scholarly journals Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System

2020 ◽  
Author(s):  
Shaun Harrigan ◽  
Ervin Zoster ◽  
Hannah Cloke ◽  
Peter Salamon ◽  
Christel Prudhomme
Keyword(s):  
Real Time ◽  
River Discharge ◽  
Skill Score ◽  
Global Scale ◽  
Forecast Skill ◽  
Western Coast ◽  
Climate Data ◽  
Service Evolution ◽  
Northern Latitudes ◽  
Awareness System

Abstract. Operational global-scale hydrological forecasting systems are widely used to help manage hydrological extremes such as floods and droughts. The vast amounts of raw data that underpin forecast systems and the ability to generate information on forecast skill have, until now, not been publicly available. As part of the Global Flood Awareness System (GloFAS; https://www.globalfloods.eu/) service evolution, in this paper daily ensemble river discharge reforecasts and real-time forecast datasets are made free and openly available through the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). They include real-time forecast data starting on 1 January 2020 updated operationally every day and a 20-year set of reforecasts and associated metadata, available through the dedicated GloFAS FTP service. This paper describes the model components and configuration used to generate the real-time river discharge forecasts and the reforecasts. An evaluation of ensemble forecast skill using the Continuous Ranked Probability Skill Score (CRPSS) was also undertaken for river points around the globe. Results show that GloFAS is skilful in over 93thinsp;% of catchments in the short- (1- to 3-days) and medium-range (5- to 15-days) against a persistence benchmark forecast, and skilful in over 80 % of catchments out to the extended-range (16- to 30-days) against a climatological benchmark forecast. However, the strength of skill varies considerably by location with GloFAS found to have no or negative skill at longer lead times in broad hydroclimatic regions in tropical Africa, western coast of South America, and catchments dominated by snow and ice in high northern latitudes. Forecast skill is summarised as a new headline skill score to be added as a layer on the GloFAS forecast Web Map Viewer at the next GloFAS cycle release, expected Autumn 2020, to aid user’s interpretation and understanding of forecast quality.

scholarly journals Sensitivity of simulated global-scale freshwater fluxes and storages to input data, hydrological model structure, human water use and calibration

2014 ◽  
Vol 18 (9) ◽  
pp. 3511-3538 ◽  
Author(s):  
H. Müller Schmied ◽  
S. Eisner ◽  
D. Franz ◽  
M. Wattenbach ◽  
F. T. Portmann ◽  
...  
Keyword(s):  
Land Cover ◽  
Water Use ◽  
River Discharge ◽  
Hydrological Model ◽  
Grid Cell ◽  
Global Scale ◽  
Climate Forcing ◽  
Actual Evapotranspiration ◽  
Climate Data ◽  
Freshwater Fluxes

Abstract. Global-scale assessments of freshwater fluxes and storages by hydrological models under historic climate conditions are subject to a variety of uncertainties. Using the global hydrological model WaterGAP (Water – Global Assessment and Prognosis) 2.2, we investigated the sensitivity of simulated freshwater fluxes and water storage variations to five major sources of uncertainty: climate forcing, land cover input, model structure/refinements, consideration of human water use and calibration (or no calibration) against observed mean river discharge. In a modeling experiment, five variants of the standard version of WaterGAP 2.2 were generated that differed from the standard version only regarding the investigated source of uncertainty. The basin-specific calibration approach for WaterGAP was found to have the largest effect on grid cell fluxes as well as on global AET (actual evapotranspiration) and discharge into oceans for the period 1971–2000. Regarding grid cell fluxes, climate forcing ranks second before land cover input. Global water storage trends are most sensitive to model refinements (mainly modeling of groundwater depletion) and consideration of human water use. The best fit to observed time series of monthly river discharge or discharge seasonality is obtained with the standard WaterGAP 2.2 model version which is calibrated and driven by daily reanalysis-based WFD/WFDEI (combination of Watch Forcing Data based on ERA40 and Watch Forcing Data based on ERA-Interim) climate data. Discharge computed by a calibrated model version using monthly CRU TS (Climate Research Unit time-series) 3.2 and GPCC (Global Precipitation Climatology Center) v6 climate input reduced the fit to observed discharge for most stations. Taking into account uncertainties of climate and land cover data, global 1971–2000 discharge into oceans and inland sinks ranges between 40 000 and 42 000 km3 yr−1. Global actual evapotranspiration, with 70 000 km3 yr−1, is rather unaffected by climate and land cover uncertainties. Human water use reduced river discharge by 1000 km3 yr−1, such that global renewable water resources are estimated to range between 41 000 and 43 000 km3 yr−1. The climate data sets WFD (available until 2001) and WFDEI (starting in 1979) were found to be inconsistent with respect to shortwave radiation data, resulting in strongly different actual evapotranspiration. Global assessments of freshwater fluxes and storages would therefore benefit from the development of a global data set of consistent daily climate forcing from 1900 to present.

