Could you describe your professional career and/or personal experiences related to space technology and water? Where does your interest in those sectors come from?
I started my research career in 2013, with research interests revolving around various environmental concerns that were deeply rooted in water related issues of Pakistan. Having an educational background in Space Science, it was quite intuitive to possess understanding of the very high potential of applicability of Geospatial technologies in the water sector. My interest in the exploitation of space technologies for comprehending and modelling various hydrological variables, and presenting simplified solutions for better water management also came from the fact that I come from a water-stressed developing country; burdened with burgeoning urbanization, rapid climate change, gross inadequacies in water management sector and limited fiscal support. Residing in a major metropolitan city, I was startled to observe that a vast majority of urban population had to grapple with the issues of rapid groundwater depletion, poor drinking water quality, pollution of surface water resources and lack of planning for water storage. Hence, equipped with the tools of space technologies and GIS support systems, the beginning years of my research quest began with assessing and solving issues of Lahore metropolitan area. The focus of my research was to analyse the polluting effects of leachate to the drinking water supply units in the down slope regions of municipal solid waste dumping sites, and studying spatial patterns of groundwater levels susceptibility to degradation and contamination through various controlling factors.
In 2020, my team and I were successful in winning a prestigious monetary grant from Higher Education Commission of Pakistan to establish a research lab under the first-ever National Centre of GIS and Space Applications. This research organization, namely Remote Sensing GIS and Climatic Research Lab (RSGCRL), is a pioneer research facility for promoting sustainable development in Pakistan through monitoring, assessing and mitigating effects of climate change and environmental pollution using space technologies. As a head of the Geospatial Indices Development Unit in this lab, I was accustomed with the task of creating Indigenous and cost-effective solutions for monitoring environmental variables and the status of natural resources, including water. Henceforth, a new dimension was opened for exploration in space-based water research, wherein I worked upon water sustainability themes such as rain-water management, surface water quality monitoring and groundwater storage anomaly. Many of the works conducted in this domain were critically acclaimed and published in highly ranked peer-reviewed journals.
Most of the work that you have been involved in includes the development and comparing of several satellite-based environmental indices for monitoring of various environmental issues. What goes into the process of developing a scientifically robust set of indicators that would help policymakers to make informed decisions and support policies and programs to protect aquatic environments?
Satellite-based environmental indices are a mathematical linkage of proxy indicators that govern or influence a particular composite environmental variable or phenomena. These proxy indicators, which act as surrogate evidence to quantify the target variable, range from the most simplistic to complex semantic independent variables. To have a scientifically robust set of indicators for monitoring of various environmental issues, a few considerations are warranted during the index development procedure. Most importantly, the chosen proxy indicators should be explicitly linked to specific and measurable surface processes. Similarly, the chosen proxy indicators should maximize the sensitivity to phenomena of interest. Ideally, the response of a target variable must change linearly to allow both, ease of scaling and use over a wide range of surface conditions. Additionally, the set of indicators would be incorporated in such a way as to normalize or reduce effects due to extraneous factors, in order to allow consistent spatial and temporal comparisons.
Can you elaborate on the state of space-based water-related indices? How well do the existing indices reflect the situation on the ground?
Currently, space-based water-related indices are generally semi-empirical models, that is, models usually based on several assumptions, approximations and generalizations. These semi-empirical models are composed of multi-variable combinations, which largely focus on the measurement of water clarity indicators such as chlorophyll, cyanobacteria, coloured dissolved organic matter, Secchi Disk depth and total suspended matter. Similar to terrestrial indices for monitoring vegetation and soils, these deliberately enhance the spectral response of the aqueous constituents of interest, while suppressing extraneous signal noise from other optically active substances.
Researchers all over the world have been reporting a number of robust semi-empirical water-related indices. Application of these satellite-based indices has successfully contributed to efficient algal bloom detection, harmful cyanobacteria concentration determination, assessment of trophic status of water, and modelling sediment influx in rivers and deltas across the globe. The results of performance metrics of water-related indices have generally been on the higher side for their potential use in regular water quality monitoring. Due to their basis in physical optical properties of aqueous environments, these indices are simplified models, more generalizable than purely empirical approaches. However, unless high quality radiometrically corrected imagery is used, these can result into over-simplified representations of particular variable of interest. For true reflection of the situation on the ground through satellite-based indices, accurate radiometric measurement at specific spectral wavelengths is required, necessitating the use of remote sensors with suitably placed band centres and adequate spectral resolution. These indices also do not incorporate any inverse modelling of the inherent optical properties (IOPs) of a waterbody, which is sometimes not suitable for more optically complex and turbulent waterbodies.
