[1] This paper presents the first application and validation of a 2D hydrodynamic model of the Amazon at a large spatial scale. The simulation results suggest that a significantly higher proportion of total flow is routed through the floodplain than previously thought. We use the hydrodynamic model LISFLOOD-FP with topographic data from the Shuttle Radar Topography Mission to predict floodplain inundation for a 240 Â 125 km section of the central Amazon floodplain in Brazil and compare our results to satellite-derived estimates of inundation extent, existing gauged data and satellite altimetry. We find that model accuracy is good at high water (72% spatial fit; 0.99 m root mean square error in water stage heights), while accuracy drops at low water (23%; 3.17 m) due to incomplete drainage of the floodplain resulting from errors in topographic data and omission of floodplain hydrologic processes from this initial model. Citation: Wilson, M.,

The first issue of WRR appeared eight years after the launch of Sputnik, but by WRR's 25 th anniversary, only seven papers that used remote sensing had appeared. Over the journal's second 25 years, that changed remarkably, and remote sensing is now widely used in hydrology and other geophysical sciences. We attribute this evolution to production of data sets that scientists not well versed in remote sensing can use, and to educational initiatives like NASA's Earth System Science Fellowship program that has supported over a thousand scientists, many in hydrology. We review progress in remote sensing in hydrology from a water balance perspective. We argue that progress is primarily attributable to a creative use of existing and past satellite sensors to estimate such variables as evapotranspiration rates or water storage in lakes and reservoirs and to new and planned missions. Recent transforming technologies include the Gravity Recovery and Climate Experiment (GRACE), the European Soil Moisture and Ocean Salinity (SMOS) and U.S. Soil Moisture Active Passive (SMAP) missions, and the Global Precipitation Measurement (GPM) mission. Future missions include Surface Water and Ocean Topography (SWOT) to measure river discharge and lake, reservoir, and wetland storage. Measurement of some important hydrologic variables remains problematic: retrieval of snow water equivalent (SWE) from space remains elusive especially in mountain areas, even though snow cover extent is well observed, and was the topic of 4 of the first 5 remote sensing papers published in WRR. We argue that this area deserves more strategic thinking from the hydrology community.

[1] Floodplain processes are driven by water flows that seasonally change in direction and consist of a myriad of interacting streams of varying depth, velocity, source, sediment concentration and chemistry. Here we show, using spaceborne interferometric synthetic aperture radar (SAR) JERS-1 measurements, the first spatially dense hydraulic mapping of the passage of a flood wave through a large, topographically complex floodplain. We find that temporal changes in flood water heights (@h/@t) are more complex than typically assumed. During the passage of a flood wave, sharp variations in @h/@t are localized along some floodplain channels. These channels separate adjacent locations with different rates of infilling. Near the peak of the flood wave, some of the channels are no longer evident as controls on @h/@t. During the passage of the flood wave, flow paths change from bathymetrically influenced to hydraulically controlled (and back again), thus it is difficult to know the flow path a-priori from bathymetry alone.

No abstract

We find that the standard deviation, hence error, of the water surface elevation data from the Shuttle Radar Topography Mission (SRTM) is 5.51 m for basin‐wide, regional and local Amazon mainstem reaches. This error implies a minimum reach length of 733km in order to calculate a reliable water‐surface slope. Resulting slopes are 1.92 ± 0.19 cm/km for Manacapuru, 2.86 ± 0.24 cm/km for Itapeua and 3.20 ± 0.34 cm/km for Tupe. Manning's equation is applied with these slopes and with channel width measurements from the Global Rain Forest Mapping project synthetic aperture radar mosaics (GRFM SAR), channel depths averaged from nautical charts, and reasonable estimates of Manning's n. Resulting discharge values are 84,800 m3/s at Manacapuru, 79,800 m3/s at Itapeua, and 62,900 m3/s at Tupe averaged over the SRTM mission period. These values are within 6.2% at Manacapuru, 7.6% at Itapeua, and 0.3% at Tupe of the in‐situ gage‐based estimates for the same or similar time period.