scholarly journals Global-scale analysis of river flow alterations due to water withdrawals and reservoirs

2009 ◽  
Vol 6 (4) ◽  
pp. 4773-4812 ◽  
Author(s):  
P. Döll ◽  
K. Fiedler ◽  
J. Zhang
Keyword(s):  
Water Use ◽  
River Discharge ◽  
River Flow ◽  
Global Scale ◽  
Low Flow ◽  
Western Coast ◽  
Land Area ◽  
Operation Rules ◽  
Western Asia ◽  
Water Withdrawals

Abstract. Global-scale information on natural river flows and anthropogenic river flow alterations is required to identify areas where aqueous ecosystems are expected to be strongly degraded. Such information can support the identification of environmental flow guidelines and a sustainable water management that balances the water demands of humans and ecosystems. This study presents the first global assessment of the anthropogenic alteration of river flow regimes by water withdrawals and dams, focusing in particular on the change of flow variability. Six ecologically relevant flow indicators were quantified using an improved version of the global water model WaterGAP. WaterGAP simulated, with a spatial resolution of 0.5 degree, river discharge as affected by human water withdrawals and dams, as well as naturalized discharge without this type of human interference. Mainly due to irrigation, long-term average river discharge and statistical low flow Q90 (monthly river discharge that is exceeded in 9 out of 10 months) have decreased by more than 10% on one sixth and one quarter of the global land area (excluding Antarctica and Greenland), respectively. Q90 has increased significantly on only 5% of the land area, downstream of reservoirs. Due to both water withdrawals and dams, seasonal flow amplitude has decreased significantly on one sixth of the land area, while interannual variability has increased on one quarter of the land area mainly due to irrigation. It has decreased on only 8% of the land area, in areas with little consumptive water use that are downstream of dams. Areas most affected by anthropogenic river flow alterations are the western and central USA, Mexico, the western coast of South America, the Mediterranean rim, Southern Africa, the semi-arid and arid countries of the Near East and Western Asia, Pakistan and India, Northern China and the Australian Murray-Darling Basin, as well as some Arctic rivers. Due to a large number of uncertainties related e.g. to the estimation of water use and reservoir operation rules, the analysis is expected to provide only first estimates of river flow alterations that should be refined in the future.

scholarly journals Measuring compound flood potential from river discharge and storm surge extremes at the global scale

2020 ◽  
Vol 20 (2) ◽  
pp. 489-504 ◽  
Author(s):  
Anaïs Couasnon ◽  
Dirk Eilander ◽  
Sanne Muis ◽  
Ted I. E. Veldkamp ◽  
Ivan D. Haigh ◽  
...  
Keyword(s):  
Storm Surge ◽  
River Discharge ◽  
Flood Hazard ◽  
Joint Probability ◽  
Global Scale ◽  
Statistical Dependence ◽  
Dependence Structure ◽  
Meteorological Forcing ◽  
Continental Scale ◽  
Bivariate Dependence

