HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus GmbHGöttingen, Germany10.5194/hess-19-4783-2015South Asia river-flow projections and their implications for water resourcesMathisonC.camilla.mathison@metoffice.gov.ukWiltshireA. J.FalloonP.ChallinorA. J.Met Office Hadley Centre, Fitzroy Road, Exeter, EX1 3PB, UKSchool of Earth and Environment, Institute for Climate and Atmospheric Science, University of Leeds, Leeds, LS2 9AT, UKC. Mathison (camilla.mathison@metoffice.gov.uk)7December201519124783481016April201516June20155November20159November2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://hess.copernicus.org/articles/19/4783/2015/hess-19-4783-2015.htmlThe full text article is available as a PDF file from https://hess.copernicus.org/articles/19/4783/2015/hess-19-4783-2015.pdf
South Asia is a region with a large and rising population, a high dependence
on water intense industries, such as agriculture and a highly variable
climate. In recent years, fears over the changing Asian summer monsoon (ASM)
and rapidly retreating glaciers together with increasing demands for water
resources have caused concern over the reliability of water resources and the
potential impact on intensely irrigated crops in this region. Despite these
concerns, there is a lack of climate simulations with a high enough
resolution to capture the complex orography, and water resource analysis is
limited by a lack of observations of the water cycle for the region. In this
paper we present the first 25 km resolution regional climate projections of
river flow for the South Asia region. Two global climate models (GCMs), which
represent the ASM reasonably well are downscaled (1960–2100) using a
regional climate model (RCM). In the absence of robust observations,
ERA-Interim reanalysis is also downscaled providing a constrained estimate of
the water balance for the region for comparison against the GCMs
(1990–2006). The RCM river flow is routed using a river-routing model to
allow analysis of present-day and future river flows through comparison with
available river gauge observations. We examine how useful these simulations
are for understanding potential changes in water resources for the South Asia
region. In general the downscaled GCMs capture the seasonality of the river
flows but overestimate the maximum river flows compared to the observations
probably due to a positive rainfall bias and a lack of abstraction in the
model. The simulations suggest an increasing trend in annual mean river flows
for some of the river gauges in this analysis, in some cases almost doubling
by the end of the century. The future maximum river-flow rates still occur
during the ASM period, with a magnitude in some cases, greater than the
present-day natural variability. Increases in river flow could mean
additional water resources for irrigation, the largest usage of water in this
region, but has implications in terms of inundation risk. These projected
increases could be more than countered by changes in demand due to depleted
groundwater, increases in domestic use or expansion of water intense
industries. Including missing hydrological processes in the model would make
these projections more robust but could also change the sign of the
projections.
Introduction
South Asia, the Indo-Gangetic Plain in particular, is a region of rapid
socio-economic change where both population growth and climate change is
expected to have a large impact on available water resources and food
security. The region is home to almost 1.6 billion people and the population
is forecast to increase to more than 2 billion by 2050 . The
economy of this region is rural and highly dependant on climate sensitive
sectors such as the agricultural and horticultural industry, characterised by
a large demand for water resources. As a result, over the coming decades, the
demand for water from all sectors; domestic, agricultural and industrial is
likely to increase .
The climate of South Asia is dominated by the Asian summer monsoon (ASM),
with much of the water resources across the region provided by this
climatological phenomena during the months of June–September
. The contribution from glacial melt to water
resources is less certain but likely to be important outside the ASM period
during periods of low river flow . Glaciers and
seasonal snowpacks are natural hydrological buffers releasing water during
the drier periods, such as spring and autumn, when the flows of some catchments
in this region are at their lowest. Similarly they may act to buffer
inter-annual variability as well releasing water during warmer drier years
and accumulating during wetter colder years . However,
showed that the influence of glacial melt reduces with
distance downstream, as other influences such as evaporation and
precipitation increase in importance. found that by
the 2050s, the main upstream water supply could decrease due to a reduction in
snow and glacial melt (reductions of 8 % for the upper Indus and more
than 18 % for the Ganges and Brahmaputra). Meltwater plays an important
role for the Indus and Brahmaputra particularly, accounting for a larger
percentage of the downstream flow than the Ganges (where meltwater is
approximately 10 % of the downstream flow). However,
also showed that these reductions in meltwater are
offset by an increase in precipitation in all three basins.
used coarse-resolution general circulation models (GCMs)
known to have difficulties in capturing monsoon precipitation and in
estimating the relationship between daily mean temperature and melting of
snow and ice.
Recent studies have highlighted uncertainty in both glacier mass balance and
ASM rainfall. showed a negative mass balance for three
benchmark glaciers in the Nepal Himalayas. and
highlighted losses more generally from western,
eastern and central Himalayan glaciers. These observed changes in Himalayan
glaciers can be attributed to the increase in temperature already experienced
across the region, with warming more pronounced at higher elevations and
during winter months . There are however some
glaciers in the Karakoram region showing increases in mass, which has been
attributed to a decrease in temperature for this region .
Projections of future glacial change are challenging due
to poor understanding of glacial processes, diversity in climate extremes and
the complex orography of the region . Complex orography
contributes to other processes such as avalanching and therefore debris
cover. The relationship between debris cover and melt is complex with a wide
variety of responses across different glaciers across the Himalayan arc
. The thickness of debris cover is widely thought to
significantly affect the response of the glacier to climate, with thick
debris cover tending to slow down surface melting .
However, on the regional scale found,
using satellite data, similar thinning rates between clean and debris covered
ice despite insulation by debris cover at some sites.
suggested that the insulating effect of debris layers with thicknesses
exceeding a few centimetres depends on the continuity of the coverage.
Therefore, changes in the thickness of debris across a glacier could change
the melt rate on a local scale even across a single glacier tongue.
The ASM is also uncertain, highlighted two climate
features that could influence the ASM, including a general weakening of
monsoonal flows while enhanced moisture convergence could increase
precipitation. Any reduction in water availability from either resource is
likely to put more pressure on groundwater resources, which is not sustainable
in the longer term . There is some disagreement in the
literature regarding the main effects of climate change on this region.
suggested that the availability and quality of groundwater for irrigation could be more important factors influencing food
security than the direct effects of climate change, particularly for India.
However, suggested that an increase in extremes (both
temperature and precipitation) could lead to instability in food production
and it is this variability in food production that is potentially the most
significant effect of climate change for the South Asia region.
Despite the general uncertainty in the reliability of water resources and the
impacts of climate change for this region, there are few simulations available
with a high enough resolution for capturing the complex topography of the
Himalayan region. The water balance for the South Asia region as a whole is
generally poorly understood with limited observing networks and data
availability for both precipitation and river flows presenting a real
challenge for validating models and estimates of water balance. This analysis
seeks to use regional climate simulations to develop our understanding of the
water cycle for the region in the context of the complete climate system,
while acknowledging that more needs to be done to address the missing
hydrological processes in the model. Regional climate model (RCM) simulations
are a widely used method across climate science for downscaling GCMs,
including the regional IPCC assessment but are used in many other regional
climate projects .
RCMs are based on the same physical equations as GCMs and therefore represent
the entire climate system including the carbon and water cycle. Though there
are some limitations due to missing processes, their higher resolution allows
a better representation of the regional-scale processes; especially in
regions of complex topography such as the Himalayas
. RCMs are designed to maintain the conservation
of water, mass, energy and momentum, essential for analysis on climate
timescales. conducted a comprehensive assessment of
four RCMs run over South Asia demonstrating their ability to capture the
monsoon; this analysis includes the RCM used here.
compared GCM and RCM outputs for temperature and precipitation specifically
for the RCM used in this analysis.
