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The exposure of High Mountain Asia (HMA) to disaster risks is heightened by extreme weather conditions and the impacts of climate change. Obtaining knowledge about the long-term response of the landscape to hydroclimatic variations in HMA is paramount, as millions of people are affected by these changes every year. During monsoons, substantial human suffering, and damage to crops and infrastructure in populated communities result from the flooding and debris flow caused by the increase in precipitation extremes each year. Although a few initiatives have undertaken the estimation of flood risk locally, the use of traditional techniques in ungauged basins is, unfortunately, not always possible because of the lack of extensive data required. To address this problem, we present in this study a geomorphologically guided machine learning (ML) approach for mapping flood impacts across HMA. We defined socioeconomic flood impact using the Lifeyears Index (LYI), a systematic index that measures the economic cost and loss of life caused by flooding. This index quantifies the importance of the destruction to infrastructure, capital, and housing in an overall assessment. We trained the proposed model with over 6000 flood events, from 1980 to 2020, and their computed five-year and ten-year LYIs. We used as predictors, (1) the five-year rainfall concentrations (which correlate the magnitude of precipitation events with the time of occurrence) of events retrieved from ERA5 daily data; (2) a geomorphic classifier (flood geomorphic potential) based on hydraulic scaling functions automatically derived from an 8 and 30-meter digital elevation model (DEM) for the region and (3) population. This model proved capable of identifying the hotspots of flood susceptibility on a national scale and showing its variability from 1980 to 2022. The study also highlights the severity of the impacts of hydroclimatic extremes in the entire HMA region. The framework is generic and can be used to derive a wide variety of flood vulnerability and subsequent risk maps in data-scarce regions. [ABSTRACT FROM AUTHOR]

The changing climate and anthropogenic activities raise the likelihood of damage due to compound flood hazards, triggered by the combined occurrence of extreme precipitation and storm surge during high tides and exacerbated by sea-level rise (SLR). Risk estimates associated with these extreme event scenarios are expected to be significantly higher than estimates derived from a standard evaluation of individual hazards. In this study, we present case studies of compound flood hazards affecting critical infrastructure (CI) in coastal Connecticut (USA). We based the analysis on actual and synthetic (considering future climate conditions for atmospheric forcing, sea-level rise, and forecasted hurricane tracks) hurricane events, represented by heavy precipitation and surge combined with tides and SLR conditions. We used the Hydrologic Engineering Center's River Analysis System (HEC-RAS), a two-dimensional hydrodynamic model, to simulate the combined coastal and riverine flooding of selected CI sites. We forced a distributed hydrological model (CREST-SVAS) with weather analysis data from the Weather Research and Forecasting (WRF) model for the synthetic events and from the National Land Data Assimilation System (NLDAS) for the actual events, to derive the upstream boundary condition (flood wave) of HEC-RAS. We extracted coastal tide and surge time series for each event from the National Oceanic and Atmospheric Administration (NOAA) to use as the downstream boundary condition of HEC-RAS. The significant outcome of this study represents the evaluation of changes in flood risk for the CI sites for the various compound scenarios (under current and future climate conditions). This approach offers an estimate of the potential impact of compound hazards relative to the 100-year flood maps produced by the Federal Emergency Management Agency (FEMA), which is vital to developing mitigation strategies. In a broader sense, this study provides a framework for assessing the risk factors of our modern infrastructure located in vulnerable coastal areas throughout the world. [ABSTRACT FROM AUTHOR]

