Search

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. Using machine learning algorithms, this study estimates sea level rise and high tide flooding thresholds every 10 km along the United States' coasts, complementing conventional linear-/point-based estimates and offering insights for ungauged areas. [ABSTRACT FROM AUTHOR]

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. Introduces a precise, machine-learning-based Socio-Economic-Infrastructure Vulnerability index for natural hazards that uncovers stark variations in vulnerability at the block level emphasizing crucial information for risk-informed decision making. [ABSTRACT FROM AUTHOR]

The current Tropical Cyclones (TCs) scaling system, Saffir‐Simpson Hurricane Wind Scale (SSHWS), characterizes the hazardousness of these events solely based on wind speed. This is despite the fact that TCs are classic examples of compound hazards during which multiple hazard drivers that are wind, storm surge, and intense rainfall interact and yield in impacts greater than the sum of individuals. Studies have shown that people's decision to evacuate is highly related to the estimated SSHWS category. Thus, the current SSHWS ‐based classification of TCs yields an underestimation of the hazardousness of TCs and so may misguide the threatened communities. Here, we propose a new scaling system that uses Copulas for categorizing TCs based on the likelihood of a given set of severity for rainfall, surge, and wind speed. We use a variety of data sources to obtain the timing and intensity of wind speed, rainfall along the track, and the associated maximum surge for 102 TCs that have made landfall in the United States' Atlantic and Gulf coasts between 1979 and 2020. Comparing the outputs of our scaling system with official damage reporting for the costliest TCs in the history of the United States, we show that the proposed approach significantly improves TC hazard communication and can be useful for informing decision makers and emergency responders. Key Points: The current Tropical Cyclones (TCs) scaling system solely based on wind speed, underestimates the hazardousness of eventsWe propose a new scaling system that takes into account the compound hazard of TCs and uses Copulas to categorize themThe proposed approach can significantly improve TCs' hazard communication [ABSTRACT FROM AUTHOR]

Soil moisture (SM) and evapotranspiration (ET) are key variables of the terrestrial water cycle with a strong relationship. This study examines remotely sensed soil moisture and evapotranspiration data assimilation (DA) with the aim of improving drought monitoring. Although numerous efforts have gone into assimilating satellite soil moisture observations into land surface models to improve their predictive skills, little attention has been given to the combined use of soil moisture and evapotranspiration to better characterize hydrologic fluxes. In this study, we assimilate two remotely sensed datasets, namely, Soil Moisture Operational Product System (SMOPS) and MODIS evapotranspiration (MODIS16 ET), at 1-km spatial resolution, into the VIC land surface model by means of an evolutionary particle filter method. To achieve this, a fully parallelized framework based on model and domain decomposition using a parallel divide-and-conquer algorithm was implemented. The findings show improvement in soil moisture predictions by multivariate assimilation of both ET and SM as compared to univariate scenarios. In addition, monthly and weekly drought maps are produced using the updated root-zone soil moisture percentiles over the Apalachicola-Chattahoochee-Flint basin in the southeastern United States. The model-based estimates are then compared against the corresponding U.S. Drought Monitor (USDM) archive maps. The results are consistent with the USDM maps during the winter and spring season considering the drought extents; however, the drought severity was found to be slightly higher according to DA method. Comparing different assimilation scenarios showed that ET assimilation results in wetter conditions comparing to open-loop and univariate SM DA. The multivariate DA then combines the effects of the two variables and provides an in-between condition. [ABSTRACT FROM AUTHOR]

Heat waves have pronounced impacts on human health, ecosystems, and society. Heat waves have become more frequent and intense globally and are likely to intensify further in a warming climate. Across the United States there is a warming trend in average surface temperatures, but concordant increase in heat wave severity appears absent. Limitations in heat waves studies may be responsible for limited detection of a heat wave warming signal. We track daily spatiotemporal evolution of heat waves using geometric concepts and clustering algorithms to investigate how heat manifests on the land surface. We develop a spatial metric combining heat wave frequency, magnitude, duration, and areal extent. We find mixed trends in some individual heat wave characteristics across the United States during 1981–2018. However, exploration of the spatiotemporal evolution of combined heat wave characteristics shows considerable increases during this period and indicates a substantial increase in heat wave hazard across the United States. Plain Language Summary: Heat waves are detrimental to human health and the environment and are projected to intensify in the future. However, observed upward trends in heat wave severity have not previously been detected in some regions of the globe, including the United States. This study considers heat waves as spatiotemporal events allowing for detection of significant increases in severity and coverage across the United States. Our study outlines a novel approach that combines geometric concepts with a clustering algorithm to investigate how heat waves evolve. We track heat waves daily across the ground surface and capture the combination of frequency, magnitude, duration, and areal extent using a new index that provides a more complete picture of heat waves. Our results indicate significant increases in heat waves across the United States during the last four decades. Key Points: A new spatial metric tracks daily heat wave evolution finding a significant upward trend in heat wave severity and coverage 1981–2018The observed upward trend is not captured by examining individual heat wave characteristics such as frequency, magnitude, or durationFindings suggest a substantial increase in the heat wave burden across the United States during the last four decades [ABSTRACT FROM AUTHOR]

