Evaluation of remotely sensed precipitation estimates using PERSIANN-CDR and MSWEP for spatio-temporal drought assessment over Iran

Journal Pre-proofs Research papers Evaluation of Remotely Sensed Precipitation Estimates using PERSIANN-CDR and MSWEP for Spatio-Temporal Drought Assessment over Iran Mohammadali Alijanian, Gholam Reza Rakhshandehroo, Ashok Mishra, Maryam Dehghani PII: DOI: Reference:

S0022-1694(19)30924-2 https://doi.org/10.1016/j.jhydrol.2019.124189 HYDROL 124189

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Journal of Hydrology

Received Date: Revised Date: Accepted Date:

17 July 2019 26 September 2019 27 September 2019

Please cite this article as: Alijanian, M., Reza Rakhshandehroo, G., Mishra, A., Dehghani, M., Evaluation of Remotely Sensed Precipitation Estimates using PERSIANN-CDR and MSWEP for Spatio-Temporal Drought Assessment over Iran, Journal of Hydrology (2019), doi: https://doi.org/10.1016/j.jhydrol.2019.124189

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Evaluation of Remotely Sensed Precipitation Estimates using PERSIANN-CDR and MSWEP for Spatio-Temporal Drought Assessment over Iran Mohammadali Alijanian1*, Gholam Reza Rakhshandehroo2, Ashok Mishra3, Maryam Dehghani4 1. PhD, Department of Civil and Environmental Engineering, Shiraz University, Iran, and Visiting Scholar at Glenn Department of Civil Engineering, Clemson University, SC, USA. [email protected] & [email protected] * Corresponding Author 2. Professor, Department of Civil and Environmental Engineering, Shiraz University, Iran. [email protected] 3. Associate Professor, Glenn Department of Civil Engineering, Clemson University, SC, USA. [email protected] 4. Assistant Professor, Department of Civil and Environmental Engineering, Shiraz University, Iran. [email protected] Abstract Satellite Rainfall Estimates (SREs) can provide rainfall information at finer spatial and temporal resolutions, however their performance varies with respect to gauged precipitation data in different climatic regions. A limited number of studies investigated the performance of SREs for spatio-temporal (regional) drought analysis, which is a key component for developing tools for regional drought planning and management. In this study, the performance of two recent SREs (data length > 30 years), which includes Artificial Neural Networks Climate Data Record (PERSIANN-CDR) and the Multi-Source Weighted-Ensemble Precipitation (MSWEP) are selected for spatio-temporal drought assessment over different climatic regions located in Iran. Firstly, the accuracy of SREs was evaluated for deriving standardized precipitation index (SPI) at different time scales (1, 3, 6, 9 and 12 months) for four climatic regions during the period of 1983-2012. Secondly, the performance of SREs was evaluated for regional drought assessment based on the concept of the Severity-Areal-Frequency (SAF) curves. It was observed that the 1

performance of SREs can be different with respect to gauge data in terms of quantifying drought characteristics (e.g., severity, duration, and frequency), identification of major historical droughts, and a significant difference can be observed based on the SAF analysis. For example, the number of drought events based on shorter time scales (SPI-1 and 3) found to be greater for SREs in comparison to gauge information for all climatic regions. While investigating the major historical droughts, discrepancies can be observed between these two types of data sets. For example, gauge data suggests wetness (i.e., SPI-3 > 0.5) near southern Iran, whereas, SREs show droughts (SPI< -1.0) in the same spatial domain. The performance of SREs with respect to gauge data varies largely in terms of quantifying the frequency component embedded in the SAF curves for selected climatic regions located in Iran. Our research findings can be useful for drought assessment in ungagged basins, as well as to develop regional drought management plans to improve water security by integrating multivariate nature of drought events. Keywords: Regional Drought Assessment; Satellite Rainfall Estimates (SREs), Severity-AreaFrequency (SAF) Curves 1. Introduction Droughts are triggered by reduction in the amount of precipitation over an extended period of time (e.g. season, year). Additional variables, such as, temperatures, low relative humidity, and distribution of rainy days, play a significant role in the occurrence of droughts (Mishra and Singh, 2010). The drought events are quantified based on different types of drought indices derived based on a combination of precipitation, temperature and soil moisture deficits. The drought indices considered to be a prime variable for monitoring and assessing the impact of drought, as well as to characterize the droughts based on their intensity, duration, severity and

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spatial extent. Typically, a longer time series (> 30 years) of hydro-climatic variables are required for deriving drought indices to quantify the drought for different time scales (e.g., month to season) (Mishra and Singh, 2010). These drought indices are generally normalized with respect to a long-term climatology, which allows the results to be compared at different locations over different climatic regimes (McKee ae al., 1993; Mishra and Singh, 2010; Sahoo et. al, 2015). The most commonly used meteorological drought indices are; the Palmer Drought Severity Index (PDSI) (Palmer, 1965) and the Standardized Precipitation Index (SPI) (McKee et al., 1993). An overview of different drought indices along with their strength and weakness are discussed in Heim (2002) and Mishra and Singh (2010). Drought events are multivariate in nature, and they can be better quantified by integrating duration, severity, area and frequency based on the concept of spatio-temporal analysis or in other words regional drought analysis (Mishra and Singh, 2009; Mishra and Singh, 2011). In order to analyze regional drought over a specific area, drought indices as well as hydro climatic variables such as rainfall, stream flow and soil moisture are commonly used (Sadeghipour and Dracup, 1985; Clausen and Pearson, 1995; Hisdal et al., 2001; Mishra and Singh, 2009; Dabanli et al., 2017). The spatio-temporal drought analysis typically combines more than one drought characteristics, for example, total areal deficit and maximum deficit intensity (Tase, 1976); combination of probability distributions of drought duration, intensity and area (Santos, 1983); intensity-duration-frequency curves (Alegria and Watkin, 2007); severity- area- duration (SAD) curves (Anderias et al., 2005); and spatial and temporal variability of droughts (Shin and Salas, 2000). Using this severity- area- frequency (SAF) curves, several studies investigated the regional droughts using SPI based on gauged precipitation (Kim et al. 2002, Loukas and Vasiliades 2004, Mishra and Desai 2005, Mishra and Singh 2008, Bonaccroso et al., 2015, Cai et

