1. Introduction
Drought is a severe natural disaster that can have serious environmental and social consequences. It directly affects the growth of vegetation and accelerates the degradation of grasslands and the disappearance of wetlands [
1,
2]. Rapid depletion of rivers and lakes due to drought poses a greater risk to ecosystems. Accurate and timely drought assessment is especially important as the incidence of drought has increased due to the increasing trend of climate warming around the world [
3]. Therefore, detailed studies on drought are very important to take necessary measures to mitigate the severe effects of drought. Thus, detailed studies of drought have attracted extensive research attention, and a large number of drought monitoring indices have been developed over the past few decades as a robust tool for qualitative and quantitative estimation of different types of drought [
4,
5,
6].
Every drought is different because of the affected environment, the drought system, the population, the economy, and the attitudes of the people. Simply, the effects of drought are not limited to water resources and agriculture [
7]. Despite technological advancement, the effects of the drought have been intensified by the expansion of hydropower generation, increasing demand for water, population growth, expansion of irrigated lands, and increasing per capita water use [
8].
There are four types of droughts, namely meteorological, agricultural, hydrological, and socio-economic [
9]. The occurrence of hydrological drought is usually indicated by a significant decrease in the surface water quality of rivers, lakes, reservoirs, and groundwater. The hydrological drought is especially observed after meteorological and agricultural droughts. The main reason for this is that groundwater, as well as surface water sources, are not recharged due to the long-term gradual decrease in rainfall [
10]. Among other droughts, hydrological drought has been gaining more attention because water is an integral part of daily human life. However, a relatively small number of indicators have been introduced for hydrological drought monitoring compared to meteorological and agricultural drought monitoring indices [
11].
Various researchers developed standardized indices for drought monitoring using a reliable and flexible method used for Standardized Precipitation Index (SPI) calculation [
12]. Standardized Runoff Index (SRI), Standardized Reservoir Supply Index (SRSI), Standardized Streamflow Index (SSFI/SSI), and Standardized Snow Melt and Rain Index (SMRI) can be introduced as standardized indices designed for hydrological drought monitoring [
13,
14,
15]. The most commonly used data for calculating these standardized indices were location-specific measurements such as river discharge, reservoir water level, groundwater level, and surface flow [
16,
17].
Since the onset of hydrological drought is well reflected by a continuous decrease in surface water resources, monitoring its long-term variation is more effective for studying hydrological drought. Previous studies suggested that the measurement and monitoring of surface water area using remote sensing technology provides valuable information for hydrological drought-related studies [
18,
19]. Although not directly relevant to drought studies, many studies have shown that different data and algorithms are used to map the surface water area at different spatial and temporal resolutions with greater accuracy [
20,
21]. However, the low cost and high spatial and temporal resolution of satellite data have led researchers to focus more on satellite-based methods than conventional methods for surface water area extraction.
Various remote sensing data captured from different satellite missions such as Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) [
22,
23], Moderate Resolution Imaging Spectroradiometer (MODIS) [
24,
25,
26], and National Ocean, Atmospheric Administration (NOAA), Advanced Very High Resolution Radiometer (AVHRR), and Sentinel-1 Synthetic Aperture Radar (SAR) have been used to detect and monitor the surface water bodies under various spatial and temporal resolutions from the 1970s to the present [
27,
28,
29].
On the other hand, remote sensing (RS) based methods for surface water detection can be roughly divided into two classes; feature classification methods and thematic water surface extraction algorithms. Even though the most commonly used feature classification methods can be identified as spectral mixing models [
30,
31], maximum probability classification [
32], and artificial intelligence methods [
33,
34,
35], these methods are not easy to use for a large area and long-term use of multiple-temporal images to extract the water surface area. The thematic water surface extraction algorithm can be classified as row satellite imagery classification and the water indexes calculation [
36]. Water indices created through multi-satellite imagery are more appropriate than single-satellite image bands because they take into account the reflective differences that exist between water and land [
37]. It is more effective to extract surface water using remotely sensed data due to its accuracy, ease of use, speed, and reproducibility, as well as the ability to analyze time-series data [
38].
