INTRODUCTION
As a city-dweller residing for most parts in the tropical wetland that is Kolkata, I've always found the city to be water-positive: the South-Westerlies bring copious quantities of rainfall, the murky Hooghly river never ceases to appear menacing, the vegetation is evergreen, and the soft soil seems to emanate a distinct watery-smell throughout the year. Yet, underneath this seeming abundance lies a worrying prospect - the city's groundwater levels are receding.
HYPERLINKED SECTIONS
Much Thanks to RUS Copernicus for the tutorial & ArcGIS Pro for the trial software.
BACKGROUND
For all the misery that a Drought brings (it is the most costly weather-event after a Cyclone), it is rather innocuously defined by USGS as 'a period of drier-than-normal conditions that results in water-related problems'. While this phenomenon can largely be attributed to natural causes such as lack of Precipitation and dry Seasons, humans are exacerbating it through their misuse of resources which contribute towards Global Warming and broader Climate Change.
Having read National Geographic's succinct explanation as to why tracking the onset of drought is complicated, I can draw parallels to my study on Deformation at volcanic sites as this phenomena also develops gradually.
Droughts can be categorized as-
Meteorological - When precipitation is below average for an extended period of time
Hydrological - When the water level in natural or man-made storage sites such as aquifers, lakes and reservoirs fall below a threshold
Agricultural/Ecological - When poor farming practises or excessive heat or low precipitation reduces soil moisture availability for crops
The repercussions from droughts can be wide-ranging, from destruction of crops and vegetation, famine, migration, soil erosion and resulting wildfires to loss of biodiversity, increase in water pollution, scarcity of water for irrigation & urban consumption, and low power generation at dams. There is actually a category of drought known as socio-economic drought which occurs when there is a shortage of produce that is dependent on water supply i.e. when the demand for products such as grains and vegetables exceeds the supply.
'Does a Drought end when it rains?'
Not really. The continuity of rains matter. Essentially, the precipitation needs to soak the soil and replenish the groundwater for the drought conditions to subside and for vegetation to reappear.
Developing & underdeveloped economies are most vulnerable to drought due to their high dependence on agriculture, as can be ascertained from UNCCD's report. I was taken aback to know the drought conditions in India - nearly 50% of land area in India was affected by it as of 2019 (30% as of 2023) and in the five years from 2016-17 to 2021-22, an astounding 35 million hectares of farmlands have been damaged. To give you a perspective of how large this figure is, it is roughly 22% of India's total arable land! (160 million hectares as estimated in 2011). The vagaries of weather - too much rainfall at certain locations and too little at others - is particularly brutal.
All is not lost however: Gujarat, a state at the receiving end of prolonged dry weather, has taken substantial measures to monitor and combat drought and can be a role model across the country. Globally, there is an emphasis on developing Early Warning Systems to proactively identify this phenomenon as well as on practical methods such as irrigation planning, rainwater harvesting, resistant crops cultivation, sustainable farming practises and even funky techniques such as cloud-seeding to combat it.
DROUGHT MONITORING USING REMOTE SENSING
I have used Sentinel-2 Multispectral Satellite Imagery to monitor the (hydrological) drought conditions at Indirasagar Dam's Reservoir in Madhya Pradesh, India over the last four years (2019-2022). This man-made reservoir is the largest in the country in terms of area and water storage capacity and the water is utilized extensively for irrigation purposes across the state which is experiencing increasing mean annual temperatures making it susceptible to drought.
The video below contains an elaborate walkthrough demonstrating the processing chain involved in Satellite Imagery Analytics for Drought Monitoring - from downloading the Multispectral datasets, processing it on an imagery analytics tool, and mapping it out using GIS applications-
Prior to monitoring Drought conditions at Indirasagar, I had opted to study Theewaterskloof Dam's reservoir in South Africa, the time period being the same (2019-2022). The latter was, for me, an extension to the tutorial which monitored drought conditions at this location for five years until 2020.
