Master Thesis
Moisture recycling and the effect of land-use change
This thesis was carried out as part of the degree of Master of Science in Civil
Engineering and Geosciences at TU Delft, under the supervision of Ir. Ruud van der
Ent, Professor Huub Savenije and researcher Holger Hoff.
Student:
Revekka Nikoli
Graduation Committee:
Prof. H.H.G. Savenije
Ir. R.J. van der Ent
Prof. R.F. Hanssen
H. Hoff
Diploma NTUA, Rural and Surveying Engineering
TU Delft, Hydrology
TU Delft, Hydrology
TU Delft, Mathematical Geodesy and Positioning
PIK, Climate Impacts & Vulnerabilities
Moisture recycling and the effect of land-use change
R. Nikoli1, R.J. van der Ent1, H.H.G. Savenije1, H.Hoff2, K. Waha2
1
Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of
Technology, the Netherlands
2
Potsdam Institute of Climate Impact Research (PIK), Germany

Corresponding author: [email protected]
Abstract.
Although the research and quantification of moisture recycling (i.e. the process by
which terrestrial evaporation returns as precipitation on land) has a long history and
many different approaches (depending on its definition and the assumptions used in
each method) the direct effects of land-use change on moisture recycling have not
received so much attention. In this paper we stepwise analyze land-use change effects
on global moisture recycling and on the precipitation of the ‘Challenge Program (CP)
River basins’ for the period 1997-2006. We investigate two land-use cases, one being
the current actual land-use case and the second being the ‘undisturbed case’, using
potential natural vegetation data and subsequently we compare them at a global scale.
This is done by computing precipitation of continental origin differences caused by
the associated continental evaporation differences between the land-use cases. The
seasonal variations of both moisture fluxes are discussed by focusing on characteristic
grid-cells. We observe that in regions, where intense irrigation takes place in the
current land-use case both continental evaporation and the resulting precipitation have
increased compared to the potential natural vegetation scenario. On the other hand,
certain regions like Western/Central Africa and South-eastern South America
experience reductions in the range of 30-50 mm/yr of evaporation and 15-25 mm/yr of
precipitation of continental origin. Among the basins we examined, the Indus shows
the highest increase in evaporation due to the land-use change and the Ganges
Brahmaputra the highest precipitation (of continental origin) increase, whereas in the
Volta both fluxes are reduced by this change. Moreover we conclude that the
topography and wind patterns play a very important role on the creation of these
either positive or negative differences between the different land-use cases.
1. Introduction
Human induced land-cover change has altered a considerable part of the Earth’s
surface and is expected to amplify in the 21st century via deforestation, afforestation
and agricultural intensification (Pitman, 2003). Accepting that moisture recycling (i.e.
the process by which terrestrial evaporation returns as precipitation on land) can be
greatly affected by land surface processes, not only locally but also at continental
scales, its magnitude can indicate the sensitivity of local, regional and possibly even
global climate to land-use change (see e.g. Brubaker et al., 1993; Dallmeyer and
Claussen, 2011; Eltahir and Bras, 1996; Kunstmann and Jung, 2007; Lettau et al.,
1979; Savenije, 1995; van der Ent et al., 2010). Therefore the quantification of
moisture recycling as a response to land-use change is of key relevance for water
resources, especially in water scarce areas.
Climate modelling groups have looked into the impacts of land-cover perturbation on
surface temperatures, turbulent energy fluxes and precipitation (Henderson-Sellers et
al., 1993; Chase et al., 2000; Werth and Avissar, 2002; Findell et al., 2006, van der
1
Ent et al., 2010). Many of these studies focused on the effects of regional land-use
change on the climate of the same region (De Groen and Savenije, 1996; Mohamed et
al., 2005; Pielke et al., 2006; Findell et al., 2011). By isolating specific regions,
however, these studies neglected both the climatic effects of land-use change in
neighbouring regions and the effects of regional land-use change on other regions
(remote effect).
On the other hand, there are studies that took into account the remote effect
(Dallmeyer and Claussen, 2011; Werth and Avissar, 2002). Dallmeyer and Claussen
(2011) investigated the influence of land-cover change in the Asian monsoon region
by comparing 3 experiments; one assuming current land-cover, one with complete
afforestation of the region and one with total replacement of the forest with grass.
They observed major decreases in the regional precipitation due to deforestation and
both increases and decreases due to afforestation in different parts of the region, for
each experiment. They also found that the climate in North Africa and the Middle
East is largely affected by the modification of the large-scale circulation that the
regional land-use causes and concluded that regional climate models can not capture
the impact of vegetation changes on the large scale circulation and so they may not
capture the complete effect of vegetation on the regional climate change (Dallmeyer
and Claussen, 2011).
Savenije (1995) estimated the recycling of moisture in the Sahelian belt being the first
to use recycling ratios for precipitation of continental origin (Appendix B.1, Equation
7), using all the continental areas as the mother region. For this he distinguished
continental from oceanic moisture sources. Due to the fact that in his model moisture
can not leave the region through the atmosphere but only through surface runoff, his
computed recycling values are considered to be overestimated (van der Ent et al.,
2010).
Global studies on land-use change and the effect on moisture recycling are also
available (Chase et al., 2000; Goessling and Reick, 2011; Gordon et al., 2005; Rost et
al,. 2008a; Rost et al., 2008b; Pitman et al., 2009). Except for Goessling and Reick
(2011) who explored the extreme scenario of non-evaporating continents the rest of
the studies compared the climatic effects of the current land-use globally with a
potential natural vegetation scenario. Among them only Pitman et al., (2009) did not
find any remote impact of their regionally imposed land-use changes. This is probably
explained by the lack of consistency between the seven models that they used to
simulate the land-use changes or the fixed sea surface temperatures that they assumed
in them. For the remaining studies the transport of moisture between land regions
(continental moisture recycling) is a common finding. For the length and time scale of
the remote impact between regions we refer to Van der Ent and Savenije (2011).
Van der Ent et al. (2010) further developed the continental precipitation recycling
ratios, that other researchers used before (Savenije, 1995; 1996a; 1996b; Bosilovich
and Schubert, 2002, Bosilovich and Chern, 2006, Dominguez et al., 2006, Koster et
al., 1986, Nieto et al., 2006; Numaguti, 1999, Stohl and James, 2005). Their research
permits a quantified first order estimate of the impact that land-use change may have
on global rainfall and water resources. Most importantly, they highlighted the
significant role of global wind patterns, topography and land cover in moisture
recycling patterns and the distribution of global water resources. In their work, Keys
2
et al. (2011) also highlight the importance of upwind land-cover change and its effects
on precipitation in water stressed downwind regions.
