which were greatly dependent on the observations extracted from a wide-range of global remotely-sensed satellite data products (TRMM, SSM/I, Landsat TM, AVHRR, AMSR-E, and ASTER). SWAT (Soil Water and Assessment Tool model is a watershed-scale, unceasing, and partly-dispersed hydrological model,) has been developed in this study for the calibration versus the observed runoff values, and then applied to simulate the overland flow, channel flow, transmission losses, bare soils evaporation and ET as well as groundwater recharge for the main catchments of these regions. Their results demonstrated that in spite of the fact that the regionalized remote sensing approach wouldn’t be considered as an appropriate substitute for the conventional methodologies, this method is still able to reveal the first-order estimation for the rainfall, runoff, and recharge over extensive regions of the arid lands with the shortage of adequate spatial and temporal precipitation and field data. In a study conducted by (Vanderkelen 4

et al., 2018), remote sensing observations, high-resolution downscaling of reanalysis data and outflow values recorded at the dam were used to present a water balance model for Lake Victoria. The uncalibrated calculation of the individual water balance components showed that fluctuations of lake level were roughly coordinated with the levels retrieved from satellite altimetry. In a study, (Nigatu, 2013) studied the water balance components including surface water inflow, overlake rainfall and evaporation pattern variation as well as their effect on the water balance of Tana Lake. Three different scenarios of climate change for future time horizons were the basis for this study: 2020s (2010-2039), 2050s (2040-2069) and 2080s (2070-2099). Hardgrave’s approach was applied to measure over-lake evaporation; inverse distance weighing (IDW) method was used to calculate over-lake rainfall, and simulation of surface inflows was performed by HBV model. Urmia Lake is the largest salt-water lake in the Middle East, which is located in northwestern Iran, west of the Caspian Sea (Hassanzadeh et al., 2012; Karbassi et al., 2010; Zarghami, 2011). Urmia Lake is a typical example of many parts of the world, where infrastructure for data monitoring is still limited to the manual method in many monitoring sites with poor spatial coverage. Over past two decades, this lake has been shrinking substantially, and a combination of over-exploitation of upstream rivers and drought as well as the lack of efficient water management have been main causes of the lake shrinkage (Eimanifar and Mohebbi, 2007; Hassanzadeh et al., 2012; Mohebzadeh, 2018). During 1995 to 2011 lake’s surface area has been diminishing from nearly 6100 km2 (Eimanifar and Mohebbi, 2007) to only 2366 km2. Based on a research conducted by (Sima and Tajrishy, 2013), this decreasing tendency in Urmia Lake area and volume can be divided into two periods: before and after 2003. 5

From 1996 to the end of 2002, the area and water level of Urmia Lake declined considerably, while from 2003 up to the end of May 2011, these parameters declined by milder slopes. However, since 2003, the lake water level, area and volume dropped below the minimum values observed over the past four decades, and the Urmia Lake water balance components are not wellinvestigated due to shortage of the observational data. Since the observational data are not widely available, remote sensing techniques can play a considerable role in the water balance analysis of the Urmia Lake because of the distributed spatial data for the certain parameters such as ET and precipitation. As the first research to estimate the annual water surface temperature (WST) cycle for Urmia Lake, (Sima et al., 2013) applied MODIS day/night Land Surface Temperature (LST) data to acquire WST maps of the lake during 2007–2010. Moreover, spatial distribution of the lake WST has been analyzed on its evaporation rate, but up to now, no in-depth study has been performed to analyze water balance components of Urmia Lake at the beginning of declining trends in the lake area and volume to determine major causes of declining inflow and increasing outflow from the lake. The main aim of this study was to conduct qualitative analysis of the water balance of Urmia Lake for the year 2005, as the beginning of historical negative trends, based on remote sensing methods. For this purpose, the water balance technique was used. Firstly, the water balance components of the lake were divided into all inflows and outflows in a given period. The term inflow refers to ground water leakage yielded by rainfall recharge, lake precipitation, and surface runoff from the basin into the lake, while outflow refers to evaporation. Secondly, SEBAL approach has been applied to estimate monthly ET; precipitation was derived from the monthly data sets of the Tropical Rainfall Measuring Mission (TRMM); runoff amount was calculated by 6

the empirical formula; and monthly recharge estimation was calculated by applying a simple water balance, where actual monthly ET (AET) derived from SEBAL and runoff is subtracted from the monthly precipitation (P). Finally, based on spatial distribution of derived components, water balance of Urmia Lake was examined.

2. Study area and materials 2.1.

Study area description

Urmia Lake is located between 37° 04′ N and 38° 17′ N latitude and 45° E and 46° E longitude in the northwest of Iran (Fig.1). It is approximately 140 km long in a north-south direction, and about 85 km wide in its east-west direction with total catchment area of 51,876 Km2 (Jalili et al., 2011), and it is also nearly 3.15 % of that of the entire country, while including 7 % of the total surface water in Iran. Urmia Lake covers an average area of 5100 km2. The maximum and average depth of this lake are 16 and 5 meters, respectively (Ghorbanali and Nouri, 2007). The basin is located at an altitude of 1250 to 3721 m, the lowest part is the lake and its adjacent flat plains (Eimanifar and Mohebbi, 2007). Groundwater also supplies a very small fraction of the water inputs to the lake. Weather condition in the Urmia Lake basin is largely controlled and influenced by the mountains surrounding the lake, which lead to the harsh and continental climate of the catchment. (Ghaheri and Baghal Vayjooee, 1999; Kelts and Shahrabi, 1986).There are remarkable seasonal fluctuations in the air temperature of this area having semiarid climate with an average yearly precipitation about 200 to 300 mm. (Touloie, 1998), and the mean annual evaporation rate of the lake is approximately between 900 to 1170 mm (Sima et al., 2013). Annual rainfall variation for the seven key stations is shown in Fig. 2. The air temperature 7

