Landslides are one of the most damaging disasters and have killed tens of thousands of people over the 21st century. Slow-moving landslides (i.e., those with surface velocities on the order of 10-2-101 m a-1) can be highly disruptive but are often overlooked in hazard inventories due to their subtle surface signatures and slow movement. Here, we discuss an approach to automatically map slow-moving landslides using feature tracking of freely- and globally-available Sentinel-2 optical satellite imagery.We evaluate this method through case studies from different environments in the USA, Chile, Italy, and Nepal. Our workflow identifies both known landslides and previously unknown slow-moving landslides in these case studies across very different geographical environments. In particular, in a test case on the well-documented Slumgullion earthflow, our workflow successfully delineates the active portion of the earthflow with velocity magnitudes consistent with field measurements. In another test case on the margin of the Southern Patagonian Icefield, Chile, we identified a very large (>6 km2) composite landslide in the eastern lateral moraine of Glacier Occidental, part of which catastrophically collapsed onto the glacier in early 2023. Finally, we tested our tool to the Ponzano landslide in central Italy which failed catastrophically in 2017.We are able to detect slow-moving landslides in complex environments using 10-m resolution globally available satellite imagery, all without any manual intervention. Taken together, this means that our workflow can be applied to any region on Earth, regardless of the availability of prior information. We leverage this workflow to conduct a preliminary national-scale survey of slow-moving landslides in Nepal, identifying over 10,000 deforming hillslopes across the country, many of which are populated. Improved mapping of the spatial distribution and surface displacement rates of slow-moving landslides will improve our understanding of their role in the multi-hazard chain and can direct detailed investigations into their dynamics.Figure: Large slow-moving landslide complex in the lateral moraine of Glaciar Oriental, Chilean Patagonia detected using our workflow.

This study introduces a new approach to multi-hazard risk assessment, leveraging hypergraph theory to model the interconnected risks posed by cascading natural hazards. Traditional single-hazard risk models fail to account for the complex interrelationships and compounding effects of multiple simultaneous or sequential hazards. By conceptualising risks within a hypergraph framework, our model overcomes these limitations, enabling efficient simulation of multi-hazard interactions and their impacts on infrastructure. We apply this model to the 2015 Mw 7.8 Gorkha earthquake in Nepal as a case study, demonstrating its ability to simulate the primary and secondary effects of the earthquake on buildings and roads across the whole earthquake-affected area. The model predicts the overall pattern of earthquake-induced building damage and landslide impacts, albeit with a tendency towards over-prediction. Our findings underscore the potential of the hypergraph approach for multi-hazard risk assessment, offering advances in rapid computation and scenario exploration for cascading geo-hazards. This approach could provide valuable insights for disaster risk reduction and humanitarian contingency planning, where the anticipation of large-scale trends is often more important than the prediction of detailed impacts.

Mapping exposure to landslides is necessary to mitigate risk and increase resilience. Exposure maps can be constructed from building databases, akin to seismic risk assessments, but there has been little investigation of the predictive relationship between building damage from landslides and risk to human life. Our study investigates this relationship globally and in Nepal (47 213 and 5664 landslides, respectively). While a correlation exists for nationwide totals (R2 = 0.75), it is negligible for individual events (R2 = 0.025). It is important to not construct landslide exposure maps from building datasets alone, else building damage may be inadvertently prioritised over human lives in disaster planning.

This study analyses the evolution of annual streamflow across Europe between 1962 and 2017, focusing on the connection of streamflow trends with climate dynamics and physiographic and land cover characteristics and changes. The spatial pattern of trends in streamflow shows strong agreement with the spatial patterns of climate trends, suggesting a climate control of these trends. However, analysing temporal evolution at the basin scale shows that the strong decrease in streamflow in southern Europe cannot be directly associated with climate dynamic. In fact, a negative trend related to non-climate factors clearly emerges. Rather, we show that forest growth and irrigated agriculture are the leading drivers of negative streamflow trends in southern Europe, particularly during dry years due to the greater proportion of green water consumption compared to blue water generation. These findings have significant implications, particularly in the context of widely embraced nature-based solutions for mitigating climate change, including carbon sequestration through forests and the planned expansion of irrigated agricultural lands in central and northern European countries as a response to rising crop water demands. These developments could potentially diminish water resources availability, leading to an increased occurrence and severity of low flow periods.

