Transboundary early-warning

We can only get to my community by using ice bridges to brings fuel and food to the community, so we need data to know levels of ice quality for safety during transportation of resources needed by the community.

The Athabasca glacier is our source of water and it is being reduced significantly because of climate change. We need to understand what is the data showing from the increases in temperature due to climate change, will the melting glacier increase more water loading in the Athabasca River, will that cause more flooding and at what point will we have no more glacier? What are the future predictions for the glacier as our source water? This information we will enable us to make a decision on whether we need to relocate or not. On the Peace River side, there are three hydroelectric projects, and we are talking to the companies to see if they can release water in the delta. But we need data to understand how much water they can release in the delta without causing flooding in the other areas. For example, how much water need released and in which season in order to have a healthy ecosystem downstream. Another set of data that will be useful is about landform stability. For example, tailings ponds are built to international dam standards, so if there are landform changes there could be consequences of tailings breach where the tailings will basically give out the dam will fail. We need digital topography modelling to understand what will happen in cases where tailing breach and how catastrophic that event will be.

Hydrometeorological disasters (HMDs) in the Hindu Kush Himalayan (HKH) area have led to multiple water-related issues that resulted from extreme rainfall, glacial melt, and changing river flows, all of which are made worse by climate change and land use changes. Accurate warnings of these disasters are difficult due to sparse gauging and rugged topography in the Garhwal Himalaya region, which increases the likelihood of disasters during the monsoon. The same region experiences water shortage and drought especially during non-monsoon periods. The use of wide coverage remote sensing data from the study region as well as from neighboring countries with access to space-based data can play a significant role in the monitoring and analysing of these challenges. This study applies spatiotemporal clustering and multi-criteria decision-making (MCDM) to map high-risk zones, which will allow policymakers to reinforce infrastructure providing disaster resilience. There is a need for a solution that uses multi-criteria decision making (MCDM) and spatiotemporal clustering to map areas in Uttarakhand, Himalaya, that are prone to disasters with the help of satellite-based data. To determine which tehsils (smaller administrative units) are vulnerable, it is suggested to examine more than 150 years of recorded disaster data with location and fatalities. Further vulnerable regions can be mapped using high-resolution satellite data (procured through Sentinel, Landsat, Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and Tropical Rainfall Measuring Mission (TRMM)) and analysed in the QGIS platform. This solution could use spatiotemporal clustering and MCDM to map high-risk zones, which will allow policymakers to reinforce infrastructure providing disaster resilience. Data of the Garhwal Himalayan region (India), which lies in the Hindu Kush Himalayan (HKH) region are needed. The topography of the HKH region is almost the same over eight countries, and all bear similar kinds of disasters and climate patterns. The Garhwal region occupies about 64 per cent of the area of the Uttarakhand state and is also the origin of the river Ganga.

Los Glaciares National Park is located southwest Argentina. It covers more than 700000 ha. and has many large glaciers which are responsible for feeding two huge lakes (Viedma and Argentino lake) with melting water. This meltwater crosses arid Patagonia and provides drinking water to the entire basin. There is poor information about how much ice and snow melts every year. Elevation data for mass balance calculations is provided by remote sensing technologies, however free access is scarce. Changes in elevation can only be measured between four free digital elevation models (DEM) of mid-resolution, and each one corresponds to a specific year. That means only three mass balance calculations can be done. The area is remote and vast, therefore we rely on remote sensing information.

Bangladesh is one of the most vulnerable countries to natural hazards and disasters. Among the natural hazards and disasters, flooding is one of the most frequent and severe due to the country's geographical location, dense river networks, and monsoon climate. Flood events displace populations and communities, damage infrastructure, and disrupt livelihoods. This challenge focuses on assessing social vulnerability to flooding by considering factors such as population density, poverty levels, access to resources, and adaptive capacity. By understanding vulnerability at a granular level, targeted interventions can be designed to enhance resilience.

Transboundary rivers such as Koshi, Indus, Ganges and Brahmaputra flow through multiple countries presenting challenges for flood management and early warning systems. In 2016, a glacial lake outburst flood originating in the Xizang Region of China resulted in huge damage to the 45 MW Upper Bhote Koshi Hydropower project. In 2008, floods in the Koshi river impacted more than 4 million people living in the downstream and killing more than 500. The absence of effective coordination and information sharing between countries sharing a transboundary river basin hinders the development and implementation of robust early warning systems. To minimize these impacts, a multi-faceted approach is essential, including enhancing early warning systems, improving infrastructure resilience, promoting sustainable land management, and implementing community-based disaster risk reduction strategies. In a data sparse region such as the Hindu Kush Himalayan (HKH) Earth observations from space are crucial to manage water resources for the benefit of people and the environment, to support in providing important forecasting services for better preparedness to prevent water-related disasters such as floods and droughts.

This challenge addresses the increasing occurrence of floods and landslides in Peru, particularly in Piura region and Catacaos city, caused by extreme weather events such as massive storms which are exacerbated by climate change or by "El Niño" and "La Niña" phenomena. The goal is to simulate flood scenarios using a combination of rainfall data, satellite imagery, and digital elevation models (DEMs). This developed model aims to predict how water will advance across the landscape, which areas are likely to be affected, and to estimate river level rises. The relevant decision-makers and stakeholders include the following: • Local and regional disaster risk management authorities. • Municipal planning offices. • Emergency response units and civil protection agencies. • NGOs working on climate resilience. • Community leaders in flood-prone areas. These decision-makers require real-time and predictive information on: • Areas likely to be inundated. • Timing and extent of floodwater advancement. • Population density in affected areas. • Infrastructure at risk (roads, hospitals, schools, etc.).