The loss of mass from glaciers, ice caps, and polar ice sheets has accelerated over the last 3 decades as a result of climate change. This has made land ice the major contributor to sea level rise and the main cause of its acceleration. However, the evolution of the land-based cryosphere over the course of the 21st century and beyond adds considerable uncertainties to sea level rise projections, particularly if instability mechanisms are triggered, leading to rapid retreat of marine basins in Antarctica. Critical knowledge gaps pose challenges for predicting the land ice response to the evolution of climate and the resulting impact on sea level, from cryospheric process understanding, ice sheet and glacier modelling, and coupling with the atmosphere and ocean to bridging the gap with sea level and coastal-impact sciences. This special issue includes contributions related to the following:

• Earth observations that help to constrain glacier and ice sheet surface conditions, dynamics, or mass loss;
• theoretical or numerical modelling of cryospheric processes or coupling with the ocean and atmosphere;
• standalone or coupled projections of ice surface mass balance;
• Arctic and Antarctic ocean conditions promoting and/or responding to ice sheet loss;
• glacier or ice sheet dynamics and mass balance;
• approaches to analysing multi-model ensembles or computing global and regional sea level rise projections;
• coastal impacts of sea level rise and climate change, adaptation needs, and related climate services.

This special issue aims to (1) provide a high-quality collection of papers showcasing methodological advances in compound- and multi-risk analysis and management, (2) consolidate and foster learning across the compound-risk and (multi-hazard) multi-risk research fields, and (3) identify future research avenues.

Recent years have demonstrated the immense challenges faced by society as a result of the increasing complexity of disaster risk and due to climate change. Societies impacted by multiple natural hazards (either in sequence or at the same time) face different challenges than when impacted by a single hazard that occurs in isolation (AghaKouchak et al., 2020; Hillier and Dixon, 2020; Raymond et al., 2020a). The impacts of compound- and multi-hazard disasters are complex and may be driven by the consecutive nature of the (drivers of) hazards themselves (Hillier et al., 2020; Mora et al., 2018; Ridder et al., 2020; Zscheischler et al., 2018), the spatiotemporal dynamics in exposure and vulnerability caused by earlier events (de Ruiter et al., 2020; de Ruiter and Van Loon, 2022; Reichstein et al., 2021), or the influences of risk management on the dynamics of risk (Simpson et al., 2022). This makes managing compound- and multi-risk disasters especially complex, and several studies have noted that their management may require more comprehensive approaches than single-hazard disasters (Simpson et al., 2023; De Ruiter et al., 2021; Schippers, 2020).

In recent years, international agreements such as the Paris Agreement (2015) and the UN’s Sendai Framework for Disaster Risk Reduction (SFDRR) (UNDRR, 2015) have called upon the disaster risk science community to move away from siloed hazard thinking (i.e. assessing the risk from hazards one by one) and toward improving our understanding of these spatiotemporal complexities of disaster risk. Similarly, the latest series of Intergovernmental Panel on Climate Change (IPCC) reports recognizes the importance of accounting for multiple and complex risks. In a recent survey of members of the natural hazard research community, respondents noted that multi-hazards and resulting risks remain one of the core scientific challenges to be tackled (Sakic Trogrlic et al., 2022).

Subsequently, the past years have seen a rise in compound- and multi-risk (multi-hazard) studies that try to capture some of these complexities through advanced statistical methods (e.g. Zscheischler, 2017; Bevacqua et al., 2022; Couasnon et al., 2020), physically based models (Eilander et al., 2023; Couasnon et al., 2022), and multi-risk system analysis (e.g. Simpson et al., 2022; De Angeli et al., 2022; Van Westen and Greiving, 2017; Gill and Malamud, 2017; Ward et al., 2022). As a result, the compound- and multi-risk communities have developed largely in parallel with each other, and only in recent months have significant efforts been made to bring these two communities together, for example, as demonstrated by the American Geophysical Union (AGU) 2022 session focusing specifically on breaking silos between the two communities.

However, there is some interesting methodological and conceptual overlap between these communities and thus strong potential for catalyzing learning and innovation for (advancing) risk studies. The call from the international community has resulted in a proliferation of innovative methodological approaches across different disciplines, offering a vast array of possible options for multi- and systemic-risk reduction in practice. The importance of this topic is also apparent in recently funded research and networking projects including Damocles, The HuT, MIRACA, MYRIAD-EU, MEDiate, PARATUS, RECEIPT, CLIMAAX, Tomorrow’s Cities, Risk KAN, and NOAA’s Climate Adaptation Partnerships (formerly RISA), among others.

