Variabilities and changes due to natural and anthropogenic causes in the water cycle always presented a challenge for water management planning. Practitioners traditionally coped with variabilities in the hydrological processes by assuming stationarity in the probability distributions and attempted to address non-stationarity by revising this probabilistic properties via continued hydro-climatological observations. Recently, this practice was questioned and more reliance on Global Circulation Models was put forward as an alternative for water management plannig.
This paper takes a brief assessment of the state of Global Circulation Models (GCM) and their applications by presenting case studies over Global, European and African domains accompanied by literature examples. Our paper demonstrates core deficiencies in GCM based water resources assessments and articulates the need for improved Earth system monitoring that is essential not only for water managers, but to aid the improvements of GCMs in the future.
Climate and direct anthropogenic change altering the water cycle has called
into question the traditional water management planning that is based on past
records of water resources as a means to characterize the likelihood of
extreme conditions affecting water availabilities
Hydrologists recognized long ago that the stationarity assumption is often
violated due to changes other than climate (e.g. land cover and land use
change, engineering alteration of the river channel, constructions of
reservoirs, etc.). Water managers were very much aware of the need to test
the statistical homogeneity of past observations
A growing number of institutions run GCMs and use dynamical downscaling of
GCM with Regional Climate Models (GCM-RCM) to produce large sets of
projections for both present climate and future scenarios. The resulting
datasets are expected to thoroughly sample the climate system phase space,
where each simulation corresponds to different trajectory, starting from
different initial conditions and determined by different choices of model
parameters and structural uncertainties. Coordinated efforts such as the
Coupled Model Intercomparison Project (CMIP) offers a collection of
standardized GCM simulations that are the backbone of the regularly revised
assessment reports of the Intergovernmental Panel on Climate Change (IPCC)
Unfortunately, GCMs have difficulties properly reproducing contemporary
climate casting doubts on their abilities in projecting future changes in key
climate variables (e.g. air temperature and precipitation)
It is generally assumed (without real scientific basis beyond very few
empirical evidence) that multi-model averages outperform individual model
projections, as individual biases are expected to at least partly cancel out.
This expectation might have merit for a large number of truly independent
models, but in reality GCMs share lot of commonalities
In addition to the inherent biases from GCM, their coarse resolutions
prevents them from capturing regional spatial variability that is essential
for water management applications. The coarse resolution GCM projections are
either downscaled “statistically” considering spatial climate variability
from observed records or dynamically by performing higher resolution regional
climate model simulations forced by coarse resolution GCM as boundary
condition. Just like bias correction, statistical downscaling carried out on
multiple variables inevitably breaks their integrity resulting in forcing
data sets that are inconsistent with the plausible states of the climate
variables. The validity of dynamic downscaling is disputed as a viable
strategy
Impact assessment models designed for assessing the sensitivity of various
economic sectors (e.g. water resources management, agriculture, human health,
etc.) play a key role in translating the projected climatic changes into
corresponding societal consequences. A growing number of hydrological models
intended to support water managers and policy makers offer capabilities to
assess water resources
Our paper presents a couple of examples applying a well established
hydrological modeling framework based on the Water Balance Model introduced
by
The presented applications span the global and regional domains and show the challenges in applying GCMs for multi-decadal water resources management planning. Our paper intends to present a couple of case studies highlighting the limitations in applying GCMs and GCM driven RCM climate forcings for long-term water resources projections.
In the presented case study examples, WBM
WBM was criticized in the past for being too simplistic by neglecting the
full energy balance in the vertical water exchange processes and lumping
together various components of the evapotranspiration (evaporation from soil,
from the canopy and transpiration through the stomates of the leaves). Our
team resisted for years to apply the more elaborated water balance
configuration in “production” experiments, where the uncertainties in
forcing data and land cover parameterization appeared to outweigh the
anticipated gain in more realistic representation of the hydrological
processes. In recent years, a number of papers came to similar conclusion
that added complexity does not necessary improve model performance
Impact model assessments rarely consider “raw” GCM climate forcings.
Instead they either rely on a single GCM or an ensemble of multiple bias
corrected GCMs as their starting points and expect the GCM community to
characterize the discrepancies between GCMs.