Perhaps due to the lack of adequate resolution regional climate simulations
available for this region, there are relatively few studies that consider the
value of downscaling using RCMs for hydrological applications for this
region. However, found that RCM data produced better
results when used with a hydrological model than using poor-quality
observation data; this implies greater confidence in the RCM simulated
meteorology than available observational data for this region
. Therefore, in the literature hydrological analysis is
typically at the global scale using GCMs coupled with hydrological models
or at the basin scale using stand-alone hydrological
models
such as the soil water assessment tool SWAT;. Weather data in SWAT are either simulated within the
model using a weather generator or taken from observations of daily
precipitation and maximum/minimum temperature . This
approach may be appropriate for small domains within which there is
consistency in rainfall patterns but may not be suitable for large domains in
South Asia due to the high temporal and spatial variability in precipitation
across the region . used the
SWAT model with 50 km resolution daily RCM weather data to conduct a
climate change impact assessment of the hydrology of several individual
basins over India for two 20-year periods representing the present day
(1981–2000) and future (2041–2060). compared the
differences between the two periods, rather than focussing on absolute
values, to find that climate change causes an increase in precipitation,
river flow and evaporation for the Ganges basin. High variability across
basins and sub-basins means that parts of the Ganges basin could experience
seasonal or regular water-stressed conditions under climate change
, although it is not exactly clear which climate change
scenario has been used for these simulations. There are more examples of the
application of RCMs for hydrological analysis for other regions such as the
UK and Europe. used 25 km RCM data in a catchment-based
rainfall–runoff model to estimate the flood frequency of small UK river
basins to good effect. used an RCM to evaluate the
benefits of using high spatial resolution climate information for the Danube
basin. have also demonstrated the importance of the
resolution of precipitation data for a region of Ireland for hydrological
impact modelling.
The typical domain and resolution of RCM simulations enables the analysis of
areas spanning multiple river basins covering a larger area than is usually
possible with hydrological models. This means that there is consistent
forcing across different basins. The use of the RCM generated runoff within
the hydrological model also preserves the consistency of the projections with
atmospheric forcing, which is not possible if the runoff is derived within a
hydrological model. However, there are few regional river-flow analyses
currently available, where these consistencies are maintained.
analysed the RCM projections used in this analysis in
terms of water availability for food production for selected river basins
using a coupled hydrology and dynamic vegetation model; however, so far no
specific analysis of river flows has been done for these RCMs. Therefore, we
present the first 25 km resolution regional climate projections of
river flow for the South Asia region by using RCM generated runoff within a
routing model to estimate river flow thereby enabling consistency to be
maintained across basins and with the driving climate scenario. This is a new
application of the highest resolution RCM data currently available for this
region, to enable analysis of the impacts of climate on river flows in
conjunction with the strategic sampling of climate variability from selected
GCMs. We use a novel approach to the consideration of variability of river
flows through analysis of the upper and lower parts of the distribution, in
addition to the mean flows.
The aim of this analysis is to examine how useful RCM simulations are for
understanding how river flows could change in South Asia in the future.
Irrigation is an important part of the agricultural industry for this part of
the world, with the Indo-Gangetic Plain traditionally providing the staple
crops of rice and wheat for India and South Asia as a
whole; the continued success of these crops is therefore important for the
food and water security of the region. We discuss the potential implications
of projected changes in the water resources needed to maintain yields of
these crops in a changing climate. The models, observations and the analysis
used are described in Sect. , while a
brief evaluation
of the driving data and the river flow analysis is presented in
Sect. . The implications of the potential changes in river
flows on water resources and conclusions are discussed in
Sects. and , respectively.
MethodologyModels
Figure summarises the methodology described
in this section in a flow chart, highlighting the main stages in the
generation of the presented river-flow projections and the approximate
resolution of the model data used.
A flow chart showing the methodology for the presented analysis.
GCM and RCM forcing
This analysis utilizes 25 km resolution regional climate modelling of the
Indian sub-continent to provide simulations across the Hindu–Kush Karakoram
Himalaya mountain belt. These RCM simulations form part of the ensemble
produced for the EU-HighNoon project (referred to hereafter as HNRCMs), for
the whole of the Indian subcontinent (25∘ N,
79∘ E–32∘ N, 88∘ E), for the period 1960–2100. The
other simulations in the HighNoon ensemble, which used another RCM, the
Regional Model from the Max Planck Institute for Meteorology
REMO;, were unavailable for use in this analysis.
Therefore, one RCM, the Hadley Centre Regional Climate Model (HadRM3) RCM
is used to generate the river-flow projections
presented here. While the additional RCM would be useful here, analysis of
the ERA-Interim driven HadRM3 and REMO
simulations over the western Himalayas by show that
both models run at 25 km resolution over comparable domains have similar
distributions of precipitation, temperature and inter-annual variability,
despite having different representations of orography. Analysis of the
complete HighNoon ensemble for the Ganges–Brahmaputra basin in
also indicates a small spread between HNRCMs for the
30-year mean climatologies of temperature. Precipitation is more variable for
this basin, with a larger spread between HNRCMs. However, the RCM uncertainty
defined by these two models (REMO and HadRM3) is still smaller than the
climate uncertainty represented by the selected GCMs with the influence of
the GCM on the projections of precipitation as great as the variability
between RCMs . Therefore, the most important
contribution to the input uncertainty is from the GCM
and using two GCMs to provide boundary data to one RCM provides a better
estimate of climate uncertainty than using a single GCM to drive two RCMs,
which would be the computational equivalent. On this basis we use HadRM3
driven by two carefully selected GCMs for this analysis. However, other RCMs,
not yet applied to this region could produce different projections.
In order to sample climate uncertainty, we use two GCM simulations that have
been shown to capture a range of temperatures and variability in
precipitation similar to the AR4 ensemble for Asia
. Although using just two ensemble members is
unlikely to capture the full range of uncertainty of a larger ensemble, the
two models used for these simulations have been shown to capture the main
features of the large-scale circulation (particularly the ASM)
, which is not true
of all GCMs. The experimental design of the HighNoon ensemble compromises
between the need for higher-resolution climate information for the region and
the need for a number of ensemble members to provide a range of uncertainty.
The length of the simulations needed and the limited number of GCMs that are
able to simulate the ASM also affect the number of ensemble members. These
factors are all important given the limited computational resources
available. The GCMs are the following: the third version of the Met Office
Hadley Centre Climate Model HadCM3;a version of the Met Office
Unified Model and ECHAM5 third
realization; are downscaled using the HadRM3 RCM
. These two GCMs capture the uncertainty in the sign of
the projected change in precipitation with one showing an increase (HadCM3)
and the other a decrease (ECHAM5). This feature is a key reason for the
selection of these two GCMs. In addition to the GCMs, ERA-Interim data
are also downscaled using the HadRM3
RCM. ERA-Interim is a reanalysis product that combines model and observations
to provide a constrained estimate of the water balance of the region. The
ERA-Interim (also referred to as ERAint) simulation has also
been shown to capture the role of steep topography on moisture transport
fluxes and vertical flow for the western Himalayas .
Therefore, for this region, where there is a lack of robust observations,
particularly of the water cycle (see Sects.
and ), it provides a useful benchmark against which
to compare the GCM driven simulations. A similar approach is described in a
previous study by .
These RCM simulations are currently the finest resolution climate modelling
available for this region . HadRM3 has 19 atmospheric levels and the
lateral atmospheric boundary conditions are updated 3 hourly
and interpolated to a 150 s time step. These simulations include a detailed
representation of the land surface in the form of version 2.2 of the Met
Office Surface Exchange Scheme MOSESv2.2;, which
includes a full physical energy-balance snow model
. MOSESv2.2 treats subgrid land cover
heterogeneity explicitly with separate surface temperatures, radiative fluxes
(long wave and shortwave), heat fluxes (sensible, latent and ground), canopy
moisture contents, snow masses and snowmelt rates computed for each surface
type in a grid box . However, the air temperature,
humidity and wind speed above the surface are treated as homogenous across
the grid box and precipitation is applied uniformly over the different
surface types of each grid box. The relationship between the precipitation
and the generation of runoff is complicated, depending on not only the
intensity, duration and distribution of the rainfall but also the
characteristics of the surface. The infiltration capacity of the soil, the
vegetation cover, steepness of the orography within the catchment and the
size of the catchment are important influencing factors on runoff generation
. In GCMs and even 25 km RCMs such as the one
presented here, the resolution is often too coarse to explicitly model the
large variations of soil moisture and runoff within a catchment and therefore
the major processes are parameterized . The method
used within MOSES2.2 for generating surface and subsurface runoff across a
grid box is through partitioning the precipitation into interception by
vegetation canopies, throughfall, runoff and infiltration for each surface
type . The infiltration
excess mechanism generates surface runoff; this assumes an exponential
distribution of point rainfall rate across the fraction of the catchment
where it is raining . Moisture fluxes are allowed
between soil layers; these are calculated using the Darcy equation, with the
water going into the top layer defined by the grid-box average and any excess
removed by lateral flow . Excess moisture in the bottom
soil layer drains from the bottom of the soil column at a rate equal to the
hydraulic conductivity of the bottom layer as subsurface runoff
. The performance of MOSESv2.2 is discussed in the
context of a GCM in ; however, no formal assessment of
MOSESv2.2 and the runoff generation in particular has been done for the RCM.