Goessling and Reick (2011) questioned the importance of continental precipitation
recycling in climate change and tested its magnitude on resulting precipitation
changes caused by comparing an extreme land-use scenario (where all land
evaporation equals zero) to the current land-use case. They concluded that most of the
land-use change response on rainfall can be attributed to changes in the large-scale
atmospheric circulation, and that the response by moisture recycling is smaller.
However, their results should be encountered with caution as such an extreme
perturbation doesn’t represent realistic land cover changes and hence doesn’t allow
solid conclusions on the capability of the recycling ratios and additional experiments
are required.
In this paper we quantify the impact of land-use change on precipitation of continental
origin via continental moisture recycling globally and in the so called ‘CP river
basins’ by comparing the current land-use case globally with a potential natural
vegetation scenario. Annual average differences of land evaporation and resulting
precipitation between the land-use cases are presented both globally and for the CP
basins. Seasonal variations are discussed by focusing on characteristic grid-cells in
regions, where these differences are most prominent.
The models and the data we use are described in section 2 as part of our methods. In
section 3 our results are presented and discussed in comparison with previous studies.
Section 4 summarises the main conclusions of our research and offers some
recommendations for further research.
2. Methods
For the determination of the moisture influxes resulting from the land-use change we
combine two different models; The LPJmL (Lund-Potsdam-Jena managed Land,
Dynamic Global Vegetation and Water Balance Model, Bondeau et al., 2008) and the
WAM (Water Accounting Model, van der Ent et al.,2010), which was modified to be
forced with output evaporation data of the LPJmL.
2.1 The LPJmL model
LPJmL simulates vegetation composition and distribution as well as stocks and landatmosphere exchange flows of carbon and water across the land-atmosphere interface,
for both natural and agricultural ecosystems. It uses a combination of ecophysiological relations, generalised empirically established functions and plant trait
parameters to compute processes such as photosynthesis, plant growth, maintenance
and regeneration losses, fire disturbance, runoff, evaporation and irrigation. Grid-cells
of 0.5°x0.5° resolution contain mosaics of up to 9 plant functional types of natural
and 12 crop functional types for agricultural vegetation (see Appendix A).
3
2.1.1 Input data
The LPJmL model uses climate data from the CRU TS 3.0 climate database (Mitchell
and Jones, 2005; http://badc.nerc.ac.uk/data/cru/) for the time period between 1901
and 2006 on a monthly basis. The monthly input consists of values of: air
temperature, precipitation amounts, the number of wet days and cloud cover.
2.1.2 Output data
The outputs of the model include all the partitioning fluxes of the incoming
precipitation (runoff, percolation, evaporation and transpiration) for all land surface
grid-cells. From the monthly precipitation data used as input, daily values are
generated with a weather generator; these generated values are also stored as an
output.
2.2 The WAM
WAM, described by Van der Ent et al. (2010) and Van der Ent and Savenije (2011),
consists of a series of MATLAB scripts and is based on the atmospheric water
balance (Section 2.2.2, Equation 1). It can be used to calculate moisture fluxes in
land-atmosphere interaction studies and continental precipitation recycling ratios (i.e.
the fraction of precipitation originating from continental evaporation) in the time
period of interest, using the assumption of the well-mixed atmosphere (Section 2.2.2,
Equation 3).
2.2.1 Input data
The data used as forcing for the determination of the moisture influxes are 3-hour
accumulated monthly precipitation (P) and evaporation (E) values; 6-hour
accumulated zonal and meridional wind speed values and specific humidity at the
lowest 24 pressure levels (175 – 1000 hPa) and the surface pressure. These data are
taken from the ERA-Interim Reanalysis dataset (Berrisford et al., 2009).
2.2.2 Mathematical principles of the (modified) WAM
WAM divides the globe in grid-cells of different sizes; the size depending on the
latitudinal location of each grid-cell on the map. A certain grid-cell (called source
region) contributes a certain amount of moisture to the same or another grid-cell (sink
region), depending on the global topography and the horizontal moisture flux (Figure
1). So in order to calculate the total precipitation falling in a basin for example, the
precipitation amount ending up in each of the basin’s grid-cells is summed up.
The underlying principle of WAM is the atmospheric water balance (Equation 1),
through which the evaporation (E) flux from the source regions and the precipitation
(P) flux ending up in the sink regions are computed. The inclusion of the moisture
storage term in the water balance equation -neglected by many other researchersmakes WAM a dynamic model and can therefore work on small time scales too.
W (Wu) (Wv)


 E  P 
t
x
y
[L3/T] (1)
4
where W is the total atmospheric moisture, u is the wind speed in the x direction, v the
wind speed in the y direction, E the input evaporation, P the input precipitation and α
the closure error.
Van der Ent et al (2010) added α in Equation 1 because of the difference between the
moisture storage calculated with the water balance equation and the moisture storage
that is computed (instantaneously) in their model. This difference is created because
reanalysis data is not always mass-conservative. In our modification of the WAM we
ignore the closure error as we use CRU and LPJmL land surface data instead. Also in
the case of the grid-cell- and basin-scale calculations, where only one or few grid cells
are involved, α can create artificially more precipitation or evaporation in the model
as individual grid-cells can have a persistent positive or negative residual term adding
or removing much source region water locally. For this reason α will be ignored in the
rest of the analysis.
Tracking moisture of any origin O (i.e. evaporated from O) yields the following
equation:
WO (WO u ) (WO v)


 EO  PO (2)
t
x
y
Where WO is the part of the atmospheric moisture storage that has origin O, EO is the
evaporation that originates from O and PO is the part of the precipitation that has
origin O.
Using the well-mixed atmosphere assumption it is then derived from Equations 1 and
2 that:
 (WO u )  (WO v)
WO
P
y
 x 
 O
 (Wu )
 (Wv)
W
P
x
y
(3)
The well-mixed atmosphere assumption allows us to calculate desired precipitation
recycling ratios (right-hand side of Equation 3) if we simply know the amount of
atmospheric moisture of a certain origin WO and the total atmospheric moisture W. In
our case, where we are interested in the precipitation of continental origin (i.e.
recently evaporated from a continental region), we consider all the continental areas
as the origin O:
Pc  P
Wc
W
(4)
where Pc is the precipitation of continental origin and Wc is the part of the
atmospheric moisture storage that originates from continents.
