Flash flooding is the top weather-related killer, responsible for an average of 140 deaths per year across the United States. Although precipitation forecasting and understanding of flash flood causes have improved in recent years, there are still many unknown factors that play into flash flooding. Despite having accurate and timely rainfall reports, some river basins simply do not respond to rainfall as meteorologists might expect. The Flash Flood Potential Index (FFPI) was developed in order to gain insight into these “problem basins”, giving National Weather Service (NWS) meteorologists insight into the intrinsic properties of a river basin and the potential for swift and copious rainfall runoff.The goal of the FFPI is to quantitatively describe a given sub-basin’s risk of flash flooding based on its inherent, static characteristics such as slope, land cover, land use and soil type/texture. It leverages both Geographic Information Systems (GIS) as well as datasets from various sources. By indexing a given sub-basin’s risk of flash flooding, the FFPI allows the user to see which subbasins are more predisposed to flash flooding than others. Thus, the FFPI can be added to the situational awareness tools which can be used to help assess flash flood risk.

The S&P Global Composite PMI Flash is an economic indicator that measures the activity level of purchasing managers in both the manufacturing and services sectors in the USA.

The S&P Global Composite PMI Flash is an economic indicator that measures the activity level of purchasing managers in both the manufacturing and services sectors in the USA.-2025-05-22

Blockchain data query: Flash Trade FLP vs Index (sFLP.5)

Flash droughts can lead to significant agricultural and ecosystem impacts via rapid land surface desiccation. While gridded weather and climate datasets, land surface models, or widely spaced in situ observations are typically used to quantify flash drought development, coarse spatial data limits the ability to determine fine-scale spatial evolution of flash drought at landscape and ecosystem scales. In this study, a novel approach is introduced to objectively identify flash drought using the land surface water index (LSWI) derived from satellite observations. LSWI is a water-related vegetation index that represents the total water content in vegetation by using the near-infrared and shortwave infrared bands. LSWI was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance (MOD09A1) with a 500 m spatial resolution and an 8-day temporal resolution. When applied to two well-established case studies, LSWI anomalies were able to capture the temporal and spatial evolution of flash drought over Oklahoma during the years of 2011 and 2012. In addition, rapid changes of LSWI during flash drought were comparable across space and time to the reanalysis-based Standardized Evaporative Stress Ratio (SESR), while negative anomalies of LSWI following flash drought corresponded with drought impacts via the United States Drought Monitor. It was found that LSWI was able to identify flash drought with a finer spatial resolution (500 m) and revealed spatial propagation of flash drought events that would not otherwise be seen with coarser meteorological data (e.g., ∼32 km at the lowest latitude for the North American Regional Reanalysis data). Furthermore, LSWI greatly enhanced the ability to detect rapid changes in surface conditions driven by flash drought and was able to provide early warning for drought development when compared with the USDM across Oklahoma. As such, the temporal and spatial evolution of flash drought depicted by LSWI and the presented methodology improves our ability to identify flash drought at high spatial resolution using satellite remote sensing and detect rapid changes in surface conditions. In light of these results, the novel LSWI approach demonstrates that satellite remote sensing applications using an objective technique are advantageous for flash drought detection in near real-time and at fine spatial scales.

Blockchain data query: Flash Trade staked FLP vs index

Blockchain data query: Index Coop: All Products - Flash Mint

This repository contains the Flash Drought Intensity Index (FDII) datasets associated with the manuscript entitled “Multivariate Evaluation of Flash Drought Across the United States”. The FDII explicitly accounts for the two key components of a flash drought – the rapid rate of intensification and the subsequent drought severity.

The S&P Global Composite PMI Flash is an economic indicator that measures the activity level of purchasing managers in both the manufacturing and services sectors in the USA.-2025-01-24

Actual vulnerability to flash floods index, from "very low" to "high" risk.

The S&P Global Composite PMI Flash is an economic indicator that measures the activity level of purchasing managers in both the manufacturing and services sectors in the USA.-2025-11-21

We present a global dataset of flash drought events, meticulously compiled using the Soil Moisture Volatility Index (SMVI), a cutting-edge tool initially applied within the United States. This dataset marks a significant expansion of the SMVI methodology to a global context, offering an essential resource for comprehensively understanding and predicting rapidly evolving drought phenomena. Characterized by detailed information on the onset, duration, and severity of each event, the dataset covers a wide array of climatic zones, thus providing a diverse and inclusive global perspective. A key feature of this dataset is the integration of atmospheric variables, which sheds light on the meteorological factors driving and influencing flash droughts. Such integration allows for an in-depth exploration of the complex interplay between soil moisture and atmospheric conditions, enhancing our understanding of drought dynamics.