As early career researchers from both fields, we have contributed to shaping these two communities, and we perceive the need to bring them together to assess solutions for the future. However, despite these advances, there is still no single collection of high-quality scientific research papers focusing on methodological innovations for the analysis and management of both compound and multiple risks.

References:

AghaKouchak, A., Chiang, F., Huning, L. S., Love, C. A., Mallakpour, I., Mazdiyasni, O., Moftakhari, H., Papalexiou, S. M., Ragno, E., and Sadegh, M.: Climate extremes and compound hazards in a warming world. Annu. Rev. Earth Pl. Sc, 48, 519-548, https://doi.org/10.1146/annurev-earth-071719-055228, 2020.

Bevacqua, E., De Michele, C., Manning, C., Couasnon, A., Ribeiro, A. F., Ramos, A. M., Vignotto, E., Bastos, A., Blesić, S., Durante, F., Hillier, J., Oliveira, S. C., Pinto J. G., Ragno, E., Rivoire, P., Saunders, K., Van der Wiel, K., Wu, W., Zhang, T., and Zscheischler, J.: Guidelines for studying diverse types of compound weather and climate events, Earth's Future, 9, e2021EF002340, https://doi.org/10.1029/2021EF002340, 2021.

Couasnon, A., Eilander, D., Muis, S., Veldkamp, T. I. E., Haigh, I. D., Wahl, T., Winsemius, H. C., and Ward, P. J.: Measuring compound flood potential from river discharge and storm surge extremes at the global scale, Nat. Hazards Earth Syst. Sci., 20, 489-504, https://doi.org/10.5194/nhess-20-489-2020, 2020.

Couasnon, A., Scussolini, P., Tran, T. V. T., Eilander, D., Muis, S., Wang, H., Nguyen, H. Q. and Winsemius, H. C., and Ward, P. J.: A flood risk framework capturing the seasonality of and dependence between rainfall and sea levels—An application to Ho Chi Minh City, Vietnam, Water Resour. Res., 58, e2021WR030002, https://doi.org/10.1029/2021WR030002, 2022.

De Angeli, S., Malamud, B. D., Rossi, L., Taylor, F. E., Trasforini, E., and Rudari, R.: A multi-hazard framework for spatial-temporal impact analysis, Int. J. Disast. Risk Re., 73, 102829, https://doi.org/10.1016/j.ijdrr.2022.102829, 2022

de Ruiter, M. C. and Van Loon, A. F.: The challenges of dynamic vulnerability and how to assess it, IScience, 25, https://doi.org/10.1016/j.isci.2022.104720, 2022.

de Ruiter, M. C., Couasnon, A., van den Homberg, M. J., Daniell, J. E., Gill, J. C., and Ward, P. J.: Why we can no longer ignore consecutive disasters, Earth's Future, 8, e2019EF001425, https://doi.org/10.1029/2019EF001425, 2020.

de Ruiter, M. C., de Bruijn, J. A., Englhardt, J., Daniell, J. E., de Moel, H., and Ward, P. J.: The asynergies of structural disaster risk reduction measures: Comparing floods and earthquakes, Earth's Future, 9, e2020EF001531, https://doi.org/10.1029/2020EF001531, 2021.

Eilander, D., Couasnon, A., Leijnse, T., Ikeuchi, H., Yamazaki, D., Muis, S., Dullaart, J., Haag, A., Winsemius, H. C., and Ward, P. J.: A globally applicable framework for compound flood hazard modeling, Nat. Hazards Earth Syst. Sci., 23, 823-846, https://doi.org/10.5194/nhess-23-823-2023, 2023.

Gill, J. C. and Malamud, B. D.: Hazard interactions and interaction networks (cascades) within multi-hazard methodologies, Earth Syst. Dynam., 7, 659-679, https://doi.org/10.5194/esd-7-659-2016, 2016.

Hillier, J. K. and Dixon, R. S.: Seasonal impact-based mapping of compound hazards, Environ. Res. Lett., 15, 114013, https://doi.org/10.1088/1748-9326/abbc3d, 2020.