5
2.3 LPJmL – WAM coupling
2.3.1 Specific Input
The land surface precipitation values for the forcing of WAM were taken from the
CRU TS 3.0 dataset at a monthly time step and are the same for both land-use
scenarios. The land surface evaporation values were taken from LPJmL simulations of
the two scenarios, also at a monthly time step. All the other meteorological input-data
(including oceanic values of precipitation and evaporation) were taken from the ERAInterim reanalysis dataset. Precipitation and evaporation reanalysis values are given at
3 hour intervals. The zonal and meridional wind speeds and specific humidity
together with the surface pressure values are given at 6 hour intervals. All reanalysis
data are available at a 1.5° latitude x 1.5° longitude grid. All the meteorological data
described above cover the time period 1996-2006. The land-sea mask of the ERAInterim reanalysis was also used to distinguish continental from oceanic grid cells.
We further accumulated the ERA-Interim Reanalysis values of evaporation and
precipitation at a monthly interval so that we could combine the oceanic cell values
with the monthly land surface values of LPJmL. Before this could be realised, it was
necessary to perform linear interpolation on the 1.5° x 1.5° grid values of the
reanalysis dataset so as to adjust them to the grid of LPJmL (0.5° x 0.5°). The WAM
however works on the 1.5° x 1.5° grid so the merged values were linearly interpolated
back to the WAM grid again. The merged data could then be forced into the WAM
and 0.5 hour data of E and P were produced (as all the meteorological input data are
reduced to half hour resolution in order to reduce the Courant number of the model).
Furthermore, from the ERA-Interim specific humidity values we computed the
atmospheric moisture storages between pressure levels and by multiplying them with
the relevant wind speed values, we got the horizontal (vertically integrated)
atmospheric moisture flux globally. Van der Ent et al (2010) showed that the
moisture flux is a main factor in defining moisture recycling patterns. Figure 1 shows
the average horizontal moisture flux for the period 1999-2008, as prepared by van der
Ent et al (2010). The main moisture flux on the Northern Hemisphere from 30°N up
to higher latitudes has clear west-to-east orientation, whereas the main moisture flux
between 30°S and 30°N is east-to-west. At latitudes lower than 30°S, the main
moisture flux has again west-to-east orientation. On a local scale, there are some
mountain ranges that alter the direction of the main moisture fluxes. For example, the
Andes in South America are blocking any oceanic moisture from leaving the
continent, a fact that is actually supporting intracontinental moisture recycling. The
opposite is observed in North America and Africa, where the Rocky Mountains and
the Great Rift Valley respectively are blocking oceanic moisture from entering the
rest of the continent (Van der Ent et al, 2010).
6
Figure 1. Global topography: height above Mean Sea Level (MSL), major rivers, and average
horizontal (vertically integrated) atmospheric moisture flux (1999–2008) (van der Ent et al., 2010)
2.3.2 Comparison of the two different land-use cases
From the LPJmL output evaporation data for the two land-use cases, we calculated the
annual average difference ΔΕ between them as:
E  Ecc  Env
(5)
where Ecc is the evaporation in the current case situation and Env is the evaporation in
the natural vegetation scenario. Further on, we subtracted the continental precipitation
values of the two land-use cases (computed with Equation 4), to see the magnitude of
change in the resulting precipitation of continental origin by the relevant difference in
the land evaporation:
Pc  Pc,cc  Pc ,nv
(6)
where ΔPc is the annual average difference of precipitation of continental origin
between the scenarios, Pc,cc is the precipitation of the current land-use case and Pc,nv is
the precipitation in the natural vegetation scenario. The same procedure was followed
for the basin scale calculations after we defined the amount of evaporation and
resulting precipitation that correspond to each one in each land-use case.
3. Results and discussion
3.1 Annual average evaporation and resulting precipitation differences between
scenarios
The global evaporation data are presented as annual averages (Figures 2 and 3)
together with the difference plot between them (Figure 4). Figures 5 and 6 show the
computed continental precipitation values for both scenarios followed by their
difference plot in Figure 7. The first year of our simulations was used as spin-up as
the model does not know the initial conditions (the atmospheric moisture storage is
considered equal to zero in the first time step) so the images show ten year averages
(1997-2006) of both fluxes.
7
Figure 2. Global annual average continental evaporation for the current land-use case (Ecc≈ 61800
km3/yr)
Figure 3. Global annual average continental evaporation for the natural vegetation scenario (Env≈
61700 km3/yr)
Figure 4. Global annual average continental evaporation difference ΔE (≈ 80 km3/yr) (Equation 5)
8
Figure 4 shows that among other, scattered grid-cells, regions like part of the Northeastern USA, Southern Brazil, Central/Western Africa and North-eastern China show
a substantial decrease in evaporation (up to 50 mm/yr in some of them), due to the
land-use change. This means that the current case vegetation has affected these
regions negatively in terms of evaporation. Parts of Southern America and a few gridcells in Europe experience a decrease of evaporation of up to 30-40 mm/yr in some
cases. These spots actually show the regions where deforestation has taken place over
the last decades with a direct effect on evaporation.
In general Figure 4 reveals that the current land-use seems to produce smaller
evaporation amounts for most of the world regions, in a range of 2-50 mm/yr. The
white spots on the land surface are mostly deserts where no evaporation is present. On
the other hand, if we focus on India and Nepal and certain grid-cells of China we
observe that evaporation has increased enormously on an annual scale in the current
land-use case. There are grid cells there that produce more than 100 mm/yr of
evaporation compared to the natural vegetation case and this is because intense, large
scale irrigation is taking place in these parts of the world nowadays. So, these places
have become much stronger as sources of moisture in the current land-use case.
The global annual average difference calculated from Equation 5 equals 80 km3/yr
(Figure 4). This fact reveals that although most of the world regions seem to
experience negative evaporation changes, the total difference globally is positive,
although still quite negligible compared to the total amount of evaporation produced
in each scenario (Figures 2 and 3). Comparing our calculated total values and
differences with the estimates other studies we see that our total values are in
accordance with the previous estimates (Table 1).
Table 1: Comparison of our evaporation input data with previous estimates (in km3/yr)
Ecc
Env
ΔE
61795 61714
80.4
Nikoli et al., 2011
62970
-598
Rost et al., 2008a
-400
Gordon et al., 2005 66600
The differences in the estimates of Table 1 are data-related (e.g. different precipitation
data and time-periods), model-related (e.g. Rost et al. (2008a) include a river routing
scheme in their simulations with LPJmL and they also include evaporation from
canals and lakes) and post-processing-related (e.g. a critical point here is always how
each researcher calculates the global sum, how they use agricultural areas as a weight
in each grid cell etc). After all, the percentage change in evaporation (-0.9% in Rost et
al. and +0.14% in our data) is smaller than the uncertainty in the evaporation
estimations from different scenarios, only occurring from uncertainties in the
precipitation data (around 3% for Rost et al., 2008a).