This web-map accesses the openly available Winter Storm Severity Index from WPC web mapping service through the NOAA/NWS.

Flash drought monitor (FDM) is the first near-real time monitoring system for operational tracking of flash drought conditions in Spain (https://flash-drought.csic.es). FDM is based on Standardized Precipitation Evapotranspiration Index (SPEI) at short time scale (1-month) and high spatial (1.21 km²) and temporal (weekly) resolution data. Flash drought events were identified following the methodology suggested by Noguera et al. (2020). Thus, we defined a flash drought event as: (i) a minimum length of 4 weeks in the development phase; (ii) a ΔSPEI equal to or <−2 z-units; and (iii) a final SPEI value equal to or <−1.28 z-units (corresponding to return periods of 10 years). See additional details in Noguera et al. (2020).

Here, we provide the datasets used by FDM to monitor flash drought conditions in Spain for the period 1961-2021. We include the SPEI (1-month time scale) dataset used for flash drought identification, as well as meteorological datasets required for SPEI calculation [i.e., precipitation and reference evapotranspiration (ETo)]. Likewise, we include the dataset with the flash drought conditions recorded in Spain for the period 1961-2021, indicating the number of weeks elapsing from the onset of a flash drought by means of the following encoding: value = 0 (no flash drought), value = 1 (flash drought onset), value = 2 (1st week from onset), value = 3 (2nd week from onset), value = 4 (3rd week from onset). All datasets are provided in netCDF (network Common Data Form) format, and contain high spatial (1.21 km²) and temporal (weekly) resolution data for the period 1961-2021.

The precipitation and ETo datasets were created using all daily observational information of precipitation, maximum and minimum air temperature, relative humidity, sunshine duration (as a surrogate of solar radiation) and wind speed from the National Spanish Meteorological Service (AEMET). FAO-56 Penman–Monteith equation (Allen et al., 1998) was used to calculate the reference evapotranspiration (ETo), which is a spatially and temporally comparable metric for the atmospheric evaporative demand (AED). Employing precipitation and ETo data we computed SPEI at a 1-month time scale following the methodology proposed by Vicente-Serrano et al. (2010). Additional details of the datasets development and validation are available in Vicente-Serrano et al. (2017).

References:

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements.

Noguera, I., Domínguez-Castro, F., & Vicente-Serrano, S. M. (2020). Characteristics and trends of flash droughts in Spain, 1961–2018. Annals of the New York Academy of Sciences, 1472(1), 155– 172. https://doi.org/10.1111/nyas.14365

Vicente-Serrano, S. M., Beguería, S., & López-Moreno, J. I. (2010). A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. Journal of Climate, 23(7), 1696–1718. https://doi.org/10.1175/2009JCLI2909.1

Vicente-Serrano, S. M., Tomas-Burguera, M., Beguería, S., Reig, F., Latorre, B., Peña-Gallardo, M., et al. (2017). A High Resolution Dataset of Drought Indices for Spain. Data, 2(3), 22. https://doi.org/10.3390/data2030022

The S&P Global Composite PMI Flash is an economic indicator that measures the activity level of purchasing managers in both the manufacturing and services sectors in the USA.-2025-02-21

The S&P Global Composite PMI Flash is an economic indicator that measures the activity level of purchasing managers in both the manufacturing and services sectors in the USA.-2025-08-21

Russia Consumer Price Index (CPI): Prev Month=100: TV and Radio Merchandise: Memory Card (Flash Card) data was reported at 98.890 Prev Mth=100 in Dec 2018. This records a decrease from the previous number of 99.380 Prev Mth=100 for Nov 2018. Russia Consumer Price Index (CPI): Prev Month=100: TV and Radio Merchandise: Memory Card (Flash Card) data is updated monthly, averaging 100.260 Prev Mth=100 from Jan 2012 (Median) to Dec 2018, with 84 observations. The data reached an all-time high of 106.790 Prev Mth=100 in Dec 2014 and a record low of 98.690 Prev Mth=100 in Jun 2016. Russia Consumer Price Index (CPI): Prev Month=100: TV and Radio Merchandise: Memory Card (Flash Card) data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IA010: Consumer Price Index: Previous Month=100: Non Food.

