Early flood warning and real-time monitoring systems play a key role in flood risk reduction and disaster response decisions. Global-scale flood forecasting and satellite-based flood detection ...systems are currently operating, however their reliability for decision-making applications needs to be assessed. In this study, we performed comparative evaluations of several operational global flood forecasting and flood detection systems, using 10 major flood events recorded over 2012–2014. Specifically, we evaluated the spatial extent and temporal characteristics of flood detections from the Global Flood Detection System (GFDS) and the Global Flood Awareness System (GloFAS). Furthermore, we compared the GFDS flood maps with those from NASA’s two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results reveal that: (1) general agreement was found between the GFDS and MODIS flood detection systems, (2) large differences exist in the spatio-temporal characteristics of the GFDS detections and GloFAS forecasts, and (3) the quantitative validation of global flood disasters in data-sparse regions is highly challenging. Overall, satellite remote sensing provides useful near real-time flood information that can be useful for risk management. We highlight the known limitations of global flood detection and forecasting systems, and propose ways forward to improve the reliability of large-scale flood monitoring tools.
Probabilistic hydro-meteorological forecasts have over the last decades been used more frequently to communicate forecast uncertainty. This uncertainty is twofold, as it constitutes both an added ...value and a challenge for the forecaster and the user of the forecasts. Many authors have demonstrated the added (economic) value of probabilistic over deterministic forecasts across the water sector (e.g. flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty in transforming the probability of occurrence of an event into a binary decision. This paper presents the results of a risk-based decision-making game on the topic of flood protection mitigation, called "How much are you prepared to pay for a forecast?". The game was played at several workshops in 2015, which were attended by operational forecasters and academics working in the field of hydro-meteorology. The aim of this game was to better understand the role of probabilistic forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants' willingness-to-pay for a forecast, the results of the game show that the value (or the usefulness) of a forecast depends on several factors, including the way users perceive the quality of their forecasts and link it to the perception of their own performances as decision-makers.
The Global Flood Awareness System (GloFAS) is a preoperational suite performing daily streamflow simulations to detect severe floods in large river basins. GloFAS defines the severity of a flood ...event with respect to thresholds estimated based on model-simulated streamflow climatology. Hence, determining accurate and consistent critical thresholds is important for its skillful flood forecasting. In this work, streamflow climatologies derived from two global meteorological inputs were compared, and their impacts on global flood forecasting were assessed. The first climatology is based on precipitation-corrected reanalysis data (ERA-Interim), which is currently used in the operational GloFAS forecast, while the second is derived from reforecasts that are routinely produced using the latest weather model. The results of the comparison indicate that 1) flood thresholds derived from the two datasets have substantial dissimilarities with varying characteristics across different regions of the globe; 2) the differences in the thresholds have a spatially variable impact on the severity classification of a flood; and 3) ERA-Interim produced lower flood threshold exceedance probabilities (and flood detection rates) than the reforecast for several large rivers at short forecast lead times, where the uncertainty in the meteorological forecast is smaller. Overall, it was found that the use of reforecasts, instead of ERA-Interim, marginally improved the flood detection skill of GloFAS forecasts.
Early and effective flood warning is essential to initiate timely measures to reduce loss of life and economic damage. The availability of several global ensemble weather prediction systems through ...the “THORPEX Interactive Grand Global Ensemble” (TIGGE) archive provides an opportunity to explore new dimensions in early flood forecasting and warning. TIGGE data has been used as meteorological input to the European Flood Alert System (EFAS) for a case study of a flood event in Romania in October 2007. Results illustrate that awareness for this case of flooding could have been raised as early as 8 days before the event and how the subsequent forecasts provide increasing insight into the range of possible flood conditions. This first assessment of one flood event illustrates the potential value of the TIGGE archive and the grand‐ensembles approach to raise preparedness and thus to reduce the socio‐economic impact of floods.
Our paper aims to improve flood forecasting by establishing whether a global hydrological forecast system could be used as an alternative to a regional system, or whether it could provide additional ...information. This paper was based on the operational Global Flood Awareness System (GloFAS) of the European Commission Copernicus Emergency Management Service, as well as on a regional hydrological forecast system named RHFS, which was created with observations recorded in the Wangjiaba river basin in China. We compared the discharge simulations of the two systems, and tested the influence of input. Then the discharge ensemble forecasts were evaluated for lead times of 1–7 d, and the impact on the forecasts of errors in initialization and modelling were considered. We also used quantile mapping (QM) to post-process the discharge simulations and forecasts. The results showed: (1) GloFAS (KGE of 0.54) had a worse discharge simulation than RHFS (KGE of 0.88), mainly because of the poor quality of the input; (2) the average forecast skill of GloFAS (CRPSS about 0.2) was inferior to that of RHFS (CRPSS about 0.6), because of the errors in the initialization and the model, however, GloFAS had a higher forecast quality than RHFS at high flow with longer lead times; (3) QM performed well at eliminating errors in input, the model, and the initialization.