River-routing model
In this analysis the simulated 25 km grid-box runoff is converted into river
flow using the 0.5∘ Total Runoff Integrating Pathways river-routing
scheme TRIP; as a post-processing step. TRIP is a
simple model that moves water along a pre-defined 0.5∘ river network;
the Simulated Topological Network at 30 min resolution STN-30p,
version 6.01; in
order to provide mean runoff per unit area of the basin; this can be compared
directly with river gauge observations. TRIP was previously used in
, which used GCM outputs directly to assess the skill
of a global river-routing scheme. The TRIP model has been shown to agree well
with observed river-flow gauge data and largely showed
good skill when comparing runoff from several land-surface models
. Implementation of TRIP in two GCMs, HadCM3 and
HadGEM1, is described by and was found to improve the
seasonality of the river flows into the ocean for most of the major rivers.
Using TRIP ensures the river-flow forcing is consistent with the atmospheric
forcing; however, it also assumes that all runoff is routed to the river
network and as such there is no net aquifer recharge/discharge. This may not
be the case in regions with significant groundwater extraction, which is
subsequently lost though evaporation and transported out of the basin. These
simulations do not include representation of extraction, reservoirs or dams.
Many of the river gauges used in this analysis and described in
Sect. are located at large dams along rivers in these
basins, and therefore the comparison between the simulations and the river
gauges could be affected by these large features. Extraction, particularly
for irrigation purposes, is large in this region ;
this means that the extraction–evaporation, as well as subsequent recycling,
of water in a catchment is not
considered in this analysis. The routed runoff of the HNRCM simulations are
generally referred to hereafter using only the global driving data
abbreviations: ERAint, ECHAM5 and HadCM3 (except
Sect. where we refer to the HadCM3 GCM and ERAint
data sets before downscaling).
A map showing the locations of the river gauges used in this analysis.
Emission scenario
These simulations use the Special Report on Emissions Scenarios (SRES) A1B
scenario . The SRES scenarios were devised
according to the production of greenhouse gases and aerosol precursor
emissions as part of the AR4 IPCC report . The A1
storyline and scenario family represents a future world of very rapid
economic growth, global population that peaks in mid-century and declines
thereafter, and rapid introduction of new and more efficient technologies.
The A1B scenario specifically, represents this future world where there is
balance across energy sources, i.e. a mixture of fossil and non-fossil fuels
. This scenario does not represent changes in land
use, which remains fixed through the duration of these simulations. This is
useful for understanding the effect of climate change in the absence of any
adaptation.
Observations
This analysis uses observations of precipitation and river flow to assess the
present-day RCM hydrology. The precipitation observations are from the Asian
Precipitation – Highly Resolved Observational Data Integration Towards the
Evaluation of Water Resources (APHRODITE; ) data
set. APHRODITE is a daily, 0.25∘ resolution gridded data set.
The river-flow analysis focusses on a selection of river gauges from the
Global Runoff Data Centre that are located within the
three major river basins of
South Asia: the Indus and the Ganges–Brahmaputra. These gauges provide
observations that are used, in addition to downscaled ERA-Interim river
flows, to evaluate the downscaled GCM river flows. The selection of these
river gauges aims to illustrate from the perspective of river flows, as
modelled in an RCM, that the
influence of the ASM on precipitation totals increases, from west to east and
north to south across the Himalayan mountain range, while that of western
disturbances reduces . The differing influences across the Himalayan arc result in
complex regional differences in sensitivity to climate change, with western
regions dominated by non-monsoonal winter precipitation and therefore
potentially less susceptible to reductions in annual snowfall
. A brief geographical description of
the rivers and the chosen gauges is given in this section, their locations
are shown in Fig. and listed in
Table (including the abbreviations shown in
Fig. and the gauge location in terms of latitude and
longitude).
Table listing the rivers and gauges (including their location) used
in this analysis; all the observations shown here are from GRDC. The
abbreviations used in Fig. are given in column one. The
years of data column includes the number of years that data is available
since 1950 with “c” to denote where data are continuous and “u” to show
where the data are available for that number of years but not as a continuous
data set.
The Indus, originates at an elevation of more than 5000 m in western Tibet
on the northern slopes of the Himalayas, flowing through the mountainous
regions of India and Pakistan to the west of the Himalayas. The upper part of
the Indus basin is greatly influenced by western disturbances, which
contribute late winter snowfall to the largest glaciers and snow fields
outside the polar regions; the meltwater from these have a crucial role in
defining the water resources of the Indus basin . In
this analysis the Attock gauge is the furthest upstream and the Kotri gauge,
located further downstream, provide observations on the main trunk of the
Indus River. The Chenab River, located in the Panjnad basin and in this
analysis represented by the Panjnad gauge, is a major eastern tributary of
the Indus, originating in the Indian state of Himachal Pradesh. In the upper
parts of the Chenab sub-basin western disturbances contribute considerably to
precipitation, while the foothills are also influenced by the ASM
.
The Ganges River originates on southern slopes of the Himalayas
and traverses thousands of kilometres before joining
with the Brahmaputra in Bangladesh and emptying into the Bay of Bengal
. The Ganges basin has a population density 10 times the
global average making it the most populated river basin in the world
, it covers 1.09 million km2 with 79 % in India,
13 % in Nepal, 4 % in Bangladesh and 4 % in China
. The main trunk of the Ganges is represented in this
analysis by the gauge at the Farakka Barrage, located at the
India–Bangladeshi border, to the east of the Himalayas. The Bhagirathi
River, located in the upper Ganga basin, is one of the main
head streams of the Ganges. The Bhagirathi River originates from Gaumukh
3920 m a.s.l. at the terminus of the Gangotri glacier in Uttarakhand, India
. The Tehri dam is located on this tributary, providing
the most central data point on the Himalayan arc in this analysis (not a GRDC
gauge).
The Karnali River (also known as Ghaghara), drains from the Himalayas
originating in Nepal flowing across the border to India where it drains into
the Ganges. The Karnali is the largest river in Nepal and a major tributary
of the Ganges accounting for approximately 11 % of
the Ganges discharge, 5 % of its area and 12 % of its snowfall in the
HNRCMs. Two of the river gauges in this analysis, the Benighat and the
Chisapani, are located on this river. Two other sub-catchments complete those covering
the Ganges basin; the Narayani and the Arun rivers. The Narayani River (also known as the Gandaki River, represented
here by the Devghat river gauge) is reportedly very
dependant on glaciers at low flow times of the year with over 1700 glaciers
covering more than 2200 km2. The Arun River,
part of the Koshi river basin originates in Tibet, flows south through the
Himalayas to Nepal. The Arun, represented in this analysis by the Turkeghat
gauge, joins the Koshi river, which
flows in a south-west direction as a tributary of the Ganges.
The Brahmaputra originates from the glaciers of Mount Kailash at more than
5000 m a.s.l., on the northern side of the Himalayas in Tibet flowing into
India, and Bangladesh before merging with the Padma in the Ganges Delta. The
Brahmaputra is prone to flooding due to its surrounding orography and the
amount of rainfall the catchment receives . The
Brahmaputra is represented in this analysis by three gauges: Yangcun, the
highest upstream gauge, Pandu in
the middle and Bahadurabad furthest downstream but above the merge with the
Padma.
There are no known observation errors for the GRDC observations (personal
communication, GRDC). Estimates of observation errors for river gauges vary
in the literature with a recommendation in for GCMs
to be consistently within 20 % of the observations, while
suggest that errors of 5 % at the 95 %
confidence interval might be expected. proposed
a method for quantifying the uncertainty in river discharge measurements by
defining confidence bounds. In this analysis, these methods are hindered by
the lack of observations concurrent with the model simulations. Therefore, the
method for approximating the inter-annual variability in this analysis is
based on the model variability and is described in Sect. .
Methods
There are two stages to the analysis presented, comparison of the simulations
with observations (for both RCM precipitation and river flows) and analysis
of future climate. The comparison against observations aims to assess if the
RCM reproduces the regional hydrology in terms of precipitation and river
flow compared with available observations. The objective of the analysis of
future climate is to understand how these simulations compare against the
present-day high and low flows, i.e. present-day natural variability. In this
section we describe the methods used in each stage of the analysis; the
comparison against observations is described in Sect.
and the analysis of future river flows in Sect. .