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al., 2015). The information on regional droughts is critical for short as well as long-term water resources management. Therefore, there is a need for further research on the regional or spatial behavior of droughts (Rossi et al., 1992; Panu and Sharma, 2002; Mishra and Singh, 2011; Bonaccorso et al. 2015), especially for data scarce regions (Mishra and Singh, 2011). One of the major limitations of regional drought analysis is lack of high-quality rainfall data to capture adequate spatial and temporal coverage (Mishra and Singh, 2011). The long-term historical time series of precipitation data are often limited in many developing countries. In addition, establishing a gauge station can be expensive, as well as difficult task in inaccessible regions, such as deserts and mountains. On the other hand, satellite datasets can be reliable alternative sources for drought assessments in un-gauged basins. With the availability of comparatively long term (up to 30 years) Satellite Rainfall Estimators (SREs) products, such as, PERSIANN-CDR (Ashouri et al., 2015), and MSWEP (Beck et al., 2016), it is now possible to investigate whether these products can capture the regional drought information accurately. The performance of SREs for estimating the rainfall are widely investigated in different part of the world (Smith et al., 2006; Dinku et al., 2009; Tian et al., 2009; Duan and Bastiaanssen, 2013; Lockhoffet. al., 2014; Salio et al., 2015; Nastos et al., 2016; Guo et al., 2016a; Alijanian et al., 2017), and relatively a few studies on drought assessment (Sahoo et al., 2015; Guo et al., 2016b). For instance, Zambrano et al., (2017) evaluated the performances of PERSIANN-CDR (19832015) and CHIRPS 2.0 (1981-2015) for deriving SPIs and compared with indices from in-situ rainfall stations over Chile. They found that SREs can perform well for deriving SPIs for timescales of 1, 3 and 6 months. To date, the performance SREs were evaluated with respect to the ground‐based rain gauge data for multiple hydro-climate related studies, such as, extreme event analysis, streamflow

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simulation using hydrologic models, crop yield simulations, data assimilation, and water quality assessment. Even though, a number of studies evaluated the performance of SRE’s for grid (gauged) based temporal drought analysis, a limited number of studies evaluated the spatiotemporal drought analysis which is a key component for developing tools for regional drought planning and management. However, to the best of our knowledge we are not aware of any prior studies that used SREs for regional drought analysis, specifically using drought SAF curves. Therefore, it is important to quantitatively investigate the performance of SRE’s relative to the gauge-based precipitation data sets. The overall objectives of this study are: (a) to evaluate the performance of SREs (PERSIANN-CDR and MSWEP) for regional drought analysis for Iran using a 30-year time period (1983-2012), (b) to investigate and compare the SAF curves generated by SREs and gauge datasets in eight different climatic zones in Iran, and (c) to compare the performance of SREs in quantifying spatio-temporal pattern of four major historical severe drought events over the climatic regions based on drought SAF curves. The remaining structure of the paper is organized as follows: methodology is provided in section 2; study area and the data sets used in this study are discussed in section 3; results are discussed in section 4 and the conclusions are drawn in section 5.

2. Methodology 2.1 Standardized Precipitation Index The drought indices are used as proxy to quantify a drought event in terms of their intensity, duration, severity, as well as spatial extent (Mishra and Singh, 2010). Various drought indices have been derived in recent decades. In this study, SPI (Mckee et al., 1993; Hayes et al., 1999) was selected due to its advantages, such as: (a) SPI can be derived based on rainfall alone, so 5

SREs can be used for drought assessment without considering other meteorological data, and (b) its variable timescale allows describing drought conditions for a range of meteorological, hydrological and agricultural applications. The standardization of SPI ensures that the frequencies of extreme events are consistent, independent of space and time. Table 1 shows drought severity classification using SPI (Hayes et al., 1999). Table 1. Drought classification based on SPI SPI Value 2.00 and above 1.50 to 1.99 1.00 to 1.49 -0.99 to 0.99 -1.00 to -1.49 -1.50 to -1.99 -2.00 and less

Category Extremely Wet Very Wet Moderately Wet Near Normal Moderately Dry Severely Dry Extremely Dry

SPI is computed using following steps (Guttman, 1998): (a) identify a suitable probability density function that describes the long-term time series of rainfall, (b) depending on the timescale of interest, select the base time of rainfall observation series, for example running time series of total rainfall corresponding to 1, 3 and 6 months were used to generate corresponding SPI-1, SPI-3, and SPI-6 respectively, (c) fit the cumulative probability to the precipitation time series generated for different time scales, and (d) the inverse normal (Gaussian) function (zero mean and variance equals to one) is then applied to the cumulative probability distribution function to generate SPI time series. In this study, Kolmogorov-Smirnov test (K-S test) was used to select the best probability density function (among Normal, Log-normal, Gamma, EV-I and EV-III functions) for each pixel. Based on Prior Studies (Reddy and Ganguli, 2011; Chen et. al., 2011; Kwak et. al., 2012; and Chen et. al., 2013) and our analysis, both Gamma and EV-III distribution were identified as the most acceptable distributions for all the pixels (2643 pixels over the country). 6

2.2 Severity - Area - Frequency (SAF) Curves The spatio-temporal (regional) characterization of drought events can be improved by integrating frequency with severity, duration and the percentage of area influenced by droughts. This concept was previously applied to derive drought severity-area-frequency (SAF) curves using observed rainfall data (Mishra and Desai, 2005; Mishra and Singh, 2008), however, there are no prior studies that investigated the performance of SREs for analysis of spatio-temporal patterns of extreme drought events. The steps used for deriving SAF curves to investigate the regional drought patterns for different climatic regions of Iran is provided in Figure 1, and they are briefly described in the following section. 1) The study area was divided in to pixels similar to the spatial resolution available for the SREs (0.25°×0.25°). The Inverse Distance Weighting (IDW) (Loukas and Vasiliades 2004; Mishra and Desai, 2005; Bonaccorso et al., 2015; and Cai et al. 2015) approach was applied to interpolate monthly observed (in-situ) rainfall values spatially at the center of SREs' pixels in order to compare regional droughts based on these two types of data (in-situ and SREs). The concept of IDW (Eq. 1) is based on the assumption that the attribute value of an un-sampled location is the weighted average of known values within its neighborhood, and the weights are inversely related to the distances between the prediction location and the sampled locations (Lu and Wong, 2008). 1 𝑝 𝑛 𝜆𝑖 = ( 𝑑𝑖 ) ∑𝑗 = 11 𝑑𝑝𝑗

(

)

(1)

Where, λi is the weight of valued point (i) with distance di from the unvalued point. The number of valued points utilized during the interpolation process is shown by n. The power for p is typically equal to 2 that represent the Inverse Distance Squared method (Garcia et al., 2008). 7

2) After interpolating the observed precipitation data for each pixel, the SPI time series for each pixel are calculated for different time scales 1-, 3-, 6-, 9- and 12- months that results in SPI-1, SPI-3, SPI-6, SPI-9, and SPI-12 respectively. Both observed precipitation and SREs datasets are used to construct SPIs at selected temporal scale. 3) The annual drought severity is calculated for each pixel using theory of run (sum of SPIs<-1 within a given year). These annual drought severities are used for frequency analysis based on different return periods. In our case, 30 annual drought severity values (1983-2012) were obtained for each SPI time series and used for the frequency analysis. In this regard, the drought severity values were fitted with Normal, Log-normal, Gamma, Extreme Value Type 1 (EV-I or Gumbel) and Extreme Value Type 3 (EV-III or Weibull) probability distributions for both SREs and in-situ datasets. The standard K-S test was used at 5% significance level to find the best distribution for different climatic zones and for each SPI timescale. In case of in-situ datasets, it was observed that Gamma and Weibull (EV-III) distributions performed well for most of the pixels. Weibull (EV-III) distribution reflected slightly better accuracies in comparison to Gamma distribution, and it is also recommended for drought frequency analysis (Chow et al., 1988). A similar result was found when SREs data were utilized for frequency analysis. 4) The areal thresholds are calculated based on the percentage of area affected by droughts of different severity. It is expected that the areal thresholds will be different based on the SPI’s for different time scales. 5) The best probability density function (Weibull distribution) is selected to conduct the frequency analysis of annual drought severity for each pixel. In this study, the frequency analysis of annual drought severity was carried out using 5, 10, 25, and 50-year return periods.