Performance of indices can vary from place to place depending on geographical conditions. How important is choosing an appropriate index and what could be the impacts if a less suited index is chosen? How do you find out if an index is well suited, and how can you make it fit for your regional specificities?
The generalizability of indices across large spatial and temporal scales where variations in atmospheric and water composition create variability in spectral response of water parameters is a matter of warranting attention. If the appropriateness of a particular index is not evaluated during the design or subsequent application phase, it may seriously impact the representativeness of the variable of interest, modelled by the index.
One common approach to analyse the regional suitability of an index is to fit a standard regression model between spectral index values and temporally coincident in-situ measurements. The error-metric results will give an idea of the appropriateness for use of the particular index. To fine-tune the index according to regional specificities, spatial heterogeneity of water composition and environmental conditions must also be taken into account, using geographically weighted regression models.
Another solution to account for spatial non-homogeneity affecting performance of indices is to use analytical models for estimating aqueous parameters, which allow for regional corrections and more localized solutions. However, model development is inherently complicated and requires information about atmospheric composition and extensive in-situ sampling, which is often not feasible for continuous monitoring. These shortcomings of analytical models are far outweighed by the simplicity and minimal computational requirements of satellite-based indices which give reasonable accuracy at site-specific scales for determining optical qualities of water constituents.
What are the main applications of space-based technologies in the solid waste management sector? How do these applications contribute towards reducing both surface- and ground-water pollution?
Space technologies are ideal monitoring tools for mapping and enhanced spatio-temporal analysis, and are becoming increasingly popular in solid waste management sector due to their relative cost-effectiveness. Space technologies are mainly utilized in four core arenas of solid waste management, i.e., detection of waste disposal sites, site suitability analysis, waste transport optimization and environmental impact assessment of solid waste. The identification of municipal solid waste disposal sites through remote sensing imagery is majorly based on the use of bio-thermal indices that make use of vegetation health (NDVI, SAVI and MASAVI) and thermal reflectance (LST) along with spatial analysis of proximity and curve flattening technique. It is also based on classification methods to detect areas of distinct spectral characteristics with respect to their surroundings. RS data can also be coupled with GIS-based multi-criteria analysis based on various selection criteria variables derived from satellite imagery to aid in decision making for proposing suitable waste disposal sites and waste transportation routes. Semi-empirical remote sensing indices and satellite-derived surface temperature are utilized as proxy evidence of ecological degradation in the vicinity of waste dumping sites, and hence help in the environmental impact assessment of solid waste.
These applications of space-based technologies are vital for all phases of sustainable waste management i.e., the safe collection, transport, disposal and post-disposal monitoring of solid waste. In this way they contribute towards greater environmental health including reduction of ground/surface water pollution. The identification of improper waste sites in the vicinity of water bodies, or the impact assessment of openly dumped waste without precautionary measures enables us to evaluate the hazard associated with movement of harmful waste effluents towards surface water bodies or underlying aquifers. Similarly, proposal of suitable solid waste disposal sites ensures that regulatory guidelines are being followed to minimize the harmful effects of solid waste on surface/ground water resources.
How do studies about municipal solid waste / open landfill hazards monitoring using satellite-based technologies and data directly or indirectly contribute towards water resources management, whether it be ground- and/or surface-water?
Improper collection, transport, disposal or exposure of solid waste inarguably have dire consequences for ground and surface water resources. Harmful landfill effluents in the form of leachate and gases can contaminate both, an underground aquifer and neighbouring surface water bodies. Therefore, all studies concentrating on the monitoring of hazardous landfill emissions from dumped solid waste, or on associated ecological degradation tend to contribute directly or indirectly towards water resource management. Such studies provide baseline statistical evidence for environmental pollution through fugitive waste decomposition products and can help policy makers to take effective measures for protecting and improving the quality of ground and surface water.
When it comes to using remotely sensed data for monitoring, proper selection of methodology is equally important as the accuracy of the remotely sensed data. What are the key factors to consider when selecting methodology?
