A multivariate approach for statistical assessments of compound extremes

Background: A deeper understanding of how heat wave definition affects the relationship between heat exposure and health, especially as a function of rurality, will be useful in developing effective heat wave warning systems.Objective: We compared the relationships between different heat wave index (HI) definitions and preterm birth (PTB) and nonaccidental death (NAD) across urban and rural areas.Methods: We used a time-stratified case-crossover design to estimate associations of PTB and NAD with heat wave days (defined using 15 HIs) relative to non–heat wave control days in Alabama, USA (1990–2010). ZIP code–level HIs were derived using data from the North American Land Data Assimilation System. Associations with heat wave days defined using different HIs were compared by bootstrapping. We also examined interactions with rurality.Results: Associations varied depending on the HI used to define heat wave days. Heat waves defined as having at least 2 consecutive days with mean daily temperatures above the 98th percentile were associated with 32.4% (95% CI: 3.7, 69.1%) higher PTB, and heat waves defined as at least 2 consecutive days with mean daily temperatures above the 90th percentile were associated with 3.7% (95% CI: 1.1, 6.3%) higher NAD. Results suggest that significant positive associations were more common when relative—compared with absolute—HIs were used to define exposure. Both positive and negative associations were found in each rurality stratum. However, all stratum-specific significant associations were positive, and NAD associations with heat waves were consistently positive in urban strata but not in middle or rural strata.Conclusions: Based on our findings, we conclude that a relative mean-temperature-only heat wave definition may be the most effective metric for heat wave warning systems in Alabama.Citation: Kent ST, McClure LA, Zaitchik BF, Smith TT, Gohlke JM. 2014. Heat waves and health outcomes in Alabama (USA): the importance of heat wave definition. Environ Health Perspect 122:151–158; http://dx.doi.org/10.1289/ehp.1307262

High temperatures and heat waves are related but not synonymous concepts. Heat waves, generally understood to be acute periods of extreme warmth, are relevant to a wide range of stakeholders because of the impacts that these events have on human health and activities and on natural environments. Perhaps because of the diversity of communities engaged in heat wave monitoring and research, there is no single, standard definition of a heat wave. Experts differ in which threshold values (absolute versus relative), duration and ancillary variables to incorporate into heat wave definitions. While there is value in this diversity of perspectives, the lack of a unified index can cause confusion when discussing patterns, trends, and impacts. Here, we use data from the North American Land Data Assimilation System to examine patterns and trends in 15 previously published heat wave indices for the period 1979-2011 across the Continental United States. Over this period the Southeast region saw the highest number of heat wave days for the majority of indices considered. Positive trends (increases in number of heat wave days per year) were greatest in the Southeast and Great Plains regions, where more than 12 % of the land area experienced significant increases in the number of heat wave days per year for the majority of heat wave indices. Significant negative trends were relatively rare, but were found in portions of the Southwest, Northwest, and Great Plains.

To better understand error and spatial variability sources of soil moisture simulated with land surface models, observed and simulated values of soil moisture (using offline simulations with the Noah land surface model with four soil layers and approximately 1-km horizontal grid spacing) were compared. This comparison between observed and modeled daily values of soil moisture was performed over the Lower Mississippi Delta region during summer–fall months 2004–06. The Noah simulations covered the 2.5° × 2.5° latitude–longitude domain and were forced by the North American Land Data Assimilation System (NLDAS) atmospheric forcing fields. Hourly soil moisture measurements and other data, including local meteorological and soil physical properties data from 12 Soil Climate Analysis Network (SCAN) sites, were used. The results show that both the observed and simulated level of soil moisture depend critically on the specified–sampled soil texture. Soil types with a relatively high observed clay content (more than 50% of weight) retain more water as a result of low water diffusivity than silty–sandy soils with 20% or less clay, provided that other conditions are the same. This fact is in agreement with previous studies and implies a strong soil texture control (through related hydraulic parameters) on the accuracy of simulated soil moisture. Sensitivity tests using the Noah model were performed to assess the effect of using the hydraulic parameters related to the site-specific soil texture on soil moisture quality. Indeed, at some SCAN sites, the errors (root-mean-square difference and bias) were reduced. Simulated soil moisture showed at least a 50% reduction when the site-specific soil texture was used in Noah simulations compared to those derived from the State Soil Geographic (STATSGO) data. The most significant improvement of simulated soil moisture was observed within the top 0–10 cm layer where an original positive bias (an excessive wetness) was almost eliminated. Meanwhile, excessive dryness (negative soil moisture bias), which was dominant within the second and third model layers, was also reduced. These improvements are expected to be valid at sites/regions with low (<0.3) vegetation fraction.