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6) Perform the frequency analysis using the selected probability distribution for drought severity of different areal extents to associate drought severity with respect to different return periods. Finally, the drought severity-area-frequency (SAF) curves was constructed for the watershed under consideration.

Figure 1. Flowchart for constructing SAF curves based on the precipitation time series derived based on the SREs and gauge data sets. 9

3. Study Area and Data 3.1 Study Area Iran, the oldest civilization in the Middle East is located between 25°-40° Eastern latitude and 43.5°-63.5° Northern longitude. The Caspian Sea is located on the north and both Persian Gulf and Oman Sea lies on the southern boundaries (Figure 2). The two major mountain ranges, Zagros Mountains (with the highest altitude of ~3500 m) and Alborz Mountains (with altitudes >5600 m) are located along its western and northern borders, respectively. In addition to that, Mediterranean low-pressure system entering from the west, Siberian high-pressure system (Siberian High) dropping from the north, and Sudan tropical low pressure (Sudan Low) flowing in from southeastern boundaries impact on Iran's climatic conditions (Golestani et al, 2000). Because of the various features and climatic conditions, Iran may be a suitable case study to evaluate the performance of SREs for regional drought analysis. For instance, the highest monthly rainfall is observed along Zagros Mountains (on the west) and the shores of northern sea (Caspian Sea) during the months of December to April. In addition, the average temperature along both western and northern mountains are less than 2 °c between December to April (Razeie, 2017; Fick and Hijmans, 2017). For the rest of the year (May to October) the precipitation is expected mostly on the northern part of the country, while the temperature is commonly high over most parts of the country (Razeie, 2017; Fick and Hijmans, 2017).

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Figure 1. Geographic features of Iran. Figure 2. Geographic location of Iran (left), and the spatial location of climatic zones (right) (Kasmaiee, 1992).

Overall, Iran is spatially divided into eight climatic zones (Figure 2-right; Kasmaiee 1992). The northern region between Alborz Mountains and Caspian Sea are classified as 'moderate/rainy' and 'semi-moderate/rainy' climate with a mean annual rainfall of 800 to 1400 mm. The southern shores of the country witness 'very hot and humid' climate, mainly influenced by Persian Gulf and Oman Sea air flows. The aridity increases from the shoreline toward interior part of the country leading to 'very hot (dry)' and ‘hot (dry)' climatic regions next to the shoreline climate. Two major deserts (Dasht-e-Lut and Dasht-e-Kavir) are located in semi-arid regions in central part of Iran. The other two regions, 'cold' and 'very cold' regions are mainly located along the mountains. Using Koppen-Geuger classification (Raziei, 2017; Table 2), Iran is classified into four major climatic types: arid steppe (Bs), arid dessert (Bw), Cold areas with dry summer (Ds) and temperate (C). By combining these two types of classification, Iran can be grouped into four climatic regions: (a) zone 1&2 (arid steppe), (b) zone 3&4 (arid desert condition), (c) zone 5&6

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located along the mountains and is generally classified as cold areas with dry summer, and (d) zone 7&8 located in northern Iran near Caspian Sea witness temperate climatic condition. In our study, we selected these four types of climate groups the evaluation the performance of SREs for spatio-temporal drought assessment. Table 2. Classification of climate zones of Iran based on the definitions of Iran Ministry of Energy and Koppen-Geiger Zone number

Location

1 2 3 4 5 6 7 8

Shores of Persian Gulf on south South of the country Southern and central hillsides Central parts of Iran Along Zagros and Alborz Mountains Highest altitudes of mountains Shores of Caspian Sea on the north Northeast and northwest of Iran

Based on Iran Ministry of Energy (Kasmaiee, 1992) Very Hot and Humid Hot and Humid Hot and Dry Semi-arid Cold Very Cold Moderate and Rainy Semi-moderate and Rainy

Based on Koppen-Geiger climatic definition (Raziei, 2017) Bsh Bsk Bwk Bwh Csa and Dsb Dfb Cfa Csa

3.2. Satellite Rainfall Estimates (SREs) Datasets Long-term data (at least 30 year) is preferred for spatio-temporal drought analysis (Mishra and Singh, 2011). Although more than ten satellite rainfall products (Liu et al., 2011) are available, few of them have a longer dataset. The longer datasets are available for PERSIANN-CDR and MSWEP since 1983 and 1979, respectively. In this research, we selected two SREs (PERSIANN-CDR and MSWEP) for spatio-temporal drought analysis and compare its performance with in-situ gauge data. Based on our prior study (Alijnian et al., 2017), these two SREs comparatively performed well in capturing the observed precipitation pattern for Iran. For example, the correlation coefficients of 0.82 and 0.72 was observed for PERSIANN-CDR and MSWEP rainfall products with respect to in-situ data located in south and southwest (shores of Persian Gulf) regions that witness very hot and humid climate.

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PERSIANN-CDR (Ashouri et al., 2015) is obtained from the National Climatic Data Center (NCDC) Climate Data Record (CDR) program of the National Oceanic and Atmospheric Administration (NOAA). This dataset is a multi-satellite high-resolution precipitation product that provides daily precipitation estimates at 0.25° spatial resolution since 1983. The Model is based on PERSIANN algorithm (Sorooshian et al., 2000) using IR satellite data from global geosynchronous satellites as the primary source of precipitation information. To calibrate PERSIANN, the model is pre-trained using the National Centers for Environmental Prediction (NCEP) stage IV hourly precipitation data. Then, the model is run for GridSat-B1 IR data (Knapp 2008). The estimates are then adjusted using the GPCP monthly 2.5° precipitation products for bias reduction (Ashouri et al., 2015). MSWEP is a new product (Beck et al., 2016), which merges available gauge, satellite, and reanalysis data, with a 3-hourly and 0.25° global gridded precipitation dataset covering 1979– present. The long-term mean of MSWEP is based on the recently released Climate Hazards Group Precipitation Climatology (CHPclim) dataset (Funk et al., 2015). Which is derived based on both gauge observations and satellite data. The bias correction is introduced using catchmentaverage precipitation from stream flow observations at 13762 stations over the globe. The temporal variability of MSWEP is determined by weighted average of precipitation anomalies from seven datasets; two based on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). Both SREs datasets (PERSIANN-CDR and MSWEP) have the similar spatial resolution of a quarter degree with matching grid cells. The entire area of Iran is divided into 2643 quarter degree pixels based on SREs' spatial resolution, with the number of pixels 510, 1142, 913 and 78 13

