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Measuring Surface Water From Space
Abstract
Surface fresh water is essential for life, yet we have surprisingly poor
knowledge of the spatial and temporal dynamics of surface freshwater
discharge and changes in storage globally. For example, we are unable to
answer such basic questions as "What is the spatial and temporal
variability of water stored on and near the surface of all continents?"
Furthermore, key societal issues, such as the susceptibility of life to
flood hazards, cannot be answered with the current global, in situ
networks designed to observe river discharge at points but not flood
events. The measurements required to answer these hydrologic questions
are surface water area, the elevation of the water surface (h), its
slope (∂h/∂x), and temporal change (∂h/∂t). Advances
in remote sensing hydrology, particularly over the past 10 years and
even more recently, have demonstrated that these hydraulic variables can
be measured reliably from orbiting platforms. Measurements of inundated
area have been used to varying degrees of accuracy as proxies for
discharge but are successful only when in situ data are available for
calibration; they fail to indicate the dynamic topography of water
surfaces. Radar altimeters have a rich, multidecadal history of
successfully measuring elevations of the ocean surface and are now also
accepted as capable tools for measuring h along orbital profiles
crossing freshwater bodies. However, altimeters are profiling tools,
which, because of their orbital spacings, miss too many freshwater
bodies to be useful hydrologically. High spatial resolution images of
∂h/∂t have been observed with interferometric synthetic
aperture radar, but the method requires emergent vegetation to scatter
radar pulses back to the receiving antenna. Essentially, existing
spaceborne methods have been used to measure components of surface water
hydraulics, but none of the technologies can singularly supply the water
volume and hydraulic measurements that are needed to accurately model
the water cycle and to guide water management practices. Instead, a
combined imaging and elevation-measuring approach is ideal as
demonstrated by the Shuttle Radar Topography Mission (SRTM), which
collected images of h at a high spatial resolution (˜90 m) thus
permitting the calculation of ∂h/∂x. We suggest that a future
satellite concept, the Water and Terrestrial Elevation Recovery mission,
will improve upon the SRTM design to permit multitemporal mappings of h
across the world's wetlands, floodplains, lakes, reservoirs, and rivers.
... Historically, much of our observational knowledge of river dynamics has relied on in situ measurements of discharge at stream gages (Fekete et al., 2015). Although gaging stations offer detailed temporal information at single points in space, the spatial scarcity of the river gages worldwide and biases in gage placement toward large socioeconomically developed basins makes it challenging to capture global hydrologic patterns using in situ data alone (Alsdorf et al., 2007;Krabbenhoft et al., 2022;Ruhi et al., 2018). Alternatively, satellite remote sensing of rivers can bridge spatial gaps inherent to in situ data sets, better characterize key components of the global water cycle, and provide unique insights into river dynamics worldwide (Alsdorf et al., 2007;Alsdorf & Lettenmaier, 2003;Ellis et al., 2024;Famiglietti et al., 2015;Sikder et al., 2021). ...
... Although gaging stations offer detailed temporal information at single points in space, the spatial scarcity of the river gages worldwide and biases in gage placement toward large socioeconomically developed basins makes it challenging to capture global hydrologic patterns using in situ data alone (Alsdorf et al., 2007;Krabbenhoft et al., 2022;Ruhi et al., 2018). Alternatively, satellite remote sensing of rivers can bridge spatial gaps inherent to in situ data sets, better characterize key components of the global water cycle, and provide unique insights into river dynamics worldwide (Alsdorf et al., 2007;Alsdorf & Lettenmaier, 2003;Ellis et al., 2024;Famiglietti et al., 2015;Sikder et al., 2021). A range of remote sensing approaches, including optical, Synthetic Aperture Radar (SAR), and altimetry instruments, can be used to observe extent, elevation, and slope of global rivers, which together can be combined to estimate discharge (Alsdorf et al., 2007;Durand et al., 2023;Gleason & Durand, 2020). ...
... Alternatively, satellite remote sensing of rivers can bridge spatial gaps inherent to in situ data sets, better characterize key components of the global water cycle, and provide unique insights into river dynamics worldwide (Alsdorf et al., 2007;Alsdorf & Lettenmaier, 2003;Ellis et al., 2024;Famiglietti et al., 2015;Sikder et al., 2021). A range of remote sensing approaches, including optical, Synthetic Aperture Radar (SAR), and altimetry instruments, can be used to observe extent, elevation, and slope of global rivers, which together can be combined to estimate discharge (Alsdorf et al., 2007;Durand et al., 2023;Gleason & Durand, 2020). ...
The Earth's rivers vary in size across several orders of magnitude. Yet, the relative significance of small upstream reaches compared to large downstream rivers in the global water cycle remains unclear, challenging the determination of adequate spatial resolution for observations. Here, we use monthly simulations of river stores and fluxes to investigate the intrinsic spatial scales of the global river water cycle. We frame these scale‐dependent river dynamics in terms of observational capabilities, assessing how the size of rivers that can be resolved influences our ability to capture key global hydrologic stores and fluxes. By filtering reaches by estimated river widths, we quantify the relative contribution of global river reaches by size and estimate that over 17% of global discharge to ocean and nearly 9% of the world's river storage lies within rivers smaller than 100 m—hence revealing both strengths and limitations of current observational capabilities.
... Usually, such optical satellite data are suffered from cloud covers, especially of those which are accompany the heavy rains and cause flooding (Henshaw et al., 2013;Schumann et al., 2018). Meanwhile, synthetic aperture radar (SAR) systems emerge as robust tools for flood monitoring in near-real-time (NRT), owing to their ability to operate effectively in diverse weather conditions (Mertes, 2002;Alsdorf et al., 2007) and around the clock (Franceschetti & Lanari, 1999;Schumann & Moller, 2015). SAR technology has demonstrated proficiency in detecting water surfaces, estimating flood depths, and identifying submerged areas under canopy cover. ...
... Change Detection (CD) method involves technology that compares backscatter intensity before and during flooding to identify pixel change (Mertes, 2002;Alsdorf et al., 2007;Lu et al., 2014). The main water body thus identified from the reference image is used on the flood image to isolate water pixels within these bodies, facilitating the derivation of statistical curves that describe them. ...
... Indeed, despite the existence of these high-resolution datasets, there is still a need for point q measurements either ground-based observations from a relatively dense network of gauges. An alternative will be provided by the recent Surface Water and Ocean Topography (SWOT) NASA-CNES mission [17][18][19]. SWOT uses a Kaband radar interferometer, to estimate water surface elevations of rivers globally at 100 m resolution that are used to obtain river discharges at global scale (but with a complex spatio-temporal sampling). Such data can be leveraged to estimate dense regular river discharge estimates, but a convincing approach is required to fully exploit their information at nominal resolution. ...
... The SWOT mission [17,19,47] launched in 2023 should provide high-resolution river discharges at the global scale , a true revolution in hydrology. However, SWOT will give those with a complex time/space sampling [19]. ...
Estimating river discharge Q at global scale from satellite observations is not yet fully satisfactory in part because of limited space/time resolution. Furthermore, on highly anthropized basins, it is essential to anchor the analysis to reliable Q measurements. Gauge networks are however very sparse and limited in time, and SWOT (Surface Water Ocean Topography) river discharge estimates at global scale are not yet available. The method proposed here is able to obtain continuous daily Q estimates at 1 km/daily resolution, using indirect satellite data and ground-based estimates. We focus here on the Ebro. Over such an anthropized basin (e.g. change of land use, irrigation), the exploitation of 205 available gauges at their nominal resolution (i.e., daily point measurements) is a necessity. The hydrological Continuum model is used to help interpolate spatially and temporally the observations into our optimal interpolation scheme. The proposed Q-mapping is similar to an assimilation scheme were Earth observations (precipitation, evapotranspiration and total water storage change) and model simulations are constrained by in situ gauge measurements. The Q estimates are evaluated using a rigorous leave-one-out experiment, showing a good agreement with the in situ data: a correlation of 0.72 (median), and a 75th percentile of Nash-Sutcliffe Efficiency up to 0.62. Our spatio-temporal continuous Q estimates at high spatial/temporal resolution can describe complex continental water dynamics, including extreme events. SWOT estimates will soon be available, at the global scale but with irregular space/time sampling: our method should help exploit them to obtain a regular space-temporal description of the water cycle at high resolution.
... Indeed, our search on the Web of Science did not return any relevant reference on the notion of "spatial" hydrographs, with a few exceptions. These exceptions include Hovmöller diagrams of discharge displayed by Biancamaria et al. (2011) and Tourian et al. (2017) and space-based river widths longitudinal profiles measured from optical and SAR imagery to estimate discharge series in the context of flow wave propagation (Alsdorf et al., 2007;Brakenridge et al., 1998;Smith & Pavelsky, 2008). On a broader level, the connection between spatial and temporal scales in hydrological processes has been explored to a greater extent in the past (e.g., the heterogeneity between temporal hydrographs at different locations, or the relationship between catchment area and precipitation variability or flood frequency; Blöschl & Sivapalan, 1997, 1995Breinl et al., 2020;Skøien et al., 2003). ...
The study of river dynamics has long relied on the analysis of traditional in situ hydrographs. This graphical representation of temporal variability at a given location is so ubiquitous that the mere term “hydrograph” is widely recognized as a time series. While such a “temporal hydrograph” is well suited for in situ data analysis, it fails to represent hydrologic variability across space at a given time; a perspective that characterizes satellite‐based hydrologic observations. Here we argue that the concept of “spatial hydrograph” should be the focus of its own dedicated scrutiny. We build “space series” of river discharge and present their analysis in the context of peak flow event detection. We propose the use of peak event spatial coverage, referred to as “length”, as an analog to event duration. Our analysis is performed in the Mississippi basin using a dense in situ network. We reveal that peak flow events range in length from around 75 to 1,800 km with a median (mean) value of 330 (520) km along the basin's largest rivers. Our analysis also suggests that spatial sampling needs to be a factor of 4 (2) finer in resolution than peak flow lengths to detect 81% ± 13% (70% ± 20%) of events and to estimate their length within 84% ± 3% (67% ± 12%) median accuracy. We evaluate the connection between temporal and spatial scales of peak flows and show that events with longer durations also affect larger extents. We finally discuss the implications for the design of satellite missions concerned with capturing floods across space.
... Water Surface Elevation (WSE) is a key indicator for studying the dynamic changes in surface water. Enhancing the capacity for WSE monitoring and accurately and comprehensively tracking river and lake water level changes is of great significance for flood control and disaster reduction, water resource management and allocation, and the protection of river and lake ecosystems [1]. Traditional water level monitoring data is primarily obtained through continuous observations at fixed hydrological stations [2], a method that requires substantial human resources, material support, and financial investment. ...
The Surface Water and Ocean Topography (SWOT) satellite mission, jointly developed by NASA and several international collaboration agencies, aims to achieve high-resolution two-dimensional observations of global surface water. Equipped with the advanced Ka-band radar interferometer (KaRIn), it significantly enhances the ability to monitor surface water and provides a new data source for obtaining large-scale water surface elevation (WSE) data at high temporal and spatial resolution. However, the accuracy and applicability of its scientific data products for inland water bodies still require validation. This study obtained three scientific data products from the SWOT satellite between August 2023 and December 2024: the Level 2 KaRIn high-rate river single-pass vector product (L2_HR_RiverSP), the Level 2 KaRIn high-rate lake single-pass vector product (L2_HR_LakeSP), and the Level 2 KaRIn high-rate water mask pixel cloud product (L2_HR_PIXC). These were compared with in situ water level data to validate their accuracy in retrieving inland water levels across eight different regions in the middle and lower reaches of the Yangtze River (MLRYR) and to evaluate the applicability of each product. The experimental results show the following: (1) The inversion accuracy of L2_HR_RiverSP and L2_HR_LakeSP varies significantly across different regions. In some areas, the extracted WSE aligns closely with the in situ water level trend, with a coefficient of determination (R2) exceeding 0.9, while in other areas, the R2 is lower (less than 0.8), and the error compared to in situ water levels is larger (with Root Mean Square Error (RMSE) greater than 1.0 m). (2) This study proposes a combined denoising method based on the Interquartile Range (IQR) and Adaptive Statistical Outlier Removal (ASOR). Compared to the L2_HR_RiverSP and L2_HR_LakeSP products, the L2_HR_PIXC product, after denoising, shows significant improvements in all accuracy metrics for water level inversion, with R2 greater than 0.85, Mean Absolute Error (MAE) less than 0.4 m, and RMSE less than 0.5 m. Overall, the SWOT satellite demonstrates the capability to monitor inland water bodies with high precision, especially through the L2_HR_PIXC product, which shows broader application potential and will play an important role in global water dynamics monitoring and refined water resource management research.
... As a technique, remote sensing distinctly differs from more 'traditional' approaches to investigating and understanding hydrological processes. Studies have established that satellite observation is efficient and reliable for water resource assessment [48,49]. Thus, hydrological models have been enhanced to achieve extensive coverage, adequate time continuity, and data accuracy. ...
Iraq and other semi-arid regions are facing severe climate change impacts, including increased temperatures and decreased rainfall. Changes to climate variables have posed a significant challenge to groundwater storage dynamics. In this regard, the Gravity Recovery and Climate Experiment (GRACE) mission permits novel originate groundwater storage variations. This study used the monthly GRACE satellite data for 2002–2023 to determine variations in groundwater storage (GWS). Changes in GWS were implied by extracting soil moisture, acquired from the Global Land Data Assimilation System (GLDAS), from the extracted Territorial Water Storage (TWS). The results demonstrated that an annual average ΔGWS trend ranged for the Goddard Space Flight Center (GSFC) mascon and Jet Propulsion Laboratory (JPL) mascon was from 0.94 to −1.14 cm/yr and 1.64 to −1.36 cm/yr, respectively. Also, the GSFC illustrated superior performance in estimating ΔGWS compared with the JPL in Iraq, achieving the lowest root mean square error at 0.28 mm and 0.60 mm and the highest coefficient of determination (R²) at 0.92 and 0.89, respectively. These data are critical for identifying areas of depletion, especially in areas where in situ data are lacking. These data allows us to fill the knowledge gaps; provide critical scientific information for monitoring and managing dynamic variations.
