Gauge stations are vital for monitoring water levels worldwide. However, many remote basins suffer from having gauges that are not tied to a common datum, making it impossible to know absolute water elevations, and therefore slope. This problem is known to exist on the River Amazon, for example, where water flux modeling efforts have been hampered by inconsistently leveled gauge data that serve as boundary conditions for these models. This paper presents a methodology for using Ice, Cloud, and land Elevation Satellite (ICESat) laser altimetry observations to geodetically level gauge stations. A proof‐of‐concept study was carried out to ascertain the feasibility and accuracy of the approach, and a mean absolute error of 19 cm was found. Once this was established, gauges within the Amazon Basin were geodetically leveled. This produced offsets for six gauges using a method that can be transferred to other locations and allows slope and discharge estimates to be calculated. The results are significant, with offsets as large as 13.37 m being added. The approach could provide improvements in modeling floodplain flow, processes, and fluxes in the Amazon Basin and worldwide.
Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at AGU Books. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]New Measurements of Water Dynamics and Sediment Transport along the Middle Reach of the Congo River and the Kasai TributaryAuthorsRaphael MTshimangaiDMark ATriggiDJeffNealiDPreksidesNdombaDenis AHughesAndrew BCarriDPierre MKabuyaiDGode BBolaiDCatherine AMushiiDJules TBeyaFelly KNganduGabriel MMokangoFelixMtaloPaulBatesiDSee all authors Raphael M TshimangaiDCorresponding Author• Submitting AuthorCongo Basin Water Resources Research Center (CRREBaC) & Dpt. Natural Resources ManagementUniversity of KinshasaiDhttps://orcid.org/0000-0002-4726-3495view email addressThe email was not providedcopy email addressMark A TriggiDSchool of Civil Engineering, University of LeedsiDhttps://orcid.org/0000-0002-8412-9332view email addressThe email was not providedcopy email addressJeff NealiDSchool of Geographical Sciences, University of BristoliDhttps://orcid.org/0000-0001-5793-9594view email addressThe email was not providedcopy email addressPreksides NdombaDepartment of Water Resources Engineering, University of Dar es Salaamview email addressThe email was not providedcopy email addressDenis A HughesInstitute for Water Research, Rhodes Universityview email addressThe email was not providedcopy email addressAndrew B CarriDSchool of Civil Engineering, University of LeedsiDhttps://orcid.org/0000-0001-8970-7149view email addressThe email was not providedcopy email addressPierre M KabuyaiDCongo Basin Water Resources Research Center (CRREBaC) & Dpt. Natural Resources Management, University of KinshasaiDhttps://orcid.org/0000-0002-4969-0538view email addressThe email was not providedcopy email addressGode B BolaiDCongo Basin Water Resources Research Center (CRREBaC) & Dpt. Natural Resources Management, University of KinshasaiDhttps://orcid.org/0000-0002-3072-4646view email addressThe email was not providedcopy email addressCatherine A MushiiDDepartment of Water Resources Engineering, University of Dar es SalaamiDhttps://orcid.org/0000-0002-2123-838Xview email addressThe email was not providedcopy email addressJules T BeyaCongo Basin Water Resources Research Center (CRREBaC) & Dpt. Natural Resources Management, University of Kinshasaview email addressThe email was not providedcopy email addressFelly K NganduCongo Basin Water Resources Research Center (CRREBaC) & Dpt. Natural Resources Management, University of Kinshasaview email addressThe email was not providedcopy email addressGabriel M MokangoRegie des Voies Fluvialesview email addressThe email was not providedcopy email addressFelix MtaloDepartment of Water Resources Engineering, University of Dar es Salaamview email addressThe email was not providedcopy email addressPaul BatesiDSchool of georgraphical sciences, University of BristoliDhttps://orcid.org/0000-0001-9192-9963view email addressThe email was not providedcopy email address