Despite some data gaps, Landsat data represent the world’s longest-running terrestrial satellite record, making it more successful for the study of long-term variability in hydrological activity and climate change [
39]. Even though Landsat data have been available since 1972, it is more challenging to analyze them over the entire period (1972 to 2020) with traditional Geographical Information System (GIS) and RS methods [
40]. As a result of significant technological improvement, various cloud computing platforms around the world have been set up to facilitate the easy analysis of large-scale geographical data [
41]. The Google Earth Engine (GEE) is a high-performance cloud-based platform that incorporates free multi-petabyte remote sensing data freely available worldwide [
42]. The GEE has successfully been used to map regional, continental and global level land cover [
43,
44], cropland areas, forests [
45], and open surface water bodies [
46,
47].
It is not easy to capture historical droughts through more successful hydrological drought monitoring using such data as it is common in most reservoirs to have large data gaps and irregular data in the depth of manually measured reservoirs. Although more successful and complex methods have been introduced to calculate surface water extent using both optical and SAR satellite data, as discussed above, this study demonstrates the possibility of defining an index for hydrological drought monitoring using reservoir water surface area as a reasonable substitute for depth measurements in shallow and gently sloping reservoirs. Thus, the main objective of this study was to introduce a novel index, the Standardized Water Surface Index (SWSI), for hydrological drought monitoring, by measuring the time-series water surface area (WSA) using both optical and SAR satellite data. The GEE was employed to calculate the long-term water surface area on a monthly basis from 2001 to 2020. On the other hand, since both optical and SAR data are used to calculate the surface water area, the accuracy of the area calculated from them was studied using a correlation coefficient. Furthermore, the relationship of the novel index, from SWSI to the SPI and Vegetation Condition Index (VCI), was also analyzed to investigate the performance of the SWSI index and determine its validity using the available information, including the surface water area of the five tanks in Sri Lanka by considering the specific hydrological setting.
3. Results
The results generated to achieve the objectives of the study are described in the following sections. The spatial and temporal changes in the water surface areas of the tanks extracted through optical and SAR satellite data are shown to vary for drought and non-drought years, and the numerical values of the surface areas of the tanks are also represented.
3.1. Landsat Base Reservior Water Dynamics
The spatial variation in the water surface area of five tanks called Tank A (Iranamadu), Tank B (Mahavilachchiya), Tank C (Kantale), Tank D (Senanayaka Samudraya), and Tank E (Udawalawa) was studied to analyze whether the surface water area of the tanks could be significantly changing during the drought.
Figure 3 shows a spatial pattern of the monthly water surface area changes in Tank D during drought (2019) and non-drought (2015) years.
The Tank D catchment area received significantly low rainfall during the 2018–2019 NEM, which directly affected crop cultivation in the 2019 Yala season. Due to the low water inflow to the tank, the depth of the water decreased rapidly, and the water surface area also decreased accordingly. This suggests that changes in surface water area can be used effectively instead of the tank’s water depth data for hydrological drought monitoring. At the maximum capacity of Tank D, the surface water area is estimated to be 90.3 (sq. km), which dropped to 6.5 (sq. km.) during the 2019 drought, which is about 7.2% of the total area. This was the lowest value recorded in the last 20 years (Irrigation department).
According to the Disaster Management Center (DMC) reports on Sri Lanka’s drought, the dry zone of Sri Lanka was severely affected by the drought from August 2016 to November 2017. In contrast, from November 2019 to November 2020, significant rainfall was received, and more than 90% of Sri Lanka’s tanks reached their maximum capacity.
Table 3 shows the maximum monthly surface water area calculated through the RS data. Furthermore, the maximum water surface area calculated by RS data for the period 2000 to 2020 obtains values that are closer to the actual (measured) value when the maximum supply level of the relevant reservoir is reached. This implies that the water surface areas of the reservoir calculated from the satellite data are more accurate.
Figure 4 depicts the spatial variability in the water surface area of Tank A, Tank B, Tank C, and Tank E in 2017 (drought year) and 2020 (non-drought year). Analysis of the water surface area changes in the above four tanks shows that the water surface area values are very low in the drought years and very high in the wet years. The 2017 drought was identified as a severe drought that occurred in most parts of the dry zone, and the water surface area of Tank A, Tank C, Tank B, and Tank E dropped up to 17.2%, 19.8%, 36.5%, and 12.9%, respectively, to its full capacity. These numbers are also one of the lowest in the last twenty years.
