Reasonable agreements and mismatches between land-surface-water-area
estimates based on a global river model and Landsat data
Abstract
Land surface water is a key component of the global water cycle.
Compared to remote sensing by satellites, both temporal extension and
spatial continuity is superior in modeling of water surface area.
However, overall evaluation of models representing different kinds of
surface waters at the global scale is lacking. We estimated land surface
water area (LSWA) using the Catchment-based Macro-scale Floodplain model
(CaMa-Flood), a global hydrodynamic model, and compared the estimates to
Landsat with 3″ spatial resolution at the global scale. Results show
that the two methodologies show agreement in the general spatial
patterns of LSWA (e.g., major rivers and lakes, open-to-sky
floodplains), but globally consistent mismatches were found under
several land surface conditions. CaMa-Flood underestimates LSWA in high
northern latitudes (e.g., the Canadian Shield) and coastal areas, as the
presence of isolated lakes in local depressions or small coastal rivers
is not considered by the model’s physical assumptions. In contrast,
model-estimated LSWA is larger than Landsat estimates in forest-covered
areas (e.g., Amazon basin) due to the opacity of vegetation for optical
satellite sensing, and in cropland areas due to the lack of dynamic
water processes (e.g., re-infiltration, evaporation, water consumption)
and constraints of water infrastructure (e.g., canals, levees). These
globally consistent differences can be reasonably explained by the
model’s physical assumptions or optical satellite sensing
characteristics, and applying filters (e.g., floodplain topography mask,
forest and cropland mask) to the two datasets allows the remaining
local-scale discrepancies to be attributed to locally varying factors
(e.g., channel parameters, atmospheric forcing).