usually ranges from 0 and -20°C in winter and up to 40°C in summer. Because of this, Urmia Lake is of great importance for the region, due to the fact that it plays a vital role to moderate these extremes (Street-Perrott and Roberts, 1983). The annual surface water in the lake basin is 6900 Million Cubic Meters (MCM), of which 4900 MCM is from rivers, 500 MCM from flood water (through rainfall), and 1500 MCM from precipitation (Jalali, 1984). Underground springs are also a source of water, but the volume is not well-determined. Iran Department of Environment (2010) reported that the long-term average water discharge to the lake is approximately equal to 5300 MCM yr-1. There have been a large number of dams and diversion projects regulating the rivers in this basin, which prevent surface water from entering the lake, while only spilled water and wastewaters may flow in. There are increasing number of projects (about 275 projects) under study, which 231 of them have been supposed to construct in near future containing 71 reservoir dams, 124 weirs and conduction facilities, 17 pumping stations as well as 10 flood controlling and artificial feeding (Hassanzadeh et al., 2012). Regulating water projects operated until 2006 in Urmia Lake basin contained 1712 MCM of water. The proposed projects that are under construction regulate 1499.9 MCM water and those which are under study regulate 657.2 MCM water; therefore total regulated water volume will be 3869.1 MCM in nearly 20 years (Ministry of Energy, 2007). This lake is the major seasonal habitat for a large number of migratory birds species, and also its watershed is of vital importance for the agricultural area with an approximate population of 6.4 million people, while 76 million people were estimated to live within a radius of 500 km (SEDAC, 2010). Because of the fact that the saline lakes like Urmia Lake occur principally in endorheic basins, these lakes are particularly susceptible to the environmental changes due to 8

their extent, salt level and variations in their hydrological regime affecting its yearly mixing regimes. The lake has been considerably shrinking over the past decade resulting in further concentration of salts and therefore raising salinity level to more than 300 g/L in the lake (Eimanifar and Mohebbi, 2007), which are 8 times as salty as typical Sea water. The concentrations of sodium chloride over 320 g/L are conceived to be lethal to the lake’s brine shrimp species, Artemia urmiana as the key link of the lake’s food chain, whereas optimal salt concentrations for the growth and survival of these shrimps are under 200 g/L, and as salinity goes up much above this level, it can lead to the negative effect on the growth rate, reproduction and mortality of these species (Abbaspour and Nazaridoust, 2007; Agh et al., 2008; Dahesht Esmaeili et al., 2010). As water levels of the lake decrease, salt levels will increase, and subsequently a vast area of lake bed will be exposed to the covering of salts, mainly sodium chloride, which is the reason for a great salt desert on more than the 400 km2 of lost surface area (Golabian, 2010). These regions with high levels of salinity, and also negative impact on most agricultural crops will not support agriculture, and prevent growth of most natural vegetation. Consequently, Urmia Lake is suffering from environmental problems, which may have severe consequences if the proper conservation measures is not taken into account. 2.2.

Data sources

2.2.1.

Meteorological data

For this study, mean monthly meteorological data for the year 2005 were obtained from seven meteorological stations around Urmia Lake. Meteorological data were used in SEBAL algorithm, calculation of reference potential ET in FAO Penman-Monteith model and validation of the TRMM precipitation estimates. Field data including surface temperature, net radiation, and 9

heat fluxes are not accessible for the calibration of SEBAL ET estimates. However, SEBAL has been validated by comparing its results with FAO Penman–Monteith estimations. The stations were located in different parts of Urmia Lake basin, which measured all meteorological variables (i.e. rainfall, maximum and minimum temperature, relative humidity, and wind speed). Fig. 1 shows station locations; Table 1 provides the station features and a summary of the meteorological data.

2.2.2.

Remotely sensed data

Two types of remote sensing datasets are used for the study area: (1) Moderate Resolution Imaging Spectroradiometer (MODIS) Level-3 MOD09A1 and MOD11A2 (daytime) products and (2) Tropical Rainfall Measuring Mission (TRMM) 3B43.v6 data (Table 2). The MODIS surface reflectance products represent an estimate of the surface spectral reflectance as it would be estimated at ground level without any atmospheric scattering or absorption. MOD09A1 provides 7 bands (1-7) at 500-meter resolution in an 8-day gridded level3 product in the Sinusoidal projection. Data sets available for this product contain quality assessment, reflectance values for bands 1-7, and the day of the year for the pixel along with solar, view, and zenith angles (Vermote, 2015). The level-3 MODIS global LST and emissivity 8-day data consist of values generated using mapping the level-2 LST product on 1-kilometer (actual 0.928-km) grids in a Sinusoidal projected tile. In addition, MOD11A2 is composed of daytime and night-time LSTs, quality assessment, observation times, view angles, coverage of clear sky and estimated emissivities in bands 31 and 32 form types of land cover.

10

Since TRMM 3B43.v6 is the monthly product with the highest spatial resolution, it was used in this study. The aim of Algorithm 3B43 was to provide the TRMM and other Data with bestestimate precipitation rate and root-mean-square (RMS) precipitation-error estimates. These gridded estimates were on a calendar month temporal resolution and a 0.25-degree by 0.25degree spatial resolution global band expanding from 50 degrees South to 50 degrees North latitude. Most MODIS and TRMM data products provide users with free accessibility, which may be downloaded directly from NASA Goddard Space Flight Center website.

