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Interview with Dr Khalid Mahmood, Assistant Professor at the University of the Punjab

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.

Interview with Sawaid Abbas, Assistant Professor at the Centre for Geographical Information, University of the Punjab, Lahore, Pakistan

Sawaid Abbas, Assistant Professor at the Centre for Geographical Information System, University of the Punjab, Lahore, Pakistan discussed his extensive work in addressing water-related challenges through the nexus between smart sensing and space technologies. His thematic focus spans water scarcity, food security, climate risks, and environmental monitoring with an emphasis on the Asia-Pacific region, including Pakistan and China. Key Sustainable Development Goals (SDGs) guiding his work include SDG2 (Zero Hunger), SDG13 (Climate Action), SDG15 (Life on Land), and SDG11 (Sustainable Cities and Communities).  Abbas's passion for water emerged during his early career at the World Wide Fund for Nature (WWF), where he was involved in Pakistan’s Wetland Program and witnessed the impact of water on associated ecosystems. This sparked his interest in understanding and managing water, forestry, and wildlife resources. He recently studied coastal ecosystems and their responses to climate and anthropogenic stressors in the Asia-Pacific region. The Living Indus – Investing in Ecological Restoration has become a new focus of interest for him, addressing sustainability challenges related to food security, river basin management, and efficient water use in alignment with the UN Decade of Ocean objectives.  Abbas shared his fascination with water, recognizing its complex and essential nature. He is captivated by its beauty in all forms and acknowledges its fundamental importance for life on Earth. This water connection further motivates his commitment to addressing global water challenges and promoting sustainable water use through innovative solutions.  Sawaid Abbas's work, stimulated by both professional commitment and personal fascination, stresses the critical role of space technologies, particularly earth observation, smart sensing nexus, and artificial intelligence in addressing water-related challenges. His research contributes to the development of innovative solutions for sustainable water use, environmental protection, and disaster response, aligning with global goals for a more resilient and water-secure future. 

Use of space-based technology to search for alternate sources of water in Tharparkar

In Pakistan’s southern province, Sindh, lies the world’s only fertile desert in the world. The Tharparkar Desert stretches till the southeastern parts of Punjab, joining the Cholistan Desert. Tharparkar District is the largest of 29 districts in Sindh. According to Integrated Water Resource Management Practices to Alleviate Poverty – A Model of Desert Development in Tharparkar, Pakistan, the Thar is, people of Thar, have their livelihoods dependent on 'rainfall and livestock rearing, which is critical to household food security.'

Hydro-diplomacy: The role of space-derived data in advancing water security

Water scarcity is one of the greatest threats faced by humanity of our time – in 2019, more than two billion people experience high water stress (UN-Water 2019) and approximately four billion people suffer from severe water scarcity for at least one month per year (Mekonnen and Hoekstra 2016). This worsening problem increases the risk of international conflict over water resources breaking out, given that there are over 270 transboundary river basins, and three-quarters of UN Member States share at least one river or lake basin with a neighbour (UN News 2017).

Interview with Sawaid Abbas, Assistant Professor at the Centre for Geographical Information, University of the Punjab, Lahore, Pakistan

Sawaid Abbas, Assistant Professor at the Centre for Geographical Information System, University of the Punjab, Lahore, Pakistan discussed his extensive work in addressing water-related challenges through the nexus between smart sensing and space technologies. His thematic focus spans water scarcity, food security, climate risks, and environmental monitoring with an emphasis on the Asia-Pacific region, including Pakistan and China. Key Sustainable Development Goals (SDGs) guiding his work include SDG2 (Zero Hunger), SDG13 (Climate Action), SDG15 (Life on Land), and SDG11 (Sustainable Cities and Communities).  Abbas's passion for water emerged during his early career at the World Wide Fund for Nature (WWF), where he was involved in Pakistan’s Wetland Program and witnessed the impact of water on associated ecosystems. This sparked his interest in understanding and managing water, forestry, and wildlife resources. He recently studied coastal ecosystems and their responses to climate and anthropogenic stressors in the Asia-Pacific region. The Living Indus – Investing in Ecological Restoration has become a new focus of interest for him, addressing sustainability challenges related to food security, river basin management, and efficient water use in alignment with the UN Decade of Ocean objectives.  Abbas shared his fascination with water, recognizing its complex and essential nature. He is captivated by its beauty in all forms and acknowledges its fundamental importance for life on Earth. This water connection further motivates his commitment to addressing global water challenges and promoting sustainable water use through innovative solutions.  Sawaid Abbas's work, stimulated by both professional commitment and personal fascination, stresses the critical role of space technologies, particularly earth observation, smart sensing nexus, and artificial intelligence in addressing water-related challenges. His research contributes to the development of innovative solutions for sustainable water use, environmental protection, and disaster response, aligning with global goals for a more resilient and water-secure future. 