... In contrast, in the Garonne River (Figure 6a), the run is degraded due to the interpolation of the surveyed sections, the water surface elevation (h) filling between the in situ stations, and the constant roughness assumption. For this river, the hydraulic modeling could benefit from more spatially distributed data, such as from SWOT [98,99] or Fully Focused SAR with Sentinel-6 [100,101]. ...
Knowledge of river bathymetry is crucial for accurately simulating river flows and floodplain inundation. However, field data are scarce, and the depth and shape of the river channels cannot be systematically observed via remote sensing. Therefore, an efficient methodology is necessary to define effective river bathymetry. This research reconstructs the bathymetry from existing global digital elevation models (DEMs) and water surface elevation observations with minimum human intervention. The methodology can be considered a 1D geometric inverse problem, and it can potentially be used in gauged or ungauged basins worldwide. Nine global DEMs and two sources of water surface elevation (in situ and remotely sensed) were analyzed across two study areas. Results highlighted the importance of preprocessing cross-sections to align with water surface elevations, significantly improving discharge estimates. Among the techniques tested, one that combines the slope-break concept with the principles of mass conservation consistently provided robust discharge estimates for the different DEMs, achieving good performance in both study areas. Copernicus and FABDEM emerged as the most reliable DEMs for accurately representing river geometry. Overall, the proposed methodology offers a scalable and efficient solution for cross-section reconstruction, supporting global hydraulic modeling in data-scarce regions.
... Other satellites, like the NASA/German Aerospace Center Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On, track the movement of water around the world on relatively large spatial scales (∼300 km) through precise measurements of gravity that reflect total water storage change (e.g., Landerer et al., 2020;Peidou et al., 2022). Additionally, satellite altimeters can provide along-track measurements of water height (Crétaux & Birkett, 2006;Duan & Bastiaanssen, 2013), and in certain conditions, measure the depth of shallow water bodies (Alsdorf et al., 2007;Caballero & Stumpf, 2023;Stumpf et al., 2003). Each of these satellites, however, is limited in what they can provide for understanding water on Earth by factors such as low spatial resolution that prohibits detection of smaller water bodies, infrequent revisit times, and/or environmental conditions like cloud cover. ...
Plain Language Summary
Climate change and human activity are dramatically reshaping how water is distributed on Earth. Both new measurements and data products are needed to understand and effectively respond to changes in water availability today and in the future. These needs are being met by a new satellite, the Surface Water and Topography (SWOT) mission, which is measuring water with unprecedented detail, and the Observational Products for End‐Users from Remote Sensing Analysis (OPERA) project, which is turning satellite observations into clear and interpretable maps of surface water extent. Together, these data represent a major advance in our ability to measure and monitor water from space. We demonstrate their capability by tracking the transformation of Badwater Basin in Death Valley–one of the driest, hottest places on Earth–into an ephemeral lake following extreme precipitation events starting with Hurricane Hilary in August of 2023. As a particularly challenging area to understand water movement, Badwater Basin serves as a model for how these new observations can be used around the world for more effective water management.
... The river slope estimation follows the '' ℎ∕ '' definition, as used by Alsdorf et al. (2007) and Paz and Collischonn (2007). The GNSS-R river slope is determined based on the phase altimetry, i.e., the retrieval of the relative WSE along the SP track, with respect to the geoid. ...
River slope, a crucial parameter in hydrological modeling, has historically been difficult to measure continu-ously on a regional or global scale. Satellite altimetry missions often have long revisit times, such as 10 to 20 days for the Surface Water and Ocean Topography (SWOT) mission. In this paper, a novel approach is presented utilizing spaceborne GNSS Reflectometry (GNSS-R) to measure river slopes with high accuracy and potentially short revisit times. Our Earth is enveloped in radio signals from over 100 GNSS satellites. These signals can be coherently reflected from river surfaces and detected by low Earth orbit (LEO) satellites with sufficient energy to estimate carrier phase. The carrier phase measurement captures water surface height variations, which can be extracted through modeling of the reflection signal propagation geometry and space environment effects to estimate river slopes. This study processes both the raw intermediate frequency (IF) data obtained by NASA’s Cyclone GNSS (CYGNSS) microsatellites and the grazing-angle GNSS-R data generated by Spire Global nanosatellites to demonstrate the feasibility and performance of the GNSS-R based river slope retrieval. This paper focuses on selected river sections with width greater than ∼500 meters. Detailed methodologies and error analyses are presented, indicating total uncertainty of approximately 0.38 cm/km plus ionospheric TEC model error for CYGNSS and 0.69 cm/km for Spire (with dual-frequency ionospheric correction) over an ideal 5-km river section at 30◦ elevation angle. The retrieval results are validated in areas with nearby flat water surfaces (such as lakes or wide and slow river sections) and against in situ gauge measurements and satellite altimetry, consistently demonstrating the high accuracy and reliability of spaceborne GNSS-R for measuring river slopes.
... Synthetic Aperture Radars (SAR) can be used to monitor surface water extent in almost all-weather conditions (Arnesen et al., 2013;Schumann & Moller, 2015). This becomes important in flood events when cloud cover may obscure the vision of optical satellites (Alsdorf et al., 2007). Several studies have been carried out to assess the impact of floods in the Indus basin during 2022 using satellite remote sensing (Hegyi & Agapiou, 2023;Nanditha et al., 2023;Qamer et al., 2023;Roth et al., 2023;Sohail & Muhammad, 2023). ...
Pakistan witnessed one of its most devastating floods during the monsoon of 2022, affecting millions of people and causing significant economic losses. The focus of this study is to analyse the spatial and temporal attributes of the 2022 floods in the lower Indus basin using multi-satellite observations and hydrological modelling. We used observations from satellite sensors including Synthetic Aperture Radar (SAR), passive microwave radiometer, radar altimeters, optical sensors, and gravity measurements to assess the dynamics of flood. SAR images revealed an inundation extent of 14,361 km 2 in the lower Indus basin and optical data showed the extent of Lake Manchar swell from 333.76 km 2 on 11 August to 512.25 km 2 on 26 August 2022. A novel approach to assess flood intensity using brightness temperature observations from a passive microwave radiometer (36.5 GHz) was used to analyse the onset, progression and persistence of inundation. Water level observations from altimeter and water storage change from satellite-based gravity measurements provided additional information to understand the temporal dynamics of flood event. Weather Research and Forecasting-Hydro model simulations revealed a peak discharge of 69,217 m 3 /s on 26 August 2022 which was ~ 50% higher than the previous year. Model simulated discharge for the Indus River correlated well (R 2 = 0.85) with the satellite-observed mean flood index. This paper demonstrates the use of available satellite sensors for effective assessment and modelling of floods, which may prove useful for monitoring and mitigating future flood events.
... Identification of flooding is a very challenging task from SAR compared to optical systems because it is difficult to identify flood pixels in SAR images. However, the roughening of water surfaces, caused by factors like intense rainfall or wind, can lead to the radar signal being backscattered, potentially causing difficulties in highlighting inundated areas 11,17,18 . ...
... With much higher across-and along-track resolution than radar altimeters, a 363 new contribution from ICESat-2 (0.7 m) is the estimation of fine-scale cross-sectional geometry using 364 accurate elevation transects. Despite the low revisit frequency of these systems (91 days for ICESat-2), 365 the dense ground sampling pattern (30 km between reference ground tracks) has been leveraged to 366 parameterize basin-scale hydraulic models, i.e., to calibrate unobserved hydraulic parameters, for 367 The ongoing SWOT mission benefits from a long legacy of radar nadir altimetry and interferometry 369 (Alsdorf et al., 2007(Alsdorf et al., , 2000Alsdorf and Lettenmaier, 2003;Farr et al., 2007;Fu et al., 2024;Rosen et al., 370 2000) and is the first wide-swath radar altimetry mission that was specifically designed to observe inland 371 water surfaces in addition to oceans. The Ka-band Radar Interferometer (KaRIn) of SWOT allows for 372 unprecedented spatial coverage through 2-dimensional radar images encompassing two 50 km swaths 373 compared to traditional nadir-pointing 1-dimensional radar traces in previous missions (Peral et al., 2024). ...
... It is important to note that nadir altimeters are profiling instruments and do not yield a two-dimensional image. As proposed by Alsdorf et al. (2007), radar altimeters can be enhanced for imaging and height determination by using a slightly off-nadir imaging concept, as demonstrated in the Surface Water and Ocean Topography (SWOT) mission and providing two-dimensional high-resolution water elevations (Fu and Rodriguez, 2004;Peral and Esteban-Fernandez, 2018;Fu et al., 2024). Boy et al. (2023) obtained longitudinal river profiles from Sentinel-6A FFSAR mode with off-nadir processing by aligning the waveform while considering compensation for reference retracking range and slant range correction, and applying supervised retracking. ...
... Datasets on global water surface extent have been generated by using optical remote sensing (Pekel et al. 2016;Pickens et al. 2020;Yamazaki, Trigg, and Ikeshima 2015), though the spatial gaps caused by the unavoidable cloud contaminations still exist in some land surface water products (Huang et al. 2018;Ogilvie et al. 2018). Retrieving water depth is much more challenging and it requires the knowledge of the underlying topography, which is only available for a small subset of the world's lakes and reservoirs due to the high cost of in-situ measurements (Alsdorf, Rodríguez, and Lettenmaier 2007;Peng et al. 2006;Weekley and Li 2021). ...
Water depth, a fundamental characteristic of a lake, is important for understanding climatic, ecological, and hydrological processes. However, lake water depth data are still scarce due to the high cost of in-situ measurements and the limitations of remote sensing observations. In this study, a novel method was developed to estimate time series of pixel-wise water depths of lakes that have ever exposed their bottom by remote sensing observations. Lake water depths were calculated as the difference between the elevations of the dynamic water surface and the historical lakebed elevations using optical images and DEM data. The method was applied in the Sahel-Sudano-Guinean region of Africa where complex climatic conditions and rare in-situ measurements. Experiments showed that the proposed method could get consistent water depths compared with the HydroLAKES data, i.e. with a MAE of 0.86 m and a RMSE of 1.69 m, and water surface elevations similar to the estimates derived from ICESat/ICESat-2 measurements with a MAE of 3.79 m and a RMSE of 5.92 m. The method can provide pixel-wise information on lake water depth at high temporal frequency, and is expected to provide an efficient solution to gather essential information on lakes.
... (1) For instance, HR observations from SAR or visible data could be used to build an "empirical floodability index". (2) The Surface Water and Ocean Topography (SWOT) french/american mission was launched by NASA/CNES in 15 December 2022 [30,31]. It will be providing, among others, surface water estimates at 100 m resolution (https://swot.jpl.nasa.gov/ ...
A surface water extent downscaling framework was developed in the past using a floodability index based on topography. We presented here a new downscaling approach including several improvements. (1) The use of a new Floodability Index (FI), including better integration of auxiliary permanent waters (i.e., presence of water during the whole time record). By using this updated FI, the new downscaling became a true data-fusion with permanent water databases originating mainly from visible observations. (2) Some discontinuities between low resolution cells have been reduced thanks to a new smoothing algorithm. (3) Finally, a coastal extrapolation scheme has been presented to deal with coarse resolution data contaminated by the ocean. This new and complex downscaling framework was tested here on the GIEMS (Global Inundation Extent from Multi-Satellite) database but the approach is generalizable and any surface water database could be used instead. It was shown that this new downscaling procedure (including several processing steps, algorithms and data sources) is a significant improvement compared to the previous version thanks to the new floodability index and the downscaling processing chain. Compared to the previous version, the downscaling results (GIEMS-D) were more coherent with the permanent water database and preserved better the original low-resolution information (e.g., mean scare error water fraction (0–1) of 0.0041 for the old version, and 0.0018 for the new version, over flooded areas in the Amazon). GIEMS-D has also been evaluated at the global scale and over the Amazon basin using independent datasets, showing an overall good performance of the downscaling.
... Of these datasets, surface water and soil moisture estimates are the most significant contributors to ΔTWS and as such, tend to exhibit the highest uncertainty in the isolation of GRACE-groundwater storage changes (Ndehedehe, 2022a;Ndehedehe et al., 2023), especially in humid areas (e.g., Congo Basin) and semi-arid regions like Australia where canopy and snow water are negligible. Given that surface water is a visible resource, the estimation of its changes, depth, extent, discharge and quality, using insitu monitoring networks and remote sensing systems based on optical and radar altimetry technologies provides high accuracy readings and increases our confidence in its use in the water budget (Alsdorf et al., 2007;Viney, 2016;Millard et al., 2020;Pickens et al., 2020;Cooley et al., 2021;Das et al., 2021;Nair et al., 2021;Papa et al., 2023). In contrast, quantifying soil moisture storage changes over regional scales is extremely challenging. ...
... The regular gauging stations do not provide enough information on the variability of the hydrodynamic conditions and the runoff distribution in the main river deltas, especially under the tidal and surge influence. This problem is common for many major river systems, such as Ganges, Brahmaputra, and Meghna, Amazon, Indus, Irrawaddy, Salween, Mekong and Niger (de Paiva et al. 2013;Maswood, Hossain 2016;Alsdorf et al. 2007). The length of tidal wave propagation upstream varies greatly depending on the river runoff. ...