Remotely sensed satellite data ̶̶̶ in its raw form ̶̶̶ always needs to be processed by a methodology that synchronizes well with natural settings of the phenomenon under investigation. At first, this synchronization includes selection and sequencing of tools to be embedded in the model such that it not only explains the phenomenon of interest, but also covers up limitations of the capturing process. Secondly, the simplification of such a model allows its use by persons with limited knowledge of the subject. The latter is a much-needed aspect not only in current economic and technical situation of developing and the underdeveloped world, but also to allow for the use of these models by people who have others professional backgrounds. So, for framing the methodology, important factors to be considered are its synchronization with the nature of phenomenon, the ability to cover up limitations of data capturing process and the simplification of use.
You are currently the Principal Investigator for the project entitled “Geotechnical support for surface water monitoring framework.” Could you describe the role of space-based technologies and data in this project? What are the main opportunities and challenges of the use of space-based technologies for surface water monitoring with a focus on a specific regional context?
The whole project revolves around the use of space-based technologies and datasets in water monitoring. Unfortunately, in Pakistan, in situ monitoring records of freshwater bodies’ and reservoirs’ water quality are non-existent due to constraints of limited monetary, infrastructural and legal provisions. With the water quality and ecological status of many of these freshwater bodies under threat, there was an urgent need to develop locally tailored geospatial solutions to be able to cope with the enormity of the problem. In order to assess the water quality of inland surface water reservoirs, both, qualitative and quantitative indicators were needed to determine the impacts of human and natural factors. We utilized freely available satellite observations and geospatial processing tools, which we considered the most practical method of surface water monitoring. These cost-effective data alternatives from satellites presented a great opportunity to advance our understanding of dynamic freshwater ecosystems compared to expensive in-situ data acquisition. To improve the accuracy of the results, we aim at using ground sampled data to validate and calibrate the models.
Since you researched the “Synergic Use of Neural Networks Model and Remote Sensing Algorithms to Estimate Water Clarity Indicators”, I am curious to learn if you have any ideas where the potential of remote sensing and machine learning will take us next in terms of monitoring water quality indicators. Is there unharnessed potential in space technologies that can be used to monitor water quality parameters?
The very same challenges we discussed in the previous questions regarding the spatio-temporal complexity of water bodies and generalizability of satellite-based indices takes us to seek solutions towards less conventional realms. Machine learning is one such domain which undoubtedly has significant potential in solving water-related problems. The rapidly expanding field of machine learning (ML) provides many methodological advantages which match very well with the needs and challenges of hydrological research. This is mainly the case because of extensive sensor networks, comprehensive automatic measurements of hydrological variables, and access to voluminous remote sensing data. The era of big data surely has arrived in water research. Conventional process-based models for water quality monitoring are usually developed for some fixed spatio-temporal scales, and pose challenges when used with new datasets. In contrast, automatic methods that learn patterns and generalizations are distinct in terms of their ability to operate in multidimensional space and to model complex non-linear relationships. Most machine learning models are also superior, because iterative learning is employed to reduce the overall error of the model. Such model optimization often produces generalizable results that capture complex, non-linear relationships between feature reflectance and physical parameters. So, the future holds exciting opportunities for the exploitation of these machine-based automatic methods for water quality monitoring. With each new advancement and improvement in technology we are better able to understand the dynamic Earth systems around us. Surely, in the case of space technology too, we are eager to explore more with better combinations of spectral and spatial details, and new data analysis methods.
In your opinion, what are major water-related issues that command the attention of policymakers, are plagued by significant in-situ data gaps, and have the potential to be developed into indicators using satellite data?
Although, there has been considerable progress in the development of space-based solutions in almost all domains of water-related issues, in my humble opinion, there are certain fields which can still benefit from enhanced research in satellite-based indicators as they currently suffer from non-availability of in-situ data for basic design and validation of algorithms. These fields include groundwater modelling, surface water quality assessment, rainwater harvesting sites/artificial groundwater recharge sites proposal and drought assessment.
What are key challenges in satellite remote sensing of water-related environmental issues? How can they be overcome?
