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?

Arriving at the Agriculture College of Jimma University, I was determined to contribute to sustainable resources management, and joined the Natural Resources Management Department. In the courses I attended, I learned about causes and impacts of drought events that occurred over Eastern Africa, the region I am from. Among others, the 1984/85 severe drought event led to complete crop failure, and death of people and livestock, which I have been frequently hearing about since my childhood. I had always been curious why it occurred. That was the first time my career path crossed hydrological extremes. Although I thought I could make use of the knowledge I leant to contribute to sustainable use of natural resources at that time, I also realized that my success to bring about the desired change in natural resources management depends on the use of available technologies. That is the moment when I started to be fascinated about earth observation and geospatial technologies for water resources management. Graduating with Great Distinction enabled me to join the university department I had studied at as a Graduate Assistant, Assistant Lecturer, and later as Lecturer and Researcher.

I received the Netherland Fellowship Program and got accepted to study MSc in Geo-information Science and Earth Observation for Water Resources and Environmental Management at ITC, University of Twente, The Netherlands. Beyond the knowledge and skills gained (e.g., to use Earth Observation and geospatial technologies for sustainable water management and monitoring hydrological hazards), my study at ITC further motivated me to develop predictions and projections of those hazards so that their impacts on both, human and natural systems can be minimized. To learn more about this, I received Danish Government Scholarship, and joined the MSc in Climate Change program at the University of Copenhagen, Denmark. Despite the lessons learnt about multiple aspects of climate change, impacts, adaptation, and mitigation, I became more curious about droughts and water related hazards, which I considered most important at a younger age. Just after graduating from University of Copenhagen, I joined the “Climate Change – Uncertainties, Thresholds and Coping Strategies” project funded by the Austrian Science Fund at WEGC and to study my PhD at University of Graz.

What are the most important human activities contributing to hydrological extreme events?

Hydrological extremes occur in both, pre-industrial and industrial periods. The types of contributions of human activity and their magnitude to changes in hydrological extremes depend on the type of hydrological extreme we are referring to, although it has become apparently clear that human activities that lead to emission of greenhouse gases (drivers of climate change) are most important. Human-induced climate change alters the spatiotemporal distribution of rain belts, and consequently precipitation. This creates deficit (droughts) in some regions and heavy precipitation in others. In addition, the warming effect of climate change, through increased evapotranspiration, exacerbates moisture deficit resulted from low precipitation. These influences of human activities alter the frequency, intensity, and duration of hydrological extremes. For example, if droughts used to occur once in 100 years during the pre-industrial time, emission of greenhouse gasses can change the frequency of droughts to once every 10 years in recent decades.

Another human contribution to changes in hydrological extremes is related to water management practices. If we take floods as an example, high irrigation frequency can lead to over saturation of soil and creates a precondition so that flooding occurs regardless of precipitation intensity. Our decisions on land use land cover, for example, urbanization influences infiltration capacity and show their effect in the rainfall-runoff processes, e.g., due to paving of streets.

Can we measure the contributions of human activities towards climatic or hydrological extreme events, and if so how?

Yes, it is possible to provide quantitative information on the contribution of human-induced climate change to changes in hydrological extremes. As mentioned before, the influence of human activity is changing the frequency, intensity, and duration of hydrological extremes. Nonetheless, these extremes can also occur without anthropogenic climate change. Disentangling the contributions of human-induced climate change and natural climate variability to changes in hydrological extremes is called Attribution Science.

Attribution science involves the detection of change in the variable (hydrological extreme) referred to. That is why it is commonly termed “Detection and Attribution”. Change detection is the first stage where it is assessed whether there is change or not by using observational data sets. This could, for example, be assessing change in the frequency of drought events. At this stage, emphasis is given to the presence of change with statistical confidence, regardless of what causes the change. Then, this is followed by disentangling whether the change (if there is any change detected) is due to unforced (internal climate variability) and forced (anthropogenic and natural) causes. This requires simulating the response of Earth’s climate system with and without human-induced causes of climate change. Natural causes of climate change are solar and volcanic forcings. Human-induced causes of climate change are greenhouse gas emissions and aerosols resulting from human activity.