3.2. SAR Base Reservior Water Dynamics
Figure 5 and
Figure 6 show the monthly surface water dynamics successfully extracted for Tank D and Tank A, which are located in mountainous and plane topographic areas using Sentinel-1 images. Those figures also indicate the monthly variability in the water surface area in drought and non-drought years of both tanks. Analysis of that variability shows that during a hydrological drought, the water surface area of the tanks reached less than 25% of the total water surface area for more than three months or more. During the year 2017, the water surface area of Tank D was less than 25% of the total water surface area for more than five months. In 2017, the maximum water surface area of Tank D was about 43%, with a minimum of 15.6%, and for about six months (July to December), the percentage was less than 30%.
The water surface area of Tank A continued to be less than 30% of the maximum area from July to October 2016, with the lowest area of 20% being recorded in September. The specialty is that in 2019, the minimum water surface area was only 37%, and it was reported only in one month. In 2019 the surface water area was more than 80% for 7 months, and in 2016, only one month exceeded 80%; since January 2016, the surface water area has been gradually declining.
Table 4 shows the numerical values of the monthly water surface area of Tank A and Tank D.
Since the temporal resolution of S1 data in Sri Lanka range from 7 to 13 days, it is possible to calculate the surface water area using about 3–6 satellite images per month. The greatest advantage of using SAR (Sentinel-1) satellite data is that they provide more accurate values of water surface area consistently under any environmental conditions. However, long-term water area data can be easily calculated by combining Landsat data and Sentinel-1 data via GEE, making the SWSI index calculation even more efficient.
3.3. Water Surface Area and Rainfall Dynamics
The analysis of the tank’s water surface area dynamics clearly shows that changes in water surface area in the main tanks show strong seasonal patterns
Figure 7. Although the timing of low and high-water surface area occurrence slightly varies more or less from tank to tank, the five tanks considered show more similarities in temporal patterns. For example, during the 2014 and 2015 North Eastern Monsoon (NEM) seasons, the maximum water surface areas of all tanks are shown, while the lowest water area levels from 2016 to 2019 are shown with the same increase shown in the NEM in 2019.
By considering the changes in the water surface of all five major tanks, it appears that more water is recharged to the tanks during the northeast monsoon season. Considering the main peaks in the surface water area variability graph of the tanks (
Figure 7), it is implied that they correspond to the main rainy season, and the sub-peaks show the short-term fluctuations in rainfall during the respective rainy season. The recurrences show large peaks at the beginning of the year, with generally high rainfall and relatively high water areas from December to the end of April. Similarly, the decrease in the surface area of the tanks can be detected early from May to the end of August/September. The main reason for this is that the dry zone of Sri Lanka receives less rainfall during the period from May to August of the year. Although four tanks out of five have shown the same behavior as described above but the water surface dynamics of Tank E contradict as it receives rainfall from both NEM and SIM of Sri Lanka.
Furthermore, the variability in rainfall and water surface area reflects the fact that not only one month of heavy rainfall but also several months of continuous rainfall contribute significantly to the rising water level in tanks (2014 and 2019). For example, in the case of Tank C, it reached its maximum capacity with a significant speed due to the heavy rainfall experienced in December 2014, and the gradual increase in water surface areas can be identified in 2019 due to the effect of accumulated rainfall that occurred during December 2018 and January and February 2019. The variability of rainfall and water surface area in the years 2016–2018 is particularly indicative that if two or three consecutive rainfall periods show a significant decrease, there is a high possibility to occur a hydrological drought. From all the facts identified above, it is clear that the WSA is directly related to rainfall. Therefore, a WSA-based index could be more appropriately used to identify the occurrence, existence, and severity of hydrological drought. Therefore, satellite technology provides a more accurate and convenient basis for calculating the water surface area of a reservoir.
The primary focus of this study was on the development of a new hydrological drought indicator called the Standardized Water Surface Index (SWSI) using optical and SAR satellite data. This section introduces the variability of the SWSI index shown in the five tanks used in this study from 2001 to 2020 and details the validation process of the SWSI index performed using the SPI index in more detail.
