DEVELOPMENT OF A GLOBAL RIVER DISCHARGE DATA SET AND ANALYSES ON THE TEMPORAL VARIATIONS OF ANNUAL RUNOFF

IntroductionHYDROCOASTAL is a two year project funded by ESA, with the objective to maximise exploitation of SAR and SARin altimeter measurements in the coastal zone and inland waters, by evaluating and implementing new approaches to process SAR and SARin data from CryoSat-2, and SAR altimeter data from Sentinel-3A and Sentinel-3B. Optical data from Sentinel-2 MSI and Sentinel-3 OLCI instruments will also be used in generating River Discharge products.New SAR and SARin processing algorithms for the coastal zone and inland waters will be developed and implemented and evaluated through an initial Test Data Set for selected regions. From the results of this evaluation a processing scheme will be implemented to generate global coastal zone and river discharge data sets.A series of case studies will assess these products in terms of their scientific impacts.All the produced data sets will be available on request to external researchers, and full descriptions of the processing algorithms will be provided&#160;ObjectivesThe scientific objectives of HYDROCOASTAL are to enhance our understanding&#160; of interactions between the inland water and coastal zone, between the coastal zone and the open ocean, and the small scale processes that govern these interactions. Also the project aims to improve our capability to characterize the variation at different time scales of inland water storage, exchanges with the ocean and the impact on regional sea-level changesThe technical objectives are to develop and evaluate&#160; new SAR&#160; and SARin altimetry processing techniques in support of the scientific objectives, including stack processing, and filtering, and retracking. Also an improved Wet Troposphere Correction will be developed and evaluated.Project&#160; OutlineThere are four tasks to the project<ul><li>Scientific Review and Requirements Consolidation: Review the current state of the art in SAR and SARin altimeter data processing as applied to the coastal zone and to inland waters</li> <li>Implementation and Validation: New processing algorithms with be implemented to generate a Test Data sets, which will be validated against models, in-situ data, and other satellite data sets. Selected algorithms will then be used to generate global coastal zone and river discharge data sets</li> <li>Impacts Assessment: The impact of these global products will be assess in a series of Case Studies</li> <li>Outreach and Roadmap: Outreach material will be prepared and distributed to engage with the wider scientific community and provide recommendations for development of future missions and future research.</li> </ul>&#160;PresentationThe presentation will provide an overview to the project, present the different SAR altimeter processing algorithms that are being evaluated in the first phase of the project, and early results from the evaluation of the initial test data set.&#160;

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Benefits of dynamically modelled river discharge input for ocean and coupled system.

10.5194/egusphere-egu2020-8564 ◽

2020 ◽

Author(s):

Hao Zuo ◽

Eric de Boisseson ◽

Ervin Zsoter ◽

Shaun Harrigan ◽

Patricia de Rosnay ◽

...

Keyword(s):

River Discharge ◽

River Mouth ◽

Sea Surface Salinity ◽

Global Network ◽

Global Ocean ◽

Management Service ◽

Freshwater Input ◽

Data Set ◽

Discharge Data ◽

Ocean Models

River freshwater input is crucial in modelling global ocean. Most ocean models used in CMEMS services rely on climatological river discharge data with various deficiencies, which can lead to biased simulated ocean states. The Copernicus Emergency Management Service (CEMS) Global Flood Awareness System (GloFAS) provides state-of-the-art global flood forecasts and downstream river discharge. A GloFAS-ERA5 global river discharge reanalysis dataset has been produced using the same system, modelled by routing runoff from ECMWF&#8217;s (European Centre for Medium- Range Weather Forecasts) atmospheric reanalysis ERA5 via a river network. As a global gridded data set that covers from 1979 to near-real-time, GloFAS-ERA5 reanalysis can provide an improved and standardized input of land freshwater input for global, regional and coastal ocean models. Evaluation results suggest that the overall performance of this new river discharge reanalysis is reasonably good in general when verified against a global network of 1801 discharge observation stations. A new method has been developed for conversion of the GloFAS-ERA5 reanalysis data into land freshwater input for the NEMO ocean model. This method has been tested with a climatology of GloFAS-ERA5 river discharge. Compared to the DRAKKAR climatology of land freshwater input (BT06, hereafter) used by most CMEMS services, this new data set has an increased global mean value of ~1.33 Sv, but with reduced seasonal variations. Assessment of this GloFAS-ERA5 land freshwater input has been carried out with the operational ECMWF ocean analysis system-OCEAN5, driven by the same ERA5 atmospheric forcing. Evaluation of simulated ocean state against in-situ observations show improvements in regions affected by Amazon freshwater input when use GloFAS-ERA5 instead of BT06, by reducing a negative sea surface salinity bias in these regions. However, negative impact from switching to GloFAS-ERA5 land freshwater input is also visible in several regions, e.g. in the Maritime Continent and west coast of central America, which is associated with a large positive bias in the GloFAS-ERA5 river discharge at these regions. This issue can be mitigated by applying bias-correction to the GloFAS-ERA5 land freshwater input, and by adding extra vertical mixing in several affected regions that are close to the river mouth. Assessments of module simulated ocean Essential Climate Variables (ECVs) have been carried out to quantify the benefit of this realistic freshwater time series input. Improvements in climate signals like the Atlantic Meridional Overturning transports is also recorded.