The Pechora is the greatest river of the European Russian Arctic, flowing into the Barents Sea. Its estuarine area includes a vast delta, represented by extensive lowlands that are dissected by the complicated network of arms and branches. Despite the Pechora Delta is considered to be microtidal, tides with a range of 0.5–1 m during the low water period have a significant impact on the nature of currents in the main branches and the distribution of runoff among them during the tidal cycle. Tidal sea level fluctuations as well as storm surges determine the reversing pattern of currents over a significant extent of the delta branches. The modern field equipment combined with 2D hydrodynamic modeling has allowed to understand the contemporary flow features and evaluate their possible alterations under climate changes. The climate impact under considered scenarios is more pronounced during the low flow period, and this can lead to the propagation of tidal currents and an increase in water levels in the city of Naryan-Mar (100 km upstream from the mouth). From a flood risk perspective, sea level rise can be offset by a reduction in flood runoff.
... This storage engenders significant temporal disparities between downstream and upstream hydrographs (Fleischmann et al., 2016). Consequently, floodplains offer a multitude of ecosystem services, including flood mitigation, sediment deposition, and preservation of water quality (Alsdorf et al., 2007). ...
The Paraguay River Basin forms part of the La Plata River Basin in South America. Its streamflow is significantly attenuated by a high evapotranspiration rate, very gentle slopes and the presence of a vast wetland known as the Pantanal. Modeling the hydrology of watersheds in which the flood pulse is affected by the presence of large floodplains can pose issues for hydrological models that do not account for spatial complexity and simplify water routing using linear assumptions. The new version of the Soil and Water Assessment Tool, known as SWAT+, routes water using variations of the kinematic wave model. However, with the inclusion of connectivity and Landscape Units, SWAT+ provides more flexibility in terms of representing the hydrologic fluxes in the watershed. The main objective of this study is to use the concept of Landscape Units and connectivity to represent the water exchanges between uplands, floodplains and channels. We developed code routines to (1) temporally retain surface and subsurface water coming from the upland into the floodplain, by assuming a reservoir-like floodplain behavior, and (2) represent overbank flow, aiming to fully simulate the interactions between channels and floodplains. The model was calibrated based on monthly discharge for the period 1990 to 2020. The simulated average annual water storage in the floodplains of the Paraguay River is ~108.81 mm accounting for 56.5% of the total annual discharge at the outlet. Furthermore, ~61% of the total annual surface runoff in the Paraguay River Basin flows through the floodplains. Results indicate that the model is able to capture the hydrologic regime in the Paraguay River representing an improvement of SWAT+.
... However, no contactless measurement techniques for bulk velocity currently exist and deployment of in situ techniques such as Acoustic Doppler Current Profiler (ADCP) can be difficult or impossible during extreme flows and in remote and hard-to-reach areas. Contactless river discharge measurement techniques therefore often use river surface velocity as a surrogate for bulk velocity and several methods exist to estimate bulk velocity from surface velocity (Alsdorf et al., 2007;Bahmanpouri, Eltner, et al., 2022;Bandini et al., 2021;Luce et al., 2013;Shi et al., 2019). Therefore, it is urgent to develop more effective and efficient contactless river surface velocity monitoring technologies and to systematically assess the performance of such techniques against established in situ monitoring technology such as electromagnetic flow sensors (e.g., OTT MF pro). ...
Using Unoccupied Aerial Systems (UAS) equipped with optical RGB cameras and Doppler radar, surface velocity can be efficiently measured at high spatial resolution. UAS‐borne Doppler radar is particularly attractive because it is suitable for real‐time velocity determination, because the measurement is contactless, and because it has fewer limitations than image velocimetry techniques. In this paper, five cross‐sections (XSs) were surveyed within a 10 km stretch of Rönne River in Sweden. Ground‐truth surface velocity observations were retrieved with an electromagnetic velocity sensor (OTT MF Pro) along the XS at one m spacing. Videos from a UAS RGB camera were analyzed using both Particle Image Velocimetry (PIV) and Space‐Time Image Velocimetry (STIV) techniques. Furthermore, we recorded full waveform signal data using a Doppler radar at multiple waypoints across the river. An algorithm fits two alternative models to the average amplitude curve to derive the correct river surface velocity based on Gaussian models with: (a) one peak, and (b) two peaks. Results indicate that river flow velocity and propwash velocity caused by the drone can be found in XS where the flow velocity is low, while the drone‐induced propwash velocity can be neglected in fast and highly turbulent flows. To verify the river flow velocity derived from Doppler radar, a mean PIV value within the footprint of the Doppler radar at each waypoint was calculated. Finally, quantitative comparisons of OTT MF Pro data with STIV, mean PIV and Doppler radar revealed that UAS‐borne Doppler radar could reliably measure the river surface velocity.
... In uniform river reaches, increased river discharge leads to increased WSE, while the WSS remains unchanged. This unique relationship between the WSE and discharge forms the basis for many algorithms estimating river discharge from satellite altimetry observations (e.g., [14][15][16][17][18]). However, in non-uniform hydraulic conditions, the WSS changes significantly with river discharge and observed changes in the WSS provide new insights into the hydraulic phenomena occurring in such river reaches. ...
The ICESat-2 and SWOT satellite earth observation missions have provided highly accurate water surface slope (WSS) observations in global rivers for the first time. While water surface slope is expected to remain constant in time for approximately uniform flow conditions, we observe time varying water surface slope in many river reaches around the globe in the ICESat-2 record. Here, we investigate the causes of time variability of WSSs using simplified river hydraulic models based on the theory of steady, gradually varied flow. We identify bed slope or cross section shape changes, river confluences, flood waves, and backwater effects from lakes, reservoirs, or the ocean as the main non-uniform hydraulic situations in natural rivers that cause time changes of WSSs. We illustrate these phenomena at selected river sites around the world, using ICESat-2 data and river discharge estimates. The analysis shows that WSS observations from space can provide new insights into river hydraulics and can enable the estimation of river discharge from combined observations of water surface elevation and WSSs at sites with complex hydraulic characteristics.
... Unlike discharge, various hydrological and hydraulic variables, such as river water level, river width, and river slope can be directly measured through remote sensing data (e.g., Smith, 1997;Alsdorf et al., 2007;Tang et al., 2009;Birkinshaw 35 et al., 2010Birkinshaw 35 et al., , 2014. By utilizing these observable variables, one can estimate discharge beyond the gauge records through the use of rating curves. ...
River discharge is a crucial measurement, indicating the volume of water flowing through a river cross-section at any given time. However, the existing network of river discharge gauges faces significant issues, largely due to the declining number of active gauges and temporal gaps. Remote sensing, especially radar-based techniques, offers an effective means to this issue. This study introduces the Satellite Altimetry-based Extension of the global-scale in situ river discharge Measurements (SAEM) data set, which utilizes multiple satellite altimetry missions and estimates discharge using the existing worldwide networks of national and international gauges. In SAEM, we have explored 47 000 gauges and estimated height-based discharge for 8 730 of them which is approximately three times the number of gauges of the largest existing remote sensing-based data set. These gauges cover approximately 88 % of the total gauged discharge volume. The height-based discharge estimates in SAEM demonstrate a median Kling-Gupta Efficiency (KGE) of 0.48, outperforming current global data sets. In addition to the river discharge time series, the SAEMdata set comprises three more products, each contributing a unique facet to better usage of our data: (1) A catalog of Virtual Stations (VSs), defined by certain predefined criteria. In addition to each station’s coordinates, this catalog provides information on satellite altimetry missions, distance to the discharge gauge, and relevant quality flags.(2) The altimetric water level time series of those VSs are included, for which we ultimately obtained good-quality discharge data. These water level time series are sourced from both existing Level-3 water level time series and newly generated ones within this study. The Level-3 data are gathered from pre-existing data sets, including Hydroweb.Next (former Hydroweb), the Database of Hydrological Time Series of Inland Waters (DAHITI), the Global River Radar Altimeter Time Series (GRRATS), and HydroSat. (3) SAEM’s third product is rating curves for the defined VSs, which map water level values into discharge values, derived using a Nonparametric Stochastic Quantile Mapping Function approach. The SAEM data set can be used to improve hydrological models, inform water resource management, and address non-linear water-related challenges under climate change. The SAEM data set is available from (Saemian et al., 2024) https://doi.org/10.18419/darus-4475 during the review process.
... Lake water levels can be measured on-site with gauges. However, this is done only in a few locations worldwide, and the number of gauging stations is declining due to the costly maintenance and installation in remote lakes (Alsdorf et al., 2007;Aminjafari et al., 2024a;Cooley et al., 2021;Shiklomanov et al., 2002). Lake water levels can also be tracked using airborne technologies such as Lidar/Radar and photogrammetry (Bandini et al., 2017;Ridolfi and Manciola, 2018), which, although accurate, can only provide a snapshot in time of water levels and expensive flight operations do not permit high temporal resolutions. ...
Lakes provide societies and natural ecosystems with valuable services such as freshwater supply and flood control. Water level changes in lakes reflect their natural responses to climatic and anthropogenic stressors; however, their monitoring is costly due to installation and maintenance requirements. With its advanced hardware and computational capabilities, altimetry has become a popular alternative to conventional in-situ gauging, although subject to the temporal availability of altimetric observations. To further improve the temporal resolution of altimetric measurements, we here combine radar altimetry data with Differential Interferometric Synthetic Aperture Radar (D-InSAR), using ten lakes in Sweden as a testing platform. First, we use Sentinel-1A and Sentinel-1B SAR images to generate consecutive six-day baseline interferograms across 2019. Then, we accumulate the phase change of coherent pixels to construct the time series of InSAR-derived water level anomalies. Finally, we retrieve altimetric observations from Sentinel-3, estimate their mean and standard deviation, and apply them to the D-InSAR standardized anomalies. In this way, we build a water-level time series with more temporal observations. In general, we find a strong agreement between water level estimates from the combination of D-InSAR and Satellite Altimetry (DInSAlt) and in-situ observations in eight lakes (Concordance Correlation Coefficient - CCC > 0.8) and moderate agreement in two lakes (CCC > 0.57). The applicability of DInSAlt is limited to lakes with suitable conditions for double-bounce scattering, such as the presence of trees or marshes. The accuracy of the water level estimates depends on the quality of the altimetry observations and the lake's width. These findings are important considering the recently launched Surface Water and Ocean Topography (SWOT) satellite, whose capabilities could expand our methodology's geographical applicability and reduce its reliance on ground measurements.
... For the dataset of orbit 153, mean wind speed values were also derived from the hourly east and north surface wind component values of the ERA5-Land database [14]. For wind dependence analysis, we used the 05 UTC series, the closest in time to the Sentinel-1A satellite orbit 153 at about 04:45 UTC. ...
... As inland water bodies play a key role in the global hydrological cycle, there is need for a homogeneous continuing monitoring system [1,2]. However, the network of in-situ for the retrieval of water level heights is sparse, and in some cases the scientific community faces restricted access to historical gauge data [3]. ...
Inland water level and its dynamics are key components in global water cycle and land surface hydrology, significantly influencing water resource management, biodiversity, and climate change impacts. Space-based altimetry observations of inland water surface elevation is an important data source to supplement the limited in-situ measurements. In this study, the evaluation of Sentinel-3 and Sentinel-6 MF derived water levels was conducted over Douro and Danube rivers. However, it is still very challenging to obtain accurate water level measurements over narrow rivers, with width less than 300m. Gauge measurements were utilized for the validation of altimetry-derived water levels. Two outlier detection methods were applied for data pre-processing to improve the accuracy of measurements. The root-mean-square-error (RMSE) of the Sentinel-6 mission over Danube River is 0.57m, while the mean-RMSE of the Sentinel-3 over Douro River is 2.12m.
... However, monitoring lake water levels faces ongoing challenges due to the need to maintain equipment, particularly in inaccessible locations [8,9]. Furthermore, global lake water level monitoring is limited and declining, as gauging stations are unevenly distributed and face financial and logistic constraints, especially for smaller and remote lakes [3,6,10,11]. ...
Monitoring lake water level fluctuations is critical for managing water resources, predicting the impacts of climatic change, and preserving ecosystem services lakes provide. However, traditional gauging stations are insufficient to monitor all lakes worldwide due to the large number of existing lakes, the challenges of installation and maintenance, and the remote locations of some. Although satellite altimetry is an alternative for measuring water levels, it cannot monitor small lakes effectively. This study evaluates the potential of Differential Interferometric Synthetic Aperture Radar (D-InSAR) for tracking minor water level changes in small lakes, a method more typically used in wetland studies. We investigate two Swedish lakes using Sentinel-1A and Sentinel-1B data from 2019, generating six-day interferograms and filtering out those with in situ water level changes exceeding one phase cycle. Our results show that D-InSAR can detect small water level changes with Lin’s correlations up to 0.63 and 0.89 and RMSE values of approximately 9 and 4 mm, respectively. These results evidence the potential of future L-band SAR missions with larger wavelengths, such as the NASA-ISRO SAR (NISAR) of the National Aeronautics and Space Administration (NASA) and the Indian Space Research Organisation (ISRO), to track water level changes in lakes and aid water tracking missions such as the SWOT (Surface Water and Ocean Topography).
... The river slope estimation follows the '' ℎ∕ '' definition, as used by Alsdorf et al. (2007) and Paz and Collischonn (2007). The GNSS-R river slope is determined based on the phase altimetry, i.e., the retrieval of the relative WSE along the SP track, with respect to the geoid. ...
