Distinguishing between unorganized and organized convection when examining land-atmosphere relationships

In this study, the robustness of a previously developed classification system that categorizes convective thunderstorm events initiated during various synoptic and dynamic conditions is analyzed. This classification system was used to distinguish between organized and unorganized convection and then used to determine whether unorganized convection occurs preferentially over wet or dry soils. The focus is on 12 events that occurred in synoptically benign (SB) environments where the Great Plains low-level jet was not present (noLLJ), and whether these events were accurately classified as unorganized convection is evaluated. Although there is a small sample size, the results show that the classification system fails to differentiate between local unorganized convection and large-scale organized convection under SB–noLLJ conditions. The authors conclude that past studies that have used this classification to study how soil moisture influences unorganized convection should be revisited. Additional variables and/or alternative precipitation datasets should be employed to enhance the robustness of the classification system

Utilizing Objective Drought Severity Thresholds to Improve Drought Monitoring

Drought is a prominent climatic hazard in the south-central United States. Drought severity is frequently classified using the categories established by the U.S. Drought Monitor (USDM). This study evaluates whether the thresholds for the standardized precipitation index (SPI) used by the USDM accurately classify drought severity. This study uses the SPI based on PRISM precipitation data from 1900 to 2015 to evaluate drought severity in Texas, Oklahoma, and Kansas. The results show that the fixed SPI thresholds for the USDM drought categories may lead to a systematic underestimation of drought severity in arid regions. To address this issue, objective drought thresholds were developed at each location by fitting a cumulative distribution function at each location to ensure that the observed frequency of drought in each severity category (D0–D4) matched the theoretical expectations of the USDM. This approach reduces the systematic biases in drought severity across the western portion of the study region. Therefore, we recommend devel-oping objective drought thresholds for each location and SPI time scale (e.g., 1, 3, and 6 months). This method can be used to develop objective drought thresholds for any drought index and climate region of interest

Assessment of observed and model-derived soil moisture-evaporative fraction relationships over the United States Southern Great Plains

The relationship between soil moisture (SM) and evaporative fraction (EF) is an important component of land-atmosphere interactions. Frequently, land-atmosphere studies are based on land-surface models and not on observations. This study examines SM-EF interactions over the United States Southern Great Plains using both in situ observations and simulations from the Variable Infiltration Capacity hydrologic model. Specifically, we evaluate how the relationship between SM and EF varies by season, we determine why these variations occur, and we compare model-derived and observed SM-energy flux relationships. Data from four sites (2004-2008) that are part of the United States Department of Energy's Atmospheric Radiation MeasurementSouthern Great Plains network are used in this study. Results show that SM-EF interactions in both the model and observations are in general agreement with the evaporative regime theory described in past studies. That is, EF is a linear function of SM when SM is between the wilting point and the critical value, and when SM is above the critical value, EF is not dependent on SM. However, SM-EF relationships vary substantially from year to year. EF is a linear function of SM only when daily net radiation is above normal. Our results suggest that the strength of SM-EF interactions is not solely controlled by soil wetness but is also strongly influenced by daily net radiation and meteorological conditions

A Data‐Driven Framework to Characterize State‐Level Water Use in the U.S.

Access to credible estimates of water-use are critical for making optimal operational de-15 cisions and investment plans to ensure reliable and affordable provisioning of water. Fur-16 thermore, identifying the key predictors of water use is important for regulators to pro-17 mote sustainable development policies to reduce water use. In this paper, we propose18 a data-driven framework, grounded in statistical learning theory, to develop a rigorously19 evaluated predictive model of state-level, per capita water use in the US as a function20 of various geographic, climatic and socioeconomic variables. Specifically, we compare the21 accuracy of various statistical methods in predicting the state-level, per capita water use22 and find that the model based on the Random Forest algorithm outperforms all other23 models. We then leverage the Random Forest model to identify key factors associated24 with high water-usage intensity among different sectors in the US. More specifically, ir-25 rigated farming, thermoelectric energy generation, and urbanization were identified as26 the most water-intensive anthropogenic activities, on a per capita basis. Among the cli-27 mate factors, precipitation was found to be a key predictor of per capita water use, with28 drier conditions associated with higher water usage. Overall, our study highlights the29 utility of leveraging data-driven modeling to gain valuable insights related to the water30 use patterns across expansive geographical areas

Assessing United States county-level exposure for research on tropical cyclones and human health