Interview with Dr Khalid Mahmood, Assistant Professor at the University of the Punjab

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.

Interview with Hafsa, Aeman, National Researcher, International Water Management Institute (IWM), CGIAR

In the interview, Hafsa Aeman discusses her passion for integrating water resource management with space technologies. She uses remote sensing and AI to tackle challenges like seawater intrusion and coastal erosion, focusing on vulnerable coastal ecosystems. By leveraging satellite data, her work provides critical insights for sustainable water management, crucial for communities impacted by climate change. Ms Aeman highlights the significant role of space technology in water management, especially through remote sensing, which helps monitor precipitation, soil moisture, and groundwater levels. Her proudest achievement is a publication on seawater intrusion, recognized for its innovative use of AI and remote sensing, contributing to Pakistan’s Living Indus initiative. At the International Water Management Institute (IWMI), Hafsa’s research integrates AI and remote sensing to optimize water and irrigation management systems. She emphasizes the importance of addressing seawater intrusion, which poses threats to agriculture, ecosystems, and global food security. She also underscores the role of community engagement in sustainable water management through capacity-building workshops for farmers, promoting smarter irrigation practices. She advocates for leadership opportunities for young scientists and believes AI can revolutionize water management by enabling more accurate and efficient data analysis. Rain, symbolizing renewal and sustenance, is her favorite aggregate state of water.

Interview with Hafsa, Aeman, National Researcher, International Water Management Institute (IWM), CGIAR

In the interview, Hafsa Aeman discusses her passion for integrating water resource management with space technologies. She uses remote sensing and AI to tackle challenges like seawater intrusion and coastal erosion, focusing on vulnerable coastal ecosystems. By leveraging satellite data, her work provides critical insights for sustainable water management, crucial for communities impacted by climate change. Ms Aeman highlights the significant role of space technology in water management, especially through remote sensing, which helps monitor precipitation, soil moisture, and groundwater levels. Her proudest achievement is a publication on seawater intrusion, recognized for its innovative use of AI and remote sensing, contributing to Pakistan’s Living Indus initiative. At the International Water Management Institute (IWMI), Hafsa’s research integrates AI and remote sensing to optimize water and irrigation management systems. She emphasizes the importance of addressing seawater intrusion, which poses threats to agriculture, ecosystems, and global food security. She also underscores the role of community engagement in sustainable water management through capacity-building workshops for farmers, promoting smarter irrigation practices. She advocates for leadership opportunities for young scientists and believes AI can revolutionize water management by enabling more accurate and efficient data analysis. Rain, symbolizing renewal and sustenance, is her favorite aggregate state of water.

Capacity Building and Training Material

UN SPIDER Recommended Best Practice: Flood Hazard Assessment

Overview:

Flood hazard assessments are critical to identifying areas at risk and taking relevant preparation and mitigation measures to address the hazard. Using the HEC-RAS 2D model for preparing flood hazard maps, this Recommended Practice explains how to identify flood-prone areas and exposed infrastructure. Through its focus on the prevention and mitigation stages of the disaster management cycle, it complements the Recommended Practice on Flood Mapping and Damage Assessment with Sentinel-2, also developed by SUPARCO.

Event

Local Perspectives Case Studies

Integrated water resource management for sustainable agriculture: data-driven approaches to optimize crop patterns and water use in Pakistan

Image of dry landscape and solar panels in the distance
The environmental impacts of irrigated agriculture, which demands between 3,000 to 5,000 litres of water to produce just one kilogram of rice, are profound. Considering that 35 per cent of Pakistan’s freshwater is used for rice cultivation, often for crops destined for export, the need for a strategic realignment of water use priorities is evident. Current practices often treat water as an unlimited resource, a perspective that is unsustainable in the face of increasing domestic and international demands for food. The urgent need for systemic change is clear: only through the adoption of innovative technologies and the integration of up-to-date environmental data can Pakistan hope to meet the Zero Hunger (SDG 2) goal and achieve sustainable development. This project proposes using advanced remote sensing and land use modelling to effectively quantify agricultural land use practices and their changes over time. This integrated assessment framework is vital for building resilience against future climate extremes and for ensuring sustainable agricultural practices that align with societal and environmental priorities. By bridging the gap between current practices and agro-ecological suitability, this project aims to achieve a sustainable, food-secure future for Pakistan. We aim to interact with multiple stakeholders and agencies with diverse expertise to support data-driven approaches for sustainable water and crop management. Our goal is to build a network of professionals and researchers, facilitate knowledge and technology sharing, and contribute geospatial and analytical solutions to address the challenge.