Empirically derived sensitivities of streamflow to precipitation are often assumed to be temporally unchanging. This assumption may be unrealistic because changes in climate and storage are known to alter this relationship. We present a non-stationary regional regression approach which is functionally similar to typical elasticity estimation approaches. This is applied to 2967 catchments in the United States to estimate variability in interannual, and trends in long-term, streamflow elasticity to precipitation over a 39-year period. We show that interannual elasticity is highly variable in water-limited catchments, indicating that these are especially sensitive to year-to-year climate variability, as compared to other regions. Interannual elasticity is more often correlated with the one-year lagged standardized precipitation index than with temperature or in-phase standardized precipitation index, suggesting that antecedent soil moisture, groundwater storage, and precipitation seasonality influence streamflow sensitivity. Finally, statistically significant long-term trends in elasticity exist in some regions, but trend magnitude is generally small. These findings suggest that an assumption of stationarity in long-term average elasticity may still be appropriate at the regional scale, however, year-to-year variation in streamflow responsiveness to precipitation is often substantial.

This research examines the changes in annual streamflow across Europe from 1962 to 2017, with a specific focus on the correlation between streamflow trends and climate dynamics, as well as physiographic and land cover characteristics. The spatial distribution of streamflow trends aligns closely with climate patterns, suggesting a climate-related influence. However, a detailed analysis at the basin scale reveals that the significant decline in streamflow in southern Europe cannot be solely attributed to climate dynamics. Instead, a discernible negative trend linked to non-climate factors becomes apparent. Specifically, our study indicates that the primary drivers of negative streamflow trends in southern Europe, especially during dry years, are forest growth and irrigated agriculture. This is attributed to the higher proportion of green water consumption compared to blue water generation. These findings hold substantial implications, particularly in the context of widely adopted nature-based solutions for addressing climate change. This includes concerns about carbon sequestration through forests and the planned expansion of irrigated agricultural lands in central and northern European countries to meet growing crop water demands. Such developments may potentially reduce the availability of water resources, leading to an increased frequency and severity of low flow periods.

Global warming is projected to result in changes in streamflow in West Africa with implications for frequent droughts and floods. This study investigates projected shifting in the timing, seasonality and magnitude of mean annual minimum (MAM) and annual maximum flows (AMF) in the Volta River basin (VRB) under climate change, using the method of circular statistics. River flow is simulated with the mesoscale hydrologic model (mHM), forced with bias-corrected climate projection datasets consisting of 43 regional and global climate model combinations under three representative concentration pathways (RCPs). Projected changes indicate that AMF increases between +1 % and +80 % across sub-basins, particularly in the near future (2021–2050), whereas MAM decreases between −19 % and −7 %, mainly from the late century (2071–2100), depending on RCPs. The date of occurrence of AMF is projected to change between −4 and +3 d, while MAM could shift between −4 and +14 d depending on scenarios over the 21st century. Annual high flows denote a strong seasonality with negligible future changes, whereas the seasonality of low flows has a higher variation, with a slight drop in the future.

Modelling risk systems, in which natural hazards and exposure elements are intricately intertwined, poses a significant challenge, especially over large spatial and temporal scales. To address this issue, this study introduces the use of hypergraphs as a modelling framework for dynamic multi-hazard systems. Hypergraphs have found applications across disciplines for effectively capturing complexities in various systems.The study demonstrates the suitability of hypergraphs to multihazard risk assessment through a case study of the 2015 Gorkha earthquake in Nepal and its subsequent coseismic landslides. The initial test case is followed by the generation of cascading scenarios initiated by thirty high-magnitude simulated earthquakes across Nepal and analysis of the subsequent cascading impacts arising from landsliding on buildings and roads. The modelling is being developed to provide scientific evidence to inform preparedness planning at a range of scales.Our results show that this approach is effective, offering several key advantages. First, the easy compatibility with spatial data enables a more accurate representation of real-world scenarios. Second, the proposed method is hazard-agnostic, allowing it to accommodate various types of natural hazards. Third, the high computational efficiency of the hypergraph-based model enables the use of large scenario ensembles. Finally, the capability to handle complex interactions between hazard processes and exposure elements streamlines the risk assessment process.We emphasise that the adoption of hypergraphs as a modelling framework has the potential to substantially enhance multi-hazard risk assessment in natural systems. By providing a comprehensive and flexible approach, this method offers a promising avenue for improving risk management strategies and bolstering preparedness measures to mitigate the impacts of environmental disasters.