This study reports results from an analysis of the relationship between atmospheric forcing and model‐simulated water and energy fluxes for the North American Land Data Assimilation System Project Phase 2 (NLDAS‐2). The relationships between mean monthly precipitation and total runoff are stronger in the Sacramento (SAC) and variable infiltration capacity (VIC) models, which grew out of the hydrological community, than in the Noah and Mosaic models, which grew out of the soil‐vegetation‐atmosphere transfer (SVAT) community. The reverse is true for the relationship between mean monthly precipitation and evapotranspiration. In addition, surface energy fluxes in VIC are less sensitive to model forcing (except for air temperature) than those in the Noah and Mosaic model. Notwithstanding these general conclusions, the relationships between forcings and model‐simulated water and energy fluxes for all models vary for different seasons, variables, and regions. These findings will ultimately inspire a combination of SVAT‐type model energy components with hydrological model water components to develop a SVAT‐hydrology model to improve both evapotranspiration and total runoff simulations. Copyright © 2011 John Wiley & Sons, Ltd.

Accurate quantitative precipitation forecasts are essential for water resources management, agriculture, and other applications. Soil moisture is among the most important boundary conditions affecting the overlying atmosphere. It can affect precipitation either directly via evapotranspiration, affecting the moisture supply to the atmosphere, or indirectly, changing sensible heat fluxes, which can affect the structure of storms and their associated distributions of precipitation. In this study we investigate the impact of soil moisture uncertainty on simulated precipitation over the conterminous United States (CONUS) with the Weather Research and Forecasting model. We selected two 30‐day periods to conduct simulation scenarios with 3‐km horizontal grid spacing over the CONUS, where the different simulations were identical except for their initial soil moisture and temperature states which were derived from three different products: Global Forecast System analyses, the National Center for Atmospheric Research (NCAR) continuously cycling ensemble Kalman filter (EnKF) data assimilation system, and the North American Land Data Assimilation System (NLDAS‐2). Results indicate different characteristics of the soil moisture–precipitation relationship during the cold and warm periods and under different climate and meteorological conditions. Better representation of initial soil moisture does not consistently provide an improvement in the simulation of precipitation. Our results suggest that soil moisture affects both the magnitude and spatial distribution of precipitation. The impact of soil moisture on precipitation is more pronounced under drier soil and strong thermal and weak synoptic forcing, while in areas with complex terrain the soil moisture‐precipitation coupling becomes weaker.

Soil moisture observations from seven observational networks (spanning portions of seven states) with different biome and climate conditions were used in this study to evaluate multimodel simulated soil moisture products. The four land surface models, including Noah, Mosaic, Sacramento soil moisture accounting (SAC), and the Variable Infiltration Capacity model (VIC), were run within phase 2 of the North American Land Data Assimilation System (NLDAS-2), with a ⅛° spatial resolution and hourly temporal resolution. Hundreds of sites in Alabama, Colorado, Michigan, Nebraska, Oklahoma, West Texas, and Utah were used to evaluate simulated soil moisture in the 0–10-, 10–40-, and 40–100-cm soil layers. Soil moisture was spatially averaged in each state to reduce noise. In general, the four models captured broad features (e.g., seasonal variation) of soil moisture variations in all three soil layers in seven states, except for the 10–40-cm soil layer in West Texas and the 40–100-cm soil layer in Alabama, where the anomaly correlations are weak. Overall, Mosaic, SAC, and the ensemble mean have the highest simulation skill and VIC has the lowest simulation skill. The results show that Noah and VIC are wetter than the observations while Mosaic and SAC are drier than the observations, mostly likely because of systematic errors in model evapotranspiration.

