Augusto Getirana

The Hydrological Modeling and Analysis Platform (HyMAP) : evaluation in the Amazon Basin

AbstractRecent advances in global flow routing schemes have shown the importance of using high-resolution topography for representing floodplain inundation dynamics more reliably. This study presents and evaluates the Hydrological Modeling and Analysis Platform (HyMAP), which is a global flow routing scheme specifically designed to bridge the gap between current state-of-the-art global flow routing schemes by combining their main features and introducing new features to better capture floodplain dynamics. The ultimate goals of HyMAP are to provide the scientific community with a novel scheme suited to the assimilation of satellite altimetry data for global water discharge forecasts and a model that can be potentially coupled with atmospheric models. In this first model evaluation, HyMAP is coupled with the Interactions between Soil–Biosphere–Atmosphere (ISBA) land surface model in order to simulate the surface water dynamics in the Amazon basin. The model is evaluated over the 1986–2006 period against an ...

Extreme Water Deficit in Brazil Detected from Space

AbstractExtreme droughts have caused significant socioeconomic and environmental damage worldwide. In Brazil, ineffective energy development and water management policies have magnified the impacts of recent severe droughts, which include massive agricultural losses, water supply restrictions, and energy rationing. Spaceborne remote sensing data advance our understanding of the spatiotemporal variability of large-scale droughts and enhance the detection and monitoring of extreme water-related events. In this study, data derived from the Gravity Recovery and Climate Experiment (GRACE) mission are used to detect and quantify an extended major drought over eastern Brazil and provide estimates of impacted areas and region-specific water deficits. Two structural breakpoint detection methods were applied to time series of GRACE-based terrestrial water storage anomalies (TWSA), determining when two abrupt changes occurred. One, in particular, defines the beginning of the current drought. Using TWSA, a water loss...

Water Balance in the Amazon Basin from a Land Surface Model Ensemble

AbstractDespite recent advances in land surface modeling and remote sensing, estimates of the global water budget are still fairly uncertain. This study aims to evaluate the water budget of the Amazon basin based on several state-of-the-art land surface model (LSM) outputs. Water budget variables (terrestrial water storage TWS, evapotranspiration ET, surface runoff R, and base flow B) are evaluated at the basin scale using both remote sensing and in situ data. Meteorological forcings at a 3-hourly time step and 1° spatial resolution were used to run 14 LSMs. Precipitation datasets that have been rescaled to match monthly Global Precipitation Climatology Project (GPCP) and Global Precipitation Climatology Centre (GPCC) datasets and the daily Hydrologie du Bassin de l’Amazone (HYBAM) dataset were used to perform three experiments. The Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme was forced with R and B and simulated discharges are compared against observations at 165 gauges. Simu...

Assimilation of Gridded GRACE Terrestrial Water Storage Estimates in the North American Land Data Assimilation System

AbstractThe objective of the North American Land Data Assimilation System (NLDAS) is to provide best-available estimates of near-surface meteorological conditions and soil hydrological status for the continental United States. To support the ongoing efforts to develop data assimilation (DA) capabilities for NLDAS, the results of Gravity Recovery and Climate Experiment (GRACE) DA implemented in a manner consistent with NLDAS development are presented. Following previous work, GRACE terrestrial water storage (TWS) anomaly estimates are assimilated into the NASA Catchment land surface model using an ensemble smoother. In contrast to many earlier GRACE DA studies, a gridded GRACE TWS product is assimilated, spatially distributed GRACE error estimates are accounted for, and the impact that GRACE scaling factors have on assimilation is evaluated. Comparisons with quality-controlled in situ observations indicate that GRACE DA has a positive impact on the simulation of unconfined groundwater variability across th...

Evaluating Global Streamflow Simulations by a Physically-based Routing Model Coupled with the Community Land Model

AbstractAccurately simulating hydrological processes such as streamflow is important in land surface modeling because they can influence other land surface processes, such as carbon cycle dynamics, through various interaction pathways. This study aims to evaluate the global application of a recently developed Model for Scale Adaptive River Transport (MOSART) coupled with the Community Land Model, version 4 (CLM4). To support the global implementation of MOSART, a comprehensive global hydrography dataset has been derived at multiple resolutions from different sources. The simulated runoff fields are first evaluated against the composite runoff map from the Global Runoff Data Centre (GRDC). The simulated streamflow is then shown to reproduce reasonably well the observed daily and monthly streamflow at over 1600 of the world’s major river stations in terms of annual, seasonal, and daily flow statistics. The impacts of model structure complexity are evaluated, and results show that the spatial and temporal va...