Landslides are one of the most damaging natural hazards and have killed tens of thousands of people around the world over the past decade. Slow-moving landslides, with surface velocities on the order of 10-2-102 m a-1, can damage buildings and infrastructure and be precursors to catastrophic collapses. However, due to their slow rates of deformation and at times subtle geomorphic signatures, they are often overlooked in local and large-scale hazard inventories. Here, we present a remote-sensing workflow to automatically map slow-moving landslides using feature tracking of freely and globally available optical satellite imagery. We evaluate this proof-of-concept workflow through three case studies from different environments: the extensively instrumented Slumgullion landslide in the United States, an unstable lateral moraine in Chilean Patagonia and a high-relief landscape in central Nepal. This workflow is able to delineate known landslides and identify previously unknown areas of hillslope deformation, which we consider as candidate slow-moving landslides. Improved mapping of the spatial distribution, character and surface displacement rates of slow-moving landslides will improve our understanding of their role in the multi-hazard chain and their sensitivity to climatic changes and can direct future detailed localised investigations into their dynamics. image

Widespread afforestation has been proposed internationally to reduce atmospheric carbon dioxide; however, the specific hydrological consequences and benefits of such large-scale afforestation (e.g. natural flood management) are poorly understood. We use a high-resolution land surface model, the Joint UK Land Environment Simulator (JULES), with realistic potential afforestation scenarios to quantify possible hydrological change across Great Britain in both present and projected climate. We assess whether proposed afforestation produces significantly different regional responses across regions; whether hydrological fluxes, stores and events are significantly altered by afforestation relative to climate; and how future hydrological processes may be altered up to 2050. Additionally, this enables determination of the relative sensitivity of land surface process representation in JULES compared to climate changes. For these three aims we run simulations using (i) past climate with proposed land cover changes and known floods and drought events; (ii) past climate with independent changes in precipitation, temperature, and CO 2 ; and (iii) a potential future climate (2020-2050). We find the proposed scale of afforestation is unlikely to significantly alter regional hydrology; however, it can noticeably decrease low flows whilst not reducing high flows. The afforestation levels minimally impact hydrological processes compared to changes in precipitation, temperature, and CO 2 . Warming average temperatures ( + 3 degrees C) decreases streamflow, while rising precipitation (130 %) and CO 2 (600 ppm) increase streamflow. Changes in high flow are generated because of evaporative parameterizations, whereas low flows are controlled by runoff model parameterizations. In this study, land surface parameters within a land surface model do not substantially alter hydrological processes when compared to climate.

Streamflow elasticity is the ratio of the expected percentage change in streamflow to a 1 % change in precipitation – a simple approximation of how responsive a river is to precipitation. Typically, streamflow elasticity is estimated for average annual streamflow; however, we propose a new concept in which streamflow elasticity is estimated for multiple percentiles across the full distribution of streamflow. This “elasticity curve” can then be used to develop a more complete depiction of how streamflow responds to climate. Representing elasticity as a curve which reflects the range of responses across the distribution of streamflow within a given time period, instead of as a single-point estimate, provides a novel lens through which we can interpret hydrological behaviour. As an example, we calculate elasticity curves for 805 catchments in the United States and then cluster them according to their shape. This results in three distinct elasticity curve types which characterize the streamflow–precipitation relationship at annual and seasonal timescales. Through this, we demonstrate that elasticity estimated from the central summary of streamflow, e.g. the annual median, does not provide a complete picture of streamflow sensitivity. Further, we show that elasticity curve shape, i.e. the response of different flow percentiles relative to one another in one catchment, can be interpreted separately from between-catchment variation in the average magnitude of streamflow change associated with a 1 % change in precipitation. Finally, we find that available water storage is likely the key control which determines curve shape.

