scholarly journals Study on the influence of infiltration on flood propagation with different peak shape coefficients and duration

Water Policy ◽  
2021 ◽  
Author(s):  
Jingming Hou ◽  
Zhaoan Zhang ◽  
Dawei Zhang ◽  
Baoshan Shi ◽  
Guangzhao Chen ◽  
...  
Keyword(s):  
Northern China ◽  
Peak Shape ◽  
Maximum Difference ◽  
Soil Infiltration ◽  
Discharge Data ◽  
Considerable Uncertainty ◽  
Flood Duration ◽  
Inundation Area ◽  
Flood Simulations ◽  
The Impact

Abstract Traditional flood simulations fail to properly consider the impact of soil infiltration in floodplain areas with high soil infiltration rates. Notably, ignoring soil infiltration will lead to considerable uncertainty in flood simulations. In this paper, a fully hydrodynamic model coupled with the Green–Ampt infiltration model was used. Taking a natural reach in northern China (HTH in this paper) as a case study, observed flood discharge data were used to analyze the influence of soil infiltration on flood propagation based on the flood propagation simulation results for various inflow conditions. The maximum difference of inundation area is about 25%. The results show that soil infiltration has little effect on the inundation area during the rising stage of a flood. In the late period of a flood, the inundation area considering the effect of infiltration is smaller than that without infiltration, and the smaller the peak coefficient is, the longer the flood duration is, the larger the impact of infiltration on the inundation area. When the peak shape coefficient is 0.42 and the flood duration is 44.4 h, the maximum difference of the inundation area is about 28%. The research results provide a reference for flood management and post-disaster rescue efforts.

scholarly journals Developing Flood Inundation Map Using RRI and SOBEK Models: A Case Study of the Bago River Basin, Myanmar

2020 ◽  
Vol 15 (3) ◽  
pp. 277-287 ◽  
Author(s):  
Zin Mar Lar Tin San ◽  
Win Win Zin ◽  
Akiyuki Kawasaki ◽  
Ralph Allen Acierto ◽  
Tin Zar Oo ◽  
...  
Keyword(s):  
River Basin ◽  
Coefficient Of Determination ◽  
Flood Inundation ◽  
Floodplain Management ◽  
User Friendliness ◽  
Discharge Data ◽  
Flood Duration ◽  
Inundation Area ◽  
Forecasting System ◽  
Inundation Map

The Bago River Basin in Myanmar is highly flood-prone. To develop a flood forecasting system, an inundation map of the Bago River Basin is required. This study applied the Rainfall-Runoff-Inundation (RRI) model and SOBEK model to simulate flood discharges and inundation to determine the model most suitable for analysis of the study basin in terms of user friendliness, cost, type of output, and correlation between simulated and observed data. In this study, five flood events were selected to calibrate and validate the models, using discharge data measured at Bago station. The Nash–Sutcliffe efficiency (ENS) and coefficient of determination (R2) were used to evaluate the performance of the models. The simulated flood inundation area was validated with satellite images. According to the comparison, the SOBEK model is more accurate than the RRI model, and the simulated and observed discharges are closely related. However, when the calculation time and cost are included in the consideration, the RRI model is preferable, as it is faster and freely available. For the Bago River Basin, the RRI model is efficient in predicting the potential flood duration and areas of inundation in near-real time, whereas the SOBEK model is useful for floodplain management. This study shows that the RRI and SOBEK models are applicable to any basin in Myanmar that is similar to the Bago River Basin.

scholarly journals Effect of the Assimilation Frequency of Radar Reflectivity on Rain Storm Prediction by Using WRF-3DVAR

2021 ◽  
Vol 13 (11) ◽  
pp. 2103
Author(s):  
Yuchen Liu ◽  
Jia Liu ◽  
Chuanzhe Li ◽  
Fuliang Yu ◽  
Wei Wang
Keyword(s):  
Data Assimilation ◽  
Northern China ◽  
Horizontal Resolution ◽  
Radar Reflectivity ◽  
Small Scale ◽  
Time Interval ◽  
Outer Domain ◽  
Storm Prediction ◽  
The Impact ◽  
Assimilation Time