Comparison against observations
The total precipitation from each of the downscaled GCM simulations are
compared against a downscaled ERA-Interim simulation and APHRODITE
observations. This comparison is on the basin scale, focussing on the basins
included in the river-flow analysis (see Sect. ): the Indus
and the Ganges–Brahmaputra. The TRIP model basin boundaries for each of these
basins are shown in Fig. . The Ganges and Brahmaputra
catchments are considered together in this analysis as these rivers join
together in the Ganges Delta and are not clearly delineated in TRIP (see
Fig. b). The precipitation patterns for each basin are
useful for understanding the changes in the river flows within the catchments
although rain gauges in the APHRODITE data set are particularly sparse at
higher elevations (see , Fig. 1). This leads to
underestimation of the basin wide water budgets particularly for mountainous
regions . This is confirmed by
for the Indus basin where they find a high altitude
precipitation of up to 10 times higher than current gridded data sets is
needed to close the water balance for this basin. We compare the observations
and simulations in terms of their annual time series and the climatology for
each basin. The climatologies are calculated using the 1971–2000 period for
HadCM3 and ECHAM5 and 1990–2006 for the ERAint simulation in order to capture
a typical seasonal cycle for each simulation and basin.
This analysis is repeated for river flows in Sect.
for each of the 12 gauges described in Sect. . We also
calculate the 1.5 SD (standard deviation) over a 30-year period to define the
inter-annual variability. A value of plus 1.5 SD indicates an ∼ 1 in
10-year wet event, and a value of minus 1.5 SD indicates a 1 in 10-year dry event.
This approach is taken to indicate the possible impact of such a change under
the hypothesis that current socio-economic levels of climate adaptation can
cope with a 1 in 10-year event. The change driving mechanism could be
anthropogenic climate or decadal variability. This assumes that inter-annual
variability is independent of climate change whether that is due to decadal
variability or externally forced change. In this context it is indicative of
the timing and magnitude of possible changes under the A1B emissions
scenario. More work and ensemble members would be required to control the
role of decadal variability while the substantial computation expense in
running high-resolution RCM experiments currently precludes the use of
initial condition ensembles.
Future analysis
In Sect. we use the annual time series of the whole
simulation period to highlight any trends in future precipitation,
evaporation (at the basin scale) and river flows (for each gauge) over the
century. We also calculate the climatologies for two future 30-year periods:
2040–2070 (referred to as the 2050s) and 2068–2098 (referred to as the
2080s). The monthly climatology for the two periods is compared against the
1971–2000 range of natural variability. The purpose of the climatology
analysis is twofold. The first objective is to establish if there is any
change in the seasonality of the river flow. The second objective is to
establish if there is any increase in the future 30-year-mean river flows
that is outside the present-day variability, thereby indicating an increase
in future events that are equivalent to the 1971–2000 1 in 10-year wet (dry)
events (see Sect. ).
Analysis of the 30-year mean is useful for understanding the general
climatology of the region, but often it is the periods of high and low river
flow that are critical in terms of water resources.
highlighted the importance of potential changes in the seasonal maximum and
minimum river flows for the agricultural sector. The analysis in
Sect. uses kernel density estimation
KDE; to calculate the probability density
functions (pdfs) of the river flows for each river gauge and 30-year period.
The main aim of this analysis is to establish if there is any change in the
distribution of the highest and lowest river flows for the 2050s and 2080s
compared with the 1971–2000 period (see Sect. ).
Given these distributions, we then attempt to quantify the changes in the
highest and lowest river flows for the two future periods by focussing on the
changes in the lowest and highest 10 % of flows using two different
approaches. In the first approach, in
Sect. , we apply the upper and lower 10 %
of river flows for the 1971–2000 period as thresholds for the 2050s and
2080s. In Sect. , we take the principle of the
threshold analysis one step further by calculating the 10th and
90th percentile thresholds for each decade, simulation and gauge. The aim of
this second approach is to establish if there is any systematic change in the
upper and lower parts of the distribution through the century.
Results
The results are divided into three sections. Precipitation has a key
influence on river flows; therefore, in Sect. we
consider the previous evaluation of the HNRCM simulations comparing the RCM
precipitation for major South Asia basins with observations and ERAint. In
Sect. we focus on river flows themselves for
12 gauges within these basins distributed across the Himalayan arc. The methods
used in Sect. and are
described in Sect. . In Sect.
we analyse the future projections of precipitation, evaporation and river
flow to understand the water cycle of the region (see Sect.
for the methods used).
Comparison of present-day driving data with observations
The HNRCM simulations have been evaluated in several previous publications.
evaluated the ability of RCMs to capture the ASM
using ERA-40 data. analysed the HNRCMs forced with
ERA-Interim data. The GCM and HNRCM simulations are also evaluated against
a range of observations for the Ganges–Brahmaputra river basin in
. Figure shows the
observed spatial distribution of total precipitation for the monsoon period
(June to September; ) together with the HadCM3 and
ERAint prior to and post-downscaling. The HNRCMs
(Fig. d and e) improve the spatial
distribution of precipitation and therefore compare well with the
observations shown in Fig. c. This is
highlighted by the additional detail shown in the precipitation fields
through comparison of the pre-downscaled data sets for the HadCM3 GCM
(Fig. a) with those downscaled using HadRM3
(Fig. e). This comparison is also possible
for the downscaled ERA-Interim reanalysis data set shown in
Fig. d, which also shows an improved
precipitation representation compared with the pre-downscaled data set
(Fig. b). The higher-resolution orography
used in the 25 km RCMs is more able than the much coarser-resolution data sets
to capture the particularly varied terrain of this region and the effects of
this on the precipitation distribution. In general the HNRCM simulations
capture the spatial characteristics of the ASM, successfully reproducing
regions of high convective precipitation, maximum land rainfall and the rain
shadow over the east coast of India . Through adequately
representing the spatial precipitation characteristics across the region, the
areas of maximum and minimum precipitation can have a direct impact on the
river flows for the appropriate basin. This is shown by the improvement in
the timing and magnitude of the maximum precipitation for the RCM (HadRM3)
compared with the GCM (HadCM3) shown in Fig. c
(Indus) and Fig. d (Ganges–Brahmaputra). The RCMs
are also able to reproduce the inter-annual variability of the region
although they underestimate the magnitude of the variation
. The GCMs in the AR4 ensemble tend to exhibit cold and
wet biases compared to observations both globally and
for South Asia . Although these are generally
reduced in the RCM simulations there is a cold bias in the RCM that is
probably carried over from the larger bias in the GCMs .
The spatial distribution of the seasonal mean total precipitation
for the monsoon period (June, July, August, September) for the HadCM3 GCM (a),
ERAint (b), APHRODITE observations (c) and the three HadRM3
simulations:
HadRM3-ERAint (d), HadRM3-HadCM3 (e) and HadRM3-ECHAM5 (f).
The outline of the basins within the TRIP model; Indus (a) and
Ganges–Brahmaputra (b).
Annual mean total precipitation for the Indus (a) and
Ganges–Brahmaputra (b) catchments for each model run (HadCM3 – red,
ECHAM5 – blue, ERAint – cyan lines) plotted against APHRODITE observations (black
line). Paler observations are annual averages and darker lines are a 5-year
rolling smoothed average.
Seasonal cycle of total precipitation for the Indus (a, c) and
Ganges–Brahmaputra (b, d) catchments. The RCM simulations are shown
in (a) and (b) (HadCM3 – red, ECHAM5 – blue, ERAint – cyan lines). A
comparison of the HadCM3 GCM (cyan line) and HadCM3-HadRM3 (red line)
seasonal cycles are shown in (c) and (d). APHRODITE observations are also
shown (black line) on all plots.
The remaining analysis focusses on the downscaled simulations of HadCM3,
ECHAM5 and ERAint using the HadRM3 RCM. The RCM simulations shown in
Fig. appear to overestimate the seasonal cycle
of total precipitation compared with the APHRODITE observations; this is
highlighted by the annual mean of the total precipitation shown in
Fig. . However, given the limitations of the
observations at high elevations discussed in Sect.
we compare HadCM3 and ECHAM5 against an ERAint simulation. The annual mean
(Fig. ) and the monthly climatology
(Fig. ) show that, for these catchments, the
ERAint simulation lies between the two HighNoon ensemble members for much of
the year. However, during peak periods of precipitation the magnitude of
total precipitation for ERAint is larger.