3. Methodology 3.1.

Preprocessing of satellite images

3.1.1.

Land–water mask

When extracting water surface evaporation of a lake, it is very important to make a clear distinction between the vegetation, land and water areas in advance. In this research, Normalized Difference Vegetation Index (NDVI) has been used to map Urmia Lake and water bodies, which can be recognizable by negative values on the NDVI maps. Because of relatively shallow lake, the lake areal extent is subject to considerable fluctuation due to the climatic variations within the year (Sima and Tajrishy, 2013). Therefore, the red (band 1) and near infrared bands (band 2) of MODIS 8-day composite products (MOD09A1) with 500-meter resolution were acquired to map monthly variations in the lake area more precisely. Eventually, the monthly delineated areas of Urmia Lake were applied to extract evaporation and precipitation over lake.

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3.2.

Estimating evapotranspiration processing

In this study the SEBAL model was applied for all months of year 2005 in order to estimate the spatial distribution of the actual ET via remotely-sensed data. The SEBAL model is a widely used, which is popular model developed by Bastiaanssen et al (1998a). For this purpose, SEBAL requires spatially distributed, visible, near-infrared and thermal infrared input data taken from any suitable satellite imageries. A complete radiation and energy balance have been computed using SEBAL algorithm together with the resistances for momentum, heat and water vapor transport for each pixel. ET was obtained with regard to instantaneous latent heat flux, ET (W m−2), which is calculated as the residual of the surface energy balance equation when satellite traverse on a pixel-by-pixel basis:

ET 

 Rn  G  H

(1)

Where Rn is net radiation (W m−2), G is the soil heat flux (W m−2), and H is the sensible heat flux (W m−2). In the framework of SEBAL modeling, the net radiation, Rn, can be computed on the basis of the balance between incoming and outgoing short-wave (0.3–2.5  m) and long-wave (2.5– 100

 m) radiation: R n  1-   R Sin  R Lin  R Lout  1  0  R Lin

(2)

4 = 1-   R Sin  a Ta4  0 TRAD  1   0   a Ta4

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Where R Sin is incoming short wave radiation (W m-2), R Lin is long wave incoming radiation (W m-2), R Lout is outgoing long wave radiation,  is surface albedo,  a is atmospheric emissivity,  0 is surface emissivity,  is Stefan–Boltzmann constant (5.67 × 10-8 Wm-2 K-4), Ta is the air temperature (K), whereas TRAD is radiometric surface temperature (K) derived from thermal remote sensing data. The surface albedo was extracted from the MODIS sensor using a weighted linear combination of the observed reflectances of 8-day composite images (Price, 1990)b with different weights coefficient proposed by Liang (2001):   0.160r1  0.291r2  0.243r3  0.116r4  0.112r5  0.081r7  0.0015

(3)

Where ri represents the reflectance of the ith MODIS spectral band. This constant albedo has been assumed that there was not remarkable variation in the vegetation cover within an 8-day period. The soil heat flux, G, is computed as a fraction of Rn, using a semi-empirical approach, through a reduction coefficient that is a function of surface albedo and vegetation cover ( using NDVI), along with radiometric surface temperature (Bastiaanssen et al., 1998a) :

G  TRAD  273.16   2  0.32  C1   0.62  C1   1  0.98NDVI 4    Rn 100

(4)

Where C1 is a factor to convert the instantaneous values of albedo to daily averages (default=1.1).

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Since sensible heat flux, H, is a function of the surface roughness, temperature gradient, and wind speed, its computation is not straightforward as a result of the interrelationship between temperature gradient and surface roughness. The following equation is the classical presentation for H given by Farah and Bastiaanssen (2001).

H  Cp

(5)

dT rah

Where  represents the air density (kg m−3) as a function of atmospheric pressure, Cp is the specific heat capacity of air (≈1004 J kg-1 K-1), dT is the near surface temperature difference (K), and rah shows the aerodynamic resistance to heat transport (s m-1). The extreme pixels of the image, which is called dry and wet pixels were applied by SEBAL algorithm in order to develop a relationship between surface temperature ( TRAD ) and the difference between ( TRAD - Ta ) as follows: TRAD  Ta  dT  aTRAD  b

(6)

The definition of a and b coefficients requires a choice of the two pixels, representing the extreme conditions of temperature and humidity, called the dry pixels and wet pixels. The dry pixel shows a dry bare farming land supposing that ET is 0, while the wet pixel indicates a well-watered and fully-covered crop surface , and the surface temperature ( TRAD ) close to the air temperature ( Ta ). Selection of wet pixel and dry pixel was done based on the relationship between temperaturealbedo and the NDVI albedo in a particular image. Usually a pixel with low temperature and high 14

NDVI is selected as the wet pixel and a pixel with low albedo, low NDVI and high temperature is selected as the dry pixel. These two pixels are used for the computation of all remaining pixels between these two points. In this study, only one wet pixel and dry pixel was selected over the whole area of study for each month. The aerodynamic resistance used in Eq. (5) was obtained between the same two heights according to the relationship suggested by Brutsaert (1982), adopting zero plane displacement (d0), roughness length for momentum (zom) and heat (zoh) transfer parameters retrieved as a function of Soil Adjusted Vegetation Index (SAVI) (Bastiaanssen et al., 2002):

SAVI 

1  L  NIR  RED 

(7)

NIR  RED  L

zom  exp  5.809  5.62  SAVI 

(8)

zoh  0.01 zom

(9)

 1  e C1LAI  d 0  h 1   C1LAI  

(10)

Where L is correction factor (default=0.5), RED and  NIR are the reflectance for MODIS bands 1 and 2, respectively, C1 is a constant given as 20.6, LAI is Leaf Area Index, and h is crop height. To estimate the sensible heat flux, an iterative way began from neutral stability assumptions was performed by atmospheric stability corrections according to Monin–Obukhov.