Developments in climate modelling enable us to simulate the response of the climate system to climate change forcings under counterfactual (only with natural forcings) and factual (with both natural and human-induced forcings) worlds. The difference between the two worlds is the absence and presence human-induced forcings. These two worlds can be considered as pre-industrial and industrial periods. In the pre-industrial period, only natural causes are responsible for changes in the climate system. In the industrial period, both natural and anthropogenic forcings are responsible for the change in the climate system. If there were no human-induced forcings in the present-day climate, the climate system would not change, which implies the frequency and intensity of extreme events would not be different from the counterfactual period. The counterfactual and factual world simulations enable us to assess the probability of class of extreme events under counterfactual (P0) and factual (P1) worlds, putting the details aside. The contribution of human-induced climate change and natural climate variability is quantified using the Fraction of Attributable Risk (FAR) and Risk Ratio (RR) metrices. These metrices are ratios of probabilities of a single (or class of) extreme event(s) under factual (P1) and counterfactual (P0) worlds. Risk Ratio is defined as a ratio of probability of a class of extreme events under factual worlds (P1) to the probability of the same class of extreme events under counterfactual (P0) world, (RR= P1/ P0 ). For example, a risk ratio of 10 implies that human-induced climate change has made the extreme event (s) 10 times more likely in the factual world.

You are currently developing your Ph.D. dissertation on the attribution of temperature and hydrological extreme events to human-induced climate change. Could you develop on the methodology and data sources you are using in your research? So far, what are the main insights and trends you got from this research? What are main implications of the results of this research?

The first component of my PhD focuses on assessing changes in temperature extremes and heat waves. The second component shows an attribution of changes detected in the first component. Currently, I am working on assessing changes in compound hydrological and soil moisture drought extreme events over Eastern Africa, following the unusually persistent 2020-2022 drought.

Robustly detecting and attributing changes in extreme events requires high spatial and temporal resolution data. However, getting such quality data is still challenging in many parts of the world, to some extent this is not because the data is unavailable but due to lack of willingness to share, or unfunctional data sharing infrastructure. Given the short lifespan of extreme events, the temporal scale of observational data sets is essential. Therefore, to make use of advantages of different data sets, I used multiple data sets from multiple data sources (observations, reanalysis data sets and climate model simulations). In addition to these, I am currently using multiple Earth Observation data sets and blended satellite products, for assessing changes in compound hydrological and soil moisture extreme events over Eastern Africa.

Although climate models reasonably simulate the mean state of the climate/hydrological variables, their performance vary with climate variables and aspects of change they simulate (intensity, frequency duration). Limitations of climate models, due to parameterization and representation of processes in simulating variables of interest contributes to uncertainties. Therefore, the method I used addresses these uncertainties in climate modelling (parameterization and structural uncertainties). Considering climate modelling uncertainty in attribution analysis enables to take uncertainties in patterns of response into account, in addition to uncertainties in simulating observed magnitude.

Despite difference in data sets and data sources the results show, temperature extremes and heat waves showed increasing trends over regions of Africa. The data sets showed difference in temporal evolution and trend strengths. The assessment of time of emergence indicated the time when those extreme events emerged and provides information for climate change adaptation. Due to low year-to-year variability, these extremes showed early emergence in tropical regions. In addition, assessing the time of emergence enhanced the robustness of changes detected. The results imply the need for accelerated climate action in adaptation and mitigation. Highly vulnerable communities will continue to suffer the most if timely climate change adaptation is not in place.

Which role do satellite-based technology and data play in monitoring and mitigating the impacts of climate change on the water domain?

Satellite-based technologies and data play vital role in different stages of climate change impact assessment from monitoring hazards and identifying affected areas to assessment of an impacts’ magnitude and resource allocation for recovery. Their application in monitoring hydrological hazards have evolved quickly and nowadays allow real-time monitoring. Real-time monitoring of hydrological hazards and other water-related variables (in the atmosphere, the ocean and on land) is possible and enables to reduce adverse impacts through enhancing the efficiency and effectiveness of early warning systems. Given billions of people are residing along coastal areas, satellite-based technologies are vital components to plan settlement areas. Furthermore, satellite-based technologies play a role in assessing suitability analysis for climate change adaptation projects by allowing for the prioritization of regions for adaptation interventions and efficient use of resources.
In addition to monitoring climate change, satellite-based technologies and data can contribute to reducing impacts of climate change in the water domain. They enable us to monitor emission of greenhouse gases and evaluating the success and failure of negative emission technologies in climate change mitigation.