... | NOT PEER-REVIEWED | Posted: 26 June 2024 doi:10.20944/preprints202406.1856.v1 2 significant role in solving these two problems. Since the 1990s, substantial progress has been made in remote sensing of river stage [22][23][24]. There are two types of remote sensing approaches for monitoring river stage or WSE [25][26][27], i.e., directly measuring river WSE using spaceborne or airborne LiDAR or altimetry radar [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46], and indirectly estimating river WSE from remote sensing imagery based on relationships between river width or river inundation area versus WSE established from the topographic information [25][26][27]39,40,[47][48][49][50][51][52][53][54][55][56][57][58]. ...
A method for estimating river water surface elevation (WSE) from Landsat imagery using the river inundation area - water surface elevation (RIA-WSE) rating curve constructed from the U.S. Geological Survey Topobathymetric Elevation Model (TEM) data was developed and tested at six gauging stations along the upper Mississippi River. The Otsu's automatic threshold selection algorithm was employed for the image classification and estimation of inundation areas within each pre-defined polygon around each gauging station. In addition to the commonly used green-band based water indices, Landsat 8 and 9 OLI's ultra-blue, blue, and red band-based water indices were also tested in this study, which resulted in twenty different water indices: NDWIv (Normalized Difference Water Index), MNDWI1v and MNDWI2v (Modified Normalized Difference Water Index), AWEIsv (Automatic Water Extraction Index with shadow), and AWEInsv (AWEI without shadow), where v represents the visible light band used in the water index. At each station, about 60-80 Landsat 8 or 9 images during 2013-2023 were used for assessing the performances of the twenty water indices through comparing the estimated WSEs with the measured WSEs. Results showed that the ultra-blue or red band-based AWEIs yielded the most accurate estimations of WSEs among the twenty tested water indices.
... To fill data gaps at gauging stations or to monitor WSEs at any ungauged river cross-sections, remote sensing is one of the economical, effective and efficient approaches that can play a significant role in solving these two problems. Since the 1990s, substantial progress has been made in remote sensing of river stage [22][23][24]. ...
A method for estimating river water surface elevation (WSE) from Landsat imagery using the river inundation area–water surface elevation (RIA-WSE) rating curve constructed from the U.S. Geological Survey Topobathymetric Elevation Model (TEM) data was developed and tested at six gauging stations along the Upper Mississippi River. Otsu’s automatic threshold selection algorithm was employed for the image classification and estimation of inundation areas within each predefined polygon around each gauging station. In addition to the commonly used green-band-based water indices, Landsat 8 and 9 OLI’s ultra-blue, blue, and red band-based water indices were also tested in this study, which resulted in twenty different water indices: NDWIv (Normalized Difference Water Index), MNDWI1v and MNDWI2v (Modified Normalized Difference Water Index), AWEIsv (Automatic Water Extraction Index with shadows), and AWEInsv (AWEI without shadows), where v represents the visible light band used in the water index. At each station, about 60–80 Landsat 8 or 9 images during 2013–2023 were used to assess the performances of the twenty water indices by comparing the estimated WSEs with the measured WSEs. The results showed that the ultra-blue or red band-based AWEIs yielded the most accurate estimations of WSEs among the twenty tested water indices.
... Distance from rivers critically controls the expansion of inundated surfaces near riverbanks to cause flooding [99,100]. The size of the river determines the flood plain and carrying capacity of peak runoff; beyond the carrying capacity, stagnation creates the flooding event. ...
Comprehensive flood risk assessment is often constrained by a lack of appropriate data in high-altitude watersheds, particularly in developing countries like Nepal, where institutional capacities are limited for mapping and monitoring flood-prone communities. This study, one of the first of its kind, produced spatial multi-criteria-based flood susceptibility, vulnerability, and risk index maps for the Kathmandu Valley (KV) watershed in Nepal using an Analytical Hierarchy Process (AHP) approach and Geographical Information System (GIS). The result shows that most parts of the KV (around 80%) have moderate to low flood susceptibility around the watershed but susceptibility is prominent in southern areas. Highly flood-susceptible regions (about 14%) are found mainly along the riverbanks. Flood vulnerability, primarily influenced by population density and literacy rate, is moderate to low in most areas of the watershed (around 86%), predominantly higher in the central urban areas, and gradually lower towards the edge of the watershed. Flood risks in the major portion of the watershed (around 72%), mainly in the southern and eastern parts, are estimated as moderate to low risk, whereas higher risk zones are found in the central urban areas. The high contrast in flood vulnerability scores across the watershed has mainly contributed to the variation of flood risk zones, as flood susceptibility scores are fairly distributed over the watershed. The study findings will help policymakers develop location-specific sustainable flood risk management strategies for the flood-vulnerable communities in the KV watershed.
... To improve the quantity and quality of monthly inundated area retrieval, the integration of optical remote sensing data with higher temporal resolution (e.g., Moderate Resolution Imaging Spectroradiometer and Sentinel-2) can help distinguish between barren land and surface water on a monthly scale (Khandelwal et al., 2017;Tulbure et al., 2022). However, accurately differentiating frozen waters and near-shore aquatic vegetation from surrounding ice/snow and vegetation remains a challenge (Alsdorf et al., 2007). Utilizing more advanced machine learning techniques such as contextual complex Wishart classification (Goumehei et al., 2019) and deep convolutional neural networks (B. ...
High‐frequency monitoring of reservoir inundation and water storage changes is crucial for reservoir functionality assessment and hydrological model calibration. Although the integration of optical data with synthetic aperture radar (SAR) backscattering coefficients (backscatters) offers an effective approach, conventional methods struggle to consistently provide accurate retrievals over diverse regions and seasons. In this study, we introduce reservoir‐ and monthly‐specific classification models to enhance the integration of Sentinel‐1 SAR backscatters with optical‐based water dynamics. Our method covers 721 reservoirs with a capacity greater than 0.1 km³ in China during 2017–2021. Furthermore, we leverage multisource satellite altimetry records (e.g., ICESat‐2, CryoSat‐2, and GEDI) and digital elevation models to derive hypsometry relationship (i.e., water level–water area relationship) for reservoirs, enabling the transformation of inundated areas into monthly water storage changes for 662 reservoirs, representing 93% of the total storage capacity of large reservoirs. Validation against in‐situ measurements at 80 reservoirs reveals improved monthly inundated area monitoring compared to existing data sets. Additionally, our reservoir water storage change estimates exhibit an average R² of 0.79 and a mean relative root mean square error (rRMSE) of 21%. Our findings highlight reservoir water increases from May/June to November and declines in winter–spring in most regions. However, the inter‐annual patterns vary among regions, with increases in Northeast China, the Yellow River basin (YR), and Southwest China, contrasted by declines in Eastern and Northwest China. Inter‐ and intra‐annual variability in reservoir water storage is mainly influenced by natural inflow in Northeast and Northwest China, while anthropogenic factors dominate in the YR, Eastern, and Southwest China.
Plain Language Summary
River discharge is the volume of water passing a location on a river over a given interval of time. This quantity determines how much water and energy are available for humans, ecosystems, and sedimentary processes and is therefore essential knowledge for understanding, monitoring, and managing water movement and storage. Global knowledge of river discharge is limited by the sparsity of on‐the‐ground measurements, especially in countries without active monitoring programs or difficult to access sites, resulting in the vast majority of the world's rivers being unmeasured. The Surface Water and Ocean Topography (SWOT) satellite could revolutionize how we understand global water availability by providing comprehensive data on river flow and related variables without the need for ground‐based measurements. In this study, we share the first estimates of river discharge from SWOT observations during the first 15 months of the mission. Our initial assessment indicates that it is in fact possible to effectively monitor river discharge from space, and correlations of SWOT discharge estimates with ground measurements range from moderate to strong. Despite unavoidable limitations in our study's river selection, these preliminary results suggest that SWOT holds promise for estimating river discharge as SWOT collects more data and its measurements become more accurate with time.
While measured streamflow is commonly used for hydrological model evaluation and calibration, an increasing amount of data on additional hydrological variables is available. These data have the potential to improve process consistency in hydrological modeling and consequently for predictions under change, as well as in data‐scarce or ungauged regions. Here, we show how these hydrological data beyond streamflow are currently used for model evaluation and calibration. We consider storage and flux variables, namely snow, soil moisture, groundwater level, terrestrial water storage, evapotranspiration, and altimetric water level. We aim at summarizing the state‐of‐the‐art and providing guidance for the use of additional hydrological variables for model evaluation and calibration. Based on a review of the current literature, we summarize observation methods and uncertainties of currently available data sets, challenges regarding their implementation, and benefits for model consistency. The focus is on catchment modeling studies with study areas ranging from a few km ² to ~500,000 km ² . We discuss challenges for implementing alternative variables that are related to differences in the spatio‐temporal resolution of observations and models, as well as to variable‐specific features, for example, discrepancy between observed and simulated variables. We further discuss advancements required to deal with uncertainties of the hydrological data and to integrate multiple, potentially inconsistent datasets. The increased model consistency and improvement shown by most reviewed studies regarding the additional variables often come at the cost of a slight decrease in streamflow model performance.
When we started our involvement in flood monitoring a few years ago, we were
aware of the large number of researchers coming from different fields who
contribute to several different aspects of producing and validating high-resolution
flood maps from remotely sensed and other data. Each of the relevant research fields,
including image and data processing, computing, hydrology, and geomorphology,
builds upon a mature and robust tradition.
Nevertheless, the steadily increasing flow of high-resolution, open-access remote
sensing data is leading to a paradigm change in the field of flood and, more generally,
hazard monitoring.
The availability of high-resolution images of large portions of the Earth’s surface
in several electromagnetic bands – on a regular, high-frequency basis – is triggering
the development of new algorithms and technologies to extract information and
knowledge about subtle effects and temporal developments of inundation events.
This in turn requires a synthesis work from traditionally separate research commu�nities to develop common languages, standards, and methods.
This book presents a snapshot of some of the most up-to-date examples of
approaches to such a daunting endeavor. By reading the various chapters from
the outstanding research groups who agreed to contribute, whom we warmly
thank, the reader will gain an understanding of the wide spectrum of skills and
background necessary to extract meaningful flood information from remotely
sensed and ancillary data. More importantly, we hope this book will serve as a
reference toward the long-term objectives mentioned above, with the final goal of
increasing awareness and easing the impact of such catastrophic events on people.
Bari, Italy Alberto Refice
July 2017 Annarita D’Addabbo
Domenico Capolongo
Global navigation satellite system reflectometry (GNSS-R) uses the reflection characteristics of navigation satellite signals reflected from the earth’s surface to provide an innovative tool for remote sensing, especially for monitoring surface and atmospheric environmental variables, such as wind speed, soil moisture, vegetation, and sea ice parameters. This paper focuses on the current application and future potential of spaceborne GNSS-R in vegetation remote sensing and the retrieval of inland water environmental and physical parameters. This paper reviews the technical progress of GNSS-R in detail, from early feasibility studies to multiple application examples at this stage, from the United Kingdom Disaster Monitoring Constellation (UK-DMC) satellite in 2003 to other recent GNSS-R missions. These cases demonstrate the unique advantages of GNSS-R in terms of global coverage, low cost, and real-time monitoring. This paper explores the application of GNSS-R technology in vegetation parameters and inland water monitoring, especially its potential in vegetation parameters and surface water monitoring applications. The article also mentioned that the accuracy and efficiency of parameter retrieval can be significantly improved by improving models and algorithms, such as using neural networks and data fusion technology. Finally, the article points out the future direction of spaceborne GNSS-R technology in vegetation remote sensing and the retrieval of inland water environment and physical parameters, including expanding its application areas to a broader range of environmental monitoring and resource management. It emphasized its essential role in monitoring the global ecosystem and monitoring water resources.
70 percent of Azerbaijan's water resources come from neighboring upstream countries (Turkey, Armenia, Georgia and Iran) through transboundary rivers. Water supply in Azerbaijan closely depends on natural and human made factors in the upstream countries. The analyzed scenarios show that in order to fully meet the water needs of the country and reduce water shortages, effective water management must be established in the country, as well as transboundary water cooperation with upstream countries. The analysis of the developed water availability scenarios shows that if no action is taken, Azerbaijan will become a country experiencing severe water stress, which in turn will have a negative impact on all water-related livelihoods.
Snow and glacier melt provide freshwater to millions of people in the Indus basin. However, the unprecedented increase in demand for freshwater and depleting resources due to climate warming has put the region's water resources at risk. Therefore, quantifying water mass variation and anticipating changes in hydrological regimes that affect downstream freshwater supply are of utmost importance. To address this, we used Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On derived terrestrial water storage anomaly (TWSA) data from April 2002 to May 2023 over the Indus basin. Several gaps in these data, totaling 33 months, significantly impact regional trends and predictions of water mass changes. We apply a machine learning‐based MissForest algorithm to fill these gaps and compare our results with four previous studies. Annual TWSA shows a declining trend (−0.65 cm/yr) before 2015/16, with a significantly higher (−2.16 cm/yr) after 2015/16. Based on the estimate for the annual groundwater storage anomaly (GWSA), a major portion (83.7%) of the basin is experiencing a significant declining trend (>−0.15 cm/yr, p < 0.05). Glaciated region has a less severe decreasing trend (−0.78 cm/yr) compared to the non‐glaciated region (−1.44 cm/yr). Among sub‐basins, the upper Indus shows the lowest decline (−0.42 cm/yr), while Panjnad exhibits the highest (−1.70 cm/yr). Annual precipitation and runoff are decreasing, while temperature shows no trend. However, evapotranspiration is increasing might be due to a significant increase in vegetation (0.23%/yr) over the basin. The trends of hydroclimatic variables, vegetation, and anthropogenic factors, indicate a consistently decreasing GWSA in the region.
