Introduction
The accurate representation of hydrologic processes in mathematical
models remains a key challenge in water resources research and
applications (Baroni et al., 2019; Clark et al., 2015; Kirchner, 2006;
Nearing et al., 2016; Semenova & Beven, 2015) due to uncertainties in
model structure (Wagener et al., 2003), parameterization (Gharari et
al., 2014; Shafii & Tolson, 2015), and observations (Di Baldassarre &
Montanari, 2009). These uncertainties might lead to inaccurate
predictions of hydrological variables for water resources and natural
hazards management (Grimaldi et al., 2019; Montanari & Koutsoyiannis,
2014), and for quantification of impacts of climate change and
anthropogenic effects on the water cycle (Haddeland et al., 2006;
Teutschbein & Seibert, 2012; C. Y. Xu et al., 2005). This problem has
led for instance to initiatives to better constrain the terrestrial
water budget by fusing models and Earth Observation datasets (M. Pan &
Wood, 2006; Pellet et al., 2019).
Traditionally, hydrological models are calibrated against gauged
streamflow data, which might hamper predictions in ungauged sites, since
it does not guarantee an accurate representation of other water cycle
components (e.g., soil moisture and evapotranspiration), thus leading to
uncertainty in hydrologic predictions (Hrachowitz et al., 2013).
Moreover, many parameter sets can provide equally acceptable
performances for streamflow evaluation (i.e., the equifinality thesis),
but they might be “right for the wrong reasons” (Beven, 2006;
Kirchner, 2006). Several solutions have been proposed to improve process
representation and reduce uncertainty in model predictions, such as the
generalized likelihood uncertainty estimation (Beven & Binley, 1992),
dynamic identifiability analysis (Wagener et al., 2003), multiscale
parameter regionalization (Samaniego et al., 2010), and multi-objective
calibration (Yapo et al., 1998). However, these are ongoing
developments, and stand out as one of the twenty-three unsolved problems
in hydrology (Blöschl et al., 2019): “how can we disentangle and reduce
model structural/parameter/input uncertainty in hydrological
prediction?”.
In addition to the presented solutions, an alternative is the use of
complementary datasets besides streamflow observations for model
calibration (e.g., Crow et al., 2003; Franks et al., 1998; Lo et al.,
2010; López et al., 2017; Rajib et al., 2016), data assimilation (e.g.,
Brêda et al., 2019; Houser et al., 1998; Mitchell et al., 2004; Paiva et
al., 2013; Pathiraja et al., 2016; Reichle et al., 2002; Vrugt et al.,
2005), or validation (e.g., Alkama et al., 2010; Motovilov et al., 1999;
Neal et al., 2012; Siqueira et al., 2018). Such approaches are promising
to improve representation of processes in hydrological models (Clark et
al., 2015), reduce uncertainty in hydrological predictions (Gharari et
al., 2014), understand equifinality (Beven, 2006), and perform
predictions in ungauged or poorly-gauged sites (Sivapalan et al., 2003).
However, distributed data of complementary hydrological variables (e.g.,
evapotranspiration, soil moisture) are scarce, and in-situ measurements
present poor spatial and temporal representativeness.
In this context, remote sensing (RS) observations have stood out in the
last decade because of their increasing spatial and temporal
resolutions, free availability in many cases, and capability to record
less monitored hydrological variables such as soil moisture,
evapotranspiration, and terrestrial water storage (Lettenmaier et al.,
2015). For instance, GRACE mission provided monthly estimates of changes
in water storage on a global coverage with an accuracy of 2 cm when
uniformly estimated over land and oceans (Tapley et al., 2004). Missions
such as SMOS, SMAP, AMSR-E and ASCAT were estimated to provide soil
moisture data with a median RMSE of 0.06-0.10 m³/m³ for the CONUS
(Karthikeyan et al., 2017). Altimeters such as Envisat, Jason-2 and
ICESat-1 and ICESat-2 can yield water level data with an accuracy
ranging from 0.04 m to 0.42 m, involving trade-offs between temporal
resolution from 10 to 91 days, and cross-track separation from 15 to 315
km (Jarihani et al., 2013), while the future SWOT mission will provide
at least one water level measurement every 21 days for global rivers
wider than 100 m (Biancamaria et al., 2016).