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Figure 5. Global annual average continental precipitation for the current land-use case (Pc,cc≈ 58520
km3/yr)
Figure 6. Global annual average continental precipitation for the natural vegetation scenario
(Pc,nv≈58460 km3/yr)
Figure 7. Global Annual Average Continental Precipitation Difference values (ΔPc≈61 km3/yr)
(Equation 6)
10
Focusing on the precipitation of continental origin differences between the scenarios,
Figure 7 reveals that the current land-use case has resulted in less precipitation of
continental origin for Central and Western Africa and a small part of South America,
in a range of 2-30 mm/yr. On the other hand though, for certain parts of Asia
continental precipitation is boosted in the current case; especially Nepal but also
China receive much more precipitation of continental origin nowadays; Nepal even
more than 80 mm/yr. This country is favoured immensely by the direction of the
horizontal moisture flux (heading from India towards it) and the Himalayas mountain
range right next to it (Figure 1), as the evaporation that is produced from India’s
agriculture returns as precipitation in Nepal due to the orographic lifting effect.
The total annual average value of precipiation of continental origin difference
globally is calculated equal to 61 km3/yr (Figure 7), also negligible compared to the
total average values for each scenario (=0.1% difference) (Figures 5 and 6). Our result
is in accordance with the estimate of Chase et al. (2000), who calculated the January
precipitation difference between natural and current vegetation and found that the
global change is very small (-0.03% difference for a 10-year average of Januaries over
latitudes 30°S-30N).
3.2 Seasonal moisture flux differences between scenarios on specific grid-cells
In order to zoom into the seasonal variations of the moisture fluxes’ differences, we
selected 5 grid-cells around the world that showed some of the most prominent
changes in their moisture fluxes between the different land-use cases and plotted them
in time at a monthly step. Figure 8 shows the location of these grid-cells with a red
square pin on each of them. They are located in the North-eastern USA, Nigeria,
Northern India, Nepal and South-eastern China as can be seen on the world map
(Figure 8).
Figure 8. The location of the five characteristic grid-cells with prominent differences in their moisture
fluxes between the land-use cases
In these regions, we observe clear patterns of evaporation and continental
precipitation difference during a year that change per season and per area of the world
we are looking at (Figures 4 and 7). Looking at the grid-cell in North-eastern USA
(Figure 9) we see that although Pc does not change much between the two scenarios
(it stays in the range of 5mm/month during the 10 year period we examined and is
often close to zero), the E changes are quite substantial. They follow the pattern of Pc
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(negative change in April-May/September-October – positive change in June-July due
to the land-use change from natural to the current case - Eq. 6) but the reduction in the
current case reaches the -24mm/month in some years and the increase goes up to
18mm in a month. This fact has made this specific grid-cell quite weaker as a source
region in the current land-use case but it has not been affected much as a sink region;
probably because the precipitable moisture that comes into the region according to
Figure 1 belongs to grid-cells in Southern North America which have no substantial
change in their evaporation (less than 10 mm/yr) and to the ocean.
Figure 9. Monthly Evaporation (ΔE) and Precipitation Difference (ΔPc) in the USA grid-cell (19972006)
In Northern Nigeria (Figure 10), the grid-cell has a similar behaviour; during the
winter months both the ΔE and ΔPc are close or equal to zero. Negative E values of up
to -38mm/month are noted during the late spring-early summer months (May-June)
and in Autumn (September-October), meaning that during these months the land-use
change up to now has decreased the evaporation in this region up to 38 mm/month.
ΔPc follows the same pattern with its highest decrease observed in November (up to 11mm/month). This pattern is in accordance with the wet and the dry seasons of
Southern Nigeria; it experiences a long and a short rainy period during March-July
and October respectively as well as a long and a short dry season during late OctoberMarch and in the last weeks of August respectively. In general this grid-cell gets
fairly weaker as a source and less weak as a sink due to the land-use change.
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Figure 10. Monthly Evaporation (ΔE) and Precipitation Difference (ΔPc) in the Nigerian grid-cell
(1997-2006)
The situation in India (Figure 11) changes abruptly (as we already saw in the annual
average difference plots); very large changes of E are noted but in this case they are
only positive in response to the land-use change. Only from October to December in
1997 is the ΔE negative (a bit more than -5 mm/month). For the rest of all months and
years E has risen tremendously even up to 96mm/month (in April, 2005). April is the
month of the greatest E additions every year but the rising limbs of the graphs have
their start in December or January of every year. In this region we observe a great
increase of evaporation on an annual scale because of the large scale irrigation that
takes place there nowadays. Land-use change has rendered this place much stronger
as a source of evaporation as crops are a vast source of moisture in comparison to the
natural land cover. It is also not a coincidence that India is known as the "land of the
endless growing season" (Facts about India: http://www.facts-aboutindia.com/seasons-in-india.php). This probably explains why we observe increases
almost all year round. Pc has again no substantial change as India’s moisture is mostly
provided by the Indian Ocean.
Figure 11. Monthly Evaporation (ΔE) and Precipitation Difference (ΔPc) in the Indian grid-cell (19972006)
13
The grid-cell in Nepal (Figure 12) exhibits a totally opposite behaviour from its
neighbouring cell in India. Here E is almost not affected by the land-use change. In
contrast to India though, E in Nepal is reduced, though only between 0-3 mm/month.
Its Pc however follows almost the exact same pattern as India’s E and reaches its
highest value in May 2006 (17 mm/month rise in the current case). Consulting the
moisture flux map (Figure 1) we see that the grid-cell of India is a source region of
moisture for the Nepalese grid-cell, and the substantial increase in evaporation in
India explains the positive effect on the ΔPc of its sink regions. The Himalayan
mountain range also blocks most of the moisture coming from the West to continue to
the East side of the continent and Nepal’s location next to this range favours it as a
sink, due to the local moisture recycling that the orographic lifting effect causes to the
blocked moisture.
Figure 12. Monthly Evaporation (ΔE) and Precipitation Difference (ΔPc) in the Nepalese grid-cell
(1997-2006)
The Southeast Chinese grid-cell (Figure 13) has almost the same response to the landuse change as the Nepalese cell. The current land-use causes mostly less E compared
to the natural land-cover although only up to -6mm/month - and increases appear
mainly during January-February and June. Pc however, has an almost steady positive
trend meaning that land-use change has caused an increase of Pc in it, of a maximum
of 18mm/month in April 2001. Such a large rise in the precipitation of continental
origin could only mean that the source regions supplying moisture to this part of
China experience a rise of evaporation in the current case. Figure 1 shows that India
and Nepal are source regions for this grid-cell, and the substantial increase in their
evaporation explains the positive effect on the Pc of their sinks.