are located in climatic zones of 1&2, 3&4, 5&6 and 7&8, respectively. The SPIs of different time scales are calculated for each pixel based on the observed and SREs precipitation time series for the period of 1983-2012. 3.3. In-situ gauge data In-situ rain gauge data are collected from two different sources: (a) six hourly data available from Synoptic gauge-network data center managed by Iran Meteorological Organization, and (b) daily rainfall data available by Ministry of Energy. The number of operating gauges varies from 421 at 1983 to 2541 at 2012. Gauges with missing data (unrecorded) are removed from the dataset. Table 3 shows number of operating gauges, gauges with missing data, and gauges with complete dataset over Iran and its climatic zones during the study period (1983-2012). In addition, the spatial distribution of gauges over Iran and its climatic zones are illustrated in Figures 3. Based on Table 3 and Figures 3, the number of gauges after the year of 2000 increased over the climatic regions, showing almost one gauge for every two pixels. Generally, more number of gauges are available over the mountainous areas (zone 5&6) and the ratio of number of gauges to number of pixels increased from 0.13 (1983) to 0.51 (1994), and later to >1.14 after 2000 (Table 3 and Figure 3). Table 3. Total number of operating gauges and pixels during the study period (1983-2012) Number of Pixels Year 1983 1985 1990 1995 2000 2005 2010 2012

Total gauges 421 377 871 1240 2570 3275 3469 2541

Iran: 2643 Missing Data Gauges 165 127 292 387 686 1160 1193 259

Complete Data Gauges 256 250 579 853 1884 2115 2276 2282

Zone 1&2: 510 Gauges on Zone 1&2 33 39 76 79 244 282 299 290

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Zone 3&4: 1142 Gauges on Zone 3&4 56 87 197 264 488 547 645 696

Zone 5&6: 913 Gauges on Zone 5&6 122 112 281 468 1034 1135 1179 1141

Zone 7&8: 78 Gauges on Zone 7&8 15 12 25 42 118 151 153 155

Figure 3. Distribution of Rain Gauges over climatic regions located in Iran during 2005 (left) and 2012 (Right).

The Inverse Distance Weighting (IDW) method was used to interpolate the observed precipitation at the center of each SREs pixel utilizing gauges datasets. Further, the rainfall data may have lost their homogeneity properties due to source of errors coming from various sources. The double mass curve analysis (DMCA) technique was widely used for detecting and correcting inconsistent precipitation data (Linsley et al. 1982; Singh, 1994; Ponce 2014). The consistency of rainfall data is made by comparing data for a single station with that of a pattern composed of the data from several other stations in the area. Before estimating the precipitation at the center of each pixel based on neighboring gauges (i.e. 10 nearest gauges), DMCA was carried out and incompatible gauges were corrected based on Equation 2.

𝑃𝑐 = 𝑃

𝑀𝑐

(2)

𝑀𝑎

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Where, P represents the original (inconsistency) dataset, Mc is the corrected slope for the double mass curve, Ma is the slope for the inconsistency data records and Pc is the corrected precipitation dataset (Singh, 1994). 4. Results and Discussions 4.1. Temporal and spatial distribution of droughts The SPIs of different time scales were used to investigate the temporal and spatial distribution of drought. The SPI-1, SPI-3, SPI-6, SPI-9 and SPI-12 time series were calculated based on gauge (in-situ), PERSIANN-CDR, and MSWEP datasets over Iran during the study period of 19832012 (Figure 4). The temporal pattern of SPIs based on these three datasets behaves similar and generally close to each other. However, the temporal pattern for MSWEP data seems slightly closer to the gauge measurements, especially for SPI 9 and SPI-12 (Figures 4-d and 4-e). A moderate to higher drought severity period can be defined when the magnitude of SPI remains below a threshold of -1.0 (Mishra and Desai 2005). Applying this definition the number of drought periods (events), their duration (months), and number of drought months (while SPI< 1.0) for all three datasets are shown in Table 4. It was observed that number of drought events, based on shorter SPI time scales (SPI-1 and SPI-3) is generally greater for SREs than those based on observed data for all climatic regions. This is more apparent for PERSIANN-CDR, which may be due to underestimation of rainfall over study area (Alijanian et al., 2017). However, for longer SPI time scales (>6 months), the observed drought events based on SREs matches well with gauge information (Table 4). As shown in Figure 4 and Table 4, when the SPI timescales increase (from 1 to 12), the number of drought months generally increase and the number of drought events decrease for all datasets and the maximum drought duration increases as well. For

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example, in the case of gauge observations, when the SPI timescale increases from 1 to 12 months, the maximum drought duration increases over climatic zones: (a) 4 to 59 months over zone 1&2 (hot and humid regions close to Persian Gulf), and (b) 6 to 28 months over zone 3&4 (hot and dry regions at the center of the country). Table 4. Statistical properties of drought indices derived based on all three datasets during the period of 2003 to 2012 for different climatic regimes. [G: Gauge, C: PERSIANN-CDR, M: MSWEP]

Zone 1&2

SPI-1 SPI-3 SPI-6 SPI-9 SPI-12

Zone 3&4

SPI-1 SPI-3 SPI-6 SPI-9 SPI-12

Zone 5&6

SPI Series

SPI-1 SPI-3 SPI-6 SPI-9 SPI-12

Zone 7&8

Climatic Region

SPI-1 SPI-3 SPI-6 SPI-9 SPI-12

Total number of drought months (SPI< -1.0) 1983-2012 G C M 15 23 23 21 35 36 26 40 43 31 43 41 34 40 41 G C M 7 8 24 62 64 63 61 63 62 44 50 48 32 57 39 G C M 41 49 57 52 65 61 47 62 55 29 46 40 21 37 40 G C M 43 54 50 35 60 47 32 64 46 17 49 43 1 53 45

Drought duration (months) Number of drought events G 9 11 10 7 6 G 5 26 26 17 3 G 21 21 18 6 2 G 24 14 12 5 1

C 18 17 14 9 6 C 6 26 29 15 5 C 24 24 22 12 3 C 27 23 20 11 4

M 18 14 14 9 6 M 16 26 28 16 5 M 24 24 20 9 4 M 27 19 13 11 6

Minimum G 1 2 5 2 9 G 1 2 2 2 11 G 1 2 3 3 20 G 1 2 2 5 --

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C 1 2 1 2 3 C 2 3 2 2 10 C 1 1 3 3 16 C 1 2 4 2 10

M 1 2 2 3 3 M 2 3 2 2 2 M 2 2 2 4 10 M 1 1 4 5 11

Maximum G 4 8 15 49 59 G 6 6 7 28 28 G 6 7 7 28 21 G 8 8 8 20 25

C 5 11 13 43 40 C 7 6 6 27 26 C 6 7 6 18 35 C 7 7 8 41 37

M 8 11 16 46 46 M 5 6 6 29 31 M 7 7 7 40 26 M 8 9 17 18 36

Average G 1.67 1.91 2.60 4.43 5.67 G 1.4 2.38 2.35 2.59 10.67 G 1.95 2.48 2.61 4.83 10.5 G 1.79 2.50 2.67 3.40 1