The seasonal cycles of total precipitation are distinctly different between
the basins shown. The Indus basin (Fig. a),
indicates two periods of precipitation: one smaller peak between January and
May and another larger one between July and September. The timings of the
largest peaks
compare well; however, the smaller peak occur later than both
ERAint and APHRODITE for ECHAM5 and HadCM3. The magnitude of the peaks in
precipitation in the APHRODITE observations are consistently lower throughout
the year than the simulations. The magnitude of the ERAint total
precipitation is typically the largest while the ECHAM5 simulation is the
lowest and closest to the APHRODITE observations. HadCM3 is between ECHAM5
and ERAint for most of the year. In contrast the Ganges–Brahmaputra
catchment (Fig. b) has one strong peak between
July and September. In general this seasonal cycle is captured reasonably
well by the simulations, both in terms of magnitude and timing of the highest
period of precipitation. However, there is a tendency for the simulations to
overestimate rainfall between January and June compared to the observations,
thus lengthening the wet season .
also showed that in these simulations, the region of
maximum precipitation along the Himalayan foothills is displaced slightly to
the north of that shown in the observations. One explanation for this could
be that the peak in total precipitation is due to the distribution of
observations already discussed. Alternatively, it could be due to the model
resolution, which may still be too coarse to adequately capture the influence
of the orography on the region of maximum precipitation. The downscaled
ERAint simulation also indicates a higher total precipitation for
January–May that is within the range of uncertainty of the GCM driven
simulations. However, for the remainder of the monsoon period, ERAint has a
higher total precipitation than the GCM driven simulations.
Figure d illustrates this, showing a slightly
larger and more intense area of maximum rainfall over the eastern Himalayas
for the downscaled ERAint simulation than shown in the other RCM simulations
(Fig. e and f) or APHRODITE
(Fig. c).
Present-day modelled river flows
In this section we compare present-day modelled river flows with observations
and a downscaled ERAint simulation, using annual average river flows (see
Fig. ) and monthly climatologies (see
Fig. ). It is clear from Fig.
that observed river-flow data are generally limited, which makes statistical
analysis of the observations difficult. River-flow data for this region are
considered sensitive and is therefore not readily available particularly for
the present day. For each of the gauges shown here, there are generally
several complete years of data but often the time the data collected
pre-dates the start of the model run. The ERAint simulation is also shown
(cyan line-ERAint) to provide a benchmark in the absence of well-constrained
observations (see Sect. ). The comparison between the
model and observations shown in Figs.
and is therefore to establish if the model and
observations are comparable in terms of the average seasonal cycle and mean
river-flow rate without over-interpreting how well they replicate the
observations. The Tehri dam on the Bhagirathi River is not a GRDC gauge;
therefore, observations are not shown. Observations for this gauge were
received via personal communication from the Tehri dam operator and therefore
could not be adequately referenced.
Annual mean evaporation for the Indus (a) and
Ganges–Brahmaputra (b) catchments for each model run (HadCM3 –
red, ECHAM5 – blue, ERAint – cyan lines) from 1971 to 2100. Paler lines are
annual averages and darker lines are a 5-year rolling smoothed
average.
Time series of river flows showing available observations (black) and
RCM runs (HadCM3 – red, ECHAM5 – blue, ERAint – cyan lines) from
1971 to 2100. Paler lines are annual averages and darker lines are a 20-year
rolling smoothed average.
Seasonal cycle of river flow at individual river gauges; observed
(black solid line) and for each of the RCMs (HadCM3 – red, ECHAM5 – blue,
ERAint – cyan lines) from 1971 to 2000; with shaded regions showing 1.5 SD from
the mean for the two simulations for the same period.
The Kotri gauge on the Indus (Fig. a) and the Yangcun
gauge on the Brahmaputra (Fig. k) are the only two
gauges where the modelled river flow is higher than the observations and not
within the estimated variability (1.5 SD) of the region. The ERAint
simulation is also outside the estimated variability (1.5 SD) for the
Benighat gauge on the Karnali River (Fig. e). The
differences in these gauges are also reflected in the annual mean river flows
(Fig. ) for these river gauges, which are higher than
observed. The high bias in modelled river flow at the Kotri gauge could be
due to the extraction of water, which is not included in the model. The Indus
has the largest irrigation scheme in the world and a semi-arid climate
, which means the extraction rate for this basin is
large . This gauge is also located relatively close to
the river mouth to the west of the Himalayas (see Fig.
and Table ); therefore, the river flows are less
likely to be affected by the ASM and more likely to be affected by meltwater
from winter precipitation. The Yangcun gauge is a more upstream gauge and the
differences between the model and observations for this gauge are more likely
to be related to the precipitation distribution.
Figure shows a region of intense
precipitation in the simulations (Fig. d–f)
for the ASM period close to this gauge. The APHRODITE data
(Fig. c) also show a region of higher rainfall
although this is not as large as that shown for the simulations. This could
be having a direct effect on the river flow.
The other two gauges on the Brahmaputra are located downstream of the Yangcun
gauge: the Pandu (Fig. l) and Bahadurabad
(Fig. j). At these two gauges, the seasonal cycle of
river flow has a very broad peak particularly in the modelled river flows
compared to the other gauges. In the simulations the snowfall climatology for
the Ganges–Brahmaputra basin (not shown) has a similar seasonal cycle to
that of the river flow for the Bahadurabad and the Pandu gauges. It is
therefore likely that the broad peak in river flow is related to the broad
peak in snowfall and subsequent snowmelt. The Pandu gauge is also one of only
two gauges where the modelled river flow is less than the observations for at
least part of the year, the other being the Devghat gauge on the Narayani River
(Fig. g). Both of these gauges are located in the
Himalayan foothills close to the region of simulated maximum total
precipitation. If the simulations put the location of this maximum below
these gauges this could cause the river flows at the gauges to be lower than
observed. The river flow on the main trunk of the Ganges at the Farakka
Barrage (shown in Fig. i) is a reasonable
approximation to the observations in terms of magnitude; however, the timing
of the peak flow seems to be later in the models. It could be argued this
also happens in some of the other gauges although it is more noticeable for
the Farakka Barrage. All the gauges shown here are for glacierized river
basins. Snow fields and snowmelt are represented in the simulations in this
analysis and will therefore replicate some aspects of melt affecting river
flow. However, glacial melt is not explicitly represented in the RCM used for
these simulations. Including glacial processes specifically could act to
reduce runoff because more snow is stored as ice or increase runoff where
there is an increased melting . Therefore, including
glacial processes could be important for the timing and magnitude of the
maximum and minimum river flows for these catchments.
Future river flows
In this section we consider the future HNRCM simulations.
Figure highlights the variability in the future
projections of total precipitation for South Asia between basins. In these
simulations the Ganges–Brahmaputra catchment shows an increasing trend in
total precipitation and there is considerable variation between the
simulations (Fig. b). The Indus basin
(Fig. a), however, has a much flatter trajectory
to 2100 and the simulations are more similar. The annual time series of
evaporation (Fig. ) over these catchments shows a
similar picture, with an increasing trend for the Ganges–Brahmaputra basin
(Fig. b) but no real trend for the Indus
(Fig. a). The annual mean runoff efficiency (not
shown), defined here as the ratio of annual runoff (streamflow per unit area)
to annual precipitation, shows no real trend for either basin. The trends in
river flow (see Fig. ) vary between gauges, although
none indicate decreasing river flows. There is an upward trend in river flows
at some of the gauges, in particular, the Narayani-Devghat
(Fig. g), Arun-Turkeghat
(Fig. h) and Ganges-Farakka
(Fig. i). These gauges suggest an upward trend toward
the 2100s that actually represents a doubling of the river-flow rate. The
increase in river flow for the Narayani-Devghat gauge
(Fig. g) are consistent with analysis by
using a hydrological model for the Narayani
basin. Ganges-Farakka is the most downstream gauge in the Ganges–Brahmaputra
basin in this river-flow analysis, therefore providing an approximation for
the whole Ganges basin. These simulations show an increase in precipitation
for the Ganges–Brahmaputra basin of approximately 20 % (see
Fig. ) and an increase of approximately 10 %
in evaporation (see Fig. ), over the course of the
century. This suggests the changes in runoff over the Ganges catchment are
predominantly driven by precipitation on the annual scale. However, regional
analysis by covering the humid north-eastern part of
India and a global analysis by suggested there has been
a decline in the evaporation caused by lighter surface winds and reduced
radiation. A future reduction in evaporation could also contribute to future
increases in runoff. Analysis using a conceptual hydrological model by
suggested that the type of precipitation being
received at different elevations and the changes in melt and evaporation from
snowpacks in a warmer climate could also be important for changes in runoff.