This study focuses on the Iberian Peninsula and investigates the propagation of precipitation uncertainty, and its interaction with hydrologic modeling, in global water resource reanalysis. Analysis is based on ensemble hydrologic simulations for a period spanning 11 years (2000–2010). To simulate the hydrological variables of surface runoff, subsurface runoff, and evapotranspiration, we used four land surface models (LSMs) – JULES (Joint UK Land Environment Simulator), ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems), SURFEX (Surface Externalisée), and HTESSEL (Hydrology – Tiled European Centre for Medium-Range Weather Forecasts – ECMWF – Scheme for Surface Exchanges over Land) – and one global hydrological model, WaterGAP3 (Water – a Global Assessment and Prognosis). Simulations were carried out for five precipitation products – CMORPH (the Climate Prediction Center Morphing technique of the National Oceanic and Atmospheric Administration, or NOAA), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), 3B42V(7), ECMWF reanalysis, and a machine-learning-based blended product. As a reference, we used a ground-based observation-driven precipitation dataset, named SAFRAN, available at 5 km, 1 h resolution. We present relative performances of hydrologic variables for the different multi-model and multi-forcing scenarios. Overall, results reveal the complexity of the interaction between precipitation characteristics and different modeling schemes and show that uncertainties in the model simulations are attributed to both uncertainty in precipitation forcing and the model structure. Surface runoff is strongly sensitive to precipitation uncertainty, and the degree of sensitivity depends significantly on the runoff generation scheme of each model examined. Evapotranspiration fluxes are comparatively less sensitive for this study region. Finally, our results suggest that there is no single model–forcing combination that can outperform all others consistently for all variables examined and thus reinforce the fact that there are significant benefits to exploring different model structures as part of the overall modeling approaches used for water resource applications. [ABSTRACT FROM AUTHOR]

Rainfall-induced debris flows in recently burned mountainous areas cause significant economic losses and human casualties. Currently, prediction of post-fire debris flows is widely based on the use of power-law thresholds and logistic regression models. While these procedures have served with certain success in existing operational warning systems, in this study we investigate the potential to improve the efficiency of current predictive models with machinelearning approaches. Specifically, the performance of a predictive model based on the random forest algorithm is compared with current techniques for the prediction of post-fire debris flow occurrence in the western United States. The analysis is based on a database of post-fire debris flows recently published by the United States Geological Survey. Results show that predictive models based on random forest exhibit systematic and considerably improved performance with respect to the other models examined. In addition, the random-forest-based models demonstrated improvement in performance with increasing training sample size, indicating a clear advantage regarding their ability to successfully assimilate new information. Complexity, in terms of variables required for developing the predictive models, is deemed important but the choice of model used is shown to have a greater impact on the overall performance. [ABSTRACT FROM AUTHOR]

This study investigates the use of a nonparametric, tree-based model, quantile regression forests (QRF), for combining multiple global precipitation datasets and characterizing the uncertainty of the combined product. We used the Iberian Peninsula as the study area, with a study period spanning 11 years (2000-2010). Inputs to the QRF model included three satellite precipitation products, CMORPH, PERSIANN, and 3B42 (V7); an atmospheric reanalysis precipitation and air temperature dataset; satellite-derived nearsurface daily soil moisture data; and a terrain elevation dataset. We calibrated the QRF model for two seasons and two terrain elevation categories and used it to generate ensemble for these conditions. Evaluation of the combined product was based on a high-resolution, ground-reference precipitation dataset (SAFRAN) available at 5 km1h-1 resolution. Furthermore, to evaluate relative improvements and the overall impact of the combined product in hydrological response, we used the generated ensemble to force a distributed hydrological model (the SURFEX land surface model and the RAPID river routing scheme) and compared its streamflow simulation results with the corresponding simulations from the individual global precipitation and reference datasets. We concluded that the proposed technique could generate realizations that successfully encapsulate the reference precipitation and provide significant improvement in streamflow simulations, with reduction in systematic and random error on the order of 20-99 and 44-88 %, respectively, when considering the ensemble mean. [ABSTRACT FROM AUTHOR]

The systematic underestimation observed in debris flow early warning thresholds has been associated with the use of sparse rain gauge networks to represent highly non-stationary rainfall fields. Remote sensing products permit concurrent estimates of debris-flow-triggering rainfall for areas poorly covered by rain gauges, but the impact of using coarse spatial resolutions to represent such rainfall fields is still to be assessed. This study uses fine-resolution radar data for ~100 debris flows in the eastern Italian Alps to (i) quantify the effect of spatial aggregation (1-20km grid size) on the estimation of debris-flow-triggering rainfall and on the identification of early warning thresholds and (ii) compare thresholds derived from aggregated estimates and rain gauge networks of different densities. The impact of spatial aggregation is influenced by the spatial organization of rainfall and by its dependence on the severity of the triggering rainfall. Thresholds from aggregated estimates show 8-21% variation in the parameters whereas 10-25% systematic variation results from the use of rain gauge networks, even for densities as high as 1/10km-2. [ABSTRACT FROM AUTHOR]