Suchergebnisse

Abstract. In this communication, we present the exposure of agricultural
lands to the flooding caused by extreme precipitation in western Europe from
12 to 15 July 2021. Overlaying the flood inundation maps
derived from the near-real-time RAdar-Produced Inundation Diary (RAPID)
system on the Coordination of information on the environment (CORINE) Land
Cover map we estimate a 1920 km2 area affected by the flooding,
with 64 % representing agricultural land. Among the inundated agricultural
land, 36 % of the area is pastures while 34 % is arable land. Most
agricultural flood exposure is found in eastern France along the Rhône
River, the southern Netherlands along the Meuse River, and western Germany along the
Rhine River.

Abstract. In this communication, we present application of the
automated near-real-time (NRT) system called RAdar-Produced Inundation Diary
(RAPID) to European Space Agency Sentinel-1 synthetic aperture radar (SAR) images to produce flooding
maps for Hurricane Dorian in the northern Bahamas. RAPID maps, released 2 d after the event, show that coastal flooding in the Bahamas reached
areas located more than 10 km inland, covering more than 3000 km2 of
continental area. RAPID flood estimates from subsequent SAR images show the
recession of the flood across the islands and present high agreement scores
when compared to Copernicus Emergency Management Service (Copernicus EMS)
estimates.

Abstract. 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
machine-learning 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. Wildfire is a critical ecological disturbance in terrestrial ecosystems. Australia, in particular, has experienced increasingly large and severe wildfires over the past 2 decades, while globally fire risk is expected to increase significantly due to projected increases in extreme weather and drought conditions. Therefore, understanding and predicting fire severity is critical for evaluating current and future impacts of wildfires on ecosystems. Here, we first introduce a vegetation-type-specific fire severity classification applied to satellite imagery, which is further used to predict fire severity during the fire season (November to March) using antecedent drought conditions, fire weather (i.e. wind speed, air temperature, and atmospheric humidity), and topography. Compared to fire severity maps from the fire extent and severity mapping (FESM) dataset, we find that fire severity prediction results using the vegetation-type-specific thresholds show good performance in extreme- and high-severity classification, with accuracies of 0.64 and 0.76, respectively. Based on a "leave-one-out" cross-validation experiment, we demonstrate high accuracy for both the fire severity classification and the regression using a suite of performance metrics: the determination coefficient (R2), mean absolute error (MAE), and root-mean-square error (RMSE), which are 0.89, 0.05, and 0.07, respectively. Our results also show that the fire severity prediction results using the vegetation-type-specific thresholds could better capture the spatial patterns of fire severity and have the potential to be applicable for seasonal fire severity forecasts due to the availability of seasonal forecasts of the predictor variables.

Abstract. 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. For this brief communication, we analyzed the crop area and number of livestock exposed to flooding from the historic precipitation caused by Storm Daniel in central Greece on 3–8 September 2023. We derived from the near-real-time RAdar Produced Inundation Diary (RAPID) system an inundated area totaling 1150 km2, located mainly in the Thessalian plain. By overlaying a land cover map on the RAPID inundation map, we found that ∼ 820 km2 (70 %) of the inundated area was agricultural land. A detailed distribution map of crop type and animal farms revealed that the crop most affected by the flooding was cotton; the inundated area of more than 282 km2 comprised ∼ 30 % of the total area planted with cotton in central Greece. In terms of livestock, we estimated more than 14 000 ornithoids and 21 500 sheep and goats were affected. Consequences for agriculture and animal husbandry in Greece are expected to be severe.

This article compares two nonparametric tree‐based models, quantile regression forests (QRF) and Bayesian additive regression trees (BART), for predicting storm outages on an electric distribution network in Connecticut, USA. We evaluated point estimates and prediction intervals of outage predictions for both models using high‐resolution weather, infrastructure, and land use data for 89 storm events (including hurricanes, blizzards, and thunderstorms). We found that spatially BART predicted more accurate point estimates than QRF. However, QRF produced better prediction intervals for high spatial resolutions (2‐km grid cells and towns), while BART predictions aggregated to coarser resolutions (divisions and service territory) more effectively. We also found that the predictive accuracy was dependent on the season (e.g., tree‐leaf condition, storm characteristics), and that the predictions were most accurate for winter storms. Given the merits of each individual model, we suggest that BART and QRF be implemented together to show the complete picture of a storm's potential impact on the electric distribution network, which would allow for a utility to make better decisions about allocating prestorm resources.