Climatology analysis
In this section we use climatologies to compare future river flows with the
present-day inter-annual variability (defined in
Sect. ). South Asia is a very variable region, yet
these models suggest the future mean river flow could lie outside the
present-day variability for peak flows for some of the gauges in this study.
This could have important implications for water resources for the region.
The gauges that show an increase in maximum river flows (see
Fig. ) are mainly those in the middle of the
Himalayan arc (see Fig. ). The seasonal cycle for the
western most gauges (located in the Indus basin) and the
eastern most gauges (located in the Brahmaputra basin) are
typically still within the range of present-day variability. This could be
due to the changes in the influence on river flow from west to east becoming
more influenced by the ASM and less by western disturbances, with basins in
the centre of the Himalayas and to the north influenced by both phenomena.
Figure also suggests that the maximum river flows
still occur mainly during the ASM for many of the gauges shown.
High and low flow analysis
The analysis of the high and low flows is of particular importance to water
resources and future availability, therefore in this section we calculate the
distributions of the river flows for each of the gauges (see
Sect. ). These are shown in the form of pdfs in
Fig. for the 1971–2000, 2050s and the 2080s.
Figure illustrates how the lowest flows dominate the
distributions for each of the three periods. In most of the gauges 1971–2000
period has the highest frequency of the lowest flows, the curves then tend to
flatten in the middle of the distribution before tailing off toward zero for
the highest flows. The two future periods also follow a similar trajectory,
although in general there is a reduction in the frequency of the lowest flows
and an increase in the magnitude of the highest flows for all of the gauges
and both simulations towards 2100.
The Yangcun gauge on the Brahmaputra (Fig. k) shows the
least change of all the gauges between the 1971–2000 period, future periods
and simulations. The distributions for the gauges downstream of Yangcun, the
Pandu (Fig. l) and the Bahadurabad
(Fig. j), are notable for their differences from all the
other gauges. All the other gauges shown have a single peak toward the lower
end of the river-flow distribution. The Pandu and Bahadurabad gauges have two
distinct peaks in frequency with a second peak occurring toward the middle of
the distribution, where the distribution for most other gauges flattens out.
This is consistent with the broader peak in the 30-year mean seasonal cycle
shown for these gauges in Fig. and is probably
similarly explained by snowmelt (see Sect. ). In some
of the other gauges there is a small increase in the middle of the river flow
distribution but this tends to be smaller and restricted to the two future
periods, e.g. the two Karnali River gauges (Fig. e and f).
Seasonal cycle of river flow in each of the RCMs (HadCM3 – red,
ECHAM5 – blue) for the two future periods: 2050s (solid lines) and 2080s
(dashed lines), with shaded regions showing 1.5 SD from the mean for
1971–2000 for each river gauge.
The distribution of the river flow in the HadCM3 and ECHAM5 (HadCM3
– red, ECHAM5 – blue) runs for three periods: historical (1971–2000 –
solid lines) and two future periods (2050s – dashed lines and 2080s –
dotted lines) plotted as a pdf for each river gauge.
Comparison of the lowest 10 % of monthly river flows at the
Farakka Barrage on the Ganges River against the 10th percentile for the
1971–2000 period for 1971–2000 (top panel), 2050s (middle panel) and 2080s
(bottom panel) for HadCM3 (red triangles) and ECHAM5 (blue stars). Each star
or triangle represents a month within the 30-year period where the value is
less than the 10th percentile of the 1971–2000 period with the total number
for each of the simulations given in the top right corner of each
plot.
Comparison of the highest 10 % of monthly river flows at the
Farakka Barrage on the Ganges River against the 90th percentile for the
1971–2000 period for 1971–2000 (top panel), 2050s (middle panel) and 2080s
(bottom panel) for HadCM3 (red triangles) and ECHAM5 (blue stars). Each star
or triangle represents a month within the 30-year period where the value is
greater than the 90th percentile of the 1971–2000 period with the total
number for each of the simulations given in the top right corner of each
plot.
Threshold analysis
The pdfs shown in Fig. and described in
Sect. suggest future changes in the lower and
upper ends of the river-flow distribution. In this section we consider these
parts of the distribution in order to confirm this pattern. We compare the
two future periods (2050s and 2080s) against the 1971–2000 period explicitly
using thresholds defined by the 10th and 90th percentiles for this
present-day period for each river gauge. Graphical examples from the results
of this analysis are shown for all three periods – historical (a),
2050s (b), 2080s (c) – for the Farakka Barrage on the Ganges in
Figs. and . In
Fig. the number of months where river flow is
below the present-day 10th percentile reduces in each of the future decades.
However, for flows greater than the present-day 90th percentile there is an
increase in each of the future decades
(Fig. ). Table
illustrates that the patterns shown in Figs.
and are generally true for almost every other
gauge in the analysis. The Tehri dam (Bhagirathi) is the only exception of
the gauges shown in Table , showing an increase of
12 % in the number of incidences where the river flow is less than the
1971–2000 10th percentile for the 2080s. This is mainly due to the ECHAM5
model which has a high number of incidences. The Yangcun gauge (Brahmaputra)
is the only gauge where there is no change in the number of incidences where
the river flow is less than the 10th percentile for 1971–2000 in either of
the future periods. This is probably because the lowest river flows are
already very low at this gauge.
At every gauge there is an increase in the number of incidences where river
flows are greater than the 90th percentile for 1971–2000 for the two future
periods. Several of the gauges have increases in the number of events above
the 90th percentile for the 1971–2000 period of more than 100 %. This
confirms the conclusions drawn visually from the analysis in
Fig. that the general distributions move toward the higher
flows for these gauges and simulations.
Decadal percentile analysis
The annual time series shown in Fig. is very
variable and systematic changes throughout the century could be masked by
this variability. The 10th and 90th percentiles for each decade and each
simulation are calculated on the basis that there are changes in the upper
and lower parts of the future river-flow distributions. At the lower end of
the distribution, there is little change in the 10th percentile (not shown)
for most of the gauges, probably because of very low flows at the lowest
times of the year. Only the Pandu and Bahadurabad gauges on the Brahmaputra
and the Farakka gauge on the Ganges show a non-zero value for the lowest
10 % of river flows through to the 2100s. These three gauges indicate a
slight increase for the
10th percentile for each decade through to 2100.
The 90th percentile values (Fig. ) are generally
much more variable throughout the century than those for the 10th percentile
to the 2100s. We consider the gauges according to their location across the
Himalayan arc from west to east (see Fig. ). The HadCM3
simulation projects an increase in river flows for the most westerly gauges
in this analysis; Attock and Kotri gauges located on the Indus (see
Fig. a and b) and the Chenab-Panjnad gauge (see
Fig. c). ECHAM5, on the other hand, shows a much
flatter trajectory for these gauges. This may be explained by the HadCM3
simulation depicting an increase in the occurrence of western disturbances and
an increase in total snowfall which is not simulated by ECHAM5 .
The gauges located toward the middle of the Himalayan arc generally show
increases across the decades to 2100 in both models; these are the
Bhagirathi-Tehri (Fig. d), both Karnali River
gauges (Benighat – Fig. e and
Chisapani – Fig. f), Narayani-Devghat and
Arun-Turkeghat (Fig. g and h). There is very close
agreement between the two simulations for the Narayani-Devghat,
Arun-Turkeghat (Fig. g and h) and Bhagirathi-Tehri
(Fig. d) gauges, with the former two showing less
variability between decades than the others in the analysis. The
Karnali-Benighat gauge (Fig. e) also has less
variability between the decades; however, there is a systematic difference
between the two simulations that remains fairly constant across the decades.
From the subset of gauges in this analysis that are the most central on the
Himalayan arc, the Karnali-Chisapani gauge (Fig. f)
has the largest variability between simulations and decades. However, this
gauge still shows an increase overall in both simulations with a steeper
increase for HadCM3 than ECHAM5. The closer agreement between simulations at
these more central gauges may be due to the reducing influence of the western
disturbances in the HadCM3 simulation from west to east across the Himalayas,
therefore resulting in smaller differences between the two simulations.
Table showing the average percentage change for the two models in
the number of times the modelled river flow is less than the 10th percentile
and greater than the 90th percentile of the 1970–2000 period for the 2050s
and 2080s future periods.
