Developing a Multivariate Agro‐Meteorological Index to Improve Capturing Onset and Persistence of Droughts Utilizing Vapor Pressure Deficit and Soil Moisture

Abstract Drought is associated with adverse environmental and societal impacts across various regions. Therefore, drought monitoring based on a single variable may lead to unreliable information, especially about the onset and persistence of drought. Previous studies show vapor pressure deficit (VPD) data can detect drought onset earlier than other drought indicators such as precipitation. On the other hand, soil moisture (SM) is a robust indicator for assessing drought persistence. This study introduces a nonparametric multivariate drought index Vapor Pressure Deficit Soil moisture standardized Drought Index (VPDSDI) which is developed by combining VPD with SM information. The performance of the multivariate index in terms of drought onset detection is compared with the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI) for six major drought events across the United States including three rapidly developing drought events (this term refers to flash droughts that develop on monthly scales) and three conventional drought events. Additionally, the performance of the proposed index in detecting drought persistence is compared with the Standardized Soil moisture Index (SSI), which is an agricultural drought index. Results indicate the multivariate index detects drought onset always earlier than SPI for conventional events, but VPDSDI detects drought onset earlier than or about the same time as SPEI for rapidly developing droughts. In terms of persistence, VPDSDI detects persistence almost identical to SSI for both rapidly developing and conventional drought events. The results also show that combining VPD with SM reduces the high variability of VPD and produces a smoother index which improves the onset and persistence detection of drought events leveraging VPD and SM information

Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States

Wildfire danger assessment is essential for operational allocation of fire management resources; with longer lead prediction, the more efficiently can resources be allocated regionally. Traditional studies focus on meteorological forecasts and fire danger index models (e.g., National Fire Danger Rating System-NFDRS) for predicting fire danger. Meteorological forecasts, however, lose accuracy beyond 10 days; as such, there is no quantifiable method for predicting fire danger beyond 10 days. While some recent studies have statistically related hydrologic parameters and past wildfire area burned or occurrence to fire, no study has used these parameters to develop a monthly spatially distributed predictive model in the contiguous United States. Thus, the objective of this study is to introduce Fire Danger from Earth Observations (FDEO), which uses satellite data over the contiguous United States (CONUS) to enable two-month lead time prediction of wildfire danger, a sufficient lead time for planning purposes and relocating resources. In this study, we use satellite observations of land cover type, vapor pressure deficit, surface soil moisture, and the enhanced vegetation index, together with the United States Forest Service (USFS) verified and validated fire database (FPA) to develop spatially gridded probabilistic predictions of fire danger, defined as expected area burned as a deviation from "normal". The results show that the model predicts spatial patterns of fire danger with 52% overall accuracy over the 2004-2013 record, and up to 75% overall accuracy during the fire season. Overall accuracy is defined as number of pixels with correctly predicted fire probability classes divided by the total number of the studied pixels. This overall accuracy is the first quantified result of two-month lead prediction of fire danger and demonstrates the potential utility of using diverse observational data sets for use in operational fire management resource allocation in the CONUS.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Global Intercomparison of Atmospheric Rivers Precipitation in Remote Sensing and Reanalysis Products

Atmospheric rivers (ARs) play an important role in the total annual precipitation regionally and globally, delivering precious freshwater to many arid/semiarid regions. On the other hand, they may cause intense precipitation and floods with huge socioeconomic effects worldwide. In this study, we investigate AR-related precipitation using 18 years (2001-2018) of globally gridded AR locations derived from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). AR precipitation features are explored regionally and seasonally using remote sensing (Integrated Multi-satellitE Retrievals for GPM version 6 [IMERG V6], daily Global Precipitation Climatology Project version 1.3 [GPCP V1.3], bias-adjusted CPC Morphing Technique version 1 [CMORPH V1], and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks [PERSIANN-CDR]) and reanalysis (MERRA-2 and ECMWF Reanalysis 5th Generation [ERA5]) precipitation products. The results show that most of the world (except the tropics) experience more intense precipitation from AR-related events compared to non-AR events. Over the oceans (especially the Southern Ocean), the contribution of ARs to the total precipitation and extreme events is larger than over land. However, some coastal areas over land are highly affected by ARs (e.g., the western and eastern United States and Canada, Western Europe, North Africa, and part of the Middle East, East Asia, and eastern South America and part of Australia). Although spatial correlations for pairs of IMERG/CMORPH and GPCP/PERSIANN-CDR are fairly high, considerable discrepancies are shown in their estimation of AR-related events (i.e., overall IMERG and CMORPH show a higher fraction of AR-related precipitation). It was found that the degree of consistency between reanalysis and satellite-based products is highly regionally dependent, partly due to the uneven distribution of in situ measurements.6 month embargo; first published online 12 October 2020This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Identifying the psychological components in the book buying behavior of students of medical sciences universities during the corona epidemic