<p>Wetland processes considerably influence the flow regime of the downstream river channel, and are important to consider for a better representation of runoff generation within a basin scale hydrological model. The need to understand these processes lead to the development of a wetland sub-model for the monthly time step Pitman hydrological model. However, previous studies highlighted the need to provide guidance to explicitly estimate the wetland parameters rather than using a trial and error calibration approach. In this study, a 2D hydrodynamic river-wetland model (LISFLOOD-FP) is used to explicitly represent the inundation process exchanges between river channels and wetland systems and thereby inform the choice of Pitman wetland model parameters. The hysteretic patterns of these river-wetland processes are quantified through the use of hysteresis indices. Additionally, the hysteretic patterns are connected with the spill and return flow parameters of the wetland sub-model and eventually with the wetland morphometric characteristics. The results show that there is a consistent connection between the degree of hysteresis found in the channel-wetland exchange processes and the Pitman wetland parameters which are also explicitly linked to the wetland morphometric characteristics. The channel capacity to spill (Qcap) is consistently correlated with the hysteresis found between the channel inflow and the wetland storage volume. This anti-clockwise hysteresis represents the time delay between the inundation and drainage processes. The channel spill factor (QSF), in addition to the inundation processes, is also connected with the drainage processes represented by the wetland storage volume and channel outflow anti-clockwise hysteresis. On the other hand, the parameters of the return flow equation have shown a strong consistent relationship with the channel inflow-wetland storage hysteresis. It has also been observed that the wetland average surface slope and the proportion of the wetland storage below the channel banks are the morphometric characteristics that influence the spill and the return flow parameters of the Pitman wetland sub-model. This understanding has a practical advantage for the estimation of the Pitman wetland parameters in the many areas where it is not possible to run complex hydrodynamic models.</p>
The topography of many floodplains in the developed world has now been surveyed with high resolution sensors such as airborne LiDAR (Light Detection and Ranging), giving accurate Digital Elevation Models (DEMs) that facilitate accurate flood inundation modelling. This is not always the case for remote rivers in developing countries. However, the accuracy of DEMs produced for modelling studies on such rivers should be enhanced in the near future by the high resolution TanDEM-X WorldDEM. In a parallel development, increasing use is now being made of flood extents derived from high resolution Synthetic Aperture Radar (SAR) images for calibrating, validating and assimilating observations into flood inundation models in order to improve these. This paper discusses an additional use of SAR flood extents, namely to improve the accuracy of the TanDEM-X DEM in the floodplain covered by the flood extents, thereby permanently improving this DEM for future flood modelling and other studies. The method is based on the fact that for larger rivers the water elevation generally changes only slowly along a reach, so that the boundary of the flood extent (the waterline) can be regarded locally as a quasi-contour. As a result, heights of adjacent pixels along a small section of waterline can be regarded as samples with a common population mean. The height of the central pixel in the section can be replaced with the average of these heights, leading to a more accurate estimate. While this will result in a reduction in the height errors along a waterline, the waterline is a linear feature in a two-dimensional space. However, improvements to the DEM heights between adjacent pairs of waterlines can also be made, because DEM heights enclosed by the higher waterline of a pair must be at least no higher than the corrected heights along the higher waterline, whereas DEM heights not enclosed by the lower waterline must in general be no lower than the corrected heights along the lower waterline. In addition, DEM heights between the higher and lower waterlines can also be assigned smaller errors because of the reduced errors on the corrected waterline heights. The method was tested on a section of the TanDEM-X Intermediate DEM (IDEM) covering an 11 km reach of the Warwickshire Avon, England. Flood extents from four COSMO-SKyMed images were available at various stages of a flood in November 2012, and a LiDAR DEM was available for validation. In the area covered by the flood extents, the original IDEM heights had a mean difference from the corresponding LiDAR heights of 0.5 m with a standard deviation of 2.0 m, while the corrected heights had a mean difference of 0.3 m with standard deviation 1.2 m. These figures show that significant reductions in IDEM height bias and error can be made using the method, with the corrected error being only 60% of the original. Even if only a single SAR image obtained near the peak of the flood was used, the corrected error was only 66% of the original. The method should also be capable of improving the final TanDEM-X DEM and other DEMs, and may also be of use with data from the SWOT (Surface Water and Ocean Topography) satellite.