In the 30 years of its availability, satellite altimetry has established itself as an important tool for understanding the Earth system. Originally developed for oceanography and geodesy, it has also proven valuable for monitoring water level variation of lakes and rivers. However, when using altimetry for inland waters, there is always a critical issue: retracking i.e. the procedure in which the range from the satellite to the water surface is (re)estimated. The current retracking methods heavily rely on single waveforms, which results in a high sensitivity to every individual peak in the waveform and in a strong dependency on the waveform's shape. Here, we propose the Bin-Space-Time (BiST) retracking method that moves beyond finding a single point in a 1D waveform and instead seeks a retracking line within a 2D radargram, for which the temporal information over different cycles is also considered. The retracking line divides the radargram into two segments: the left (
Front
) and right-hand side (
Back
) of the retracking line. Such a segmentation approach can be interpreted as a binary image segmentation problem, for which spatiotemporal information can be incorporated. We follow a Bayesian approach, exploiting a probabilistic graphical model known as a Markov Random Field (MRF). There, the problem is arranged as a Maximum A Posteriori estimation of an MRF (MAP-MRF), which means finding a retracking line that maximizes a posterior probability density or minimizes a posterior energy function. Our posterior energy function is obtained by a prior energy function and a likelihood energy function, both of them depending on signal intensity and bin: 1) The prior: the bin-space energy function defined between first-order neighbouring pixels of a radargram modeling the spatial dependency between their labels for given intensities and bins and 2) The likelihood: the temporal energy function of a pixel for labeling
Front
or
Back
given its overall temporal evolution. The realization of the field with the minimum sum of the bin-space and the temporal energy functions is then found through the maxflow algorithm. Consequently, the retracking line, the boundary between the
Back
and
Front
region is obtained. We apply our method to both pulse-limited and SAR altimetry data over nine lakes and reservoirs in the USA with different sizes and different altimetry characteristics. The resulting water level time series are validated against in situ data. Across the selected case studies, on average, the BiST retracker improves the RMSE by approximately 0.5m compared to the best existing retracker. The main benefit of the proposed retracker, which operates in bin, space, and time domains, is its robustness against unexpected waveform variations, making it suitable for diverse inland water surfaces.
Surface water in rivers is vital for human society. However, our current understanding of the dynamics and drivers of river flows relies predominantly on stream gauging data, which are limited in spatial coverage and involve significant costs. Remote sensing techniques have emerged as complementary tools for monitoring river discharge, but these satellite-based methods often require complex data processing. This paper introduces a simple, yet effective and robust methodology to understand river discharge, using Australian rivers as a case study. Our findings reveal that changes in relative surface water extent within river basins can effectively capture river discharge dynamics. Moreover, we observe a linear relationship between relative surface water extent and river discharge in hilly basins and a quadratic relationship in flat basins. The new approach helps monitor river flows in ungauged river basins, and it has the potential to bridge the gap between local and regional understandings of water dynamics.
Satellite Radar altimetry has emerged as a powerful tool for monitoring inland water bodies including rivers, lakes, and coastal regions. However, challenges persist regarding its accuracy, particularly over complex terrains. This study addresses these challenges by enhancing water level estimation accuracy in complex environments. Although the scope of our study may appear limited in terms of temporal and spatial scales, it aims to contribute additional validation results for a comprehensive water-level database utilizing satellite radar altimetry data. In this paper, we applied two pulse filter criteria on Sentinel-3A radar data—Pulse Peakiness (PP) and Misfit (Mf)—to enhance the selection of altimetry waveforms. After applying the filters, the estimated water levels show improved accuracy when validated against in-situ observations. Pulses from non-water surfaces were excluded using riverbank boundaries to ensure higher-quality pulses. A total of six scenarios were considered where the PP and Mf criteria were used to optimize water level estimates. The results indicate that Mf < 1.5 performs well (RMSE = 0.27 m, R = 0.93), and PP > 0.3 yields even better outcomes (RMSE = 0.28 m, R = 0.95). These findings underscore the significance and efficacy of filtering in enhancing the accuracy of water level estimation. The dataset correlation (R) increased substantially, from 0.088 to 0.935–0.953, with the implementation of filters. Additionally, the RMSE significantly improved, reducing from 4.2 m to 0.26–0.27 m with filter parameters. As anticipated, filters removed data points but vastly improved results. Integrating multi-mission satellite data can compensate for data loss and augment overall quantity and quality.
This study demonstrates the potential for applying passive microwave satellite sensor data to infer the discharge dynamics of large river systems using the main stem Amazon as a test case. The methodology combines (1) interpolated ground-based meteorological station data, (2) horizontally and vertically polarized temperature differences (HVPTD) from the 37-GHz scanning multichannel microwave radiometer (SMMR) aboard the Nimbus 7 satellite, and (3) a calibrated water balance/water transport model (WBM/WTM). Monthly HVPTD values at 0.25° (latitude by longitude) resolution were resampled spatially and temporally to produce an enhanced HVPTD time series at 0.5° resolution for the period May 1979 through February 1985. Enhanced HVPTD values were regressed against monthly discharge derived from the WBM/WTM for each of 40 grid cells along the main stem over a calibration period from May 1979 to February 1983 to provide a spatially contiguous estimate of time-varying discharge. HVPTD-estimated flows generated for a validation period from March 1983 to February 1985 were found to be in good agreement with both observed arid modeled discharges over a 1400-km section of the main stem Amazon. This span of river is bounded downstream by a region of tidal influence and upstream by low sensor response associated with dense forest canopy. Both the WBM/WTM and HVPTD-derived flow rates reflect the significant impact of the 1982–1983 El Niño-;Southern Oscillation (ENSO) event on water balances within the drainage basin.
1] Using a suite of satellite observations, including passive and active microwave along with visible and infrared observations developed to estimate wetlands on a global scale, the present study examine wetlands spatial and temporal dynamics over the Indian subcontinent, a region subject to an annual monsoon. Monthly wetlands extent estimates over a 2-year (1993 – 1994) period and their consistency are analyzed using independent data sets, such as rainfall rate in-situ rain-gauge observations and the Global Precipitation Climatology Project product, as well as river water levels derived from radar altimeter observations. The extent variations show a good agreement with the rain-gauge data, a high correlation with the GPCP rain data and a similar seasonal cycle with the altimeter estimates over the 2 years. This case study evaluation shows the potential of the remote sensing to give spatial-temporal consistent information on the variability of flooded areas and encourages the development of longer satellite wetlands estimates. Citation: Papa, F., C. Prigent, F. Durand, and W. B. Rossow (2006), Wetland dynamics using a suite of satellite observations: A case study of application and evaluation for the Indian Subcontinent, Geophys. Res. Lett., 33, L08401,
As part of the World Climate Research Program's (WCRPs) Global Energy and Water-Cycle Experiment (GEWEX) Continental-scale International Project (GCIP), a preliminary water and energy budget synthesis (WEBS) was developed for the period 1996-1999 fromthe "best available" observations and models. Besides this summary paper, a companion CD-ROM with more extensive discussion, figures, tables, and raw data is available to the interested researcher from the GEWEX project office, the GAPP project office, or the first author. An updated online version of the CD-ROM is also available at http://ecpc.ucsd.edu/gcip/webs.htm/. Observations cannot adequately characterize or "close" budgets since too many fundamental processes are missing. Models that properly represent the many complicated atmospheric and near-surface interactions are also required. This preliminary synthesis therefore included a representative global general circulation model, regional climate model, and a macroscale hydrologic model as well as a global reanalysis and a regional analysis. By the qualitative agreement among the models and available observations, it did appear that we now qualitatively understand water and energy budgets of the Mississippi River Basin. However, there is still much quantitative uncertainty. In that regard, there did appear to be a clear advantage to using a regional analysis over a global analysis or a regional simulation over a global simulation to describe the Mississippi River Basin water and energy budgets. There also appeared to be some advantage to using a macroscale hydrologic model for at least the surface water budgets.
currently available land cover classification products are remarkably poor indicators of vegetation type and water body extent in this northern wetland environment. The ground-truth data are compared with (1) the Global Land Cover Characteristics database derived from Advanced Very High Resolution Radiometer data (GLCC.AVHRR), (2) the Global Land Cover Classification derived from Moderate Resolution Imaging Spectroradiometer data (GLCC.MODIS), (3) the Global Lakes and Wetlands Database (GLWD), and (4) the West Siberian Lowland Peatland Database (WSLPD) using: (1) all land cover categories and (2) permanent wetland and water body categories only. Overall agreement with ground observations of land cover is only 21% for the GLCC.AVHRR database and 11% for the GLCC.MODIS database. However, at a much broader scale (one degree of latitude) there is improved qualitative agreement between vegetation classes, with some notable exceptions: (1) at low latitudes (� 54N-58N), both the GLCC.AVHRR and GLCC.MODIS databases underestimate woody savannas in favor of croplands; (2) at midlatitudes (� 58N-64N), the GLCC.AVHRR database underestimates evergreen needleleaf forest in favor of mixed forest; and (3) at high latitudes (� 64N-68N), both the GLCC.AVHRR and GLCC.MODIS databases underestimate deciduous needleleaf forest in favor of woody savannas or open shrublands, respectively. It is clear that all four data databases significantly underestimate permanent wetlands and water bodies, although those specifically developed for this purpose (GLWD and WSPLD) perform better than the more widely used, multiclass land cover products. For permanent wetlands, agreement with our ground data is only 2% for GLCC.MODIS, 23% for GLCC.AVHRR, 45% for GLWD and 56% for WSLPD. Agreement with open water bodies is even poorer (0-5%). These results raise into question the efficacy of incorporating currently available land cover products into terrestrial ecosystem models in northern wetland environments.
Satellite radar altimetry has the ability to monitor variations in surface water height (stage) for large wetlands, rivers, and associated floodplains. A clear advantage is the provision of data where traditional gauges are absent. As part of an international program, a complete altimetric analysis of the Amazon Basin is being undertaken. Here, an updated and more rigorous evaluation of the TOPEX/POSEIDON (T/P) data set is presented for the first ~7.5 years of the mission. With an initial study group of 230 targets, height variability at many ungauged locations can be observed for 30-50%, the range reflecting the clarity of the variations in lieu of instrument limitations. An assessment of the instrument performance confirms that the minimum river width attainable is ~1 km in the presence of some inundated floodplain. This constraint does allow observation of the main stem (Solimões/Amazon) and the larger tributaries, but rugged terrain in the vicinity of the target additionally places severe limitations on data retrieval. First-order validation exercises with the deduced 1992-1999 time series of stage fluctuations reveal accuracies ranging from tens of centimeters to several meters (mean ~1.1 m rms). Altimetric water levels in the Solimões and Amazon are particularly well defined with amplitudes
We demonstrate that interferometric processing of JERS-1 SAR data over an Amazon lake containing ∼1500 islands yields centimeter-scale changes in the height of the water surface from February 14 to March 30, 1997. For the method to work, we qualitatively find that inundation of about one or two leafless trees per 25 m² multi-look SAR pixel is sufficient to return the radar pulse to the side-looking antenna. Validation is provided by multi-temporal TOPEX-POSEIDON altimetry profiles, which directly measure surface heights relative to a fixed datum. Because SAR provides an image, the water height changes (∼12 cm) can be converted to a net volume measurement (280 million m³) over the 44 days separating the JERS-1 acquisitions. Compared to historical gauge records, removal of this volume from the lake required a ∼50% greater flow.
Using multitemporal ERS 1 synthetic aperture radar (SAR) satellite imagery and simultaneous ground measurements of streamflow, a strong correlation (R2 = 0.89) was found between water surface area and discharge for a braided glacial river in British Columbia, Canada. Satellite-derived effective width (We) was found to vary with discharge (Q) as We = 27.5Q0.42, where We is defined as the total water surface area within a 10 km × 3 km control section, divided by the section length. This “area/discharge rating curve” yields instantaneous discharge estimates with a mean error of ±275 m3/s for ground-measured flows that ranged from 242 to 6350 m3/s.
Observations of river inundation areas, water levels, and flow variability from orbital sensors have the potential to directly measure the runoff component of the Earth's hydrologic cycle [ Birkett et al ., 2002; Brakenridge et al ., 1998; Sippel et al ., 1994, 1998; Townsend , 2001]. A remote‐sensing‐based measurement strategy for rivers and streams is emerging: Surface water data can be collected, their accuracy evaluated, and the results disseminated without regard to political boundaries. The results can be used to address a wide variety of applications.
In this article, the needs for such measurements, a river reach‐based methodology for their collection, and some sample results are presented. Because the international observational capability is increasing, some future opportunities for improving this strategy are also described.
We describe the sources and routing of the Amazon River flood wave
through a 2000-km reach of the main channel, between São Paulo de
Olivença and Obidos, Brazil. The damped hydrograph of the main
stem reflects the large drainage basin area, the 3-month phase lag in
peak flows between the north and south draining tributaries due to
seasonal differences in precipitation, and the large volume of water
stored on the floodplain. We examined several aspects of the valley
floor hydrology that are important for biogeochemistry. These include
volumes of water storage in the channel and the floodplain and the rates
of transfer between these two storage elements at various seasons and in
each segment of the valley. We estimate that up to 30% of the water in
the main stem is derived from water that has passed through the
floodplain. To predict the discharge at any cross section within the
study reach, we used the Muskingum formula to predict the hydrograph at
downriver cross sections from a known hydrograph at upstream
cross-sections and inputs and outputs along each reach. The model was
calibrated using three years of data and was successfully tested against
an additional six years of data. With this model it is possible to
interpolate discharges for unsampled times and sites.
