Supernovae (SNe) may have affected Earth's atmosphere during Late Quaternary (50 ka-present) time and be detectible in cosmogenic isotopic records. Supernova remnants (SNRs) at distances <2.3 kpc provide a revised chronology of SNe and predicted hard photons received by Earth. Calculated fluences assume X-ray and gamma isotropic emissions of 4 x 10(49) erg within 2 yr. Such are compatible with high-energy observations of extragalactic SNe. Earlier values may be unrealistically small given current knowledge. The radiation events associated with nearby SNRs are compared to dated records of terrestrial environmental changes. Eight SNe may have produced hard photon fluences of 1-6 x 10(24) erg on the terrestrial disc; they were at distances <= 0.6 kpc. The Vela SN (0.29 kpc) produced the highest fluence, at similar to 13 ka. Its predicted environmental effects include abruptly elevated atmospheric C-14, reductions in upper atmosphere O-3 and CH4, increased solar UVB at Earth's surface, possible cooling of the global climate, selective animal extinctions, increased wildfires, and Pt-group dust deposition. All are recorded in terrestrial records commencing at 12.76 ka and the start of the Younger Dryas cold period. Several thousand years earlier, the Hoinga SN (similar to 0.35 kpc, similar to 15 ka) may have caused a single year 30 parts per thousand Delta C-14 rise at 14.32 ka and the Older Dryas cool period. The C-14 production dropped to its previous level by 14.23 ka but a subsequent increase occurred 14-13.9 ka and may record the arrival of associated cosmic radiation. Delta C-14 events at 9.126, 7.209, 2.764, 2.614, 1.175 ka, and 0.957 ka were apparently global and each have plausible SNe candidates of appropriate distances and ages. The nearest SNe appear to be associated with the largest isotope anomalies.

The Mekong Basin’s rapidly growing population and changing water infrastructure (e.g., dams and canals) requires major improvements in observations of river discharge and reservoir storage changes. Floods and droughts can affect food supplies, requiring frequent and long-term observations for evaluation. We use satellite passive microwave radiometry (PMR) to monitor rivers and reservoirs, and compare performance at different frequencies and polarization combinations. PMR from TRMM, AMSR-E, AMSR2, GPM, SMOS, and SMAP sensitively monitors water surface area change at selected satellite gauging reaches (SGRs). These reaches can be measured globally at daily or near-daily intervals from 1998 to present. Rating curves that translate PMR signal to stage and discharge units can be obtained from nearby gauging stations (even if now discontinued) or from hydrologic modeling. We demonstrate the PMR capability to measure river stage/discharge/runoff and lake/reservoir water level as verified with in-situ gauging data for selected locations in the Lower Mekong Basin.

River flow is a fundamental observable in hydrology, but there is no consistent global ground measurement network. Various types of orbital remote sensing are, therefore, well positioned to meet an important observational need, including for hydrological modeling and for understanding trends through time. In previous studies, we showed that passive microwave radiometry (PMR) can measure streamflow over selected locations around the globe with a high correlation to colocated in situ discharge observations. This article demonstrates the potential of low-frequency, L-band NASA Soil Moisture Active and Passive (SMAP) satellite observations for streamflow measurement: an unanticipated but exceptionally valuable use of this sensor. By using the fully polarimetric capability of SMAP with full Stokes parameters, we optimize the polarization combinations of the observations to retrieve accurate river hydrographs from space. Flow measurements over 150 satellite gauging reaches (SGR) are retrieved over different continents, and 14 SGRs provide comparisons to available in situ river gauging data. Results from linear correlation calculations provide coefficients of determination $r^{2}$ of approximately 0.75 for SMAP-based discharge measurements when compared to in situ streamflow observations. SMAP river observations thereby improve river gauging results compared to ESA’s Soil Moisture Ocean Salinity (SMOS) satellite L-band PMR as the analysis indicates typically lower $r^{2}$ values of approximately 0.68 for SMOS.

