Full Paper - PAWEES, Masumoto

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PAWEES 2011 Intl. Conference, 27 Oct. 2011 @ Taipei (National Taiwan University)
Impact assessment method of climate change on agricultural
water use by using a distributed water circulation model
circulation model
Takao Masumoto, 1, 2 Ryoji Kudo,1 Takeo Yoshida,1 and Naoki Horikawa 1
1
2
National Institute for Rural Engineering, NARO, Tsukuba, Ibaraki, Japan.
Graduate School of Life and Environment Science, University of Tsukuba, Tsukuba, Ibaraki, Japan.
This study demonstrates a series of methods of assessing climate change impacts on agricultural water
use. First, we applied a distributed water circulation model incorporating various water uses to the
Mekong River Basin (7,950,000 km2; a representative international basin), and Nam Ngum River
Basin (16,800 km2; a river basin with high hydropower potential). Then, we summarized the
application of the model’s tools to the assessment of future human activities such as dam construction
and irrigation development. Estimations of future climate conditions and impacts on agricultural water
use were made by incorporating a GCM (general circulation model; MRI-CGCM2.3.2) to this model.
Our results can be summarized as follows: 1) Bias corrections were essentially performed to assimilate
probability distributions between the observed and re-created (GCM/RCM) meteorological data series.
2) As the variation in annual maximum daily discharge increases, the maximum discharge will also
change dramatically in the future, possibly increasing the likelihood of flooding. 3) Although the
extent of rain-fed rice-cropping areas will increase owing to increased rainfall, the areas affected by
drought years in the future (2015–2039, 2075–2099) will decrease compared to the present (1979–
2003). 4) The effects of climate change on agricultural water use at any point and at any time period in
the target basins in monsoon Asia were successfully assessed. 5) The proposed assessment method is
an effective tool for examining the effects of human activities, such as dam construction.
Keywords Climate change, Impact assessment, Distributed water use model, Agricultural water use,
Regional scale
Introduction
The use of water resources and agricultural water in the Asian monsoon region belonging to the humid
zone is absolutely different from that in the arid and semiarid areas. The former features distinct dry
and rainy seasons as well as a great diversity in the same area. The people there have maintained
paddy fields while taking full advantage of the regional characteristics for a long time, but not
sufficiently investigated how to evaluate changes in water circulation and their factors as well as the
sustainability of and measures for rice production by the effective use of the regional water use
characteristics. Moreover, there is no in-depth research on the effect of future climate change on the
water circulation and agricultural facilities. Particularly important things are to examine the effect of
climate change on the hydrology, water resources, and disaster prevention by using an effect
evaluation method, and to show concrete examples of whether scenario analysis is possible and
measures can be proposed.
In general, human activities exert global-scale impacts on our environment with significant
implications for freshwater-driven services and hazards for humans and nature [Wagener et al., 2010].
Climate change impacts can be devastating, giving rise to economic disruption and mass migration as
agricultural systems fail, either through drought or floods. Then, the future of hydrology and
vulnerability and uncertainty are discussed by using historical climate simulations and climate
projections [Brekke et al., 2008] and hydrological index or models [Sullivan et al., 2005; Bastola et al.,
2011].
1
It is important to accurately predict and assess climate-driven impacts on water resources for the
relevant scales of planning. However, process-based small-scale hydrologic modeling is data
demanding and large uncertainties exist in data-sparse areas. There are several examples of climate
change assessment on water resources [Leimer et al., 2011], river flows [Kerkhoven and Gan, 2011;
Tao et al., 2011; Frampton et al., 2011], groundwater, recession curves [Wang and Cai, 2010] and
Evapotranspiration (ET) [Donohue et al., 2010; Liu and Yang, 2010].
Most studies on the impact of climate change on regional water resources, however, focus on
long-term average flows or mean water availability, and they rarely take the effects of altered human
water use into account. When analyzing extreme events such as floods and droughts, the assessments
are typically confined to smaller areas and case studies. Changes of regional-based flood and drought
risks arising from global warming are analyzed based on the precipitation observation records
obtained in Japan over the past 100 years [Wada et al., 2005] and as another study presents the
selected study area of Europe [Lehner et al., 2006]. Climate change impact studies have been done
with respect to fluvial flood risk in the UK [Prudhomme et al., 2010] and for changes in flooding
caused by climate change in Finland [Veijalainen et al., 2010]. Linde et al. [2010] aim to enhance the
simulation of future low-probability flood peak events in the Rhine basin and Khatibi [2011] tries to
explain the interactions between flood defence and flood risk management.
On the other hand, a scale problem for the analysis is another issue to discuss. There are many
difficulties in precisely estimating the impacts of climate change on water resources. Several studies
have pointed out the importance of basin-scale investigations for determining regional impacts on
water resources, including scale effects of floods and droughts [Zaitchik et al., 2010; Liuzzo et al.,
2010; Caballero et al., 2007; Chang and Jung, 2010; Gray and McCabe, 2010; Senatore et al., 2011;
Sulis et al., 2011; and Nester et al., 2011].
Monsoon is an integral part of the hydrologic cycle and water availability in South Asia. Global
Climate Models (GCM) are in general agreement that future climate change will have a profound
impact on monsoon. In addition, there are results of multi-country research activities in Africa [Dinar
et al., 2008].' Rainwater harvesting (RWH), the small-scale collection and storage of runoff to
augment groundwater stores, has been seen as a solution to the deepening groundwater crisis in India
[Glendenning and Vervoort, 2010] and for the sustainability of irrigated agriculture [Glendenning and
Vervoort, 2011]. Tu [2006] examined the potential impacts of climate variability and change on water
resources of South Asia, such as Nepal, India, and Bangladesh, while cases of South Asia are
examined for transboundary river basins [Mirza and Ahmad, 2005]. For those areas, risk-based
planning offers a robust way to identify strategies that permit adaptive water resources management
under climate change, such as climate change risk assessments involving reservoir operations [Brekke
et al., 2009], for an example. Meanwhile, the feature of Monsoon areas is categorized as
ecohydrologic factors including vegetation pattern, which are closely related to flood characteristics.
Therefore, the changes to ecohydrologic factors are critical for cataloging existing ecosystem
resources and for understanding the effects of climate change scenarios [Moradkhani et al., 2010] and
for understanding the future climate and the responses of small prairie wetlands [Zhang et al., 2011].
Recent developments of global models and data sets enable a new, spatially explicit and
process-based assessment of green and blue water in food production and trade [Hoff et al., 2010; Liu
and Yang, 2010; Siebert and Doll, 2010]. Knowledge of the virtual water content (VWC) of crops and
especially its possible future developments is helpful for improvements in water productivity and
water management [Fader et al., 2010]. Recent improvements in global hydrological models
consisting of both physically based hydrological and anthropogenic activity modules enabled us to
simulate the virtual water content of major crops consistent with their global hydrological simulation,
such as green water and blue water [Hanasaki et al., 2010], seven case studies distributed over Africa,
Europe and Central Asia [Menzel and Matovelle, 2010], a macro-scale hydrological model [Wisser et
al., 2010], and the IFPRI’s International Model for Policy Analysis of Agricultural Commodities and
Trade (IMPACT) [Sulser et al., 2010], trade-off between economic welfare and environmental
sustainability [Calzadilla et al., 2010], the potential impacts of climate change and CO2 fertilization on
global agriculture [Calzadilla et al., 2011] and to seasonal climate predictions and their applications to
small holder agriculture in developing countries [Sivakumar and Hansen, 2007]. Six spatial
econometric models are estimated to explore over space and time in the Great Plains [Polsky, 2003].
