PIAHSProceedings of the International Association of Hydrological SciencesPIAHSProc. IAHS2199-899XCopernicus GmbHGöttingen, Germany10.5194/piahs-371-17-2015Hydrologic nonstationarity and extrapolating models to predict the
future: overview of session and proceedingChiewF. H. S.francis.chiew@csiro.auVazeJ.CSIRO Land and Water Flagship, GPO Box 1666, Canberra, ACT 2601, AustraliaF. H. S. Chiew (francis.chiew@csiro.au)12June2015371371172118March201518March2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://piahs.copernicus.org/articles/371/17/2015/piahs-371-17-2015.htmlThe full text article is available as a PDF file from https://piahs.copernicus.org/articles/371/17/2015/piahs-371-17-2015.pdf
This paper provides an overview of this IAHS symposium and PIAHS proceeding
on “hydrologic nonstationarity and extrapolating models to predict the
future”. The paper provides a brief review of research on this topic,
presents approaches used to account for nonstationarity when extrapolating
models to predict the future, and summarises the papers in this session and
proceeding.
Hydrologic nonstationarity and implications – Overview
The commentary by Milly et al. (2008) has initiated significant discussions
and continuing progression of research on hydrologic nonstationarity. The
term “hydrologic nonstationarity” has been used to describe many things,
ranging from different climate-runoff relationships evident in different
periods within a long hydroclimate time series to changes in hydroclimate
characteristics and dominant hydrological processes in an increasingly
warmer and higher CO2 world. Hydrologists have always represented
stationarity and nonstationarity (which is difficult to distinguish
statistically in natural systems) as best they could and their implications
on water resources and related systems, but modelling this adequately will
become increasingly challenging in a world driven by anthropogenic changes.
The constancy of laws and patterns has always been and will always be
“stationary”. It is our understanding or lack of these and the constancy of
variables or characteristics at different times that may appear
“nonstationary”. For example, a hydroclimate time series can be considered
“stationary” over thousands or millions of years, in that we can represent
statistically or stochastically the characteristics and variability over
time and space scales or even develop a precise understanding of the
processes from the very long record. But of course, the characteristics of
the different periods will always be different (exhibiting variability over
different time and space scales), that is, nonstationary over time. The
practical issue then is not whether hydroclimate systems are stationary or
nonstationary, but whether the nonstationarity is substantial enough to
require a change in existing system characterisation, conceptualisation or
modelling for a particular hydrologic design, operation and planning.
Hydrologists have excelled in developing models for numerous applications,
through analysing and interpreting climate and hydrologic data to understand
hydrologic processes, conceptualising the processes in hydrological models,
and calibrating and testing models against observations. These models are
particularly good in predicting the streamflow response to changes in the
climate inputs and catchment characteristics. These models, when developed
adequately using relatively long historical records that encapsulate the
range of hydroclimate conditions, should be able to predict hydrologic
responses to changes in the climate inputs over the near and medium term.
However, extrapolating hydrological models to predict further into the
future that is influenced by anthropogenic change is challenging as we will
then be predicting system behaviours that are beyond the range of observed
variability in the instrumental record (changed rainfall characteristics,
higher temperature, higher CO2) or that result from significant
alterations of the physical system characteristics. Therefore, whilst
near-term future projections of water availability (and streamflow,
hydrological fluxes and stores) are influenced mainly by the large
uncertainty in the rainfall projections (Teng et al., 2012), water
projections further into the future will be increasingly influenced also by
the uncertainty in hydrological modelling.
The Millennium Drought in far south-eastern Australia and its
influence on hydrology and climate-runoff relationship.
Chiew et al. (2014) presents an example of hydrologic nonstationarity and
the implications on hydrologic prediction exposed by the prolonged
1997–2009 Millennium Drought in far south-eastern Australia (Fig. 1). The
unprecedented runoff decline during the drought was caused not only by the
lower annual rainfall, but also by changes in other climate characteristics
(lack of high rainfall years, change in rainfall seasonality and higher
temperatures) and dominant hydrological processes (reduced
surface-groundwater connection and farm dams intercepting proportionally
more water during dry periods). Because of the significantly different
climate-runoff relationship and model conceptualisations that do not
adequately represent surface-groundwater connection through long dry spells,
it is not surprising then that models developed and calibrated against the
pre-1997 data were not able to estimate the flow volumes and runoff
characteristics during the drought. However, because the Millennium Drought
has exposed these extreme conditions, models can now be developed or adapted
to also represent these conditions.
There are many similar examples of models not being able to simulate the
hydrology of a period with very different hydroclimate characteristics from
the period used to develop the models. However, when the models are
developed or calibrated using a long data set that encapsulates the
different hydroclimate characteristics of different data periods, the models
can generally reasonably simulate the hydrology through the different times
(although not as well as if the model was calibrated only against data from
the period it is simulating) (Vaze et al., 2010; Merz et al., 2011; Coron et
al., 2012). Therefore, following on from the above Millennium Drought
example, hydrological models developed and tested against long historical
records are generally reliable until there is a significantly `changed'
condition (like the Millennium Drought). After the changed hydroclimate
conditions have been observed (following the end of the Millennium Drought),
new conceptualisations can be introduced to the models to also represent
these conditions. These newly developed models will then continue to be
robust until there is another significant and unexpected changed condition.
As we can never have a perfect and complete understanding of the
ecohydroclimatological processes and interactions, this future prediction
problem can only be overcome if we can anticipate all the plausible changes
and conceptualise them adequately in models.
