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 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.

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.