2
Among the urgent research priorities, more comprehensive assessments of impacts that better
represent the uncertainties are needed. One paper presents an assessment of the cost of water scarcity
in Cyprus, today and the next 20 years [Zachariadis, 2010]. Tao et al. [2010] demonstrate the
approach in addressing the probabilistic changes of maize production in the North China Plain in
future. The farming systems model APSIM (Agricultural Production Systems Simulator) was first
calibrated and validated using 3 years of experimental data [Chen et al., 2010]. Based on future
climate change projections offered by IPCC, the responses of yields and water use efficiencies of
wheat and maize to climate change scenarios are explored over the North China Plain [Guo et al.,
2010]. The effects of climate change on grain production of a winter wheat-summer maize cropping
system were investigated [Liu et al., 2010]. On the other hands, a continued challenge in watershed
management is information related to future land cover and its impact on crops [Marshall and Randhir,
2008; Li et al., 2009]. Although agriculture and its water use are obviously sensitive to climatic
conditions, past research has seldom identified the effects of climate variability and climate change on
the fully developed relationship between crop yield and irrigation [Brumbelow and Georgakakos,
2007]. There is potentially great value in understanding the role of climatic uncertainty on this
relationship because of the dependence of agriculture on irrigation and the scale of water consumption
for irrigation. There have been several trials surface irrigation in southeastern Colorado [Elgaali et al.,
2007] and in China [Li et al., 2010]. Fertile land and freshwater constitute two of the most
fundamental resources for food production. With a global forest and agricultural sector model, Sauer
et al. [2010] quantify the impacts of increased demand for food due to economic development on
potential land and water use until 2030. The work analyzed the repercussions that alternative irrigation
management can cause on water use and on the quality of irrigation return flows [Garcia-Garizabal
and Causape, 2010; Xie and Cui, 2011], which develops the SWAT model by incorporating new
processes for irrigation and drainage.
However, in the analysis of basin-scale impacts of climate change on agricultural water use, we
have to incorporate rain-fed agricultural lands as well as irrigated ones, which dominate in monsoon
Asia over 90 % of the whole areas. There also exist several types for irrigation patterns relating to the
respective water use mechanism. In addition, the combination of multiple land covers should be
combined in a analysis unit of surface hydrologic models. In addition, The SWAT model is a
quasi-distributed model, which disaggregates the target basin into pieces of sub-catchments, so that it
is difficult to able to provide us with arbitrary point values for the necessary output information. The
models mentioned above are able to produce with crop yields, but not with the planting and harvesting
timing as well as planting periods closely related to availability of agricultural water resources. Those
points are not solved yet.
Climate change is influencing our future. In practice, however, enough knowledge is not yet
implemented in spatial and agricultural structures’ planning. The spatial challenge or spatial task is to
implement and initiate a shift from this sectoral standardized thinking, with higher risks, towards
multifunctional and flexible thinking based on the dynamics of the natural system. Spatial planning
and design should not only implement the shift, but also needs to play a leading role in the transition.
The key characteristics needed in such a transition are creativity and innovative thinking without
boundaries. New pathways need to be discovered and the future needs to be visualized. Cross-sectoral
thinking and integrated design needs to be enhanced. A highly adaptive, resilient and less vulnerable
environment can be designed if the pressure to fulfill all kinds of standards is minimized. This will
result in a new paradigm: adaptation inclusive planning [Roggema, 2009]. The critical importance of
water is undeniable. It is particularly vital in semitropical regions with noticeable wet and dry seasons,
such as the southern Maya lowlands, in which society dealt with the annual seasonal extremes as well
as a series of droughts [Lucero et al., 2011].
The objective of this research is to develop a series of assessment methods of impacts of climate
change on water resources in the Mekong River basin, which is based on the distributed water
circulation model incorporating agricultural water use [Masumoto et al, in preparation]. In the past, the
effect of global warming on agricultural water has been evaluated on a field basis through
hydrological and water circulation models, into which neither the circulation of agricultural water,
particularly for paddy fields, nor the process of a flood are incorporated. Developing a model that
takes these effects into consideration is essential to improving the estimation accuracy of the
3
agricultural use of water. Meanwhile, it is forecasted that global warming will increase the annual
precipitation of the Asian monsoon region, resulting in a rise in the possibility of the same area having
two disasters: one is a flood due to frequent torrential rain and the other is a severe drought. Both
extreme phenomena revealed require integrated measures rather than independent ones. In addition,
basin analysis and effect evaluation require in-depth work on how to adopt spatial information
included in various data shown by a global climate model as a result from a global warming
experiment. Accordingly, we aim to develop a technique of impact assessment for climate change on
agricultural water use and irrigation facilities and then forecast and evaluate its effect precisely and the
downstream basin of the Mekong River, the Nam Ngum River in Laos, with proposed simulation
models.
Site Description
Whole Basin of the Mekong River
The Asian monsoon zone, where water is variously used for paddies for food production, features high
rainfall exceeding an annual precipitation of 1,000 mm, belonging to the humid climate zone, land use
focusing on rice farming, and multi-functionality of paddies, such as flood prevention, fostering of
groundwater resources, hillside erosion control, and so on [Japanese Institute of Irrigation and
Drainage, 2003]. In this research, we selected the basin of the Mekong River [Mekong River
Commission, 2005] as a typical example that had the features above to verify models.
The Mekong River is an international river that originates its source in the Plateau of Tibet and
that flows in Vietnam via the southwestern part of China, Myanmar, Laos, Thailand, and Cambodia.
The length is about 4,800 km, the basin area is about 795,000 km2, and about 85 percent of the land
area of Laos or Cambodia belongs to the basin. The agricultural area is 350,000 km2 and accounts for
44 percent of the basin area. It includes a paddy area of about 230,000 km 2 (about 66 percent share),
and the most part gathers around the Mekong River running in Laos, the northeastern part of Thailand,
the whole area of Cambodia, and the Mekong River Delta of Vietnam. The irrigated area is only
30,000 km2 (irrigation rate: 8.6%)—most paddy fields are the rain-fed type without irrigation facilities.
However, we can see a variety of irrigation methods in the basin [Shimizu and Masumoto, 2006]. In
the northeastern part of Thailand, irrigation using dams as a water source is distributed widely, while
in a mainstream irrigation system used in the Lao PDR, water pumped from the river is delivered by
gravitation. Cambodian people apply floods caused every year
to rice farming during the flooded water reduction period, feed
water from the river to farmland via earth canals, or take water
out of wells with small pumps. In the Mekong River Delta,
irrigation using a tidal range makes double and triple cropping
possible.
