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.

scholarly journals Estimating water discharge from large radar altimetry datasets

2013 ◽  
Vol 17 (3) ◽  
pp. 923-933 ◽  
Author(s):  
A. C. V. Getirana ◽  
C. Peters-Lidard
Keyword(s):  
Land Surface ◽  
Amazon Basin ◽  
Water Discharge ◽  
Land Surface Model ◽  
Global Scale ◽  
Surface Model ◽  
Rating Curve ◽  
Radar Altimetry ◽  
Discharge Data ◽  
The Impact

Abstract. The objective of this study is to evaluate the potential of large altimetry datasets as a complementary gauging network capable of providing water discharge in ungauged regions. A rating curve-based methodology is adopted to derive water discharge from altimetric data provided by the Envisat satellite at 475 virtual stations (VS) within the Amazon basin. From a global-scale perspective, the stage–discharge relations at VS are built based on radar altimetry and outputs from a modeling system composed of a land surface model and a global river routing scheme. In order to quantify the impact of model uncertainties on rating-curve based discharges, a second experiment is performed using outputs from a simulation where daily observed discharges at 135 gauging stations are introduced in the modeling system. Discharge estimates at 90 VS are evaluated against observations during the curve fitting calibration (2002–2005) and evaluation (2006–2008) periods, resulting in mean normalized RMS errors as high as 39 and 15% for experiments without and with direct insertion of data, respectively. Without direct insertion, 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 streamflow volume errors (RE) of altimetry-based discharges varied from 15 to 84% for large and small drainage areas, respectively. Rating curves produced a mean RE of 51% versus 68% from model outputs. Inserting discharge data into the modeling system decreases the mean RE from 51 to 18%, and mean NRMSE from 24 to 9%. These results demonstrate the feasibility of applying the proposed methodology to the continental or global scales.

scholarly journals Impact of the Mean Daily Air Temperature Calculation on the Rainfall-Runoff Modelling

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3175
Author(s):  
Nejc Bezak ◽  
Lazar Cerović ◽  
Mojca Šraj
Keyword(s):  
Air Temperature ◽  
Input Data ◽  
Absolute Error ◽  
Rainfall Runoff ◽  
Rating Curve ◽  
Discharge Data ◽  
Temperature Calculation ◽  
Maximum Discharge ◽  
The Mean ◽  
The Impact

Conceptual rainfall-runoff models besides precipitation and discharge data generally require estimates of the mean daily air temperature as input data. For the estimation of the mean daily air temperature, there are different methods available. The paper presents an evaluation of the impact of the mean daily air temperature calculation on the rainfall-runoff modelling results. Additionally, other measured variables and rating curve uncertainty were assessed. Differences in the mean daily air temperature values were evaluated for the 33 meteorological stations in Slovenia and additional investigations were conducted for four selected meso-scale catchments located in different climates. The results of the application of four equations for the mean air temperature calculation yielded the mean absolute error values between 0.56–0.80 °C. However, the results of rainfall-runoff modelling showed that these differences had an almost negligible impact on the model results. Differences in the mean simulated discharge values were no larger than 1%, while differences in the maximum discharge values were a bit larger, but did not exceed 5%. A somewhat larger impact on the model results was observed when precipitation and water level measurements’ uncertainty was included. However, among all analysed input data uncertainties, the rating curve uncertainty can be regarded as the most influential with differences in the simulated mean discharge values in the range of 3% and differences in the maximum discharge values up to 14%.

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 Analysis of Suspended Sediment in the Anavilhanas Archipelago, Rio Negro, Amazon Basin

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1073 ◽  
Author(s):  
Rogério Ribeiro Marinho ◽  
Naziano Pantoja Filizola Junior ◽  
Édipo Henrique Cremon
Keyword(s):  
Suspended Sediment ◽  
Amazon Basin ◽  
Water Discharge ◽  
Left Bank ◽  
River Channels ◽  
Total Suspended Sediment ◽  
Negro River ◽  
Discharge Velocity ◽  
The Mean ◽  
The Right

This article analyzes the flows of water and total suspended sediment in different reaches in the lower course of the Negro River, the largest fluvial blackwater system in the world. The area under study is the Anavilhanas Archipelago, which is a complex multichannel reach on the Negro River. Between the years 2016 and 2019, data about water discharge, velocity, and concentration of total suspended solids (TSS) were acquired in sample sections of the Negro River channels located upstream, inside, and downstream of the Anavilhanas Archipelago. In the study area, the Negro River drains an area greater than 700,000 km2, and the mean water discharge observed before the Anavilhanas was about 28.655 m3·s−1, of which 97% flows through two channels of the Archipelago close to the right and left banks. The mean TSS concentration of the Negro River upstream and downstream the Archipelago was 3.28 mg·L−1 and 1.63 mg·L−1, respectively. Within the Archipelago, we observed more TSS in the channel on the left bank of the Negro River (mean of 4.50 mg·L−1). The total suspended sediment discharge of the Negro River before (3.14 Mt·year−1) and after (1.43 Mt·year−1) the Anavilhanas Archipelago indicates a 55% retention of the suspended load due to the low water slope and reduced flow velocity caused by the backwater effect of Solimões River on the Negro River. The hydro-sedimentary scenario of the low course of the Negro River characterized in this study indicates a slow and continuous sedimentation process in the Anavilhanas Archipelago. The results presented will serve as a baseline to assess the impacts of the dams on the Branco River, the main tributary for both water and sediment in the Negro River basin.