Theewaterskloof, which stores and supplies water to the city of Cape Town, was affected by severe drought from 2016 to 2018 and water levels were estimated to have receded by as much as 89%.
I will include snippets from the Theewaterskloof study in this post as well.
a. TIMELINE OF IMAGERY USED
For the Indirasagar study, I had selected two imagery datasets from each of the four years, acquired during summer and monsoon respectively.
For the Theewaterskloof study, I had selected three imagery datasets from each of the four years, two acquired during summer and one during monsoon. (P.S. the seasons are reversed in the Southern Hemisphere).
It took me a while to acknowledge that drought conditions can exist during the monsoons (less or no rainfall).
Maintaining consistency in terms of the timeline felt appropriate - not just in the selection of the month but also in the selection of the day from the month. For example, one can reasonably expect May 15th - 31st to be reasonably warmer than May 1st - 15th in an Indian summer. To compare a May 28th image to a May 2nd image from the previous year wouldn't be a like-for-like comparison: the water body would be expected to be drier (more evaporation), all else remaining constant.
Also, while Sentinel-2 datasets are available cyclically throughout the year, the tricky aspect is the presence of clouds in the imagery - it is very important that the cloud cover over the study area (the reservoir) in particular is negligible as they hinder the analysis of surface features due to lack of adequate reflectance data. I had to be mindful of this and had to move to another date range if there were no cloud-free images in the desired date-range. For the Indirasagar study, most of the datasets which I had selected were relatively cloud-free (<1% cloud cover for most and 2-3% occasionally).
Just to clarify, free-to-use earth observation satellites such as Sentinel-2 travel on a fixed path and offer standard-sized coverage (based on swath width). If the study area is very large or does not lie within a single pass of the satellite, one may need to stitch two or more imagery datasets together prior to processing. In contrast, if the study area lies within the imagery, one prefers to clip the geographic extent of the image as it helps in reducing the size of the imagery (Sentinel-2 L2A dataset is ~1 GB per image) allowing subsequent processing steps to run quicker. Technically known as subset, clipping is not restricted to geographic extent alone - multispectral datasets capture surface reflectances across multiple ranges of wavelengths (called spectral bands) and one can choose to remove the bands that are not relevant to the study. Band subset has the same effect as Geographic subset in that the size of the image is reduced which saves up on processing time.
The polygon on the left in Figure 6 depicts the geographic extent of a Sentinel-2 multispectral dataset - you can observe the water body which lies within - it is the Indirasagar Reservoir, our area of interest. Notice the clouds that lie south-west in the satellite view on the right of Figure 6. While the cloud cover percentage in the image is miniscule (<1 % as mentioned in the product page), had that chunk of clouds been directly over the reservoir, I would be hesitant to select this dataset for the study.
This product (Figure 7) is not useful at all though-
b. PROCESSING TOOLS UTILIZED
Given that L2A products over this region have started to be released for public consumption relatively recently (from December 2018), I was fortunate that I could use them for my study instead of Level-1C products. L2A products are atmospherically-corrected i.e. they contain Bottom of Atmosphere (BoA) reflectance values instead of Top of Atmosphere (ToA) radiance ones which L1C carries - this makes analysis of surface features much more accurate, as desired in the Drought monitoring study where I shall be monitoring a reservoir. Converting Level-1C product to Level-2A product manually would entail deploying the Sen2Cor processing algorithm which is a time-consuming process.
To analyse and subsequently drought-map the Level-2A products, I have used three applications - SNAP, QGIS & ArcGIS Pro. SNAP software was predominantly used for the main analysis (identifying water pixels) whereas the latter two GIS applications were used to refine, organize and map the extracted output from SNAP. All the steps have been demonstrated in the video walkthrough.
c. PROCESSING THE IMAGERY ON SNAP
SNAP (Sentinel Application Platform) is a powerful, free-to-use application that can perform a wide variety of analysis workflows on remotely-sensed data. At first, let me visualize the Sentinel-2 L2A product in RGB mode (natural color)-
Sentinel-2A products capture data across 12 spectral bands (ranges of wavelength) as can be seen in Figure 11. L1C products have an additional B10 band (cirrus).