Nam Ngum River Basin
The Nam Ngum River running in the north and middle part of
Laos is one of the Mekong branches (Fig. 1). The river has its
source in the Xieng Khouang Heights about 1,500 m above sea
level and runs down toward the southwestern direction. The
basin of the Nam Ngum Dam accounts for a large area (8,460
km2) and the dam supports the regional water industry. The
annual mean flow rate is 666 m3/s at the confluence of the Nam
Ngum and Mekong Rivers. The regional precipitation varies
greatly, so a flood occurs periodically in the rainy season. The
dam reservoir not only catches runoff in the basin but also
receives water from the Nam Leuk and Nam Mang Rivers for
electric power generation. In recent years, the Laotian
government plans the construction of three dams: Nam Ngum 2,
4
Fig. 1. Location of the Nam Ngum
River Basin
3, and 5. The basin size of the Nam Ngum River is 16,841 km2 at the point where it meets the Mekong
River. The height ranges from 155 m at the confluence to 2,820 m at the source. The basin includes a
mountainous area accounting for a large part, the Vientiane Plain containing a flood area at the
confluence of the Nam Ngum and Nam Lik Rivers, and Jarres Plain situated in Xieng Khouang
Province in the upstream part of the river. The length of the Nam Ngum River is 400 km from the
source (near to Peak District, Xieng Khouang Province) to the confluence with the Mekong River. The
river runs across areas that have sharp climbs made from graywacke or limestone, or that have gentle
slopes made from weak sandstone or mudstone.
The basin belongs to both subtropical and tropical zones and has the distinct rainy season from
May through October. The remainder is the dry season, which may be classified into two: cool dry
(November to February) and hot dry (March and April). The maximum temperature changes from 28
degrees Celsius in December and January to 34 degrees in March and April. The minimum
temperature ranges from 14 to 24 degrees. The historical highest and lowest temperatures are 40 and
4 degrees respectively. The annual mean precipitation of the basin is about 2,000 mm but changes
widely from 3,500 mm at Vaig Vieng Town to 1,400 mm at Xieng Khouang Province. In general, the
southern and northern parts are dry, while the middle part is humid. It is reported that the Penman's
method presents potential evaportranspirations of 1,060 mm/y at Xieng Khouang and 1,360 mm/y at
Vientiane.
A method of predicting impacts of global warming on agricultural water use
Structure of an assessment method of climate change on agricultural water use
An assessment method of global warming on agricultural use is making use of the distributed water
circulation model incorporating agricultural water use [Taniguchi et al., 2009a, b, c; Masumoto et al.,
2009; Masumoto et al., in preparation]. It consists of analyses of climate scenario and social scenario,
the usage of the distributed hydrological model and the display of the analyzed results (Fig. 2). We
will show the features of the distributed water circulation model that is applicable to the Asian
monsoon region and that takes account of crop planting patters and yields relating to a variety of water
use and food production in paddy regions, and describes the process of improving the model to apply
to special basins extracted from the Mekong River, later. We focus on how to develop a dam control
model and incorporate it into the distributed model, and to apply the combined to an area that floods
the downstream part. As the input into the above model, the predicted results, such as daily values of
temperature, relative humidity, solar radiation, precipitation and wind velocity, from GCMs are
utilized (Kudo et al., 2012). Social
scenarios are taken into account as
land use, topographic conditions,
irrigation, cropping patterns and so
on.
Features of the distributed water
circulation model
In the Asian monsoon region, water
is used mainly for paddy fields. The
area features various kinds of
irrigation type, the presence of the
dry and rainy seasons, and the
occurrence of droughts and floods.
However, there is no existing
hydrological and runoff model that
takes the diversity of agricultural
water use into consideration, and
that evaluates the effect of water
Fig. 2. Structure of a proposed assessment method of climate change
on agricultural water use
5
use change on the whole
water
circulation
comprehensively.
Therefore, Taniguchi et
al. [2009] and Masumoto
et al. [2009] have
developed and proposed
a
distributed
water
circulation mod el that
consists
of
multiple
component
models
(evapotranspiration,
planting time and area,
paddy water use, and
runoff) for analyzing the
features above, and that
take account of a variety
of paddy water use.
The whole model
consists
of
four Fig. 3. Structure of the arbitrary mesh of the distributed water circulation model (a
sub-models:
an
hexagon, parallelogram, and rectangle represent input data, output, and
―evapotranspiration
submodel respectively)
model‖ for forecasting
the actual value, a ―planting time and area model‖ for estimating the rice planting state that varies
depending on the paddy field type and precipitation, a ―paddy water use model‖ for evaluating
agricultural water use and control, and a ―runoff model‖ for representing water circulation (Fig. 3).
To validate the model, we applied it to the basin of the Mekong River as a typical example, used the
flow rates measured at five points on the main river and one point on the tributary, and installed our
special instruments for observation. The results of comparing the measured and estimated
evapotranspiration show that the model has good estimation ability. Moreover, we have gotten
important information as shown below [Masumoto et al., in preparation].
1) Inputting the daily precipitation and paddy water use type of the basin in question makes
it possible to estimate the planting state according to the annual water conditions. In an region
whose rain-fed area ratio is high, there is the distinct difference between the reference
evapotranspiration (given by the Modified Penman-Monteith Equation) and the actual one estimated
by the model. Moreover, since the proposed model holds a land use area ratio in each cell, this
makes it easy to evaluate the effect of changes in land use.
2) We classify agricultural water use into rain-fed (3 types) and irrigated (6 types) paddies
and incorporate the features of each type into the paddy water use model to take account of the
ponding/delayed effects of the rainwater in paddies. In this model, we specify various data on each
irrigation type and each facility to find the actual water intake rate by comparing paddy water
demand, facility capacity, and availability of water for intake. Accordingly, it can reproduce the
irrigation state similar to the real one and cover the repeated use of water in a paddy region.
3) The model can estimate the paddy planting area, availability for water intake, and soil
moisture content at an arbitrary point of time and space. In addition, it can evaluate and forecast the
effect of various human activities (changes in agricultural practices) on the water circulation of the
basin, for example, the forecast of the effect of irrigation development on the discharge of the river
shows that such development in a rain-fed paddy field reduces the discharge significantly in the dry
season.
The above shows the result of applying the distributed water circulation model to the Mekong
River having a variety of water circulation properties, but it is also applicable to the whole area or
basin of the Asian monsoon region. Moreover, the model can be used not only to estimate changes in
water circulation due to global warming and the effect on irrigation facilities, water for irrigation,
drainage, and food but also to evaluate flood mitigation and adaptation measures for keeping food.
6
Improvement of the distributed water circulation model by incorporating a dam storage/control model
(1)
Calculation of the water storage
The reservoir storage Vres(t) [m3] is given by the following equation including the storage
Vres(t-1) of the previous period (or the previous day in the case of day-by-day calculation), the
reservoir inflow rate Qresin(t) [m3/d], and the reservoir discharge Qresout(t) [m3/d]:
Vres (t )  Vres (t 1)  Qresin (t )  Qresout (t )t
(1)
where Δt[d] is the duration of one calculation step and Vres(t) = 0 when the water level is the lowest
(so-called dead water level).
The runoff model presents the reservoir inflow rate Qresin(t), and the previous storage Vres(t-1) is
known. Therefore, we can solve the water balance equation by finding the reservoir discharge Qresout(t).
In this model, the reservoir discharge Qresout(t) consists of the water-use discharge Qresuout(t) [m3/d], the
full-water-level discharge Qspill(t) [m3/d], and the retention flow rate Qrf(t) [m3/d] as follows:
Qresout (t )  Qresuout(t )  Qspill (t )  Qrf (t )
(2)
The water-use discharge Qresuout(t) is given by the following equation:


When Qresiout (t )  Qresdout(t )  Qrespout(t ) t  Vres (t  1) ,
Qresuout(t )  Qresiout (t )  Qresdout(t )  Qrespout(t )

(3)

When Qresiout (t )  Qresdout(t )  Qrespout(t ) t  Vres (t  1) ,
Qresuout(t ) 
Vres (t  1)
t
(4)
where Qresiout(t) [m3/d] is the irrigation discharge, Qresdout(t) [m3/d] is the domestic-use discharge, and
Qrespout(t) [m3/d] is the power-generation discharge. We assume that the domestic-use discharge
Qrespout(t) is equal to the planned supply Qdplan[m3/d] shown in the statistical documents.