Avec les récents développements des technologies pour étudier le transport des sédiments dans les plans d'eau, comme la télédétection et les améliorations des modèles de prédiction de l'érosion, les mesures de concentration des sédiments sur le terrain sont toujours nécessaires pour calibrer et valider les résultats de ces études. Les détails de l'établissement d'une station d'échantillonnage des sédiments à haute fréquence sur la rivière Kasai, un affluent majeur du fleuve Congo, sont décrits. La station d'échantillonnage est utilisée pour mesurer la concentration de sédiments en suspension et le débit du fleuve à partir d'un bassin versant de ~900,000 km 2 , qui représente le plus grand flux de sédiments dans le fleuve Congo, avec des conséquences importantes pour la navigation fluviale et pour l'hydroélectricité. Les résultats préliminaires indiquent des concentrations moyennes de sédiments en suspension (SSC) de ~518 mg/L avec un pourcentage moyen de matière organique du sol (SOM) de 1.86 %. Les concentrations de SSC enregistrées sont beaucoup plus élevées que les valeurs précédentes rapportées pour le sous-bassin, indiquant un besoin urgent d'études plus détaillées sur la dynamique actuelle du transport des sédiments dans celui-ci. Les moyennes de turbidité néphélométrique et rétrodiffusive sont respectivement de 281.47 FNU et 539.93 FBU, tandis que la température moyenne de l'eau dans la section transversale est de 28.25 °C. Il s'agit de l'une des très rares stations de surveillance des sédiments dans la CRB et la première à tenter ce niveau d'échantillonnage des sédiments à haute fréquence, visant à faire la lumière sur le transport des sédiments dans l'affluent du Kasaï.
Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at AGU Books. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]Towards a framework of catchment classification for hydrologic predictions and water resources management in the ungauged basin of the Congo River: An a priori approachAuthorsRaphaelTshimangaiDGodeBolaiDPierreKabuyaiDLandryNkabaJefferyNealiDMarkTriggiDPaulBatesiDDenisHughesAlainLaraqueRossWoodsiDThorstenWageneriDSee all authors Raphael TshimangaiDCorresponding Author• Submitting AuthorCongo Basin Water Resources Research Center (CRREBaC) & Dpt. Natural Resources ManagementUniversity of KinshasaiDhttps://orcid.org/0000-0002-4726-3495view email addressThe email was not providedcopy email addressGode BolaiDCongo Basin Water Resources Research Center (CRREBaC) & Dpt. Natural Resources ManagementUniversity of KinshasaiDhttps://orcid.org/0000-0002-3072-4646view email addressThe email was not providedcopy email addressPierre KabuyaiDCongo Basin Water Resources Research Center (CRREBaC) & Dpt. Natural Resources ManagementUniversity of KinshasaiDhttps://orcid.org/0000-0002-4969-0538view email addressThe email was not providedcopy email addressLandry NkabaCongo Basin Water Resources Research Center (CRREBaC) & Dpt. Natural Resources Management, University of Kinshasaview email addressThe email was not providedcopy email addressJeffery NealiDSchool of Geographical Sciences, University of BristoliDhttps://orcid.org/0000-0001-5793-9594view email addressThe email was not providedcopy email addressMark TriggiDSchool of Civil Engineering, University of LeedsiDhttps://orcid.org/0000-0002-8412-9332view email addressThe email was not providedcopy email addressPaul BatesiDSchool of Geographical Sciences, University of BristoliDhttps://orcid.org/0000-0001-9192-9963view email addressThe email was not providedcopy email addressDenis HughesInstitute for Water Research, Rhodes Universityview email addressThe email was not providedcopy email addressAlain LaraqueCNRS/IRDview email addressThe email was not providedcopy email addressRoss WoodsiDDepartment of Civil Engineering, University of BristoliDhttps://orcid.org/0000-0002-5732-5979view email addressThe email was not providedcopy email addressThorsten WageneriDDepartment of Civil Engineering, University of BristoliDhttps://orcid.org/0000-0003-3881-5849view email addressThe email was not providedcopy email address
Over the last two decades, several datasets have been developed to assess flood risk at the global scale. In recent years, some of these datasets have become detailed enough to be informative at national scales. The use of these datasets nationally could have enormous benefits in areas lacking existing flood risk information and allow better flood management decisions and disaster response. In this study, we evaluate the usefulness of global data for assessing flood risk in five countries: Colombia, England, Ethiopia, India, and Malaysia. National flood risk assessments are carried out for each of the five countries using global datasets and methodologies. We also conduct interviews with key water experts in each country to explore what capacity there is to use these global datasets nationally. To assess national flood risk, we use 6 datasets of global flood hazard, 7 datasets of global population, and 3 different methods for calculating vulnerability that have been used in previous global studies of flood risk. We find that the datasets differ substantially at the national level, and this is reflected in the national flood risk estimates. While some global datasets could be of significant value for national flood risk management, others are either not detailed enough, or too outdated to be relevant at this scale. For the relevant global datasets to be used most effectively for national flood risk management, a country needs a functioning, institutional framework with capability to support their use and implementation.
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)