Besides spectral bands, S-2 L2A package has atmospherical corrections stored in separate masks - such as the extracted cloud pixels (refer Figure 12 below).
The raw L2A will undergo three processing phases - I'd like to call them refining, analysing and extracting - for the purposes of this Drought monitoring study-
In Phase 1 (Refining), the L2A imagery product will be resampled first so that all the spectral bands have the same 10m spatial resolution. This is because certain types of processing only work on SNAP if there is an equivalence in spatial resolution across the bands that are being used i.e. it is not a multi-sized product. Thereafter, I will proceed to subset the product discarding the unnecessary geographic extent from the product.
Phase 2 (Analysis) involves manipulating the resized spectral bands to isolate the pixels depicting water from the dataset. In hydrological drought monitoring, what we are essentially setting out to do is to see how the area of the reservoir changes over a period of time. If the reservoir shrinks by a significant margin, then it is indicative of drought.
I've made use of a few type of water radiometric indices to identify the water pixels-
Each indice has its own advantages and disadvantages - typically a suitable indice is chosen based on the type of water and/or based on surrounding features (eg. urban area, vegetated land).
The way to input these indices on SNAP is just like applying a formula in mathematics. For example NDWI can be derived using - (B3 band − B8 band) ÷ (B3 band + B8 band) where B3, the green band has a central wavelength of 560 nanometers and B8, the Near Infrared band has a central wavelength of 842 nanometers).
'But what is the rationale?', you may wonder,
As you'll infer from Figure 14 above, Water reflects wavelengths within the Visible Light spectrum in low amounts (~10% ), however it doesn't reflect Near Infrared wavelengths at all i.e. absorbs NIR completely. Meanwhile, Soil and Green Vegetation have a considerable increase in reflectance when exposed to NIR when compared to visible light (RGB). What NDWI essentially does is it magnifies this contrasting reflectance characteristic (Green minus NIR / Green + NIR) in order to make it easy for the researcher to discern and isolate Water pixels from Land pixels.
Please note that Multispectral imagery captured using Earth Observation satellites use a passive radiation source - Sunlight - whose reflectance from Earth is captured by the satellite's receiver. Solar radiation comprises wavelengths of Visible Light, Near Infrared (NIR), Short Wave Infrared (SWIR) and Ultraviolet (UV) and hence NDWI and other water radiometric indices can be applied on Sentinel-2 Multispectral imagery.
In Phase 3 (Extraction), I have utilized the output of all the four water indices and asked SNAP to select only those pixels that are water pixels across all the indices. The advantage is that all the shortlisted pixels are highly likely to be water pixels. The disadvantage is the removal of false negatives i.e. those pixels which are water or predominantly water but are incorrectly removed by SNAP as one or more water indice wasn't able to classify it as so.
Thereafter, I will create a Mask layer which will remove all NaN (No data values) and only keep the shortlisted water pixels. After repeating the step on the other seven NDWI outputs across four years, I will export them to my desktop as GeoTIFF files. These will be used as inputs in QGIS subsequently.
Next, I've computed the area occupied by the water pixels in the Mask layer below-
Next, I'll perform Time Series Analysis - refer to the video below where I am comparing the pixel values of the NDWI output across all the eight images acquired over four years simultaneously on a graph upon hovering the cursor over a product's visualization.