SUMMARY Crustal deformation caused by hydrological processes has long been detected using space geodetic techniques, yet questions remain about the relative contributions of surface water and groundwater to the geodetic signals in different regions. Here, we investigate forward models of elastic loading deformation caused by a variety of water-storage changes within the Great Lakes region, including fluctuations in lake-water volume, soil moisture and snow load. We use lake-level data from the Great Lakes Environmental Research Laboratory, soil-moisture content from the North American Land Data Assimilation System (NLDAS), snow load from the Snow Data Assimilation System (SNODAS) and background hydrological load at the global scale from Gravity Recovery and Climate Experiment (GRACE). We compare the modelled surface deformation with estimates of hydrological loading deformation inferred from Global Navigation Satellite System (GNSS) measurements. We find that seasonal deformation measured by GNSS is dominated by regional-scale hydrological loading based on strong correlations with the modelled loading displacements. The mean correlation coefficient for the study network is 0.56. The correlation coefficients vary spatially within the study region and exceed 0.9 at some stations near to the Great Lakes. We assess the relative contribution of each individual hydrological component to the total integrated hydrological load. We find that soil moisture consistently explains the largest percentage (27–69 per cent) of the total vertical loading deformation for 87 per cent of GNSS stations in the Great Lakes region. Snow loading and soil moisture contribute relatively equally in the northern reaches of the study area (e.g. Canadian shield, northern Superior basin). Lake loading accounts for about 10–25 per cent of the total loading signal in the immediate vicinity of the lakes. We also investigate the sensitivities of the surface loading displacements to three different Earth models, including two with lateral variations in structure. The structural variations considered here have limited impact (<0.2 mm) on the predicted hydrological loading displacements and could be neglected at the current level of observational precision.

If not properly account for, autocorrelated retrieval errors can lead to inaccurate results in soil moisture data analysis and reanalysis. Here we propose a more generalized form of the triple collocation analysis algorithm (GTC) capable of decomposing the total error variance of remotely sensed surface soil moisture retrievals into their autocorrelated and the serially white components. Synthetic tests demonstrate the robustness and accuracy of GTC—even in the presence of significant temporal data gaps. However, the accuracy of GTC error autoregressive parameter estimates is relatively more sensitive to temporal data availability. In addition, land surface model soil moisture predictions collected from phase 2 of the North American Land Data Assimilation System and remotely sensed surface soil moisture retrievals obtained from the European Space Agency Climate Change Initiative (ESA CCI) are applied for a real data demonstration. Despite expectations to the contrary, significant error autocorrelation is found in the remotely sensed‐based ESA CCI soil moisture data sets. In particular, ESA CCI‐Act (i.e., the subset of ESA CCI soil moisture retrievals based on active scattomotter data) demonstrates the largest autoregressive parameters over low biomass areas. Conversely, ESA CCI‐Pas retrievals (based on passive radiometer data) have larger error autoregressive parameters over high biomass areas. As such, results clarify circumstances in which errors in remotely sensed surface soil moisture retrievals cannot be considered serially white.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> We examine the Naval Research Laboratory (NRL) blended satellite (NRL-Blend) High-Resolution Precipitation Product (HRPP) as a proxy for a Global Precipitation Mission (GPM)-era HRPP by using the NRL-Blend for precipitation forcing in land surface models (LSM). We use the existing (late 2008) constellation of low Earth orbiting (LEO) microwave-based satellite platforms as a baseline to examine the impact of omitting several satellite and sensor types from future GPM-era HRPPs. A response of 1-m soil water content (SWC) to different precipitation forcing represented by six NRL-Blend satellite/sensor omission scenarios was investigated using simulations over the central United States with the Noah and Mosaic land surface models (LSM). The LSMs were integrated over a domain encompassing the Arkansas-Red River basin, using the North American Land Data Assimilation System (NLDAS) atmospheric forcing (except for precipitation). Both spatial and temporal statistical properties of the SWC response were examined. Both LSMs predicted a rather consistent geographical response of the 1-m SWC to different precipitation inputs, having positive/negative SWC monthly mean anomalies in western/eastern parts of the domain. The biggest impact was due to the omission of either the crosstrack microwave sounders, or the morning local time crossing satellites. On the other hand, omission of afternoon local time crossing satellites in the NRL-Blend resulted in the smallest impact upon the soil moisture simulated both with the Noah and Mosaic models. Although the relative magnitude of these SWC changes is small, these results suggest the importance of the crosstrack microwave sounders for future GPM constellations. </para>