C 1.28 2.06 2.86 4.78 6.67 C 1.33 2.46 2.17 3.33 11.4 C 2.05 2.7 2.82 3.83 12.33 C 2 2.60 3.2 4.45 13.25

M 1.28 2.57 3.07 4.56 4.83 M 1.5 2.42 2.21 3.0 7.8 M 2.38 2.54 2.75 4.44 10 M 1.85 2.47 3.54 3.91 7.50

Figure 4. Time series of spatially averaged SPI-1, -3, -6, -9, -12 over Iran derived based on three type of data sets. 18

The performance of SREs for drought characterization varies with respect to climatic pattern. The number of drought events based on both SREs over climatic zone 1&2 decreases from 18 to 6 months (for SPI-1 to SPI-12); while the corresponding drought events based on gauge information vary from 9 to 6 (Table 4). In addition, for shorter SPI timescales (SPI-1 and SPI-3), the maximum duration of drought based on SREs are closer to the ones shown by gauge data. For example, the maximum drought duration for SPI-3 is 11 months for SREs and 8 months for the gauge datasets. However, in the case of longer SPI timescales (i.e., SPI-12), the maximum drought durations vary, and the maximum drought duration found to be 59, 40, and 46 months based on gauge, PERSIANN-CDR, and MSWEP data sets respectively. It was observed that the SREs able to perform well in capturing the drought durations at shorter SPI timescales. However, for the number of drought events, SREs perform better at longer SPI timescales, and no particular trend was observed in terms of total number of drought months. The average drought duration based on the three rainfall datasets for central part of Iran (zone 3&4) are almost similar to climatic zone 1&2 based on all SPIs except SPI-12. The average drought duration for SPI-12 vary widely with 10.67, 11.4 and 7.8 months for gauge, PERSIANCDR, and MSWEP respectively for zone 3&4. Over mountainous areas with 'cold' climatic conditions (zone 5&6), the average drought duration for SPIs with short timescales (<12 months) are typically less than 4.83 months. However, the drought durations for SPI-12 seems to be longer and differs among datasets with 10.5, 12.33 and 10 months for gauge, PERSIANN-CDR, and MSWEP respectively. While for the climatic zone 7&8 ('moderate and rainy') located in northern part of Iran (shores of Caspian Sea), similar average drought duration (<4.45) was observed for all SPIs less than 12 months. However, only one drought was detected for SPI-12 by gauge data which continues for 25 months, while 19

PERSIANN-CDR and MSWEP data indicated 4 and 6 drought events with average duration of 13.25 and 10 months respectively. The performance of SREs for drought assessment with respect to gauge data is further assessed using statistical criteria, such as, Correlation Coefficient (CC) and Root Mean Square Error (RMSE) for different SPIs over Iran and its various climatic zones (Table 5). As shown, MSWEP data generally reflects better CCs, compared to PERSIANN-CDR for all SPIs over Iran and its climatic regions. Among the SPIs, best CCs are associated with SPI-3 and SPI-6 for all climatic regions, ranging from 0.43 (zone 7&8) to 0.88 (zone 1&2). Also, RMSE for MSWEP is generally less in comparison to PERSIANN-CDR reflecting a better accuracy for MSWEP in drought assessments. Comparing SPIs for different timescales, it was observed that shorter timescales (<6 months) show higher CCs and lower RMSEs, resulting in better assessments, across the country and SREs. Table 5. Comparison between SREs and observed data sets for calculating SPIs over Iran and different climate zones. Region Iran Zone 1&2 Zone 3&4 Zone 5&6 Zone 7&8

Criteria CC RMSE CC RMSE CC RMSE CC RMSE CC RMSE

SPI-1 0.43 0.98 0.77 0.66 0.37 1.04 0.32 1.04 0.26 1.06

PERSIANN-CDR vs. Gauges SPI-3 SPI-6 SPI-9 SPI-12 0.55 0.66 0.39 0.35 0.88 0.77 1.01 0.97 0.80 0.80 0.71 0.67 0.62 0.62 0.73 0.70 0.53 0.68 0.30 0.23 0.90 0.74 1.08 1.02 0.43 0.56 0.31 0.28 0.96 0.84 1.04 1.03 0.30 0.32 0.27 0.33 1.04 1.02 1.05 1.00

SPI-1 0.49 0.93 0.84 0.54 0.41 1.01 0.39 0.99 0.38 0.98

MSWEP vs. Gauges SPI-3 SPI-6 SPI-9 0.62 0.73 0.44 0.81 0.69 0.96 0.88 0.88 0.80 0.48 0.47 0.61 0.58 0.72 0.33 0.85 0.70 1.06 0.52 0.66 0.37 0.88 0.75 1.00 0.43 0.50 0.38 0.94 0.89 0.98

SPI-12 0.40 0.95 0.77 0.59 0.25 1.03 0.34 1.00 0.49 0.90

The average annual SPI (SPI-3 and SPI-6) values are further compared between SREs and gauge data for climatic zones for the study period of 1983-2012 (Figure 5). Generally, the yearly averaged SPIs for SREs matched well with gauge data at higher values of both positive and 20

negative SPIs. This indicates that SREs may accurately assess the droughts as well as wet spells. It was also observed that the SREs based drought indicators can be able to identify four most severe drought events during 1985, 1990, 2000 and 2008 (Figure 5). The spatial distributions of drought based on SPI-3 for severe drought years (1985, 1990, 2000 and 2008) are illustrated in Figure 6. Both SREs (PERSIANN-CDR and MSWEP) performed comparatively well in capturing the drought during 1985; however, the difference was noticed between SREs and in-situ (gauge) datasets for zone 1&2. For example, gauge data suggests wetness (i.e., SPI-3>0.5) near southern Iran, whereas, SREs show droughts (SPI< -1.0) in the same spatial domain. In addition, this issue was observed in mountain regions of northwestern Iran. The severe droughts during 1990 that spread across northern part of Iran were not captured by SREs. Similarly, the spatial droughts observed in central part of Iran during 2000 were not captured by SREs, although the SREs nearly detect the spatial distribution of drought in dryer regions (zones 1 to 4). Both SREs indicated wet spells during 2008, whereas the observed data indicated droughts in several parts of Iran.

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Figure 5. Comparison between observed and SREs data sets based on annual average of SPI (SPI-3 and SPI-6) during the study period 1983-2012. [Note: Blue, red and green bar represents PERSIANN-CDR and MSWEP datasets, respectively].

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Figure 6. Spatial distribution of major droughts (using SPI 3) over Iran based on Gauge, PERSIANNCDR and MSWEP data sets. The SREs can able to detect the spatial distribution of drought in dryer regions (zones 1 to 4), except for drought event at 2008.