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Figure 13. Monthly Evaporation (ΔE) and Precipitation Difference (ΔPc) in the Chinese grid-cell
(1997-2006)
3.3 Effects of the land-use change on the ‘Challenge Program Basins’
Nine large river basins were chosen to be analysed further, which comprise of the so
called ‘Challenge Program basins’ (CGIAR Challenge Program on Water and Food,
http://www.waterandfood.org/). These are water stressed river basins scattered over
Africa, Asia and America. In Africa the river basins of interest are the Limpopo, the
Nile, the Niger and the Volta. In Asia the Ganges-Brahmaputra, the Indus, the
Mekong and the Yellow River basin are investigated. Finally, in South America the
São Francisco basin is examined. Figure 14 shows the location of forty-six major
basins on the world map (Döll et al., 2002). The nine basins that comprise our study
regions are shown in yellow colour.
Figure 14. The nine basins of interest as shown on the world map (Döll et al., 2002)
The differences in evaporation and continental precipitation for each of the basins
between the two land-use cases are presented in Tables 2 and 3.
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Table 2. Annual ΔΕ in each basin (1997-2006, Equation 5)
ΔE (mm/yr)
1997 1998 1999 2000 2001 2002 2003
São Francisco
-3.81 -2.59 -3.44 -2.98
-6.30 -1.94 -3.26
Volta
-23.30 -27.87 -27.49 -24.42 -25.36 -26.98 -28.30
Niger
-16.71 -22.68 -25.77 -15.66 -16.26 -19.13 -20.64
Nile
-3.63 -5.48 -5.05 -3.71
-2.51 -3.88 -4.28
Limpopo
-19.54 -7.16 11.06 19.23
1.53
-2.90 -2.36
Indus
79.02 79.95 91.73 106.23 104.96 97.32 83.24
Ganges-Brahmaputra 53.35 47.63 57.36 63.01 56.72 59.26 57.09
Mekong
9.39 12.10 12.19 10.92
11.02 12.72 11.17
Yellow
6.28
2.77
8.01
9.60
5.76
2.49
4.83
2004 2005 2006 AVG
-3.83
-27.30
-19.97
-3.20
10.58
81.38
51.65
13.83
-1.34
-3.40
-27.42
-20.22
-3.37
-1.36
79.91
57.25
12.34
14.05
-5.33 -3.7
-29.20 -26.8
-19.15 -19.6
-4.61 -4.0
-22.48 -1.3
87.95 89.2
57.56 56.1
10.93 11.7
6.91
5.9
Table 3. Annual ΔPc in each basin (1997-2006, Equation 6)
ΔPc(mm/yr)
São Francisco
Volta
Niger
Nile
Limpopo
Indus
Ganges-Brahmaputra
Mekong
Yellow
1997 1998 1999 2000 2001 2002 2003 2004
2005
2006
AVG
-0.5
-11.1
-7.6
-3.1
-3.3
18.5
25.1
9.6
3.0
-0.2
-12.3
-9.0
-2.7
-1.0
15.6
29.9
6.6
2.0
-1.5
-10.1
-6.9
-2.0
-3.7
15.2
27.6
6.4
1.5
-0.9
-10.8
-8.0
-2.6
-1.1
16.9
28.9
7.9
2.9
-1.1
-11.5
-7.3
-2.7
-3.8
15.0
25.0
6.9
3.6
-1.5
-10.9
-8.5
-2.6
0.2
19.3
25.8
8.4
4.2
-0.2
-8.4
-8.5
-2.4
1.6
14.6
38.0
9.8
3.1
-1.7
-10.4
-7.2
-2.4
-0.9
19.7
26.7
7.3
1.1
-0.6
-9.4
-7.9
-2.3
-0.2
16.8
34.2
6.4
4.8
-0.8
-12.4
-8.2
-3.5
-0.8
19.8
27.7
8.9
3.6
-0.7
-11.8
-8.7
-2.1
0.5
14.8
28.7
8.7
1.9
From Tables 2 and 3 we conclude that land-use change has resulted in increased E
and Pc in all the basins that belong to the Asian continent. Especially Indus has gained
on average 90mm/yr of E and around 17mm/yr of Pc. Ganges-Brahmaputra comes
second in terms of evaporation and first in terms of precipitation increase. These two
basins fall in the location of the 2 characteristic grid cells that we chose in India and
Nepal so their annual values of differences confirm the conclusions we made in
Section 3.2. Mekong and especially Yellow River basins are not so vastly affected by
the land-use change. Mekong receives most of its moisture from the ocean according
to Figure 1 and Yellow from parts of the Eurasian continent, where land-use change
has not affected the evaporation significantly (Figures 1 and 4). Still the Asian River
basins confirm their dependence on irrigation in terms of their reinforced role as
sources and the exchange of moisture between them.
The rest of the basins that lie in Africa and South America experience only negative
flux changes by the land-use change. Volta has the most negative response,
misplacing 27mm/yr of E and 11mm/yr of Pc on average with the current land-use.
Niger, as a neighbouring basin has also some remarkable change of E yearly (-20
mm/yr on average) and -8 mm/yr of Pc. Nile, Limpopo and São Francisco do not
show any significant fluctuations annually (-1-4mm/yr for both fluxes on average)
mainly because they are situated close to the oceans and receive some of their
moisture from there and because their land moisture sources are also not greatly
affected by the land-use change.
In Appendix B a further analysis of the basins’ dependence on moisture of continental
origin is given and the relation of this dependence with the average wetness or
dryness of a year is investigated.
16
4. Conclusions and recommendations
In this paper we quantified the effect of land-use change on moisture recycling on a
global and on the basin scale. Our results show that there is a dynamic relation
between the two processes. Although there are no significant changes of the global
average moisture fluxes’ values between the land-use cases, strong effects are
observed if we focus on specific regions.
We found that in regions, where intense irrigation takes place in the current land-use
case especially evaporation but also resulting precipitation have increased
significantly compared to the natural vegetation scenario. On the other hand, in
regions like Western-Central Africa and South-eastern South America evaporation
has been reduced in the range of 30-50 mm/yr and precipitation between 15-25 mm/yr
due to the land-use change.
The topography and global wind patterns play a very important role in this respect.
When land-use change is combined with mountain ranges like in the case of Nepal its
effect is very prominent on the local moisture recycling. However, when the change
occurs at a region where the resulting moisture difference will finally be transferred to
the oceans by the wind, no difference is caused on the moisture recycling of a land
region.
From the seasonal variations examined on the characteristic grid-cells we conclude
that each region has its own temporal response to land-use change, depending on its
wet and dry seasons and therefore also on its location and geographical zone on the
global map as well as on the type of vegetation change and the seasonal activity
patterns of potential natural and current actual vegetation.