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. Key Points: A joint sequential and variational data assimilation method was developed for superior and robust dual‐state‐parameter estimationThe proposed HEAVEN approach accounts for all sources of uncertainties involved in model predictionsThe effectiveness and usefulness of HEAVEN was evaluated by both deterministic and probabilistic measures [ABSTRACT FROM AUTHOR]

The changing climate and the associated future increases in temperature are expected to have impacts on drought characteristics and hydrologic cycle. This paper investigates the projected changes in spatiotemporal characteristics of droughts and their future attributes over the Willamette River Basin (WRB) in the Pacific Northwest U.S. The analysis is performed using two subsets of downscaled CMIP5 global climate models (GCMs) each consisting of 10 models from two future scenarios (RCP4.5 and RCP8.5) for 30 years of historical period (1970–1999) and 90 years of future projections (2010–2099). Hydrologic modeling is conducted using the Precipitation Runoff Modeling System (PRMS) as a robust distributed hydrologic model with lower computational cost compared to other models. Meteorological and hydrological droughts are studied using three drought indices (i.e. Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, Standardized Streamflow Index). Results reveal that the intensity and duration of hydrological droughts are expected to increase over the WRB, albeit the annual precipitation is expected to increase. On the other hand, the intensity of meteorological droughts do not indicate an aggravation for most cases. We explore the changes of hydrometeolorogical variables over the basin in order to understand the causes for such differences and to discover the controlling factors of drought. Furthermore, the uncertainty of projections are quantified for model, scenario, and downscaling uncertainty. [ABSTRACT FROM AUTHOR]

ABSTRACT Drought is a natural hazard developing slowly and affecting large areas which may have severe consequences on society and economy. Due to the effects of climate change, drought is expected to exacerbate in various regions in future. In this study, the impact of climate change on drought characteristics is assessed, and statistical methods are employed to analyse the significance of projections. This is the first study utilizing 21 recently available downscaled global climate models generated by NASA ( NEX-GDDP) to evaluate drought projections over various regions across the United States. Drought is investigated through a multi-model dual-index dual-scenario approach to probabilistically analyse drought attributes while characterizing the uncertainty in future drought projections. Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index values at the seasonal scale (3 months) are used to project and analyse meteorological drought conditions from 1950 to 2099 at 0.25° spatial resolution. Two future concentration pathways of RCP4.5 and RCP8.5 are considered for this analysis. Accounting for the combined effects of precipitation and temperature variations reveals a considerable aggravation in severity and extent of future drought in the western United States and a tendency toward more frequent and intense summer droughts across the Contiguous United States. [ABSTRACT FROM AUTHOR]

Summary: A first step in understanding the impacts of sediment and controlling the sources of sediment is to quantify the mass loading. Since mass loading is the product of flow and concentration, the quantification of loads first requires the quantification of runoff volume. Using the National Weather Service’s SNOW-17 and the Sacramento Soil Moisture Accounting (SAC-SMA) models, this study employed particle filter based Bayesian data assimilation methods to predict seasonal snow water equivalent (SWE) and runoff within a small watershed in the Lake Tahoe Basin located in California, USA. A procedure was developed to scale the variance multipliers (a.k.a hyperparameters) for model parameters and predictions based on the accuracy of the mean predictions relative to the ensemble spread. In addition, an online bias correction algorithm based on the lagged average bias was implemented to detect and correct for systematic bias in model forecasts prior to updating with the particle filter. Both of these methods significantly improved the performance of the particle filter without requiring excessively wide prediction bounds. The flow ensemble was linked to a non-linear regression model that was used to predict suspended sediment concentrations (SSCs) based on runoff rate and time of year. Runoff volumes and SSC were then combined to produce an ensemble of suspended sediment load estimates. Annual suspended sediment loads for the 5years of simulation were finally computed along with 95% prediction intervals that account for uncertainty in both the SSC regression model and flow rate estimates. Understanding the uncertainty associated with annual suspended sediment load predictions is critical for making sound watershed management decisions aimed at maintaining the exceptional clarity of Lake Tahoe. The computational methods developed and applied in this research could assist with similar studies where it is important to quantify the predictive uncertainty of pollutant load estimates. [Copyright &y& Elsevier]