Includes bibliographical references (pages 067007-12-067007-13).Background: Tropical cyclone epidemiology can be advanced through exposure assessment methods that are comprehensive and consistent across space and time, as these facilitate multiyear, multistorm studies. Further, an understanding of patterns in and between exposure metrics that are based on specific hazards of the storm can help in designing tropical cyclone epidemiological research. Objectives: a) Provide an open-source data set for tropical cyclone exposure assessment for epidemiological research; and b) investigate patterns and agreement between county-level assessments of tropical cyclone exposure based on different storm hazards. Methods: We created an open-source data set with data at the county level on exposure to four tropical cyclone hazards: peak sustained wind, rainfall, flooding, and tornadoes. The data cover all eastern U.S. counties for all land-falling or near-land Atlantic basin storms, covering 1996–2011 for all metrics and up to 1988–2018 for specific metrics. We validated measurements against other data sources and investigated patterns and agreement among binary exposure classifications based on these metrics, as well as compared them to use of distance from the storm’s track, which has been used as a proxy for exposure in some epidemiological studies. Results: Our open-source data set was typically consistent with data from other sources, and we present and discuss areas of disagreement and other caveats. Over the study period and area, tropical cyclones typically brought different hazards to different counties. Therefore, when comparing exposure assessment between different hazard-specific metrics, agreement was usually low, as it also was when comparing exposure assessment based on a distance-based proxy measurement and any of the hazard-specific metrics. Discussion: Our results provide a multihazard data set that can be leveraged for epidemiological research on tropical cyclones, as well as insights that can inform the design and analysis for tropical cyclone epidemiological researc

Response of crop yield to different time-scales of drought in the United States: spatio-temporal patterns and climatic and environmental drivers

This article presents an analysis of the response of the annual crop yield in five main dryland cultivations in the United States to different time-scales of drought, and explores the environmental and climatic characteristics that determine the response. For this purpose we analysed barley, winter wheat, soybean, corn and cotton. Drought was quantified by means of the Standardized Precipitation Evapotranspiration Index (SPEI). The results demonstrate a strong response in the interannual variability of crop yields to the drought time-scales in the different cultivations. Moreover, the response is highly spatially variable. Crop types showed considerable differences in the month in which their yields are most strongly linked to drought conditions. Some crops (e.g. winter wheat) responded to drought at medium to long SPEI time-scales, while other crops (e.g. soybean and corn) responded to short or long drought time-scales. The study confirms that the differences in the patterns of crop yield response to drought time-scales are mostly controlled by average climate conditions, in general, and water availability (precipitation), in particular. Generally, we found that there is a weaker link between crop yield and drought severity in humid environments and also that the response tends to occur over longer time-scales

Effectiveness of drought indices in identifying impacts on major crops across the USA

In North America, the occurrence of extreme drought events has increased significantly in number and severity over the last decades. Past droughts have contributed to lower agricultural productivity in major farming and ranching areas across the US. We evaluated the relationship between drought indices and crop yields across the US for the period 1961-2014. In order to assess the correlations with yields from the major cash crops in the country, we calculated several drought indices commonly used to monitor drought conditions, including 4 Palmer-based and 3 multiscalar indices (Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, Standardized Precipitation Drought Index). The 3 multiscalar drought indices were aggregated at 1 to 12 mo timescales. Besides the quantification of the similarities or differences between these drought indices using Pearson correlation coefficients, we identified spatial patterns illustrating this relationship. The results demonstrate that the flexible multiscalar indices can identify drought impacts on different types of crops for a wide range of time periods. The differences in spatial and temporal distribution of the correlations depend on the crop and timescale analyzed, but can also be found within the same type of crop. The moisture conditions during summer and shorter timescales (1 to 3 mo) turn out to be a determining factor for barley, corn, cotton and soybean yields. Therefore, the use of multiscalar drought indices based on both precipitation and the atmospheric evaporative demand (SPEI and SPDI) seems to be a prudent recommendation

Developing a strategy for the national coordinated soil moisture monitoring network

Soil moisture is a critical land surface variable, affecting a wide variety of climatological, agricultural, and hydrological processes. Determining the current soil moisture status is possible via a variety of methods, including in situ monitoring, remote sensing, and numerical modeling. Although all of these approaches are rapidly evolving, there is no cohesive strategy or framework to integrate these diverse information sources to develop and disseminate coordinated national soil moisture products that will improve our ability to understand climate variability. The National Coordinated Soil Moisture Monitoring Network initiative has developed a national strategy for network coordination with NOAA’s National Integrated Drought Information System. The strategy is currently in review within NOAA, and work is underway to implement the initial milestones of the strategy. This update reviews the goals and steps being taken to establish this national-scale coordination for soil moisture monitoring in the United States

Elevated risk of tropical cyclone precipitation and pluvial flood in Houston under global warming

Pluvial floods generated by tropical cyclones (TCs) are one of the major concerns for coastal communities. Choosing Houston as an example, we demonstrate that there will be significantly elevated risk of TC rainfall and flood in the future warming world by coupling downscaled TCs from Model Intercomparison Project Phase 6 models with physical hydrological models. We find that slower TC translation speed, more frequent stalling, greater TC frequency, and increased rain rate are major contributors to increased TC rainfall risk and flood risk. The TC flood risk increases more than the rainfall. Smaller watersheds with a high degree of urbanization are particularly vulnerable to future changes in TC floods in a warming world