&lt;p&gt;River stage (surface water level H), discharge (volumetric water flow rate Q), and seasonal ice cover (freeze-up timing F, and break-up timing B) are crucial observables for hydrology and water cycle science.&amp;#160; In-situ river gauging measurements of H, Q, F, and B are laborious and costly to install and maintain at a limited number of locations.&amp;#160; It will be a breakthrough to use satellite data for global river measurements on a nearly-daily basis with multi-decadal data records.&amp;#160; Passive microwave radiometer (PMR) data have been collected from space globally since the 1980s.&amp;#160; Nevertheless, the typical satellite PMR resolution is coarse (10s km), which is much larger than general river widths.&amp;#160; The key question is how PMR can possibly measure the river parameters.&amp;#160;&lt;/p&gt; &lt;p&gt;The answer is physically founded on the first principle of Maxwell equations to derive vector wave equations for all polarization combinations in heterogeneous multi-layered geophysical media.&amp;#160; The wave equations are solved with dyadic Green&amp;#8217;s functions subject to boundary conditions. The renormalization method is applied to determine the effective permittivity in each layer while all multiple wave-boundary interactions are included. To circumvent the limitation of the isothermal condition in the Kirchhoff approach, the fluctuation-dissipation theorem is used to calculate the brightness temperature&lt;sub&gt; &lt;/sub&gt;Tb(h) for the horizontal polarization (the first modified Stoke parameter), Tb(v) for the vertical polarization (the second parameter), the polarization cross-correlation amplitude U (the third parameter), and the phase V (the fourth parameter).&lt;/p&gt; &lt;p&gt;Based on this physical foundation, a protocol to derive the river observables (H, Q, F, and B) is developed due to the high sensitivity of microwave emissivity of water versus ice and soil conveyed in the brightness temperatures. This overcomes and renders the high spatial resolution requirement unnecessary for river remote sensing by wide-swath PMR for global river observations on a daily or near-daily basis. The PMR method relies on the total areal change of river water within the footprint rather than depending on the river width per se.&amp;#160; As such, PMR can measure a narrow river when its meandering makes a sufficient total surface area in the PMR footprint.&amp;#160; The PMR method is also robust against short-term river channel migration and in-stream sand bars that can be changed by river sedimentation and dynamic processes.&lt;/p&gt; &lt;p&gt;To demonstrate the PMR capability for river monitoring, examples of satellite results for river measurements are compared and validated with in-situ river gauging time-series data records for various rivers from the tropics to cold land regions using PMR data at Ka-band such as AMSR-E, AMSR2, TRMM, and GPM and at L-band such as SMOS and SMAP.&amp;#160; The capability to measure global rivers allows PMR satellite missions to address hydrology and water cycle science as a key contribution, including the future Copernicus Imaging Microwave Radiometer (CIMR) to be launched in the 2025+ time frame, further extending the existing long-term data records for river measurements. Moreover, a significant advance of water cycle science is expected with the synergy of PMR together with SWOT successfully launched by NASA in December 2023.&lt;/p&gt;

1. Overview This repository contains datasets used to evaluate potential improvements to flood detectability afforded by combining data collected by Landsat, Sentinel-2, and Sentinel-1 for the first time globally. The datasets were produced as part of the manuscript "A multi-sensor approach for increased measurements of floods and their societal impacts from space" which is currently in review. 2. Dataset Descriptions There are two datasets included here. (a) A global grid of revisit periods of Landsat, Sentinel-1, Sentinel-2 Satellites and their combination [GlobalMedianRevisits.zip] A global dataset of revisit periods of individual satellites and their combination based on a 0.5-degree resolution grid.Revisit periods are defined as the time between two consecutive observations of a particular point on the surface, for the satellite missions Landsat, Sentinel-2 and Sentinel-1. The grid was created using ArcMap 10.8.1 and intersections of the grid were used to create points. For each individual point, average revisit times (i.e., to account for irregular revisits, downlink issues) were calculated for each individual satellite and the composite of the three satellites. Averaged revisit times for each of these points were calculated based on the number of image tiles that intersected a particular grid point with more than a 30-minute time difference between each other acquired between 01 Jan 2016 and 31 Dec 2020.The following equation is used to calculate revisit periods: Average revisit time for a grid point = (Number of days between 01 Jan 2016 and 31 Dec 2020 (1827)) / (Total Number of Images captured) Only revisits occurring between 82.5 N and 55 S of land grid points are considered; Antarctica is omitted from analysis. For satellite missions that consist of two spacecraft orbiting simultaneously (Sentinel-1 A/B, and Sentinel-2 A/B), images acquired by both satellites were used in average revisit period calculation for a given grid point. Sum totals of image tiles of all three missions are used to calculate composite point-based revisit times. (b) Average revisit periods of satellites for flood records in the DFO database [FloodInfo.zip] Average Revisit Times of Landsat, Sentinel-1, Sentinel-2 and their ensemble are calculated for 5130 flood records in the Dartmouth Flood Observatory's (DFO) flood record database. These were appended to the already existing attributes of the database.

