Global Surface Water Product Reliability for Amazon Floodplain Hydrology
Résumé
Freshwater wetlands ensure fundamental functions such as flood mitigation,
groundwater recharge, water purification, and nutrient and sediment retention, as well as
supporting high levels of biodiversity. Amazonian floodplains support one of Earth´s largest
reservoirs of biodiversity, yet are increasingly threatened by land cover and land use changes
induced by large-scale agriculture expansion, waterway network development, and
hydroelectric dam construction. These pressures conjointly with climate change may have
dramatic impacts on floodplain biodiversity and endemic plant and animal species. Because
flooding dynamics is an important driver of floodplain biodiversity and productivity,
characterizing and monitoring floodplain hydrology are important to supporting biodiversity
conservation. Several global- or regional-scale wetland or flood extent maps have been
produced. Until recently, these maps were of coarse spatial resolution, inadequate to support
wetlands biodiversity conservation. Recently, based on Landsat imagery, Pekel et al. (2016)
produced a global surface water (GSW) map at 30 m and analyzed changes in
minimum/maximum flood extent and flood duration over the past three decades. However, in
tropical regions, clouds and vegetation may significantly impact the accuracy of surface water
mapping based on optical data. On the other hand, SAR sensors acquire data regardless of
weather conditions and SAR imagery has been widely used over the past decades to monitor
and map wetland inundation and vegetation worldwide, including in the Amazon region.
In this study, we use Sentinel-1 Synthetic Aperture Radar (SAR) time series (12-day
repeat cycle at this latitude, 10 m resolution) to monitor the flood dynamics of a segment of
the Solimões/Amazon river encompassing the Curuai floodplain (eastward) and the Janauacá
floodplain (westward) for the year 2017 (covering 6 S1 tiles). The Curuai floodplain (4000
km2
, including the local watershed) forms a vast complex system of temporally connected
lakes, flooded forest and fringing wetlands along the Amazon river right margin. Several
perennial or intermittent channels of various size link the floodplain lakes system with the
Amazon River. The Janauacá floodplain is a medium size system (786 km2
, including the
local watershed), composed by a lake and associated flooded forest and other wetlands, linked
to the Solimões River by a single channel.
Images from the S1 time series were stacked (around 30 images per tile), a mean
image was calculated and a thresholding classification was applied on the basis of optical data
(Sentinel 2). In this mean classification, areas always flooded will appear in black, areas never
flooded in light grey and areas occasionally flooded in shades of grey on flood duration. For
each date of the stack, the same thresholding classification is performed and compared to the
mean classification in order to produce 4 land cover classes: open water, potentially flooded
vegetation, low vegetation and forest. Classification results are then refined applying posttreatments: we used the HAND index combined with spatial and temporal rules to avoid
overestimation of water in areas that are not compatible with the hydrodynamics of the area.
We compare our results with the GSW products in terms of maximum water extent and
inundation duration in order to assess the reliability of GSW for large Amazonian floodplains.
Maximum open water extent
Both studies are in good agreement, with an estimated open water maximal extent of
27 000 km2
(our study) and 30 000 km2
(Pekel et al., 2016). Most of the discrepancies are
observed along floodplain and mainstream margins, and differences are greater at Curuai than
at Janauacá. The lengths of the time series used to construct our product and GSW products
are very different. Pekel et al. (2016) used a Landsat chronology over the 32 last years, while
we used only the year 2017. Consequently, the maximum water level recorded at Óbidos
gauge for our study was 760 cm while it was 860 cm over the last 32 years included in Pekel
et al.’s study. As reported in Sippel et al. (1998), flood extent and main stream water level are
directly related. According to their relationship between water level and flood extent, a 1 m
water level variation induces an increase of roughly 11% of the flood extent, comparable with
the expected flood extent increase for the same water level variation in the Curuai floodplain
(Bonnet et al, 2008). Applying this percentage to our results leads to a maximal flood extent
of circa 30 000 km2
, similar to the extension found by Pekel et al. (2016).
Flood duration
Discrepancies between the GSW and Sentinel-based products are larger in the case of
flood duration. These differences are not related to upstream or downstream position but to
lateral flow propagation across the floodplain. Throughout the entire study area, strong
heterogeneities are observed with variations between both results of several months.
At the level of the floodplains, we evidence smaller water residence duration in the
main lake of the Janauacá floodplain (between 0 and 2 months). In Curuai, we observe longer
water residence duration throughout the floodplain (up to 8 months). Part of the discrepancies
might be explained by water level differences between the time series used to build the
product (2014-2015 for GSW vs 2017 in this study). Thanks to the repetitivity of cloud-free
Sentinel 1, we provide a finer quantification of the temporal dynamics of floods in the
floodplains and explain the over-estimation and under-estimation of flood duration in
Janauacá and Curuai respectively by the GSW product. Therefore, compared with the GSW
product, the flood duration dynamics of our product correspond more closely with
hydrodynamic modelling results obtained by Bonnet et al. (2017) for the Janauacá floodplain
and Rudorff et al. (2014) and Bonnet et al. (2008) for the Curuai floodplain.
We conclude that GSW provides realistic maximum open water extents even at local
scale and with accuracies suitable for supporting hydrologic applications, for example model
calibration or validation. On the other hand, GSW should be used cautiously when looking at
flood duration and subsequent hydrological connectivity analysis, which are fundamental
properties to support biodiversity conservation. The Sentinel 1 and Sentinel 2 constellation
should provide improved mapping of flood duration at global scale.