Wetlands are widely recognized as vital components of watersheds that provide various hydrological and ecological functions and valuable services for the society, such as water storage, groundwater recharge, water quality improvement, carbon storage, and biodiversity conservation [1
]. In the Amazon basin, wetlands cover 800,000 km2
, or 14% of the lowland basin [3
]. The flooded area nearly doubles between the low and high water periods, at Óbidos (1.93°S, 55.5°W) the most downstream gauge, in response to the pronounced discharge seasonality of the Amazon River. Water storage clearly contributes to the flood wave attenuation and delay in its propagation [4
]. In comparing two regional approaches for channel hydrodynamic modeling, including or omitting floodplain storage, these authors found a difference in travel time of approximately 1 month at the scale of the Solimões and Amazon basin. When flood waters travel across the floodplain, a significant part of the main channel sediment load is trapped [6
]. Floodplains also significantly alter the fate of carbon and nutrients in the basin [9
]. They are considered as hot spots of biodiversity [11
], these characteristics being mainly interpreted in the framework of the flood pulse concept [12
Floodplains mainly receive regional water through channelized and diffusive overbank flow from the main stem. Water in the floodplains also comes from local runoff, groundwater seepage and direct rainfall, whose contributions vary seasonally and inter-annually [13
]. The seasonal contribution and the mixture of these different water sources could be a key driving factor of biochemical and ecological processes in floodplains [16
]. Because of their hydrological connectivity, these systems are threatened by both local and distant anthropic pressures, particularly through regional and local deforestation and dam construction [18
]. Thus far, the possible impacts of climatic trends and anthropic pressure on floodplain hydrology and ecology have not clearly been stated. Gloor et al.
] recently showed that the Amazon basin has presented wetter climatic conditions since 1990.
Considering the strong interactions between water circulation, biogeochemical and ecological processes, it is important to quantify the river-floodplain fluxes exchanged and the water circulation patterns within the floodplains. Hydrodynamics models, such as LISFLOOD-FP, originally developed by Bates and De Roo [20
] and progressively improved [21
], are attractive in this context. This model (or an adapted version) has been applied in the Amazon, from regional (thousands of km) [5
] to medium (hundreds of km) spatial scales [25
]. However, relatively high quality topographic data are obviously required to produce consistent results. Indeed, hydrodynamics features in floodplains are in part controlled by small topographic features [28
], such as narrow channels that drive floodplain connectivity at low and medium water levels [27
To date, the best topographic dataset readily available for the Amazon is from the Shuttle Radar Topographic Mission (SRTM), described in Farr et al.
]. Nevertheless, the SRTM includes various types of errors, the dominant sources of which are summarized in Rodriguez et al.
]. These errors, gathered hereafter under the term of interferometric bias, make the data inappropriate for use as they stand in hydrodynamics models. In moderate topography and low vegetation regions, the residual motion error of the interferometric baseline mainly contributes to the interferometric bias in a significant source of absolute height errors. The baseline rolls error results in a residual long-wavelength ±2 m error with peak values of ±10 m [31
]. In addition, as in most remote-sensed digital elevation models (DEM), land cover impacts the vertical accuracy of the SRTM: the denser the vegetation, the more important the elevation offset is. This effect is due to the inability of C-band radar (5.6 cm) to fully reach the bare earth in the presence of a forest canopy. The capacity of SRTM to detect bare earth is a function of vegetation characteristics such as tree height, density, branching angle, soil moisture, and wood moisture [32
]. In the Amazon lowland basin, Carabajal and Hardling [34
] estimated the height error to be 22.4 m and the SRTM elevation to be 40% of the distance from the canopy top to the ground. The reported error value in height estimation may prevent accurate estimation of the water exchange between the main stream and its floodplains. It is higher than the flood wave amplitude tide in most of water level gauges along the Solimões/Amazon River (approximately 12 m in Manacapuru (3.317°S, 60.583°W), for example).
Interferometric bias might be estimated from ground control points (GCPs), as explained in Rodriguez et al.
]. However, no systematic method has been developed to remove the positive vegetation-related offset. At the regional scale, the simplest method consists of removing a uniform vegetation bias independently of vegetation type. For example, in the view of hydrological modeling at the Amazonian Basin scale, Coe et al.
] used a value of 23 m, whereas Paiva et al.
] assumed 17 m in forested areas. Such a methodology, however, assumes a uniform spatial error, which is unlikely and can lead to inconsistent elevations in floodplain regions where spatial variations in vegetation heights occur naturally [37
]. This procedure could result in certain areas of the SRTM being over or under-elevated, as shown in Paiva et al.
]. An alternative approach consists of using a vegetation height map and subtracting a percentage of the vegetation height from the SRTM. From this perspective, Wilson et al.
] made vegetation height field measurements in different representative land cover classes and, using the wetland cover class proposed by Hess et al.
], built a vegetation map of their study area consisting of a 285 km-long floodplain segment. Comparing in situ
measurements made at the edge of deforested areas and SRTM elevations along the same ground profile, they found that the percentage of penetration of the radar signal within the canopy was 50%. Combining these two sources of information allowed them to produce a corrected DEM. Based on vegetation height field data, however, this method was difficult to extrapolate to other areas or larger scales. Going further, and taking advantage of a global remote-sensed vegetation height map produced by Simard et al.