Purpose As stated in the United Nations Global Assessment Report 2022 Concept Note, decision-makers everywhere need data and statistics that are accurate, timely, sufficiently disaggregated, relevant, accessible and easy to use. The purpose of this paper is to demonstrate scalable and replicable methods to advance and integrate the use of earth observation (EO), specifically ongoing efforts within the Group on Earth Observations (GEO) Work Programme and the Committee on Earth Observation Satellites (CEOS) Work Plan, to support risk-informed decision-making, based on documented national and subnational needs and requirements. Design/methodology/approach Promotion of open data sharing and geospatial technology solutions at national and subnational scales encourages the accelerated implementation of successful EO applications. These solutions may also be linked to specific Sendai Framework for Disaster Risk Reduction (DRR) 2015–2030 Global Targets that provide trusted answers to risk-oriented decision frameworks, as well as critical synergies between the Sendai Framework and the 2030 Agenda for Sustainable Development. This paper provides examples of these efforts in the form of platforms and knowledge hubs that leverage latest developments in analysis ready data and support evidence-based DRR measures. Findings The climate crisis is forcing countries to face unprecedented frequency and severity of disasters. At the same time, there are growing demands to respond to policy at the national and international level. EOs offer insights and intelligence for evidence-based policy development and decision-making to support key aspects of the Sendai Framework. The GEO DRR Working Group and CEOS Working Group Disasters are ideally placed to help national government agencies, particularly national Sendai focal points to learn more about EOs and understand their role in supporting DRR. Originality/value The unique perspective of EOs provide unrealized value to decision-makers addressing DRR. This paper highlights tangible methods and practices that leverage free and open source EO insights that can benefit all DRR practitioners.

The global water cycle is accelerating in a changing climate. A key element of the hydrology cycle is surface streamflow, which lacks a global river gauging network with open data shared among international stakeholders. As an alternative, rivers have been monitored from space with multiple orbital sensors that continue providing river measurements worldwide for the past several decades and into the future. With its all-weather and day-and-night capabilities, passive microwave satellite sensors provide a unique data source for near-daily global streamflow monitoring.

Merging observations from multiple satellites is necessary to ensure that extreme hydrological events are consistently observed. Here, we evaluate the potential improvements to flood detectability afforded by combining data collected globally by Landsat, Sentinel-2, and Sentinel-1. The enhanced temporal sampling increased the number of floods with at least 1 useful image (≤20% clouds) from 7% for single sensors to up to 66% for a potential multi-sensor product. As dramatic as the increased coverage is, the socioeconomic impacts are even more tangible. In the pre-Sentinel era, only 22% of the total population displaced by flood events benefitted from having high-resolution images, whereas a potential multi-sensor product would serve 75% of the displaced population. Additionally, the merged dataset could observe up to 100% of floods caused by challenging drivers, e.g., tropical cyclones, tidal surges, including those rarely seen by single sensors, and thereby enable insights into governing mechanisms of these events.

Abstract The timing of ice freeze‐up and break‐up in the Arctic may be responding to climate change. Passive microwave remote sensing is a powerful technique for monitoring this timing. We processed low‐frequency microwave time series from the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission for a set of 31 satellite gauging reaches (SGRs) above 65°N between 2010 and 2020 to determine timing of freeze‐up and break‐up and annual river ice durations. We found indication of progressive ice cover reduction over more than half of the monitored river reaches, with possibly the fastest rate occurring over northeast Russia. Some rivers in high‐latitude North America experienced a slight increase in ice cover. Across the data set, we observed an average 2.2 days shift toward later ice freeze‐up in autumn and an average 0.6 days shift toward earlier ice break‐up in spring, resulting in an average decrease of 3.4 days in ice duration between 2010 and 2020. River reaches with the longest duration of ice cover appeared to have experienced the fastest rate of decrease. A possible reduction of the time lag between air temperature rise or fall and corresponding river ice break‐up and freeze‐up was also observed. Yet results on variability are carefuly interpreted given the short length of the time series (2010–2020) and the low statistical confidence rates calculated for the decadal tendency. Still outcomes are consistent with increases in global and Arctic surface air temperature. Following these time series over the next decade using passive microwave satellite sensors can monitor ice cover duration in the Arctic and will further determine temporal and regional trends.