Mora, C., Spirandelli, D., Franklin, E. C., Lynham, J., Kantar, M. B., Miles, W., Smith, C. Z., Freel, K., Moy, J., Louis, L. V., Barba, E. W., Bettinger, K., Frazier, A. G., Colburn IX, J. F., Hanasaki, N., Hawkins, E., Hirabayashi, Y., Knorr, W., Little, C. M., Emanuel, K., Sheffield, J., Patz, J. A., and Hunter, C. L.: Broad threat to humanity from cumulative climate hazards intensified by greenhouse gas emissions, Nat. Clim. Change, 8, 1062-1071, https://doi.org/10.1038/s41558-018-0315-6, 2018.

Raymond, C., Horton, R. M., Zscheischler, J., Martius, O., AghaKouchak, A., Balch, J., Bowen, S. G., Camargo, S. J., Hess, J., Kornhuber, K., Oppenheimer, M., Ruane, A. C., Wahl, T., and White, K.: Understanding and managing connected extreme events, Nat. Clim. Change, 10, 611-621, https://doi.org/10.1038/s41558-020-0790-4, 2020.

Reichstein, M., Riede, F., and Frank, D.: More floods, fires and cyclones—plan for domino effects on sustainability goals, Nature, 592, 347-349, https://doi.org/10.1038/d41586-021-00927-x, 2021.

Ridder, N. N., Pitman, A. J., Westra, S., Ukkola, A., Do, H. X., Bador, M., Hirsch, A. L., Evans, J. P., Di Luca, A., and Zscheischler, J.: Global hotspots for the occurrence of compound events, Nat. Commun., 11, 5956, https://doi.org/10.1038/s41467-020-19639-3, 2020.

Šakić Trogrlić, R., Donovan, A., and Malamud, B. D.: Invited perspectives: Views of 350 natural hazard community members on key challenges in natural hazards research and the Sustainable Development Goals, Nat. Hazards Earth Syst. Sci., 22, 2771-2790, https://doi.org/10.5194/nhess-22-2771-2022, 2022.

Schipper, E. L. F.: Maladaptation: when adaptation to climate change goes very wrong, One Earth, 3, 409-414, https://doi.org/10.1016/j.oneear.2020.09.014, 2020.

Simpson, N. P., Mach, K. J., Constable, A., Hess, J., Hogarth, R., Howden, M., Lawrence, J., Lempert, R. J., Muccione, V., Mackey, B., New, M. G., O’Neill, B., Otoo, F., Pörtner, H.-O., Reisinger, A., Roberts, D., Schmidt, D. N., Seneviratne, S., Strongin, S., Van Aalst, M., Totin, E., and Trisos, C. H.: A framework for complex climate change risk assessment, One Earth, 4, 489-501, https://doi.org/10.1016/j.oneear.2021.03.005, 2021.

Simpson, N. P., Williams, P. A., Mach, K. J., Berrang-Ford, L., Biesbroek, R., Haasnoot, M., Segnon, A. C., Campbell, D., Musah-Surugu, J. I., Joe, E. T., Nunbogu, A. M., Sabour, S., Meyer, A. L. S., Andrews, T. M., Singh, C., Siders, A. R., Lawrence, J., Van Aalst, M., and Trisos, C. H.: Adaptation to compound climate risks: A systematic global stocktake, IScience, 26, https://doi.org/10.2139/ssrn.4205750, 2023.

UNDRR: Sendai framework for disaster risk reduction 2015–2030, United Nations Office for Disaster Risk Reduction, Geneva, Switzerland, https://doi.org/10.1163/2210-7975_hrd-9813-2015016, 2015.

van Westen, C. J. and Greiving, S.: Multi-hazard risk assessment and decision making, Environmental Hazards Methodologies for Risk Assessment and Management, 31, https://doi.org/10.2166/9781780407135_0031, 2017.

Ward, P. J., Daniell, J., Duncan, M., Dunne, A., Hananel, C., Hochrainer-Stigler, S., Tijssen, A., Torresan, S., Ciurean, R., Gill, J. C., Sillmann, J., Couasnon, A., Koks, E., Padrón-Fumero, N., Tatman, S., Tronstad Lund, M., Adesiyun, A., Aerts, J. C. J. H., Alabaster, A., Bulder, B., Campillo Torres, C., Critto, A., Hernández-Martín, R., Machado, M., Mysiak, J., Orth, R., Palomino Antolín, I., Petrescu, E.-C., Reichstein, M., Tiggeloven, T., Van Loon, A. F., Vuong Pham, H., and de Ruiter, M. C.: Invited perspectives: A research agenda towards disaster risk management pathways in multi-(hazard-)risk assessment, Nat. Hazards Earth Syst. Sci., 22, 1487-1497, https://doi.org/10.5194/nhess-22-1487-2022, 2022.