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Having estimated all the other components in the energy balance equation, the instantaneous latent heat flux, ET , was calculated for each pixel of the image, and it was then used to compute the instantaneous evaporative fraction  ins :

ins 

ET Rn  G

(11)

The instantaneous evaporative fraction  ins represents the ratio of the real to the vegetation evaporative demand while the atmospheric moisture conditions are in equivalence with the moisture condition of soil. As long as moisture is available, energy will be used for its evaporation. When there is no more moisture left, all existing energy will be conducted into the sensible heat flux and  ins approaches zero. Therefore, the available moisture in the root zone and its consumption by plants are integrated using the evaporative fraction, while it is not requisite to know the actual content of moisture stored in the soil (Khalaf and Donoghue, 2012). Generally,  ins is supposed to be in the range of 0 and 1 in daytime. However, these boundaries are not representative of theoretical restrictions (Cammalleri et al., 2012). In several studies (Brutsaert and Sugita, 1992; Crago, 1996; Farah et al., 2004; Shuttleworth et al., 1989), it was indicated the slight daytime change of  ins based on the hypothesis of selfpreservation, which resulted in using  ins as a temporal integration parameter. Furthermore, under this hypothesis, the instantaneous  ins is generally used to extract either daytime (  day ) or 24-h ET (Van Niel et al., 2011). Daytime behavior of  ins depends on the environmental factors such as air temperature, soil water content and leaf area index (LAI), while it is almost independent of wind speed despite the fact that wind is considered to be a major forcing factor causing turbulent 16

exchange at the surface (Lhomme and Elguero, 1999). However, Gentine et al. (2007) found that the low correlation of  ins with wind speed leads to slight increases in the wet conditions. According to the following listed hypotheses: ( i )  ins , self-preservation during daytime ( ins  day ) and ( ii ) negligible contribute of soil heat flux on 24-h available energy ( G 24  0 ),

net available energy (Rn − G) reduces to the net radiation (Rn), and ET24 (mm d-1) can be obtained by following equation :

ET24 

8.64 107 ins R n 24 w

(12)

Where Rn24 (W m-2) is the 24-h averaged net radiation,  ( 2.47 106 J kg-1) is the latent heat of vaporization, and  w (1000 kg m-3) is the density of water. The 24-h integrated meteorological variables was used to estimate the daily net radiation based on the approach suggested by the FAO-56 paper (Allen et al., 1998). The SEBAL model was applied to the MODIS 8-day composite images of surface reflectance bands 1–7 with 500 m spatial resolution, and LST with 1 km resolution. In addition to the satellite data, the SEBAL algorithm needs some meteorological data, such as air temperature and relative humidity. In this study, monthly records of these variables for the year 2005 were obtained from the Oroomieh Station. Then some parameters were derived for the selected images of the study area, including surface albedo, emissivity, evaporative fraction, surface roughness and resistance. Monthly records of air temperature, wind speed, humidity, and sun duration from seven meteorological stations were used for the ET modeling by FAO Penman–Monteith method, and also the SEBAL ET calculations were validated by comparing the derived results with those 17

from the FAO Penman–Monteith method. Evaporation estimation approaches such as Penman, Hargreaves and Penman-Monteith were compared by (Carrillo‐González et al., 2008) using in situ measured weather data and remote sensing data. Furthermore, the standard Penman-Monteith approach which was recommended by FAO (2012) revealed the best result while incorporating into the remote sensing techniques. 3.3.

Estimation of recharge and runoff

The estimation of groundwater recharge is one of the concerns of active research (Manfreda et al., 2005; Schneider et al., 2007), which has received a great deal of attention from the hydrogeologists not only because of its importance for the long-term sustenance and monitoring of groundwater storages but also due to the challenges in its estimation (El Yaouti et al., 2008). The water balance method which was used to estimate the recharge rate is represented as follows:

R  P  Q  AET  W

(13)

Where R is recharge (mm yr-1), P is precipitation (mm yr-1), Q is net runoff (mm yr-1), AET is actual ET (mm yr-1), and W is change in soil moisture storage that is considered negligible in semi-arid regions. Since groundwater recharge does not occur during the whole months of the year, an approach is necessary to find the months receiving the significant recharge rates. Hence, according to the Thornthwaite and Mather (1957) method, a monthly soil-water budget has been applied in this research, and also the balance sheet has been computed per month for the duration of study using

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the monthly rain (P, in mm), the potential ET of the month (PET-Thornthwaite, in mm) and the soil-water storage (RFU, in mm). The Thornthwaite and Mather approach is an appropriate tool to measure the excess of water, which is not stored in the soil profile and is based on two cases: when the difference between precipitation (P) and potential ET (PET) (P - PET) is positive, water penetrates deeply into the soil reservoir until the soil medium becomes saturated; and therefore the excess of water leads to the surface or groundwater flow. While the negative difference represents the amount by which the climatic demand for water cannot be met by precipitation and it may be pumped from the soil storage. When there is no water stock, the evaporation will be equal to the precipitation. There is deficiency regarding the quantity of water, which needs to be added in the AET in order to obtain the PET (AET is always lower or equal to PET) (El Yaouti et al., 2008). The computation of yearly value of surface runoff through rainfall has been carried out by the empirical equation of Berkaloff and Tixeront (1958) when precipitation is lower than 600 mm calculate: (14)

P3 Q 3  ET 2

Where Q is the value of surface runoff (mm yr-1), P is the precipitation (mm yr-1), and ET is the ET (mm yr-1). 3.4.