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Stochastic generation of spatially coherent river discharge peaks for large-scale, event-based flood risk assessment

10.5194/nhess-2018-231 ◽

2018 ◽

Author(s):

Dirk Diederen ◽

Ye Liu ◽

Ben Gouldby ◽

Ferdinand Diermanse ◽

Sergiy Vorogushyn

Keyword(s):

Risk Assessment ◽

Flood Risk ◽

River Discharge ◽

Large Scale ◽

Time Window ◽

Insurance Industry ◽

Flood Risk Assessment ◽

Data Set ◽

Discharge Data ◽

Short Time Window

Abstract. Flood risk assessments are required for long-term planning, e.g. for investments in infrastructure and other urban capital. Vorogushyn et al. (2018) call for new methods in large-scale Flood Risk Assessment (FRA) to enable the capturing of system interactions and feedbacks. With the increase of computational power, large-scale, continental FRAs have recently become feasible (Ward et al., 2013; Alfieri et al., 2014; Dottori et al., 2016; Vousdoukas, 2016; Winsemius et al., 2016; Paprotny et al., 2017). Flood events cause large damages worldwide (Desai et al., 2015). Moreover, widespread flooding can potentially cause large damage in a short time window. Therefore, large-scale (e.g. pan-European) events and for instance maximum probable damages are of interest, in particular for the (re)insurance industry, because they want to know the chance of their widespread portfolio of assets getting affected by large-scale events (Jongman et al., 2014). Using a pan-European data set of modelled, gridded river discharge data, we tracked discharge waves in all major European river basins. We synthetically generated a large catalogue of synthetic, pan-European events, consisting of spatially coherent discharge peak sets.

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The Rise of Climate-Driven Sediment Discharge in the Amazonian River Basin

Atmosphere ◽

10.3390/atmos11020208 ◽

2020 ◽

Vol 11 (2) ◽

pp. 208 ◽

Cited By ~ 3

Author(s):

Nazzareno Diodato ◽

Naziano Filizola ◽

Pasquale Borrelli ◽

Panos Panagos ◽

Gianni Bellocchi

Keyword(s):

River Basin ◽

Amazon Basin ◽

Global Climate ◽

Climate Extremes ◽

Temporal Variations ◽

Sediment Discharge ◽

Storm Events ◽

Discharge Data ◽

Magdalena River ◽

Sediment Export

The occurrence of hydrological extremes in the Amazon region and the associated sediment loss during rainfall events are key features in the global climate system. Climate extremes alter the sediment and carbon balance but the ecological consequences of such changes are poorly understood in this region. With the aim of examining the interactions between precipitation and landscape-scale controls of sediment export from the Amazon basin, we developed a parsimonious hydro-climatological model on a multi-year series (1997–2014) of sediment discharge data taken at the outlet of Óbidos (Brazil) watershed (the narrowest and swiftest part of the Amazon River). The calibrated model (correlation coefficient equal to 0.84) captured the sediment load variability of an independent dataset from a different watershed (the Magdalena River basin), and performed better than three alternative approaches. Our model captured the interdecadal variability and the long-term patterns of sediment export. In our reconstruction of yearly sediment discharge over 1859–2014, we observed that landscape erosion changes are mostly induced by single storm events, and result from coupled effects of droughts and storms over long time scales. By quantifying temporal variations in the sediment produced by weathering, this analysis enables a new understanding of the linkage between climate forcing and river response, which drives sediment dynamics in the Amazon basin.

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Infectious Disease Hospitalizations Among American Indian/Alaska Native and Non–American Indian/Alaska Native Persons in Alaska, 2010-2011

Public Health Reports ◽

10.1177/0033354916679807 ◽

2016 ◽

Vol 132 (1) ◽

pp. 65-75 ◽

Cited By ~ 4

Author(s):

Prabhu P. Gounder ◽

Robert C. Holman ◽

Sara M. Seeman ◽

Alice J. Rarig ◽

Mary McEwen ◽

...

