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Wildfires are increasingly impacting social and environmental systems in the United States (US). The ability to mitigate the adverse effects of wildfires increases with understanding of the social, physical, and biological conditions that co-occurred with or caused the wildfire ignitions and contributed to the wildfire impacts. To this end, we developed the FPA FOD-Attributes dataset, which augments the sixth version of the Fire Program Analysis Fire-Occurrence Database (FPA FOD v6) with nearly 270 attributes that coincide with the date and location of each wildfire ignition in the US. FPA FOD v6 contains information on location, jurisdiction, discovery time, cause, and final size of >2.3×106 wildfires in the US between 1992 and 2020. For each wildfire, we added physical (e.g., weather, climate, topography, and infrastructure), biological (e.g., land cover and normalized difference vegetation index), social (e.g., population density and social vulnerability index), and administrative (e.g., national and regional preparedness level and jurisdiction) attributes. This publicly available dataset can be used to answer numerous questions about the covariates associated with human- and lightning-caused wildfires. Furthermore, the FPA FOD-Attributes dataset can support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models. The FPA FOD-Attributes dataset is available at 10.5281/zenodo.8381129 (Pourmohamad et al., 2023). [ABSTRACT FROM AUTHOR]

Wildfires are increasingly impacting social and environmental systems in the United States. The ability to mitigate the adverse effects of wildfires increases with understanding of the social, physical, and biological conditions that co-occurred with or caused the wildfire ignitions and contributed to the wildfire impacts. To this end, we developed the FPA FOD-Attributes dataset, which augments the sixth version of the Fire Program Analysis-Fire Occurrence Database (FPA FOD v6) with nearly 270 attributes that coincide with the date and location of each wildfire ignition in the United States. FPA FOD v6 contains information on location, jurisdiction, discovery time, cause, and final size of >2.3 million wildfires from 1992–2020 in the United States. For each wildfire, we added physical (e.g., weather, climate, topography, infrastructure), biological (e.g., land cover, normalized difference vegetation index), social (e.g., population density, social vulnerability index), and administrative (e.g., national and regional preparedness level, jurisdiction) attributes. This publicly available dataset can be used to answer numerous questions about the covariates associated with human- and lightning-caused wildfires. Furthermore, the FPA FOD-Attributes dataset can support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including development of machine learning models. [ABSTRACT FROM AUTHOR]

Precipitation prediction at seasonal timescales is important for planning and management of water resources as well as preparedness for hazards such as floods, droughts and wildfires. Quantifying predictability is quite challenging as a consequence of a large number of potential drivers, varying antecedent conditions, and small sample size of high‐quality observations available at seasonal timescales, that in turn, increases prediction uncertainty and the risk of model overfitting. Here, we introduce a generalized probabilistic framework to account for these issues and assess predictability under uncertainty. We focus on prediction of winter (Nov–Mar) precipitation across the contiguous United States, using sea surface temperature‐derived indices (averaged in Aug–Oct) as predictors. In our analysis we identify "predictability hotspots," which we define as regions where precipitation is inherently more predictable. Our framework estimates the entire predictive distribution of precipitation using copulas and quantifies prediction uncertainties, while employing principal component analysis for dimensionality reduction and a cross validation technique to avoid overfitting. We also evaluate how predictability changes across different quantiles of the precipitation distribution (dry, normal, wet amounts) using a multi‐category 3 × 3 contingency table. Our results indicate that well‐defined predictability hotspots occur in the Southwest and Southeast. Moreover, extreme dry and wet conditions are shown to be relatively more predictable compared to normal conditions. Our study may help with water resources management in several subregions of the United States and can be used to assess the fidelity of earth system models in successfully representing teleconnections and predictability. Key Points: We present a rigorous probabilistic framework and novel statistical metrics to assess the predictability of seasonal precipitation totalsHotspots of winter precipitation predictability occur in the Southwest and Southeast of contiguous United States (CONUS)Sea Surface Temperature (SST) information may help with predicting dry or wet conditions in some areas, while normal conditions are difficult to predict [ABSTRACT FROM AUTHOR]

Precipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical downscaling (SD) methods, which relate precipitation with model resolved dynamics, often provide more accurate precipitation estimates compared to model's raw precipitation products. We introduce the convolutional neural network model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation‐related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation‐related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the convolutional neural network model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Based on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: First, we offer a novel approach to enhance numerical precipitation estimation; second, the proposed model provides important implications for improving precipitation‐related parameterization schemes using a data‐driven approach. Plain Language Summary: The precipitation process is not well simulated in numerical weather models, since it takes place at the scales beyond the resolution of current models. We develop a statistical model using deep learning technique to improve the estimation of precipitation in numerical weather models. Key Points: We offer a novel approach to enhance numerical precipitation estimation using deep convolutional neural network (CNN)The model provides important implications for improving precipitation‐related parameterization schemes using a data‐driven approachThe CNN model outperforms existing precipitation statistical downscaling approaches by learning dominant spatial dynamics features [ABSTRACT FROM AUTHOR]