Notice the cyclical patterns when I hover around the edge of the reservoir - this is because during the summer months, the water in the reservoir dries up and the edges turn into land i.e. negative NDWI data value. During the monsoons, those pixels have water and the NDWI becomes positive.
d. SUBSEQUENT PROCESSING ON QGIS
I'd have preferred to perform the ensuing processing steps on SNAP itself as moving data from one application to another is slightly discomforting besides the fact that one needs to know how to operate different applications as well. This isn't a slight on QGIS though - it is a very powerful geospatial tool adept at performing a wide variety of workflows. The best aspect is that it is open-source and free-to-use just like SNAP is, making it a user-favorite.
In QGIS, I will perform data transformation and simultaneous water pixels extraction across all the eight images in the timeline. By data transformation, I mean that I will convert the data in the GeoTIFF file, which is in a Raster form, to a Vector form (Vector vs Raster explained).
In a Raster image, each pixel has a data value but there are no containers for clusters of similar data values. Converting the imagery products into vector format helps to have distinct shapes in the visualization - all of which shall contain water pixels. This will help me to extract the Indirasager reservoir, which will be wrapped in a distinct container/shape, from the rest of the extent which contains a few other tiny clusters of water pixels - I'll perform this step on ArcGIS Pro software.
The next steps could have been done on QGIS itself, however, I am more comfortable using ArcGIS Pro software to perform these type of steps.
f. SUBSEQUENT PROCESSING ON ArcGIS Pro
Esri's ArcGIS Pro is a premium application which is adept at performing a wide variety of mapping, location analytics and imagery analytics workflows. It is highly user-friendly and a trial version is also available (which I have utilized myself). The software also offers several dedicated extensions for performing specialized workflows specific to certain sectors and industries such as solar, airports, military, urban planning among others. The workflows performed on SNAP earlier can also be performed on ArcGIS Pro by using the Image Analyst extension.
Watching my video walkthrough would be the ideal if you'd like to know the processing steps on ArcGIS in detail as there are several minor steps which are difficult to outline in this post.
Explore the slider below - what have I done here?
Slider 1: Before - After comparison of an important processing step in ArcGIS Pro
As indicated in the previous section, I've refined the vector file to do away with several small water pools surrounding the reservoir. This allows me to have a more accurate computation of the area of water within the reservoir - an important aspect for drought monitoring purposes.
One can locate the shapefile for the reservoir from within ArcGIS' repository (from ArcGIS Online or from Living Atlas) or from other online sources on the web and use it to clip out the other water features. Instead, I've opted for a practical approach which is to just keep the largest shape from the attribute table as Indirasagar is clearly the largest feature in the whole extent - the other shapes can be removed. For doing so, I've used the Buffer tool which allows me to generate an outline of the shapes within the vector layer and subsequently I've used the Clip tool to keep only the largest outline.
Besides helping to clean up, organize, and refine the data, ArcGIS Pro is particularly useful when it comes to symbolizing a layer. I can visualize the output in multiple ways using a variety of colors, shapes and effects. That is the essence of map-making - to present the information in a highly impactful way to the viewer.
Figure 20 is a nice way to observe whether there was drought and if so, which portions of the reservoir receded the most. In Theewaterskloof's case, it is evident that the reservoir was recovering from the drought which it had faced between 2016-18 - there is an increase in water area almost every year between 2019-22.
Besides visual storytelling, one can also analyze the data with dynamic charts, statistical tools and infographs using ArcGIS Pro-
At Indirasagar, it becomes evident from Figure 21 that there was a significant decline in water area in the period between 2021 monsoon and 2022 summer when compared to the previous years.
The charts below enhance our understanding-
ArcGIS Pro finally helps me to combine all the useful components into a beautiful printable map-
I cannot say for sure whether there is a 'drought' in technical terms - a hydrological expert with access to historical data and benchmarks would be the right person to assess. That being said, just based on the four years worth of data, certainly there is a dip in the water content at Indirasagar Reservoir.
I hope you found this article to be interesting and appreciated the importance of water as a precious resource. Feel free to share your feedback on projects@mapmyops.com or on the YouTube's comment section.
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