Keyword(s):

Infectious Disease ◽

American Indian ◽

Alaska Native ◽

Rate Ratio ◽

Common Reason ◽

Data Set ◽

Discharge Data ◽

Public Health Programs ◽

Hospitalization Rates ◽

Population Rate

Objective: Reports about infectious disease (ID) hospitalization rates among American Indian/Alaska Native (AI/AN) persons have been constrained by data limited to the tribal health care system and by comparisons with the general US population. We used a merged state database to determine ID hospitalization rates in Alaska. Methods: We combined 2010 and 2011 hospital discharge data from the Indian Health Service and the Alaska State Inpatient Database. We used the merged data set to calculate average annual age-adjusted and age-specific ID hospitalization rates for AI/AN and non-AI/AN persons in Alaska. We stratified the ID hospitalization rates by sex, age, and ID diagnosis. Results: ID diagnoses accounted for 19% (6501 of 34 160) of AI/AN hospitalizations, compared with 12% (7397 of 62 059) of non-AI/AN hospitalizations. The average annual age-adjusted hospitalization rate was >3 times higher for AI/AN persons (2697 per 100 000 population) than for non-AI/AN persons (730 per 100 000 population; rate ratio = 3.7, P < .001). Lower respiratory tract infection (LRTI), which occurred in 38% (2486 of 6501) of AI/AN persons, was the most common reason for ID hospitalization. AI/AN persons were significantly more likely than non-AI/AN persons to be hospitalized for LRTI (rate ratio = 5.2, P < .001). Conclusions: A substantial disparity in ID hospitalization rates exists between AI/AN and non-AI/AN persons, and the most common reason for ID hospitalization among AI/AN persons was LRTI. Public health programs and policies that address the risk factors for LRTI are likely to benefit AI/AN persons.

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Toward continental hydrologic–hydrodynamic modeling in South America

Hydrology and Earth System Sciences ◽

10.5194/hess-22-4815-2018 ◽

2018 ◽

Vol 22 (9) ◽

pp. 4815-4842 ◽

Cited By ~ 29

Author(s):

Vinícius A. Siqueira ◽

Rodrigo C. D. Paiva ◽

Ayan S. Fleischmann ◽

Fernando M. Fan ◽

Anderson L. Ruhoff ◽

...

Keyword(s):

South America ◽

Land Surface ◽

River Discharge ◽

Hydrological Modeling ◽

Water Levels ◽

Scale Model ◽

Global Models ◽

Discharge Data ◽

Continental Scale ◽

Terrestrial Water

Abstract. Providing reliable estimates of streamflow and hydrological fluxes is a major challenge for water resources management over national and transnational basins in South America. Global hydrological models and land surface models are a possible solution to simulate the terrestrial water cycle at the continental scale, but issues about parameterization and limitations in representing lowland river systems can place constraints on these models to meet local needs. In an attempt to overcome such limitations, we extended a regional, fully coupled hydrologic–hydrodynamic model (MGB; Modelo hidrológico de Grandes Bacias) to the continental domain of South America and assessed its performance using daily river discharge, water levels from independent sources (in situ, satellite altimetry), estimates of terrestrial water storage (TWS) and evapotranspiration (ET) from remote sensing and other available global datasets. In addition, river discharge was compared with outputs from global models acquired through the eartH2Observe project (HTESSEL/CaMa-Flood, LISFLOOD and WaterGAP3), providing the first cross-scale assessment (regional/continental × global models) that makes use of spatially distributed, daily discharge data. A satisfactory representation of discharge and water levels was obtained (Nash–Sutcliffe efficiency, NSE > 0.6 in 55 % of the cases) and the continental model was able to capture patterns of seasonality and magnitude of TWS and ET, especially over the largest basins of South America. After the comparison with global models, we found that it is possible to obtain considerable improvement on daily river discharge, even by using current global forcing data, just by combining parameterization and better routing physics based on regional experience. Issues about the potential sources of errors related to both global- and continental-scale modeling are discussed, as well as future directions for improving large-scale model applications in this continent. We hope that our study provides important insights to reduce the gap between global and regional hydrological modeling communities.

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Temporal variations in bedload transport rates and sediment storage in gravel-bed rivers

Progress in Physical Geography Earth and Environment ◽

10.1177/030913339201600303 ◽

1992 ◽

Vol 16 (3) ◽

pp. 319-338 ◽

Cited By ~ 90

Author(s):

Trevor Hoey

Keyword(s):

Three Dimensional ◽

Temporal Variations ◽

Sampling Interval ◽

Bedload Transport ◽

Sediment Storage ◽

Data Set ◽

Gravel Bed ◽

Wide Range ◽

Gravel Bed Rivers ◽

Bed Waves

Temporal variability in bedload transport rates and spatial variability in sediment storage have been reported with increasing frequency in recent years. A spatial and temporal classification for these features is suggested based on the gravel bedform classification of Church and Jones (1982). The identified scales, meso-, macro-, and mega- are each broad, and within each there is a wide range of processes acting to produce bedload fluctuations. Sampling the same data set with different sampling intervals yields a near linear relationship between sampling interval and pulse period. A range of modelling strategies has been applied to bed waves. The most successful have been those which allow for the three-dimensional nature of sediment storage processes, and which allow changes in the width and depth of stored sediment. The existence of bed waves makes equilibrium in gravel-bed rivers necessarily dynamic. Bedload pulses and bed waves can be regarded as equilibrium forms at sufficiently long timescales.