Zscheischler, J., Westra, S., van den Hurk, B. J. J. M., Seneviratne, S. I., Ward, P. J., Pitman, A., AghaKouchak, A., Bresch, D. N., Leonard, M., Wahl, T., and Zhang, X.: Future climate risk from compound events, Nat. Clim. Change, 8, 469477, https://doi.org/10.1038/s41558-018-0156-3, 2018.

This special issue gathers already-published and future papers that describe and/or apply the global water resources and use model WaterGAP.

WaterGAP (www.watergap.de) is a global freshwater model that calculates human water use as well as water flows and storage on all continents (except Antarctica), taking into account the human influence on the natural freshwater system such as climate change, water abstractions, and dams. As one of the pioneers in the field of global hydrological modelling, it supports our understanding of the global freshwater system since 1996 for historical periods and the future. The model is continuously being improved to answer scientific questions driven by societal demands. WaterGAP is applied to assess water scarcity, droughts, and floods and to quantify the human impact on, for example, groundwater, wetlands, streamflow, and sea-level rise.

Landslide inventory maps (LIMs) are a basic tool for spatially representing landslides, forming a cornerstone for subsequent analyses in landslide research. Traditional methods of landslide mapping have historically relied on heuristic interpretation, resulting in varied accuracy, coverage, and timeliness. Their reliability is influenced by mapping errors arising from diverse techniques and base data. Recent research emphasizes geographic accuracy, thematic accuracy, and completeness/statistical representativeness as key factors defining the quality of LIMs.

The classification of susceptibility adds to the complexity of mapping efforts. Conventional methods often struggle with differences between the types of landslides due to variations in morphological and environmental factors. The integration of machine learning (ML) has revolutionized landslide mapping and modelling. ML's capacity to extract critical patterns from heterogeneous data sources enables precise classification of landslides, addressing challenges faced by conventional methods. Additionally, ML techniques offer a comprehensive view of the landscape and its dynamic changes and a comprehensive solution for assessing and mitigating landslide hazards by addressing challenges related to threshold determination, classification accuracy, and uncertainty evaluation.

We invite contributions addressing the following:

• metrics for evaluating mapping accuracy, errors, and uncertainty;
• statistical modelling of mapping errors and ML-based classification;
• quality assessment methods for landslide inventory maps;
• the impact of error propagation on susceptibility models, hazard assessment, and risk evaluation;
• model inter-comparisons;
• relating LIM quality to use limitations and decision-making at different land-management levels.

Landslide inventory maps (LIMs) are a basic tool for spatially representing landslides, forming a cornerstone for subsequent analyses in landslide research. Traditional methods of landslide mapping have historically relied on heuristic interpretation, resulting in varied accuracy, coverage, and timeliness. Their reliability is influenced by mapping errors arising from diverse techniques and base data. Recent research emphasizes geographic accuracy, thematic accuracy, and completeness/statistical representativeness as key factors defining the quality of LIMs.

The classification of susceptibility adds to the complexity of mapping efforts. Conventional methods often struggle with differences between the types of landslides due to variations in morphological and environmental factors. The integration of machine learning (ML) has revolutionized landslide mapping and modelling. ML's capacity to extract critical patterns from heterogeneous data sources enables precise classification of landslides, addressing challenges faced by conventional methods. Additionally, ML techniques offer a comprehensive view of the landscape and its dynamic changes and a comprehensive solution for assessing and mitigating landslide hazards by addressing challenges related to threshold determination, classification accuracy, and uncertainty evaluation.

We invite contributions addressing the following:

• metrics for evaluating mapping accuracy, errors, and uncertainty;
• statistical modelling of mapping errors and ML-based classification;
• quality assessment methods for landslide inventory maps;
• the impact of error propagation on susceptibility models, hazard assessment, and risk evaluation;
• model inter-comparisons;
• relating LIM quality to use limitations and decision-making at different land-management levels.

This special issue gathers already-published and future papers that describe and/or apply the global water resources and use model WaterGAP.