Among the basins we examined, the ones that belong to the Asian continent show
increased evaporation and continental precipitation amounts from the land-use
change, due to the current large scale irrigation in India and Nepal and the support by
the local topography (Himalayan mountain range) and the wind directions. Therefore
Indus shows the highest increase in evaporation due to the land-use change and
Ganges Brahmaputra the highest continental precipitation differences. On the other
hand, Niger and Volta belong to this part of the world (Western Africa) where landuse change has reduced the moisture fluxes and this effect is depicted on the basins’ E
and Pc values.
The land-use change also causes differences in the atmospheric temperatures and the
circulation patterns (through the evaporation change and therefore the changes in the
surface pressure distribution (Goessling and Reick, 2011)). The data we used to
calculate the average horizontal moisture flux in the given time period (Figure 1), was
assumed to be the same for both land-use scenarios. It is therefore suggested that in a
future study, the change in the circulation patterns is taken into account, so that a
more detailed analysis as to where the moisture travels to, is performed.
Furthermore, it would be interesting if a second order estimate of the impact that landuse change has on global precipitation is quantified. The results that we presented in
this paper offer a first order estimate of this impact, because we used precipitation
17
input data that correspond to the current land-use case also for the natural vegetation
simulation. By performing a second simulation of the natural vegetation scenario,
adding to the initial input data the precipitation of continental origin that we
calculated here, will give as a better understanding of the extent to which rainfall
depends on land evaporation. Nevertheless, we do not expect major changes either in
the patterns or in the difference between the current and the second order natural
vegetation moisture fluxes. The differences in the fluxes are expected to be enhanced
but only to a small extent, as the small global differences reveal in our first order
simulation of land-use change.
Finally, a combination of our analysis with additional socio-economic data could lead
to a quantitative assessment of a region’s (or basin's) resilience to global change.
Future scenarios of relevant socio-economic and environmental input on land-use
could also be taken into account.
Appendix A
An overview of the distribution of potential natural vegetation scenario and
agricultural vegetation in the LPJmL simulations are presented in Figures 15, 16 and
17. Natural vegetation is represented by nine plant functional types (PFTs) (Figure
15) and agricultural vegetation by 12 crop functional types (CFTs) representing field
crops as well as pasture (Figures 16 and 17). Agricultural vegetation can be either
rainfed or irrigated (Rost et al., 2008a).
Figure 15: Natural vegetation scenario: LPJmL-simulated dominant plant functional types in 2000 (7
woody and 2 herbaceous) (LPJmL version 3.5.003, prepared by K.Waha, PIK)
18
Figure 16: Distribution of rain-fed and irrigated area of 11 crops for the period 1998-2002 (LPJmL
version 3.5.003, prepared by M.Fader, PIK)
Figure 17: Distribution of rain-fed and irrigated maize area (fraction of cell area) for the period 19982002 (LPJmL version 3.5.003, prepared by M.Fader, PIK)
Appendix B
B.1 Continental precipitation recycling ratios of the CP Basins
Here, we have calculated what van der Ent et al (2010) call the continental
precipitation recycling ratios for all the CP basins, per year:
 c (t , x, y ) 
Pc (t , x, y )
[-]
P(t , x, y )
(7)
where ρc: continental precipitation recycling ratio
Pc: precipitation of continental origin (falling in the basin)
P: total precipitation (falling in the basin)
By means of this ratio we define the fraction of precipitation that is of continental
origin for all the basins and both scenarios and we investigate each basin’s
dependence on precipitation of continental origin.
19
The yearly averaged continental precipitation recycling ratios of each basin for both
land-use cases are presented in Tables 3 and 4. There is no need to compare the two
scenarios in terms of their ρc values, as we have already compared the differences in
their Pc in section 3.3 and the ρc difference is proportional to the Pc difference
(Equation 7). After all, the differences in the ρc values are not very prominent between
the scenarios as one can see in Tables 4 and 5. So for the comparison between the
basins’ dependence on moisture of continental origin, we just focus on the values of
the current case scenario for ρc.
Table 4. Annual continental precipitation recycling ratio for all basins in the current land-use case
(1997-2006)
ρc,cc (%)
São Francisco
Volta
Niger
Nile
Limpopo
Indus
Ganges-Brahmaputra
Mekong
Yellow
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 AVG
26.3
45.9
50.7
38.1
29.6
43.1
43.9
22.3
68.1
25.8
44.2
46.2
42.1
31.6
42.3
38.3
20.8
66.2
26.9
40.7
43.3
40.9
27.4
43.0
37.1
20.5
71.4
26.3
44.9
49.7
38.9
30.2
40.5
37
21
69.7
21.5
45.7
50.4
44.7
29.7
48
39.3
20.8
71.5
28
44.5
49.2
40.7
23.4
39.2
39.6
21.2
69.4
23.2
44.2
47.8
46.2
25.4
42.4
37
22.2
70.5
29.8
51
53.2
41.8
28
42.4
35.7
21.9
69.6
30
51.1
54.4
44.8
28.7
46
36.1
21
68
31.6
45.3
46.7
45
31.1
44.9
38.3
22.7
68
26.94
45.75
49.16
42.32
28.51
43.18
38.23
21.44
69.24
Table 5. Annual continental precipitation recycling ratio for all basins in the natural vegetation
scenario (1997-2006)
ρc,nv (%)
São Francisco
Volta
Niger
Nile
Limpopo
Indus
Ganges-Brahmaputra
Mekong
Yellow
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 AVG
26.4
47.1
51.7
38.6
30.1
39.9
41.7
21.7
67
25.9
45.4
47.1
42.5
32.2
39.3
36.6
20.4
65.3
27.1
41.7
44.3
41.2
27.3
39.0
35.2
20
70.3
26.3
45.9
50.8
39.3
30.1
36.1
34.1
20.4
68.9
21.7
46.9
51.4
45.1
29.8
43
37.1
20.4
71.2
28.1
45.6
50.3
41
23.4
35.2
36.7
20.8
68.3
23.3
45.3
48.8
46.7
25.5
39
34.8
21.6
69.8
29.9
52.3
54.4
42.2
27.9
38.8
33.4
21.4
69.1
30.1
52.4
55.6
45.2
28.9
42.7
33.7
20.5
67.4
31.7
46.4
47.7
45.3
31.7
41.9
36
22.2
67.5
27.05
46.9
50.21
42.71
28.69
39.49
35.93
20.94
68.48
Yellow has the highest values of ρc among all the basins, reaching 69% in the current
land-use case. This reveals the basin’s strong dependence on rainfall that has its origin
in upwind continents (mainly the Eurasian continent). In case the land-use changes
more drastically for the source continents of Yellow in the future then its annual
precipitation can be greatly affected either positively or negatively depending on the
change.
