Establishing flood thresholds for sea level rise impact communication.

Sea level rise (SLR) affects coastal flood regimes and poses serious challenges to flood risk management, particularly on ungauged coasts. To address the challenge of monitoring SLR at local scales, we propose a high tide flood (HTF) thresholding system that leverages machine learning (ML) techniques to estimate SLR and HTF thresholds at a relatively fine spatial resolution (10 km) along the United States' coastlines. The proposed system, complementing conventional linear- and point-based estimations of HTF thresholds and SLR rates, can estimate these values at ungauged stretches of the coast. Trained and validated against National Oceanic and Atmospheric Administration (NOAA) gauge data, our system demonstrates promising skills with an average Kling-Gupta Efficiency (KGE) of 0.77. The results can raise community awareness about SLR impacts by documenting the chronic signal of HTF and providing useful information for adaptation planning. The findings encourage further application of ML in achieving spatially distributed thresholds.

Nature-based solutions as buffers against coastal compound flooding: Exploring potential framework for process-based modeling of hazard mitigation.

As coastal regions face escalating risks from flooding in a changing climate, Nature-based Solutions (NbS) have garnered attention as promising adaptation measures to mitigate the destructive impacts of coastal flooding. However, the challenge of compound flooding, which involves the combined effects of multiple flood drivers, demands a deeper understanding of the efficacy of NbS against this complex phenomenon. This manuscript reviews the literature on process-based modeling of NbS for mitigating compound coastal flooding and identifies knowledge gaps to enhance future research efforts. We used an automated search strategy within the SCOPUS database, followed by a screening process that ultimately resulted in 141 publications assessing the functionality of NbS against coastal flooding. Our review identified a dearth of research (9 %) investigating the performance of NbS against compound flooding scenarios. We examined the challenges and complexities involved in modeling such scenarios, including hydrologic, hydrodynamic, and ecological feedback processes by exploring the studies that used a process-based modeling framework. Key research gaps were identified, such as navigating the complex environment, managing computational costs, and addressing the shortages of experts and data. We outlined potential modeling pathways to improve NbS characterization in the compound flooding framework. Additionally, uncertainties associated with numerical modeling and steps to bridge the research-to-operation gaps were briefly discussed, highlighting the bottlenecks in operational implementation.

Block-level vulnerability assessment reveals disproportionate impacts of natural hazards across the conterminous United States.

The global increase in the frequency, intensity, and adverse impacts of natural hazards on societies and economies necessitates comprehensive vulnerability assessments at regional to national scales. Despite considerable research conducted on this subject, current vulnerability and risk assessments are implemented at relatively coarse resolution, and they are subject to significant uncertainty. Here, we develop a block-level Socio-Economic-Infrastructure Vulnerability (SEIV) index that helps characterize the spatial variation of vulnerability across the conterminous United States. The SEIV index provides vulnerability information at the block level, takes building count and the distance to emergency facilities into consideration in addition to common socioeconomic vulnerability measures and uses a machine-learning algorithm to calculate the relative weight of contributors to improve upon existing vulnerability indices in spatial resolution, comprehensiveness, and subjectivity reduction. Based on such fine resolution data of approximately 11 million blocks, we are able to analyze inequality within smaller political boundaries and find significant differences even between neighboring blocks.

Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting.

This perspective discusses the importance of characterizing, quantifying, and accounting for various sources of uncertainties involved in different layers of hydrometeorological and hydrodynamic model simulations as well as their complex interactions and cascading effects (e.g., uncertainty propagation) in forecasting compound flooding (CF). Over the past few decades, CF has come to attention across the globe as this natural hazard results from a combination of either concurrent or successive flood drivers with larger economic, societal, and environmental impacts than those from isolated drivers. A warming climate and increased urbanization in flood-prone areas are expected to contribute to an escalation in the risk of CF in the near future. Recent advances in remote sensing and data science can provide a wide range of possibilities to account for and reduce the predictive uncertainties; hence improving the predictability of CF events, enabling risk-informed decision-making, and ensuring a sustainable CF risk governance.

Integrated socio-environmental vulnerability assessment of coastal hazards using data-driven and multi-criteria analysis approaches.