The present era of climate change and expanding population requires major improvements in sustained observation of global river discharge. Floods and droughts are affecting food supplies, and suspected long‐term trends require appropriate data for evaluation. Orbital remote sensing can address this observational need. Here we use satellite Ka‐ (36.5 GHz) and L‐band (1–2 GHz) passive microwave radiometry (PMR) to monitor river discharge changes and determine what size rivers can be measured and the frequencies and polarization configurations that yield the most robust results. Selected satellite gauging reaches (SGRs) can be measured at near‐daily intervals from 1998 to present (Ka‐band) and 2010 to present (L‐band). The SGRs are 10–36 km in length; the dynamic proportion of water surface area within each varies with river discharge. Due to contrasting dielectric properties, water and land emit different intensities of microwave radiation; thus emission from a mixed water/land pixel decreases as the proportion of water within the pixel increases. Depending on the river and floodplain morphology, water flow area can be a robust indicator of discharge and the microwave sensors can retrieve daily discharge to ±20%. Instead of spatial resolution, it is the sensor measurement precision, geolocation accuracy, and channel and floodplain morphology that most strongly affect accuracy. Calibration of flow area signals to discharge can be performed using nearby ground stations (even if now discontinued) or by comparison to hydrologic modeling.

Flooding is one of the deadliest and costliest natural disasters. Climate change-induced flooding events are increasing worldwide, disproportionately impacting low-income and developing communities. While early warning systems save lives, satellite-based observation systems are vital for the disaster relief and recovery phases. Current satellite-based operational flood products are largely based on either optical remote-sensing methods, which exhibit a limited ability to detect water through clouds and vegetation, or microwave remote sensing, which provides relatively low spatial and temporal resolution. New small satellite constellations using radar or GNSS reflectometry (GNSS-R) have been shown to enhance our ability to overcome these deficiencies. In this work, we quantify the performance of using GNSS-R measurements from the NASA Cyclone Global Navigation Satellite Systems (CYGNSS) satellite constellation to map surface water in South Sudan and the Sudd wetland in comparison with a set of representative operational products. We make quantitative comparisons of our results with operational flood products based on Visible Infrared Imaging Radiometer Suite (VIIRS) and MODIS and with C-band Sentinel-1 synthetic aperture radar. We find that our method detects 35.4% more surface water than Sentinel-1, while the VIIRS- and MODIS-based products underestimate by 4.8% and 83.7%, respectively. We use several metrics commonly used to evaluate classification performance: precision, true positive rate (TPR), true negative rate (TNR), F2-score, and the Matthews correlation coefficient (MCC) and assess the comparisons in this statistical framework. We discuss the consequences of our findings, including ways CYGNSS data may enhance current flood products and assist decision-makers and emergency managers.

As climate change-induced global flooding increases in both frequency and magnitude, having accurate and timely flood maps becomes essential for humanitarian and future flood mitigation efforts. The Dartmouth Flood Observatory (DFO) aids humanitarian organizations and inundation observation research efforts through its archive of historical flood events extending back through 1985, as well as by providing current daily flood maps derived from a combination of observations, and also precipitation-based modeling products. Both could benefit from the addition of microwave observations that penetrate through clouds, rain, and vegetation, such as GNSS-R data now becoming available on a daily basis. In this work, we discuss a current collaboration to combine CYGNSS data with operational MODIS flood maps and evaluate the expected benefits for an example scenario over the recent anomalous flooding in South Sudan.

Flooding affects more people than any other environmental hazard and hinders sustainable development1,2. Investing in flood adaptation strategies may reduce the loss of life and livelihood caused by floods3. Where and how floods occur and who is exposed are changing as a result of rapid urbanization4, flood mitigation infrastructure5 and increasing settlements in floodplains6. Previous estimates of the global flood-exposed population have been limited by a lack of observational data, relying instead on models, which have high uncertainty3,7–11. Here we use daily satellite imagery at 250-metre resolution to estimate flood extent and population exposure for 913 large flood events from 2000 to 2018. We determine a total inundation area of 2.23 million square kilometres, with 255–290 million people directly affected by floods. We estimate that the total population in locations with satellite-observed inundation grew by 58–86 million from 2000 to 2015. This represents an increase of 20 to 24 per cent in the proportion of the global population exposed to floods, ten times higher than previous estimates7. Climate change projections for 2030 indicate that the proportion of the population exposed to floods will increase further. The high spatial and temporal resolution of the satellite observations will improve our understanding of where floods are changing and how best to adapt. The global flood database generated from these observations will help to improve vulnerability assessments, the accuracy of global and local flood models, the efficacy of adaptation interventions and our understanding of the interactions between landcover change, climate and floods. Satellite imagery for the period 2000–2018 reveals that population growth was greater in flood-prone regions than elsewhere, thus exposing a greater proportion of the population to floods.