Qresdout(t )  Qdplan
(5)
The power-generation discharge is classified into three types: i) a given amount of water is
always discharged, ii) water is discharged depending on the storage, and iii) water is discharged
according to the storage and period. In this model, the dam releases water depending on the storage
regardless of the season. Accordingly, the power-generation discharge is given by the following
equation including the maximum power-generation discharge Qrespmax [m3/d] and the effluent
coefficient OR(t).
Qrespout(t )  OR(t )  Qresp max
(6)
The effluent coefficient is proportional to the initial storage regardless of the period.
OR(t ) 
Vres (t  1)
Vres max (t )
(7)
The full-water-level discharge (spillway overflow) Qspill(t) is given by the following equation:
7
When Vres (t  1)  Qre sin (t )t  Vres max ( D) ,
Qspill (t )  0
(8)
When Vres (t  1)  Qre sin (t )t  Vres max ( D) ,
Qspill (t )  V (t  1)  Qre sin (t )t  Vres max ( D)
(9)
where Vresmax(D) [m3] is the period-by-period available storage and D is the number of days from
January 1 at the calculation step t. The available storage is the difference between the amounts of
water at the full and lowest water levels (the latter: the dead water level). Note that if a
flood-limitation water level is specified on a period basis, the available storage is the difference the
amounts of water between the specified and lowest water levels. The storage Vres(t) does not exceed
the period-by-period available storage Vresmax(t).
Water is released so that the discharge does not exceed the full or flood-limitation water level,
which is called the flood-regulation discharge in Japan. In this model, such a situation is called the
full-water-level discharge (spillway overflow) when a flood occurs. The model takes neither storage
limitation nor compensation discharge into consideration, so the retention flow rate Qrf(t) is zero.
Qrf (t )  0
(10)
(2)
Discharge for irrigation
1) Outline
In an irrigation area relying on a reservoir, if the flow rate of the river at the intake point (when
no water is discharged from the reservoir), strictly speaking the runoff of the downstream basin of the
reservoir, is lower than the necessary intake rate, then the reservoir discharges water to compensate the
shortage. Therefore, assuming that the irrigation area has the gross water requirement Qresgw(t) [m3/d]
and the reservoir-downstream-basin runoff Qrsf(t) [m3/d], the necessary irrigation discharge is given by
the following equation, provided that there are neither the limitation flow rate at the intake point nor
the third-party water intake between the intake point and reservoir.
Qresiout (t )  Qresgw (t )  Qrsf (t )
(Qresgw(t )  Qrsf (t ))
(11)
Qresiout (t )  0
(Qresgw(t )  Qrsf (t ))
(12)
The irrigation discharge is too high unless it is given to compensate the flow rate of the river at
the intake point. However in many cases, the reservoir operation model incorporated into the outflow
model does not take such compensation discharge into consideration. The distributed water circulation
model can present the flow rate and the gross water requirement—varying demand for agricultural
water—at an arbitrary intake point. Therefore, it can use the irrigation discharge to compensate the
shortage at the intake point.
2) Runoff of the downstream basin of the reservoir
The reservoir-downstream-basin runoff Qrsf(t) is derived from the flow rate Qsf(t) at the previous
intake cell Dreswd and the reservoir discharge Qresout(t).
Qrsf (t )  Qsf (t  1)  Qresout (t  1)
(Qsf (t  1)  Qresout (t  1))
(Qsf (t  1)  Qresout (t  1))
Qrsf (t )  0
(13)
(14)
Note that Qrsf(t) = 0 if water is taken out of the reservoir and there are beneficial areas on the
right and left sides of the downstream part of the reservoir.
3) Gross water requirement
8
If the gross water requirement Qresgw(t) [m3/d] of the irrigation area is not put to the reservoir
operation model, then we can use an alternative method of finding it: multiplying the gross water
requirement at the intake cell IDreswd by the irrigation area ratio Cresrt. The value on the day is unknown
when we make a reservoir calculation, so we use that on the previous day.
Qresiout (t )  CresrtQgw (t  1)  Qrsf (t )
(CresrtQgw (t  1)  Qrsf (t ))
(15)
Qresiout (t )  0
(CresrtQgw (t  1)  Qrsf (t ))
(16)
The irrigation area ratio Cresrt is the ratio of the irrigation area Aresirmax to the area Ai0 of a cell in
the beneficial region.
C resrt 
Aresir max
Ai 0
(17)
In an irrigation region relying on a reservoir in Southeast Asia, the irrigation area varies
between the rainy and dry seasons, so we use the different values. In the rainy season, the beneficial
region is almost completely irrigated, so the irrigation area is equal to the reservoir-beneficial area
Aresirmax [m2]. In the dry season, the irrigation area is a value depending on the water storage at the
beginning.
[Rainy season]
C resrt 
Aresir max
Ai 0
C resrt 
1 Vres (t dis  1)
Ai 0 WDdry
(
Cresrt 
Aresir max
Ai 0
(
(18)
[Dry season]
Vres (t dis  1)
 Aresir max )
WDdry
(19)
Vres (t dis  1)
 Aresir max )
WDdry
(20)
where tdis is the latest irrigation start day in the dry season and WDdry [m] is the planned paddy water
level in the dry season. We use a water level of 2 m derived from a field survey.
(3) Incorporation with the distributed water circulation model
We set up reservoirs, such as dams and ponds, between the cells of the distributed water
circulation model. We input the discharge Qsfout(t) of a cell in the upstream part of the reservoir to the
dam control model as the reservoir inflow Qresin(t). Meanwhile, we input the reservoir rerease Qresout(t)
presented by the dam control model to the downstream cell as the diacharge Qsfout(t) from the upstream
cell. That is to say, in the downstream cell, the surface inflow rate Qsfin(t) is replaced with the reservoir
rerease Qresout(t). The reservoir irrigation discharge is derived from the gross water requirement Qgw(t)
given by the paddy water use model.
The following shows information (six parameters) necessary to the reservoir: i) Cell number
IDreswd where an intake point is present, ii) Period-by-period available storage Vresmax(t) [m3], iii)
Reservoir-beneficial area Aresirmax [m2], iv) Planned domestic-use supply Qdplan [m3/d], v) Maximum
power-generation discharge Qrespmax [m3/d], vi) Reservoir purpose IDrespps.
Results and Discussion
Extraction of climate forecasts from the GCM
(1)
Super-high-resolution atmospheric model
9
We put future climate forecasts resulting from the general circulation model (GCM) to the
distributed water circulation model incorporating agricultural water use to predict the effect of climate
change on the river flow and dam control in the Nam Ngum River basin.