Even without analyses (discussed in
GCMs consistently project rising temperatures for the upcoming decades
(although they have marked differences in the rate of change), but projected
precipitation trends from different GCMs have a wide spread in the magnitude
and direction of change. GCMs and their weather forecast model cousins are
known to have deficiencies in representing cloud formation and precipitation
processes for decades
Annual estimates from GCM simulations under present and projected future conditions in contrast with observed climate forcings from the Climate Research Units, University of East Anglia.
The Inter-Sectorial Impact Model Intercomparison Project
(ISI-MIP;
Figure
Contemporary (1950–2006) global average air temperature,
precipitation and runoff (estimated using WBM
SD
Linear regression slope of the projected air temperature, precipitation and WBMplus simulated runoff for the 2006–2099 period.
Global continental runoff estimates based on bias corrected present and projected future climate conditions using the ISI-MIP forcing data.
Ensemble spread (the difference between the minimum and the maximum
from the ensemble members) of mean annual air temperature
The projected linear trends in global air temperature (in
Predicted changes in mean annual temperature
Perhaps, one of the most disturbing outcome of the ISI-MIP model
intercomparison was the recognition that model uncertainties in the impact
models were comparable to the uncertainties in the GCM forcings
The large difference between global scale hydrological model simulations is
not entirely new as the European Water and Global Change (EU-WATCH) program
(a precursor to the ISI-MIP project) already came to the similar conclusions
IMPACT2C made use of the growing ensemble of high-resolution (
Robust signals could only be detected in temperature showing a
2
It has been documented, however, that a winter positive bias in surface
temperature and precipitation rates can be induced in RCM simulations over
Northern Europe through the prescribed large scale boundary conditions, which
can lead to a too deep Icelandic low precipitation extending too far into the
Nordic seas. Such feature also causes too low temperature and precipitation
rates over Southern Europe. On the other hand, in summer warm and dry biases
have been observed in RCMs simulations over Eastern Europe and to a lesser
degree the Mediterranean, where too little simulated rainfall can dry out
soil water reservoirs causing very high surface temperatures
It is worth noting that the high resolution of the Med-CORDEX simulations
might be expected to locally reduce the fraction of convective precipitation
to total precipitation, therefore possibly reducing the summer warm and dry
biases, thus improving both the spatial pattern and temporal evolution of
precipitation. On the contrary, winter circulation being dominated by large
scale features, it can mainly benefit from a finer representation of the
flow-topography interactions in the domain interior
Quite unfortunately, the bias correction procedures (in this case, quantile
mapping) to the surface temperature and precipitation fields required the
original RCM outputs to be upscaled from 12 to 25 km resolution, in order
to match that of the E-OBS gridded dataset selected for reference
When combined with the different representations of orography in the
different RCMs, in vast areas this led to virtually correct altitude rather
than intrinsic biases in the projected fields, as demonstrated by the
comparison between the patterns of the uncorrected RCM ensemble spread in
present and future climate for both precipitation and surface temperature,
clearly carrying a stable topography signature, and between the correspondent
average fields before and after bias correction
(Fig.
Such features are even more evident over specific regions, e.g. the Alps, over which individual model fields have been compared: single peaks can be identified and directly compared to high resolution geographic maps. The overall effect is a dramatic decrease and over-smoothing in precipitation projections and a similarly non-negligible increase in temperature at high altitudes, both severely inconsistent with the rest of modeled fields and, quite ironically, neutralizing just what is one of the major improvements expected from high resolution regional simulations.
Precipitation is the most critical input variable in hydrological modeling
Basin-wide seasonal cycles of key water balance components (
For four main European catchments discharging into the Mediterranean or the
Black seas, WBM
By removing apparent systematic errors in the projected fields, bias
correction effectively reduces model spread in control simulations, whereas
it only partly succeeds in limiting noise in the projected water balance for
the
Figure
The climate change signal in temperature alone also exhibits a definite meridional gradient, with the highest increments located over northern Europe, and southern countries experiencing lower though consistent warming. It is worth noting again, that both effects might originate in the reported model deficiencies, a hypothesis that is confirmed if the spatial patterns of both corrections to the data and model spread are considered. Under the assumption that model spread and correction magnitude together concur to give a rough estimate of uncertainties in model projections, if not as a disclosure of both model and data inadequacy, the climate change signal is in fact severely obscured by noise.