Historical and current information regarding river discharge is essential, not only from a water management, energy, or global change perspective but also to better analyze, control and forecast flooding. However, globally the number of ground-based gauging stations declines, and data that is measured by ground-based gauging stations is often not, or shared with a considerable delay. It has been demonstrated that existing satellite sensors can be utilized for useful discharge measurements without requiring ground-based information. The DFO – Flood Observatory uses the Advanced Microwave Scanning Radiometer band at 36.5 GHz (e.g. TRMM, AMSR‐E, AMSR2, GMP), pre-processed by the Joint Research Center (JRC) to estimate discharges. With a nearly-daily repeat interval, this microwave signal has been successfully applied to measure water discharge at a global scale, where the calibration of the microwave discharge signal to discharge units is accomplished by comparison to results from a global hydrological numerical model, the Water Balance Model (WBM), for a calibration period. Once calibrated, daily discharge can be back-calculated to January 1998, providing a daily discharge record for more than 20 years. Here we present the methods used to utilize remote sensing to measure discharge. We indicate the challenges and how to overcome these when using a multiple sensor approach to capture daily discharges for over a 20-year period. And we show an example for the Amazon river, comparing the remote sensed discharge data with ground observations for multiple locations. Additionally, applications are shown on how this discharge can be combined with flood extent maps to analyze flood frequency.

River floods and daily runoff have long been measured on the ground at in situ gauging stations. However, today's global hydrologic models require improvements to the quantity and quality of such observations, in order to calibrate flow routing calculations and to monitor areas where any ground information is sparse. Satellite remote sensing of the Earth's water cycle has recently been extended to measurements of daily discharge and runoff, and thereby to flood events. We demonstrate that passive microwave radiometry can monitor river flow changes with considerable accuracy at an appropriate temporal sampling interval for characterizing floods (daily) regardless of cloud cover, over multiple decades and continuing into the future. Ka-band data from the AMSR-2, AMSR-E, TMI and GMI passive microwave radiometers are now being used to provide important river flow status information, with period of records commencing in 1998. Also, the L-band sensors now returning data from the NASA SMAP and ESA SMOS satellites can provide even more sensitive and accurate information over forested floodplains. Moreover, the timing of ice cover establishment and break up can also be tracked along cold region rivers: the annual spring flood can be immediately detected and compared to all previous years. These public observational data can be used to address important science and flood risk issues, such as the effects of climate change on flood frequencies and magnitudes.

Every year riverine flooding affects millions of people in developing countries, due to the large population exposure in the floodplains and the lack of adequate flood protection. Preparedness and monitoring are effective ways to reduce flood risk. State-of-the-art technologies relying on satellite remote sensing as well as numerical hydrological and meteorological predictions can detect and monitor severe flood events at the global scale. This chapter describes the emerging role of the Global Flood Partnership (GFP), a global network of scientists, users, and private and public organizations active in global flood risk management. Currently, a number of GFP partner institutes regularly share results from their experimental products, developed to predict and monitor where and when flooding is taking place in near real time. Products of the GFP have already been used on several occasions by national environmental agencies and humanitarian organizations to support emergency operations and to reduce the overall socioeconomic impacts of disasters. The chapter includes a discussion on existing challenges and ways forward to improve rapid access to flood information and increase resilience to flooding.

Abstract Planetary habitability may be affected by exposure to γ radiation from supernovae (SNe). Records of Earth history during the late Quaternary Period (40 000 years to present) allow testing for specific SN γ radiation effects. SNe include Type Ia white dwarf explosions, Type Ib, c and II core collapses, and many γ burst objects. Surveys of galactic SNe remnants offer a nearly complete accounting for this time and including SN distances and ages. Terrestrial changes in records of the cosmogenic isotope 14C are here compared to SN-predicted changes. SN γ emission occurs mainly within 3 years; average per-event total emissions of 4 × 1049 erg are used for comparison of close events There are 18 SNe ≤ 1.5 kpc, and brief 14C anomalies are reported for eight of the closest. Four are notable (BP is year before 1950 CE): the older Vela SNR and an abrupt 30‰ del 14C rise at 12 740 BP; S165 and a 20‰ rise at 7431 BP; Vela Jr. and a 14‰ rise at 2765 BP; and HB9 and a 9‰ rise at 5372 BP. Rapid-increase anomalies in 14C production have been attributed to cosmic rays from exceptionally large solar flares. However, the proximity and ages of these SNe, the probable size and duration of their γ emissions, the predicted effects on 14C, and the agreement with 14C records together support SNe causation. Also, the supposed solar-caused 14C anomalies at CE 774 and 993 may instead have been caused by the SNe associated with the G190.9-2.2 and G347.3-00.5 remnants. Both are of appropriate age and distance.