], Baugh et al.
] proposed a sensitivity analysis to assess the influence of the fixed value to remove the vegetation bias from SRTM data for hydrodynamic modeling. They created a set of distributed vegetation offsets by applying a fixed percentage of radar signal penetration to the Simard et al.
] vegetation height map. Then, they generated a set of DEMs by subtracting these sets of distributed offset from the SRTM elevation; the best DEM was selected as the one leading to the most consistent results when comparing the LISFLOOD-FP hydrodynamic model results with independent hydrometric data. Their study suggested that subtracting 50%–60% of the Simard et al.
] vegetation height from the SRTM was most appropriate for the vegetation encountered in the Amazon floodplains. One weak point of this method is the target criterion, which requires basing the DEM assessment upon a hydrodynamic modeling application. As models do not represent reality but only approach it, we should be careful in using models to validate forcing components of the models themselves [40
In this study, we propose a correction methodology for the latest release of the SRTM, the SRTM Global 1 arc-second (SRTMGL1) product, with the aim of making this product available for the hydrological and hydrodynamic modeling of a floodplain located in the Amazon basin. The correction methodology considers interferometric and vegetation offset corrections and aims to keep the DEM coherent with hydrologic knowledge acquired during field campaigns, such as canals of communication between the Solimões and the lake. The elevation dataset merges the corrected SRTMGL1 elevations and in situ
bathymetric data and is interpolated by the ANUDEM v5.3 algorithm [41
], which is forced by a ground truth drainage network. The methodology remains relatively simple, and independent of the model. We compare the improvement of the DEMs generated by each methodology step in terms of vertical accuracy, drainage network quality and flood extent against GCPs derived from altimetry sensors (e.g., Envisat, ICESat) and inundation maps deduced from free available remote-sensed product imagery. For each DEM, we also assess the consequences for the morphological and hydrological characteristics of the floodplain local catchment. This analysis permits investigation of the usefulness of each data source on the DEM performance, evaluating the possibility of using the methodology on a larger scale.
Hydrodynamic models are attractive tools for quantifying river-floodplain water exchanges and for studying water circulation patterns in the floodplain, but they require relatively high quality topography to produce realistic results. To date, the best free readily available topographic information for the lower Amazon basin is the most recent SRTMGL1 release, at 1 arc-second of resolution. However, this dataset still presents inconsistent elevations.
We proposed a method to remove some of the errors related to interferometric and vegetation bias, mobilizing in situ bathymetric data, altimetry data and flooding extent mapped from remote sensing.
The interferometric bias was estimated to be −2.0 m. The mean total offset correction over the study area was 5.9 m, and 7.4 m for pixels located in the upland region. As the SRTM elevation is located approximately 40% of the distance from the canopy top to the ground, the latter value led to a mean canopy height of 12.3 m, which was reasonable considering the proportion of secondary forest in the region.
In a second step, unbiased elevations were interpolated using the ANUDEM v5.3 algorithm. Several DEMs were generated to control for the respective influence of using or not using a GCP dataset or a ground-truth drainage network in the interpolation process. The vegetation correction and the interpolation process made it possible to reduce the DEM roughness by almost 50%. As expected, using GCPs clearly improved the vertical accuracy: the RMSE value decreased from 4.7 m to less than 2 m for all DEMs that included the GCP dataset. The use of GCPs also improved the flooding extent predicted by the DEM, independently of using or not using a drainage network at low water levels. The correction method improved the agreement between the flooding extent derived from the DEMs and from remote sensed products at low and high water levels (+10% and +27%, respectively), whereas accuracy at a medium water level was difficult to evaluate using the Landsat product. At medium and high water levels, the use of a ground truth drainage network as input to the interpolation algorithm permitted the algorithm to achieve relatively reasonable results regarding flooding extent and watershed hydrological characteristics, even with DEMs that did not include GCPs. This result is promising from the perspective of the application of the method, at least for hydrological studies at larger scale as radar altimetry can likely furnish a sufficient GCP dataset to estimate the interferometric bias.
Finally, we investigated the influence of the different DEMs on the numerical retrieval of morphological watershed characteristics and consequently on some hydrological properties. Using the D8 algorithm, the best generated DEM (identified as DEM_COR in this study) according to our criteria led to a 786 km2-wide watershed area, whereas we obtained 795 km2 with SRTMGL1. The minimum flooding extent was 23 km2, representing 3% of the generated DEM watershed, instead of 88 km2 (i.e., 10% of the watershed) for SRTMGL1. The maximum flooding extent derived from the best generated DEM was 391 km2, whereas we obtained 397 km2 for SRTMGL1, thus neglecting error during high waters. In both cases, the result represented 50% of the Janauacá catchment extent. The longest flow path deduced from the generated DEM was 30% greater than when deduced from SRTMGL1. These differences produced an almost double Kirpich concentration time if computed from SRTMGL1 (26 h) or the generated DEM (50 h). In terms of local runoff and assuming a linear runoff formula, the difference in terms of the watershed and flooded area between SRTMGL1 and the generated DEM would lead to an increase of 10% in terms of runoff at low water level and to a reduction of less than 1% at high water level if using the corrected DEM.