Figure 11 - uploaded by Shaun Harrigan
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The GloFAS-ERA5 river discharge reanalysis landing page in the C3S Climate Data Store (CDS; https://cds.climate.copernicus. eu/cdsapp#!/dataset/cems-glofas-historical?tab=overview).
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Estimating how much water is flowing through rivers at
the global scale is challenging due to a lack of observations in space and
time. A way forward is to optimally combine the global network of earth
system observations with advanced numerical weather prediction (NWP) models
to generate consistent spatio-temporal maps of land, ocean, and atmosphe...
Context in source publication
Context 1
... GloFAS-ERA5 river discharge reanalysis product is available on the CDS: https://cds.climate.copernicus.eu/ cdsapp#!/dataset/cems-glofas-historical?tab=overview with the following DOI: https://doi.org/10.24381/cds.a4fdd6b9 (C3S, 2019). The CDS landing page for the GloFAS-ERA5 reanalysis dataset is shown in Fig. 11. Both the long-term consolidated and the near-real-time intermediate reanalysis data are available in two ways. First, through the "Down-load data" tab whereby users can manually select options in a form for which data they would like to download. Second, data can be retrieved through the dedicated Python CDS API; an example API ...
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Almost every year, Vietnam suffers floods resulting in the loss of many lives and considerable costs for damaged and lost properties. This study proposes a forecasting system that couples the dynamical downscaling technique with hydrologic models to forecast real-time flood events with a lead time ranging from one to three days. This approach is de...
Citations
... With respect to solar radiation, Urraca et al. (2018) and Yang and Bright (2020) indicated a good performance of ERA5 compared to other data. Ultimately, to characterize river discharge the GloFAS-ERA5 gridded global river discharge v3.1 time series data (Harrigan et al. 2020) was employed. In this regard, it needs to be emphasized that the modeled river discharge has not been validated over the investigated study area. ...
... In this regard, it needs to be emphasized that the modeled river discharge has not been validated over the investigated study area. However, a validation of this data with globally distributed observations indicated a good performance (Harrigan et al. 2020). ...
With temperatures in Central Asia (CA) increasing more than the global average, this region is one of the global hotspots affected by climate change. CA is mostly characterized by arid climate, which is why available water resources are of paramount importance for the societies, economies, and the environment. In this regard, quantifying changes on the land surface and controlling factors that influence land surface dynamics are of great interest to improve our understanding of climate change impacts in this region. Hence, this study analyzes multivariate time series covering climatic, hydrological and Earth observation (EO)-based land surface variables. The used EO time series characterize the land surface and include data on the normalized difference vegetation index (NDVI), surface water area (SWA), and snow cover area (SCA) between December 2002 to November 2021. To analyze these time series, we employ trend analyses and a causal discovery algorithm. Both analyses were carried out at multiple spatial and temporal scales. The results show that NDVI trends were mostly significantly negative in the Northwest and positive in the Northeast of CA in summer. In summer and autumn, the percentage of significant negative NDVI trends outweighed the positive trends. For SWA, the detected trends were mostly significant negative throughout all scales. Significant negative trends were retrieved for SCA across all seasons, except for autumn regionally. Particularly the Tian Shan and Pamir mountains show significant declines of SCA in winter and spring. The causal analyses revealed that the NDVI is mostly controlled by water availability in summer. In spring and autumn, temperature is the leading driver on the NDVI. Likewise, temperature is found to largely control SWA in spring and autumn. SCA is mostly negatively coupled to temperature during spring and autumn. A positive coupling between SCA and precipitation is identified in winter.
... Also note, confusion matrices of these results are provided in Table S5-S6. (Harrigan, Zsoter, et al., 2020). As such, Fig.13a indicates that GloFASv3.1 ERA5 reanalysis clearly captures well the seasonality and the timing of observed river flow. ...
