Description: Pokhara (ca. 850 m a.s.l.), Nepal's second-largest city, lies at the foot of the Higher Himalayas and has more than tripled its population in the past 3 decades. Construction materials are in high demand in rapidly expanding built-up areas, and several informal settlements cater to unregulated sand and gravel mining in the Pokhara Valley's main river, the Seti Khola. This river is fed by the Sabche glacier below Annapurna III (7555 m a.s.l.), some 35 km upstream of the city, and traverses one of the steepest topographic gradients in the Himalayas. In May 2012 a sudden flood caused 〉70 fatalities and intense damage along this river and rekindled concerns about flood risk management. We estimate the flow dynamics and inundation depths of flood scenarios using the hydrodynamic model HEC-RAS (Hydrologic Engineering Center’s River Analysis System). We simulate the potential impacts of peak discharges from 1000 to 10 000 m3 s−1 on land cover based on high-resolution Maxar satellite imagery and OpenStreetMap data (buildings and road network). We also trace the dynamics of two informal settlements near Kaseri and Yamdi with high potential flood impact from RapidEye, PlanetScope, and Google Earth imagery of the past 2 decades. Our hydrodynamic simulations highlight several sites of potential hydraulic ponding that would largely affect these informal settlements and sites of sand and gravel mining. These built-up areas grew between 3- and 20-fold, thus likely raising local flood exposure well beyond changes in flood hazard. Besides these drastic local changes, about 1 % of Pokhara's built-up urban area and essential rural road network is in the highest-hazard zones highlighted by our flood simulations. Our results stress the need to adapt early-warning strategies for locally differing hydrological and geomorphic conditions in this rapidly growing urban watershed.

Description: The disastrous consequences of the July 2021 flood in Western Europe have again demonstrated that current flood risk management is too strongly focused on design events. For instance, the 100-year flood is often used as the target safety level for flood defense, and events beyond such design scenarios are neglected. This disregard of ‘High-Impact / Low-Probability’ (HILP) events would not be advisable in a stationary system, but is even more inappropriate given the widespread climatic, environmental and socio-economic changes. We discuss methods to develop HILP flood scenarios, such as downward counterfactuals and perfect storms. Taking the Ahr catchment as an example, which experienced massive destruction and more than 130 fatalities during the 2021 flood, we demonstrate how a flood risk model chain can be used to develop HILP scenarios. The model chain, consisting of hydrological, hydrodynamic and damage models, is driven by a stochastic weather generator, which generates a very long time series of synthetic weather. In combination with modifying processes along the model chain, for instance, assuming failure of early warning, this setup allows (1) understanding how HILP events could evolve, and (2) generating a large range of flood scenarios beyond design events.

Description: In July of 2021, extreme precipitation over the Netherlands, Belgium, and western Germany triggered flash floods in many hilly catchments and caused an estimated loss of EUR 30 billion in Germany. This event dramatically demonstrated the extreme destructive power that this highly-energetic flood type exerts also on companies. Studies of company flood losses are impeded by larger heterogeneity and fewer available data in comparison with private households. We address the later limitation by compiling a new survey data set of flash flood losses, which we acquired by telephone interviews with 434 companies located in heavily affected parts of western Germany. We asses the importance of several potential loss predictors in regard to the response variable relative loss of company assets (i.e., building, equipment, goods and stocks) by applying an ensemble of machine learning algorithms, including LASSO regression and Conditional Inference Trees. Potential predictors are variables describing the flood impact, company characteristics, previous flood experience, and precautionary measures. Apart from new insights into loss processes during such unprecedented flash flood events, identifying the variables with highest predictive powers will also enable us to develop a new, probabilistic and multivariate model for company flash flood loss on the object-level.

Description: Simulating the West African monsoon (WAM) system using numerical weather and climate models suffers from large uncertainties, which are difficult to disentangle due to highly non-linear interactions between different components of the WAM. We propose a new approach to this problem by emulating a full-blown numerical model, the ICON model of the German Weather Service, through statistical surrogate models. The ICON model was run during the rainy seasons in four years in a nested limited-area mode. The uncertainty contributions of six selected model parameters were investigated. To this end, we employed a sampling strategy to obtain model parameter combinations for a manageable number of ICON model runs. Surrogate models were then constructed to describe a relationship between the model parameters and selected Quantities of Interest (e.g. characteristics of the African and Tropical easterly jets or the Saharan heat low) to employ sensitivity and parameter studies. For better interpretation a local parameter analysis based on the output fields was conducted using the same setup. Results reveal the complex nature of the WAM system and indicate for which parameters (and thus processes) uncertainties need to be reduced to lower the spread in the outputs. Among the considered parameters, the entrainment rate and the terminal fall velocity of ice show the greatest effects, where larger values lead to a decrease of cloud cover and precipitation, and to an intensification of the Saharan heat low, despite distinct regional differences. The evaporative soil surface also shows a significant effect, mostly on temperature and cloud cover.

Description: Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system1. Remote-sensing estimates to quantify carbon losses from global forests2,3,4,5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced6 and satellite-derived approaches2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets.

Description: Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17–34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling.

Description: The emergence of alternative stable states in forest systems has significant implications for the functioning and structure of the terrestrial biosphere, yet empirical evidence remains scarce. Here, we combine global forest biodiversity observations and simulations to test for alternative stable states in the presence of evergreen and deciduous forest types. We reveal a bimodal distribution of forest leaf types across temperate regions of the Northern Hemisphere that cannot be explained by the environment alone, suggesting signatures of alternative forest states. Moreover, we empirically demonstrate the existence of positive feedbacks in tree growth, recruitment and mortality, with trees having 4–43% higher growth rates, 14–17% higher survival rates and 4–7 times higher recruitment rates when they are surrounded by trees of their own leaf type. Simulations show that the observed positive feedbacks are necessary and sufficient to generate alternative forest states, which also lead to dependency on history (hysteresis) during ecosystem transition from evergreen to deciduous forests and vice versa. We identify hotspots of bistable forest types in evergreen-deciduous ecotones, which are likely driven by soil-related positive feedbacks. These findings are integral to predicting the distribution of forest biomes, and aid to our understanding of biodiversity, carbon turnover, and terrestrial climate feedbacks.