Background and Aim: The emerging corona virus has presented new challenges to the printing industry that were unprecedented before, and these challenges include new opportunities and threats. Also, the coincidence of Corona with the paralyzing American sanctions on Iran's economy has caused an unbridled increase in the cost of books and a sharp decrease in the financial capacity of book buyers, which requires a comprehensive research in accordance with the tremendous changes that the simultaneous impact of these two emerging components of Iran's economy (Corona) and the crippling US sanctions) on the pattern of book buying behavior and makes it necessary for the publishing industry. Therefore, this research has identified the psychological components in the book buying behavior of students of medical sciences universities during the corona epidemic as its main goa. Method: The statistical sample was also selected based on the purposeful/judgmental sampling method from among the managers and decision makers of the University of Medical Sciences in Iran, academic professors and prominent organizational consultants. In order to identify the factors affecting the book buying behavior of students of medical sciences universities during the corona epidemic, the qualitative method of data theory of the foundation has been used. Results: Based on the results, social and cultural factors, branding in the field of books, study values, sales promotion and quality of content and structure are the known factors affecting the book buying behavior of students of medical sciences universities during the Corona epidemic. Conclusion: In relation to the social and cultural factor, we find that most people in our country devote limited minutes to study, while in some countries there is an average of 4 hours of study per day. In relation to branding in the field of books, marketing is particularly important, because attracting the satisfaction of the audience and increasing the purchase of books will promote the culture of reading and reading in the country and will bring about the cultural development of the country

Satellite hydrology observations as operational indicators of forecasted fire danger across the contiguous United States

Traditional methods for assessing fire danger often depend on meteorological forecasts, which have reduced reliability after similar to 10 d. Recent studies have demonstrated long lead-time correlations between pre-fire-season hydrological variables such as soil moisture and later fire occurrence or area burned, yet the potential value of these relationships for operational forecasting has not been studied. Here, we use soil moisture data refined by remote sensing observations of terrestrial water storage from NASA's Gravity Recovery and Climate Experiment (GRACE) mission and vapor pressure deficit from NASA's Atmospheric Infrared Sounder (AIRS) mission to generate monthly predictions of fire danger at scales commensurate with regional management. We test the viability of predictors within nine US geographic area coordination centers (GACCs) using regression models specific to each GACC. Results show that the model framework improves interannual wildfire-burned-area prediction relative to climatology for all GACCs. This demonstrates the importance of hydrological information to extend operational forecast ability into the months preceding wildfire activity.Jet Propulsion LaboratoryOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

2019–2020 Australia Fire and Its Relationship to Hydroclimatological and Vegetation Variabilities

Wildfire is a major concern worldwide and particularly in Australia. The 2019–2020 wildfires in Australia became historically significant as they were widespread and extremely severe. Linking climate and vegetation settings to wildfires can provide insightful information for wildfire prediction, and help better understand wildfires behavior in the future. The goal of this research was to examine the relationship between the recent wildfires, various hydroclimatological variables, and satellite-retrieved vegetation indices. The analyses performed here show the uniqueness of the 2019–2020 wildfires. The near-surface air temperature from December 2019 to February 2020 was about 1 °C higher than the 20-year mean, which increased the evaporative demand. The lack of precipitation before the wildfires, due to an enhanced high-pressure system over southeast Australia, prevented the soil from having enough moisture to supply the demand, and set the stage for a large amount of dry fuel that highly favored the spread of the fires