Flood-related impacts and losses have been rising. Therefore, understanding flood characteristics, drivers, and predictability is critical for informed decisions in the ongoing flood early warning (FldEWS) projects. This study presents an in-depth analysis of hydro-meteorological, Sentinel Mission (SM), and ensemble hydrological model datasets. We examine flood characteristics using observed hydro-meteorological and SM datasets, followed by statistical analysis of climate drivers of flood events at inter-annual and sub-seasonal (S2S) time scales. Finally, reforecasts from Global Flood Awareness System (GloFAS) are assessed against observed river flows. New hydrological insights for the study region: There is a high inter-annual variability of flood events with flood peaks occurring in May and December. SM satellites have the ability to map flooded areas in near-real time. At inter-annual timescales, positive Indian Ocean Dipole (IOD) and warm El Niño Southern Oscillation (ENSO) drives short rains (October to December). At Sub-Seasonal (S2S) timescales, Madden Julian Oscillation (MJO; phases 2-4) is a notable driver of flood related extreme rainfall. GloFAS offers reliable forecasts depending on the flood magnitude, trigger probability, and 'anticipation window' and it meets the tolerable skill requirements for flood preparedness actions (FAR < 50% and POD > 50%) with up to a 20-day lead time for 1 and 2-year return periods. We subsequently discuss how our research findings can inform the development of FldEWS in Kenya, with an emphasis on improved co-production of flood forecast information with relevant stakeholders.
... Thus, lakes that experience the greatest relative decline in mean surface extent also typically experience the greatest increase in the frequency of low water extremes. The primary factors that influence mean water extent and, in turn, the frequency of low water extremes in lakes include changes in flow regime (i.e., streamflow) that are linked to global climate change, the volume of water withdrawn for agricultural and domestic purposes, and the volume of water lost via evaporation, all of which have changed in recent decades 17,[22][23][24] (Supplementary Fig. 3). The two dominant drivers of change in the occurrence of low water extremes include streamflow and water use. ...
... Data on the naturalized streamflow between the historic (1985 to 1999) and contemporary (2010 to 2019) period were retrieved from the GloFAS-ERA5 dataset with a spatial resolution of 0.1°(ref. 22). Long-term trend of total water (surface water and groundwater) withdrawal from 1971 to 2010 were retrieved from the reconstructed gridded (0.5°spatial resolution) water withdraw dataset 61 . ...
Extreme within-lake conditions have the potential to exert detrimental effects on lakes. Here we use satellite observations to investigate how the occurrence of multiple types of extremes, notably algal blooms, lake heatwaves, and low lake levels, have varied in 2724 lakes since the 1980s. Our study, which focuses on bloom-affected lakes, suggests that 75% of studied lakes have experienced a concurrent increase in at least two of the extremes considered (27% defined as having a notable increase), with 25% experiencing an increase in frequency of all three extremes (5% had a notable increase). The greatest increases in the frequency of these extremes were found in regions that have experienced increases in agricultural fertilizer use, lake warming, and a decline in water availability. As extremes in lakes become more common, understanding their impacts must be a primary focus of future studies and they must be carefully considered in future risk assessments.
... The worldwide Global Flood Awareness System (GloFAS; Harrigan et al., 2020), created collaboratively by the European Commission and the European Centre for Medium-Range Weather Forecasts, is a global hydrological forecasting and monitoring system that is not constrained by administrative or political boundaries. It combines cutting-edge meteorological predictions with a hydrological model, and due to its continental scale setup, it can deliver information on upstream river conditions as well as continental and global overviews to downstream nations. ...
