@article{fdi:010093550,
  title = {{S}atellite {A}ltimetry-based {E}xtension of global-scale in situ river discharge {M}easurements ({SAEM})},
  author = {{S}aemian, {P}. and {E}lmi, {O}. and {S}troud, {M}. and {R}iggs, {R}. and {K}itambo, {B}. {M}. and {P}apa, {F}abrice and {A}llen, {G}. {H}. and {T}ourian, {M}. {J}.},
  editor = {},
  language = {{ENG}},
  abstract = {{R}iver discharge is a crucial measurement, indicating the volume of water flowing through a river cross-section at any given time. {H}owever, the existing network of river discharge gauges faces significant issues, largely due to the declining number of active gauges and temporal gaps. {R}emote sensing, especially radar-based techniques, offers an effective means to this issue. {T}his study introduces the {S}atellite {A}ltimetry-based {E}xtension of the global-scale in situ river discharge {M}easurements ({SAEM}) data set, which utilizes multiple satellite altimetry missions and estimates discharge using the existing worldwide networks of national and international gauges. {I}n {SAEM}, we have explored 47 000 gauges and estimated height-based discharge for 8730 of them, which is approximately 3 times the number of gauges of the largest existing remote-sensing-based data set. {T}hese gauges cover approximately 88 % of the total gauged discharge volume. {T}he height-based discharge estimates in {SAEM} demonstrate a median {K}ling-{G}upta efficiency ({KGE}) of 0.48, outperforming current global data sets. {I}n addition to the river discharge time series, the {SAEM} data set comprises three more products, each contributing a unique facet to better usage of our data. (1) {A} catalog of virtual stations ({VS}s) is defined by certain predefined criteria. {I}n addition to each station's coordinates, this catalog provides information on satellite altimetry missions, distance to the discharge gauge, and relevant quality flags. (2) {T}he altimetric water level time series of those {VS}s are included, for which we ultimately obtained good-quality discharge data. {T}hese water level time series are sourced from both existing {L}evel-3 water level time series and newly generated ones within this study. {T}he {L}evel-3 data are gathered from pre-existing data sets, including {H}ydroweb.{N}ext (formerly {H}ydroweb), the {D}atabase of {H}ydrological {T}ime {S}eries of {I}nland {W}aters ({DAHITI}), the {G}lobal {R}iver {R}adar {A}ltimetry {T}ime {S}eries ({GRRATS}), and {H}ydro{S}at. (3) {SAEM}'s third product is rating curves for the defined {VS}s, which map water level values into discharge values, derived using a nonparametric stochastic quantile mapping function approach. {T}he {SAEM} data set can be used to improve hydrological models, inform water resource management, and address nonlinear water-related challenges under climate change. {T}he {SAEM} data set is available from https://doi.org/10.18419/darus-4475 .},
  keywords = {{MONDE}},
  booktitle = {},
  journal = {{E}arth {S}ystem {S}cience {D}ata},
  volume = {17},
  numero = {5},
  pages = {2063--2085},
  ISSN = {1866-3508},
  year = {2025},
  DOI = {10.5194/essd-17-2063-2025},
  URL = {https://www.documentation.ird.fr/hor/fdi:010093550},
}