Automatic parameterization of a flow routing scheme driven by radar altimetry data: Evaluation in the Amazon basin

ISI Document Delivery No.: 129GR Times Cited: 2 Cited Reference Count: 36 Cited References: BAMBER JL, 1994, INT J REMOTE SENS, V15, P925 Birkett CM, 2000, REMOTE SENS ENVIRON, V72, P218, DOI 10.1016/S0034-4257(99)00105-4 Boone A, 1999, J APPL METEOROL, V38, P1611, DOI 10.1175/1520-0450(1999)0382.0.CO;2 Boyle DP, 2000, WATER RESOUR RES, V36, P3663, DOI 10.1029/2000WR900207 Calmant S, 2008, SURV GEOPHYS, V29, P247, DOI 10.1007/s10712-008-9051-1 Chow V. T., 1988, APPL HYDROLOGY Coe MT, 2008, HYDROL PROCESS, V22, P2542, DOI 10.1002/hyp.6850 Cogley J. G., 2003, 20031 TRENT U DEP GE Cretaux JF, 2011, ADV SPACE RES, V47, P1497, DOI 10.1016/j.asr.2011.01.004 Dadson SJ, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2010JD014474 Decharme B, 2012, CLIM DYNAM, V38, P1389, DOI 10.1007/s00382-011-1054-9 Durand M, 2010, IEEE J-STARS, V3, P20, DOI 10.1109/JSTARS.2009.2033453 Getirana A. C. 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I., 2012, HYDROL EARTH SYST SC, V9, P3203, DOI [10.5194/hessd-9-3203-2012, DOI 10.5194/HESSD-9-3203-2012] Noilhan J, 1996, GLOBAL PLANET CHANGE, V13, P145, DOI 10.1016/0921-8181(95)00043-7 Pereira-Cardenal SJ, 2011, HYDROL EARTH SYST SC, V15, P241, DOI 10.5194/hess-15-241-2011 Roux E, 2010, HYDROLOG SCI J, V55, P104, DOI 10.1080/02626660903529023 Sheffield J, 2006, J CLIMATE, V19, P3088, DOI 10.1175/JCLI3790.1 Shuttleworth W. J., 1993, HDB HYDROLOGY, P1 SOROOSHIAN S, 1983, WATER RESOUR RES, V19, P251, DOI 10.1029/WR019i001p00251 Wilson M, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL030156 Yamazaki D, 2011, WATER RESOUR RES, V47, DOI 10.1029/2010WR009726 Yamazaki D, 2009, HYDROL EARTH SYST SC, V13, P2241 Yapo PO, 1998, J HYDROL, V204, P83, DOI 10.1016/S0022-1694(97)00107-8 Getirana, Augusto C. V. Boone, Aaron Yamazaki, Dai Mognard, Nelly Yamazaki, Dai/J-3029-2012 Centre National d'Etudes Spatiales (CNES) The first author thanks the Centre National d'Etudes Spatiales (CNES) for the financial support. The study benefited from data made available by Agencia Nacional de Aguas (ANA) and by the European Space Agency (ESA) under the form of Geophysical Data Records (GDRs). The multimission database of GDRs is maintained by the Centre de Topographie des Oceans et de l'Hydrosphere (CTOH) at LEGOS. The authors also thank G. Cochonneau (IRD) and M. C. Gennero (IRD) for their help in data acquisition and processing and three anonymous reviewers for their valuable comments. 2 AMER GEOPHYSICAL UNION WASHINGTON WATER RESOUR RESThis paper describes and evaluates a procedure that integrates radar altimetry data into the automatic calibration of large-scale flow routing schemes (LFRS). The Hydrological Modeling and Analysis Platform, coupled in off-line mode with the Interactions between Soil, Biosphere, and Atmosphere land surface model, is used to simulate daily surface water dynamics of the Amazon basin at a 0.25 degrees spatial resolution. The Multiobjective Complex Evolution optimization algorithm is used to optimize one parameter (subsurface runoff time delay) and other three parameter multiplier factors (Manning roughness coefficient for rivers, river width, and bankfull height) by minimizing two objective functions for the 2002 to 2006 period. Four calibration experiments are performed by combining water discharge observations and Envisat data to evaluate the potential of using radar altimetry in the automatic calibration of LFRS. One experiment is based on daily discharge observations, other combines discharge with altimetric data, and the other two ones are driven exclusively by radar altimetry data, at 16 or four virtual stations, depending on the experiment. The calibration process is validated against discharge observations at five gauging stations located on the main tributaries. This study shows the feasibility of calibrating LFRS using radar altimetry data. Results demonstrate that reasonable parameters can be obtained by using radar altimetry in an optimization procedure with competitive computational costs. However, there is evidence of equifinality among model parameters. Furthermore, the automatic calibration driven by altimetric data can reliably reproduce discharges time series, and significant improvements are noticed in simulated water level variations. Citation: Getirana, A. C. V., A. Boone, D. Yamazaki, and N. Mognard (2013), Automatic parameterization of a flow routing scheme driven by radar altimetry data: Evaluation in the Amazon basin, Water Resour. Res., 49, doi: 10.1002/wrcr.20077