* This value is the only positive value in the table.
The Farakka-Ganges gauge (Fig. i) and two of the
Brahmaputra gauges – Bahadurabad (Fig. j) and
Pandu (Fig. l) – represent three of the most
easterly river gauges in the analysis. These gauges show an increase in both
simulations through to the 2100s, in this case more pronounced in ECHAM5 than
HadCM3 for these two Brahmaputra gauges. There is much closer agreement
between the two simulations at the Farakka-Ganges gauge
(Fig. i), which is located slightly further west
than the two Brahmaputra gauges. The other Brahmaputra gauge, the Yangcun
(Fig. k) is very variable through the century,
there is a period with consecutive decades of increasing river flows in the
middle of the century but over the whole century neither model shows a
consistent change.
This analysis shows that neither simulation is consistently showing
a systematic increase in the 90th percentile of river flows across all the
gauges. Instead it highlights the changing conditions and the different
behaviour of the two simulations across the Himalayan arc.
Implications of changes in future river flows
In this section we consider the implications of the projected future changes
in river flows for South Asia on water resources. We highlight the broader
challenges facing the region and where the current RCMs need development to
represent key processes for this region. The key points from this discussion
are summarised in Table . In the present day, water
resources in South Asia are complicated, precariously balanced between excess
and shortage. Parts of South Asia receive some of the largest volumes of
precipitation in the world and are therefore at frequent risk of flooding and
yet others regularly endure water stress. The complexity is increased by the
competition between states and countries for resources from rivers that flow
large distances crossing state and country borders, each with their own
demands on resource. There is a considerable gap between the amount of water
resources flowing through South Asia and the actual usable amount
, for example the total flow for the Brahmaputra
basin is approximately 629 km3 of which only 24 km3 is
usable . There is therefore huge potential for
improvements in the efficiency of systems for irrigation and domestic water
supply that could ease pressures on water resources, currently and predicted,
as demand increases.
The 90th percentile of river flow for each decade for HadCM3 (red
triangles) and ECHAM5 (blue circles) for each river gauge.
Table of implications of changes in water resources.
Types of changeImplications for water resourceAdaptation optionsOther issuesLarge annualAbundance some years andBuilding storage capacityType of water storage isvariabilityscarcity in others makee.g. rainwater harvesting.important e.g. reservoirs/damsit difficult to plan budgetsImprovement of irrigations systems.have both politicalfor different users.Development of water efficient,and ecological implications.high-yielding crop varieties.Developing new crops takes time.Changes inIncreases in peak flows could beImproving river channel capacity.Flood protection levelspeak flow – timingpositive for irrigation andDiverting excess water to a different valley.do not match demographicand magnitudedomestic supply but could increaseStoring the excess water for low flow periodstrends so vulnerabilitythe risk of flooding.e.g. through rainwater harvesting.to flooding remains highPeak flows occurring later and/orImproving drainage and water recycling.in this regiondecreases in peak flows could reduceAdopting varieties of crops that grow.availability of water for irrigationwhen water for irrigation is moreMarket development forat crucial crop development stagesreadily availablenew crops takes timenegatively impacting yields.Changes in lowIncreases in the magnitude of the lowAdaptations to avoid flooding duringflows – timingflows could be positive for irrigationpeak flow periods could provide resourcesand magnitudeand domestic supply.during low flow periods.Decreases could mean less resourcesDevelopment of water efficient,available for irrigationhigh-yieldingleading to reduced yieldscrop varieties
In the last 50 years there have already been efficiency improvements,
such as development of irrigation systems and use of high-yielding water
efficient crop varieties. These improvements have fuelled the rapid
development in agriculture across South Asia making the region more
self-sustained and alleviating poverty . However, these
advances have also had a large impact on the regions river ecosystems
resulting in habitat loss, reduced biodiversity and
water pollution . Historically arbitrary thresholds
based on a percentage of the annual mean flow have been used to estimate
minimum flows, but these simplistic estimates do not take account of the flow
variability that is crucial for sustaining river ecosystems
, referred to as environmental
flows. Environmental flows are defined by as the
ecologically acceptable flow regime designed to maintain a river in an agreed
or predetermined state. The variability in river flows through the year have
important ecological significance; for example low flows are important for
algae control and therefore maintaining water quality. High flows are
important for wetland flooding and preserving the river channel. When
considering the implications of future changes in climate on river flows and
therefore surface water resources, estimates of flow variability and minimum
flows are an important consideration. However these are not easily quantified
in general terms with many methods requiring calibration for applications to
different regions and basins. In our simulations there is an intensification
of the seasonal cycle and therefore an increase in the flow variability and a
reduction in the occurrence of the lowest flows. These changes could have
implications for the biodiversity of these catchments.
In India the domestic requirement for water is the highest priority but is
only 5 % of the total demand. Irrigation is the second highest priority
accounting for a much greater proportion, approximately 80 % of India's
total demand for water. A significant proportion of domestic and irrigation
resource comes from both ground and surface water.
studied future water resources for food production using the LPJmL (Lund-Potsdam-Jena managed Land) model and the HNRCMS. LPJmL also simulates groundwater extractions
these are thought to be important for the Indus and
parts of the Ganges but not the Brahmaputra. The LPJmL simulated extraction
varies considerably between basins; the largest occurring in the Indus
(343 km3 yr-1) followed by the Ganges (281 km3 yr-1)
and Brahmaputra (45 km3 yr-1). The Brahmaputra has the smallest
percentage of irrigated crop production (approximately 40 %) followed by
the Ganges (less than 75 %) and the Indus where more than 90 % of crop
production is on irrigated land. The Indus has the largest proportion of
water sourced from rivers and lakes of the three basins and the largest
proportion of the river flow is glacial melt .
use a perturbed physics ensemble of HadCM3 GCM
simulations and find an increase in water resources for
South Asia at the annual timescale due to climate change. The analysis shown
here shows a similar result with increases in river flow, particularly the
magnitudes of the higher river flows at these gauges, in some cases above the
range of variability used for this analysis (1.5 SD). However, the analysis
shown here on the monthly timescale, also highlights that these increases in
resources tend to occur during the ASM, when river flow is at its maximum.
This could mean that the benefits of an increase in water resources may not be
realized due to the timing of this increase within the year. Although these
projected changes in river flow are not critical for water resources they
could still be beneficial where there is the capacity to store the additional
flow for use during periods of low flow. Additional water storage capacity
for example through rainwater harvesting, could greatly increase the useable
water resources for the Ganges–Brahmaputra catchments
and potentially alleviate the increased risk of flooding during the ASM when
rainfall is most persistent and rivers are already at their peak flow. South
Asia, even in the current climate, is particularly susceptible to flooding
due to the high temporal and spatial variability of rainfall of the region.
It is estimated that approximately 20 % of Bangladesh floods annually
. Several studies have highlighted increases in both the
extremes and
the variability of precipitation in recent years that
cause extreme rainfall events resulting in catastrophic levels of river
flooding. Over 30 million people in India alone are affected by floods and
more than 1500 lives are lost each year , the economic
cost of flooding is also considerable with the cumulative flood related
losses estimated to be of the order of USD 16 billion between 1978 and
2006 .
The timing of the peak flows of major rivers in this region is also very
important in terms of flooding. In 1998 the peak flows of the Ganges and the
Brahmaputra rivers occurred within 2 days of each other resulting in
devastating flooding across the entire central region of Bangladesh.
Approximately 70 % of the country was inundated, the flood waters then
remained above danger levels for more than 60 days . This
event caused extensive loss of life and livelihood in terms of damaged crops,
fisheries and property and the slow recedence of flood waters hindered the
relief operations and recovery in the region. This analysis does not suggest
any change to the timing of the peak flows, only the magnitude. However, given
the high probability of two rivers in this region having coincident peak
flows in any given year and the likelihood that severe
flooding will result, an increase in the magnitude of the peak could still be
significant. Flooding can have a large impact on crops, for example in
Bangladesh over 30 % of the total flood related damages are due to the
loss of crops. The estimated crop damage from the 1998 floods was estimated
to be 3 million t . The slow receding of flood water
can also mean the ground is not in a suitable condition to sow the next crop,
restricting the growing time and potentially affecting crop yields for the
following year. On the other hand a limited amount of flooding could also be
a benefit, particularly for rice crops. Inundation of clear water can benefit
crop yield, due to the fertilization effect of nitrogen producing blue-green
algae in the water .