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Project / Mission / Initiative / Community Portal

Strategic Strengthening of Flood Warning and Management Capacity of Pakistan

This project aims at developing a flood early warning system and building capacity in Pakistan to manage floods. The flood early warning system is primarily composed of Indus-IFAS, an interface mounted with two hydrological models IFAS (Integrated Flood Analysis System ) and RRI (Rainfall Runoff Innundation) models developed by International Centre for Hazard and Risk Management (ICHARM)  and the use of satellite based rainfall estimates "Global Satellite Mapping of Precipitation (GSMaP)" including a correction interface developed by the Japan Aerospace Exploration Agency (JAXA).

Stakeholder

Stimson Center

The Energy, Water, & Sustainability Program at the Stimson Center addresses important and timely policy issues and technical opportunities concerning energy, water, and sustainable development in the Global South from a multidisciplinary perspective.

Our work on transboundary river basins identifies pathways towards enhancing water security and optimizing tradeoffs between water, energy, and sustainable development options in the Mekong, Ganges-Brahmaputra, Indus, Aral Sea and Euphrates-Tigris river basins.

International Research Center of Big Data for Sustainable Development Goals

With the aim of addressing global challenges in the implementation of the 2030 Agenda for Sustainable Development, CBAS is committed to harnessing big data to support the Sustainable Development Goals (SDGs) by reducing technological barriers and filling in data gaps. Since its inauguration, CBAS identified key areas of interest and has made significant progress. 

International Water Management Institute

IWMI is a research-for-development (R4D) organization, with offices in 13 countries and a global network of scientists operating in more than 30 countries. For over three decades, our research results have led to changes in water management that have contributed to social and economic development. IWMI’s Vision reflected in its Strategy 2019-2023, is ‘a water-secure world’.

Space and Upper Atmosphere Research Commission

Realizing the importance of Space Science and Technology applications for sustainable national development, the Government of Pakistan established Pakistan Space and Upper Atmosphere Research Commission. Being the National Space Agency of Pakistan, SUPARCO is mandated to conduct research and development work in the field of space science, technology and its applications for peaceful purposes and socio-economic uplift of country. Its headquarter is located at Islamabad and technical facilities are spread over Karachi, Lahore, Multan, Quetta, Peshawar and Gilgit.

Global Water Partnership

The Global Water Partnership (GWP) is a global action network with over 3,000 Partner organisations in 179 countries. The network has 69 accredited Country Water Partnerships and 13 Regional Water Partnerships.

The network is open to all organisations involved in water resources management: developed and developing country government institutions, agencies of the United Nations, bi- and multi-lateral development banks, professional associations, research institutions, non-governmental organisations, and the private sector.

Person

Ali

Photo of Dr. Ali

Amjad Ali

Director general/Chief scientist Space and Upper Atmosphere Research Commission

Dr. Amjad Ali is serving as a senior scientist in the Pakistan Space and Upper Atmosphere Research Commission (SUPARCO) in the capacity of remote sensing of the environment. He obtained MSc and Doctoral degrees in Geo-Information Science and Earth Observation from the Faculty of Geo-Information Science and Earth Observation of the University of Twente, the Netherlands. His scientific contributions are reflected in international scientific journals and conferences in the fields of Remote Sensing and GIS.

Photo of Dr. Sawaid Abbas

Sawaid Abbas

Assistant Professor Smart Sensing for Climate and Development, GIS Centre, University of the Punjab Centre for Geographical Information, University of the Punjab

Sawaid is a spatial data scientist who works at the nexus of earth science, ecology and climate change through leveraging remote sensing, machine learning, and strong domain knowledge. His key work involves forest succession, drought, and rangelands which were accomplished through collaboration with institutions like WWF, ICIMOD, ICRAF, AFCD, and KFBG.

picture showing the person

Hafsa Aeman

Senior Research Officer - Geoinformatics, CGIAR International Water Management Institute

Hafsa Aeman is a Senior Research Officer at the International Water Management Institute (IWMI) in Pakistan. In this capacity, she is deeply involved in various projects, notably the Water Resource Accountability in Pakistan (WRAP) and NEXU Gains initiatives, both supported by the UK Foreign, Commonwealth, and Development Office (FCDO). These projects are geared towards augmenting capacity for water resource management at the provincial and district levels.