To know the current and future climate, we employ global warming test results from the
super-high-resolution atmospheric model (hereinafter referred to as the 20 km global model) being
developing by the Meteorological Research Institute through the innovative program for forecasting
climate change in the 21st century (innovative program) [Kusunoki and Mizuta, 2011]. The 20 km
global model divides the earth into meshes 20 km square (0.1875-degree) to precisely forecast extreme
phenomena, such as changes in typhoon, hurricane, and seasonal rain [Mizuta et al., 2006; Kitoh et al.,
2009; Kusunoki and Mizuta, 2011]. The innovative project conducts the pretest of a warming forecast
with the 20 km global model to predict the climate for three periods: the present time (1979-2003), the
near future (2015-2039), and the end of the 21st century (2075-2099) [Ministry of Education, Culture,
Sports, Science and Technology-Japan, 2009]. The test is based on the time slicing method. For this
research, we obtained the three-period calculation results of the region including the basin of the
Mekong River (Lat.: 8.6-33.8 degrees N; Long.: 93.8-108.8 degrees E) for the area in question on the
20 km global model. The obtained data includes precipitation, daily minimum and maximum
temperatures, specific humidity, wind speed, and sea level-calibrated atmospheric pressure. The first
two are forecasted daily, while the remainder is predicted every six hours, which indicates the
1.25-degree mesh for forecasting the specific humidity.
(2) Extraction of the Nam Ngum basin and allocation to the 0.1-degree mesh
We extracted the Nam Ngum River basin from the region and allocated the 0.1-degree mesh to
it so that it was consistent with one for the water circulation model. We employed the inverse distance
weighted method as an interpolation. We could get no data on the relative humidity RH as a result
from the 20 km global model. Therefore, we estimated it from available data—the specific humidity qa
and the sea level-calibrated atmospheric pressure P. The sea level-calibrated atmospheric pressure is
the local one corrected according to the mesh-by-mesh altitude and atmospheric temperature [Ogura,
2003].
We estimated the solar radiation (short waves) from the following equation including the
minimum and maximum temperatures [FAO, 1998]:
Rs  K Rs Tmax  Tmin Ra
(21)
where Rs is the short-wave radiation [MJm-2d-1], KRs is a factor (parameter: 0.16 to 0.19C-0.5), Tmax is
the daily maximum temperature, Tmin is the daily minimum temperature, and Ra is the
extra-atmospheric solar radiation [MJm-2d-1].
(3) Changes in precipitation and reference evapotranspiration in the future
According to the comparison of changes in the present and future precipitation and reference
evapotranspiration, which result from the 20 km global model and the Penman-Monteith equation
respectively, examining the average values in the basins of the Nam Ngum Dam and Hinheup, there is
no large difference in monthly precipitation between both regions, and it will slightly increase in the
second half of the rainy season (September and October) at the end of the 21st century. Meanwhile,
the reference evapotranspiration will increase over a year in proportion of a rise in atmospheric
temperature in the future. These results suggest that climate change will bring a tendency to reduce the
potential quantity of water resources due to a rise in evapotranspiration.
Results of the model improvement
(1)
Land use in the Nam Ngum River basin
Four types of land use are combined in each mesh, namely the ratios (land use area/mesh area)
of forest, rain-fed and irrigated paddy fields, and farmland on a 0.1-degree mesh basis. In addition to
the four kinds of land use, the water area is allotted to each mesh of the distributed water circulation
model.
10
The forest area ratio of each mesh ranges widely from 7 to 100 percent and most meshes
exceeds a 90% share, which means that forest widely spreads over the basin. It reduces in the
downstream part of the Nam Ngum Dam. This area is the Vientiane plain where farmland gathers
around the river. There is farmland in Xieng Khouang Province, the upstream part of the Nam Ngum
Dam, and around the main course of the river in the downstream part of the dam. Forest accounts for
most part of the Nam Ngum basin, so most meshes have no paddy field. However, there are relatively
many rain-fed paddy fields in the downstream part of the dam (Vientiane plain) and in the upstream
part (Xieng Khouang Province). Like the rain-fed paddy field, the irrigated type is not so many but
exists in the downstream part of the dam (Vientiane plain) and in the upstream part (Xieng Khouang
Province). The area ratio is lower than that of the rain-fed paddy field, but there are irrigation areas
mainly along the main course of the Nam Ngum River in the downstream part of the dam. In Xieng
Khouang Province in the upstream part of the dam, water is fed mainly from the river to paddy fields
as a supplementary irrigation means [Asian Development Bank, 2008].
(2) Main reservoirs in the Nam Ngum River basin
1) Existing dams and a weir
The Nam Ngum River basin has three major reservoirs (whose available capacity exceeds 10
million m3): the Nam Ngum 1, Nam Souang, and Nam Houm Dams. Moreover, water for power
generation flows in the basin from the Nam Man 3 and Nam Leuk Dams in the adjacent basin. There is
the Nam Song Weir on the Nam Song River, a branch of the Nam Ngum River, and it feeds water to
the Nam Ngum 1 Dam. In addition of these existing reservoirs, as is mentioned, many dams for power
generation are being constructed or planned.
Nam Ngum 1 dam was completed for power generation in 1971. It has an available capacity of
4.7 billion m3, which is the largest in the downstream basin of the Mekong River. The dam is equipped
with five turbines whose total power is 155 MW and whose water consumption is 465.9 m3/s in total.
The spillway has four radial gates, each being 10 m high and 12.5 m wide, and the design flood rate is
about 4500 m3/s. The operator defines a period-by-period target reservoir level, which is called a rule
or switching curve. When the storage is enough, the dam generates power at a maximum rating of 155
MW around the clock. When it reduces, water is released for power generation as planned in
consideration of demand.
The Nam Souang dam is a dam for irrigation, which is on a tributary of the Nam Ngum River
and whose effective capacity is 94 million m3. The beneficial area is about 3,000 ha and extends over
the Vientiane Special City and Vientiane Province. The Nam Houm dam for irrigation is on a tributary
of the Nam Ngum River with the effective capacity of 54 million m3. The beneficial area is 2,400 ha
and the dam was constructed in 1982. In 2007, the irrigation area for rainy-season cropping is 2,263.2
ha, and the rice field, cash crop land, and fishpond command 98.8%, 0.2%, and 1% shares respectively.
The area planted from 2006 to 2007 is 1,526 ha, and the rice field, cash crop land, and fishpond
command 97.4%, 1.2%, and 1.4% shares respectively. In this region, the main agriculture is double
cropping for rice, and the irrigation area for dry-season cropping varies depending on the reservoir
level at the beginning of the dry season. In the rainy season, supplementary irrigation is performed
during puddling or no rain. The Nam Leuk Dam is for power generation that was constructed on the
Nam Leuk River, a tributary of the Nam Man River in 2000. The dam changes its basin to discharge
water into the Nam San River in the upstream part of the Nam Ngum Dam for power generation. The
available capacity is 200 million and 3,000
Table 1. Parameters of the Nam Ngum 1 Dam
m3 and the maximum power is 60 MW. The
Item
Parameter
Nam Mang 3 Dam is a water use dam for
power generation and irrigation that was
Effective capacity Vresmax [m3]
4,700  106
completed in 2004 and whose effective
Beneficial area Aresirmax [m2]
0
capacity is 45 million m3. The dam sits in the
3
upstream part of th e Nam Mang River
Planned city-use supply Qdplan [m /d]
0
adjacent to the Nam Ngum basin, but
Maximum power-generation
40.3  106
changes its basin to discharge water into the
3
discharge Qrespmax [m /d]
Nam Than River, a branch of the Nam Ngum
Reservoir purpose IDrespps
Power
River, to generate power because the branch
generation
has a higher effective head. The water used
11
for power generation flows into the Nam Mang irrigation region (beneficial area of 2,600 ha) on the
left side of the Nam Ngum River for agricultural use.