Niger and Volta follow Yellow with a 49% and 46% dependence on continental
moisture recycling respectively. These numbers are quite impressive if we take into
account that the basins are situated next to the Atlantic Ocean; one would expect that
the rainfall would be mostly supplied by it; however the wind patterns and the
topography of the region obviously boost continental moisture recycling (Figure 1).
The same holds for the São Francisco and the Limpopo river basins, which get a
contribution of continental rainfall close to 30%, a respectful percent if we consider
their location right next to the oceans.
20
Indus, Ganges-Brahmaputra and Nile River basins are three of the largest basins
among all and receive around 40% of their moisture from continental sources.
Mekong seems to be somewhat more dependent on oceanic moisture though. This can
be explained by its location too (lies between the Indian and the Pacific Ocean at
around 30°N latitude), where the moisture flux seems to be coming from both
directions (easterly and westerly) to the regions that are situated there.
To conclude, Yellow proves to be most reliant on precipitation of continental origin
and Mekong the least one, due to their location and the prevailing circulation patterns
(Figure 1), which in the first case render the Eurasian continent as the source of
Yellow whereas in the case of Mekong, the ocean is the main supplier of moisture.
The rest of the basins are quite dependent on moisture of continental origin (with
ratios ranging from 27-50%), making moisture recycling of key relevance for the
distribution of their water resources, keeping in mind that they are all water scarce
river basins.
B.2 Impact of the annual wetness on the basins’ precipitation recycling ratios
We further compared the annual temporal variability of the basins’ continental
precipitation recycling ratios for the current land-use case with their total annual
precipitation. In Table 6 we cite the values of the averaged total precipitation that
every basin receives in a year. An average value of precipitation has been calculated
per basin for the 10-year period too and compared to this we classify the annual
values as ‘wet’ or ‘dry’, depending on whether they are higher or lower than the
average value respectively. In Table 6 we have marked all the ‘wet’ years with a cyan
colour and all the ‘dry’ ones with a pale yellow colour.
Table 6. Averaged Total Precipitation for all the basins (same for both scenarios, 1997-2006)
Ptotal (mm/yr)
São Francisco
Volta
Niger
Nile
Limpopo
Indus
Ganges-Brahmaputra
Mekong
Yellow
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
AVG
969
935
729
692
640
585
1160
1525
277
850
1001
805
701
597
513
1406
1424
411
880
1106
871
709
554
487
1339
1703
360
1025
841
764
648
965
329
1316
1605
380
920
889
709
643
602
393
1191
1622
312
925
902
710
629
426
427
1164
1585
421
876
1099
867
673
438
582
1229
1395
485
1144
924
740
571
592
409
1244
1503
379
1086
953
742
599
500
473
1244
1515
324
953
905
686
639
638
523
1206
1462
361
963
956
762
650
595
472
1250
1534
371
Further on we plotted all the ρc values against the total precipitation of each basin per
year to observe possible trends between the ratio’s distribution and the wetness of
each year (Figure 18). We then forced some linear trend-lines in each graph and we
saw that most basins with the exception of São Francisco, Limpopo, Indus and
Yellow have higher continental recycling ratio values during dry years. The relation
between the two variables is proportional in São Francisco and Limpopo whereas
Indus and Yellow do not really respond to the wetness or the dryness of the years and
they show a similar behaviour throughout all the years.
21
Figure 18. Annual continental precipitation recycling ratio plotted against the total precipitation in the
basins (1997-2006)
For the basins that have an inversely proportional relation between the two variables,
it means that the origin of the moisture that they receive in dry years is mostly
continental. The amount of total precipitation decreases during the dry years and the
continental precipitation recycling ratio increases in comparison to the wet years,
meaning that these basins are more dependent on continental moisture during the dry
years.
For Indus and Yellow (and also Ganges-Brahmaputra), we observed that even due to
the land-use change, their precipitation of continental origin did not change
significantly, especially in the Yellow River basin (Table 2). This means that their ρc
values do not generally respond to changes of the land-use, mainly because their
sources of moisture are not really affected by land-use changes. Especially for Indus
and Ganges-Brahmaputra the orographic effect due to the Himalayas is dominant and
the effect of relatively wet or dry years on the moisture recycling is therefore absent.
The proportional relationship for São Francisco and Limpopo shows however that
probably mechanisms of local moisture recycling are taking place there as ρc and
therefore Pc increases together with the total precipitation in the basin. The São
Francisco Ridge in the Brazilian Highlands could be a possible mechanism that boosts
local moisture recycling throughout all years for São Francisco and especially during
the wet ones as more moisture is supplied to be locally recycled then. The orographic
effect of a mountain range that is present in the Limpopo River basin could also be
responsible for the same effect there.
Generally, no solid conclusions can be drawn from the relationship between the two
variables, but we suggest that further research is executed on how the local
22
topography and circulation patterns affect the continental moisture recycling in these
regions.
References
Berrisford, P., D. Dee, K. Fielding, M. Fuentes, P. W. Kållberg, S. Kobayashi, and S. M.
Uppala (2009): The ERA‐Interim archive, http://www.ecmwf.int/research/era/do/get/erainterim, Eur. Cent. for Medium Range Weather Forecasts, Reading, U. K.
Bonan, G. B. (2008): Forests and Climate Change: Forcings, Feedbacks, and the Climate
Benefits of Forests, Science, 320(5882), 1444-1449.
Bondeau, A., Smith, P.C., Zaehle, S., Schaphoff, S., Lucht, W., Cramer, W.,Gerten, D.
(2007): Modelling the role of agriculture for the 20th century global terrestrial carbon
balance. Global Change Biology. 13, 679-706.
Bosilovich, M.G., and S.D. Schubert (2002): Water Vapor Tracers as Diagnostics of the
Regional Hydrologic Cycle. Journal of Hydrometeorology 3, no. 2: 149-165.
Bosilovich, M.G., and J-D. Chern (2006): Simulation of Water Sources and Precipitation
Recycling for the MacKenzie, Mississippi, and Amazon River Basins. Journal of
Hydrometeorology 7, no. 3: 312-329.
Brubaker, K. L., D. Entekhabi, and P. S. Eagleson (1993): Estimation of continental
precipitation recycling, Journal of Climate, 6(6), 1077–1089.
CGIAR Challenge Program on Water and Food, http://www.waterandfood.org/, last
accessed on 11/11/2011.
Chase, T. N., Pielke Sr, R. A., Kittel, T. G. F., Nemani, R. R. & Running, S. W. (2000):
Simulated impacts of historical land cover changes on global climate in northern winter.