An attempt was made to evaluate the impact of assimilating Doppler Weather Radar (DWR) reflectivity together with Global Telecommunication System (GTS) data in the three-dimensional variational data assimilation (3DVAR) system of the Weather Research Forecast (WRF) model on rain storm prediction in Daqinghe basin of northern China. The aim of this study was to explore the potential effects of data assimilation frequency and to evaluate the outputs from different domain resolutions in improving the meso-scale NWP rainfall products. In this study, four numerical experiments (no assimilation, 1 and 6 h assimilation time interval with DWR and GTS at 1 km horizontal resolution, 6 h assimilation time interval with radar reflectivity, and GTS data at 3 km horizontal resolution) are carried out to evaluate the impact of data assimilation on prediction of convective rain storms. The results show that the assimilation of radar reflectivity and GTS data collectively enhanced the performance of the WRF-3DVAR system over the Beijing-Tianjin-Hebei region of northern China. It is indicated by the experimental results that the rapid update assimilation has a positive impact on the prediction of the location, tendency, and development of rain storms associated with the study area. In order to explore the influence of data assimilation in the outer domain on the output of the inner domain, the rainfall outputs of 3 and 1 km resolution are compared. The results show that the data assimilation in the outer domain has a positive effect on the output of the inner domain. Since the 3DVAR system is able to analyze certain small-scale and convective-scale features through the incorporation of radar observations, hourly assimilation time interval does not always significantly improve precipitation forecasts because of the inaccurate radar reflectivity observations. Therefore, before data assimilation, the validity of assimilation data should be judged as far as possible in advance, which can not only improve the prediction accuracy, but also improve the assimilation efficiency.

Do Hospitals with a Higher Level of Maternal Care Designation Have Better Maternal Outcomes?

2018 ◽  
Vol 36 (06) ◽  
pp. 653-658 ◽  
Author(s):  
Sindhu Srinivas ◽  
Katy Kozhimannil ◽  
Peiyin Hung ◽  
Laura Attanasio ◽  
Judy Jou ◽  
...  
Keyword(s):  
High Risk ◽  
Maternal Care ◽  
Maternal Morbidity ◽  
Cross Sectional Study ◽  
Severe Maternal Morbidity ◽  
Maternal Outcomes ◽  
Cross Sectional ◽  
Discharge Data ◽  
Logistic Regression Models ◽  
The Impact

Background A recent document by the American Congress of Obstetricians and Gynecologists (ACOG) and the Society for Maternal-Fetal Medicine introduced the concept of uniform levels of maternal care (LMCs). Objective We assessed LMC across hospitals and measured their association with maternal morbidity, focusing on women with high-risk conditions. Study Design We collected data from hospitals from May to November 2015 and linked survey responses to Statewide Inpatient Databases (SID) hospital discharge data in a retrospective cross-sectional study of 247,383 births admitted to 236 hospitals. Generalized logistic regression models were used to examine the associations between hospitals' LMC and the risk of severe maternal morbidity. Stratified analyses were conducted among women with high-risk conditions. Results High-risk pregnancies were more likely to be managed in hospitals with higher LMC (p < 0.001). Women with cardiac conditions had lower odds of maternal morbidity when delivered in level I compared with level IV units (adjusted odds ratio: 0.29; 95% confidence interval: 0.08–0.99; p = 0.049). There were no other significant associations between the LMC and severe maternal morbidity. Conclusion A higher proportion of high-risk pregnancies were managed within level IV units, although there was no overall evidence that these births had superior outcomes. Further prospective evaluation of LMC designation with patient outcomes is necessary to determine the impact of regionalization on maternal outcomes.

scholarly journals Routine use of immunosuppressants is associated with mortality in hospitalised patients with COVID-19