The Nam Song Weir sits in the catchment area (1,050 km2) of the Nam Song River, a branch of
the Nam Ngum River. The purpose is to transfer water to the Nam Ngum 1 Dam. The maximum
conveyance rate is 210 m3/s and the weir was completed in 1996.
2) Reservoirs being constructed or planned
In the Nam Ngum basin, many dams for power generation are being constructed or planned.
The ones under construction include the Nam Ngum 2 Dam (whose effective capacity is 2,900 million
and 9,400 m3), the Nam Ngum 5 Dam (250 million m3), the Nam Lik 2 Dam (826 million m3), and the
Nam Ngum 3 Dam (979 million m3). The ones being planned include the Nam Ngum 4A, Nam Ngum
4B, and Nam Pay Dams.
3) Necessary data for applying the improved model to the Nam Ngum River basin
The Nam Ngum basin has three dams whose effective capacity exceeds 10 million m3: Nam
Ngum 1, Nam Souang, and Nam Houm. At this time, we model the Nam Ngum 1 Dam that has the
biggest capacity and an significant effect on the basin. Table 1 lists the parameters of the dam.
The following describes key precautions when the dam control model applies to the Nam Ngum
River basin. The dam has the largest power generation plant in Laos. The operator defines a daily
target water level and derives the discharge from the target and reservoir levels. The target level is the
lowest and highest at the beginning of the rainy and dry seasons respectively. In the model, we define
the power-generation discharge according to the storage (reservoir level), but it is constant over the
year without period-by-period change. In addition to the Nam Ngum 1 Dam, the Nam Souang and
Nam Houm Dams for irrigation have a reservoir whose available capacity exceeds 10 million m3. We
collected information, such as the location and irrigation area, necessary to modeling the two
reservoirs. However, we could show no results in this research because the catchment area was as
small as the area of a single cell, resulting in the difficult calculation of the inflow rate. We need to
review the scale of a reservoir to be modeled in the future. The Nam Ngum basin receives water
released from dams in the adjacent basin. Moreover, the Nam Song River, a branch in the basin, is
changed so that the water flows in the Nam Ngum Dam. We determine that the effect is small in this
year, and do not take the water transmission into consideration. We need to work on how to deal with
water transmission in the future.
(3) Creation of a pseudo river network
We create the network so that the 0.1-degree mesh (about 10 km square) is consistent with the
calculation one of the distributed water circulation model. The altitude of each mesh is given by
averaging 1 km-mesh height data included (GTOP30, USGS). However, this mean altitude is affected
by mountains, which makes it difficult to identify the flowing direction of a river correctly. Therefore,
we determined the flowing direction by sorting the 1 km-mesh height data of each 0.1-degree mesh in
ascending order and averaging the first several to dozen values from the smallest value. We used 25
percent of the data from the smallest value. Firstly, we use the GIS to extract 0.1-degree meshes that
overlap the main course of the river, then determine the flowing direction of each river mesh, and
finally use all the meshes to create a pseudo river network. We identify the flowing direction of all the
meshes except the river ones in the maximum grade method. However, we expect that a problem—the
pseudo network does no pass through some water facilities—is avoidable because the key ones are
constructed on the main river course in many cases. We made such modifications so that the in-depth
data on the river course network is taken into consideration and the basin area of each point where the
water level and discharge are measured is similar to the announced one.
(4) Data for analysis and water balance
1) Meteorological data
For analysis, we got meteorological data from the Department of Meteorology and Hydrology
(DMH) in Laos. The daily data at 13 points includes maximum and minimum temperatures, maximum
and minimum humidity, precipitation, mean wind speed, sunshine duration. As for the sunshine
durations, the data is observed at only 4 of the 13 points. There is no solar radiation data on the Nam
Ngum basin, so the solar radiation Rs is estimated from the following equation including the daylight
hours [FAO, 1998]:
12
n

R s   a s  bs  R a
N

(22)
where n is the daylight hour(s), N is the possible duration of sunshine (h), Ra is the extra-atmospheric
solar radiation (MJm-2day-1), and as, bs are constants (as = 0.25, bs = 0.50).
The daily mean temperature and humidity are given by averaging the maximum and minimum
values. We allocate the meteorological data to the 0.1-degree mesh by the interpolation method.
2) Interpolation of the meteorological data
We employed 8 observation points of the 13 stations at which measurements were made for
eight years from 1994 through 2001. We employed the Inverse Distance Weighted (IDW) method as
an interpolation in which the inverse of the distance from each observation point to each mesh is
defined as a weight—the nearer the mesh, the larger the weight—to find the weighted average for
interpolation. We used the meteorological data at three points that are the nearest to each 0.1-degree
mesh. We corrected the atmospheric temperature of each mesh according to the detemperature rate
(0.65C/100m) and the difference in height from the observation point. Moreover, we estimated the
reference evapotranspiration from the interpolated meteorological data and the Penman-Monteith
equation [FAO, 1998].
3) Hydrological data
As for the inflow rate of the Nam Ngum Dam over 20 years, it is 309.3, 517.4, and 98.1 m3/s in
annual average, in the rainy season, and in the dry season, respectively. According to the reservoir
level over 20 years, the level gradually increases from August, the second half of the rainy season but
decreases from October because the dam discharges water.
The Nam Ngum Dam has received water from the Nam Lik River via the Nam Song Dam since
1996 and from Nam Leuk since 2000. The annual conveyance rate of water fed from the Nam Song
Dam to the Nam Ngum Dam is 68.9 m3/s (117.9 m3/s in the rainy season and 23.7 m3/s in the dry
season), and commands an about 20% share of the average inflow rate from 1996 to 1999. The
conveyance rate is important to the calculation of the water balance of the Nam Ngum Dam or the
basin of the Nam Lik River, but the hydrograph has a missing measurement and no data on the inflow
rate of the Nam Song Dam, which makes it difficult to create a model. Accordingly, we carry on our
analysis without taking this water transmission into consideration. We got other hydrological data
from HYMOS, which includes the water levels and flow rates at four points.
4) Water balance
To analyze the Nam Ngum basin, we
investigated the water balance first. The basin can
be roughly classified into two areas: the Nam Lik
and Nam Ngum Dam basins. We examine the
water balance of each basin. As the flow at the
downstream end, the one measured at Hinheup is
used for the former basin, while the inflow of the
dam for the latter basin. As mentioned before, the
Nam Ngum Dam receives water from other basins,
so we use 80 percent of the inflow of the dam as
the measured one. The factors α and β are found
so that the following equation holds.
R  Ep  Qobs
(23)
where R is the mean precipitation of the basin
(mm), Ep is the mean reference evapotranspiration
of the basin (mm) estimated by using the
Penman-Monteith equation, and Qobs is the
measured flow (mm). Water balance was
13
Fig. 4. Meteorological and hydrological stations in
the Nam Ngum basin
calculated for eight years
from 1994 to 2001. It
presents α = 1.2 and β =
0.7 in the Nam Lik basin
and α = 1.0 and β = 0.7 in
the Nam Ngum Dam
basin. The actual annual
evapotranspiration of the
former and latter basins is
about 900 and 800 mm
respectively.
Ac
cordingly, it is necessary
to
determine
the
parameters
of
the
distributed
water
(a) Hinnup
circulation model so that
the
actual
annual
evapotranspiration is near
to these values in addition
to the observed flow.