This paper investigates surface temperature variables as they relate to passive microwave-derived surface freeze/thaw (FT) state and assesses the accuracy of such FT products relative to surface temperature. Utilizing retrievals from the soil moisture active/passive (SMAP), advanced microwave scanning radiometer, and special sensor microwave imager instruments, surface FT records have previously been derived globally. Moderate Resolution Imaging Spectroradiometer skin temperature, North American Land Data Assimilation System (NLDAS) skin, 0–10-cm soil layer, and 2-m air temperatures are compared to the various FT state products (FTSPs) by defining the threshold for FT state transitions at 0 °C. This paper utilizes the 2015–2016 overlap period in FT records within the NLDAS domain. Spatial variability of classification accuracy (CA) is then investigated over the study area. A proportional differencing method also enables the identification of biases between FTSPs and surface temperature variables. Additionally, by analyzing probability distribution functions of FTSPs associated with temperature values, we assess the distribution of temperature variables as they relate to FT classifications. Classification agreement is shown to vary with sensor configuration, seasonality, and retrieval time. Air temperature is found to have the highest CAs across FTSPs (81%–91%), while NLDAS soil exhibits a close relationship to FTSPs, especially in regard to SMAP products. Finally, ascending ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p.m.</small> ) retrievals are shown to be increasingly linked to the selected temperature parameters as compared to descending ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a.m.</small> ) observations. This paper contributes to an improved understanding of current FTSPs and will benefit efforts to enhance future FT products.

Accurate rainfall-runoff modelling is particularly challenging due to complex nonlinear relationships between various factors such as rainfall characteristics, soil properties, land use, and temporal lags. Recently, with improvements to computation systems and resources, data-driven models have shown good performances for runoff forecasting. However, the relative performance of common data-driven models using small temporal resolutions is still unclear. This study presents an application of data-driven models using artificial neural network, support vector regression and long-short term memory approaches and distributed forcing data for runoff predictions between 2010 and 2019 in the Russian River basin, California, USA. These models were used to predict hourly runoff with 1–6 h of lead time using precipitation, soil moisture, baseflow and land surface temperature datasets provided from the North American Land Data Assimilation System. The predicted results were evaluated in terms of seasonal and event-based performance using various statistical metrics. The results showed that the long-short term memory and support vector regression models outperforms artificial neural network model for hourly runoff forecasting, and the predictive performance of the models was greater during the wet seasons compared to the dry seasons. In addition, a comparison of the data-driven model results with the National Water Model, a fully distributed physical-based hydrologic model, showed that the long-short term memory and support vector regression models provide comparable performance. The results demonstrate that data-driven models for hourly runoff forecasting are sufficiently predictive and useful in areas where observation systems are not available.

A relationship between the likelihood of wildfires and various drought metrics (soil moisture-based fire potential indices) were examined over the southern part of Mississippi. The following three indices were tested and used to simulate spatial and temporal wildfire probability changes: (1) the accumulated difference between daily precipitation and potential evapotranspiration (P - E); (2) simulated moisture content of the top 10 cm of soil; and (3) the Keetch-Byram Drought Index (KBDI). These indices were estimated from gridded meterological data and Mosaic-simulated soil moisture data available from the North American Land Data Assimilation System (NLDAS-2). The relationships between normalized fire potential index deviations and the probability of at least one fire occurring during the following five consecutive days were evaluated using a 23-year (1986-2008) forest fire record for an evenly spaced grid (0.25° x 0.25°) across the state of Mississippi's coastal plain. Two periods were selected and examined (January-mid June and mid September-December). There was good agreement between the observed and logistic model-fitted fire probabilities over the study area during both seasons. The fire potential indices based on the top 10 cm soil moisture and KBDI had the largest impact on wildfire odds, increasing it by almost 2 times in response to each unit change of the corresponding fire potential index during January-mid-June period and by nearly 1.5 times during mid-September-December. These results suggest that soil moisture-based fire potential indices are good indicators of fire occurrence probability across this region.