Atmospheric rivers lie behind many extreme precipitation and flood episodes in the mid-latitudes. Better forecasts of atmospheric rivers and their impacts could help with preparedness. Here we argue ...that a comprehensive and systematic observational campaign could help advance numerical weather prediction, and thereby provide a path towards much improved forecasts of atmospheric rivers. We envision an interdisciplinary European–American observational campaign in the North Atlantic to identify and address numerical weather prediction errors in atmospheric rivers, and the associated extratropical cyclones. Insights gained could be applied in other regions. With improved understanding of the physiography of river basins and insights into their flood response to extreme precipitation, the impacts of atmospheric rivers can also be forecast more reliably.
A concerted observational campaign in the North Atlantic could help improve forecasts of atmospheric rivers and their impacts via a synthesis of advances in reconnaissance and numerical weather prediction.
Heatwaves are increasing in frequency, duration, and intensity due to climate change. They are associated with high mortality rates and cross‐sectional impacts including a reduction in crop yield and ...power outages. Here we demonstrate that there are large deficiencies in reporting of heatwave impacts in international disasters databases, international organization reports, and climate bulletins. We characterize the distribution of heat stress across the world focusing on August in the Northern Hemisphere, when notably heatwaves have taken place (i.e., 2003, 2010, and 2020) for the last 20 years using the ERA5‐HEAT reanalysis of the Universal Thermal Comfort Index and establish heat stress has grown larger in extent, more so during a heatwave. Comparison of heat stress against the emergency events impacts database and climate reports reveals underreporting of heatwave‐related impacts. This work suggests an internationally agreed protocol should be put in place for impact reporting by organizations and national government, facilitating implementation of preparedness measures, and early warning systems.
Plain Language Summary
Heat extremes are increasing in frequency, duration, and intensity due to climate change. Their impacts include a rise in death rates, a decrease in how much of a crop is produced and power outages. Here we show that there is a lack of reporting of impacts in international organization reports, international disaster databases. Further, heat stress, an impact of heat extremes is characterized using an index that is human‐centric. With a focus on August and the Northern Hemisphere, we show that heat stress has grown in extent and is presenting a growing risk to more of the population over this millennium. This work suggests that organizations and national governments should come together to agree a protocol for how to best report heat impacts, so that we can be better prepared for them.
Key Points
In the Northern Hemisphere, heat stress during the month of August is growing in area and is larger during a heatwave
Heat stress area increase is greater over the populated land surface than the total land surface during August
Impacts of Heatwaves are not sufficiency captured by international meteorological organization reports and emergency events impacts database
Abstract
Atmospheric River Reconnaissance (AR Recon) is a targeted campaign that complements other sources of observational data, forming part of a diverse observing system. AR Recon 2021 operated ...for ten weeks from January 13 to March 22, with 29.5 Intensive Observation Periods (IOPs), 45 flights and 1142 successful dropsondes deployed in the northeast Pacific. With the availability of two WC-130J aircraft operated by the 53
rd
Weather Reconnaissance Squadron (53 WRS), Air Force Reserve Command (AFRC) and one National Oceanic and Atmospheric Administration (NOAA) Aircraft Operations Center (AOC) G-IVSP aircraft, six sequences were accomplished, in which the same synoptic system was sampled over several days.
The principal aim was to gather observations to improve forecasts of landfalling atmospheric rivers on the U.S. West Coast. Sampling of other meteorological phenomena forecast to have downstream impacts over the U.S. was also considered. Alongside forecast improvement, observations were also gathered to address important scientific research questions, as part of a Research and Operations Partnership.
Targeted dropsonde observations were focused on essential atmospheric structures, primarily atmospheric rivers. Adjoint and ensemble sensitivities, mainly focusing on predictions of U.S. West Coast precipitation, provided complementary information on locations where additional observations may help to reduce the forecast uncertainty. Additionally, Airborne Radio Occultation (ARO) and tail radar were active during some flights, 30 drifting buoys were distributed, and 111 radiosondes were launched from four locations in California. Dropsonde, radiosonde and buoy data were available for assimilation in real-time into operational forecast models. Future work is planned to examine the impact of AR Recon 2021 data on model forecasts.
We introduce the Precipitation Probability DISTribution (PPDIST) dataset, a collection of global high-resolution (0.1°) observation-based climatologies (1979–2018) of the occurrence and peak ...intensity of precipitation (P) at daily and 3-hourly time-scales. The climatologies were produced using neural networks trained with daily P observations from 93,138 gauges and hourly P observations (resampled to 3-hourly) from 11,881 gauges worldwide. Mean validation coefficient of determination (R^(2)) values ranged from 0.76 to 0.80 for the daily P occurrence indices, and from 0.44 to 0.84 for the daily peak P intensity indices. The neural networks performed significantly better than current state-of-the-art reanalysis (ERA5) and satellite (IMERG) products for all P indices. Using a 0.1 mm 3 per h threshold, P was estimated to occur 12.2%, 7.4%, and 14.3% of the time, on average, over the global, land, and ocean domains, respectively. The highest P intensities were found over parts of Central America, India, and Southeast Asia, along the western equatorial coast of Africa, and in the intertropical convergence zone.
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