Urmia Lake water balance components

The derived water balance components (ET, precipitation, recharge, and runoff) were combined in a GIS format to quantify and map the Urmia Lake water balance. Water balance 19

components were divided into two separate parts including: ( i ) precipitation and evaporation over the lake surface leading to the direct inflow and outflow from the lake, which were calculated during the 12 months of year 2005, and ( ii ) the lake basin water balance when the difference between precipitation (P) and ET (ET) (P-ET) was positive and the surplus represented rainfall recharge and runoff flow. Projection of the TRMM data was converted to the same projection with the MODIS data in order to match the MODIS data, and also TRMM at 0.25degree×0.25-degree resolution was resampled to 500 m ×500 m MODIS resolution.

4. Results and discussion 4.1.

Validation and interpretation

4.1.1.

SEBAL-estimated evapotranspiration

The monthly ET for 12 months of the year 2005 were computed via the SEBAL model using the MODIS images and routine meteorological data. Due to the scarce direct fluxes measurements, the estimated reference ET derived by FAO Penman–Monteith model was used to validate the ET calculated by SEBAL. However, since the FAO Penman–Monteith method gives only point measurements and does not represent the actual conditions at a specific pixel, the relationship was only applied as an index of relative variation of ET within the study area. Fig. 3 shows the scatter plots between the ET derived by SEBAL and the reference ET calculated by FAO Penman–Monteith at seven meteorological stations within the study area. The results indicated that the SEBAL estimations was in accordance with the FAO Penman–Monteith model estimations with the R2 ranging from 0.717 to 0.938. The best agreement was obtained in Oroomieh station (R2= 0.938), which was located in an agricultural area and represented that 20

SEBAL has more suitable estimations in areas with high moisture content. In contrast, the minimum level of agreement occurred in Takab station (R2= 0.717), which was located in a semiarid area and showed that the SEBAL results are not desirable for dry lands (Fig. 3). The discrepancy in the SEBAL estimations is due to two primary assumptions in the SEBAL algorithm. Firstly, surface temperature computed by the remotely-sensed data is considered as the main factor for the estimation of fluxes and total net radiation estimated by albedo from visible channels, and vapor pressure from infrared channels, is used to estimate latent heat flux. Since the SEBAL utilize surface reflectance and emittance from different satellite channels to calculate evapotranspiration, water supply and moisture in the soil, which are the important factors for the estimation of evapotranspiration, are disregarded. Secondly, instantaneous SEBAL estimations are extrapolated to daily estimations using some hypotheses in the instantaneous evaporative fraction, and since the evaporative fraction has different values during the day, the SEBAL is not successful to compute the daily heating, and therefore computed sensible and latent turbulent heat flux are different from actual daily values. Overall, the SEBAL had tendency toward an underestimation of ET with the mean bias error (MBE) of -14.4%, which was considered to be reasonable. Fig. 4 shows a graphical representation of the average pixel values of SEBAL ET estimates, extracted from each pixel of the SEBAL according to the location of the stations, and average ET estimates of FAO Penman–Monteith method during year 2005. As can be seen from Fig. 4, the difference between ET rates of these two methods was small during the winter months (November to May), while the considerable discrepancies in ET rates of these two methods were observed during the summer months (June to October), which could be referred to the variations 21

in performance of each approach based on the physical condition of the study area on a certain date. Meanwhile, according to Allen et al. (2005), it should be noted that evaporation from open water bodies like lakes and rivers is lower than the pan evaporation or reference ET0. In spite of the fact that SEBAL was able to catch main specifications of the ET distribution at the regional scale, some uncertainties were still left arising from the assumptions including: ( i ) the existence of a dry (zero evaporation) and a wet pixel (zero sensible heat) in the same image, ( ii ) the speed of wind at the blending height (~100 m) is supposed constant all over the study area, and ( iii ) there was a linear relationship between temperature difference , T and surface temperature,

TRAD ( for more details see Khalaf and Donoghue (2012)). There was a bias in the estimation of the SEBAL model from the actual measurements on the regional scale, which may be elucidated by the instable and harsh weathering conditions over the study area. Meteorological measurements revealed substantial changes in the conditions, and therefore the application of SEBAL having only one mean value for the whole area could bring about incorrect results. 4.1.2.

TRMM-estimated precipitation

The monthly data of rain gauge measurements from the seven meteorological stations were used to validate the TRMM precipitation estimates over the study area. By validation of TRMM data, this data can be used in the regions where there are sparse rain gauge stations. This data was provided for all months of the year 2005 in the regions where time series of accumulated monthly precipitation from TRMM 3B43.v6 was used. Fig. 5 presents the scatter plot of the TRMM estimates against the rain gauge records from the meteorological stations. The results indicated that the estimations of TRMM had good accordance with the rain gauge measurements with R2 ranging from 0.694 to 0.905, and the average R2 of 0.748. The best correlation between TRMM 22

and rain gauge measurements were obtained in Tabriz station (R2 = 0.905) and the weakest correlation occurred in Takab station (R2 = 0.694) (Fig. 5). TRMM estimates, with the MBE of 55%, showed an overestimation in the precipitation rates over the whole area, which was because of the fact that in some cases, TRMM may overestimate precipitation due to having no record by gauge data in some areas (Milewski et al., 2009a; Milewski et al., 2009b) as an illustration, where rain gauges do not properly measure rainfall in high wind conditions (Franchito et al., 2009). Moreover, the TRMM sensor may sometimes identify incorrectly a variety of earth surfaces for precipitating clouds (Bauer et al., 2002) leading to a wrong indication for the rainfall less than 0.5 mm h-1 (Bauer, 2001).