Coastal hazard vulnerability assessment has been centered around the multi-variate analysis of geo-physical and hydroclimate data. The representation of coupled socio-environmental factors has often been ignored in vulnerability assessment. This study develops an integrated socio-environmental Coastal Vulnerability Index (CVI), which simultaneously combines information from five vulnerability groups: biophysical, hydroclimate, socio-economic, ecological, and shoreline. Using the Multi-Criteria Decision Making (MCDM) approach, two CVI (CVI-50 and CVI-90) have been developed based on average and extreme conditions of the factors. Each CVI is then compared to a data-driven CVI, which is formed based on Probabilistic Principal Component Analysis (PPCA). Both MCDM and PPCA have been tied into geospatial analysis to assess the natural hazard vulnerability of six coastal counties in South Carolina. Despite traditional MCDM-based vulnerability assessments, where the final index is estimated based on subjective weighting methods or equal weights, this study employs an entropy weighting technique to reduce the individuals' biases in weight assignment. Considering the multivariate nature of the coastal vulnerability, the validation results show both CVI-90 and PPCA preserve the vulnerability results from biophysical and socio-economic factors reasonably, while the CVI-50 methods underestimate the biophysical vulnerability of coastal hazards. Sensitivity analysis of CVIs shows that Charleston County is more sensitive to socio-economic factors, whereas in Horry County the physical factors contribute to a higher degree of vulnerability. Findings from this study suggest that the PPCA technique facilitates the high-dimensional vulnerability assessment, while the MCDM approach accounts more for decision-makers' opinions.

How does precipitation data influence the land surface data assimilation for drought monitoring?

Droughts are among the costliest natural hazards that occur annually worldwide. Their socioeconomic impacts are significant and widespread, affecting the sustainable development of human societies. This study investigates the influence of different forcing precipitation data in driving Land Surface Models (LSMs) and characterizing drought conditions. Here, we utilize our recently developed LSM data assimilation system for probabilistically monitoring drought over the Contiguous United States (CONUS). The Noah-MP LSM model is forced with two widely used precipitation data including IMERG (Integrated Multi-satellitE Retrievals for GPM) and NLDAS (North American Land Data Assimilation System). Soil moisture and evapotranspiration are known to have a strong relationship in the land-atmospheric interaction processes. Unlike other studies that attempted the individual assimilation of these variables, here we propose a multivariate data assimilation framework. Therefore, in both modeling scenarios, the data assimilation approach is used to integrate remotely sensed MODIS (Moderate Resolution Imaging Spectroradiometer) evapotranspiration and SMAP (Soil Moisture Active Passive) soil moisture observations into the Noah-MP LSM. The results of this study indicate that the source of precipitation data has a significant impact on the performance of LSM data assimilation system for drought monitoring. The findings revealed that NLDAS and IMERG precipitation can result in a significant difference in identifying drought severity depending on the region and time of the year. Furthermore, our analysis indicates that regardless of the precipitation forcing data product used in the land surface data assimilation system, our modeling framework can effectively detect the drought impacts on crop yield. Additionally, we calculated the drought probability based on the ensemble of soil moisture percentiles and found that there exist temporal and spatial discrepancies in drought probability maps generated from the NLDAS and IMERG precipitation forcings.

A Place-based Assessment of Flash Flood Hazard and Vulnerability in the Contiguous United States.

Flash flood is among the most catastrophic natural hazards which causes disruption in the environment and societies. Flash flood is mainly initiated by intense rainfall, and due to its rapid onset (within six hours of rainfall), taking action for effective response is challenging. Building resilience to flash floods require understanding of the socio-economic characteristics of the societies and their vulnerability to these extreme events. This study provides a comprehensive assessment of socio-economic vulnerability to flash floods and investigates the main characteristics of flash flood hazard, i.e. frequency, duration, severity, and magnitude. A socio-economic vulnerability index is developed at the county level across the Contiguous United States (CONUS). For this purpose, an ensemble of social and economic variables from the US Census and the Bureau of Economic Analysis were analyzed. Then, the coincidence of socio-economic vulnerability and flash flood hazard were investigated to identify the critical and non-critical regions. Results show that the southwest U.S. experienced severe flash flooding with high magnitude, whereas the Northern Great Plains experience lower severity and frequency. Critical counties (high-vulnerable-hotspot) are mostly located in the southern and southwestern parts of the U.S. The majority of counties in the Northern Great Plains indicate a non-critical status.