scholarly journals Influence of ENSO and tropical Atlantic climate variability on flood characteristics in the Amazon basin

2020 ◽  
Author(s):  
Jamie Towner ◽  
Andrea Ficchí ◽  
Hannah L. Cloke ◽  
Juan Bazo ◽  
Erin Coughlan de Perez ◽  
...  
Keyword(s):  
Climate Variability ◽  
Early Warning ◽  
Large Scale ◽  
Amazon Basin ◽  
Early Warning Systems ◽  
Discharge Data ◽  
Flood Magnitude ◽  
Highly Correlated ◽  
The Impact ◽  
Flood Characteristics

Abstract. Flooding in the Amazon basin is frequently attributed to modes of large-scale climate variability, but little attention is paid to how these modes influence the timing and duration of floods despite their importance to early warning systems and the significant impacts that these flood characteristics can have on communities. In this study, river discharge data from the Global Flood Awareness System (GloFAS 2.1) and observed data at 58 gauging stations are used to examine whether positive/negative phases of several Pacific and Atlantic indices significantly alter the characteristics of river flows throughout the Amazon basin (1979–2015). Results show significant changes in both flood magnitude and duration, particularly in the north-eastern Amazon for negative ENSO years when the SST anomaly is positioned in the central tropical Pacific. This response is not identified for the eastern Pacific index, highlighting how the response can differ between ENSO types. Although flood magnitude and duration were found to be highly correlated, the impacts of large-scale climate variability on these characteristics are non-linear; some increases in annual flood maxima coincide with decreases in flood duration. The impact of flood timing however does not follow any notable pattern for all indices analysed. Finally, observed and simulated changes are found to be much more highly correlated for negative ENSO years compared to the positive phase, meaning that GloFAS struggles to accurately simulate the differences in flood characteristics between El Niño and neutral years. These results have important implications for both the social and physical sectors working towards the improvement of early warning action systems for floods.

scholarly journals Sediment load estimation in the Mellegue catchment, Algeria

2016 ◽  
Vol 31 (1) ◽  
pp. 129-137 ◽  
Author(s):  
Kaouther Selmi ◽  
Kamel Khanchoul
Keyword(s):  
Sediment Transport ◽  
Suspended Sediment ◽  
Sediment Yield ◽  
Agricultural Land ◽  
Water Discharge ◽  
Sediment Concentration ◽  
Sediment Loads ◽  
Recorded Data ◽  
The Mean ◽  
The Impact

AbstractSoil erosion by water and the impact of sediment transport on lakes and streams, can seriously degrade soil and create problems for both agricultural land and water quality. The present study has been carried out to assess suspended sediment yield in Mellegue catchment, northeast of Algeria. Regression analysis was used to establish a relationship between the instantaneous water discharge (Q) and the instantaneous suspended sediment concentration (C) based on all recorded data and seasonal ratings for the period 1970–2003. The regression technique used in this paper involved a division of data into discharge – based classes, the mean concentrations and discharges of which are used to develop power regressions, according to single and season ratings, through log-transformation. Sediment loads estimated by stratified rating curves reduced underestimations to a range from 2 to 4%. The mean annual sediment yield during the 34 years of the study period was 589.23 t·km−2·y−1. Sediment transport is dominated by fall rainstorms accounting for 41% of the annual load. The big supply of sediment during this season confirms the intense geomorphic work by fall storms caused by high intensity rainfall and low vegetation cover.

Comparing Limited-Area 3DVAR and Hybrid Variational-Ensemble Data Assimilation Methods for Typhoon Track Forecasts: Sensitivity to Outer Loops and Vortex Relocation

2013 ◽  
Vol 141 (12) ◽  
pp. 4350-4372 ◽  
Author(s):  
Craig S. Schwartz ◽  
Zhiquan Liu ◽  
Xiang-Yu Huang ◽  
Ying-Hwa Kuo ◽  
Chin-Tzu Fong
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Wrf Model ◽  
Limited Area ◽  
Background Error ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Typhoon Track ◽  
The Mean ◽  
The Impact