Abstract. The interaction between physical drivers from oceanographic, hydrological, and meteorological processes in coastal areas can result in compound flooding. Compound flood events, like Cyclone Idai and Hurricane Harvey, have revealed the devastating consequences of the co-occurrence of coastal and river floods. A number of studies have recently investigated the likelihood of compound flooding at the continental scale based on simulated variables of flood drivers, such as storm surge, precipitation, and river discharges. At the global scale, this has only been performed based on observations, thereby excluding a large extent of the global coastline. The purpose of this study is to fill this gap and identify regions with a high compound flooding potential from river discharge and storm surge extremes in river mouths globally. To do so, we use daily time series of river discharge and storm surge from state-of-the-art global models driven with consistent meteorological forcing from reanalysis datasets. We measure the compound flood potential by analysing both variables with respect to their timing, joint statistical dependence, and joint return period. Our analysis indicates many regions that deviate from statistical independence and could not be identified in previous global studies based on observations alone, such as Madagascar, northern Morocco, Vietnam, and Taiwan. We report possible causal mechanisms for the observed spatial patterns based on existing literature. Finally, we provide preliminary insights on the implications of the bivariate dependence behaviour on the flood hazard characterisation using Madagascar as a case study. Our global and local analyses show that the dependence structure between flood drivers can be complex and can significantly impact the joint probability of discharge and storm surge extremes. These emphasise the need to refine global flood risk assessments and emergency planning to account for these potential interactions.

scholarly journals Yearly evolution of Euro-Atlantic weather regimes and of their sub-seasonal predictability

2021 ◽  
Author(s):  
Nicola Cortesi ◽  
Verónica Torralba ◽  
Llorenó Lledó ◽  
Andrea Manrique-Suñén ◽  
Nube Gonzalez-Reviriego ◽  
...  
Keyword(s):  
Annual Cycle ◽  
Winter Season ◽  
Skill Score ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Seasonal Forecasts ◽  
Sea Level Pressure ◽  
Weather Regimes ◽  
Start Dates ◽  
Weekly Frequency

AbstractIt is often assumed that weather regimes adequately characterize atmospheric circulation variability. However, regime classifications spanning many months and with a low number of regimes may not satisfy this assumption. The first aim of this study is to test such hypothesis for the Euro-Atlantic region. The second one is to extend the assessment of sub-seasonal forecast skill in predicting the frequencies of occurrence of the regimes beyond the winter season. Two regime classifications of four regimes each were obtained from sea level pressure anomalies clustered from October to March and from April to September respectively. Their spatial patterns were compared with those representing the annual cycle. Results highlight that the two regime classifications are able to reproduce most part of the patterns of the annual cycle, except during the transition weeks between the two periods, when patterns of the annual cycle resembling Atlantic Low regime are not also observed in any of the two classifications. Forecast skill of Atlantic Low was found to be similar to that of NAO+, the regime replacing Atlantic Low in the two classifications. Thus, although clustering yearly circulation data in two periods of 6 months each introduces a few deviations from the annual cycle of the regime patterns, it does not negatively affect sub-seasonal forecast skill. Beyond the winter season and the first ten forecast days, sub-seasonal forecasts of ECMWF are still able to achieve weekly frequency correlations of r = 0.5 for some regimes and start dates, including summer ones. ECMWF forecasts beat climatological forecasts in case of long-lasting regime events, and when measured by the fair continuous ranked probability skill score, but not when measured by the Brier skill score. Thus, more efforts have to be done yet in order to achieve minimum skill necessary to develop forecast products based on weather regimes outside winter season.

scholarly journals Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

2021 ◽  
Vol 13 (12) ◽  
pp. 2405
Author(s):  
Fengyang Long ◽  
Chengfa Gao ◽  
Yuxiang Yan ◽  
Jinling Wang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Model Performance ◽  
Global Scale ◽  
Radiosonde Data ◽  
Model Parameters ◽  
Measured Temperature ◽  
Weighted Mean ◽  
Weighted Mean Temperature ◽  
Application Scope