Climate-related natural disasters have increased significantly in the last few decades. However, conventional water monitoring systems such as discharge guaging stations, even if data are widely shared, have limitations for monitoring, understanding, and predicting these disasters. This chapter provides an overview of water-related products that the DFO (Dartmouth Flood Observatory)—Flood Observatory, in collaboration with other agencies and initiatives, has developed and shares. Most of these products are experimental and semi-operational and are based on freely-available satellite measurements and images. DFO preserves for public use a digital map record of the Earth’s changing surface water, conducts remote sensing-based water measurement, and mapping in “near real time” for humanitarian purposes, and supports and encourages operational uses of remote sensing-based surface water information. It is our vision that these products can support the larger hydrological community in an effort to reduce the impact of flooding and related natural disasters.

Monitoring changes in flood extent is critical for flood control and mitigation purposes in areas where flooding affects many people and dense infrastructure and properties. Remote sensing can be an effective technique to detect changes in surface water extent and its dynamics. Compared to optical remote sensing, microwave in- formation is suitable for working in any weather condition without severe cloud interference. Usually passive microwave data has a high temporal but a rather coarse spatial resolution, whereas for active microwave data this is reversed and only with ideal satellite constellation observations it can reach high sampling rates. To overcome these spatial and temporal restrictions, we proposed an integrated methodology to combine the passive and active microwave remote sensing and thus provide flood information more frequently at a high resolution. In this paper, a demonstration of the methodology is presented for a flood event occurring in Wuhan, Hubei Province of China in July 2016. The major inundation occurred along the Jushui River, part of the Middle Yangtze River basin. Using the brightness temperature data from a special data set (MEaSUREs), a daily passive microwave signal at the resolution of 3.125 km is used as an indicator to monitor flood occurrence and obtain the flood duration over time. Synthetic Aperture Radar (SAR) imagery (12-day revisit, 10-m spatial resolution) from Sentinel-1 was processed to estimate high resolution flood extents within the time span of the flood based on a threshold-based method together with the High Above Nearest Drainage (HAND) index post-processor. Surface water fraction data generated from SAR images presents a strong correlation with the passive microwave signal (R-2 = 0.84) when using a quadratic polynomial fit. The average bias between surface water fraction computed by the passive microwave signals and SAR observations for water pixels within the Jushui River basin for the validation dates is 0.34%, indicating that this relationship can be applied to interpolate surface water fraction for each pixel along the river and other permanent water bodies for days when SAR observations are not available. This integrated method takes advantage of both passive and active microwave remote sensing and enriches temporally sparse flood data. Given the global coverage of the datasets used in this study, it can be utilized to estimate flood status for other flood-prone areas and thus contribute towards societal flood preparedness and response.

The demand for timely and accurate flood information is well understood and more urgent than ever as flooding has become the most common natural hazard worldwide, impacting people of all continents in both developed and less developed countries. Population and total exposed assets by river flooding are certain to increase in the coming century making the need for flood information even more pressing. Unlike the World Meteorological Organization (WMO), the hydrological community hasn’t been very successful in establishing a global hydrological network of observations through which model simulations and measurements and novel measurement technologies could be exploited. Countries that can afford have departments in place that are tasked to develop flood risk maps and are involved in flood forecasting and relief efforts. However, the majority of countries do not or cannot allocate sufficient funds to support such efforts, nor has there been a global initiative to identify and determine global flood risk areas. Due to the lack of objective knowledge of the impact of flooding during or after an event, first relief agency assistance is often constrained and therefore less effective. These humanitarian catastrophes could be reduced with better transformation of existing observational and modeling technologies into information useful to local populations and decision makers. Here I present new efforts to produce a state-of-the-art, globally-scoped, flood prediction, monitoring capabilities and risk evaluations platform that is interactive and includes high resolution flood information to better serve local needs. The platform builds upon already operational or quasi-operational NASA-supported global flood systems, including the DFO - Flood Observatory satellite-based hydrological gauging stations, UMD Global Flood Monitoring System (GFMS) and have these integrated with the European Commission’s GloFAS, and SAR-based high-resolution flood mapping. This with the intension to have these data layers (flood forecasting, flood extent, and flood history) available to everybody.