Climate Dynamics,16, 93-105.
Budyko, M. I. (1974): Climate and Life. 508 pp., Academic, New York.
D'Almeida, C., C. J. Vorosmarty, G. C. Hurtt, J. A. Marengo, S. L. Dingman, and B. D. Keim
(2007): The effects of deforestation on the hydrological cycle in Amazonia: a review on
scale and resolution, International Journal of Climatology, 27(5), 633-647.
Dallmeyer, A., and M. Claussen (2011): The influence of land cover change in the Asian
monsoon region on present-day and mid-Holocene climate, Biogeosciences, 8(6), 1499-1519.
De Groen, M.M., and H.H.G. Savenije (1996): Do land use induced changes of evaporation
affect rainfall in Southeastern Africa?, Physics and Chemistry of the Earth, Vol. 20, No. 5-6,
pp.515-519
Döll, Petra; Lehner, Bernhard (2002): Validation of a new global 30-min drainage direction
map.- Journal of Hydrology 258, 214-231.
Dominguez, F., P. Kumar, X‐Z. Liang, and M. Ting (2006): Impact of Atmospheric
Moisture Storage on Precipitation Recycling. Journal of Climate 19, no. 8: 1513-1530.
23
Eltahir, E. A. B., and R. L. Bras (1995): Precipitation recycling, Rev. Geophys., 34(3), 367–
378.
Facts about India: http://www.facts-about-india.com/seasons-in-india.php, last accessed on
21/10/2011.
Findell, K. L., P. Gentine, B. R. Lintner, and C. Kerr (2011): Probability of afternoon
precipitation in eastern United States and Mexico enhanced by high evaporation, Nature
Geosci., 4(7), 434-439.
Goessling, H. F. and Reick, C. H. (2011): What do moisture recycling estimates tell us?
Exploring the extreme case of non-evaporating continents, Hydrol. Earth Syst. Sci., 15, 32173235.
Gordon, L. J., W. Steffen, B. F. Jönsson, C. Folke, M. Falkenmark, and Å. Johannessen
(2005): Human modification of global water vapor flows from the land surface, Proceedings
of the National Academy of Sciences of the United States of America, 102(21), 7612-7617.
Henderson-Sellers, A., R.E. Dickinson, T.B. Durbidge, P.J. Kennedy, K. McGuffie and A.J.
Pitman (1993): Tropical deforestation: modelling local to regional-scale climate change.
Journal of Geophysical Research, 98 (1993), pp. 7289-7315
Keys, P. W., R. J. van der Ent, L. J. Gordon, H. Hoff, R. Nikoli, and H. H. G. Savenije
(2011): Analyzing precipitationsheds to understand the vulnerability of rainfall dependent
regions, Biogeosciences Discuss., 8, 10487–10516.
Koster, R., et al. (1986): Global Sources of Local Precipitation as Determined by the
NASA/GISS GCM. Geophysical Research Letters 13, no. 1: 121-124.
Kunstmann, H., and G. Jung (2007): Influence of soil moisture and land use change on
precipitation in the Volta Basin of West Africa, Int. J. River Basin Manage., 5(1), 9–16.
Lettau, H., K. Lettau, and L. C. Molion (1979): Amazonia’s hydrological cycle and the role of
atmospheric recycling in assessing deforestation effects, Mon. Weather Rev., 107(3), 227–
238.
Lund-Potsdam-Jena managed Land Dynamic Global Vegetation and Water Balance Model:
http://www.pik-potsdam.de/research/research-domains/climate-impacts-and-vulnerabilities/
models/lpjml, last accessed on 26/10/2011
Mohamed, Y. A., B. J. J. M. Van den Hurk & H. H. G. Savenije and W. G. M. Bastiaanssen
(2005): Impact of the Sudd wetland on the Nile hydroclimatology, Water Resources Research,
41, W08420, 14 PP.
Nieto, R., L. Gimeno, and R. M. Trigo (2006): A Lagrangian identification of major sources
of Sahel moisture. Geophysical Research Letters, 33, L18707.
Numaguti, A. (1999): Origin and recycling processes of precipitating water over the
Eurasian continent: Experiments using an atmospheric general circulation model. J.
Geophys. Research, 104(D2), 1957–1972.
Pitman, A. J. (2003): The evolution of, and revolution in, land surface schemes designed for
climate models, International Journal of Climatology, 23(5), 479-510.
24
Pitman, A. J., N. de Noblet-Ducoudré, F. T. Cruz, E. L. Davin, G. B. Bonan, V. Brovkin, M.
Claussen, C. Delire, L. Ganzeveld, V. Gayler, B. J. J. M. van den Hurk, P. J. Lawrence, M.
K. van der Molen, C. Müller, C. H. Reick, S. I. Seneviratne, B. J. Strengers, and A. Voldoire
(2009): Uncertainties in climate responses to past land cover change: First results from the
LUCID intercomparison study, Geophysical Research Letters, 36(14), L14814.
Rost, S., D. Gerten, A. Bondeau, W. Lucht, J. Rohwer, and S. Schaphoff (2008a):
Agricultural green and blue water consumption and its influence on the global water system,
Water Resources Research, 44(9).
Rost, S., D. Gerten, and U. Heyder (2008b): Human alterations of the terrestrial water cycle
through land management, Advances in Geosciences, 18, 43-50.
Savenije, H. H. G. (1995): New definitions for moisture recycling and the relationship with
land-use changes in the Sahel, Journal of Hydrology, 167(1–4), 57–78.
Savenije, H.H.G. (1996a): The runoff coefficient as the key to moisture recycling, Journal of
Hydrology, 176: 219‐225.
Savenije, H.H.G. (1996b): Does Moisture Feedback Affect Rainfall Significantly? Physics
and Chemistry of the Earth 20, no. 5‐6: 507‐513.
Stohl, A., and P. James (2005): A Lagrangian analysis of the atmospheric branch of the
global water cycle. Part II: Moisture transports between earth’s ocean basins and river
catchments. Journal of Hydrometeorology, 6(6), 961–984.
Van der Ent, R. J., H. H. G. Savenije, B. Schaefli, and S. C. Steele-Dunne (2010): Origin and
fate of atmospheric moisture over continents. Water Resour. Res., 46, W09525.
Van der Ent, R. J., and H. H. G. Savenije (2011): Length and time scales of atmospheric
moisture recycling, Atmospheric Chemistry and Physics, 11(5), 1853-1863.
Werth, D., and R. Avissar (2002): The local and global effects of Amazon deforestation,
Journal of Geophysical Research, 107, 8087.
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