1. Impact of the 21st Century Climate Change on Surface Water Availability of the Transboundary Kabul River Basin
M. Zia ur Rahman Hashmi1, Amjad Masood1, Haris Mushtaq2, Burhan Ahmad3, Ahsan Bukhari3
Global Change Impact Studies Centre, Pakistan1
LEAD Pakistan2
Pakistan Meteorological Department3

2. Introduction
Climate change, a higher degree concern in trans-boundary basins
Co-riparian states will be impacted by climate change jointly
Hence, will feel more inter-dependency in terms of their shared water resources (Biermann and Dingwerth, 2004)
This may call for a major shift in the focus of climate related policies to not only address national or sub-national challenges but also to cater for transboundary threats
In the absence of such a shift, there is a high likelihood of disputes among the riparian leading to instability on both sides (Wolf, 2009)
First essential step: substantial improvement in the understanding of likely climatic changes and its potential impacts on the transboundary waters.

3. Kabul River Basin
Transboundary Kabul Basin: Shared by Pakistan and Afghanistan
No treaty/agreement exists
Facing a number of climate change related challenges
Lacks data, hence detailed studies to envisage likely implications of CC
Kabul River at Noshera
Source:

4. Study Objectives
Analyzing performance of the SRM for simulation of the daily stream flows in the snow and glacier fed transboundary Kabul River basin;
Determine whether rising temperature and associated climate change has significant impact on the stream flows of the transboundary Kabul River basin under different future climate change scenarios (RCP 4.5 and RCP 8.5).

5. Unique aspects
Firstly, to our knowledge, it is the only study which considers the larger transboundary Kabul basin (includes the complete area of the basin on both sides of the border) for a climate change analysis
Secondly, it uses for the first time the bias corrected APHRODITE (The Asian Precipitation Highly Resolved Observational Data Integration Toward Evaluation of Water Resources) data (Burhan et al., 2015) of fine spatial and temporal resolution (1km and daily, respectively), especially for the upper Indus Basin (UIB).

6. Data and sources Gridded Climate data (1km):
Bias corrected APHORDITE (Burhan et al. 2015)
Min. and Max temperature and Precipitation ( )
Daily flow data: Noshera (WPADA: )
DEM: SRTM 30 m
Snow cover data: MODIS 8-daily interpolated to daily
Hydrological Model: Snowmelt Runoff Model (SRM) (Martinec, 1975)
Future climate projections’ data: Statistically downscaled CMIP5 ensemble data:
Near future ( ; 2020s) and Far future ( ; 2040s)
RCPs: 4.5 and 8.5

7. Methodology

8. Shortcomings of APHRODITE Data
Burhan et al. (2015) noted that the APHRODITE precipitation was underestimated at high altitudes, likely due to the valley–bottom location of precipitation observations and due to limitation of data availability from Indian side

9. Strategy to Overcome Shortcomings
Temperature lapse rates from Tahir et al. (2011), to correct the temperature data for the historical period at 1 km resolution
Precipitation gradients for the Upper Indus Basin (UIB), by using a proxy method that uses glacier mass balance data
Three estimates of precip. gradient (0, 0.3, 0.6 [%/m]) to calculate the regional glacier mass balance taken from Kääb et al. (2012)
The gradients were applied using the model of the Hewitt et al. (2007); which says that the precipitation increases up to the altitude of 5500 m and deceases afterwards.

11. CMIP5 GCMs’ Selection and Downscaling
Four GCMs have been selected based on their goodness of fit criteria:
They have a good Pearson’s Correlation Coefficient (greater than or equal to 0.88) with baseline APHRODITE time-series.
Their normalized root mean square error is low (less than or equal to 0.15)
Their normalized standard deviations lie within ±0.4 to that of normalized standard deviation of APHRODITE dataset.
Model
Center
Spatial Resolution (Lat x Long)
CCSM4
NCAR
1.25x0.94
CanESM2
CCCMA
2.81x2.81
GFDL–ESM2M
GFDL
2.5x2.011
HadGEM2–ES
MOHC
1.87 x 1.25

12. Downscaling process

13. Hydrological Modelling

14. Bands
Elev. bands (m)
Area (Sq. km)
Mean Elev. (m) 1
12017
700 2
12278
1350 3
15268
2000 4
16246
2500 5
15347
3350 6
10665
4000 7
7260
4700 8
2212
5500

15. Hydrological Model: Calibration and Validation

16. Analysis of Results

17. Temperature change scenarios

18. Precipitation change scenarios

19. Obs high flow analysis

20. Future flows: Variability and extremes
St.dev: and 284
St.dev: and 359

21. Future flows: Availability and timing

22. Increased flooding in the Kabul River
High to Very High Flood

23. Summary of results
Increase in temperatures for all the elevational bands that receive snowfall for near future and far future under RCP 4.5 and RCP 8.5 scenarios
Winter precipitation is likely to increase significantly for middle elevation bands
Increased temperature and precipitation are going to cause;
Overall increase in the flow volume
Earlier and sharper peaks
Increased inter- and intra-annual variability
More frequent high flow events (extreme flow events)

24. Recommendations
A joint campaign for installation of gauging sites in the basin by both the riparian countries to improve data collection and availability for hydrological studies and climate change research.
Revisit existing water infrastructure regulation practices, to enhance their efficiency and resilience under altered climate/flow conditions
Measures to improve trust and coordination between national and transboundary weather and flood forecasting agencies through enhanced knowledge and data sharing.
Develop jointly run early warning systems with increased efficiency through international cooperation and participation of the vulnerable communities, especially to deal with flash floods

25. Thank you

26. Hydrological model parameters
Range
Chosen Value
Min
Max
Snowmelt runoff Coefficient, Cs
0.1 1
Rainfall runoff Coefficient, Cr
Degree Day factor
0.09
0.73
Lapse rate
0.59
0.95
0.65
Critical Temperature
0.75 3 2
Recession Coefficient
Kx Ky

27. Precipitation projections

28. Temperature projections

29. EDW (Pepin et al. 2015) in KRB