WaterGAP (www.watergap.de) is a global freshwater model that calculates human water use as well as water flows and storage on all continents (except Antarctica), taking into account the human influence on the natural freshwater system such as climate change, water abstractions, and dams. As one of the pioneers in the field of global hydrological modelling, it supports our understanding of the global freshwater system since 1996 for historical periods and the future. The model is continuously being improved to answer scientific questions driven by societal demands. WaterGAP is applied to assess water scarcity, droughts, and floods and to quantify the human impact on, for example, groundwater, wetlands, streamflow, and sea-level rise.

This special issue aims to (1) provide a high-quality collection of papers showcasing methodological advances in compound- and multi-risk analysis and management, (2) consolidate and foster learning across the compound-risk and (multi-hazard) multi-risk research fields, and (3) identify future research avenues.

Recent years have demonstrated the immense challenges faced by society as a result of the increasing complexity of disaster risk and due to climate change. Societies impacted by multiple natural hazards (either in sequence or at the same time) face different challenges than when impacted by a single hazard that occurs in isolation (AghaKouchak et al., 2020; Hillier and Dixon, 2020; Raymond et al., 2020a). The impacts of compound- and multi-hazard disasters are complex and may be driven by the consecutive nature of the (drivers of) hazards themselves (Hillier et al., 2020; Mora et al., 2018; Ridder et al., 2020; Zscheischler et al., 2018), the spatiotemporal dynamics in exposure and vulnerability caused by earlier events (de Ruiter et al., 2020; de Ruiter and Van Loon, 2022; Reichstein et al., 2021), or the influences of risk management on the dynamics of risk (Simpson et al., 2022). This makes managing compound- and multi-risk disasters especially complex, and several studies have noted that their management may require more comprehensive approaches than single-hazard disasters (Simpson et al., 2023; De Ruiter et al., 2021; Schippers, 2020).

In recent years, international agreements such as the Paris Agreement (2015) and the UN’s Sendai Framework for Disaster Risk Reduction (SFDRR) (UNDRR, 2015) have called upon the disaster risk science community to move away from siloed hazard thinking (i.e. assessing the risk from hazards one by one) and toward improving our understanding of these spatiotemporal complexities of disaster risk. Similarly, the latest series of Intergovernmental Panel on Climate Change (IPCC) reports recognizes the importance of accounting for multiple and complex risks. In a recent survey of members of the natural hazard research community, respondents noted that multi-hazards and resulting risks remain one of the core scientific challenges to be tackled (Sakic Trogrlic et al., 2022).

Subsequently, the past years have seen a rise in compound- and multi-risk (multi-hazard) studies that try to capture some of these complexities through advanced statistical methods (e.g. Zscheischler, 2017; Bevacqua et al., 2022; Couasnon et al., 2020), physically based models (Eilander et al., 2023; Couasnon et al., 2022), and multi-risk system analysis (e.g. Simpson et al., 2022; De Angeli et al., 2022; Van Westen and Greiving, 2017; Gill and Malamud, 2017; Ward et al., 2022). As a result, the compound- and multi-risk communities have developed largely in parallel with each other, and only in recent months have significant efforts been made to bring these two communities together, for example, as demonstrated by the American Geophysical Union (AGU) 2022 session focusing specifically on breaking silos between the two communities.

However, there is some interesting methodological and conceptual overlap between these communities and thus strong potential for catalyzing learning and innovation for (advancing) risk studies. The call from the international community has resulted in a proliferation of innovative methodological approaches across different disciplines, offering a vast array of possible options for multi- and systemic-risk reduction in practice. The importance of this topic is also apparent in recently funded research and networking projects including Damocles, The HuT, MIRACA, MYRIAD-EU, MEDiate, PARATUS, RECEIPT, CLIMAAX, Tomorrow’s Cities, Risk KAN, and NOAA’s Climate Adaptation Partnerships (formerly RISA), among others.

As early career researchers from both fields, we have contributed to shaping these two communities, and we perceive the need to bring them together to assess solutions for the future. However, despite these advances, there is still no single collection of high-quality scientific research papers focusing on methodological innovations for the analysis and management of both compound and multiple risks.