Trade-off between cost and accuracy in large-scale surface water dynamic modeling

Recent efforts have led to the development of the local inertia formulation (INER) for anaccurate but still cost-efficient representation of surface water dynamics, compared to the widely used kinematic wave equation (KINE). In this study, both formulations are evaluated over the Amazon basin in terms of computational costs and accuracy in simulating streamflows and water levels through synthetic experiments and comparisons against ground-based observations. Varying time steps are considered as part of the evaluation and INER at 60-second time step is adopted as the reference for synthetic experiments. Five hybrid (HYBR) realizations are performed based on maps representing the spatial distribution of the two formulations that physically represent river reach flow dynamics within the domain. Maps have fractions of KINE varying from 35.6% to 82.8%. KINE runs show clear deterioration along the Amazon river andmain tributaries, with maximum RMSE values for streamflow and water level reaching7827m(exp 3).s(exp -1) and 1379 cm near the basins outlet. However, KINE 20 is at least 25%more efficient than INER with low model sensitivity to longer time steps. A significant improvement is achieved with HYBR, resulting in maximum RMSE values of 3.9-292 m(exp 3).s(exp -1) for streamflows and 1.1-28.5 cm for water levels, and cost reduction of 6-16%, depending on the map used. Optimal results using HYBR are obtained when the local inertia formulation is used in about one third of the Amazon basin, reducing computational costs in simulations while preserving accuracy. However, that threshold may vary when applied to different regions, according to their hydrodynamics and geomorphological characteristics

Assimilation of MODIS Snow Cover Fraction Observations into the NASA Catchment Land Surface Model

The NASA Catchment land surface model (CLSM) is the land model component used for the Modern-Era Retrospective Analysis for Research and Applications (MERRA). Here, the CLSM versions of MERRA and MERRA-Land are evaluated using snow cover fraction (SCF) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Moreover, a computationally-efficient empirical scheme is designed to improve CLSM estimates of SCF, snow depth, and snow water equivalent (SWE) through the assimilation of MODIS SCF observations. Results show that data assimilation (DA) improved SCF estimates compared to the open-loop model without assimilation (OL), especially in areas with ephemeral snow cover and mountainous regions. A comparison of the SCF estimates from DA against snow cover estimates from the NOAA Interactive Multisensor Snow and Ice Mapping System showed an improvement in the probability of detection of up to 28% and a reduction in false alarms by up to 6% (relative to OL). A comparison of the model snow depth estimates against Canadian Meteorological Centre analyses showed that DA successfully improved the model seasonal bias from 0.017 m for OL to 0.007 m for DA, although there was no significant change in root-mean-square differences (RMSD) (0.095 m for OL, 0.093 m for DA). The time-average of the spatial correlation coefficient also improved from 0.61 for OL to 0.63 for DA. A comparison against in situ SWE measurements also showed improvements from assimilation. The correlation increased from 0.44 for OL to 0.49 for DA, the bias improved from 0.111 m for OL to 0.100 m for DA, and the RMSD decreased from 0.186 m for OL to 0.180 m for DA

NASAs Seasonal Hydrological Forecast System for Improved Food Insecurity Early Warning in Africa

To develop a seasonal scale drought forecasting system to strengthen FEWS NET's progressive early warning efforts in Africa and the Middle East. This presentation provides an overview of the implementation, validation, and ongoing operational applications of this system

The Land surface Data Toolkit (LDT v7.2) – a data fusion environment for land data assimilation systems

The effective applications of land surface models (LSMs) and hydrologic models pose a varied set of data input and processing needs, ranging from ensuring consistency checks to more derived data processing and analytics. This article describes the development of the Land surface Data Toolkit (LDT), which is an integrated framework designed specifically for processing input data to execute LSMs and hydrological models. LDT not only serves as a preprocessor to the NASA Land Information System (LIS), which is an integrated framework designed for multi-model LSM simulations and data assimilation (DA) integrations, but also as a land-surface-based observation and DA input processor. It offers a variety of user options and inputs to processing datasets for use within LIS and stand-alone models. The LDT design facilitates the use of common data formats and conventions. LDT is also capable of processing LSM initial conditions and meteorological boundary conditions and ensuring data quality for inputs to LSMs and DA routines. The machine learning layer in LDT facilitates the use of modern data science algorithms for developing data-driven predictive models. Through the use of an object-oriented framework design, LDT provides extensible features for the continued development of support for different types of observational datasets and data analytics algorithms to aid land surface modeling and data assimilation.</p