There were still some uncertainties left in the remote sensing approaches, which was used to estimate rainfall from space borne instruments in spite of fairly close correlation between the overall TRMM and the seven rain gauges data sets (Franchito et al., 2009). Since TRMM satellite is a low-orbiting satellite (350 km, boosted to 403 km in August 2001), its rain sensors sample any region’s atmosphere only at distinct time intervals. As sampling frequency is a function of latitude, more samples were collected per month at higher latitudes; Monthly estimations were based on the arithmetical mean of the observations, and estimates were underestimated in some areas owing to the periodicity of the TRMM sampling and the area covered by the sensor. Furthermore, because TRMM measurements were obtained every three hours, short events between two consecutive acquisitions might remain unsought (Khalaf and Donoghue, 2012).

23

4.2.

Urmia Lake water balance analysis

4.2.1.

Over lake precipitation and evaporation

Since Urmia Lake is a terminal lake, evaporation is the only component of the lake’s water loss. The spatial variation of yearly TRMM precipitation and SEBAL evaporation over Urmia Lake is shown in Fig. 6. The estimated yearly evaporation ranged from 1345 to 1734 mm yr-1 with the mean value of about 1594 mm yr-1 for the whole lake. The number available in the literature for the yearly evaporation was 1033 mm yr-1 (Sima et al., 2013), which was approximately in conformity to the remote sensing figure (coefficient = 0.64). The spatial distribution of evaporation over the lake has been almost homogeneous, and the highest values of evaporation could be observed along the shorelines of the lake. The estimated yearly precipitation ranged from 217 to 320 mm yr-1 with the mean value of about 259 mm yr-1. The highest values of precipitation were related to the northern and southern part of the lake, while the eastern part showed the lowest level of precipitation. Fig. 7 depicts the monthly pattern of TRMM precipitation over the lake and the SEBAL evaporation estimates. The total estimated precipitation and evaporation over the lake with area of 4112 km2 were about 1165.58 and 7028 MCM yr-1, respectively. 4.2.2.

Lake Basin water balance

The Thornthwaite and Mather water budget was used to identify the months that precipitation was higher than the PET and surplus water represented the runoff and recharge. For this purpose, the monthly balance sheet was calculated for the year 2005. The computation started from January and RFU (soil-water storage) was considered to be zero for the whole period. Table 3 summarizes the calculation of the balance sheet. From January to March, the rainfall was 24

generally higher than PET, and therefore the actual ET (AET) was equal to PET. Thus these were the first months of the groundwater recharge and rainfall runoff. Between April and November, the rainfall was generally lower than PET. AET and rainfall were well-balanced; thus there was no water stock in the soil medium. In December, the rainfall was higher than PET, which led to the recharge and runoff of the surplus water. It was assumed that the study area was a bound area, and hence water storage component was treated as the closing factor of the water balance. Therefore, rainfall, recharge and runoff were estimated for the winter months (January, February, March, and December). In winter months, the monthly recharge estimation was calculated by the water balance equation where AET derived from SEBAL, and runoff was subtracted from the monthly precipitation. For this purpose, the annual runoff was firstly calculated using Eq. (14). Then, it was assumed that runoff has a similar distribution during the winter months as difference between precipitation (P) and PET (P - PET). Calculations showed that January, February, March, and December allocated 33.6%, 46.3%, 7.25%, and 12.81% of the total calculated P-PET, respectively. Consequently, these amounts were used for the calculation of runoff for each month from the annual runoff. Finally, recharge has been quantified for the winter months by subtracting ET estimates and runoff from precipitation. Table 4 shows the water balance components of Urmia Lake basin. According to Table 4, precipitation occurred mostly in January and February, while ET showed the lowest amounts in these months. The areas in the South and South-Western parts of the basin (mountain areas) received higher amounts of rainfall compared to the other areas (Fig. 8(a)), while distribution of 25

ET along the lake basin was almost homogeneous, expect the wetlands around the lake that had relatively higher values of ET (Fig. 8 (b)). In winter months, the total amounts of precipitation and ET were 8474 and 4346 MCM, respectively. Rainfall runoff occurred mostly in January and February in the South and South-Western of the lake basin with values of about 199 and 276 MCM month-1, respectively (Fig. 8 (c) and Table 4). The amount of runoff from precipitation was 6.31% of the total rainfall in the winter months (average was around 30 mm yr-1). The recharge estimations indicated considerable recharge mostly in January and February, whilst there was some recharge in March and December. According to Table 4, the highest amount of recharge occurred in February. The average annual recharge rate was around 3595 MCM yr-1. Fig. 8(d) shows actual surface recharge distribution in February. The calculations indicated that the South-Western and Western parts of the basin received highest recharge. This showed that the South-Western and Western areas were much wetter than the Eastern areas. 4.2.3.