(5) Application of the
distributed
water
circulation model
incorporating a dam
control model
1) Applicaion of
the model
We interpolated the
meteorological data to the
0.1-degree
mesh
to
(b) Nam Ngum Dam
generate discharges for
Fig. 5. Results of outflow analysis with the distributed water circulation model
eight years from 1994 and
2001. The distributed
water circulation model incorporating a variety of paddy water use [Taniguchi et al., 2009a, b, and c]
is utilized. The model consists of four sub-models: reference evapotranspiration, planting period and
area, agricultural water use, and outflow to estimate the moisture content of soil, the flow rate of a
river, the cropping area, and the actual intake rate on a mesh basis (Fig. 2). It has a structure that
allows us to find the period-by-period planting stage and irrigation rate according to soil moisture
conditions because each cell has various kinds of information: namely area ratios of forest, rain-fed
and irrigated paddies, and water body as well as irrigation and planting patterns depending on paddy
systems.
Table 2. Parameters of the distributed water
The parameters needed to specify for the model
circulation model
include the saturated permeability coefficient of the
surface layer of soil Ksf [mm/d], the reduction factor
F, the thickness of a root zone Srmax [mm] (forest,
paddy field, and farmland), the thickness of a
saturated zone Szdmax [mm], and the average duration
[days] that it takes for water to reach the river. We
determined these values while taking account of the
ones in all the basins of the Mekong River shown by
Taniguchi et al. and so that the calculated flow rate at
Hinheup and the calculated inflow rate of the Nam
Ngum Dam were equal to the measured ones through
trial and error. In this process, we took account of the Rorder: River magnitude order on the model
14
actual annual evapotranspiration estimated when examining the water balance. The identification
period is from 1996 to 1999, and other than it is defined as the verification period. Table 2 lists the
finalized parameters. Concerning the storage of root and saturated zones, and the river flow rate, the
data on January 1 little vary over a year. Therefore, we made an 8-year outflow calculation once and
then set the data on the last day (December 31, 2001) as the initial values.
We determined the parameters K and P of the kinematic wave model used for tracing a river
course in consideration of the cross-sectional views of ten discharge observation points. These values
are found separately in the Hinheup and Nam Ngum Dam basins.
2) Analyzed results
The results of estimated discharges at Hinheup and those of the Nam Ngum Dam were
compared to the observed discharges with good accuracy in both the dry and rainy seasons (Fig. 5).
However, the calculated release of the dam is much higher than the measured in August and
September 1998 and 1999. We think that this is because the Nam Ngum Dam basin having only one
rain observation point obliged us to use the value measured in the downstream part of the dam in the
interpolation for finding the precipitation of each cell in the dam basin, and the precipitation at the
observation point was much higher than the average in those periods. The relative error at Hinheup is
35.6-95.7 percent (48.8% in average) and that at the Nam Ngum Dam is 38.5-140.7 percent (55.8% in
average)—the latter is slightly larger than the former. It would be reasonable to use the relative error
as information from the viewpoints of the low spatial density of meteorological stations and the
limited accuracy of measured discharge.
3) Incorporation of a dam control model
The Nam Ngum Dam basin accounts for about 50 percent of the Nam Ngum basin, so the dam
control has a significant effect on the water circulation. Accordingly, we did it and incorporated the
resulting dam control model into the distributed water circulation model. The water balance of the
reservoir, the base of the dam control model, is represented as follows:
Vres (i)  Vres (i  1)  (Qin (i)  Qout (i))t
(24)
where Vres is the storage on the ith day, Qin is the inflow rate of the reservoir on the ith day, and Qout is
the discharge of the reservoir on the ith day. The inflow rate is given by the distributed water
circulation model, so rules that apply to the determination of the discharge are important to the
reservoir control model. This model allows us to estimate the discharge for water use (irrigation,
power generation, and urban life) and the overflow rate of the spillway separately. Because the Nam
Ngum Dam runs for power generation, the following describes how to find the discharge for power
generation and the overflow rate of the spillway.
i) Discharge for power generation: The discharge for power generation Qpw may vary depending
on season-by-season power demand. In this model, we define the ratio of the current storage Vres(i) to
the available capacity Vresmax as the discharge factor OR(i), and multiply the maximum discharge for
power generation Qpwmax by the factor to find Qpw as follows:
Q pw (i)  OR(i)  Q pwmax
OR(i ) 
(25)
Vres (i  1)
Vres max
(26)
ii) Overflow rate of the spillway: The spillway overflows when the sum of the current storage
Vres(i) and the inflow Qin(i) is higher than the available capacity Vresmax. The overflow is given by the
equation ―(Storage + Inflow)  Available capacity.‖
In the same fashion as when finding the initial value of the water circulation model, we made an
8-year outflow calculation once and then defined the resulting storage and discharge as the initial
values. The results of reproducing the storage and discharge are shown in Fig. 6. We estimated the
measured storage from the measured reservoir level received from the Mekong River Commission
15
(MRC) and an H-V
curve. We derived the
latter
from
the
relationship between
the
storage
and
reservoir level at four
points (including the
available capacity and
its water level). The
resulting storage is
almost
satisfactory.
We think that this is
because the inflow
patterns and control
(a) Storage
rules in this reservoir
are simpler than ones
in Japan. Concerning
the disc harge, the
measured one is almost
constant
over
the
period, but the one
given by the model is
not constant because
the
discharge
for
power generation is
proportional to the
storage. However, the
resulting
discharge
shows a satisfactory
(b) Discharge
tendency.
Fig. 6. Comparison of the storage and discharge of the Nam Ngum Dam
(6)
Future
with the dam control model
improvement
We have applied the distributed water circulation model developed so far to the Nam Ngum
basin and investigated the reproducibility of the discharges at several points in the region. Moreover,
we have modeled the Nam Ngum Dam and incorporated the resulting model into the water circulation
model. The results of reproducing the inflows into the Nam Ngum Dam and the discharges at Hinheup
in the Nam Lik basin were relatively good to reproduce water received from the Nam Song Dam. We
put off such development in this research because we got data on the amount of water transferred from
(a) Nam Ngum Dam basin
(b) Hinheup basin
Fig. 7. Changes in actual evapotranspiration
16
(a) Nam Ngum Dam Site
(b) Hinheup
Fig. 8. Forecasting the monthly mean flows in the future
the Nam Song Dam to the Nam Ngum Dam and the overflow rate of the spillway, but no data on the
inflow rate of the former dam.
Results of impact assessment of global warming for the Nam Ngum River basin
We put the results of a warming test with the 20 km global model of the Japanese Meteorological
Research Institute [Kusunoki and Mizuta, 2011] having interpolated 0.1-degree meshes to the
distributed water circulation model in order to evaluate the effect of climate change on the flow regime
of rivers, dam control, irrigation, and t he cropping area of paddies. The evaluation periods are the
present time (1979-2003), the near future (2015-2039), and the end of the 21st century (2075-2099).
The calculated storage and discharge of the Nam Ngum Dam were satisfactory as well.
In the future, however, we need to develop a new assessment model with bias correction, so that
we can compare the values given by the GCM and the measured and to perform a bias correction.
(1) Changes in actual evapotranspiration in the future
Figure 7 shows changes in the actual evapotranspiration of the Nam Ngum Dam and Nam Lik
basins. Both basins show that the actual evapotranspiration will little change in the near future because
of not so large variation in reference evapotranspiration. At
the end of the 21st century, it will increase in both basins,
particularly in the rainy season, because of a rise in reference
evapotranspiration. In the dry season, the actual
evapotranspiration will not change though the reference one
will increase because the total amount of rainwater is small.