2021 ◽  
Vol 12 ◽  
pp. 204209862098569
Author(s):  
Phyo K. Myint ◽  
Ben Carter ◽  
Fenella Barlow-Pay ◽  
Roxanna Short ◽  
Alice G. Einarsson ◽  
...  
Keyword(s):  
Medical Attention ◽  
Close Monitoring ◽  
Plain Language ◽  
Discharge Data ◽  
Specific Patient ◽  
Risk Of Death ◽  
Hospitalised Patients ◽  
Increased Risk ◽  
The Uk ◽  
The Impact

Background: Whilst there is literature on the impact of SARS viruses in the severely immunosuppressed, less is known about the link between routine immunosuppressant use and outcome in COVID-19. Consequently, guidelines on their use vary depending on specific patient populations. Methods: The study population was drawn from the COPE Study (COVID-19 in Older People), a multicentre observational cohort study, across the UK and Italy. Data were collected between 27 February and 28 April 2020 by trained data-collectors and included all unselected consecutive admissions with COVID-19. Load (name/number of medications) and dosage of immunosuppressant were collected along with other covariate data. Primary outcome was time-to-mortality from the date of admission (or) date of diagnosis, if diagnosis was five or more days after admission. Secondary outcomes were Day-14 mortality and time-to-discharge. Data were analysed with mixed-effects, Cox proportional hazards and logistic regression models using non-users of immunosuppressants as the reference group. Results: In total 1184 patients were eligible for inclusion. The median (IQR) age was 74 (62–83), 676 (57%) were male, and 299 (25.3%) died in hospital (total person follow-up 15,540 days). Most patients exhibited at least one comorbidity, and 113 (~10%) were on immunosuppressants. Any immunosuppressant use was associated with increased mortality: aHR 1.87, 95% CI: 1.30, 2.69 (time to mortality) and aOR 1.71, 95% CI: 1.01–2.88 (14-day mortality). There also appeared to be a dose–response relationship. Conclusion: Despite possible indication bias, until further evidence emerges we recommend adhering to public health measures, a low threshold to seek medical advice and close monitoring of symptoms in those who take immunosuppressants routinely regardless of their indication. However, it should be noted that the inability to control for the underlying condition requiring immunosuppressants is a major limitation, and hence caution should be exercised in interpretation of the results. Plain Language Summary Regular Use of Immune Suppressing Drugs is Associated with Increased Risk of Death in Hospitalised Patients with COVID-19 Background: We do not have much information on how the COVID-19 virus affects patients who use immunosuppressants, drugs which inhibit or reduce the activity of the immune system. There are various conflicting views on whether immune-suppressing drugs are beneficial or detrimental in patients with the disease. Methods: This study collected data from 10 hospitals in the UK and one in Italy between February and April 2020 in order to identify any association between the regular use of immunosuppressant medicines and survival in patients who were admitted to hospital with COVID-19. Results: 1184 patients were included in the study, and 10% of them were using immunosuppressants. Any immunosuppressant use was associated with increased risk of death, and the risk appeared to increase if the dose of the medicine was higher. Conclusion: We therefore recommend that patients who take immunosuppressant medicines routinely should carefully adhere to social distancing measures, and seek medical attention early during the COVID-19 pandemic.

Improving SAR Altimeter processing over the coastal zone and inland waters - the ESA HYDROCOASTAL project

2021 ◽  
Author(s):  
David Cotton ◽  
Keyword(s):  
Coastal Zone ◽  
Test Data ◽  
River Discharge ◽  
Altimeter Data ◽  
Inland Waters ◽  
Data Sets ◽  
Data Set ◽  
Discharge Data ◽  
Processing Algorithms ◽  
The Impact