Lake water balance

The water budget of Urmia Lake is presented in Table 5. Budget terms are expressed in MCM yr-1, which are positive and negative when flowing into and out of the lake, respectively. The two last columns of Table 5 give the different water balance terms as percentages of the total inflow or outflow. Since Urmia Lake is located in the lowest part of the basin (an altitude of 1250), it was assumed that total groundwater recharge inflows to the lake were through the rivers and streams as the main source of discharging into the lake, which was about 3595 MCM yr-1, or about 67.88% of the total inflow into the lake. A second important source of water was the direct 26

precipitation into the lake. The direct precipitation was about 1165.58 MCM yr-1 representing 22% of the total inflow of water into the lake. Other inflow to the lake was through the rainfall runoff, which was about 535.46 MCM yr-1, or about 10% of the total inflow. The figures available in the literature for the precipitation and runoff were 1500 and 500 MCM yr-1, respectively (Jalali, 1984), which showed relative conformity with the remote sensing figures. On the other hand, Jalali (1984) reported the river and stream inflows to the lake equal to 4900 MCM yr-1, that was not in accordance with the remote sensing estimation. Iran Department of Environment (2010) reported that the annual water discharge to the lake was about 5300 MCM yr-1. This number was similar to the annual inflow estimated by remotely-sensed data sets. The main discharge of water from the lake was evaporation with the average value of about 7028 MCM yr-1. In addition to evaporation from the lake, the water with approximate amount of 1712 MCM yr-1, which has been regulated in the Urmia basin through the construction of dams was the second important source of water loss from the lake. The comparison between available figures reported in the literature and the remote sensingbased analysis demonstrated that the uncertainty was the inseparable factor in estimating the water balance components of lakes, which may be due to the overestimation of the SEBAL ET spatial estimates and precipitation extracted from the TRMM images of the study area. Quantitative analysis of water balance components revealed that Urmia Lake faced negative water balance about 3443 MCM yr-1 (Table 5). In addition to the negative water balance, Urmia Lake currently faces a deficit of 13.2 km3 below its minimum ecological level (Sima and Tajrishy, 2013) leading to the exposure of the Urmia Lake to the sever environmental stress. 27

Therefore, authentic quantification of negative water balance in this lake could be great assistance to the water managers and policymakers to make appropriate and informed decisions about better allocation and supply of the lake water as well as reclamation of Urmia Lake. The use of different types of remote sensing data to derive the lake water balance components has been well-represented in this study. Substituting data from satellite images for the common empirical formulas and integration with the hydrogeological data led to more accurate estimations of a lake water balance on a pixel-by-pixel basis, which in turn, provided insight to the water managers for the proper assessment and allocation of water resources to downstream ecosystems.

5. Conclusions and recommendations One of the most concerns of water managers and policymakers is the shortage of the accurate quantification of water balance components to understand changes in the hydrological components, and subsequent change in lakes water balance. The main aim of this study was to use remote sensing data for the quantitative analysis of the water balance components of Urmia Lake in the year 2005. For this purpose, monthly ET and precipitations have been derived from the SEBAL algorithm using the MODIS satellite images and TRMM sensors, respectively during all months of year 2005. The geospatial techniques were combined with the hydrogeological data in a GIS environment to quantify and map the water balance components of Urmia Lake. Results revealed that Urmia Lake faced negative water balance about 3443 MCM yr-1, which may bring about the severe consequences for the lake and surrounding environment without proper implementation of conservation measures in the near future. This estimation was close to the 28

value reported by Iran Department of Environment (2010) declaring that Urmia Lake requires an approximate volume of 3100 MCM yr-1 of water to flow into the lake in order to maintain its water level at the minimum ecological level of 1274.1 m. Since Urmia Lake faced a deficit of 13.2 km3 below its minimum ecological level, the quantification of negative water balance in Urmia Lake was a warning massage that can help water resource engineers and policymakers to make informed decisions to preserve Urmia Lake from an extremely serious water deficit problem, which has never been experienced before. Although this study has been done within a framework of few limitations, it proposed an innovative approach to estimate the water balance components in the highly variable environments like lake’s basins, particularly in Urmia Lake, which is located in a semi-arid region. Because of the fact that lake and lake’s basin show two different behaviors against variations in the precipitation and evaporation pattern, which should be considered carefully, this study tried to address this issue with the separate estimation of lake and lake’s basin water balance. Despite this study demonstrated the suitability of the remote sensing data as a cost effective alternative for the detailed water balance components of Urmia Lake; however, there were some discrepancies between the field data and remote sensing estimations. These differences were mainly owing to the lack of field data, the overestimation of AET and TRMM precipitation. For more accurate analysis of water balance components of Urmia Lake, there are a number of suggestions for the future research as follows:  One of the shortcomings of this study was the limited field measurements of hydrogeological, meteorological, and surface parameters. Good dataset about some parameters 29

such as the actual field measurements of ET, net radiation, precipitation, soil water content, and its distribution over the study area are required as inputs for the model and also the validation of the results. - The medium resolution MODIS sensor with high temporal resolution is a good source for the regional scale energy and water fluxes such as ET. However, since spatial resolution is an important function of ET estimation, in this case, the MODIS images may not able to make an accurate estimation due to the 1 km spatial footprint. The solution for this problem is to combine the MODIS and Landsat images as the datasets. The high temporal resolution of the MODIS images can be guarantee for an adequate data selection, while the fair spatial resolution of Landsat images can exhibit the spatial patterns of ET parameters.  In this study, rainfall-runoff depth was calculated by the empirical formula, while supplementary simulations should be conducted for the basin such as vegetation patterns and slope for more accurate estimation of the runoff depth over the study area.  In this study, the TRMM monthly precipitation was used with the highest spatial resolution but information on the rainfall variability at intraday time-scales and on the areas with high topographic variation, such as mountainous areas located in the South and South-Western of Urmia Lake basin, is required for the study of erosion processes and runoff simulation as well as forecasting.  The TRMM estimations were used to assess the spatial distribution of rainfall. Because of the intermittent nature of sampling of the satellite, however, there might be discrepancies 30

between the satellite sampling and field measurements. Therefore, for future studies, a suitable approach can be applied to use the TRMM satellite data in combination with the data from other satellites. In this regard, the TRMM satellite can be applied in combination with MSG-2, which has higher temporal and spatial resolutions or SSM/I which has similar sensors on board with that of the TRMM satellite.