As both the population and economic output of India continue to grow, so does its demand for electricity. Coupled with an increasing determination to transition to net zero, India has responded to this rising demand by rapidly expanding its installed renewable capacity: an increase of 60% in the last 5 years has been driven largely by a quintupling of installed solar capacity. In this study, we use broad variety of data sources to quantify potential and realized capacity over India from 1979 to 2022. For potential capacity, we identify spatiotemporal patterns in solar, wind, hydro and wave power. We show that solar capacity factor is relatively homogeneous across India, except over the western Himalaya, and is highest during the pre‐monsoon. Wind capacity factor is highest during the summer monsoon, and has high values off the southern coast, along the Western Ghats, and in Gujarat. We argue that wave power could be a useful source of renewable energy for the Andaman and Nicobar Islands, which are not connected to the main Indian power grid. Using gridded estimates of existing installed capacity combined with our historical capacity factor dataset, we create a simple but effective renewable production model. We use this model to identify weaknesses in the existing grid—particularly a lack of complementarity between wind and solar production in north India, and vulnerability to high‐deficit generation in the winter. We discuss potential avenues for future renewable investment to counter existing seasonality problems, principally offshore wind and high‐altitude solar.
... The * in the legend means M for AMIP (HadGEM3-GC31-LM) and L for COUPLED (HadGEM3-GC31-LL). The grey bands show the observational uncertainty considering a large number of observation-based estimations, including IPCC AR6 Rodell et al., 2015;Trenberth et al., 2007), ERA5 (Hersbach et al., 2020), CRU TS4.05 (Harris et al., 2020), WFDEI (Weedon et al., 2018), CPC (Chen et al., 2008), FLUXCOM (Jung et al., 2019;Dai et al., 2009;Clark et al., 2015;Müller et al., 2021a), and GLOFAS (Harrigan et al., 2020). Units are in 10 3 km 3 yr −1 . ...
There is high confidence that global warming intensifies all components of the global water cycle. This work investigates the possible effects of global warming on river flows worldwide in the coming decades. We conducted 18 global hydrological simulations to assess how river flows are projected to change in the near future (2015–2050) compared to the recent past (1950–2014). The simulations are forced by runoff from the High Resolution Model Intercomparison Project (HighResMIP) CMIP6 global climate models (GCMs), which assume a high-emission scenario for the projections. The assessment includes estimating the signal-to-noise (S/N) ratio and the time of emergence (ToE) of all the rivers in the world. Consistently with the water cycle intensification, the hydrological simulations project a clear positive global river discharge trend from ∼2000 that emerges beyond the levels of natural variability and becomes “unfamiliar” by 2017 and “unusual” by 2033. Simulations agree that the climate change signal is dominated by strong increases in the flows of rivers originating in central Africa and South Asia and those discharging into the Arctic Ocean, partially compensated for by the reduced flow projected for Patagonian rivers. The potential implications of such changes may include more frequent floods in central African and South Asian rivers, driven by the projected magnification of the annual cycles with unprecedented peaks, a freshening of the Arctic Ocean from extra freshwater release, and limited water availability in Patagonia given the projected drier conditions of its rivers. This underscores the critical need for a paradigm shift in prioritizing water-related concerns amidst the challenges of global warming.
... Drought was characterized by monthly ERA5 potential evaporation, subsidence at 500 hPa, and air temperature. Rainfall characteristics were studied via multi-satellite interpolated CPC morphed (cMorph) and Global precipitation monitoring (GPM) products [31] and daily ERA5 (GloFAS) river discharge [32] at 25 km resolution. Long-term averages were calculated for many parameters to acquaint the reader with Africa's circulation, convection, heat flux, and air chemistry. ...
A survey of African weather and climate extremes in the period 1970–2023 reveals spatial and temporal patterns of intense dry and wet spells, associated with meteorological conditions and consequences. Seasonal wind storms occur along coasts facing the Mozambique Channel, the Gulf of Guinea, the Mediterranean, and the Southern Ocean. Desiccating evaporation is found along the edge of the Sahara and Kalahari Deserts, as well as in lowland subtropical river valleys. The Palmer Drought Severity Index (PDSI) and net outgoing longwave radiation (OLR) reflect precipitation–evaporation balance and guide regional evaluation. Temporal fluctuations are dominated by inter-decadal oscillations and drying/moistening trends over Southeast/West Africa, respectively. Localized floods and droughts are frequent, but widespread impacts are rare, suggesting that the transfer of resources from surplus to deficit regions is possible. Various case studies focus on (i) tropical cyclone impacts, (ii) monsoon moisture flux, and (iii) coastal upwelling. African communities have become resilient in the face of extreme weather and have shown that adaptation is possible, but further mitigating efforts are needed so that macro-economic progress does not come with harmful secondary consequences.