3.4. Water Surface Area and Rainfall Dynamics
SWSI values were calculated using 240 months (20 years) of water surface area data from January 2001 to 2020, using five tanks of different sizes, shapes, and climatological and geographical settings of Sri Lanka (
Figure 1). For that calculation, the water surface area of tanks extracted from satellite imagery was used. The WMO’s SPI Calculation Tool was used to calculate the SWSI Index, and values are calculated according to different time series of 1 month, 3 months, 6 months, 9 months, 12 months, 24 months, and 48 months, as per the last 20 years coverage.
Figure 8 shows how the 3-month SWSI values for all five tanks have changed over the last 20 years. The SWSI index values change from −3 to +3, and using those values the severity of the hydrological drought can be determined. The classification scale for SWSI values and the corresponding event probabilities and cumulative probabilities are given in
Table 5.
As shown in
Figure 8, in the context of Tank A, it is well observed that hydrological droughts occurred in 2004, 2012 to 2014, and 2016 to 2017 in the extreme to severe, and those droughts correspond exactly to historical sources (DMC reports 2014, 2017). The other specialty is the hydrological drought that lasted for about three years for both Tank D and Tank C occurred from 2016 to 2019. On the other hand, Tank A, Tank B, and Tank C are located in the Northern and North-central Provinces, Tank D is located in the Eastern Province, and all the tanks belong to the dry and semi-arid zones of the country. The common indicator of the SWSI index is that all tanks are more prone to long-term hydrological drought after 2010. It is also important to note that from 2016 to 2018, there was an extreme to severe hydrological drought covering the dry zone of Sri Lanka. The hydrological drought of 2016–2017 is well represented by SWSI as the worst drought in the last 20 years.
3.5. Standardized Precipitation Index (SPI)
Since the water capacity of a reservoir is proportional to the precipitation received by the catchment area of the study tanks, the average rainfall in the catchment area of each tank was used to calculate the SPI.
Figure 9 shows the variability of 3-month SWSI values for the last 20 years. When comparing the results of SPI calculated from different time frames (1, 3, 6, 9, 12, 24), it was shown that increase in the SPI time frame increase the duration of drought or wet condition, as the frequency of alternating positive and negative values decreases.
Analysis of the positive and negative values of SPI-3 for all three tanks above reveals that alternating periods can be identified. Tank D has 27 alternating periods, of which 14 are negative, and 13 are positive. In Tank C and Tank D, the alternate periods positive and negative were identified as 59 and 62, respectively, and those alternate periods are close to 50% between positive and negative. Furthermore, the 3-month SPI represents short-term droughts with a high frequency of changing positive and negative, while the 6–24 SPIs represent seasonal and annual droughts. However, the specialty is to better illustrate the long-term occurrences of the 2001–2002, 2013–2014, and 2016–2017 droughts through all the SPI timeframes, and drought severity becomes the highest in the 2016–2017 drought.
Table 6 can be used to obtain a detailed understanding of the number of months of drought in the last 20 years, from 2001 to 2020, depending on the severity of the drought. When comparing the number of months corresponding to the different drought classes in the three tanks shows that no significant increase or decrease in the number of months of the drought was observed during the increase in SPI time frame from 3 months to 24 months. Compared to Tank A, Tank C, and Tank D, a maximum number of months of both extreme and severe droughts were recorded in the Kantale tank as 11 and 24 months, while the lowest number was recorded in Tank D with 3 and 5 months, respectively. Meteorological drought occurrences in the three tanks in the extreme, severe, and moderate classes averaged 6.6, 12.4, and 19.4 months, respectively. Furthermore, when analyzing the maximum number of months of drought events in the last 20 years by drought classes, the extreme drought events are 4.6%, severe events are 10%, and moderate events are 12.1%.
5. Conclusions
This study introduced a novel drought index named Standardized Water Surface Index (SWSI) because of the lack of attention paid by researchers to develop hydrological drought indices and the fact that there is only a handful of hydrological drought monitoring compared to meteorological and agricultural drought indicators. The uniqueness of this novel index is the use of both optical and microwave (SAR) remote sensing data for calculations compared to other hydrological indices.