Space-based Solution

Addressed challenge(s)

Data-driven irrigation demand forecasting for rotational water management under the Warabandi system

Collaborating actors (stakeholders, professionals, young professionals or Indigenous voices)
Suggested solution

The proposed solution leverages Earth Observation (EO) and climate data to develop a machine learning-based irrigation demand forecasting system tailored for smallholder farmers operating under the Warabandi system. In regions where rotational irrigation governs water distribution, farmers often lack accurate tools to forecast short-term irrigation needs, leading to overuse or underuse of water, both of which impact productivity and efficiency. This space-based solution addresses the challenge by integrating EO-derived variables such as Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Land Surface Temperature (LST), and net radiation to estimate actual crop water requirements. The model enables data-driven decision-making for farmers and water managers, promoting more efficient and timely irrigation practices within fixed rotation systems.

Donor: Water Resource Accountability in Pakistan (WRAP), supported by the Foreign, Commonwealth & Development Office (FCDO)

Government Departments Involved: On-Farm Water Management (OFWM), Agriculture Department and Irrigation Department, Punjab

Community and Sectoral Engagement: Farmers’ associations and local water user groups, experts in water demand management from academia and the private sector

Inclusive Participation: Integrating voices from underrepresented communities, including women and Indigenous stakeholders.

Requirements

Data

  • Landsat time series 
  • PlanetScope time series 
  • Climate data: ERA5 (Copernicus), Flux Tower System (for validation)
  • Crop calendar and landcover data integrated with ML models

Software

Physical

  • Validation of land cover features, historical crop water use, and weather parameters through ground-based systems such as flux tower, along with crop information verified using crop calendars and spectral signatures collected from the field. 
  • The information regarding soil moisture will be verified through Soil moisture sensors.

Outline steps for a solution

  1. Data collection and sourcing (Completed)
  2. Workflow development and EO dataset integration (In progress)
  3. Data loader development and ML model setup (Completed)
  4. Training and initial testing of ML models (Completed)
  5. Automation of input data prediction via GEE/Colab (To do)
  6. Continuous irrigation forecast generation and output delivery (To do)

Steps to a solution

The solution workflow begins by collecting and preprocessing key spectral indices derived from historical satellite datasets. These include Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Land Surface Temperature (LST), Land Use Land Cover (LULC), and Net Radiation (Rn) data. This includes the:

  1. Dataset Preparation:
  • Extract temporal identifiers from each dataset.
  • Group datasets by matching dates across all indices and the target variable Evapotranspiration (ET).
  • Data pre-processing to clean datasets to remove NaN values and outliers for consistent temporal-spatial alignment.

 

  1. Model Development:
  • Features are stacked into multi-channel tensors for CNN models (e.g., 5 input channels for NDVI, SAVI, LST, Rn, and LULC).
  • For Random Forest models, the same data is flattened into tabular format with each pixel representing a row.

 

  1. Convolutional Neural Network (CNN):
  • A deep CNN model is trained with 5 layers including convolution (Conv2D) and Batch Normalization, activated using ReLU functions.
  • The final layer outputs a single channel of predicted Evapotranspiration (crop water requirement) for each crop pixel by pixel.

 

  1. Random Forest Ensemble:
  • A bootstrapped ensemble of Random Forest regressors is trained on flattened data.
  • Each model votes on ET prediction, and the final output is an average of these predictions

 

Results

Initial model testing achieved accurate crop water requirement estimation using CNN and ML. Results indicated high R² values (e.g., NDVI = 0.81, SAVI = 0.81, Net Radiation = 0.83, LST = 0.78). A 7-day irrigation forecast was generated for rice, providing actionable advisories. The model testing phase has been completed and is now in the process of being brought into a continuous irrigation advisory system to generate crop driven irrigation forecasts.

The irrigation demand forecasting model was validated across two cropping seasons with Kharif (June 2024) and Rabi (February 2024), using observed evapotranspiration (ET) from PySEBAL and flux tower data. During the Kharif season, CNN predictions closely aligned with observed ET for rice (CNN: 6.798 mm/day vs. PySEBAL: 6.370 mm/day; Flux Tower: 6.99 mm/day), while RF and XGB models showed moderate underestimations.

Similarly, in the Rabi season, wheat ET prediction by CNN (2.041 mm/day) was close to the flux tower estimate (1.86 mm/day), with XGB and RF providing slightly conservative outputs. Across both seasons, CNN consistently performed better in spatial alignment and magnitude, demonstrating its robustness in capturing seasonal irrigation demand variations across diverse crops like maize, potato, guava, and citrus orchards.

Infographic showing methodology for datasets, models development and the output
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Climate Zone (addressed by the solution)
Habitat (addressed by the solution)
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