DAHITI – an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry
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A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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- Teacher spread
- 0.223 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Abstract. Satellite altimetry has been designed for sea level monitoring over open ocean areas. However, for some years, this technology has also been used to retrieve water levels from reservoirs, wetlands and in general any inland water body, although the radar altimetry technique has been especially applied to rivers and lakes. In this paper, a new approach for the estimation of inland water level time series is described. It is used for the computation of time series of rivers and lakes available through the web service "Database for Hydrological Time Series over Inland Waters" (DAHITI). The new method is based on an extended outlier rejection and a Kalman filter approach incorporating cross-calibrated multi-mission altimeter data from Envisat, ERS-2, Jason-1, Jason-2, TOPEX/Poseidon, and SARAL/AltiKa, including their uncertainties. The paper presents water level time series for a variety of lakes and rivers in North and South America featuring different characteristics such as shape, lake extent, river width, and data coverage. A comprehensive validation is performed by comparisons with in situ gauge data and results from external inland altimeter databases. The new approach yields rms differences with respect to in situ data between 4 and 36 cm for lakes and 8 and 114 cm for rivers. For most study cases, more accurate height information than from other available altimeter databases can be achieved.
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The record
- Venue
- Hydrology and earth system sciences
- Topic
- Flood Risk Assessment and Management
- Field
- Environmental Science
- Canadian institutions
- —
- Funders
- U.S. Geological SurveyNational Oceanic and Atmospheric AdministrationTechnische Universität MünchenCentre National d’Etudes SpatialesGovernment of CanadaDeutsche ForschungsgemeinschaftIndian Space Research OrganisationAgência Nacional de ÁguasNational Aeronautics and Space Administration
- Keywords
- AltimeterSatelliteWater levelSeries (stratigraphy)Remote sensingWetlandSatellite altimetryEnvironmental scienceTime seriesTide gaugeRadar altimeterGeologySea levelGeographyComputer scienceOceanographyCartography
- Has abstract in OpenAlex
- yes