Precise modeling of weighted mean temperature (Tm) is critical for realizing real-time conversion from zenith wet delay (ZWD) to precipitation water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology applications. The empirical Tm models developed by neural network techniques have been proved to have better performances on the global scale; they also have fewer model parameters and are thus easy to operate. This paper aims to further deepen the research of Tm modeling with the neural network, and expand the application scope of Tm models and provide global users with more solutions for the real-time acquisition of Tm. An enhanced neural network Tm model (ENNTm) has been developed with the radiosonde data distributed globally. Compared with other empirical models, the ENNTm has some advanced features in both model design and model performance, Firstly, the data for modeling cover the whole troposphere rather than just near the Earth’s surface; secondly, the ensemble learning was employed to weaken the impact of sample disturbance on model performance and elaborate data preprocessing, including up-sampling and down-sampling, which was adopted to achieve better model performance on the global scale; furthermore, the ENNTm was designed to meet the requirements of three different application conditions by providing three sets of model parameters, i.e., Tm estimating without measured meteorological elements, Tm estimating with only measured temperature and Tm estimating with both measured temperature and water vapor pressure. The validation work is carried out by using the radiosonde data of global distribution, and results show that the ENNTm has better performance compared with other competing models from different perspectives under the same application conditions, the proposed model expanded the application scope of Tm estimation and provided the global users with more choices in the applications of real-time GNSS-PWV retrival.

scholarly journals Evaluation of the satellite-based Global Flood Detection System for measuring river discharge: influence of local factors

2014 ◽  
Vol 18 (11) ◽  
pp. 4467-4484 ◽  
Author(s):  
B. Revilla-Romero ◽  
J. Thielen ◽  
P. Salamon ◽  
T. De Groeve ◽  
G. R. Brakenridge
Keyword(s):  
Real Time ◽  
River Discharge ◽  
Detection System ◽  
Poor Performance ◽  
Model Verification ◽  
Area Index ◽  
General Terms ◽  
Skill Scores ◽  
Flood Detection ◽  
Discharge Measurements

Abstract. One of the main challenges for global hydrological modelling is the limited availability of observational data for calibration and model verification. This is particularly the case for real-time applications. This problem could potentially be overcome if discharge measurements based on satellite data were sufficiently accurate to substitute for ground-based measurements. The aim of this study is to test the potentials and constraints of the remote sensing signal of the Global Flood Detection System for converting the flood detection signal into river discharge values. The study uses data for 322 river measurement locations in Africa, Asia, Europe, North America and South America. Satellite discharge measurements were calibrated for these sites and a validation analysis with in situ discharge was performed. The locations with very good performance will be used in a future project where satellite discharge measurements are obtained on a daily basis to fill the gaps where real-time ground observations are not available. These include several international river locations in Africa: the Niger, Volta and Zambezi rivers. Analysis of the potential factors affecting the satellite signal was based on a classification decision tree (random forest) and showed that mean discharge, climatic region, land cover and upstream catchment area are the dominant variables which determine good or poor performance of the measure\\-ment sites. In general terms, higher skill scores were obtained for locations with one or more of the following characteristics: a river width higher than 1km; a large floodplain area and in flooded forest, a potential flooded area greater than 40%; sparse vegetation, croplands or grasslands and closed to open and open forest; leaf area index > 2; tropical climatic area; and without hydraulic infrastructures. Also, locations where river ice cover is seasonally present obtained higher skill scores. This work provides guidance on the best locations and limitations for estimating discharge values from these daily satellite signals.

scholarly journals A framework for estimating global-scale river discharge by assimilating satellite altimetry

2020 ◽  
Author(s):  
Menaka Revel ◽  
Daiki Ikeshima ◽  
Dai Yamazaki ◽  
Shinjiro Kanae
Keyword(s):  
River Discharge ◽  
Satellite Altimetry ◽  
Global Scale

scholarly journals Forecast skill score assessment of a relocatable ocean prediction system, using a simplified objective analysis method

Ocean Science ◽  
2017 ◽  
Vol 13 (6) ◽  
pp. 925-945 ◽  
Author(s):  
Reiner Onken
Keyword(s):  
Observational Data ◽  
Skill Score ◽  
Forecast Skill ◽  
Objective Analysis ◽  
Prediction System ◽  
Regional Ocean Modeling System ◽  
Data Set ◽  
Vast Number ◽  
Ocean Prediction ◽  
Forecast Skill Score