The calculation of SWSI used the changes in the water surface area at the monthly timeframe in a particular water-bearing formation such as a reservoir, lake, or tank. Thus, accurate and long-term water surface area extractions are essential for the calculation of the SWSI. Therefore, calculation of the surface water area using satellite data is more effective as the traditional methods used for that are more complex, inefficient, and slow process. It can be concluded that the use of a cloud computing platform, such as Google Earth Engine (GEE), made it possible to extract water surface area efficiently and quickly. On the other hand, water surface area extraction was more successful because GEE enabled to process of a large volume of satellite data through the new algorithm. In order to calculate the SWSI index, five tanks, Tank A (Iranamadu), Tank B (Mahavilachchiya), Tank C (Kantale), Tank D (Senanayaka Samudraya), and Tank E (Udawalawa), were selected. The maximum water surface area extracted from both the optical and the Synthetic Aperture Radar (SAR) satellite show greater accuracy (more than 95%) than the ground-measured maximum water surface area. It can be concluded without any doubt that the higher spatial resolution (30 m and 10 m) of both the Landsat and Sentinel-1 data is the main reason for such greater accuracy.
Various methods were introduced in water surface extraction with SAR satellite data, from simple thresholding [
62] to more complex methods [
63]. However, in these study areas, the simple threshold method gives better results when calculating the water area, while more complex indicators can be used for other areas based on the behavior. Moreover, by limiting surface water extraction to the maximum extent of the reservoir, impacts from other wetlands can be limited. The study also revealed that the difference in water surface area for severe drought years was less than 25% of the total and, in some cases, as low as 7%. Furthermore, the study observed that in the event of extreme hydrological drought, the surface water area decreases by more than 25%, which gradually decreases and persists for more than three to four months or even more. Furthermore, while the water level of a reservoir decreases, the change in watershed area is determined by the nature of the slope of the area where the reservoir was created, so the applicability and usability of this index in steep slope reservoirs can be studied using Digital Elevation Model (DEM) as a factor for future studies. When considering satellite data-based drought indices, the Temperature–Vegetation–soil Moisture–Precipitation Drought Index (TVMPDI) was developed by combining the parameters rainfall, temperature, soil moisture, and vegetation, which is much more successful for meteorological and agricultural drought monitoring [
64] while the SWSI index is much useful for hydrological drought monitoring.
Since there is a direct correlation between precipitation and changes in water surface area in the tanks, it can be said that the SPI index, which was generated from the rainfall data, is more suitable for determining the validity of the SWSI index. Therefore, in order to study the validity of the SWSI index, a comparison analysis was carried out between the SWSI and SPI indices by using the Pearson correlation coefficient for the period 2001–2020 for five water tanks in Sri Lanka. The implication is that the maximum value of the Pearson correlation coefficient for the five tanks ranges from 0.58 to 0.67, indicating a strong correlation between SPI and SWSI. Even though there are differences between the values of SPI and SWSI, the visual interpretation clearly shows that the positive and negative faces of both indices are well coincide with each other. Moreover, event probabilities and cumulative probabilities of the SWSI classification suggest that it is much closer to the SPI classification, as reported in previous studies [
12].
These results further prove that the new drought index (SWSI) can be used effectively to monitor hydrological drought by integrating multi-sensor (optical and SAR) satellite data. The study is based entirely on the freely available Landsat and Sentinel-1 time series data, which have high spatial resolution and can be easily applied to different areas due to their simplicity of use. Furthermore, hydrological drought monitoring does not easily apply to monitor drought covering a large area as more than 90% of the existing indicators require location-specific data. Therefore, timely drought monitoring of relatively large geographical areas can be performed using remote sensor data with greater accuracy via the SWSI index. Moreover, the SWSI index should be more useful for areas where there is a lack of field-measured data.
However, the important factors on which the surface water level change depending on the water depletion with drought in the considered reservoirs/lakes are the depth of the reservoir and the topography of the area. In reservoirs with a gentle slope, the change in water surface area is significant, but in steep or near vertical slope reservoirs, that change is insignificant. Considering the parameters of volume, surface water area and depth of the studied reservoirs (
Table 1), it is clear that this index can be applied more successfully to shallow and gentle slope reservoirs. Therefore, a major limitation is the use of SWSI in reservoirs with vertical slopes. Another limitation that can be introduced is the unavailability of SAR data with all the polarization for more successful water surface area detection.