References:

AghaKouchak, A., Chiang, F., Huning, L. S., Love, C. A., Mallakpour, I., Mazdiyasni, O., Moftakhari, H., Papalexiou, S. M., Ragno, E., and Sadegh, M.: Climate extremes and compound hazards in a warming world. Annu. Rev. Earth Pl. Sc, 48, 519-548, https://doi.org/10.1146/annurev-earth-071719-055228, 2020.

Bevacqua, E., De Michele, C., Manning, C., Couasnon, A., Ribeiro, A. F., Ramos, A. M., Vignotto, E., Bastos, A., Blesić, S., Durante, F., Hillier, J., Oliveira, S. C., Pinto J. G., Ragno, E., Rivoire, P., Saunders, K., Van der Wiel, K., Wu, W., Zhang, T., and Zscheischler, J.: Guidelines for studying diverse types of compound weather and climate events, Earth's Future, 9, e2021EF002340, https://doi.org/10.1029/2021EF002340, 2021.

Couasnon, A., Eilander, D., Muis, S., Veldkamp, T. I. E., Haigh, I. D., Wahl, T., Winsemius, H. C., and Ward, P. J.: Measuring compound flood potential from river discharge and storm surge extremes at the global scale, Nat. Hazards Earth Syst. Sci., 20, 489-504, https://doi.org/10.5194/nhess-20-489-2020, 2020.

Couasnon, A., Scussolini, P., Tran, T. V. T., Eilander, D., Muis, S., Wang, H., Nguyen, H. Q. and Winsemius, H. C., and Ward, P. J.: A flood risk framework capturing the seasonality of and dependence between rainfall and sea levels—An application to Ho Chi Minh City, Vietnam, Water Resour. Res., 58, e2021WR030002, https://doi.org/10.1029/2021WR030002, 2022.

De Angeli, S., Malamud, B. D., Rossi, L., Taylor, F. E., Trasforini, E., and Rudari, R.: A multi-hazard framework for spatial-temporal impact analysis, Int. J. Disast. Risk Re., 73, 102829, https://doi.org/10.1016/j.ijdrr.2022.102829, 2022

de Ruiter, M. C. and Van Loon, A. F.: The challenges of dynamic vulnerability and how to assess it, IScience, 25, https://doi.org/10.1016/j.isci.2022.104720, 2022.

de Ruiter, M. C., Couasnon, A., van den Homberg, M. J., Daniell, J. E., Gill, J. C., and Ward, P. J.: Why we can no longer ignore consecutive disasters, Earth's Future, 8, e2019EF001425, https://doi.org/10.1029/2019EF001425, 2020.

de Ruiter, M. C., de Bruijn, J. A., Englhardt, J., Daniell, J. E., de Moel, H., and Ward, P. J.: The asynergies of structural disaster risk reduction measures: Comparing floods and earthquakes, Earth's Future, 9, e2020EF001531, https://doi.org/10.1029/2020EF001531, 2021.

Eilander, D., Couasnon, A., Leijnse, T., Ikeuchi, H., Yamazaki, D., Muis, S., Dullaart, J., Haag, A., Winsemius, H. C., and Ward, P. J.: A globally applicable framework for compound flood hazard modeling, Nat. Hazards Earth Syst. Sci., 23, 823-846, https://doi.org/10.5194/nhess-23-823-2023, 2023.

Gill, J. C. and Malamud, B. D.: Hazard interactions and interaction networks (cascades) within multi-hazard methodologies, Earth Syst. Dynam., 7, 659-679, https://doi.org/10.5194/esd-7-659-2016, 2016.

Hillier, J. K. and Dixon, R. S.: Seasonal impact-based mapping of compound hazards, Environ. Res. Lett., 15, 114013, https://doi.org/10.1088/1748-9326/abbc3d, 2020.

Mora, C., Spirandelli, D., Franklin, E. C., Lynham, J., Kantar, M. B., Miles, W., Smith, C. Z., Freel, K., Moy, J., Louis, L. V., Barba, E. W., Bettinger, K., Frazier, A. G., Colburn IX, J. F., Hanasaki, N., Hawkins, E., Hirabayashi, Y., Knorr, W., Little, C. M., Emanuel, K., Sheffield, J., Patz, J. A., and Hunter, C. L.: Broad threat to humanity from cumulative climate hazards intensified by greenhouse gas emissions, Nat. Clim. Change, 8, 1062-1071, https://doi.org/10.1038/s41558-018-0315-6, 2018.