This study performs three experiments to calibrate the drought area percentages in the continental United States (CONUS), six U.S. Drought Monitor (USDM) regions, and 48 states downloaded from the USDM archive website. The corresponding three experiments are named CONUS, Region, and State, respectively. The data sets used in these experiments are from the North American Land Data Assimilation System Phase 2 (NLDAS-2). The main purpose is to develop an automated USDM-based approach to objectively generate and reconstruct USDM-style drought maps using NLDAS-2 data by mimicking 10 year (2000–2009) USDM statistics. The results show that State and Region have larger correlation coefficients and smaller root-mean-square error (RMSE) and bias than CONUS when compared to the drought area percentages derived from the USDM, indicating that State and Region perform better than CONUS. In general, State marginally outperforms Region in terms of RMSE, bias, and correlation. Analysis of normalized optimal weight coefficients shows that soil moisture percentiles (top 1 m and total column) play the dominant role in most of the 48 states. The optimal blended NLDAS drought index (OBNDI) has higher simulation skills (correlation coefficient and Nash-Sutcliffe efficiency) in the South, Southeast, High Plains, and Midwest regions when compared to those in the West and Northeast. The highest simulation skills appear in TX and OK. By using optimal equations, we can reconstruct the long-term drought area percentages and OBNDI over the continental United States for the entire period of the NLDAS-2 data sets (January 1979 to present).

While investigating linkages between afternoon peak rainfall amount and land–atmosphere coupling strength, a statistically significant trend in phase 2 of the North American Land Data Assimilation System (NLDAS-2) warm season (April–September) afternoon (1700–2259 UTC) precipitation was noted for a large fraction of the conterminous United States, namely, two-thirds of the area east of the Mississippi River, during the period from 1979 to 2015. To verify and better characterize this trend, a thorough statistical analysis is undertaken. The analysis focuses on three aspects of precipitation: amount, frequency, and intensity at 6-hourly time scale and for each calendar month separately. At the NLDAS-2 native resolution of 0.125° × 0.125°, Kendall’s tau and Sen’s slope estimators are used to detect and estimate trends and the Pettitt test is used to detect breakpoints. Parallel analyses are conducted on both NARR and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), subdaily precipitation estimates. Widespread breakpoints of field significance at the α = 0.05 level are detected in the NLDAS-2 frequency and intensity series for all months and 6-h periods that are absent from the analogous NARR and MERRA-2 datasets. These breakpoints are shown to correspond with a July 1996 NLDAS-2 transition away from hourly 2° × 2.5° NOAA/CPC precipitation estimates to hourly 4-km stage II Doppler radar precipitation estimates in the temporal disaggregation of CPC daily gauge analyses. While NLDAS-2 may provide the most realistic diurnal precipitation cycle overall, users should be aware of this discontinuity and its direct effect on long-term trends in subdaily precipitation and indirect effects on trends in modeled soil moisture, surface temperature, surface energy and water fluxes, snow cover, snow water equivalent, and runoff/streamflow.

Background:The geographic range of the tick Amblyomma americanum, a vector of diseases of public health significance such as ehrlichiosis, has expanded from the southeast of the United States northward during the 20th century. Recently, populations of this tick have been reported to be present close to the Canadian border in Michigan and New York states, but established populations are not known in Canada. Previous research suggests that changing temperature patterns with climate change may influence tick life cycles and permit northward range expansion of ticks in the northern hemisphere.Objectives:We aimed to estimate minimal temperature conditions for survival of A. americanum populations at the northern edge of the tick’s range and to investigate the possibility of range expansion of A. americanum into northern U.S. states and southern Canada in the coming decades.Methods:A simulation model of the tick A. americanum was used, via simulations using climate data from meteorological stations in the United States and Canada, to estimate minimal temperature conditions for survival of A. americanum populations at the northern edge of the tick’s range.Results:The predicted geographic scope of temperature suitability [ annual cumulative degree days (DD) ] included most of the central and eastern U.S. states east of longitude 110°W, which is consistent with current surveillance data for the presence of the tick in this region, as well as parts of southern Quebec and Ontario in Canada. Regional climate model output raises the possibility of northward range expansion into all provinces of Canada from Alberta to Newfoundland and Labrador during the coming decades, with the greatest northward range expansion (up to by the year 2100) occurring under the greenhouse gas (GHG) emissions of Representative Concentration Pathway (RCP) 8.5. Predicted northward range expansion was reduced by approximately half under the reduced GHG emissions of RCP4.5.Discussion:Our results raise the possibility of range expansion of A. americanum into northern U.S. states and southern Canada in the coming decades, and conclude that surveillance for this tick, and the diseases it transmits, would be prudent. https://doi.org/10.1289/EHP5668