Surface freshwater storage and variability in the Amazon basin from multi-satellite observations, 1993-2007

The amount of water stored and moving through the surface water bodies of large river basins (river, floodplains, wetlands) plays a major role in the global water and biochemical cycles and is a critical parameter for water resources management. However, the spatiotemporal variations of these freshwater reservoirs are still widely unknown at the global scale. Here, we propose a hypsographic curve approach to estimate surface freshwater storage variations over the Amazon basin combining surface water extent from a multi-satellite-technique with topographic data from the Global Digital Elevation Model (GDEM) from Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Monthly surface water storage variations for 1993-2007 are presented, showing a strong seasonal and interannual variability, and are evaluated against in situ river discharge and precipitation. The basin-scale mean annual amplitude of similar to 1200 km(3) is in the range of previous estimates and contributes to about half of the Gravity Recovery And Climate Experiment (GRACE) total water storage variations. For the first time, we map the surface water volume anomaly during the extreme droughts of 1997 (October-November) and 2005 (September-October) and found that during these dry events the water stored in the river and floodplains of the Amazon basin was, respectively, similar to 230 (similar to 40%) and 210 (similar to 50%) km(3) below the 1993-2007 average. This new 15 year data set of surface water volume represents an unprecedented source of information for future hydrological or climate modeling of the Amazon. It is also a first step toward the development of such database at the global scale.

Quantifying the Added Value of Snow Cover Area Observations in Passive Microwave Snow Depth Data Assimilation

AbstractAccurate determination of snow conditions is important for several water management applications, partly because of the significant influence of snowmelt on seasonal streamflow prediction. This article examines an approach using snow cover area (SCA) observations as snow detection constraints during the assimilation of snow depth retrievals from passive microwave sensors. Two different SCA products [the Interactive Multisensor Snow and Ice Mapping System (IMS) and the Moderate Resolution Imaging Spectroradiometer (MODIS)] are employed jointly with the snow depth retrievals from a variety of sensors for data assimilation in the Noah land surface model. The results indicate that the use of MODIS data is effective in obtaining added improvements (up to 6% improvement in aggregate RMSE) in snow depth fields compared to assimilating passive microwave data alone, whereas the impact of IMS data is small. The improvements in snow depth fields are also found to translate to small yet systematic improvement...

Evaluating LSM-Based Water Budgets over a West African Basin Assisted with a River Routing Scheme

AbstractWithin the framework of the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project phase 2 (ALMIP-2), this study evaluates the water balance simulated by the Interactions between Soil, Biosphere, and Atmosphere (ISBA) over the upper Oueme River basin, in Benin, using a mesoscale river routing scheme (RRS). The RRS is based on the nonlinear Muskingum–Cunge method coupled with two linear reservoirs that simulate the time delay of both surface runoff and base flow that are produced by land surface models. On the basis of the evidence of a deep water-table recharge in that region, a reservoir representing the deep-water infiltration (DWI) is introduced. The hydrological processes of the basin are simulated for the 2005–08 AMMA field campaign period during which rainfall and streamflow data were intensively collected over the study area. Optimal RRS parameter sets were determined for three optimization experiments that were performed using daily streamflow at five ...

Automated Generation of Lakes and Reservoirs Water Elevation Changes From Satellite Radar Altimetry

Limited access to in-situ water level data for lakes and reservoirs have been a major setback for regional and global studies of reservoirs, surface water storage changes, and monitoring the hydrologic cycle. Processing satellite radar altimetry data over inland water bodies on a large scale has been a cumbersome task primarily due to the removal of contaminated measurements as a result of surrounding land. In this study, we proposed a new algorithm to automatically generate time series from raw satellite radar altimetry data without user intervention. With this method, users with a little knowledge on the field can now independently process radar altimetry for diverse applications. The method is based on K-means clustering, interquartile range, and statistical analysis of the dataset for outlier detection. Jason-2 and Envisat radar altimetry data were used to demonstrate the capability of this algorithm. A total of 37 satellite crossings over 30 lakes and reservoirs located in the U.S., Brazil, and Nigeria were used based on the availability of in-situ data. We compared the results against in-situ data and root-mean-square error values ranged from 0.09 to 1.20 m. We also confirmed the potential of this algorithm over rivers and wetlands using the southern Congo River and Everglades wetlands in Florida, respectively. Finally, the different retracking algorithms in Envisat; Ice-1, Ice-2, Ocean, and Sea-Ice were compared using the proposed algorithm. Ice-1 performed best for generating water level time series for in-land water bodies and the result is consistent with previous studies.

Tradeoff 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 an accurate 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 and main tributaries, with maximum RMSE values for streamflow and water level reaching 7827m3.s-1 and 1379cm near the basins outlet. However, KINE 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 3.9-292m3.s-1 for streamflows and 1.1-28.5cm 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.