Abstract. Soil moisture is a key preconditioning factor influencing hillslope stability and the initiation of landslides. Direct measurements of soil moisture on a large scale are logistically complicated, expensive, and therefore sparse, resulting in large data gaps. In this study, we calibrate a numerical land surface model to improve our representation of post-monsoon soil drying in landslide-prone Nepal. We use a parameter perturbation experiment to identify optimal parameter sets at three field monitoring sites and evaluate the performance of those optimal parameter sets at each location. This process enables the calibration of key soil hydraulic parameters, in particular a higher hydraulic conductivity and a lower saturation moisture content relative to the default parameter setting. Runs with the calibrated model parameters provide a substantially more accurate (50 % or greater reduction in root mean squared error) soil moisture record than those with the default model parameters, even when calibrated from sites as much as 250 km apart. This process enables meaningful calculation of post-monsoon soil moisture decay at locations with no in situ monitoring, so as to inform a key component of landslide susceptibility mapping in Nepal and other regions where field measurements of soil moisture are limited.

The national-scale British Groundwater Model (BGWM) is implemented to simulate groundwater dynamics and budgets in Great Britain. Notwithstanding the challenges of integrating a very large amount of data, finding a trade-off between computational efficiency and realism, performing automatic calibration, and addressing multiple sources of structural and parameter uncertainty, a quantitative-qualitive evaluation approach showed that the BGWM provides a reasonably accurate digital representation of groundwater systems and processes at a national scale. In this work, the model was applied to understand the variability of budget components across multiple spatial and temporal scales. Comparisons showed regional differences linked to lithological and climatic factors, which in turn can be associated with more or less groundwater resilience to extreme climatic events. There is confidence that the current and future versions of the BGWM can become valuable tools for effective water resources management and adaptation strategies under future climatic and population changes.

Afforestation is of international interest for its positive benefits on carbon storage, ecology, and society, but its impacts on terrestrial and atmospheric processes are still poorly understood. This study presents the first use of a coupled land surface and convection permitting atmospheric model (CPM) to quantify hydrometeorological effects of afforestation across the United Kingdom, focusing on atmospheric processes often missing in hydrological models. Generating a scenario of 93 000 km ^2 (40%) additional woodland across the UK, the periods of 2042–2052 and 2062–2072 are analysed. Simulated afforestation alters seasonal and regional UK hydrometeorology. Countrywide runoff increases in all seasons (between 5.4–11 mm and 4.3–8.6% per season) due to elevated subsurface flows from greater soil moisture. Evaporation decreases in summer (−20.6 mm, −10%) but increases in winter (8.1 mm, 15%) whereas rainfall increases throughout all seasons (between 2.2–6.86 mm and 0.9%–2.2% per season). Greater winter rainfall is detected along Great Britain’s west coastline as increased surface roughness produces prolonged and heavier rainfall. In the summer, lower albedo increases potential evapotranspiration and reduces near surface specific humidity: water is locked in deeper soil layers as transpiration diminishes and the topsoil dries out. However, the magnitude of hydrometeorological change due to altered land cover is smaller than the uncertainty in local climate change projections. This work sets a precedent in illustrating the impacts of afforestation on hydrology using a high-resolution CPM and highlights the importance of coupled hydrometeorological processes when investigating land cover impacts on hydrological processes.

Abstract The physical river characteristics datasets described here provide spatially consistent information to support hydrological and inundation modelling at a 1 km × 1 km resolution across the United Kingdom, on the British National Grid. The datasets of physical river characteristics provide gridded datasets (outflow drainage directions, catchment areas, widths of bankfull rivers and depths of bankfull rivers) and a comma‐separated table of NRFA (National River Flow Archive) gauging station locations. These datasets are derived from a range of sources: outflow drainage directions, catchment area and bankfull river widths are derived from existing higher resolution datasets, whereas bankfull river depths were harder to source and instead are derived from sparse historical measurements. The new gridded datasets provide a derived value for each UK land cell on the British National Grid (BNG). The comma‐separated NRFA gauging station locations table provides the most appropriate locations of 1,499 river flow gauging stations on the 1 km resolution grids, together with the approximate error in the 1 km × 1 km gridded delineation of the upstream catchment area. This article explains how UK‐wide 1‐km grids of these variables were estimated, their format and how to use them. The data are available from the Environmental Information Data Centre (EIDC).