... As satellite data accessibility improves, we are advancing toward near real-time, high-resolution remote monitoring of both water quantities and qualities in rivers 34,35 , lakes [36][37][38] , and reservoirs [39][40][41][42] . Data assimilation in conjunction with meteorological and hydrological models [43][44][45][46] can further enhance the dynamic simulation of water resources and infrastructure operations, paving the way for a more comprehensive and nuanced understanding of water system behaviors and vulnerabilities. and 4V HA represent the simulated and remotely sensed storage change of the Dnipro cascade, respectively, while 4V upstream corresponds to the remotely sensed storage change for five upstream reservoirs. ...
... Given the absence of streamflow observation data for the Dnipro River, we sourced daily discharge simulation data for the site of the Kakhovka Dam from GloFAS-ERA5 45 , which have accounted the reservoir evaporation in the reservoir module, spanning the period from 1979 to 2023. We then refined the annual flow volume by implementing quantile mapping, based on the specific annual streamflow volume and corresponding reliability values stipulated in an evaluation report 29 . ...
On June 6, 2023, the Kakhovka Dam in Ukraine experienced a catastrophic breach that led to the loss of life and substantial economic values. Prior to the breach, the supporting structures downstream of the spillway had shown signs of being compromised. Here, we use multi-source satellite data, meteorological reanalysis, and dam design criteria to document the dam’s pre-failure condition. We find that anomalous operation of the Kakhovka Dam began in November 2022, following the destruction of a bridge segment, which led to persistent overtopping from late April 2023 up to the breach, contributing to the erosion of the spillway foundation. Moreover, our findings also highlight safety and risk-reduction measures pivotal in avoiding such scenarios. To help prevent future disasters, we advocate for greater transparency in the design parameters of key water structures to enable risk management, and conclude that remote sensing technology can help ensuring water infrastructure safety.
... Hydroclimatic databases, generally composed of climatic (precipitation and air temperature) and hydrological (streamflow) time series at the catchment scale, are extremely useful (Tramblay et al., 2021). They are used for water resources planning and management as well as for monitoring and forecasting floods, droughts, and changes in surface and ground-water resources (Dewandel et al., 2003(Dewandel et al., , 2004Alfieri et al., 2020;Harrigan et al., 2020). These databases are also used to evaluate the performance of "new" hydro-meteorological products based on Earth observation satellites, which are increasingly applied in poorly instrumented regions (Beck et al., 2019;Brocca et al., 2019;Prakash, 2019;Bathelemy et al., 2022). ...
Haiti, a Caribbean country, is highly vulnerable to hydroclimatic hazards due to heavy rainfall, which is partly linked to tropical cyclones. Additionally, its steep slopes generate flash floods, particularly in small catchments. Moreover, the hydrology of this region remains poorly understood and understudied. Unfortunately, there is no accessible database for the scientific community to use in this country. To fill this gap, hydroclimatic data were collected to create the first historical database in Haiti. This database, called Simbi (guardian of rivers, freshwater, and rain in Haitian mythology), includes 156 monthly rainfall series over the period 1905–2005, 59 daily rainfall series over the period 1920–1940, 70 daily streamflow series, and 23 monthly temperature series, not necessarily continuous, over the period 1920–1940. It also provides simulated streamflow series over the period 1920–1940 using the GR2M and GR4J rainfall–runoff models for 24 catchments and 49 attributes covering a wide range of topographic, climatic, geological, land use, hydrogeological, and hydrological signature indices. Simbi is the first open-access hydro-meteorological dataset for Haiti and will contribute to a better knowledge of hydrological risk in Haiti. Several sources of uncertainty associated with Simbi are acknowledged, including data quality (historical data), digitisation of paper archives, identification of relevant rain gauges, and rainfall–runoff models. It is important to consider these uncertainties when using Simbi. The database will be regularly updated to include additional historical data that will be digitised in the future. It will thus contribute toward better knowledge of the hydrology of Haitian catchments and will enable the implementation of various hydrological calculations useful for designing structures or flow forecasting. Simbi is an open-access database and is available for download at https://doi.org/10.23708/02POK6 (Bathelemy et al., 2023).