Abstract The Weather Research and Forecasting Model (WRF) “hybrid” variational-ensemble data assimilation (DA) algorithm was used to initialize WRF model forecasts of three tropical cyclones (TCs). The hybrid-initialized forecasts were compared to forecasts initialized by WRF's three-dimensional variational (3DVAR) DA system. An ensemble adjustment Kalman filter (EAKF) updated a 32-member WRF-based ensemble system that provided flow-dependent background error covariances for the hybrid. The 3DVAR, hybrid, and EAKF configurations cycled continuously for ~3.5 weeks and produced new analyses every 6 h that initialized 72-h WRF forecasts with 45-km horizontal grid spacing. Additionally, the impact of employing a TC relocation technique and using multiple outer loops (OLs) in the 3DVAR and hybrid minimizations were explored. Model output was compared to conventional, dropwindsonde, and TC “best track” observations. On average, the hybrid produced superior forecasts compared to 3DVAR when only one OL was used during minimization. However, when three OLs were employed, 3DVAR forecasts were dramatically improved but the mean hybrid performance changed little. Additionally, incorporation of TC relocation within the cycling systems further improved the mean 3DVAR-initialized forecasts but the average hybrid-initialized forecasts were nearly unchanged.

scholarly journals Large-scale hydrological model river storage and discharge correction using a satellite altimetry-based discharge product

2018 ◽  
Vol 22 (4) ◽  
pp. 2135-2162 ◽  
Author(s):  
Charlotte Marie Emery ◽  
Adrien Paris ◽  
Sylvain Biancamaria ◽  
Aaron Boone ◽  
Stéphane Calmant ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Amazon Basin ◽  
Water Cycle ◽  
State Variables ◽  
Initial Development ◽  
Discharge Product ◽  
Types Of Information ◽  
The Impact

Abstract. Land surface models (LSMs) are widely used to study the continental part of the water cycle. However, even though their accuracy is increasing, inherent model uncertainties can not be avoided. In the meantime, remotely sensed observations of the continental water cycle variables such as soil moisture, lakes and river elevations are more frequent and accurate. Therefore, those two different types of information can be combined, using data assimilation techniques to reduce a model's uncertainties in its state variables or/and in its input parameters. The objective of this study is to present a data assimilation platform that assimilates into the large-scale ISBA-CTRIP LSM a punctual river discharge product, derived from ENVISAT nadir altimeter water elevation measurements and rating curves, over the whole Amazon basin. To deal with the scale difference between the model and the observation, the study also presents an initial development for a localization treatment that allows one to limit the impact of observations to areas close to the observation and in the same hydrological network. This assimilation platform is based on the ensemble Kalman filter and can correct either the CTRIP river water storage or the discharge. Root mean square error (RMSE) compared to gauge discharges is globally reduced until 21 % and at Óbidos, near the outlet, RMSE is reduced by up to 52 % compared to ENVISAT-based discharge. Finally, it is shown that localization improves results along the main tributaries.

scholarly journals Large scale hydrological model river storage and discharge correction using satellite altimetry-based discharge product

2017 ◽  
Author(s):  
Charlotte Marie Emery ◽  
Adrien Paris ◽  
Sylvain Biancamaria ◽  
Aaron Boone ◽  
Stéphane Calmant ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Amazon Basin ◽  
Water Cycle ◽  
State Variables ◽  
Initial Development ◽  
Discharge Product ◽  
Types Of Information ◽  
The Impact

Abstract. Land Surface Models (LSM) are widely used to study the continental part of the water cycle. Yet, even though their accuracy is increasing, inherent model uncertainties can not be avoided. In the meantime, remotely-sensed observations of the continental water cycle variables such as soil moisture, lakes and rivers elevations are more frequent and accurate. Therefore, those two different types of information can be combined, using data assimilation techniques to reduce model's uncertainties on its state variables or/and on its input parameters. The objective of this study is to present a data assimilation platform that assimilates into the LSM ISBA-CTRIP a river discharge product, derived from ENVISAT nadir altimeter water elevation measurements, over the whole Amazon basin. The study presents an initial development for a localization treatment that allows to limit the impact of observations to areas close to the observation and on the same hydrological network. This assimilation platform is based on the Ensemble Kalman Filter and can either correct the river water storage or the discharge: RMSE compared to gauge discharges are globally reduced until 21 % and at Óbidos, near the outlet, RMSE are reduced up to 52 % compared to ENVISAT discharge. Finally, it is shown that localization improve results along main tributaries.

scholarly journals The fresh-water discharge in Todos os Santos Bay (BA) an its significance to the general water circulation

2001 ◽  
Vol 28 (2) ◽  
pp. 85 ◽  
Author(s):  
GERALDO MARCELO PEREIRA LIMA ◽  
GUILHERME CAMARGO LESSA
Keyword(s):  
Fresh Water ◽  
River Discharge ◽  
Water Discharge ◽  
Water Circulation ◽  
Tidal Prism ◽  
Discharge Data ◽  
Bay Area ◽  
Area Distribution ◽  
The Mean ◽  
Fresh Water Discharge

Fresh water discharge into Todos os Santos Bay is assessed on the basis of measured and estimated river discharge data. The average mean fresh water discharge amounts to 111.1 m3s-1, and might be added to another 28.2 m3s-1 that falls directly on the bay area as rainfall. The mean river discharge represents only 0.08% of the spring tidal prism, calculated by the means of a through analysis of the depth area distribution.

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