Raymond, C., Horton, R. M., Zscheischler, J., Martius, O., AghaKouchak, A., Balch, J., Bowen, S. G., Camargo, S. J., Hess, J., Kornhuber, K., Oppenheimer, M., Ruane, A. C., Wahl, T., and White, K.: Understanding and managing connected extreme events, Nat. Clim. Change, 10, 611-621, https://doi.org/10.1038/s41558-020-0790-4, 2020.

Reichstein, M., Riede, F., and Frank, D.: More floods, fires and cyclones—plan for domino effects on sustainability goals, Nature, 592, 347-349, https://doi.org/10.1038/d41586-021-00927-x, 2021.

Ridder, N. N., Pitman, A. J., Westra, S., Ukkola, A., Do, H. X., Bador, M., Hirsch, A. L., Evans, J. P., Di Luca, A., and Zscheischler, J.: Global hotspots for the occurrence of compound events, Nat. Commun., 11, 5956, https://doi.org/10.1038/s41467-020-19639-3, 2020.

Šakić Trogrlić, R., Donovan, A., and Malamud, B. D.: Invited perspectives: Views of 350 natural hazard community members on key challenges in natural hazards research and the Sustainable Development Goals, Nat. Hazards Earth Syst. Sci., 22, 2771-2790, https://doi.org/10.5194/nhess-22-2771-2022, 2022.

Schipper, E. L. F.: Maladaptation: when adaptation to climate change goes very wrong, One Earth, 3, 409-414, https://doi.org/10.1016/j.oneear.2020.09.014, 2020.

Simpson, N. P., Mach, K. J., Constable, A., Hess, J., Hogarth, R., Howden, M., Lawrence, J., Lempert, R. J., Muccione, V., Mackey, B., New, M. G., O’Neill, B., Otoo, F., Pörtner, H.-O., Reisinger, A., Roberts, D., Schmidt, D. N., Seneviratne, S., Strongin, S., Van Aalst, M., Totin, E., and Trisos, C. H.: A framework for complex climate change risk assessment, One Earth, 4, 489-501, https://doi.org/10.1016/j.oneear.2021.03.005, 2021.

Simpson, N. P., Williams, P. A., Mach, K. J., Berrang-Ford, L., Biesbroek, R., Haasnoot, M., Segnon, A. C., Campbell, D., Musah-Surugu, J. I., Joe, E. T., Nunbogu, A. M., Sabour, S., Meyer, A. L. S., Andrews, T. M., Singh, C., Siders, A. R., Lawrence, J., Van Aalst, M., and Trisos, C. H.: Adaptation to compound climate risks: A systematic global stocktake, IScience, 26, https://doi.org/10.2139/ssrn.4205750, 2023.

UNDRR: Sendai framework for disaster risk reduction 2015–2030, United Nations Office for Disaster Risk Reduction, Geneva, Switzerland, https://doi.org/10.1163/2210-7975_hrd-9813-2015016, 2015.

van Westen, C. J. and Greiving, S.: Multi-hazard risk assessment and decision making, Environmental Hazards Methodologies for Risk Assessment and Management, 31, https://doi.org/10.2166/9781780407135_0031, 2017.

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The loss of mass from glaciers, ice caps, and polar ice sheets has accelerated over the last 3 decades as a result of climate change. This has made land ice the major contributor to sea level rise and the main cause of its acceleration. However, the evolution of the land-based cryosphere over the course of the 21st century and beyond adds considerable uncertainties to sea level rise projections, particularly if instability mechanisms are triggered, leading to rapid retreat of marine basins in Antarctica. Critical knowledge gaps pose challenges for predicting the land ice response to the evolution of climate and the resulting impact on sea level, from cryospheric process understanding, ice sheet and glacier modelling, and coupling with the atmosphere and ocean to bridging the gap with sea level and coastal-impact sciences. This special issue includes contributions related to the following:

• Earth observations that help to constrain glacier and ice sheet surface conditions, dynamics, or mass loss;
• theoretical or numerical modelling of cryospheric processes or coupling with the ocean and atmosphere;
• standalone or coupled projections of ice surface mass balance;
• Arctic and Antarctic ocean conditions promoting and/or responding to ice sheet loss;
• glacier or ice sheet dynamics and mass balance;
• approaches to analysing multi-model ensembles or computing global and regional sea level rise projections;
• coastal impacts of sea level rise and climate change, adaptation needs, and related climate services.