DERIVING WATER RESOURCES INDICATORS AT THE BASIN SCALE
FROM GLOBAL HYDROLOGICAL AND LAND SURFACE MODELS
Micha Werner1,3, Eleanor Blyth2, Jaap Schellekens3, Rogier Westerhoff3
1
UNESCO-IHE Institute for Water Education
2
Centre for Ecology and Hydrology
3
Deltares
Introduction
There are currently several global hydrological and land-surface models currently available. The
resolution of many of these is rapidly increasing, providing simulated hydrological fluxes at a
scale appropriate to managing water resources in medium-sized basins. This offers clear
advantages, as these fluxes are consistently represented around the world, allowing easy
comparison between different regions. The potential for improved water resources management
in poorly gauged basins using these data is obvious. The Earth2Observe project, a recent
research initiative funded by the European Union 7th Research Framework, aims to develop a
consistent water resources re-analysis (WRR) at the global scale, based on an ensemble of such
global scale models. This is developed in two phases. In the first phase, the ensemble of
participating models is driven using the WATCH-ERA-Interim (WFDEI) meteorological re-analysis
dataset as forcing, providing hydrological data at the global scale at 0.5 arc degree resolution, at
a daily time step. These models will then be improved through improved process representation,
as well as assimilating various (satellite) products to develop the second phase WRR dataset.
Method
In this paper we use this first phase WRR to explore how these data can be applied to determine
indicators useful to water resources managers and basin planners. Nine of the participating
models are considered, including four land surface models (LSM) and five global hydrological
models (GHM). These models are run for the 32 year period from 1979 to 2012, with the common
WFDEI meteorological reanalysis dataset used as forcing.
Using the results of the participating models, we explore three water resources indicators: an
aridity index, based on the ratio of actual and potential evaporation; the water exploitation index,
which compares water withdrawal and consumption to the renewable water resource; and an
indicator that calculates the frequency of occurrence of stress in the (model) root zone, which is
defined to occur when the available moisture falls below 50% of the water available in the root
zone at field capacity.
Results
The resulting hydrological fluxes calculated by each of the nine models show quite significant
differences, despite the use of a common meteorological forcing dataset. These differences are
due to different processes representations in the models. Indicators calculated with the model
outputs reflect these differences, thus showing considerable uncertainty. We show that the
distribution of the value of the index depends on the hydro-climatology, with marked differences
between arid, semi-arid and temperate zone areas. Figure 1, panels (a), shows the distribution of
the mean value of the Aridity Index from the nine models. As expected, Northern and temperate
climates show a low index, while Mediterranean and North African climates have Index values
belonging to semi-arid and arid climates. The spread between the models, shown by the standard
deviation in panel (b) is, however, significant in some areas; for example in parts of Eastern
Europe. It is also shown that the LSM and GHM models often dominate different parts of the
distribution of the indicator value. An example is shown in panel (c). The GHM's calculate lower
values of the Aridity Index in the North African, Mediterranean, and Northern European climates,
while the LSM's dominate the lower values in the more temperate climates. While these
uncertainties are important, applying a threshold to the indices to categorise areas as arid or nonarid reveals that these uncertainties may not always be relevant. The aridity index provides a
simple example. The index ranges from 0 to 1, and in this example we consider areas with an
index value larger than 0.6 to be arid. This shows there is consensus between the models in most
areas. That consensus is, however, lower in transition areas. The Mediterranean region is one
such example, where the categorisation depends very much on the model applied (Figure 1,
panel (d)). In these areas, the uncertainty in the multi-model ensemble is relevant to decision
making based on the indicator. It is also in these areas where a calculating the index using a
model has value, as we do not need to run a model to decide that desert areas such as in
Northern Africa are arid. Similar results for the geographic distribution and uncertainty of the
values are found for the other two indicators, as well as in other project case study basins in
Australia & New Zealand, Bangladesh, Colombia and Ethiopia.
Our analysis shows that while both LSM's and GHM's can provide useful data for basins across
the world, indices derived with the results of these models for use in water resources
management planning may be quite different, depending on how hydrological processes are
represented. The analysis also identifies in which climates improvements to the models, and data
assimilation is particularly relevant to support the confidence with which decisions can be taken
based on indicators derived from model simulation results.
(a)
(b)
(c)
(d)
Figure 1 Distribution of the Aridity Index over Europe calculated with an ensemble of nine global scale
hydrological models. Panels (a) and (b) shows the ensemble mean and standard deviation. Panel (c) shows
the ID of the model that calculates the minimum Aridity Index. Models with ID's 1-4 are Land Surface
Models, while those with ID's 5-9 are Global Hydrological Models. Panel (d) shows the number of models
with an Aridity Index above 0.6.