The flash-flood susceptibility index (FFSI) represents the relative potential of Ecuador catchments to generate a flash flood when significant local rainfall occurs. The FFSI is calculated for each HydroSHEDS level 12 catchment units (Lehner et al. 2008, Yamazaki et al. 2014) of Ecuador, using a weighted mean of 7 commonly used flash flood drivers related to the hypsometry, drainage network, and surface properties of catchments.

• Hypsometry characteristics : 1-catchment mean slope and 2- mean curvature. Derived from NASA's Shuttle Radar Topography Mission (SRTM) 90 m Digital Elevation Model (DEM) v4.1 (Jarvis et al. 2008), and cleaned using the Zevenbergen and Thorne (1987) 2nd-degree polynomial adjustment algorithm.

• Drainage network characteristics : 1- Upslope contributing area of a basin, 2- Cumulative drainage density and 3- mean drainage strahler order. Extracted from the World Wildlife Fund (WWF) HydroSHEDS v1 global data, at a resolution of 15 arc-seconds, level-12 hydrological basins (Lehner et al. 2008, Yamazaki et al. 2014) and river routing networks (Lehner and Grill 2013).

• Surface properties: 1- mean Land Use Land Cover (LULC) Index calculated from Copernicus Global Land Operation products (Buchhorn et al., 2020) and 2- the mean ISRIC SoilGrid Sand Fraction to account for the infiltration potential of soils (Hengl et al. 2017).

The weights of each indicators have been estimated from PCA analysis after normalization of the indicators For more details on the methods, see Kruczkiewicz et al. 2021.

The final normalized FFSI results is available, as well as the discretized FFSI into an index (1-10) computed for this case study using a rule-based approach specific to the context of Ecuador and the normalized FFSI spatial distribution.

The data are downloadable as ESRI Shapefile format. For each of the 1903 HydroSHED (level12) catchment of Ecuador, it contains the HydroSHED unique ID field, the 7 raw indicators, the final normalized FFSI and the reclassified FFSI (1-10).

South Korea PPI: Flash memory data was reported at 73.680 2020=100 in Mar 2025. This records an increase from the previous number of 69.460 2020=100 for Feb 2025. South Korea PPI: Flash memory data is updated monthly, averaging 156.710 2020=100 from Jan 2005 (Median) to Mar 2025, with 243 observations. The data reached an all-time high of 1,677.700 2020=100 in Feb 2005 and a record low of 47.680 2020=100 in Jul 2023. South Korea PPI: Flash memory data remains active status in CEIC and is reported by The Bank of Korea. The data is categorized under Global Database’s South Korea – Table KR.I045: Producer Price Index: 2020=100.

This table presents figures on the price development of a package of goods and services that an average household on the islands of Bonaire, Sint Eustatius and Saba purchases. This is referred to as the consumer price index (CPI). In addition, the table shows the quarterly and annual change of the CPI. The figures are available for 12 product groups. For each product group, it also shows how much consumers spend on it in relation to their total expenditure. We call this the weighting coefficient.

The table also includes the flash estimate for Bonaire. The flash estimate is made only for the total level.

Data available from: first quarter 2010 and for the flash estimate from the first quarter of 2025.

Status of figures: price indices in this table for the fourth quarter of 2025 are provisional. At the publication for the first quarter of 2026, they will be final.

The flash for Bonaire gives an indication of the price development at the total level for Bonaire during one quarter. The flashes are calculated based on the first and first two months of a quarter, respectively. In the second month of a quarter the CPI changes are published based on the data of the first month. In the third month of the quarter, a second flash follows based on the data for the first two months of the quarter. The second estimate overwrites the first estimate and the second estimate is overwritten, after the end of the quarter, by the provisional quarterly index. The flash is not suitable to be used for indexation, hence no indices are published. The flash is marked as provisional.

When will there be new figures?

The next flash estimate will be published in December 2025. The new price indices will be published in January 2026.