For any new technological innovation, there are certain associated challenges in terms of its design, operation and use that must be overcome for its effective utilization. Firstly, there are traditional barriers to the access and usage of high-resolution satellite data for hydrological applications and water-related environmental issues. Based on lower-resolution satellite datasets and re-analysis products, our knowledge of the hydrological regime is often confined to regional scales only, and the local context is often still under-researched and unclear. Another serious challenge in retrieving water parameters from satellite sensors is related to their native design and orbital characteristics, which ̶̶̶ in many cases ̶̶̶ were not destined to monitor water-related issues. In such scenarios, we are relying on indirect proxy indicators that invariably necessitate substantial processing and complex mathematical modelling to derive the required variable, the accuracy of which may not be sufficient for practical purposes. Additionally, there are limitations and challenges associated with data consistency, latency, quality and resolution. However, on the brighter side, improvements in satellite dataset resolution and bit depth may be possible through improvements in sensor technologies. Specifically, water-targeting sensors and future satellite missions have great prospects for in-depth hydrological studies. To better utilize satellite measurements for water resource monitoring and management, continued validation and testing is also required to improve the accuracy of the retrievals, and the maintenance of globally distributed networks of in-situ sensors is crucial to this.
What satellite-based and in-situ technologies and data is required to determine the potential of rainwater harvesting (RWH), identify suitable locations and improve RWH systems? In arid environments, how has advanced remote sensing technologies helped improve RWH?
For determining optimum sites for rainwater harvesting and potential of rainwater harvesting structures, data on landcover/land-use, elevation and topography, geo-chemical formation of soils, stream runoff and various hydro-meteorological variables is required. While high quality data of landcover/land-use, elevation and slope with fine spatial resolution can be derived from satellite imagery, stream runoff and hydro-meteorological variable statistics with sufficient accuracy can only be obtained through ground based in-situ sensors. In many cases however, satellite remote sensing represents a critical source of information, especially in regions with limited sensor networks and where information on hydrologic conditions is not accessible. As arid and semi-arid regions around the world are mainly dependent on rain-fed agriculture and are facing water scarcity issues, rainwater harvesting is critically important for these areas. Remote sensing and geographical information technologies can play a very powerful role in addressing major challenges, since spatial patterns of aridity, climatic uncertainty or rapid climatic variability are not vividly understood or taken into account by local farmers or municipal authorities while planning for agriculture or domestic water use. Robust modelling is possible with space-based applications such as the use of RS to identify suitable locations and harvesting potentials for ponds and pans, check dams, terracing, percolation tanks, and Nala-bunds in arid environments; with very less amount of time, effort and overhead assessment cost.
How do you envision that the next generation should be educated to use remote sensing data and technology to address water-related issues? What are key challenges in playing an active role in capacity building?
Successful imparting and dissemination of scientific knowledge from experts to end-users is a pre-requisite in addressing water-related issues for minimizing the research-policy gaps. However, the successful implementation of technical assistance and capacity building for indigenized and locally-customized water solutions faces numerous caveats. Usually, there are logistical constraints of human and technical capacity, less viable environments for initial funding and ensuring sustainability of maintenance and continuity of equipment and infrastructure. For future generations to play an effective role in utilizing space technologies for water management, capacity building through training in evolving methodologies, software, databases and support from relevant water organizations is key for much needed education and training. Overall, an effective cooperation is required between scientists who develop water-related products, algorithms and solutions; water managers and other stakeholders who will be responsible for implementing such solutions at grass root level.
What can you say about the young generation in Pakistan, what do they need to be empowered to use space technology and data to contribute solving water-related issues?
The younger generation has to be empowered in terms of space education, data provision and capacity to be able to develop site-specific local solutions for water resource analysis and management. The disconnection between academia/researchers and young water professionals or managers should be eliminated and stronger partnerships between local water agencies and academic institutions are needed. Scientists with a focus on space-based applications should put more efforts on capacity building initiatives, stakeholder involvement and co-development of Indigenous water solutions. When training and educating youth, transparency in image processing methodologies and adoption of standardized approaches should be taken care of, so that users can readily understand the underlying rationale and processes of satellite data selection and methodology. Moreover, access to a wide range of datasets, software and trainings is essential to ensure the possibility of future generations playing a greater part in beneficial hydrological research.
Last, but not least, what is your favourite aggregate state of water?
"As a rule, whatever is fluid, soft, and yielding will overcome whatever is rigid and hard. This is another paradox: what is soft is strong."- Lao Tzu.