&lt;p&gt;&lt;strong&gt;Introduction&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;HYDROCOASTAL 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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;A series of case studies will assess these products in terms of their scientific impacts.&lt;/p&gt;&lt;p&gt;All the produced data sets will be available on request to external researchers, and full descriptions of the processing algorithms will be provided&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objectives&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;The scientific objectives of HYDROCOASTAL are to enhance our understanding&amp;#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 changes&lt;/p&gt;&lt;p&gt;The technical objectives are to develop and evaluate&amp;#160; new SAR&amp;#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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Project&amp;#160; Outline&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;There are four tasks to the project&lt;/p&gt;&lt;ul&gt;&lt;li&gt;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&lt;/li&gt; &lt;li&gt;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&lt;/li&gt; &lt;li&gt;Impacts Assessment: The impact of these global products will be assess in a series of Case Studies&lt;/li&gt; &lt;li&gt;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.&lt;/li&gt; &lt;/ul&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Presentation&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;The 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.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Benefits and limitations of data assimilation for discharge forecasting using an event-based rainfall–runoff model

2013 ◽  
Vol 13 (3) ◽  
pp. 583-596 ◽  
Author(s):  
M. Coustau ◽  
S. Ricci ◽  
V. Borrell-Estupina ◽  
C. Bouvier ◽  
O. Thual
Keyword(s):  
Data Assimilation ◽  
Hydrological Model ◽  
Peak Discharge ◽  
State Variables ◽  
Rainfall Runoff ◽  
Discharge Data ◽  
Southern France ◽  
Flood Peak ◽  
Event Based ◽  
The Impact

Abstract. Mediterranean catchments in southern France are threatened by potentially devastating fast floods which are difficult to anticipate. In order to improve the skill of rainfall-runoff models in predicting such flash floods, hydrologists use data assimilation techniques to provide real-time updates of the model using observational data. This approach seeks to reduce the uncertainties present in different components of the hydrological model (forcing, parameters or state variables) in order to minimize the error in simulated discharges. This article presents a data assimilation procedure, the best linear unbiased estimator (BLUE), used with the goal of improving the peak discharge predictions generated by an event-based hydrological model Soil Conservation Service lag and route (SCS-LR). For a given prediction date, selected model inputs are corrected by assimilating discharge data observed at the basin outlet. This study is conducted on the Lez Mediterranean basin in southern France. The key objectives of this article are (i) to select the parameter(s) which allow for the most efficient and reliable correction of the simulated discharges, (ii) to demonstrate the impact of the correction of the initial condition upon simulated discharges, and (iii) to identify and understand conditions in which this technique fails to improve the forecast skill. The correction of the initial moisture deficit of the soil reservoir proves to be the most efficient control parameter for adjusting the peak discharge. Using data assimilation, this correction leads to an average of 12% improvement in the flood peak magnitude forecast in 75% of cases. The investigation of the other 25% of cases points out a number of precautions for the appropriate use of this data assimilation procedure.

scholarly journals Proglacial river dataset from the Akuliarusiarsuup Kuua River northern tributary, Southwest Greenland, 2008–2010

2011 ◽  
Vol 4 (1) ◽  
pp. 71-97 ◽  
Author(s):  
A. K. Rennermalm ◽  
L. C. Smith ◽  
V. W. Chu ◽  
R. R. Forster ◽  
J. E. Box ◽  
...  
Keyword(s):  
Water Level ◽  
Statistical Tests ◽  
Ice Sheet ◽  
High Water ◽  
Stream Channels ◽  
Discharge Data ◽  
Lake Water Level ◽  
Considerable Uncertainty ◽  
Added Uncertainty ◽  
Southwest Greenland

