Introduction
Drought is one of the most frequently occurring environmental disasters,
and both historical observations and future climate projections show
increasing frequency of drought worldwide (Dai, 2013; Feng & Fu, 2013;
Trenberth et al., 2013). Droughts are mostly triggered by a reduction in
seasonal or annual precipitation (Mishra & Singh, 2010). Droughts can
have devastating impacts on regional food production, water resources
management, drinking water supply, and even the stability of governments
(Mishra & Singh, 2010; Dai, 2011; Zhang, Zhang, Cui, & Zeng, 2011).
Although drought usually has dire environmental and socio-economic
consequences, drought prediction is still a grand challenge (Dai, 2011;
Mishra & Singh, 2011; Chiew et al., 2014). Drought involves complex
interactions amongst different dimensions including meteorological
conditions, vegetation water demand, hydrological conditions,etc . (Wang, Basia, & Arie, 2003; Nalbantis & Tsakiris, 2009;
Dai, 2011; Zhang et al., 2011; Buttafuoco, Caloiero, & Coscarelli,
2015) hat can induce shifts in regional hydrological regime or
rainfall-runoff relationship, leading to failures in predicting the
onset, duration, severity, and termination of
drought
(Guardiola-Claramonte et al., 2011; Mishra & Singh, 2011; Zhang et al.,
2011; Chiew et al., 2014; Huijgevoort, Lanen, Teuling, & Uijlenhoet,
2014; Yang et al., 2017).
Determining whether drought can lead to shifts in catchment hydrological
behaviors is critical for future accurate hydrological prediction
(Huijgevoort et al., 2014; Saft, Western, Zhang, Peel, & Potter, 2015).
Previously, many studies have reported that drought can violate the
assumption of stationarity in the catchment rainfall-runoff relationship
(Conway et al., 2004; Guardiola-Claramonte et al., 2011; Cheng et al.,
2012; Hughes, Petrone, & Silberstein, 2012; Chiew et al., 2014). Chiew
et al. (2014) found that the rainfall-runoff relationship during drought
periods was simulated poorly and overestimated significantly (up to
150%) by a hydrological model previously calibrated under normal
period. Petrone, Hughes, Niel, & Silberstein (2010) found a significant
decline in the runoff coefficient and a shift in hydrological regime in
the headwater regions of southwest Western Australia after a long-term
decline in rainfall from the mid-1970s to 2008. Based on the long-term
rainfall-runoff observations of 228 catchments in south-eastern
Australia, Saft et al. (2015) showed that prolonged drought during
1997-2009 led to a statistically significant shift in the
rainfall-runoff relationship in about 46% of the studied catchments.
Although many studies have statistically demonstrated that long-term
drought can lead to shifts in catchment hydrological regimes based on
observations and modelling, there is still great uncertainty in
detecting and predicting whether drought can induce changes in catchment
hydrological behaviors and in understanding why the rainfall-runoff
relationship can change at the process level.
Insights into this challenge can be gained by combining a data
assimilation method with process-based hydrological models. This
approach accounts for hydrological non-stationarity in the
rainfall-runoff relationship for capturing shifts in the flow regime
induced by long-term drought. It also accounts for time-variant
parameters in the hydrological model. Accounting for both factors leads
to identification of possible mechanisms that cause the changes in
catchment hydrological behaviors. Parameters in a process-based
hydrological model represent catchment functional properties, and thus
can be used to detect catchment hydrological behaviors and their changes
(Pathiraja, Marshall, Sharma, & Moradkhani, 2016). Parameters in
hydrological models are traditionally assumed to be stationary
(i.e., time-invariant), and are calibrated against observed
runoff (Coron et al., 2012). There
is an accumulated body of literature showing that hydrological systems
can be non-stationary, and that parameters in hydrological models should
be time-variant. This is because substantial anthropogenic changes of
climate have occurred outside of the historically measured mode of
natural variability, and direct alteration of local water cycles has
occurred as a result of land and water management practices including
deforestation (Destouni, Jaramillo, & Prieto, 2013; Lima et al., 2014;
Cheng et al., 2017; Guimberteau et al., 2017),
groundwater extraction (Kinal &
Stoneman, 2012; Miguez-Macho & Fan, 2012), and damming of rivers for
hydroelectricity (Botter, Basso, Porporato, Rodrigueziturbe, & Rinaldo,
2010; Xue, Liu, & Ge, 2011). Recent studies have recognized that models
with time-variant parameters can reasonably account for shifts in the
catchment rainfall-runoff relationship or catchment behaviors under
changing environments (Merz, Parajka, & Blöschl, 2011; Chiew et al.,
2014; Deng, Liu, Guo, Li, & Wang, 2016). Based on time-variant
parameters obtained by a data assimilation method, not only can changes
in the catchment rainfall-runoff relationship can be detected (Deng et
al., 2016), but also the causes of the changes can be identified from
hydrological parameters (Pathiraja et al., 2016; Xiong et al., 2019).
For example, (Deng et al., 2016) combined a two-parameter monthly water
balance model to obtain time-variant hydrological parameters, and
successfully detected the impacts of land-use changes on catchment water
storage capacity in the Wudinghe Basin, which led to changes in the
catchment rainfall-runoff relationship. Pathiraja et al. (2016)
demonstrated that land cover changes can lead to significant step
changes in estimated parameters in hydrological models using an ensemble
Kalman filter with a locally evolutionary linear parameter in two paired
experimental catchments in the Western Australia. They identified
changes in the excess runoff generation process that resulted from land
use changes. Based on previous successful studies for detecting and
understanding hydrological non-stationarity under changing environments
using a data assimilation method, we employed a similar methodology to
investigate the non-stationarity in hydrological behavior induced by
long-term drought.
In this study, the Particle filter (PF) data assimilation technique was
combined with a two-parameter monthly water balance model (TWBM) to
obtain time-variant parameter series, and then to identify changes
caused by drought at the process level. The PF data assimilation
technique is one of a general class of ensemble-based statistical data
assimilation methods that is more suitable for nonlinear data
assimilation problems and retaining the water balance (Arulampalam,
Maskell, Gordon, & Clapp, 2002; Moradkhani & Weihermüller, 2011;
Field, Tavrisov, Brown, Harris, & Kreidl, 2016) , and thus was selected
in this study. The TWBM model is a widely used monthly hydrological
model that has been successfully applied to simulate the catchment
rainfall-runoff relationship in a wide range of climates, soils, and
vegetation conditions (Guo, Wang, Xiong, Ying, & Li, 2002; Guo et al.,
2005; Xiong & Guo, 2012; Shuai, Xiong, Dong, & Zhang, 2013; Zhang,
Liu, Liu, & Bai, 2013; Xiong, Yu, & Gottschalk, 2015). The specific
objectives of this study were to (1) demonstrate whether the PF data
assimilation method can be used to detect changes in catchment
hydrological behaviors induced by drought; (2) detect whether prolonged
drought can cause changes in the catchment rainfall-runoff relationship;
and (3) identify the mechanisms responsible for drought induced changes
in catchment hydrological behavior at the process level.