The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework.

This article presents a novel approach to couple a deterministic four-dimensional variational (4DVAR) assimilation method with the particle filter (PF) ensemble data assimilation system, to produce a robust approach for dual-state-parameter estimation. In our proposed method, the Hybrid Ensemble and Variational Data Assimilation framework for Environmental systems (HEAVEN), we characterize the model structural uncertainty in addition to model parameter and input uncertainties. The sequential PF is formulated within the 4DVAR system to design a computationally efficient feedback mechanism throughout the assimilation period. In this framework, the 4DVAR optimization produces the maximum a posteriori estimate of state variables at the beginning of the assimilation window without the need to develop the adjoint of the forecast model. The 4DVAR solution is then perturbed by a newly defined prior error covariance matrix to generate an initial condition ensemble for the PF system to provide more accurate and reliable posterior distributions within the same assimilation window. The prior error covariance matrix is updated from one cycle to another over the main assimilation period to account for model structural uncertainty resulting in an improved estimation of posterior distribution. The premise of the presented approach is that it (1) accounts for all sources of uncertainties involved in hydrologic predictions, (2) uses a small ensemble size, and (3) precludes the particle degeneracy and sample impoverishment. The proposed method is applied on a nonlinear hydrologic model and the effectiveness, robustness, and reliability of the method is demonstrated for several river basins across the United States.

Atmospheric Circulation Patterns Associated with Extreme United States Floods Identified via Machine Learning.

The massive socioeconomic impacts engendered by extreme floods provides a clear motivation for improved understanding of flood drivers. We use self-organizing maps, a type of artificial neural network, to perform unsupervised clustering of climate reanalysis data to identify synoptic-scale atmospheric circulation patterns associated with extreme floods across the United States. We subsequently assess the flood characteristics (e.g., frequency, spatial domain, event size, and seasonality) specific to each circulation pattern. To supplement this analysis, we have developed an interactive website with detailed information for every flood of record. We identify four primary categories of circulation patterns: tropical moisture exports, tropical cyclones, atmospheric lows or troughs, and melting snow. We find that large flood events are generally caused by tropical moisture exports (tropical cyclones) in the western and central (eastern) United States. We identify regions where extreme floods regularly occur outside the normal flood season (e.g., the Sierra Nevada Mountains due to tropical moisture exports) and regions where multiple extreme flood events can occur within a single year (e.g., the Atlantic seaboard due to tropical cyclones and atmospheric lows or troughs). These results provide the first machine-learning based near-continental scale identification of atmospheric circulation patterns associated with extreme floods with valuable insights for flood risk management.

Future drought risk in Africa: Integrating vulnerability, climate change, and population growth.

Drought risk refers to the potential losses from hazard imposed by a drought event, and it is generally characterized as a function of vulnerability, hazard, and exposure. In this study, drought risk is assessed at a national level across Africa, and the impacts of climate change, population growth, and socioeconomic vulnerabilities on drought risk are investigated. A rigorous framework is implemented to quantify drought vulnerability considering various sectors including economy, energy and infrastructure, health, land use, society, and water resources. Multi-model and multi-scenario analyses are employed to quantify drought hazard using an ensemble of 10 regional climate models and a multi-scalar drought index. Drought risk is then assessed in each country for 2 climate emission pathways (RCP4.5 and RCP8.5), 3 population scenarios, and 3 vulnerability scenarios during three future periods between 2010 and 2100. Drought risk ratio is quantified, and the role of each component (i.e. hazard, vulnerability, and exposure) is identified, and the associated uncertainties are also characterized. Results show that drought risk is expected to increase in future across Africa with varied rates for different models and scenarios. Although northern African countries indicate aggravating drought hazard, drought risk ratio is found to be highest in central African countries as a consequent of vulnerability and population rise in that region. Results indicate that if no climate change adaptation is implemented, unprecedented drought hazard and risk will occur decades earlier. In addition, controlling population growth is found to be imperative for mitigating drought risk in Africa (even more effective than climate change mitigation), as it improves socioeconomic vulnerability and reduces potential exposure to drought.