... This river section is fairly uniform, but bisected by a waterfall of several meters' altitude, as shown in panels A and B of figure 3. We exclude ICESat-2 data for the winter months (November to March), as the river is icebound in this period, which complicates water leveldischarge relationships. We do not have access to in-situ river discharge at this site and thus use the GLOFAS reanalysis dataset 17,18 to estimate river discharge. Panel B shows an overall good agreement of the ICESat-2 data with a hydraulic model simulating a critical flow section just upstream of the falls. ...
... River discharge estimates for the days of ICESat-2 overpasses were extracted from the Global Runoff Data Center (GRDC) global runoff database 16 for the sites for which observations were available in the GRDC archive. For sites without available GRDC records, we extracted discharge reanalysis data from the River Discharge and Related Forecasted Data archive produced by the Global Flood Awareness System and archived on the Copernicus Data Store 17,18 . ...
The ICESat-2 and SWOT satellite earth observation missions provide highly accurate water surface slope (WSS) observations in global rivers for the first time. While water surface slope is expected to remain constant in time for approximately uniform flow conditions, we observe time varying water surface slope in many river reaches around the globe in the ICESat-2 record. Here, we investigate the causes of time variability of WSS using simplified river hydraulic models based on the theory of steady, gradually varied flow. We identify bed slope or cross section shape changes, river confluences, flood waves and backwater effects from lakes, reservoirs, or the ocean as the main hydraulic phenomena causing time changes of WSS in rivers. We illustrate these phenomena at selected prototypical river sites around the world. These sites show that WSS observations can provide new insights into river hydraulics and can enable estimation of river discharge from water level observations at sites with complex hydraulic characteristics.
... (accessed on April 8, 2023). Both precipitation and river discharge data are from the global gridded datasets with a horizontal resolution of 0.1 0 x 0.1 0 and a daily step (Harrigan et al., 2020), extracted from January 2007 to December 2021 (15 years). ...
The optimization of marine fisheries activities can be achieved through an understanding of the timing of fishing, access to good information, and knowledge of oceanographic conditions. These conditions often lead to significant nutrient enrichment in the surface layer of the ocean, which in turn increases the sea surface chlorophyll-a (SSC). In the context of the west Borneo Island region, seasonal variability in SSC plays a crucial role in determining potential fishing grounds. The objectives of this study are examining the seasonal variability of SSC, identifying upwelling and downwelling processes through analysis of sea surface wind (SSW), and determining the climatological distribution of Sea Surface Temperature (SST) and sea surface height (SSH) within the water off Labuan Island, Malaysian Borneo, and the Karimata Strait, West Kalimantan, Indonesia. Remote sensing data spanning from 2007 to 2021 were analyzed, encompassing SSC, SST, SSH anomalies, SSW, wind stress curl, and Ekman pumping. Additionally, rainfall and river discharge were examined as supplementary indicators of these oceanographic processes. The findings indicate that SSW plays a pivotal role in driving upwelling and downwelling processes, which in turn influence SSC variability. In Labuan waters, upwelling occurs primarily from November to February, while downwelling predominates from June to September. In contrast, in the Karimata Strait, upwelling is identified from July to September, with downwelling prevalent between March and May. Upwelling events in both regions are characterized by increasing SSC, accompanied by decreasing SST and SSH, while the opposite trends are observed during downwelling events. The peak of rainfall and river discharge in December is noted to potentially enhance SSC variability in the Karimata Strait compared to Labuan Island waters.