In our simulations the reduced occurrence of the lowest flows could translate
into an increase in the surface water resource in this region especially
during periods when the river flows are traditionally very low. This could
mean that the current and increasing pressure on groundwater
may be alleviated in future years. Alternatively
increases in the lowest flows may enable adaptation to a changing climate and
the modification of irrigation practises. Current projections of future
climate suggest that temperatures could also increase for this region
. Increasing temperatures poses a threat to crop yields
of a different kind because this is a region where temperatures are already
at a physiological maxima for some crops . Rice yield,
for example, is adversely affected by temperatures above 35 ∘C at
the critical flowering stage of its development . Wheat
yields could also be affected by rising temperatures, with estimated losses
of 4–5 million t per degree of temperature rise through the growing
period . Additional water resources for irrigation at
previously low flow times of the year could allow sowing to take place at
a different time of the year in order to avoid the highest temperatures,
thereby reducing the likelihood of crop failure. However, with increasing
variability and extremes, a potential feature of the future climate for this
region , there is also the increased risk of longer
periods with below average rainfall and potentially more incidences of
drought. This could lead to additional demand for water for irrigation to
prevent crops becoming water stressed . There may
also be increases in demand from other sources other than agriculture, for
example the increasing population or the reduced availability
of groundwater of an acceptable quality for domestic use
. Any of these factors, either individually or
combined, could effectively cancel out any or all increases in resource from
increased river flow due to climate change.
In addition there are a number of processes missing from the models used for
these simulations that could change the sign of the projected changes. There
is no irrigation included in these simulations, which could be important
particularly on the basin scale. The impacts of extensive irrigation on the
atmosphere are complex but could have a positive impact on water availability
due to evaporation and water being recycled within
the basin. estimated that up to 35 % of
additional evaporation is recycled within the Ganges basin. Therefore, this
aspect of the regional water cycle is not accounted for in these simulations.
There is also no representation of glaciers that could act to increase or
reduce river flows depending on the occurrence of negative or positive mass
balance, respectively. In these simulations snowmelt is represented; however,
representing glacial processes as snowmelt could act to enhance the seasonal
cycle in the simulated river flows for both present-day and future
projections as snow melts more readily than ice. These simulations also do
not explicitly include groundwater, primarily focussing on river flows.
Groundwater is a highly exploited part of water resources for South Asia.
Representation of this would give a more complete picture of the total water
resources for this region.
Conclusions
We present the first 25 km resolution regional climate projections of
river flow for the South Asia region. A sub-selection of the HNRCMs are used
to provide runoff to a river-routing model in order to provide river-flow
rate, which can be compared directly with ERAInt and any available river gauge
data for the South Asia region. This analysis focusses on the major South Asia
river basins that originate in the glaciated Hindu–Kush Karakoram Himalayas:
the Ganges–Brahmaputra and the Indus. The aim of this analysis is firstly to
understand the river flows in the ECHAM5 and HadCM3 simulations and secondly
examine how useful they are for understanding the changes in water resources
for South Asia. We also consider what the projected changes in river flow to
the 2100s might mean for water resources across the Himalaya region.
The driving GCMs (ECHAM5 and HadCM3) have previously been shown to capture
a range of temperatures and variability in precipitation similar to the AR4
ensemble for the much larger domain of Asia .
However, using just two ensemble members cannot capture the full range of
these larger ensembles. In this analysis the seasonal cycle of precipitation,
a key influence on river flows, is captured reasonably well for the
downscaled GCMs compared to both observations and the downscaled ERAint
simulation. Although observed precipitation is lower than in the model the
underestimation inherent in precipitation observations at higher elevations
is likely to be an important factor for this analysis, which includes the
high Himalayas.
A number of GRDC gauge stations , selected to capture the
range of conditions across the Himalayan arc and sample the major river
basins, provide observations of river flow for comparison against the HNRCM
simulations. The lack of recent river flow data limited the gauges that could
be selected for analysis. In the absence of robust observations we use a
downscaled ERAint simulation in addition to the available observations to
provide a useful benchmark against which to compare the downscaled GCM
simulations. In general there is a tendency for overestimation of river-flow
rate across the selected gauges compared with GRDC observations; however,
comparison against the ERAint simulation is more mixed with some gauges
showing higher and others with lower river flows than ERAInt. In general most
of the simulations broadly agree with observations and ERAint to within the
range of natural variability (of 1.5 SD) and agree on the periods of the highest
and lowest river flow. Therefore, indicating that the RCM is able to capture
the main features of both the climate and hydrology of this region for the
present day.
The future projections indicate an increase in surface water resources, with
river-flow rates at some of the gauges almost doubled by the end of the
century. These increases in river flow occur for the gauges in the
Ganges–Brahmaputra basin, which also shows an increasing trend in both
evaporation and precipitation. Therefore, the changes in river flow are likely
to be mainly driven by precipitation on the annual scale which more than
counters the evaporation caused by increasing temperatures in the model. This
is consistent with other analyses of precipitation that also use the A1B
climate scenario , which is a useful result. The
trajectories of the annual average river flow, evaporation and precipitation
for the Indus are much flatter, showing little or no trend.
The increases in the annual mean river flows are reflected in the seasonal
cycles of river flow for the two future periods (2050s and 2080s), which
indicate that most of the changes occur during peak flow periods. Some of the
gauges toward the middle of the Himalayan arc, show changes above the range
of present-day natural variability. This could be due to the increasing
influence of the ASM and reducing influence of western disturbances from west
to east having an additive effect. The gauges located furthest west and east
in this analysis lie within the present-day natural variability. There were
also differences between the two simulations across the Himalayan arc with
HadCM3 suggesting increases in river flow at the upper end of the
distribution for western gauges that was not evident in ECHAM5. The analysis
shown here does not suggest a systematic change in the models for the timing
of the maximum and minimum river flows relative to the present day, suggesting
an over all increase in water resources at the top and bottom of the
distribution. This has positive and negative implications with potentially
more resources during usually water scarce periods. However, there are also
implications in terms of increased future flood risk during periods where the
river flow is particularly high. Increases in maximum flows for rivers in
this region could be important in terms of loss of life, livelihoods,
particularly agriculture and damage to infrastructure.
While this analysis suggests increasing surface water resources due to
climate change, there are a number of other factors that could affect this
result, both in terms of this analysis and uncertainties surrounding the
region itself. The South Asia region is changing rapidly; therefore, other
factors could have a large effect on water resources for this region. A
rising population, expansion of industry (other than agriculture) and the
continued depletion of groundwater could change the demand for surface water
resource from other parts of the South Asia economy. In addition increasing
variability of an already changeable climate could lead to extended periods
throughout the year of rainfall below the annual average, leading to an
increase in demand for irrigation resource. In terms of this analysis, this
is only one RCM and another RCM could produce a different result. Also there
are missing hydrological processes in the RCM and river-flow model that could
impact the river flows directly. The RCM and river-flow model do not include
abstraction and irrigation, groundwater recharge or explicitly include
glacial processes and their contribution to river flow. Including glacial
processes in the form of a glacier model together with river routing within
the land-surface representation will be useful to establish if the
contribution from glaciers changes the timing and/or magnitude of both the
lowest and highest flows in these gauges. Likewise including representation
of water extraction (both from rivers and groundwater) particularly for
irrigation, the biggest user of water in the region, will help to provide
a more complete picture of the demand for water resources for the South Asia
region. Including irrigation and therefore the associated evaporation will
capture part of the water cycle not possible with the current model and
maintain the regional water balance. Including representation of these
processes in the RCM or river-flow model would improve the robustness of the
future projections of water resources and further our understanding of the
water balance for this region. These processes could have a large impact on
the water balance in the model potentially changing the signal of the
projected changes in river flow. Understanding the interactions between
availability of water resources, irrigation and food production for this
region by using a more integrated approach, such as that used in
, may also help with understanding how pressures on
resources could change with time. In support of this work and others, there
is also a need for good-quality observations of both precipitation and river
flow available for long enough time periods to conduct robust water resource
assessments for this region.
Acknowledgements
The research leading to these results has received funding from the European
Union Seventh Framework Programme FP7/2007–2013 under grant agreement
no. 603864. Camilla Mathison, Pete Falloon and Andy Wiltshire were supported
by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme
(GA01101). Thanks to Neil Kaye for his GIS expertise.
Edited by: B. Schaefli
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