Altimetry for the future: Building on 25 years of progress

In 2018 we celebrated 25 years of development of radar altimetry, and the progress achieved by this methodology in the fields of global and coastal oceanography, hydrology, geodesy and cryospheric sciences. Many symbolic major events have celebrated these developments, e.g., in Venice, Italy, the 15th (2006) and 20th (2012) years of progress and more recently, in 2018, in Ponta Delgada, Portugal, 25 Years of Progress in Radar Altimetry. On this latter occasion it was decided to collect contributions of scientists, engineers and managers involved in the worldwide altimetry community to depict the state of altimetry and propose recommendations for the altimetry of the future. This paper summarizes contributions and recommendations that were collected and provides guidance for future mission design, research activities, and sustainable operational radar altimetry data exploitation. Recommendations provided are fundamental for optimizing further scientific and operational advances of oceanographic observations by altimetry, including requirements for spatial and temporal resolution of altimetric measurements, their accuracy and continuity. There are also new challenges and new openings mentioned in the paper that are particularly crucial for observations at higher latitudes, for coastal oceanography, for cryospheric studies and for hydrology. The paper starts with a general introduction followed by a section on Earth System Science including Ocean Dynamics, Sea Level, the Coastal Ocean, Hydrology, the Cryosphere and Polar Oceans and the ‘‘Green” Ocean, extending the frontier from biogeochemistry to marine ecology. Applications are described in a subsequent section, which covers Operational Oceanography, Weather, Hurricane Wave and Wind Forecasting, Climate projection. Instruments’ development and satellite missions’ evolutions are described in a fourth section. A fifth section covers the key observations that altimeters provide and their potential complements, from other Earth observation measurements to in situ data. Section 6 identifies the data and methods and provides some accuracy and resolution requirements for the wet tropospheric correction, the orbit and other geodetic requirements, the Mean Sea Surface, Geoid and Mean Dynamic Topography, Calibration and Validation, data accuracy, data access and handling (including the DUACS system). Section 7 brings a transversal view on scales, integration, artificial intelligence, and capacity building (education and training). Section 8 reviews the programmatic issues followed by a conclusion

A new automatic calibration approach based on rating curves: first results with ENVISAT altimetric data

ISI Document Delivery No.: BYC88 Times Cited: 1 Cited Reference Count: 10 Cited References: Collischonn W, 2007, HYDROLOG SCI J, V52, P878, DOI 10.1623/hysj.52.5.878 Frappart F, 2006, REMOTE SENS ENVIRON, V100, P252, DOI 10.1016/j.rse.2005.10.027 Getirana ACV, 2010, J HYDROL, V387, P244, DOI 10.1016/j.jhydrol.2010.04.013 Getirana ACV, 2009, HYDROL PROCESS, V23, P502, DOI 10.1002/hyp.7167 Getirana ACV, 2010, HYDROL PROCESS, V24, P3219, DOI 10.1002/hyp.7747 Jaccon G., 1989, Curva-chave: analise e tratado Martinez JM, 2007, REMOTE SENS ENVIRON, V108, P209, DOI 10.1016/j.rse.2006.11.012 Pareto V., 1971, MANUAL POLITICAL EC Roux E, 2010, HYDROLOG SCI J, V55, P104, DOI 10.1080/02626660903529023 Yapo PO, 1998, J HYDROL, V204, P83, DOI 10.1016/S0022-1694(97)00107-8 Getirana, Augusto C. V. Proceedings Paper 25th General Assembly of the International Union of Geodesy and Geophysics JUN 28-JUL 07, 2011 Melbourne, AUSTRALIA Int Commiss Water Resources Systems, Int Commiss Surface Water, Int Commiss Water Qual, Int Assoc Hydrol Sci, UNESCO-IHP Getirana, Augusto/G-4630-2011 INST OF HYDROLOGY, WALLINGFORD OX10 8BB, ENGLANDThis study presents preliminary results obtained with a new procedure for the automatic calibration of hydrological models based exclusively on spatial altimetry data. The technique is based on the minimization of biases between discharges computed by the hydrological model and by stage versus discharge relationships (h x Q model) derived from the combination of spatial altimetry data and modelled discharges at virtual stations. The study area is the Branco River basin, located in the northern Amazon basin. Spatial altimetry data provided by the ENVISAT satellite at our virtual stations are used in the optimization process for the 2002-2006 periods. For altimetry-based cases, the Nash-Sutcliffe (NS) coefficient varied from 0.66 to 0.94, the NS for the logarithms of streamflows from 0.61 to 0.95 and the relative error (RE) from 0.18 to 0.73. The best values for discharge-based cases were 0.94, 0.96 and 0.16, respectively. The results show that the new altimetry-based optimization approach can provide improved solutions and reliably reproduce discharges time series. It also gives results similar to those provided by discharge-based optimization approaches, with competitive computational costs