Time series of water level in major rivers of the Plata basin are examined using altimetry data from the Topex/Poseidon satellite over the period 1993-2001. Ten sites are selected on the Paraná, three on the Paraguay, one the Uruguay and seven on wetlands. It is confirmed that the seasonal cycle of river levels decreases in amplitude with increasing latitude of the site. The normalized time series for sites along the same river differ mainly by a time-lag corresponding to propagation speeds of about 0.1 ms-1. The interannual variability of the Lower Paraná, with a strong peak in early 1998 and minima in early 1996 and 2000, shows the remote effects of El Niño/Southern Oscillation (ENSO). The variability of the Upper Paraná and Paraguay shows similar ENSO impacts with smaller amplitudes after 1995, and the effects of regional features climate variability before that date (e.g. the South American monsoon). The results support the usefulness of satellite altimetry for a dense and continuous monitoring of river water levels.
We present a high-resolution shoreline data set amalgamated from two databases in the public domain. The data have undergone extensive processing and are free of internal inconsistencies such as erratic points and crossing segments. The shorelines are constructed entirely from hierarchically arranged closed polygons. The data can be used to simplify data searches and data selections or to study the statistical characteristics of shorelines and landmasses. The data set can be accessed both electronically over Internet and from the National Geophysical Data Center, Boulder, Colorado; it comes with access software and routines to facilitate decimation based on a standard line-reduction algorithm.
About 10 yr of TOPEX/Poseidon (T/P) altimetry data have been used to compute time series of lake levels at six inland lakes in China. To verify our T/P data processing strategy, the T/P-derived lake levels at Bosten Lake (west China) and Lake Huron (north America) were compared with lake gauge records: good agreement is found between the T/P and the gauge results. Wavelet spectra indicate annual and interannual variations of these lake levels, which are also sensitive to climate variability. At the interannual timescale, the lake levels of Hulun (north China), Bosten (west China) and Ngangzi (east Tibet) are correlated with precipitation and El Niño Southern Oscillation (ENSO); in particular, they all respond to the 1997-1998 El Niño. The Bosten lake level has increased monotonically since 1993 due to the increased temperature on Tianshan Mountain, which feeds water into this lake. The lake levels of Hongze and Gaoyou (east China) show minor decreasing trends. The lake level of La'nga (west Tibet) decreased steadily from 1993 to 2001, with a total drop of 4 m. The Ngangzi lake level decreased from 1993 January to 1997 December, but after the peak of the 1997-1998 El Niño the slope was reversed and the lake level has increased monotonically since then. An example given at Bosten Lake shows that waveform contamination over Chinese lakes affects the quality of T/P-derived lake levels and retracking is necessary to mitigate the problem.
Floodplains and associated lakes are important components of the biogeochemistry, ecology, and hydrology of the Amazon basin. Amazon floodplains contain thousands of lakes and associated wetlands linked to each other and to the many rivers of the immense basin. These floodplain lakes modify the passage of flood waves (Richey et al. 1989a), increase nutrient retention and recycling (Melack and Fisher 1990), and influence the chemistry of the rivers (Devol et al. 1995). The mosaic of flooded forests, open water, and floating macrophytes in the central Amazon floodplain makes a significant contribution of methane to the troposphere (Bartlett et al. 1988, Devol et al. 1990). The fishery potential of the large river systems is closely tied to the area of floodplain and the magnitude and duration of inundation (Welcomme 1979, Bayley and Petrere 1989). The majority of fishes harvested in the Amazon basin obtain nutrition in flooded forests (Goulding 1980) or from organic matter derived from floodplain algae (Araujo-Lima et al. 1986, Forsberg et al. 1993). Much progress has been made during the last fifty years toward understanding the lakes of the Amazon floodplain. Still, the vast size of the Amazon basin poses challenges to limnologists working in the region. Recent research has been enhanced by the maintenance of functional floating laboratories in several areas, use of modern ships capable of regional surveys and equipped for hydrographic studies, and applications of remote sensing. Our objective in this chapter is to examine the role of lakes in the hydrology of the floodplain and in the biogeochemistry of carbon, nitrogen, and phosphorous within the central Amazon basin. Particular emphasis is placed on how inundation patterns interplay with carbon balance and nutrient limitation. By combining numerous measurements of primary productivity with recent results from studies using isotopes of carbon, we will examine the contribution of the major plant groups to aquatic foodwebs, and offer a new paradigm for the processing of organic carbon on the Amazon floodplain. The interplay between the Amazon River and local catchments as sources of nutrients to the floodplain indicates the potential sensitivity of the lakes to basin-wide and local disturbances.
The further development of two-dimensional finite element models of river flood flow is currently constrained by a lack of data for rigorous parameterization and validation. Remote sensing techniques have the potential to overcome a number of these constraints thereby allowing a research design for model development. This is illustrated with reference to a case study of a two-dimensional finite element model applied to the Missouri River, Nebraska and compared with a synchronous Landsat TM image of flood inundation extent. The case study allows research needs for the integration of hydraulic modelling and remote sensing to be defined. © 1997 John Wiley & Sons, Ltd.
Following is a continuation of the list of titles and authors of the papers presented: Use of Thermal Infrared Scanning in Evaluating Predictive Models for Power Plant Thermal Plume Mixing in Italian Coastal Waters. By G. Dinelli, David T. Hodder, and F. Parrini. Applications of Thermal Remote Sensing to Detailed Ground Water Studies. By Joel Souto-Maior. Digital Processing Techniques in Thermal Plume Analysis. By Ronald W. Stingelin and Glen B. Avis. Utilization of Aerial Photographs for Measuring Land Use Changes in Watersheds. By Donald B. Stafford, James T. Ligon, and M. Eugene Nettles. Water Pollution Surveillance Using Local Remote Sensing Equipment. By W. B. McCoy and T. H. Lackie. Use of Remote Sensing for Mapping of Aquatic Vegetation in the Kawartha Lakes. By Ivanka Wile. Spectral Reflectance of Water Containing Suspended Sediment. By Bruce J. Blanchard and Ross W. Leamer.
Proceedings of the symposium include 32 technical papers on current reports on remote sensing applications to water resources management. Following is a list of titles and authors of the papers presented: Remote Sensing and Water Resources - U. S. Space Program. By Martin W. Molloy and Vincent V. Salomonson. Mapping of the 1973 Mississippi River Floods from the Earth Resources Technology Satellite (ERTS). By Morris Deutsch, F. H. Ruggles, Philip Guss, and Edward Yost. Mississippi Sound Remote Sensing Study. By B. H. Atwell and G. C. Thomann. Some Recent Applications of Remote Sensing to Water Resources in Hawaii. By Yu-Si Fok and William M. Adams. Water Quality Monitoring of Reservoirs on the Colorado River Utilizing ERTS-1 Imagery. By Larry K. Lepley, Kennith E. Foster, and Lorne G. Everett.
During the flood of 1993 on the upper Mississippi and Missouri Rivers, the most dramatic changes to flood plains resulted at levee-break complexes. At these sites, large discharges were concentrated through narrow levee breaks, which resulted in high water-surface slopes and intensely turbulent flow. These conditions created extensive, deep scours and permitted sedimentladen water to flow into areas behind the levees. As a result, large areas of formerly productive bottom land were eroded or covered by thick deposits of sand. The levee break at Miller City. Illinois, created a typical large levee-break complex. As much as 28 percent of the Mississippi River discharge flowed through the break during the peak of the flood. With velocities as great as 300 centimeters per second, the flow through the levee break was able to scour an area 2,200 meters long to a maximum depth of more than 20 meters. Transport of sediment from the main channel, through the break, resulted in net deposition of at least 8.2 million cubic meters of sand. Although the history of this part of the Mississippi River Valley indicates that channel changes have been extensive and frequent, the geomorphic changes that resulted from the 1993 flood seem to be unprecedented during the last 10,000 years or so. The large geomorphic changes at Miller City during the 1993 floods are attributable to the magnitude of the flood, the hydraulic head artificially increased by the levee, and the lack of energy dissipation on the agricultural flood plain relative to presettlement, forested conditions.
Day and night electrofishing catches were compared for sampling effectiveness and diel movements offish to and from near-shore waters of the Ohio and Muskingum rivers. Standardized methods were used to collect same-day paired samples by sampling during the day, displacing the catch, and resampling after twilight. Night catches contained significantly higher numbers of species, individuals (excluding Dorosoma cepedianum), weight, and biological index scores (Modified Index of Well-Being [Mlwb] and Index of Biotic Integrity [IBI]). Night versus day paired samples in the Ohio and Muskingum rivers showed, respectively, mean increases of 7.6 and 4.6 species, 229 and 417 fish per km (excluding D. cepedianum), 18.2 and 30.4 kg/km, 2.3 and 1.5 Mlwb units, and 10.8 and 8.7 IBI units. Total night catches yielded, respectively, 43% and 15% more taxa, 62% and 160% greater numbers (excluding!), cepedianum), and 50% and 70% more weight than total day catches. Catch differences were primarily attributed to diel movements from off-shore to near-shore waters during the evening-twilight period. Taxa which increased the most at night in the Ohio River were: Alosa chrysochloris, Notropis wicklijfi, Ictiobus bubalus, Moxostoma anisurum, M. duquesnei, Ictalurus punctatus, Morone saxatilis x M. chrysops, Ambloplites rupestris, Stizostedion canadense, and Aplodinotus grunniens; and in the Muskingum River: Ictiobus bubalus, Moxostoma anisurum, and Morone chrysops. Standardized night electrofishing is an effective sampling technique for many mainstem species and provides a better, more complete biological assessment than day electrofishing. Therefore, it should be incorporated into long-term monitoring programs for these large, deep rivers. The findings of this study may also be applicable to other large, deep bodies of water elsewhere.
We analyze Shuttle Radar Topography Mission (SRTM) water surface elevation data to assess the capacity of interferometric radar for future surface water missions. Elevations from three Ohio reservoirs and several Amazon floodplain lakes have standard deviations, interpreted as errors,
that are smaller in C-band compared to X-band and are smaller in Ohio than in the Amazon. These trends are also evident when comparing water surface elevations from the Muskingum River in Ohio with those of the Amazon River. Differences are attributed to increased averaging in C-band compared
to X-band, greater sensitivity to surface water motion in X-band, and generally larger off-nadir look angles in X-band. Absolute water surface elevations are greater in the C-band DEM for much of the two study areas and yield expected slope values. Height and slope differences are attributed
to differing usage of geoids and ellipsoids. These SRTM measurements suggest the great possibility for space-based, laterally-spatial (2D) measurements of water surface elevations.
1. The fashion for examining riverine landscapes is changing as our technical instruments, from microprobes to satellites, expands to be able to examine the spatial and temporal relationships among biota, hydrology and geomorphology across scales from microhabitats to channel units to valleys to catchments.
2. The range of successful applications from remote sensing analyses of riverine landscapes has especially increased with the launch of many new instruments that record data across the electromagnetic spectrum. Engineering of the instruments has also improved such that knowledge of the radiometric properties of the digital data is more complete as a result of better instrumentation and installation of on-board calibrations systems for many instruments.
3. With the development of faster processing on cheaper computers, it is now common for comprehensive data sets to be processed through algorithms that previously could only be applied to relatively small (<1 Mbyte) rasters of data. This technical advance is especially important for the statistical algorithms such as principal components and spectral mixture analysis that can decompose gradients in the spectral data. The combined effect is the production of regional views of riverine landscapes separated into components of water, vegetation and soil.
4. The landscape properties of riverine landscapes that have been most successfully measured with remote sensing data include community and habitat level classification and connectivity of waterbodies with optical and radar data. Laser and radar altimetric data measured from aircraft provide land elevations at resolutions as fine as decimetres. A remaining challenge is to achieve an exact match between the categories of landscape classification from the remote sensing analysis and data from field surveys or model outputs.
5. In contrast to many landscape properties, several water properties are now routinely measured as absolute values (water surface elevation, temperature, surface sediment concentration and algal concentration) with remote sensing. New analyses of both passive and active radar data in addition have led to measurements of inundation and wetness that are providing valuable insight into the dynamics of flooding and its effect on riverine landscapes.
6. Finally, an effective examination of the variability in landscape cover includes additional analyses of remote sensing products using pattern metrics that measure the scale of patchiness and distribution of the landscape properties. These types of variability measures at the regional scale contribute to an increased understanding of the way in which spatial heterogeneity of riverine landscapes varies across scales and how landscape filters (sensu Poff, 1997) influence the evolution of these diversity patterns.
This paper describes a new approach to making altimeter measurements
from off-nadir radar signal returns and its oceanographic and
geophysical applications. The approach is based on the technique of
radar interferomety and the new instrument is called the Wide-Swath
Ocean Altimeter (WSOA). WSOA is designed to be flown with a Jason class
conventional dual-frequency altimeter system, including a multifrequency
radiometer for the correction of the effects of water vapor in the
troposphere. WSOA will extend the measurement from a line along the
nadir to a swath of 200 km centered on the nadir track. The most
important application of WSOA is to provide the first synoptic maps of
the global oceanic eddy field with a spatial resolution of 15
km×15 km. The strong currents and water property anomalies (in
temperature, salinity, oxygen, etc.) associated with ocean eddies are a
major factor affecting the oceanic general circulation and the
biogeochemical cycles of the ocean. WSOA will also provide measurements
that allow the monitoring and study of coastal currents and tides that
affect the lives of half of the world's population. The intrinsic
resolution of WSOA in the look direction of the interferometric radar is
about 1 km, allowing the estimation of sea surface slope with an
accuracy of 1 micro radian down to a wavelength of 20 km for a 2-year
mission (15 km for a 5-year mission). This capability will make
contributions to the mapping of the details of the sea floor topography.