Flood hazard mapping in the United States (US) is deeply tied to the National Flood Insurance Program (NFIP). Consequently, publicly available flood maps provide essential information for insurance purposes, but do not necessarily provide relevant information for non-insurance aspects of flood risk management (FRM) such as public education and emergency planning. Recent calls for flood hazard maps that support a wider variety of FRM tasks highlight the need to deepen our understanding about the factors that make flood maps useful and understandable for local end-users. In this study, social scientists and engineers explore opportunities for improving the utility and relevance of flood hazard maps through the co-production of maps responsive to end-users' FRM needs. Specifically, two-dimensional flood modeling produced a set of baseline hazard maps for stakeholders of the Tijuana River Valley, US, and Los Laureles Canyon in Tijuana, Mexico. Focus groups with natural resource managers, city planners, emergency managers, academia, non-profit, and community leaders refined the baseline hazard maps by triggering additional modeling scenarios and map revisions. Several important end-user preferences emerged, such as (1) legends that frame flood intensity both qualitatively and quantitatively, and (2) flood scenario descriptions that report flood magnitude in terms of rainfall, streamflow, and its relation to an historic event. Regarding desired hazard map content, end-users' requests revealed general consistency with mapping needs reported in European studies and guidelines published in Australia. However, requested map content that is not commonly produced included: (1) standing water depths following the flood, (2) the erosive potential of flowing water, and (3) pluvial flood hazards, or flooding caused directly by rainfall. We conclude that the relevance and utility of commonly produced flood hazard maps can be most improved by illustrating pluvial flood hazards and by using concrete reference points to describe flooding scenarios rather than exceedance probabilities or frequencies. [ABSTRACT FROM AUTHOR]

The article reports on damage to the Oroville dam in North America due to an extreme water flows creating a massive hole in the primary spillway and then excessive erosion in the emergency spillway. It mentions impact of extreme events, land-cover and land-use changes, and the climatic changes on aging dams and levees. It adds need to re-evaluate failure probability of dams and levees under multihazard scenarios.

• Hydrological drought durations and severities, as identified by Fixed and Variable Drought Thresholds (FDT and VDT), showed general agreement. • The key time nodes of hydrological drought evolution were completely different as identified by FDT and VDT. • Both the FDT and VDT methods were determined to be suitable for watersheds with lower seasonal flow variability. • To enhance the characterization of hydrological droughts, it is important to consider factors such as their onset, maximum streamflow deficit, and termination. Duration and severity are two key characteristics commonly used for analyzing hydrological drought (HD); however, without considering important HD time nodes (i.e., drought onset, maximum streamflow deficit, and termination), drought monitoring and characterization remain inaccurate. For example, any HD index can identify the duration and severity of certain drought events. However, the onset and termination times of drought events identified may vary significantly among different types of drought indices. This discrepancy presents an obstacle within the realm of drought forecasting and management. The methods of fixed drought threshold (FDT) and variable drought threshold (VDT), which apply an absolute and relative threshold for drought definition, respectively, are commonly used to identify HD. We studied the difference and applicability of the FDT and VDT methods for different thresholds (i.e., streamflow percentiles Q 10 , Q 20 , Q 30 , Q 40 , and Q 50) based on monthly streamflow records of 219 watersheds in the United States, with a focus on the drought onset, maximum streamflow deficit, and termination. The results show that, while the average HD durations and severities identified by FDT and VDT were in broad agreement, the key time nodes of HD evolution were completely different between two methods, especially in watersheds with larger streamflow variability within a year. The FDT and VDT methods were determined to be suitable for watersheds with lower seasonal flow variability based on the key time nodes of HD evolution. However, the FDT method seems to be more appropriate for watersheds with notable seasonal variations in streamflow, when compared to the VDT method. Our results indicated that HD identification by only considering the commonly used drought features (duration, severity) leads to inaccurate drought characterization. It is necessary to improve HD identification and characterization by considering drought onset, maximum streamflow deficit, and termination. [ABSTRACT FROM AUTHOR]

A combination of climate events (e.g., low precipitation and high temperatures) may cause a significant impact on the ecosystem and society, although individual events involved may not be severe extremes themselves. Analyzing historical changes in concurrent climate extremes is critical to preparing for and mitigating the negative effects of climatic change and variability. This study focuses on the changes in concurrences of heatwaves and meteorological droughts from 1960 to 2010. Despite an apparent hiatus in rising temperature and no significant trend in droughts, we show a substantial increase in concurrent droughts and heatwaves across most parts of the United States, and a statistically significant shift in the distribution of concurrent extremes. Although commonly used trend analysis methods do not show any trend in concurrent droughts and heatwaves, a unique statistical approach discussed in this study exhibits a statistically significant change in the distribution of the data.