In an earlier global climate-change study, air temperature and precipitation data for the entire twenty-first century simulated from five general circulation models were used as input to precalibrated watershed models for 14 selected basins across the United States. Simulated daily streamflow and energy output from the watershed models were used to compute a range of statistics. With a side-by-side comparison of the statistical analyses for the 14 basins, regional climatic and hydrologic trends over the twenty-first century could be qualitatively identified. Low-flow statistics (95%% exceedance, 7-day mean annual minimum, and summer mean monthly streamflow) decreased for almost all basins. Annual maximum daily streamflow also decreased in all the basins, except for all four basins in California and the Pacific Northwest. An analysis of the supply of available energy and water for the basins indicated that ratios of evaporation to precipitation and potential evapotranspiration to precipitation for most of the basins will increase. Probability density functions (PDFs) were developed to assess the uncertainty and multimodality in the impact of climate change on mean annual streamflow variability. Kolmogorov--Smirnov tests showed significant differences between the beginning and ending twenty-first-century PDFs for most of the basins, with the exception of four basins that are located in the western United States. Almost none of the basin PDFs were normally distributed, and two basins in the upper Midwest had PDFs that were extremely dispersed and skewed. [ABSTRACT FROM AUTHOR]

• Regionalization is applied to stage-discharge rating curve parameters. • Homogeneous river reach catchments with similar river dynamics are identified. • The behavioral-based hierarchical clustering outperforms the attribute-based methods. Stage-discharge rating curves are empirical power functions that represent river dynamics by explaining the temporal variations of river discharge with respect to stage fluctuations. The widespread access to measured river discharge and stage at gauging stations in the United States provides a valuable opportunity to perform regionalization techniques and transfer this information to ungauged areas. In this study, a recently proposed regionalization framework that integrates the behavioral-based hierarchical clustering (BHC) with classification and aggregation, namely BHC-CA, is applied to 128 rating curves in the Arkansas-White-Red region to detect homogeneous river reach catchments with similar river dynamics. Additionally, the performance of the BHC, compared to commonly used attribute-based hierarchical clustering (AHC) algorithms, is tested. The BHC-CA framework divides the entire study area into three homogeneous clusters, creates three regional rating curves and finds a logistic regression with 7 physical/climatic attributes as the best classifier for transferring regional rating curves to ungauged catchments. Results also demonstrate the effectiveness of the BHC and its usefulness as an alternative clustering algorithm for the regionalization of environmental model problems. [ABSTRACT FROM AUTHOR]

Correction: Engström, J., et al. Drought Vulnerability in the United States: An Integrated Assessment. [Extracted from the article]

Droughts are among the costliest natural hazards in the U.S. and globally. The severity of the hazard is closely related to a region's ability to cope and recover from the event, an ability that depends on the region's sensitivity and adaptive capacity. Here, the vulnerability to drought of each state within the contiguous U.S. is assessed as a function of exposure, sensitivity, and adaptive capacity, using socio-economic, climatic, and environmental indicators. The division of vulnerability into three sub-indices allows for an assessment of the driver(s) of vulnerability of a state and as such provides a foundation for drought mitigation and planning efforts. In addition, a probabilistic approach is used to investigate the sensitivity of vulnerability to the weighting scheme of indicators. The resulting geographic distribution of relative vulnerability of the states is partially a reflection of their heterogeneous climates but also highlights the importance of sustainable adaptation of the local economy to water availability in order to reduce sensitivity and to limit the impact of drought. As such, the study at hand offers insights to local and regional planners on how to effectively distribute funds and plan accordingly in order to reduce state-level drought vulnerability today and in the future. [ABSTRACT FROM AUTHOR]