For the African domain, the CORDEX community has produced a number of GCM-RCM
combinations at a spatial resolution of 0.44
A change indicated by the majority of models is a delay in the onset of the rainy season together with a earlier cessation, leading to a total shortening of the wet period that lasts between 165 days in the South and only about 90 days in the Sahelian region in the North. With little change in precipitation this implies more precipitation falling on less days and generally more erratic rainfall.
Bias correcting the ambiguous climate model data in this region is likely not going to make the climate model for water balance modeling more meaningful. Despite the conceptual issues related to bias correction in general, the bigger problem is selecting the data sets on which the bias correction should be based on. For precipitation, for example, the observation network is very sparse and access to data is restricted by many national data sharing policies. Related to this, the satellite precipitation products (that rely on in-situ observations for calibration) in the region show large discrepancies that will translate through the bias correction chain and introduce further uncertainties.
Climate models are indispensable tools to understand atmospheric processes
and the evolution of the Earth's climate regime, but they have clear
shortcomings in providing climate projections for actual water management
planning
Relative change in ensemble median precipitation (left) and
simulated absolute values of long term annual historical and future
precipitation for 16 RCM/GCM combinations aggregated for 4 vegetation zones
(right). “
Projections of rel. changes in annual precipitation for West Africa,
from 5 GCMs, randomly picked from ISI-MIP bias corrected data (grey). The
blue line shows the observed variability in precipitation for the last 100
years (upper
In an anticipation that the computing power needed to enable GCM modelers to
carry out computations at significantly higher spatial and temporal
resolution (that are viewed as the key in improving GCM performance) is still
decades away, some scientist are envisioning a possible alternative pathways
by using supercomputers that are less accurate and don't necessary compute
the same results from identical input data
Considering the incredible increase in computational power (which was always
viewed as the major roadblock in improving GCM performance) during the last
three decades (since the climate change agenda rose to its current prominence
in geophysical research), one has to wonder if it is indeed the lack of
computing power that prevents major breakthroughs or there are fundamental
obstacles in our ability to predict the trajectories of the chaotic climate
systems
The real improvements in our understanding of the Earth system processes will
likely come only from better data that will need to come from improved Earth
system monitoring both from in-situ and remote sensing sensors. A recent
debate published in Science provided two distinct view about the role of
in-situ monitoring
Given the demonstrated inefficiencies of both the Global Circulation Models
and their operational cousins, the weather forecast models, in capturing the
water cycle, better hydrological data are much needed. While remote sensing
undoubtedly will play critical roles in monitoring precipitation and possibly
soil moisture, river discharge, which is the most accurately monitored
element of the hydrological cycle
The biggest obstacle to more monitoring is the lack of financial resources.
One would think that cost of operating high performance computing centers to
support GCM modeling pales in comparison to the investment needed to maintain
in-situ observing networks. In reality, it is not the case, as an example the
Earth Simulator built in Japan (which was the fastest computer between
2002–2004) had a USD 700 million price tag and needed a full overhaul by 2009
The only way the investments in GCM and impact model assessment capabilities can reach their full potential is if robust and reliable Earth observation can aid their development and calibration and permit sustained validations. “Historia est Magistra Vitae” – and history remains the life's teacher in our changing world.
The data used in the presented research are all publicly available listed under the assets tab of the electronic version of the paper. The water balance model results are available from the ISIMIP project of the Potsdam Institute for Climate Impact Research (Warszavszki et al., 2014; Hempel et al., 2013). Their hosting data portal is part of the Earth System Grid Federation. The results from the European water balance model experiments are accessible at the ENSEMBLES project under theme RT3. The modeling and spatial analysis tools used in the present study are available on GitHub. Updated version of Willmott et al. (1994) was retrieved from Willtmott-Matsuura (2016).
The source code for the Water Balance/transport Model used in the presented studies are available on GitHub (Fekete, 2016).
The authors would like to acknowledge the Inter-Sectoral Impact Model
Intercomparison Project, organized by the Potsdam Institute for Climate
Impact Research that not only supplied the bias corrected global circulation
model projections, but provided funding for the water balance model
simulations. The European experiments were carried out under the IMPCAT2C
project (Grant agreement#282746) funded by the European Union Seventh
Framework Programme (FP7/2007–2013). Funding for the African example were
provided by the German Ministry of Education and Research (BMBF) through the
West African Science Service Center on Climate Change and Adapted Land Use
(WASCAL,