Acknowledgements The authors would like to appreciate NASA for providing access to the MODIS and TRMM image datasets. We would also like to thank Iran Meteorological Organization (IRIMO), for providing the meteorological data required. Giovanni online data system provided analyses and visualizations of TRMM data, which is developed and maintained by the NASA GES DISC. The authors are also grateful to the anonymous reviewers for the helpful comments and suggestions. Declarations of interest: none

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Tables Table 1. Summary of the meteorological conditions for year 2005.

Station

Location

Lon. Oroomieh

45 5

Salmas

44 51

Takab

47 7

Mahabad Tabriz

45 43 46 17

Lat. 38 32 39 13 37 23 37 46 39 5

Elevatio n

Mean. (temp).

Min. (temp).

Max. (temp).

R.H .

Wind speed

Rainfall

(m.a.s.l)

(°C)

(°C)

(°C)

(%)

(m/s)

(mm/year )

1315.9

11.6

-6.7

31.2

61

2.83

167.2

1337

11.4

-7.7

33.3

58

4.42

158.7

1765

9.9

-8.2

32.6

52

6.14

253.4

1385

13.2

-5.6

34.3

51

4.94

325.3

1361

13.6

-6.1

34.6

50

3.62

233.5

38

46 38 1477.7 13.8 -4.8 34.6 50 4.71 238.3 16 24 47 38 Sarab 1682 8.8 -15.2 30.5 62 5.47 263.7 32 56 Notes: Lon. is Longitude, Lat. is Latitude, Mean. temp. is Mean temperature, Min. temp. is Minimum temperature, Max. temp. is Maximum temperature, R.H. is Relative humidity. Maragheh

Table 2. Characteristics of MODIS and TRMM data used. Data set

Description

Spatial resolutio n

Date

01/01/2005 31/12/2005 MOD09A1:surface reflectance bands 1–7 MOD11A2: daytime land surface temperature and emissivity

MODIS level 3 (8-day)

TRMM: 3B43: monthly merged TRMM and other sources estimates

Merged 3B-42 and rain gauge estimates

500 m 1000 m

0.25° × 0.25°

01/01/2005 31/12/2005

Table 3. Thornthwaite water budget. The value of the soil-water storage (RFU) has been considered to be zero (P, monthly rainfall; PET, Thornthwaite monthly potential evapotranspiration; AET, monthly actual evapotranspiration; D, deficit; Exc, excess).

T(°C) P(mm) PET(m m) PPET(m m) AET(m m)

Janu ary

Febru ary

Mar ch

-2.10

-1.50

6.60

73.7 0 21.0 9

102.8 0

86.5 0 75.1 5

30.33

52.6 1

72.47

11.3 5

21.0 9

30.33

75.1 5

Apri l 12.1 0 59.0 0 150. 00 91.0 0 59.0 0

May 15.3 0 68.8 2 180. 75 111. 93 68.8 2

June 19.9 0 16.5 4 178. 21 161. 67 16.5 4

July 24.3 0

Aug ust 23.5 0

6.22

8.36

174. 46 168. 24

159. 65 151. 29

6.22

8.36

39

Septe mber 18.50 12.20 130.37 118.17 12.20

Octo ber 12.7 0 14.7 0 95.8 7 81.1 7 14.7 0

Nove mber

Dece mber

5.90

3.40

41.30

50.60

60.74

30.55

-19.44

20.05

41.30

30.55

Year ly 11.5 5 540. 74 1287 .17 746. 43 384. 26

D(mm) Exc(m m)

0.00

0.00

0.00

91.0 0

111. 93

161. 67

168. 24

151. 29

118.17

81.1 7

19.44

0.00

52.6 1

72.47

11.3 5

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

20.05

902. 91 156. 48

Table 4. Urmia Lake basin water balance components for winter months (MCM month -1). Surface runoff

Month

Precipitation

Actual evapotranspiration (AET)

Positive Recharge

January

2523.7

818.2

199.7

1506.2

February

2661.5

628.4

276.2

1757.8

March

1873.1

1762.3

8.5

102.5

December

1416.4

1137.5

51.1

228.8

Total

8474.7

4346.4

535.5

3595.1

Table 5. Urmia Lake water balance components (MCM yr-1) 2005

Inflow

Outflow

Inflow(%)

Outflow(%)

Precipitation

1165.58

0.00

22.01

0.00

Surface runoff Groundwater discharge into the rivers and streams Evaporation

535.46

0.00

10.11

0.00

3595.14

0.00

67.88

0.00

0.00

-7028.00

0.00

80.41

Dams

0.00

-1712.00

0.00

19.59

Total

5296.18

-8740.00

Inflow-Outflow

-3443.82

40

41

42

43

44

45

46

Highlights 

We quantitatively analyzed the water balance of Urmia Lake utilizing remote sensing derived data and meteorological data.



TRMM and SEBAL estimations have good agreements with the in-situ measurements.



The water balance components calculated by remote sensing agreed well with the available numbers reported in literature.



Urmia Lake faces negative water balance about 3443 MCM yr-1.

47