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The suitability of remotely sensed soil moisture for improving operational flood forecasting

Hydrology and Earth System Sciences ◽

10.5194/hess-18-2343-2014 ◽

2014 ◽

Vol 18 (6) ◽

pp. 2343-2357 ◽

Cited By ~ 148

Author(s):

N. Wanders ◽

D. Karssenberg ◽

A. de Roo ◽

S. M. de Jong ◽

M. F. P. Bierkens

Keyword(s):

Soil Moisture ◽

Satellite Data ◽

Hydrological Model ◽

Flood Forecasting ◽

Remotely Sensed ◽

Added Value ◽

Lead Times ◽

Validation Data ◽

Data Set ◽

Discharge Data

Abstract. We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the mean absolute error (MAE) of the ensemble mean is reduced by 35%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the continuous ranked probability score (CRPS) shows a performance increase of 5–10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more observational data is assimilated into the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

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Investigation of the international comparability of population-based routine hospital data set derived comorbidity scores for patients with lung cancer

Thorax ◽

10.1136/thoraxjnl-2017-210362 ◽

2017 ◽

Vol 73 (4) ◽

pp. 339-349 ◽

Cited By ~ 4

Author(s):

Margreet Lüchtenborg ◽

Eva J A Morris ◽

Daniela Tataru ◽

Victoria H Coupland ◽

Andrew Smith ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Survival ◽

Population Based ◽

Data Sets ◽

Hospital Data ◽

Data Set ◽

Discharge Data ◽

International Differences ◽

Cancer Data ◽

Comorbidity Scores

IntroductionThe International Cancer Benchmarking Partnership (ICBP) identified significant international differences in lung cancer survival. Differing levels of comorbid disease across ICBP countries has been suggested as a potential explanation of this variation but, to date, no studies have quantified its impact. This study investigated whether comparable, robust comorbidity scores can be derived from the different routine population-based cancer data sets available in the ICBP jurisdictions and, if so, use them to quantify international variation in comorbidity and determine its influence on outcome.MethodsLinked population-based lung cancer registry and hospital discharge data sets were acquired from nine ICBP jurisdictions in Australia, Canada, Norway and the UK providing a study population of 233 981 individuals. For each person in this cohort Charlson, Elixhauser and inpatient bed day Comorbidity Scores were derived relating to the 4–36 months prior to their lung cancer diagnosis. The scores were then compared to assess their validity and feasibility of use in international survival comparisons.ResultsIt was feasible to generate the three comorbidity scores for each jurisdiction, which were found to have good content, face and concurrent validity. Predictive validity was limited and there was evidence that the reliability was questionable.ConclusionThe results presented here indicate that interjurisdictional comparability of recorded comorbidity was limited due to probable differences in coding and hospital admission practices in each area. Before the contribution of comorbidity on international differences in cancer survival can be investigated an internationally harmonised comorbidity index is required.

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Nodal Seismograph Recordings of the 2019 Ridgecrest Earthquake Sequence

Seismological Research Letters ◽

10.1785/0220200203 ◽

2020 ◽

Vol 91 (6) ◽

pp. 3622-3633

Author(s):

Rufus D. Catchings ◽

Mark R. Goldman ◽

Jamison H. Steidl ◽

Joanne H. Chan ◽

Amir A. Allam ◽

...

Keyword(s):

Wave Velocity ◽

Large Volume ◽

Ambient Noise ◽

Temporal Variations ◽

Earthquake Sequence ◽

S Wave ◽

Data Set ◽

Fault Structure ◽

Nodal Data ◽

Dense Arrays

Abstract The 2019 Ridgecrest, California, earthquake sequence included Mw 6.4 and 7.1 earthquakes that occurred on successive days beginning on 4 July 2019. These two largest earthquakes of the sequence occurred on orthogonal faults that ruptured the Earth’s surface. To better evaluate the 3D subsurface fault structure, (P- and S-wave) velocity, 3D and temporal variations in seismicity, and other important aspects of the earthquake sequence, we recorded aftershocks and ambient noise using up to 461 three-component nodal seismographs for about two months, beginning about one day after the Mw 7.1 mainshock. The ∼30,000Mw≥1 earthquakes that were recorded on the dense arrays provide an unusually large volume of data with which to evaluate the earthquake sequence. This report describes the recording arrays and is intended to provide metadata for researchers interested in evaluating various aspects of the 2019 Ridgecrest earthquake sequence using the nodal data set.

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