Extrapolating hydrological models to predict the future
With anthropogenic climate change, we know we will at the very least be
extrapolating hydrological models to predict a future under changed rainfall
distribution and characteristics, warmer conditions and higher CO2.
Changes in rainfall characteristics may trigger a change to a hydrologic
regime not seen in the past (Grayson and Bloschl, 2000; Peterson et al.,
2009; the surface-groundwater connection example earlier). Higher
temperatures will influence evapotranspiration and energy and water balance
and interactions at different scales (Roderick et al., 2009; Lockart et
al., 2009; Potter and Chiew, 2011), and in high altitudes and latitudes
change the timing of snowmelt (Woo et al., 2008) and the importance of
rain-on-snow rainfall events (Sui and Koehler, 2001). Higher CO2 will
reduce canopy conductance and increase leaf water use efficiency (CO2
fertilisation) which could be offset by increased leaf area and forest
biomass (Medlyn et al., 2001; Betts et al., 2007; Ainsworth and Rogers,
2007; Cheng et al., 2014). However, understanding these potential influences
and the complex ecohydrology and atmospheric interactions and feedbacks
under higher temperature and CO2 is very difficult and is a significant
area of current science and global research programs. In addition, any
understanding, speculation or modelling of the physical processes can only
be validated against past data, which will then be extrapolated to predict a
future that will be significantly different from the past.
“Stationarity is dead”. However, it is not apparent what if any alternative
methods should be used as a replacement for the different types of
hydrological applications. For example, existing approaches may be
sufficient for operational water management and short-term planning, but key
aspects of “nonstationarity” must be taken into account for certain
hydrologic design and long-term planning. Predicting the future is difficult
if not impossible, and hydrologic planning will always consider
probabilistic or multiple plausible realisations and adopt adaptive risk
management with systems planned for particular levels of security or
reliability.
Hydrologists have used a variety of approaches to predict a future under
nonstationarity. Hydrologic responses to changed climate inputs are
generally modelled using hydrological models informed by climate projections
from the large or entire range of global and regional climate models (Xu et
al., 2005; Christensen and Lettenmaier, 2007; Raisanan, 2007; Chiew et al.,
2009; Vaze et al., 2011). Improved understanding of vegetation behaviour and
hydrological responses to warmer climate and enhanced CO2 are
increasingly incorporated to the more complex hydrological models (Arora,
2002; Murray et al., 2011). Improved conceptualisations are being introduced
to hydrological models, particularly where they are used in studies
predicting into the future under prolonged extreme conditions. Examples
include attempts at parameterising semi-distributed hydrological models or
adapting existing models to simulate processes important under extreme
conditions like long dry spells (farm dam interception (Nathan et al., 2005)
and surface-groundwater connectivity (Puspalatha et al., 2011)) and
learning from catchments experiencing different or changing conditions
(Wagener, 2007; Fenicia et al., 2008; Buytaert and Beven, 2009). Many
studies use existing models, but with smart approaches to parameterise and
calibrate the model, for example (i) with time varying parameters dependent
on storage levels (Smith et al., 2008; Merz et al., 2011); (ii) multi-criteria
optimisation that also considers low flow simulations
(Madsen, 2000; Oudin et al., 2006; Efstratiadis and Koutsoyiannis, 2010); and
(iii) predicting the future with parameters from model calibration against a
similar climate period as the future climate projections.
Hydrological modelling under changing conditions is a problem familiar in
hydrology. This is highlighted by the two decadal initiatives of the
International Association of Hydrological Sciences (IAHS), the 2003–2012
Decade on “Prediction in Ungauged Basin” (PUB) focussed on extrapolating
model parameterisation in space (Sivapalan et al., 2003; Bloschl et al.,
2013) and the “2013–2022 Decade on Panta Rhei – Change in Hydrology and
Society” now focussing on prediction in a changing world (extrapolation in
time) (Montanari et al., 2013). There have been several useful technical
overviews and commentaries on hydrological prediction under change and these
include Clifford (2002), Wagener et al. (2010) and Peel and Bloschl (2011). The
Colorado State University (2010) workshop on hydrologic nonstationarity and
sessions in key international forums (e.g. AGU Fall Meeting 2012, IAHS
Assembly 2013) also provides useful discussions on this issue and practical
approaches to account for nonstationarity when extrapolating models to
predict the future for design, operation and planning of water resources and
related systems.
IAHS Symposium and PIAHS Proceeding
This IAHS symposium on “hydrologic nonstationarity and extrapolating models
to predict the future” directly addresses a key issue in the IAHS Panta
Rhei Decade (Change in Hydrology and Society) and builds on previous forums
on this topic. The presentations (oral and poster) and dedicated discussions
in the symposium are focussed on recent advances in hydrologic
nonstationarity research and implications on hydrologic predictions.
There are 54 abstracts and 35 full papers accepted for the symposium. This
PIAHS proceeding presents the 35 full papers. The papers can be broadly
grouped into four categories: (i) papers that characterise hydroclimate
trend and nonstationarity and discuss their implications on hydrologic
predictions; (ii) papers that largely model climate change impact on water;
(iii) papers that explore approaches to take into account hydrologic
nonstationarity in predicting the future (through process conceptualisation
and/or smart parameterisation of existing models); and (iv) papers that
address anthropogenic nonstationarity from catchment development, river
regulation and environmental disturbances.
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