Abstract. Pressing scientific questions concerning the Greenland ice sheet's climatic sensitivity, hydrology, and contributions to current and future sea level rise require hydrological datasets to resolve. While direct observations of ice sheet meltwater losses can be obtained in terrestrial rivers draining the ice sheet and from lake levels, few such datasets exist. We present a new dataset of meltwater river discharge for the vicinity of Kangerlussuaq, Southwest Greenland. The dataset contains measurements of river water level and discharge for three sites along the Akuliarusiarsuup Kuua (Watson) River's northern tributary, with 30 min temporal resolution between June 2008 and August 2010. Additional data of water temperature, air pressure, and lake water level and temperature are also provided. Discharge data were measured at sites with near-ideal properties for such data collection. Regardless, high water bedload and turbulent flow introduce considerable uncertainty. These were constrained and quantified using statistical techniques, which revealed that the greatest discharge data uncertainties are associated with streambed elevation change and measurements. Large portions of stream channels deepened according to statistical tests, but poor precision of streambed depth measurements also added uncertainty. Data will periodically be extended, and are available in Open Access at doi:10.1594/PANGAEA.762818.

scholarly journals The Impact of Air Pollution (PM2.5) on Atherogenesis in Modernizing Southern versus Northern China

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1552
Author(s):  
Kamsang Woo ◽  
Changqing Lin ◽  
Yuehui Yin ◽  
Dongshuang Guo ◽  
Ping Chook ◽  
...  
Keyword(s):  
Risk Factors ◽  
Air Pollution ◽  
Northern China ◽  
Age And Gender ◽  
Carotid Imt ◽  
Southern Chinese ◽  
Northern Chinese ◽  
Traditional Risk Factors ◽  
And Gender ◽  
The Impact

To evaluate the impact of PM2.5 air pollution on atherogenic processes in modernizing Southern versus Northern China, we studied 1323 asymptomatic Chinese in Southern and Northern China in 1996–2007. PM2.5 exposure and metabolic syndrome (MS) were noted. Brachial flow-mediated dilation (endothelial function FMD) and carotid intima-media thickness (IMT) were measured by ultrasound. Although age and gender were similar, PM2.5 was higher in Northern China than in Southern China. The Northern Chinese were characterized by lower lipids, folate and vitamin B12, but higher age, blood pressures, MS and homocysteine (HC) (p = 0.0015). Brachial FMD was significantly lower and carotid IMT was significantly greater (0.68±0.13) in Northern Chinese, compared with FMD and IMT (0.57 ± 0.13, p < 0.0001) in Southern Chinese. On multivariate regression, for the overall cohort, carotid IMT was significantly related to PM2.5, independent of location and traditional risk factors (Model R2 = 0.352, F = 27.1, p < 0.0001), while FMD was inversely related to gender, age, and northern location, but not to PM2.5. In Southern Chinese, brachial FMD was inversely correlated to PM2.5, independent of age, whereas carotid IMT was significantly related to PM2.5, independent of age and gender. In Northern Chinese, brachial FMD was inversely related to gender only, but not to PM2.5, while carotid IMT was related to traditional risk factors. Despite a higher PM2.5 pollution in Northern China, PM2.5 pollution was more significantly associated with atherogenic surrogates in Southern compared to Northern Chinese. This has potential implications for atherosclerosis prevention.

scholarly journals Prescription Opioid Abuse: Gleaning insights from hospital and vital records data

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Reka Sundaram-Stukel ◽  
Ousmane Diallo ◽  
Benjamin Wiseman ◽  
Richard E. Miller
Keyword(s):  
Mental Health ◽  
Prescription Drug ◽  
Prescription Drugs ◽  
The United States ◽  
Hospital Charges ◽  
Prescription Opioids ◽  
Opioid Prescription ◽  
Discharge Data ◽  
Operative Recovery ◽  
The Impact