The Aral Sea was one of the biggest lakes in the world before it started to shrink in the 1960s due to water withdrawal for land irrigation. Sea level decreases led to the separation of the Aral Sea into two basins - the Small Aral in the north and the Big Aral in the south. For several decades there were no continuous observations of Aral Sea level, and the few data that exist are fragmentary or unavailable. We present observations of the Big Aral Sea level estimated from the TOPEX/Poseidon (T/P) altimetry with high temporal resolution over the last decade (1993-2004). Since sea volume is one of the key parameters for the studies of water balance, we use the T/P-derived time series of sea level and a dedicated digital bathymetry model (DBM) to reconstruct temporal changes in the Aral Sea surface and volume. We introduce variations of the sea volume as the new constraint for the water budget of the Big Aral Sea. This is an important step toward estimating detailed seasonal and interannual changes of the water budget. We assess various existing components of the water budget of the Aral Sea and discuss the quality of the existing data and their applicability for establishing detailed water balance. In particular, large uncertainties in estimating the evaporation and underground water supply are addressed. Desiccation of the Aral Sea resulted in dramatic changes in the salinity regime and, consequently, affected its aquatic ecosystems. We also discuss changes in the aquatic fauna and their possible evolution under continuing desiccation of the Big Aral Sea. Combining satellite altimetry with other parameters of the water budget offers a promising potential for assessing temporal changes in the water budget of arid or semi-arid regions, even those with a poor ground monitoring network.
The growing availability of multi-temporal satellite data has increased opportunities for monitoring large rivers from space. A variety of passive and active sensors operating in the visible and microwave range are currently operating, or planned, which can estimate inundation area and delineate flood boundaries. Radar altimeters show great promise for directly measuring stage variation in large rivers. It also appears to be possible to obtain estimates of river discharge from space, using ground measurements and satellite data to construct empirical curves that relate water surface area to discharge. Extrapolation of these curves to ungauged sites may be possible for the special case of braided rivers. Where clouds, trees and floating vegetation do not obscure the water surface, high-resolution visible/infrared sensors provide good delineation of inundated areas. Synthetic aperture radar (SAR) sensors can penetrate clouds and can also detect standing water through emergent aquatic plants and forest canopies. However, multiple frequencies and polarizations are required for optimal discrimination of various inundated vegetation cover types. Existing single- polarization, fixed-frequency SARs are not suÅcient for mapping inundation area in all riverine environments. In the absence of a space-borne multi-parameter SAR, a synergistic approach using single-frequency, fixed-polarization SAR and visible/infrared data will provide the best results over densely vegetated river floodplains. #1997 John Wiley & Sons, Ltd.
[1] Extensive new data from previously unstudied Siberian streams and rivers suggest that mobilization of currently frozen, high-latitude soil carbon is likely over the next century in response to predicted Arctic warming. We present dissolved organic carbon (DOC) measurements from ninety-six watersheds in West Siberia, a region that contains the world's largest stores of peat carbon, exports massive volumes of freshwater and DOC to the Arctic Ocean, and is warming faster than the Arctic as a whole. The sample sites span ∼106 km2 over a large climatic gradient (∼55–68°N), providing data on a much broader spatial scale than previous studies and for the first time explicitly examining stream DOC in permafrost peatland environments. Our results show that cold, permafrost-influenced watersheds release little DOC to streams, regardless of the extent of peatland cover. However, we find considerably higher concentrations in warm, permafrost-free watersheds, rising sharply as a function of peatland cover. The two regimes are demarcated by the position of the −2°C mean annual air temperature (MAAT) isotherm, which is also approximately coincident with the permafrost limit. Climate model simulations for the next century predict near-doubling of West Siberian land surface areas with a MAAT warmer than −2°C, suggesting up to ∼700% increases in stream DOC concentrations and ∼2.7–4.3 Tg yr−1 (∼29–46%) increases in DOC flux to the Arctic Ocean.
The NASA/NGA Shuttle Radar Topography Mission (SRTM) collected interferometric radar data which has been used by the Jet Propulsion Laboratory to generate a near-global topography data product for latitudes smaller than 60°. One of the primary goals of the mission was to produce
a data set that was
globally consistent
and with
quantified errors
. To achieve this goal, an extensive global ground campaign was conducted by NGA and NASA to collect ground truth that would allow for the global validation of this unique data
set. This paper documents the results of this SRTM validation effort using this global data set. The table shown below summarizes our results (all quantities represent 90 percent errors in meters).
Hydrologists, water resource managers, and engineers recognize the potential of remote sensing for acquiring hydraulic measurements necessary for estimating discharge and storage changes globally, and thus have formed a community proposing the Water Elevation Recovery satellite mission (WatER). The WatER technological heritage is directly based on the highly successful Shuttle Radar Topography Mission (SRTM) and its C-band and X-band digital elevation models (DEMs). SRTM is not only a technological heritage for WatER but also a baseline measurement by which we can assess the potential of remote sensing to collect measurements of h, dh/dx and dh/dt. Water surface elevations are abundant in both the C-band and X-band SRTM DEMs. Elevations from three Ohio reservoirs and several Amazon floodplain lakes have standard deviations, interpreted as errors, that are smaller in C-band compared to X-band and are smaller in Ohio than in the Amazon. These trends are also evident when comparing water surface elevations from the Muskingum River in Ohio with those of the Amazon River. Differences are attributed to increased averaging in C-band compared to X-band, greater sensitivity to surface water motion in X-band, and generally larger off-nadir look angles in X-band. Absolute water surface elevations are greater in the C-band DEM for much of the two study areas and yield expected slope values on the Amazon River. However, X-band DEM values for the Amazon River are below sea level downstream of Sao Jose do Amatari (~600 km upstream of Obidos) and have some slope values that are greater than expected. These absolute height and slope differences are attributed to the usage of differing vertical datums.
Surface fresh water is essential for life, yet we have surprisingly poor knowledge of the spatial and temporal dynamics of surface water storage and discharge globally. The core mission objective is to describe and understand the continental water cycle and the hydrological processes (e.g., floodplain hydraulics) at work in a river basin. The key question that will be answered by WatER is: "Where is water stored on Earth's land surfaces, and how does this storage vary in space and time?" WatER will facilitate societal needs by (1) improving our understanding of flood hazards; (2) freely providing water volume information to countries who critically rely on rivers that cross political borders; and (3) mapping the variations in water bodies that contribute to disease vectors (e.g., malaria). Conventional altimeter profiles are, without question, incapable of supplying the measurements needed to address scientific and societal questions. WatER will repeatedly measure the spatially distributed water surface elevations (h) of wetlands, rivers, lakes, reservoirs, etc. Successive h measurements yield dh/dt, (t is time), hence a volumetric change in water stored or lost. Individual images of h yield dh/dx (x is distance), hence surface water slope, which is necessary for estimating streamflow. WatER's main instrument is a Ka-band radar interferometer (KaRIN) which is the only technology capable of supplying the required imaging capability of h. KaRIN has a rich heritage based on (1) the many highly successful ocean observing radar altimeters, (2) the Shuttle Radar Topography Mission (SRTM), and (3) the development effort of the Wide Swath Ocean Altimeter (WSOA). The interferometric altimeter is a near-nadir viewing, 120 km wideswath based instrument that uses interferometric SAR processing of the returned pulses to yield single-look 5m azimuth and 10m to 70m range resolution, with an elevation accuracy of approximately 50 cm. Polynomial based averaging of heights along the water body increases the height accuracy to about 3 cm. The entire globe is covered twice every 16 days and orbit subcycles allow the average visit to be about half this time at low to mid-latitudes, and almost daily at high latitudes. The WatER mission is an international effort with a large, supporting scientific community. It is already proposed as an ESA Earth Explorer Core mission and will also be jointly submitted to NASA's Earth System Science Pathfinder program. WatER is designed to meet high priority targets for all nations and will provide essential data for the EU Water Framework Directive and the European Flood Alert System. WatER will meet the United Nations call for a "greater focus on water related issues", responds to the hydroclimatological needs of the International Working Group on Earth Observations, and answers the U.S. federal government call to focus on our "ability to measure, monitor, and forecast U.S. and global supplies of fresh water".
An important control on river biogeochemistry and sediment load is the process of water exchange between primary channels and the flood plain, particularly in low-relief areas containing lakes, ephemeral channels, and other aquatic ecosystems. Flood-plain exchange may be a dominant process on the lowland rivers of Arctic Russia, which are among the world's largest in water discharge yet are strikingly deficient in their delivery of sediment to the Arctic shelf. Temporal synthetic aperture radar (SAR) amplitude and interferometric images of the Ob' River, Siberia, reveal a time-varying limnological network controlling water, sediment, and nutrient exchange between flood-plain wetlands and the main channel. The amount of hydrologic exchange decreases by one order of magnitude from June to September, enhancing sedimentation over as much as 90% of the flood plain and enriching channel waters with colloidal organic carbon. This observation, combined with Russian field measurements of water discharge and sediment load, indicates that a major sediment sink on the lower Ob' flood plain may be responsible for the low amount of sediment delivery by the Ob' River to its estuary and the Kara Sea.
Rivers differ among themselves and through time. An individual river can vary significantly downstream, changing its dimensions and pattern dramatically over a short distance. If hydrology and hydraulics were the primary controls on the morphology and behavior of large rivers, we would expect long reaches of rivers to maintain characteristic and relatively uniform morphologies. In fact, this is not the case - the variability of large rivers indicates that other important factors are involved. River Variability and Complexity presents a new approach to the understanding of river variability. It provides examples of river variability and explains the reasons for them, including fluvial response to human activities. Understanding the mechanisms of variability is important for geomorphologists, geologists, river engineers and sedimentologists as they attempt to interpret ancient fluvial deposits or anticipate river behavior at different locations and through time. This book provides an excellent background for graduates, researchers and professionals.
The transfer of water, sediment, and other materials to floodplains is a function of the hydrology of inundation. Inundation of floodplains by regional water, that is, overbank flow from the main river channel, and local water, that is, groundwater, hyporheic water, local tributary water, and direct precipitation onto the floodplain, is such that some rivers inundate dry floodplains, while other rivers inundate fully saturated floodplains. Remote sensing and field data from the large rivers Missouri, Mississippi, Amazon, Ob'-Irtysh, Taquari, and Altamaha show a variety of water types on inundated floodplains, including areas of mixing of river and local water defined as the "perirheic zone." For the rivers examined here, only the Missouri River flooded its entire valley with sediment-rich river water. Therefore the floodplains of these large rivers from the Arctic to the Amazon are only partially inundated with river water during floods and the corresponding perirheic zones may encompass a significant floodplain ecotone.
Conventional measurements of river flows are costly, time-consuming, and frequently dangerous. This report evaluates the use of a continuous wave microwave radar, a monostatic UHF Doppler radar, a pulsed Doppler microwave radar, and a ground-penetrating radar to measure river flows continuously over long periods and without touching the water with any instruments. The experiments duplicate the flow records from conventional stream gauging stations on the San Joaquin River in California and the Cowlitz River in Washington. The purpose of the experiments was to directly measure the parameters necessary to compute flow: surface velocity (converted to mean velocity) and cross-sectional area, thereby avoiding the uncertainty, complexity, and cost of maintaining rating curves. River channel cross sections were measured by ground-penetrating radar suspended above the river. River surface water velocity was obtained by Bragg scattering of microwave and UHF Doppler radars, and the surface velocity data were converted to mean velocity on the basis of detailed velocity profiles measured by current meters and hydroacoustic instruments. Experiments using these radars to acquire a continuous record of flow were conducted for 4 weeks on the San Joaquin River and for 16 weeks on the Cowlitz River. At the San Joaquin River the radar noncontact measurements produced discharges more than 20% higher than the other independent measurements in the early part of the experiment. After the first 3 days, the noncontact radar discharge measurements were within 5% of the rating values. On the Cowlitz River at Castle Rock, correlation coefficients between the USGS stream gauging station rating curve discharge and discharge computed from three different Doppler radar systems and GPR data over the 16 week experiment were 0.883, 0.969, and 0.992. Noncontact radar results were within a few percent of discharge values obtained by gauging station, current meter, and hydroacoustic methods. Time series of surface velocity obtained by different radars in the Cowlitz River experiment also show small-amplitude pulsations not found in stage records that reflect tidal energy at the gauging station. Noncontact discharge measurements made during a flood on 30 January 2004 agreed with the rated discharge to within 5%. Measurement at both field sites confirm that lognormal velocity profiles exist for a wide range of flows in these rivers, and mean velocity is approximately 0.85 times measured surface velocity. Noncontact methods of flow measurement appear to (1) be as accurate as conventional methods, (2) obtain data when standard contact methods are dangerous or cannot be obtained, and (3) provide insight into flow dynamics not available from detailed stage records alone.
Terrestrial waters—including snowpack, glaciers, water in aquifers and other geological formations, water in the plant root zone, rivers, lakes, man‐made reservoirs, wetlands, and inundated areas—represent less than a mere 1% of the total amount of water on Earth. However, they have a crucial impact on terrestrial life and human needs and play a major role in climate variability.
Land waters are continuously exchanged with the atmosphere and oceans in vertical and horizontal mass fluxes through evaporation, transpiration, and surface and subsurface runoff. Although it is now recognized that improved description of the terrestrial branch of the global water cycle is of major importance for climate research and for inventory and management of water resources, the global distribution and spatial‐temporal variations of terrestrial waters are still poorly known because routine in situ observations are not available globally. So far, global estimates of spatial‐temporal change of land water stored in soils and in the snowpack essentially rely on hydrological models, either coupled with atmosphere/ocean global circulation models and/or forced by observations.