Abstract. A relocatable ocean prediction system (ROPS) was employed to an observational data set which was collected in June 2014 in the waters to the west of Sardinia (western Mediterranean) in the framework of the REP14-MED experiment. The observational data, comprising more than 6000 temperature and salinity profiles from a fleet of underwater gliders and shipborne probes, were assimilated in the Regional Ocean Modeling System (ROMS), which is the heart of ROPS, and verified against independent observations from ScanFish tows by means of the forecast skill score as defined by Murphy(1993). A simplified objective analysis (OA) method was utilised for assimilation, taking account of only those profiles which were located within a predetermined time window W. As a result of a sensitivity study, the highest skill score was obtained for a correlation length scale C = 12.5 km, W = 24 h, and r = 1, where r is the ratio between the error of the observations and the background error, both for temperature and salinity. Additional ROPS runs showed that (i) the skill score of assimilation runs was mostly higher than the score of a control run without assimilation, (i) the skill score increased with increasing forecast range, and (iii) the skill score for temperature was higher than the score for salinity in the majority of cases. Further on, it is demonstrated that the vast number of observations can be managed by the applied OA method without data reduction, enabling timely operational forecasts even on a commercially available personal computer or a laptop.

scholarly journals A missing source of aerosols in Antarctica – beyond long-range transport, phytoplankton, and photochemistry

2016 ◽  
Author(s):  
Michael R. Giordano ◽  
Lars E. Kalnajs ◽  
Anita Avery ◽  
James D. Goetz ◽  
Sean M. Davis ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Dimethyl Sulfate ◽  
Methanesulfonic Acid ◽  
Global Scale ◽  
Formation Mechanisms ◽  
Second Phase ◽  
Aerosol Sampling ◽  
Austral Spring ◽  
The Antarctic

Abstract. Understanding the sources and evolution of aerosols is crucial for constraining the impacts that aerosols have on a global scale. An unanswered question in atmospheric science is the source and evolution of the Antarctic aerosol population. Previous work over the continent has primarily utilized low resolution aerosol filters (coupled with off-line ion chromatography) to answer questions about Antarctic aerosols. Bulk aerosol sampling has been useful in identifying seasonal cycles in the aerosol populations, especially in populations that have been attributed to Southern Ocean phytoplankton emissions. However, real-time, high resolution chemical composition data is necessary to identify the mechanisms and exact timing of changes in the Antarctic aerosol populations. The recent 2ODIAC (2-Season Ozone Depletion and Interaction with Aerosols Campaign) field campaign saw the first ever deployment of a real-time, high resolution aerosol mass spectrometer (SP-AMS or AMS) to the continent. Data obtained from the AMS, and a suite of other aerosol, gas-phase, and meteorological instruments, are presented here. In particular, this manuscript focuses on the aerosol population over coastal Antarctica and the evolution of that population in Austral Spring. Results indicate that there exists a sulfate mode in Antarctica that is externally mixed to the rest of the aerosol population with a mass mode vacuum aerodynamic diameter of 250 nm. Springtime increases in sulfate aerosol are observed and attributed to biogenic sources, in agreement with previous research identifying phytoplankton activity as the source of the aerosol. Furthermore, the total Antarctic aerosol population is shown to undergo three distinct phases during the winter to summer transition. The first phase is dominated by highly aged sulfate particles comprising the majority of the aerosol population at low wind speed. The second phase, previously unidentified, is the generation of a sub-250 nm aerosol population of unknown composition. The second phase appears as a transitional phase during the extended polar sunrise. The third phase is marked by an increased importance of biogenically-derived sulfate to the total aerosol population (photolysis of dimethyl sulfate and methanesulfonic acid [DMS and MSA]). The increased importance of MSA is identified both through the direct, real-time measurement of aerosol MSA and through the use of positive matrix factorization on the sulfur containing ions in the high-resolution mass spectral data. Given the importance of sub-250 nm particles, the aforementioned second phase suggests that early Austral spring is the season where new particle formation mechanisms are likely to have the largest contribution to the aerosol population in Antarctica.

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