Precipitation is the most important driver of the hydrological cycle, but it is challenging to estimate it over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR, hereafter PERCCDR) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against 1825 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly, and daily timescales, MSWEP followed by ERA5 demonstrated a higher correlation (CC) and Kling–Gupta efficiency (KGE) than other datasets for more than 50 % of the stations, whilst ERA5 was the second-highest-performing dataset, and it showed the highest error and bias for about 20 % of the stations. PERCCDR is the least-well-performing dataset, with a bias of up to 99 % and a normalised root mean square error of up to 247 %. PERCCDR only show a higher KGE and CC than the other products for less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region, or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products.

<p>Interest continues to grow in the benefit of afforestation for carbon sequestration, yet the potential consequences of largescale afforestation on terrestrial hydrology are still unknown. Furthermore, it is unclear how large land cover changes may alter the surface-atmosphere hydrological connection, particularly as the climate and hydrological cycle evolve. In this study, we investigate how terrestrial and atmospheric hydrological processes in the UK may alter with increases in woodland across the UK, Ireland, and parts of Western Europe. We use a convection permitting physics-based regional climate model (HadREM3-RA11M) at 2.2 km resolution to simulate and identify the responses of afforestation on hydrology. Afforestation scenarios were generated using existing datasets from regional authorities and previous studies, with tree type determined according to pre-existing landcover. We compare modelled scenarios of widespread afforestation and existing land cover for a future period up to 2080 (with Representative Concentration Pathway 8.5) to assess the consequences of expanded woodland on terrestrial and atmospheric processes within the UK in a much warmer climate.</p> <p> </p> <p>Model results show clear and substantial changes in hydrology in both the atmosphere and land surface with woodland expansion. Soil moisture increases, leading to a commensurate boost to subsurface flows, which is particularly greater in summer months. Although runoff increases throughout the country, there is a proportionally greater increase in the drier south-eastern UK. Evaporation broadly decreases across the country, primarily driven by a reduction in soil evaporation, although this varies seasonally. Precipitation patterns also alter substantially, with increases in the west and slight increases and decreases in the east of the country. These results provide unique insights into how models that couple the land surface with the atmosphere can identify potentially far-reaching consequences of afforestation in temperate regions.</p>

Geomorphologists have long debated the relative importance of disturbance magnitude, duration and frequency in shaping landscapes; for channel change during single floods, it is thought that flood duration, rather than magnitude, matters most. However, studies of flood-induced channel change have often drawn upon small datasets. By using satellite data to track channel adjustment during floods, we can now query these classic hypotheses with large datasets, and we do so here by combining 7 years of Sentinel-2 images with daily flow data from laterally active rivers. Using Earth Engine, we apply automated algorithms to map river planforms and detect their lateral shifting, and we generate a large dataset to quantify channel change during ~1000 flood events in gauged rivers across New Zealand and the Americas. We draw upon this dataset to evaluate how characteristics of the flood hydrograph (including magnitude, duration, and integrated sediment transport) correlate with the degree of geomorphic change observed. Finally, we examine the potential of predictive models for geomorphic change during floods, and consider the variables that moderate this relation between flood character and geomorphic change.

Abstract. Land surface models such as JULES (the Joint UK Land Environment Simulator) are usually run on a regular, rectilinear grid, resulting in gridded outputs for variables such as soil moisture and water fluxes. Here we investigate a method of clustering grid cells with similar characteristics together in JULES. Clustering grid cells has the potential to reduce computational expense as well as providing an alternative to tiling approaches for capturing sub-grid heterogeneity. In this study, we cluster grid cells exclusively in the land surface part of modelling, i.e., separate from river routing. We compare gridded and clustered soil moisture outputs from JULES with measurements from the UK Centre for Ecology and Hydrology (UKCEH) COSMOS-UK network and show that the clustering approach can model soil moisture well while reducing computational expense. However, soil moisture results are dependent on the characteristics used to create the clusters. We investigate the effect of using clusters on predicted river flows, and compare routed JULES outputs with NRFA gauge data in the catchment. We show that less expensive JULES clustered outputs give similar river flow results to standard gridded outputs when routed at the grid resolution, and are able to match observed river flow better than gridded outputs when routed at higher resolution.