• Soil erosion is estimated at 2.32 Mg/ha/yr across all LULC classes in the US. • Croplands are responsible for 44% of soil erosion on 17% of US land. • Cultivated and other vegetated lands contribute to 90% of soil erosion. • The highest soil erosion rates are seen in corn, soybean, and wheat lands. • Future urban and cropland expansion will increase soil erosion rates in the US. Brought on by anthropogenic actions, accelerated soil erosion inflicts extreme changes in terrestrial and aquatic ecosystems. These field-scale (30 m) changes have neither been fully surveyed in the present, nor predicted for a probable future. Water-driven soil erosion (i.e., sheet and rill erosion) rates across the contiguous United States were estimated for the present, and then predicted for the future using three alternative Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios (2.6, 4.5, and 8.5) of the Coupled Model Intercomparison Project Phase 6 (CMIP6). The G2 erosion model which is integrated with Machine Learning (ML) and Remote Sensing (RS) techniques were used to estimate soil erosion based on gauge observations of long-term precipitation, and climate and land use land cover (LULC) scenarios. The baseline model (2020) estimated soil erosion rates of 2.32 Mg ha−1 yr−1 under current conservation agriculture practices (CPs). Maintaining current CPs, future scenarios predict an 8 % to 21 % increase in soil erosion under different combinations of SSP-RCP climate and LULC change scenarios. The findings of this study can help policy makers for future conservation planning on maintaining soil fertility, mitigating environmental impacts, and promoting food security. [ABSTRACT FROM AUTHOR]

Satellite remote sensing provides unprecedented information on near-surface soil moisture at a global scale, enabling a wide range of studies such as drought monitoring and forecasting. Data Assimilation (DA) has been recognized as an effective means to incorporate such observations into hydrologic models to better predict and forecast hydroclimatic variables. In this study, we use a recently developed Evolutionary Particle Filter with Markov Chain Monte Carlo (EPFM) approach to assimilate Soil Moisture Active Passive (SMAP) soil moisture data into Variable Infiltration Capacity (VIC) hydrologic model to provide more reliable topsoil layer moisture (0~–5 cm) over the entire Continental United States (CONUS). The EPFM outperformed an Ensemble Kalman filter (EnKF) in terms of correlations and the unbiased root mean square error (ubRMSE) with in situ measurements from the Soil Climate Analysis Network (SCAN) and the United States Climate Reference Network (USCRN). Also, we used a multivariate probability distribution based on a Copula function to integrate the posterior soil moisture, precipitation (from the North American Land Data Assimilation System (NLDAS)) and evapotranspiration (from the Moderate Resolution Imaging Spectroradiometer (MODIS)) information to develop a new integrated drought index, i.e. SPESMI. To validate the usefulness of the developed integrated drought index, we compared the drought events detected by this index with those reported by the United States Drought Monitor (USDM). The results indicated a strong temporal consistency of the drought areas detected by our approach and the USDM over the entire period of study (April 2015 to June 2018). In addition to such promising results, we noticed that our approach could capture the flash drought in 2017 in the U.S. Northern Plains earlier than the USDM, and could identify some severe to extreme drought events that had been underestimated by the USDM. Moreover, the SPESMI has a high correlation with the yield loss of spring and winter wheat in the United States. This novel drought monitoring framework can serve as an independent and potentially complementary drought monitoring system. • SMAP soil moisture is assimilated by the Evolutionary Particle Filter with MCMC (EPFM). • The EPFM outperforms the EnKF method in soil moisture assimilation for most in-situ stations. • A multivariate drought index (SPESMI) is developed based on precipitation, PET and soil moisture. • The SPESMI drought index can detect some drought events that were underestimated by the USDM. [ABSTRACT FROM AUTHOR]

• This study presents a probabilistic crop yield prediction framework • Bayesian multi-modeling is used to integrate multiple Deep NNs outputs • Probabilistic soybean crop yield estimation was provided across three states in the United States An imperative aspect of agricultural planning is accurate yield prediction. Artificial Intelligence (AI) techniques, such as Deep Learning (DL), have been recognized as effective means for achieving practical solutions to this problem. However, these approaches most often provide deterministic estimates and do not account for the uncertainties involved in model predictions. This study presents a framework that employs the Bayesian Model Averaging (BMA) and a set of Copula functions to integrate the outputs of multiple deep neural networks, including the 3DCNN (3D Convolutional Neural Network) and ConvLSTM (Convolutional Long Short-Term Memory), and provides a probabilistic estimate of soybean crop yield over a hundred counties across three states in the United States. The results of this study show that the proposed approach produces more accurate and reliable soybean crop yield predictions than the 3DCNN and ConvLSTM networks alone while accounting for the models' uncertainties. [ABSTRACT FROM AUTHOR]

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., (Copyright © 2018 Elsevier Ltd. All rights reserved.)