Although previous studies have analyzed the value of integrating RS data
into hydrological modeling through calibration or data assimilation (see
review by Xu et al., 2014 and Jiang & Wang, 2019), this topic has not
been fully explored to its potential yet. Therefore, in section 1.1, we
present major knowledge gaps in the context of RS-based calibration of
hydrological models through an extensive literature review. In section
1.2, we describe the aims and contributions of this study.
Literature review on calibration of hydrological models with
RS
data
A comprehensive, yet non-exhaustive literature review of studies that
used RS datasets for parameter estimation in hydrological models is
presented in this section and summarized in Figure 1. A total of 62
research articles was found (Supporting Information Table S1). Most
publications involved large study areas (> 1000 km²), which
is expected because of the usual coarse resolution of RS products. Most
studies used RS-derived evapotranspiration for model calibration,
followed by soil moisture (Figure 1b), but there were also attempts for
calibration of up to eight different RS-derived variables (Nijzink et
al., 2018). This indicates a still existent knowledge gap regarding
which RS-derived variables are more useful for model calibration.
Indeed, many recent studies have investigated the added value of
RS-derived information to calibrate hydrological models (Figure 1d;
Table S1).
Most of the studies (69.35%) used only one RS product for model
calibration (Figure 1e, in black), while twelve studies (19.35%) used
two products, and five (8.06%) used three products. Only few studies
used more than three RS products for model calibration (Demirel et al.,
2019; Nijzink et al., 2018). Some studies addressed the use of RS data
to estimate discharge in ungauged basins (Kittel et al., 2018; Sun et
al., 2010), while others focused on narrowing the parameter search
space, and thus equifinality reduction, by combining multiple variables
for calibration (e.g., Nijzink et al., 2018; Pan et al., 2018). This is
confirmed by Figure 1e (in blue), which demonstrates that the vast
majority of researches used two variables for calibration (in general,
discharge and a RS-derived variable). Within these studies, some
analyzed model performance in terms of discharge only, while others
considered different variables (Figure 1e, in red), providing a more
comprehensive discussion on inconsistencies of hydrological models
(e.g., Koch et al., 2018; Li et al., 2018).
Regarding how RS is incorporated into the model calibration procedure
(Figure 1h), 65.6% of the articles used RS-based spatially distributed
information, thus calibrating the model with distributed objective
functions (e.g., pixel-by-pixel or by sub-basin). Within these studies,
bias-insensitive functions have been recently introduced (e.g., Koch et
al., 2018; Demirel et al., 2018; Zink et al., 2018; Dembele et al.,
2020), being important for reducing the impact of RS data uncertainty on
the parameter estimation procedure. The remaining publications (34.4%)
incorporated RS data as an average for the whole basin.
Finally, there is still a need for more studies in tropical regions
(especially South America) (Figure 1c), which have particular
hydro-climatic characteristics, and so have different requirements than
temperate regions on model process representation (e.g., snow-related
processes might not be so relevant in some tropical areas, whereas an
accurate representation of floodplains might be). In the case of basin
with complex river-floodplain interactions as in the Amazon, an accurate
flood wave routing method is required to correctly depict the water
transport along the drainage network. Our analysis shows that most
studies used simple flood wave routing schemes such as kinematic wave or
Muskingum (Figure 1g). Only 10.4% attempted to couple hydrologic and
river hydrodynamic models, highlighting the necessity of better
understanding the applicability of RS-based calibration in basins with
major flat regions with wetlands (Hodges, 2013; Neal et al., 2012;
Pontes et al., 2017).