What are the main climate/hydrological variables and parameters that can be observed from space with satisfactory accuracy and which ones rely on in-situ data?

Earth Observation provides spatially complete information with reasonable accuracy, although this is based on longer revisit times of satellites. The accuracy of Earth Observation data products varies with climate/hydrological variables (e.g. precipitation, sea level, etc.), methods of evaluation, availability of in-situ data for evaluation, terrain complexity, type of sensors (active and passive), spectral and radiometric resolutions of sensors, among others.
Observing some variables from space is more complex than others. While cloud-top temperature can be used to estimate precipitation, heterogeneous below-ground and surface geophysical factors (e.g., vegetation morphology, surface roughness and soil texture) enhance the complexity of estimating soil moisture. In addition, some satellites have shorter revisit periods, which allows more frequent observations of variables than others. Due to continuous improvements of the factors mentioned above, the accuracy of Earth Observation and its application is evolving quickly.
In-situ data sets are direct measurements. Availability of in-situ data sets allow reliable evaluation of remotely sensed data. In-situ and Earth observation data sets complement each other. Analysing a variable using both data sets enhance the robustness and completeness of results. It is this complementarity of in-situ and earth observation data sets that is a reason for the common practice to use both data sets in combination, as well as blended products.

What are key challenges in satellite remote sensing of climate change? How can they be overcome?

Climate change analysis requires data recorded over long period of time. Most climate variables have been observed by satellites for two to three decades. The short observation period had limited the data’s use in climate change analysis. Despite the short observation length, Earth Observation has been used in assessing observed changes of climate variables. Robustness of observed changes is assessed using Earth Observation data sets especially for regions where direct (in-situ) measurements are scare. The period of observing these variables has been increasing. In addition to the length of record, maintaining the continuity and consistency of instruments is important.
In addition, climate extremes such as floods, heavy precipitation, hurricanes, and wildfires have a short lifespan in the order of a few days to weeks. Depending on their revisit period, sensors may observe these extremes only once. Furthermore, higher spatial resolution of sensors would reveal more information about spatial variabilities.
Temporarily, a better accuracy of remote sensing data can be achieved by capitalizing on a combination of different instruments/sensors. However, it is necessary to improve sensors’ spatiotemporal and radiometric resolutions to be able to investigate more spatiotemporal details in assessing hazards, their impacts and for adaptation and mitigation efforts.

In your opinion, are space-based data being used to their full potential in support of the water-related sustainable development goals or is there place to improve? Which water-related SDG indicators could benefit from using space-based data that don't harness the potential so far?

I believe that there is room for improvement. There is large volume of space-based data that can substantially enhance the achievement of the sustainable development goals. Although space-based technology is used intensively in some regions, many regions in the world still lack a system that functions properly among institutions and stakeholders to integrate and intensively utilize the data sets for decision making. In some instances, technical skill and infrastructure, depending on the area of application, are limited. In addition, such systems intended to bring the technology into use are often either not established or do not properly function even though the willingness is there. I wonder if this can be considered as malfunctioning cooperation?

Given the huge amount of human and financial resources invested in data acquisition already, expanding the effective use of such data sets for decision-making to other regions can be achieved with relatively little resource investment.

Space-based data sets are playing substantial role in achieving the SDGs and improving the wellbeing of human and natural systems. SDGs and their indicators are inter-related, and the use of space-based data sets in one SDG (indicator) can benefit multiple SDGs (indicators). Integrated water resources management in SGD 6 improves production of commodities, and benefits, for example, SDG 1, SDG 2 and SDG 3. Both quantity and quality of fresh water is big challenge in various parts of the world. Some countries that can be considered as water-tower still suffer from irregularity in water availability and lack of safe drinking water. Specifically, among SDG 6 indicators, safely managed drinking water, water use efficiency, and integrated water resources management indicators can benefit more from space-based data sets.

Could you identify the unexploited potentialities of geospatial technologies in water management?

Usually, satellite missions and geospatial technologies are designed with a certain objective on mind and hence they are utilized for their intended use. In many regions of the world only limited potential of these technologies has been exploited. Their advantages could play a crucial role in accelerating economic growth, environmental and societal well-being there.

As a young professional, what do you feel is missing in the current scientific debate on climate-water-energy-food nexus?