4.2 Spatio-temporal drought analysis using SAF curves 4.2.1 Comparison between SREs and gauge data

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The performance of SREs was investigated to study regional droughts using SAF curves by quantitatively connecting severity, area and frequency using return periods of 10, 50, 100 years. For illustration purpose, we derived SAF curves based on SPI-3 (as short-term drought) and SPI12 (long-term drought) using SREs and gauge datasets for climatic regions of Iran (Figure 7 and 8), where X and Y axes represent percentage of area affected by drought and annual drought severity (sum of negative SPI values in dry spells) with different return period, respectively. For SPI-3, SAF curves based on both SREs datasets match well with frequency curves related to gauge data over the southern part of the country (zone 1&2), where the low magnitude droughts affected more than 50% of the region (Figure 7-a). Over the central part of Iran located in semiarid regions (zone 3&4) and the mountains regions (zone 5&6), the SAF curves derived based on SREs' matches well with gauges data, especially for droughts with lower return periods (Figure 7-b and 7-c). Over the moderate region located on shores of Caspian Sea (zone 7&8), the SAF curves of SREs do not matches to each other and the frequency curve remains below the observed data. For example, all the SREs frequency curves based on the return period of 50- and 100-year are illustrated between the SAF curves for gauge data with return periods of 10- and 50- year. For example, all SREs curves with the return period of 50- and 100-year are illustrated between the SAF curves for gauge data with return periods of 10- and 50- year. This result show that short-term droughts (durations less than 3 months) with low severity (annual SPI values around 6) often (return periods of 10 years) occur over Iran. The difference among drought severity was observed for zone 1&2 based on 50- and 100-year return period between observed and SREs data (Figure 7-a). For example, the drought severity based on observed data varies between 6 and 22, whereas it is between 8 and 18 for SREs’ products based on 100-year return period. Similarly, in this region over more than 40 percent of 24

the area, the drought severity was higher for SREs’ data (PERSIANN-CDR and MSWEP) compared to observed data at all frequencies (Figure 7-a). In contrast, both the severity and the percentage of area affected by drought at different return periods matches well between observed and two SREs’ products for climate zone 3&4 (Figure 7-b). However, for lower return period, comparatively better performance was observed by SREs’ products (Figure 7-b). Similar pattern was observed for other climatic regions, zone5&6 and zone7&8 (Figures 7-c and 9-d, respectively), where the observed data typically has higher magnitude across whole spatial area, specifically at higher return periods compared to SREs’ products.

Figure 7. Drought SAF curves for selected climate zones of Iran derived based on SPI-3.

Figure 8 illustrates SAF curves for long term droughts (SPI-12) over Iran's climatic regions. Overall, a wide variation in terms of drought severity was found between observed data and 25

SREs’ datasets (PERSIANN-CDR and MSWEP), especially for regions with less aridity, mountains and the northerner areas (zone 5&6 and zone 7&8, Figure 8-c and 8-d, respectively). Interestingly, at 10-year return period, the SAF curves remain flat, which indicates that the drought affected whole study area with equal magnitude, and both observed and SREs’ datasets agrees well at this return period. Typically, the observed data indicates that the study area is affected by higher drought magnitude compared to remote sensing products at 50- and 100-year return periods. Remarkably, for dryer regions, zone 1&2 and zone 3&4, the drought magnitude remains same for observed and remote sensing products at higher return periods (50 and 100 year) for about 30 to 50% of area (Figures 8-a and 8-b). However, for the rest of the country, zone 5&6 and zone 7&8, there is a large difference between observed and SREs products in terms of severity across whole study domain based on 100 year return period, however, these gaps reduced in the case of 50 year return period (Figures 8-c and 8-d). In addition, for droughts with lower frequencies (50- and 100-year return periods), PERSIANN-CDR curves show better match to gauge ones (compared to MSWEP) all around the country, especially over dryer regions of zone 1&2 and zone 3&4 (Figures 8-a and 10-b). It may be concluded that SREs’ datasets (specifically PERSIANN-CDR) have acceptable accuracies in assessing long term regional droughts over Iran.

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Figure 8. Drought SAF curves for selected climate zones of Iran derived based on SPI-12.

4.2.2 Evaluation of major historical droughts using SAF curves The performance of SREs based SAF curves was evaluated in detecting the spatio-temporal pattern of major historical drought events occurred in 1985, 1990, 2000 and 2008. The performance of SREs was compared with gauge datasets in terms of severity, area and frequency associated with major historical droughts. For illustration purpose, the drought assessment is performed over dryer regions due to the critical needs of managing water resources. The SAF curves are constructed for the southern part (zone 1&2) with arid steppe (Bs), and central part of Iran (zone 3&4) with arid dessert (Bw) climatic conditions are shown in Figures 9 and 10 respectively. Here we used SPI-3 for short-term and SPI-12 for long-term drought analysis (Mishra and Singh, 2009), and these two indices were used for deriving SAF curves. 27

The SAF curves were derived based on RSEs and gauge data based on SPI-3. Based on the SAF curves for the southern part of Iran (zone 1&2; figure 9-a~c), the variations of drought severity (from 8 to 20) based on the observed data are more in comparison to the severity associated with both SREs (ranging from 7 to 16). Based on the observed data, the most severe drought occurred in the year 2000 is associated with a return period that varies from less than 10 to 50 years (Figure 9-a), while less than 20% of this region was affected by a drought with return period of 25-50 years and more than 60% of the area experienced the drought with a return period of around 10 years. The frequency curves based on the observed data indicates that the drought event occurred during the years 1985, 1995, and 2008 witnessed a return period of less than 10 years over the entire region of zone 1&2 (Figure 911-a). In contrast to the gauged data sets, the SAF curves based on PERSIANN-CDR indicates that the most severe drought occurred in 1990 in comparison to the year 2000 (Figure 9-b), and the similar results to the observed data can be observed for MSWEP (Figure 9-c). Based on SAF curves, the drought severity over the central part of Iran (zone 3&4) are comparatively less in comparison to the southern parts (zone 1&2). It can be observed that the SAF curves for zone (3&4) indicates higher drought severity based on the observed data in comparison to SREs (Figure 9-d~f). In addition, based on all the three data sets, the more severe drought was occurred in 2000 with a return period that varies between 1050 years at different spatial domains for the given climatic zones. The second most severe observed drought occurred in 2008, whereas, the SREs’ datasets depicted it as the least severe drought over the central part of Iran with arid climate (zone 3&4). This issue was also similar to results on zone 1&2.

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Figure 9. Evaluation of major historical droughts using SPI-3 based on SAF curves derived based on Gauge, PERSIANN-CDR and MSWEP datasets.

