Conclusion
We calibrated and evaluated a hydrological-hydrodynamic model with five
different RS-based observations of the water cycle: water levels
(Jason-2), flood extent (ALOS-PALSAR), TWS (GRACE), vegetation ET
(MOD16), and soil moisture (SMOS), for a study basin in a tropical
region with floodplains (Purus River Basin in the Amazon), and analyzed
the redundancy and complementarity between different variables and
processes.
Results showed that calibration with current RS observations was able to
improve discharge estimates. For instance, in the uncalibrated setup (a
priori parameter sets), average performances for discharge were around
KGE = 0.30. By calibrating the model with ET from MOD16 (and evaluating
for the same time period), discharge average performance was improved to
KGE = 0.64, representing a Skill Score of S = 52.9%. Also in the
calibration period, a joint scheme of calibration with water level +
soil moisture led to discharge improvements of S = 59.9%. When
evaluating for a different time period, discharge performance was
improved by calibration with water level, TWS and a joint scheme of all
RS-variables (S = 25.9%, S = 27.9% and S = 17.4%, respectively). We
conclude that RS observations are useful to predict discharge estimates.
However, the utility of each RS variable might depend on the study area
characteristics and the time period considered.
Our results also showed that RS-based calibration led to an overall
improvement of the water cycle representation. For instance, calibration
with water level was able to improve estimates of water level itself,
but also flood extent, TWS and ET; calibration with soil moisture was
able to improve estimates of soil moisture itself, but also discharge,
flood extent and TWS.
Moreover, calibration with
multiple RS variables was able to highlight deficiencies that might be
related to model structure, parameterization, and observations. In the
context of model structure, for
instance, calibration with ET highlighted the model inability to
represent the root water intake in dry season in this region, thus
compensating it by misrepresenting other variables. In the context of
model parameterization, for
instance, we found a wide range of different parameters by varying the
calibration target variable.
Besides individual calibration with each RS variable, we conducted two
multi-variable calibration experiments: calibration with all variables
except discharge, and calibration with water level and soil moisture.
Calibration with all variables was useful to some extent, but
appropriately selecting complementary variables for model calibration
may result in a better overall performance. Even though we used a lumped
calibration approach, results highlighted the overall model capability
to retrieve ET spatial pattern, but not for TWS and soil moisture.
The main conclusions presented here are of great interest for the
hydrological community, and agree with previous works in that RS–based
calibration is useful to improve the water cycle representation in
hydrological models. To further investigate the potentiality of RS data,
future developments should test the methodology presented here for
multiple basins at contrasting
hydro-climatic regions. Here, we
assessed an Amazonian Equatorial basin, with particular climate and land
cover characteristics and an overall spatial homogeneity of
rainfall-runoff processes. Other basins with different hydroclimatic
regimes could be also assessed, e.g., in arid basins subject to long dry
periods, more erratic precipitation patterns, and different runoff
generation mechanisms than the Amazon, which require different model
structures.
Finally, here we used one state-of-the-art RS product for each variable,
but future developments should explore to its potential other missions
as SWOT for surface water observation (Biancamaria et al., 2016), as
well as considering different products for representing each variable
(e.g., ET could be estimated by GLEAM, MODIS, SSEBop, SEBS, ALEXI,
METRIC, etc., besides MOD16).