Note that in the dry season, plants having long roots draw up
water from the underground aquifer or water rises thanks to
capillary action, but these phenomena are not incorporated
into the present water circulation model.
(2) Changes in river flow in the future
The monthly discharges at the Nam Ngum Dam and
Hinheup at the present time and in the future are illustrated in
Fig. 8. Each vertical line shows the maximum and minimum
values for 25 years. In the Nam Ngum Dam basin, the
monthly mean flow rate will reduce, particularly between July
and September, at the end of the 21st century. This is inferred
as that the actual evapotranspiration will increase in
proportion to a rise in atmospheric temperature, resulting in a
reduction in outflow rate. In the rainy season, the maximum
Fig. 9. Changes in probable flow rate
discharge will increase but the minimum one will decrease,
(Nam Ngum Dam)
17
which suggests the
possibility
of
changing the outflow
rate extremely.
The monthly
mean flow rate at
Hinheup will not
change
compared
with the Nam Ngum
Dam (Fig. 8 (b)).
This is because a rise
in
actual
evapotranspiration
Fig. 10. Changes of dam storage in the Nam Ngum Reservoir
will be smaller in the
(Bars
in the figure depict maximum and minimum values of 15days'
Hinheup basin than
interval for 25 years.)
in the Nam Ngum
Dam basin as shown
in Fig. 8 (b). Note that even in the Hinheup basin, the maximum flow rate will increase but the
minimum one will decrease in the rainy season, which suggests a tendency to change the outflow rate.
To evaluate the effect of global warming on flood, we checked changes in probable flow rate.
To estimate the flow rate, the Gumbel distribution, one of extreme-value distributions, was used. The
year-by-year annual maximum discharges for 25 years at the present time are plotted in the near future,
and at the end of the 21st century as well as the resulting Gumbel distributions (Fig. 9). We can see
that the slope of the Gumbel distribution (lines in the figure) will be small in the future. The difference
between the maximum and minimum flow rate (symbols in the figure) will be larger in the future than
at the present time. This means that the maximum flow rate will vary year by year in the future. The
illustrated Gumbel distribution shows that the 10-year probable flow rate is 1136.7 m3/s at the present
time, but will increase slightly (1238.4 m3/s in the near future and 1254.5 m3/s at the end of the 21st
century). The figure also indicates the return period of the current 10-year probable flow rate given by
the future distribution. The 10-year probability at the present time will reduce to the 5-year one in the
future, which means a rise in the frequency of the current 10-year probable flow rate. The above
suggests that an extreme rise in probable and year-by-year maximum flow rates will variously affect
the basin of the Mekong River where people’s life relates closely to flood. In this research, we used
the Gumbel distribution as a probability distribution function, but it did not fit the data, particularly in
the near future. Therefore, the quantitative evaluation of the probable discharge requires another
distribution function.
(3) Forecasts of the effects of climate change on dam control
The following describes the effect of climate change to the control of the Nam Ngum Dam.
18
No paddy field
(a) Irrigated paddy field
(b) Rain-fed paddy field
Fig. 12. Changes in cropping area ratio (in the near future) of the future to the present
Figure 10 figures out the average storage for 25 years and the maximum and minimum storage every
15 days, which were given by the dam operation model. We can see that the average storage will
decrease, particularly at the end of the 21st century. The minimum storage will reduce year after year.
This is because like the inflow rat e, the monthly mean and minimum flow rates will decrease. It is
expected that the inflow rate will decrease in inverse proportion to a rise in evapotranspiration and the
amount of vapor from the water surface of the reservoir will increase. Accordingly, we need to control
the dam carefully in the future.
(4) Forecasts of cropping area in the future
In Fig. 11, the change of the cropping areas of irrigated and rain-fed paddies are predicted for
25 years at the present time, in the near future, and at the end of the 21st century, which are sorted in
descending order. The large area part indicates that the cropping area will decrease in the near future
and at the end of the 21st century. The 20th place and later part indicate that the cropping area will
little change in the near future, but will decrease in inverse proportional to a rise in evapotranspiration
at the end of the 21st century. As mentioned above, it is expected that the cropping area ratio of paddy
fields will decrease in the future.
Changes in the mesh-by-mesh ratio of the cropping area in the near future or at the end of the
21st century to that at the present time are depicted in Fig. 12. We can see that the cropping area will
decrease not only in the upstream part where water intake conditions are not good but also in the
vicinity of the main course of the Nam Ngum River where the conditions are good. Part of Xieng
Khouang Province in the upstream part shows a rise in cropping area, but the reliability of the data
may be low because the region basically has a small cropping area due to bad water intake conditions,
so a little rise in cropping area increases the ratio sharply. This is a problem relating to the frequency
of a surface flow on the primary river course, and we need to review the parameters and structure of
the model in the future. Anyway, the whole basin shows a tendency to decrease the cropping area in
inverse proportion to a rise in evapotranspiration.
(5) Future challenges
We have used the distributed water circulation model incorporating agricultural water use to
evaluate the effect of warming on the river flow rate and cropping area in the Nam Ngum basin. To
forecast the future climate, we have used the results of a global warming experiment with the
ultrahigh-resolution global atmospheric model developed by Meteorological Research Institute. The
evaluation results suggest that change s in actual evapotranspiration will be larger than that in
precipitation in the basin, resulting in a reduction in the inflow rate of the dam and a significant effect
on crops. In the future, we need to improve the reliability of estimating the actual evapotranspiration in
the future by modeling a phenomenon in which water rises from the aquifer thanks to capillary action.
19
In this research, we did not make the bias correction of data provided by the GCM, but we have to
make such correction while comparing with the measured data if necessary.
Conclusions
We have used the existing simulation models to forecast and evaluate the effect of climate change due
to global warming on agricultural water use and irrigation facilities in the downstream basin of the
Mekong River. As the first step, we have clarified the features of agricultural water use in the basin.
Next, we have improved the distributed water circulation model incorporating agricultural water use
and selected the basins of the Nam Ngum River (Laos) as an example in order to investigate changes
in water environment by putting changes in agricultural water use and weather forecasts derived from
the latest global warming test to the model and to report the results.
We would like to advocate the results of warming experiments we have made. Concerning data
for global warming tests, we carried out the bias correction of the latest forecasting results given by
the innovation project. Therefore, in the evaluation of the impacts of global warming on the basin of
the Nam Ngum River, we have applied the operation model to the Nam Ngum Dam and showed the
good results, so we would like to carry on the effect evaluation in consideration of the completion of a
dam being planned. Concerning the estimation of the impact of global warming on the whole basin of
the Mekong River, we should replace the experimental data given by the coexistence project with the
one presented by the innovation project.
Acknowledgements. The authors wish to acknowledge their deep gratitude for the invaluable support provided
by the Global Warming Project ―Development of Mitigation and Adaptation Technologies to address Global
Warming in the Agriculture, forestry and Fisheries (fiscal 2010-2014) ‖ sponsored by the Agriculture, Forestry
and Fisheries Research Council (AFFRC) of Japan’s Ministry of Agriculture, Forestry and Fisheries (MAFF)
and Grant-in-Aid for Scientific Research (B), ―Impact Assessment of Global Warming on Irrigation and
Drainage and Future Countermeasures using a Distributed Water Circulation Model (2009-2012),‖ Japan Society
for the Promotion of Science (JSPS) supported by Japan’s Ministry of Education, Culture, Sport, Science and
Technology (MEXT).
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