Although the gap is already recognized, regions of knowledge production and regions of application of climate-water-energy-food nexus are not sufficiently harmonized yet. This can leave important factors behind and influence the overall success of the efforts. Furthermore, the nexus research so far is concentrated in limited geographical regions. Unless expanded to other regions where it can be applied, using lessons learned from other regions risks overlooking local circumstances.

What do you personally need to innovate and who do you think should get together to bring and spread innovation into the water sector?

Although I am at the early stage of my scientific career, and finalizing my PhD, I need to make substantial contributions in research areas that I value most, hydrological extremes, and water management. It is obvious that resources are not only unavailable and competitive but can also mismatch with personal interest and value. Therefore, I afraid I may be trapped by a market-driven career path that can largely influence my personal interest and flexibility, and consequently, limit my innovation capacity. I think availability of resources for what I value most (research on climate/hydrological extremes and sustainable water management) is crucial.
In addition, innovation and successful extension of its outputs require continuous development in leadership, communication, and strategic thinking skills, which I want to improve. Therefore, amalgamating access to resources and improving my skills will enable me to innovate and bring substantial contribution, using space-based and geospatial technologies, in the water sector.  

If you had three free wishes to be fulfilled by a Space Agency, what would they be?

  • Improve temporal scales of satellite observations so that more data for short-lifespan climate/hydrological extremes will be available
  • Improve spatial resolution of sensors so that more spatially detailed information can be obtained
  • Improve retrieval algorithms, for example, in remote sensing of water quality

What is your favourite aggregate state of water and why?

Liquid water: It’s the essential input for life.



Glossary from IPCC and NASEM

1” Attribution: attribution is defined as the process of evaluating the relative contributions of multiple causal factors to a change or event with an assessment of confidence.”

1” Climate model: a qualitative or quantitative representation of the climate system based on the physical, chemical and biological properties of its components, their interactions and feedback processes and accounting for some of its known properties.”

2 “Counterfactual world: From the perspective of attribution studies, counterfactual or counterfactual world refers to a hypothetical “control” world that has only been impacted by natural forcings and internal variability.”

1 “Detection: detection of change is defined as the process of demonstrating that climate or a system affected by climate has changed in some defined statistical sense, without providing a reason for that change. An identified change is detected in observations if its likelihood of occurrence by chance due to internal variability alone is determined to be small, for example, <10%.”

2 “Factual world: from the perspective of attribution studies, factual refers to the currently observed world as it exists in the context of climate change.”

2 “Fraction of attributable risk (FAR): The fraction of the likelihood of an event that is attributable to a specific causal factor.”

2 “Internal variability: is the technical term that is often used to describe the natural, unforced, chaotic variability that occurs continually in the climate system. It is a component of natural variability.”

2 “Model: A set of ideas; a physical representation or set of formulas that describe a process or system. In climate science, and in this report, the term usually refers to a set of equations describing the physical laws governing the behaviour of the atmosphere, ocean, sea ice, land surface, and other components of the Earth system, whose solutions simulate the time evolution of the system.”

1 “Parameterization: in climate models, this term refers to the technique of representing processes that cannot be explicitly resolved at the spatial or temporal resolution of the model (sub-grid scale processes) by relationships between model-resolved larger scale variables and the area- or time-averaged effect of such sub-grid scale processes.”

2 “Risk ratio: the ratio of probabilities under two different conditions or settings; in event attribution this is generally the ratio of the probability under anthropogenic forcing (the factual scenario) to that under the counterfactual scenario. While well established in epidemiology, the term is a misnomer because it is a ratio of probabilities and does not involve risk as formally defined to account for both probability and magnitude of impact.”

1 “Uncertainty: a state of incomplete knowledge that can result from a lack of information or from disagreement about what is known or even knowable. It may have many types of sources, from imprecision in the data to ambiguously defined concepts or terminology, incomplete understanding of critical processes, or uncertain projections of human behaviour.”

Reference for glossaries

  1. IPCC, 2021: Annex VII: Glossary [Matthews, J.B.R., V. Möller, R. van Diemen, J.S. Fuglestvedt, V. Masson-Delmotte, C.  Méndez, S. Semenov, A. Reisinger (eds.)]. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 2215–2256, doi:10.1017/9781009157896.022.
  2. National Academies of Sciences, Engineering, and Medicine. (2016). Attribution of extreme weather events in the context of climate change. National Academies Press.