ObjectiveIn this paper we used hospital charges to assess costs incurred dueto prescription drug/opioid hospitalizationsIntroductionThere is a resurgence in the need to evaluate the economic burdenof prescription drug hospitalizations in the United States. We used theWisconsin 2014 Hospital Discharge data to examine opioid relatedhospitalization incidence and costs. Fentanyl, a powerful syntheticopioid, is frequently being used for as an intraoperative agent inanesthesia, and post-operative recovery in hospitals. According to a2013 study, synthetic Fentanyl is 40 times more potent than heroinand other prescription opioids; the strength of Fentanyl leads tosubstantial hospitalizations risks. Since, 1990 it has been availablewith a prescription in various forms such as transdermal patches orlollipops for treatment of serious chronic pain, most often prescribedfor late stage cancer patients. There have been reported fatal overdosesassociated with misuse of prescription fentanyl. In Wisconsin numberof total opioid related deaths increased by 51% from 2010 to 2014with the number of deaths involving prescription opioids specificallyincreased by 23% and number of deaths involving heroin increasedby 192%. We hypothesized that opioids prescription drugs, as a proxyof Fentanyl use, result in excessive health care costs.MethodsOpioid hospitalizations was defined as any mention of the ICD9codes (304,305) in any diagnostic field or the mention of (:E935.09) onthe first listed E-code. Our analysis used the Heckman 2-stage model,a method often used by Economists in absence of randomized controltrials. In presence of unobserved choice, for example opioid relatedhospitalizations, there usually is a correlation between error in anunderlying function (fentanyl prescription) and an estimated function(hospital charges) that introduces a selection bias. Heckman treats thiscorrelation between errors as an omitted variable bias. Therefore, weestimate a Heckman two step model using hospitalization: where theselection function is the probability of being hospitalized for syntheticopioid via logistic regression. Finally, we estimate the hospitalcharges realized if the patient was given opioids.ResultsMale patients are significantly more likely to be hospitalized foropioids than are female patients; while white patients are significantlymore likely to be admitted for opioid usage than other racialgroups. We also find that comorbid factors, such as mental health,significantly impact hospital charges associated with opioid use. Wefind that persons with private health insurance are associated withhigher rates of opioid use.ConclusionsUsing a Heckman two step approach we show that comorbidconditions such as mental health, Hepatitis C, injuries, etc significantlyaffect hospital charges associated with hospitalization. We usethese findings to explore the impact of the 2013 rule mandatingdoctors share opioid prescription information on the incidence ofopioid related death and hospital charges associated with opioidprescriptions. This work is policy relevant because alternatives toopioid prescription such as meditation, pain management therapiesmay be relevant.

scholarly journals Water discharge estimates from large radar altimetry datasets in the Amazon basin

2012 ◽  
Vol 9 (6) ◽  
pp. 7591-7611 ◽  
Author(s):  
A. C. V. Getirana ◽  
C. Peters-Lidard
Keyword(s):  
Data Assimilation ◽  
Amazon Basin ◽  
Water Discharge ◽  
Rating Curve ◽  
Radar Altimetry ◽  
Discharge Data ◽  
Modeling Uncertainties ◽  
The Mean ◽  
Relative Errors ◽  
The Impact

Abstract. In this study, we evaluate the use of a large radar altimetry dataset as a complementary gauging network capable of providing water discharge in ungauged regions within the Amazon basin. A rating-curve-based methodology is adopted to derive water discharge from altimetric data provided by Envisat at 444 virtual stations (VS). The stage-discharge relations at VS are built based on radar altimetry and outputs from a global flow routing scheme. In order to quantify the impact of modeling uncertainties on rating-curve based discharges, another experiment is performed using simulated discharges derived from a simplified data assimilation procedure. Discharge estimates at 90 VS are evaluated against observations during the curve fitting calibration (2002–2005) and evaluation (2006–2008) periods, resulting in mean relative RMS errors as high as 52% and 12% for experiments without and with assimilation, respectively. Without data assimilation, uncertainty of discharge estimates can be mostly attributed to forcing errors at smaller scales, generating a positive correlation between performance and drainage area. Mean relative errors (RE) of altimetry-based discharges varied from 15% to 92% for large and small drainage areas, respectively. Rating curves produced a mean RE of 54% versus 68% from model outputs. Assimilating discharge data decreases the mean RE from 68% to 12%. These results demonstrate the feasibility of applying the proposed methodology to the regional or global scales. Also, it is shown the potential of satellite altimetry for predicting water discharge in poorly-gauged and ungauged river basins.

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