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Similar to SPI-3 analysis, the SAF curves were also derived based on RSEs and gauge data based on SPI-12 to investigate long-term droughts over southern (zone 1&2) and central (zone 3&4) part of Iran (Figure 10-a~d). It can be observed from the figure that for all the datasets (in-situ and SREs’) the SAF curves remains flat for high frequency droughts (especially 5- and 10-year return period). This aspect indicates that the high frequency droughts can typically spread all over the regions and indicates less difference in the precipitation variability among the grids within the basin. The two most severe droughts occurred in 2000 and 2008 affected 50% and 30% area of the zone 1&2 (the hot regions close to Persian Gulf) with a return period between 10 and 25-year (Figure 10-a). Over this region, the SREs’ datasets (PERSIANN-CDR and MSWEP) suggests that drought in 2000 can be very severe that covered around 40% and 70% of the area with a return period between 10 and 30-year, respectively (Figures 10-b and 10-c). For the other three drought events the SAF curves related to MSWEP are mostly similar to the ones related to observed datasets. Based on PERSIANN-CDR dataset all these three drought events (1985, 2000 and 2008) have the same severity and areal extent over the shores of Persian Gulf. Based on the SAF analysis using SPI-12, it can be observed that over the central part of the Iran (zone 3&4), the drought occurred in 2008 spread over 50% of the area with a return period between 10 to 20years, while rest of the area witness a lower return period (Figure 10-d). Although, the major drought events (1985, 1990, 2000 and 2008) derived based on the gauge data sets differs based on the frequency as well as their spatial extents in zone 3&4, the similar characteristics was not observed based on PERSIANN-CDR data. Based on PERSIANN-CDR, the SAF curve patterns for historical droughts were close to each other affecting about 40% of the central part of the Iran with a return period around 10 years (Figure 10-e). In the case of MSWEP data sets, the most severe drought was observed in 2000 (affected about 70% area), and 30

the curves for other historical events varies in terms of frequency and spatial extents (Figure 10f). Overall, MSWEP data set performs well in capturing the spatio-temporal pattern of major historical droughts in comparison to PERSIANN-CDR over central part of Iran.

Figure 10. Evaluation of major historical droughts using SPI-12 based on SAF curves derived based on Gauge, PERSIANN-CDR and MSWEP datasets. 31

5. Conclusion The spatio-temporal (regional) drought analysis are often challenging due to the lack of highquality precipitation data sets for different parts of the globe.

The advancement made in

satellite-based precipitation estimates can add new capability for spatio-temporal (or, regional) drought analysis, as well as it can be a reliable alternative source for drought assessments in ungauged basins. Although, SREs can provide rainfall information at finer spatial and temporal resolutions, their performance is not consistent with respect to gauged rainfall data in different climatic regions. The regional drought analysis requires long-term rainfall data sets, which can be a key limitation for SREs as only few products have long-term data sets. Therefore, it is important to investigate whether long-term SREs products can provide the regional drought information accurately. In this study new generations of SREs, such as, PERSIANN-CDR and MSWEP with long term (more than 30 years) data sets are used to construct SAF curves for regional drought assessment by integrating drought severity (S), areal extent (A), and frequency (F) components for different climatic zones located in Iran. The following conclusions can be drawn from this study. (a) Drought events and their durations detected by SREs and in-situ datasets were compared over four different climatic regions based on SPI at different timescales (1, 3, 6, 9 and 12 months). Overall, the number of drought events based on shorter time scales (SPI-1 and -3) found to be greater for SREs in comparison to gauge information for all climatic regions. This is more significant for PERSIANN-CDR, which may be attributed to its underestimation of rainfall over

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the Iran. However, for longer time scales (>6 months), the number of drought events based on SREs matches well with gauge data. (b) Based on the shorter timescales (SPI-1 and SPI-3), the maximum duration of drought based on SREs found to be closer to the gauge datasets, and MSWEP comparatively performed well in comparison to PERSIANN-CDR. Although, the spatial pattern (excluding magnitude) of both SREs (PERSIANN-CDR and MSWEP) captures the major historical droughts (1985, 1990, 2000 and 2008) illustrated by gauge datasets, but a significant difference was noticed between both SREs’ and gauge datasets in zone1&2 for 1985 drought event. For example, gauge data suggests wetness (i.e., SPI 3 > 0.5) near southern Iran, whereas, SREs show droughts (SPI< -1.0) in the same spatial domain. The severe droughts during 1990 that spreads across northern part of Iran were not captured by SREs. (c) The SAF curves derived based on SREs matches well with gauges data, especially for droughts with lower return periods. Over the moderate region located on shores of Caspian Sea (zone 7&8), the SREs based SAF curves do not matches to each other and the frequency curve remains below the observed data. For dryer regions (zone 1 to 4), the drought magnitude remains same for observed and remote sensing products at higher return periods (50 and 100 year) for about half of the land area. However, for the remaining climatic zones (5 to 8), there is a large difference between observed and SREs products in terms of severity based on 100-year return period in comparison to 50-year return period. (d) The SAF curves derived based on SPI-12 for RSEs and gauge data sets over southern (zone 1&2) and central (zone 3&4) part of Iran remains flat for high frequency droughts (especially 5and 10-year return period). This aspect indicates that the high frequency droughts can typically

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Figure 2. Geographic location of Iran (left), and the spatial location of climatic zones (right) (Kasmaiee, 1992). Figure 3. Distribution of Rain Gauges over climatic regions located in Iran during 2005 (left) and 2012 (Right).

Figure 4. Time series of spatially averaged SPI-1, -3, -6, -9, -12 over Iran derived based on three type of data sets. 39

Figure 5. Comparison between observed and SREs data sets based on annual average of SPI (SPI-3 and SPI-6) during the study period 1983-2012. [Note: Blue, red and green bar represents PERSIANN-CDR and MSWEP datasets, respectively].

Figure 6. Spatial distribution of major droughts (using SPI 3) over Iran based on Gauge, PERSIANNCDR and MSWEP data sets. The SREs can able to detect the spatial distribution of drought in dryer regions (zones 1 to 4), except for drought event at 2008.

Figure 7. Drought SAF curves for selected climate zones of Iran derived based on SPI-3.

Figure 8. Drought SAF curves for selected climate zones of Iran derived based on SPI-12.

Figure 9. Evaluation of major historical droughts using SPI-3 based on SAF curves derived based on Gauge, PERSIANN-CDR and MSWEP datasets.

Figure 10. Evaluation of major historical droughts using SPI-12 based on SAF curves derived based on Gauge, PERSIANN-CDR and MSWEP datasets.

Tables and Figures

Table 1. Drought severity classification using SPI SPI Value 2.00 and above 1.50 to 1.99 1.00 to 1.49 -0.99 to 0.99 -1.00 to -1.49 -1.50 to -1.99 -2.00 and less

Category Extremely Wet Very Wet Moderately Wet Near Normal Moderately Dry Severely Dry Extremely Dry

Table 2. Climatic zones of Iran based on definitions of Iran Ministry of Energy and Koppen-Geiger Zone

Location

Based on Iran Ministry of

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Based on Koppen-Geiger

number 1 2 3 4 5 6 7 8

Shores of Persian Gulf on south South of the country Southern and central hillsides Central parts of Iran Along Zagros and Alborz Mounts Highest altitudes of mounts Shores of Caspian Sea on the north Northeast and northwest of Iran

Energy (Kasmaiee, 1992) Very Hot and Humid Hot and Humid Hot and Dry Semi-arid Cold Very Cold Moderate and Rainy Semi-moderate and Rainy

climatic definition (Raziei, 2017) Bsh Bsk Bwk Bwh Csa and Dsb Dfb Cfa Csa

Table 3. Total number of operating gauges and pixels during the study period (1983-2012) Number of Pixels Year 1983 1985 1990 1995 2000 2005 2010 2012