Hydrological drought persistence and recovery over the CONUS: A multi-stage framework considering water quantity and quality.

Hydrological droughts have considerable negative impacts on water quantity and quality, and understanding their regional characteristics is of crucial importance. This study presents a multi-stage framework to detect and characterize hydrological droughts considering both streamflow and water quality changes. Hydrological droughts are categorized into three stages of growth, persistence, retreat, and water quality variables (i.e., water temperature, dissolved oxygen concentration, and turbidity) are utilized to further investigate drought recovery. The framework is applied to 400 streamflow gauges across the Contiguous United States (CONUS) over the study period of 1950-2016. The method is illustrated for the 2012 US drought, which affected most of the nation. Results reveal the duration, frequency, and severity of historical droughts in various regions as well as their spatial consistencies and heterogeneities. Furthermore, duration of each stage of drought (i.e., growth, persistence, and retreat) is also assessed and the spatial patterns are diagnosed across the CONUS. Considering the water quality variables, increased water temperature (4 °C on average) and reduced dissolved oxygen concentration (2.5 mg/L on average) were observed during drought episodes, both of which impose severe consequences on ecology of natural habitats. On the contrary, turbidity was found to decrease during droughts, and indicate a sudden increase when drought terminates, due to increase in runoff. Varied drought recovery durations are perceived for different water quality variables, and in general, it takes about two more months for water quality variables to recover from a drought, following the hydrological drought termination.

Multi-dimensional assessment of drought vulnerability in Africa: 1960-2100.

Drought vulnerability is a complex concept that identifies the capacity to cope with drought, and reveals the susceptibility of a system to the adverse impacts of drought. In this study, a multi-dimensional modeling framework is carried out to investigate drought vulnerability at a national level across the African continent. Data from 28 factors in six different components (i.e. economy, energy and infrastructure, health, land use, society, and water resources) are collected for 46 African countries during 1960-2015, and a composite Drought Vulnerability Index (DVI) is calculated for each country. Various analyses are conducted to assess the reliability and accuracy of the proposed DVI, and the index is evaluated against historical observed drought impacts. Then, regression models are fitted to the historical time-series of DVI for each country, and the models are extrapolated for the period of 2020-2100 to provide three future scenarios of DVI projection (low, medium, and high) based on historical variations and trends. Results show that Egypt, Tunisia, and Algeria are the least drought vulnerable countries, and Chad, Niger, and Malawi are the most drought vulnerable countries in Africa. Future DVI projections indicate that the difference between low- and high-vulnerable countries will increase in future, with most of the southern and northern African countries becoming less vulnerable to drought, whereas the majority of central African countries indicate increasing drought vulnerability. The projected DVIs can be utilized for long-term drought risk analysis as well as strategic adaptation planning purposes.

Escalating heat-stress mortality risk due to global warming in the Middle East and North Africa (MENA).

Climate change will substantially exacerbate extreme temperature and heatwaves. The impacts will be more intense across the Middle East and North Africa (MENA), a region mostly characterized by hot and arid climate, already intolerable for human beings in many parts. In this study, daily climate data from 17 fine-resolution Regional Climate Models (RCMs) are acquired to calculate wet-bulb temperature and investigate the mortality risk for people aged over 65 years caused by excessive heat stress across the MENA region. Spatially adaptive temperature thresholds are implemented for quantifying the mortality risk, and the analysis is conducted for the historical period of 1951-2005 and two future scenarios of RCP4.5 and RCP8.5 during the 2006-2100 period. Results show that the mortality risk will increase in distant future to 8-20 times higher than that of the historical period if no climate change mitigation is implemented. The coastal regions of the Red sea, Persian Gulf, and Mediterranean Sea indicate substantial increase in mortality risk. Nonetheless, the risk ratio will be limited to 3-7 times if global warming is limited to 2 °C. Climate change planning and adaptation is imperative for mitigating heat-related mortality risk across the region.

Hydrologic Remote Sensing and Land Surface Data Assimilation.

Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface-atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation.