Global monitoring of large reservoir storage from satellite remote sensing

We studied 34 global reservoirs for which good quality surface elevation data could be obtained from a combination of five satellite altimeters for the period from 1992 to 2010. For each of these reservoirs, we used an unsupervised classification approach using the Moderate Resolution Imaging Spectroradiometer (MODIS) 16‐day 250 m vegetation product to estimate the surface water areas over the MODIS period of record (2000 to 2010). We then derived elevation‐area relationships for each of the reservoirs by combining the MODIS‐based estimates with satellite altimeter‐based estimates of reservoir water elevations. Through a combination of direct observations of elevation and surface area along with documented reservoir configurations at capacity, we estimated storage time histories for each reservoir from 1992 to 2010. We evaluated these satellite‐based data products in comparison with gauge observations for the five largest reservoirs in the United States (Lakes Mead, Powell, Sakakawea, Oahe, and Fort Peck Reservoir). The storage estimates were highly correlated with observations (R = 0.92 to 0.99), with values for the normalized root mean square error (NRMSE) ranging from 3% to 15%. The storage mean absolute error (expressed as a percentage of reservoir capacity) for the reservoirs in this study was 4%. The multidecadal reconstructed reservoir storage variations are in accordance with known droughts and high flow periods on each of the five continents represented in the data set.

[1]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[2]  Michael R. Anderberg,et al.  Cluster Analysis for Applications , 1973 .

[3]  Shokri Z. Selim,et al.  K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  J. Bezdek,et al.  c-means clustering with the l/sub l/ and l/sub infinity / norms , 1991 .

[5]  Hamdani,et al.  Freshwater , 2019, Global Environment Outlook – GEO-6: Healthy Planet, Healthy People.

[6]  C. Birkett,et al.  Radar altimetry: A new concept in monitoring lake level changes , 1994 .

[7]  C. Birkett,et al.  The contribution of TOPEX/POSEIDON to the global monitoring of climatically sensitive lakes , 1995 .

[8]  Laurence C. Smith,et al.  Control on sediment and organic carbon delivery to the Arctic Ocean revealed with space-borne synthetic aperture radar: Ob' River, Siberia , 1998 .

[9]  C. Birkett,et al.  Synergistic Remote Sensing of Lake Chad: Variability of Basin Inundation , 2000 .

[10]  John M. Melack,et al.  Water level changes in a large Amazon lake measured with spaceborne radar interferometry and altimetry , 2001 .

[11]  Anny Cazenave,et al.  Interannual lake level fluctuations (1993–1999) in Africa from Topex/Poseidon: connections with ocean–atmosphere interactions over the Indian Ocean , 2002 .

[12]  Petra Döll,et al.  Global modeling of irrigation water requirements , 2002 .

[13]  T. Schmugge,et al.  Remote sensing in hydrology , 2002 .

[14]  P. Döll,et al.  Development and validation of a global database of lakes, reservoirs and wetlands , 2004 .

[15]  D. Lettenmaier,et al.  Measuring surface water from space , 2004 .

[16]  Jean-François Crétaux,et al.  Modern hydro‐biological state of the Small Aral sea , 2005 .

[17]  P. Berry,et al.  Global inland water monitoring from multi‐mission altimetry , 2005 .

[18]  C. Revenga,et al.  Fragmentation and Flow Regulation of the World's Large River Systems , 2005, Science.

[19]  R. Scholes,et al.  Ecosystems and human well-being: current state and trends , 2005 .

[20]  Jean-François Crétaux,et al.  Evolution of Sea Level of the Big Aral Sea from Satellite Altimetry and Its Implications for Water Balance , 2005 .

[21]  D. Lettenmaier,et al.  Anthropogenic impacts on continental surface water fluxes , 2006 .

[22]  Changsheng Li,et al.  Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images , 2006 .

[23]  A. Senzanje,et al.  Estimation of small reservoir storage capacities in Limpopo River Basin using geographical information systems (GIS) and remotely sensed surface areas: Case of Mzingwane catchment , 2006 .

[24]  Seiji Hayashi,et al.  Measuring Water Storage Fluctuations in Lake Dongting, China, by Topex/Poseidon Satellite Altimetry , 2006, Environmental monitoring and assessment.

[25]  Naota Hanasaki,et al.  A reservoir operation scheme for global river routing models , 2006 .

[26]  T. Bernauer,et al.  Estimating the performance of international regulatory regimes: Methodology and empirical application to international water management in the Naryn/Syr Darya basin , 2007 .

[27]  W. Lucht,et al.  Agricultural green and blue water consumption and its influence on the global water system , 2008 .

[28]  B. Chao,et al.  Impact of Artificial Reservoir Water Impoundment on Global Sea Level , 2008, Science.

[29]  M. Friedl,et al.  Using MODIS data to characterize seasonal inundation patterns in the Florida Everglades , 2008 .

[30]  Mingsheng Liao,et al.  Using MODIS images to examine the surface extents and variations derived from the DEM and laser altimeter data in the Danjiangkou Reservoir, China , 2008 .

[31]  Ahmed M. Youssef,et al.  Rise and demise of the New Lakes of Sahara , 2008 .

[32]  Andreas Schumann,et al.  Global irrigation water demand: Variability and uncertainties arising from agricultural and climate data sets , 2008 .

[33]  John R. Townshend,et al.  A new global raster water mask at 250 m resolution , 2009, Int. J. Digit. Earth.

[34]  Dennis P. Lettenmaier,et al.  Land waters and sea level , 2009 .

[35]  Petra Döll,et al.  Global-scale analysis of river flow alterations due to water withdrawals and reservoirs , 2009 .

[36]  Bin Zhao,et al.  Detecting the spatiotemporal changes of tidal flood in the estuarine wetland by using MODIS time series data. , 2010 .

[37]  Michael Durand,et al.  Please Scroll down for Article International Journal of Remote Sensing Characterization of Surface Water Storage Changes in Arctic Lakes Using Simulated Swot Measurements Characterization of Surface Water Storage Changes in Arctic Lakes Using Simulated Swot Measurements , 2022 .

[38]  Michael Durand,et al.  Preliminary Characterization of SWOT Hydrology Error Budget and Global Capabilities , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[39]  Célia M. Gouveia,et al.  The intense 2007-2009 drought in the Fertile Crescent: Impacts and associated atmospheric circulation , 2010 .

[40]  A. Islam,et al.  Flood inundation map of Bangladesh using MODIS time‐series images , 2010 .

[41]  Michael Durand,et al.  The Surface Water and Ocean Topography Mission: Observing Terrestrial Surface Water and Oceanic Submesoscale Eddies , 2010, Proceedings of the IEEE.

[42]  B. D. Beckley,et al.  Investigating the Performance of the Jason-2/OSTM Radar Altimeter over Lakes and Reservoirs , 2010 .

[43]  A. Cazenave,et al.  SOLS: A lake database to monitor in the Near Real Time water level and storage variations from remote sensing data , 2011 .

[44]  P. Döll,et al.  High‐resolution mapping of the world's reservoirs and dams for sustainable river‐flow management , 2011 .

[45]  F. Ludwig,et al.  Impact of reservoirs on river discharge and irrigation water supply during the 20th century , 2011 .

[46]  A. Physique Water Resources Research , 2011 .

[47]  Kenton W. Ross,et al.  Verification and Validation of NASA-Supported Enhancements to Pecad's Decision Support Tools , 2013 .