Total gauges 421 377 871 1240 2570 3275 3469 2541

Iran: 2643 Missing Data Gauges 165 127 292 387 686 1160 1193 259

Complete Data Gauges 256 250 579 853 1884 2115 2276 2282

Zone 1&2: 510 Gauges on Zone 1&2 33 39 76 79 244 282 299 290

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Zone 3&4: 1142 Gauges on Zone 3&4 56 87 197 264 488 547 645 696

Zone 5&6: 913 Gauges on Zone 5&6 122 112 281 468 1034 1135 1179 1141

Zone 7&8: 78 Gauges on Zone 7&8 15 12 25 42 118 151 153 155

Table 4. Statistical properties of drought indices derived based on all three datasets during 2003 to 2012 for different climatic regimes. [G: Gauge, C: PERSIANN-CDR, M: MSWEP]

Zone 1&2

SPI-1 SPI-3 SPI-6 SPI-9 SPI-12

Zone 3&4

SPI-1 SPI-3 SPI-6 SPI-9 SPI-12

Zone 5&6

SPI Series

SPI-1 SPI-3 SPI-6 SPI-9 SPI-12

Zone 7&8

Climatic Region

SPI-1 SPI-3 SPI-6 SPI-9 SPI-12

Total number of drought months (SPI< -1.0) 1983-2012 G C M 15 23 23 21 35 36 26 40 43 31 43 41 34 40 41 G C M 7 8 24 62 64 63 61 63 62 44 50 48 32 57 39 G C M 41 49 57 52 65 61 47 62 55 29 46 40 21 37 40 G C M 43 54 50 35 60 47 32 64 46 17 49 43 1 53 45

Drought duration (months) Number of drought events G 9 11 10 7 6 G 5 26 26 17 3 G 21 21 18 6 2 G 24 14 12 5 1

C 18 17 14 9 6 C 6 26 29 15 5 C 24 24 22 12 3 C 27 23 20 11 4

M 18 14 14 9 6 M 16 26 28 16 5 M 24 24 20 9 4 M 27 19 13 11 6

Minimum G 1 2 5 2 9 G 1 2 2 2 11 G 1 2 3 3 20 G 1 2 2 5 --

C 1 2 1 2 3 C 2 3 2 2 10 C 1 1 3 3 16 C 1 2 4 2 10

M 1 2 2 3 3 M 2 3 2 2 2 M 2 2 2 4 10 M 1 1 4 5 11

Maximum G 4 8 15 49 59 G 6 6 7 28 28 G 6 7 7 28 21 G 8 8 8 20 25

C 5 11 13 43 40 C 7 6 6 27 26 C 6 7 6 18 35 C 7 7 8 41 37

M 8 11 16 46 46 M 5 6 6 29 31 M 7 7 7 40 26 M 8 9 17 18 36

Average G 1.67 1.91 2.60 4.43 5.67 G 1.4 2.38 2.35 2.59 10.67 G 1.95 2.48 2.61 4.83 10.5 G 1.79 2.50 2.67 3.40 1

C 1.28 2.06 2.86 4.78 6.67 C 1.33 2.46 2.17 3.33 11.4 C 2.05 2.7 2.82 3.83 12.33 C 2 2.60 3.2 4.45 13.25

M 1.28 2.57 3.07 4.56 4.83 M 1.5 2.42 2.21 3.0 7.8 M 2.38 2.54 2.75 4.44 10 M 1.85 2.47 3.54 3.91 7.50

Table 5. Evaluation of SREs with observed data for calculating SPIs over Iran and its climatic zones Region Iran Zone 1&2 Zone 3&4 Zone 5&6 Zone 7&8

Criteria CC RMSE CC RMSE CC RMSE CC RMSE CC RMSE

SPI-1 0.43 0.98 0.77 0.66 0.37 1.04 0.32 1.04 0.26 1.06

PERSIANN-CDR vs. Gauges SPI-3 SPI-6 SPI-9 SPI-12 0.55 0.66 0.39 0.35 0.88 0.77 1.01 0.97 0.80 0.80 0.71 0.67 0.62 0.62 0.73 0.70 0.53 0.68 0.30 0.23 0.90 0.74 1.08 1.02 0.43 0.56 0.31 0.28 0.96 0.84 1.04 1.03 0.30 0.32 0.27 0.33 1.04 1.02 1.05 1.00

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SPI-1 0.49 0.93 0.84 0.54 0.41 1.01 0.39 0.99 0.38 0.98

MSWEP vs. Gauges SPI-3 SPI-6 SPI-9 0.62 0.73 0.44 0.81 0.69 0.96 0.88 0.88 0.80 0.48 0.47 0.61 0.58 0.72 0.33 0.85 0.70 1.06 0.52 0.66 0.37 0.88 0.75 1.00 0.43 0.50 0.38 0.94 0.89 0.98

SPI-12 0.40 0.95 0.77 0.59 0.25 1.03 0.34 1.00 0.49 0.90

Abstract Satellite Rainfall Estimates (SREs) can provide rainfall information at finer spatial and temporal resolutions, however their performance varies with respect to gauged precipitation data in different climatic regions. A limited number of studies investigated the performance of SREs for spatio-temporal (regional) drought analysis, which is a key component for developing tools for regional drought planning and management. In this study, the performance of two recent SREs (data length > 30 years), which includes Artificial Neural Networks Climate Data Record (PERSIANN-CDR) and the Multi-Source Weighted-Ensemble Precipitation (MSWEP) are selected for spatio-temporal drought assessment over different climatic regions located in Iran. Firstly, the accuracy of SREs was evaluated for deriving standardized precipitation index (SPI) at different time scales (1, 3, 6, 9 and 12 months) for four climatic regions during the period of 1983-2012. Secondly, the performance of SREs was evaluated for regional drought assessment based on the concept of the Severity-Areal-Frequency (SAF) curves. It was observed that the performance of SREs can be different with respect to gauge data in terms of quantifying drought characteristics (e.g., severity, duration, and frequency), identification of major historical droughts, and a significant difference can be observed based on the SAF analysis. For example, the number of drought events based on shorter time scales (SPI-1 and 3) found to be greater for SREs in comparison to gauge information for all climatic regions. While investigating the major historical droughts, discrepancies can be observed between these two types of data sets. For example, gauge data suggests wetness (i.e., SPI-3 > 0.5) near southern Iran, whereas, SREs show

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droughts (SPI< -1.0) in the same spatial domain. The performance of SREs with respect to gauge data varies largely in terms of quantifying the frequency component embedded in the SAF curves for selected climatic regions located in Iran. Our research findings can be useful for drought assessment in ungagged basins, as well as to develop regional drought management plans to improve water security by integrating multivariate nature of drought events. Keywords: Regional Drought Assessment; Satellite Rainfall Estimates (SREs), Severity-AreaFrequency (SAF) Curves

Declaration of Interest statement/Conflict of interest: We have no conflict of interest to report.

Key points



Performance of SREs was evaluated for spatio-temporal drought analysis



SREs may not perform adequately for extreme drought events



Drought frequency curves can vary based on the climatic zones



Performance of SREs can vary with temporal resolution of drought indices

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