C‐band radar pulses backscatter from the upper canopy of swamp forests, and consequently interferometric synthetic aperture radar (InSAR) analysis of C‐band imagery has not been exploited to study water level changes in swamp forests. This article explores C‐band ERS‐1 (European Remote Sensing Satellite) and ERS‐2 InSAR data over swamp forests composed of moderately dense trees with a medium‐low canopy closure in southeastern Louisiana to measure water level changes beneath tree cover.
Wetlands cover more than 4% of the Earth's land surface and interact with hydrologic, biogeochemical, and sediment transport processes that are fundamental in understanding ecological and climatic changes [ Alsdorf et al , 2003; Prigent et al ., 2001 ; Melack and Forsberg , 2000; Dunne et al ., 1998]. Measurement of water level changes in wetlands, and consequently of changes in water storage capacity, provides a required input for hydrologic models, and is required to comprehensively assess flood hazards [e.g., Coe , 1998].
A global data base of wetlands at 1 degree resolution was developed from the integration of three independent global, digital sources: (1) vegetation, (2) soil properties and (3) fractional inundation in each 1 degree cell. The integration yielded a global distribution of wetland sites identified with in situ ecological and environmental characteristics. The wetland sites were classified into five major groups on the basis of environmental characteristics governing methane emissions. The global wetland area derived in this study is 5.3 trillion sq m, approximately twice the wetland area previously used in methane emission studies. Methane emission was calculated using methane fluxes for the major wetland groups, and simple assumptions about the duration of the methane production season. The annual methane emission from wetlands is about 110 Tg, well within the range of previous estimates. Tropical/subtropical peat-poor swamps from 20 degrees N to 30 degrees S account from 30% of the global wetland area and 25% of the total methane emission. About 60% of the total emission comes from peat-rich bogs concentrated from 50-70 degrees N, suggesting that the highly seasonal emission from these ecosystems is the major contributor to the large annual oscillations observed in atmospheric methane concentrations at these latitudes. 78 refs., 6 figs., 5 tabs.
The application of numerical models of free surface flow to fluvial
flood prediction is currently hampered by the lack of distributed
calibration and validation data. We address this shortcoming by the use
of satellite-borne synthetic aperture radar imagery to map a flood on a
15-km reach of the river Thames, England. A finite element numerical
model of shallow water flow is constructed over the reach, and analysis
shows that floodplain friction is the dominant factor affecting
inundation extent when compared to channel friction, turbulence
parameterization, and downstream boundary conditions. The area of the
domain correctly classified by the model is maximized with the
constraint of uniform floodplain friction (77% at maximum), compared
with 70% for a simple planar model of the water free surface. A simple
distributed calibration scheme is also tested, with a minor improvement
over the uniform parameterization.
The GRACE satellite mission, scheduled for launch in 2001, is designed to map out the Earth's gravity field to high accuracy every 2-4 weeks over a nominal lifetime of 5 years. Changes in the gravity field are caused by the redistribution of mass within the Earth and on or above its surface. GRACE will thus be able to constrain processes that involve mass redistribution. In this paper we use output from hydrological, oceanographic, and atmospheric models to estimate the variability in the gravity field (i.e., in the geoid) due to those sources. We develop a method for constructing surface mass estimates from the GRACE gravity coefficients. We show the results of simulations, where we use synthetic GRACE gravity data, constructed by combining estimated geophysical signals and simulated GRACE measurement errors, to attempt to recover hydrological and oceanographic signals. We show that GRACE may be able to recover changes in continental water storage and in seafloor pressure, at scales of a few hundred kilometers and larger and at timescales of a few weeks and longer, with accuracies approaching 2 mm in water thickness over land, and 0.1 mbar or better in seafloor pressure.
Remote sensing and long-term monitoring of closed and climatically
sensitive open lakes can provide useful information for the study of
climatic change. Satellite radar altimetry offers the advantages of
day/night and all-weather capability in the production of relative lake
level changes on a global scale. A simple technique which derives
relative lake level changes is described with specific relevance to the
TOPEX/POSEIDON geophysical data record data set. An assessment of the
coverage and global tracking performance of both the NASA radar
altimeter and the solid state altimeter over these lakes is discussed,
and results are presented for the first 1.75 years of the mission. Lake
level time series were acquired for 12 closed lakes, six open lakes, and
three reservoirs, providing information in many cases where ground gauge
data are unobtainable or the lake is inaccessible. The results, accurate
to ˜4 cm rms, mark the beginning of a very accurate lake level
data set, showing that TOPEX/POSEIDON can successfully contribute to the
long-term global program.
This paper presents an application of the TOPEX/Poseidon (T/P) satellite altimetry data to estimate river discharge at three sites along the Amazon River. We discuss the methodology to establish empirical relationships between satellite-derived water levels and daily estimations of river discharges based on rating curves and in situ level measurements at gauging stations. Three sites are chosen: Manacapuru (River Solimões), Jatuarana (nearby the confluence of the Solimões and Rio Negro rivers) and Óbidos (Amazon River). We then reconstruct the satellite-based river discharge over a 10-year time span (1992–2002). Comparison between satellite-derived and river discharge at the gauging stations shows that the T/P data can successfully be used for hydrological studies of large rivers, in providing in particular discharge estimates when in situ data are not available. To cite this article: E.A. Zakharova et al., C. R. Geoscience 338 (2006).
Numerous studies have demonstrated the potential usefulness of river hydraulic data obtained from satellites in developing general approaches to tracking floods and changes in river discharge from space. Few studies, however, have attempted to estimate the magnitude of discharge in rivers entirely from remotely obtained information. The present study uses multiple-regression analyses of hydraulic data from more than 1000 discharge measurements, ranging in magnitude from over 200,000 to less than 1 m3/s, to develop multi-variate river discharge estimating equations that use various combinations of potentially observable variables to estimate river discharge. Uncertainty analysis indicates that existing satellite-based sensors can measure water-surface width (or surface area), water-surface elevation, and potentially the surface velocity of rivers with accuracies sufficient to provide estimates of discharge with average uncertainty of less than 20%. Development and validation of multi-variate rating equations that are applicable to the full range of rivers that can be observed from satellite sensors, development of techniques to accurately estimate the average depth in rivers from stage measurements, and development of techniques to accurately estimate the average velocity in rivers from surface-velocity measurements will be key to successful prediction of discharge from satellite observations.
Community based efforts are underway to propose a satellite mission for measuring surface water hydraulics. Such a mission is essential for advancing our hydrologic view from 1D point-based gauge measurements of discharge to 2D measurements of surface water that capture the complex spatial dynamics of flow (e.g., measurements of elevations, h, and their change, dh/dt and dh/dx). Examples are essential for demonstrating the utility of such measurements for improving our understanding of the mass-balances in the global water cycle and of the dynamics of complex floodplain hydraulics. Interferometric SAR yields high-spatial resolution images of dh/dt, albeit with a dt greater than desired and the method only permits measurements from inundated vegetation (it fails for open-water). Nevertheless, new observations from interferometric JERS-1 data during inundation over the central Amazon floodplain show dh/dt changes marking flow corridors of several kilometers width that are particularly apparent for the Cabaliana region. Many floodplain channels are conspicuous because dh/dt changes are not equivalent on both sides suggesting that flow delivery and decantation to surrounding floodplain areas is uneven. In fact, for some floodplain channels, dh/dt is greatest on the upstream side of the adjacent main channel (e.g., the Solimoes, Purus Rivers) during inundation but at peak stage, water delivery from the Amazon dominates resulting in a greater dh/dt on the downstream side of these floodplain channels. During early rising water, inundation appears first as a patchwork bordered by small floodplain channels, whereas at mainstem peak stage, floodplain flow appears to sub-parallel the mainstem. Essentially, the hydraulics of Amazon floodplain inundation are a complex interaction of local geomorphology and water stage. These views are only apparent from a spaceborne platform, yet are key for improving our understanding of floods and their transported biogeochemical and sediment constituents.
For certain major rivers and wetlands, hydrological information can often be difficult to obtain due to the inaccessibility of the region, the sparse distribution of gauge stations, or the slow dissemination of data. Satellite radar altimeters have the potential to monitor height variations over inland waters. Here, it is shown that the NASA radar altimeter (NRA), currently operating on board the TOPEX/POSEIDON satellite, can successfully track both large wetlands and rivers of >1 km width. The coverage, performance, and limitations of the NRA altimeter are discussed with relevance to these regions, and the merits of utilizing the geophysical data records (GDRs) and the sensor data records (SDRs) are explored. Time series of relative water level variations, for the first ~3.7 years of the mission, have been obtained for a selection of the world's largest rivers, and for major wetlands of international importance. Validation shows the results can be accurate to ~11 cm rms, offering the potential to observe these regions as part of a long-term hydrological monitoring program.
Peak discharge data from catchments in the central Appalachian region of eastern United States suggest that the coefficient of variation of annual flood peaks, CV[Q], is not constant, as implied by the index flood method but varies with catchment size in a complex manner [Smith, 1992]. Gupta et al. [1994] have interpreted the data as indicating that for catchments smaller than a critical threshold size, CV[Q] increases with increasing catchment size, while for larger catchments CV[Q] decreases with catchment size. Our analysis of the same discharge data suggests further that the spatial heterogeneity of these flood frequency characteristics, for example, mean annual flood,E[Q], and coefficient of variation, CV[Q], between catchments in the region is also not constant but varies systematically with catchment size. The spatial heterogeneity of E[Q] appears to decrease with catchment size, while heterogeneity of CV[Q] appears to mirror the observed scaling behavior of CV[Q]. These observations have been made based on statistical analysis of empirical flood data without being underpinned by a physical theory to explain them. In this paper, motivated by these observations and by the need for a physical theory of regional flood frequency, we develop a simple derived flood frequency model. On the basis of insights provided by the model, we relate the increase of CV[Q] with catchment size for small catchments (smaller than a threshold size) to the scaling behavior of the ratio of storm duration to catchment response time. On the other hand, we connect the decrease of CV[Q] with catchment size for larger catchments to the spatial scaling of rainfall (excess) intensity. Our simple model also permits us to separate the relative contributions of catchment routing response and rainfall intensity to the scaling behavior of E[Q]. Our results also lead us to hypothesize that the heterogeneity ofCV[Q] between catchments is primarily due to the heterogeneity of catchment response time, while the heterogeneity of E[Q] is primarily due to the heterogeneity of runoff generation processes.
River discharge as well as lake and wetland storage of water are critical terms in the surface water balance, yet they are poorly observed globally and the prospects for improvement from in-situ networks are bleak [e.g., Shiklomanov et al., 2002; IAHS, 2001; Stokstad, 1999]. Indeed, given our basic need for fresh water, perhaps the most important hydrologic observations that can be made in a basin are of the temporal and spatial variations in discharge. Gauges measuring discharge rely on flow converging from the upstream catchment to a singular in-channel cross-section. This approach has successfully monitored many of the world's densely inhabited and typically heavily engineered basins for well over a century. However, much of the globally significant discharge occurs in sparsely gauged basins, many with vast wetlands that lack flow convergence (e.g., Figures 1 and 2), thus leading to poorly defined values of runoff at local, regional, and continental scales. The Surface Water Working Group is funded by NASA's Terrestrial Hydrology Program and is an outgrowth of a mission planning process summarized in a July 1999 white paper [Vörösmarty et al., 1999]. Based on the white paper and discussions at working group meetings over the last 2 years, we are focused on the following critical hydrologic questions. (1) What are the observational and data assimilation requirements for measuring surface storage and river discharge that will allow us to understand the dynamics of the land surface branch of the global hydrologic cycle, and in particular, to predict the consequences of global change on water resources? (2) What are the roles of wetlands, lakes, and rivers as regulators of biogeochemical cycles (e.g., carbon and nutrients) and in creating or ameliorating water-related hazards of relevance to society?
Variations in terrestrial water storage affect weather, climate, geophysical phenomena, and life on land, yet observation and understanding of terrestrial water storage are deficient. However, estimates of terrestrial water storage changes soon may be derived from observations of Earth's time-dependent gravity field made by NASA's Gravity Recovery and Climate Experiment (GRACE). Previous studies have evaluated that concept using modeled soil moisture and snow data. This investigation builds upon their results by relying on observations rather than modeled results, by analyzing groundwater and surface water variations as well as snow and soil water variations, and by using a longer time series. Expected uncertainty in GRACE-derived water storage changes are compared to monthly, seasonal, and annual terrestrial water storage changes estimated from observations in Illinois (145,800 km2). Assuming those changes are representative of larger regions, detectability is possible given a 200,000 km2 or larger area. Changes in soil moisture are typically the largest component of terrestrial water storage variations, followed by changes in groundwater plus intermediate zone storage.
Continental water storage is a key variable in the Earth system that has never been adequately monitored globally. Since variations in water storage on land affect the time dependent component of Earth's gravity field, the NASA Gravity Recovery and Climate Experiment (GRACE) satellite mission, which will accurately map the gravity field at 2-4 week intervals, may soon provide global data on temporal changes in continental water storage. This study characterizes water storage changes in 20 drainage basins ranging in size from 130,000 to 5,782,000 km2 and uses estimates of uncertainty in the GRACE technique to determine in which basins water storage changes may be detectable by GRACE and how this detectability may vary in space and time. Results indicate that GRACE will likely detect changes in water storage in most of the basins on monthly or longer time steps and that instrument errors, atmospheric modeling errors, and the magnitude of the variations themselves will be the primary controls on the relative accuracy of the GRACE-derived estimates.