Hydrological Modeling of the Upper Indus Basin: A Case Study from a High-Altitude Glacierized Catchment Hunza
<p>Map of the study area.</p> "> Figure 2
<p>Elevation bands and Hypsometric curve of the study area.</p> "> Figure 3
<p>Observed and simulated daily runoff for the calibration period (1998–2004).</p> "> Figure 4
<p>Observed and simulated daily outflow for the validation period (2008–2010).</p> "> Figure 5
<p>Comparison of observed and simulated daily outflow calibration (<b>a</b>) and validation (<b>b</b>) periods of the Danyore Gauge station.</p> "> Figure 6
<p>Monthly calibration (<b>a</b>) and validation (<b>b</b>) for 1998–2004 and 2008–2010.</p> "> Figure 7
<p>Monthly projected discharge, average for our future reference years (2030–2059 and 2070–2099) for the Hunza River at the Danyore gauge station; (<b>a</b>) RCP2.6; (<b>b</b>) RCP4.5; and (<b>c</b>) RCP8.5.</p> ">
Abstract
:1. Introduction
2. Study Area
3. Data and Methods
3.1. SWAT Data
3.2. Future Climate Change Scenario (2010–2100)
3.3. Model Calibration and Validation
4. Results and Discussion
4.1. Future Climate Projections (2010–2100)
4.2. Future Hydrological Flows (2010–2100)
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Immerzeel, W. Spatial Modeling of Mountainous Basins: An Integrated Analysis of the Hydrological Cycle, Climate Change and Agriculture; Netherlands Geographical Studies; Utrecht University: Utrecht, The Netherlands, 2008; Volume 369. [Google Scholar]
- Tahir, A.; Chevallier, P.; Arnaud, Y.; Ahmad, B. Snow cover dynamics and hydrological regime of the Hunza river basin, Karakoram range, Northern Pakistan. Hydrol. Earth Syst. Sci. 2011, 15, 2259–2274. [Google Scholar] [CrossRef] [Green Version]
- Debele, B.; Srinivasan, R.; Gosain, A. Comparison of process-based and temperature-index snowmelt modeling in SWAT. Water Resour. Manag. 2010, 24, 1065–1088. [Google Scholar] [CrossRef]
- Young, G.; Hewitt, K. Hydrology research in the Upper Indus Basin, Karakoram Himalaya, Pakistan. Hydrol. Mt. Areas. IAHS Publ. 1990, 190, 139–152. [Google Scholar]
- Hewitt, K.; Wake, C.; Young, G.; David, C. Hydrological investigations at Biafo glacier, Karakorum Range, Himalaya; an important source of water for the Indus river. Ann. Glaciol. 1989, 13, 103–108. [Google Scholar]
- Bookhagen, B.; Burbank, D.W. Toward a complete Himalayan hydrological budget: Spatiotemporal distribution of snowmelt and rainfall and their impact on river discharge. J. Geophys. Res. Earth Surf. 2010, 115, F03019. [Google Scholar] [CrossRef]
- United Nations Development Programme (UNDP). Community Based Survey for Assessment of Glacier Lake Outburst Flood Hazards (GLOFs) in Hunza River Basin; UNDP Report; UNDP: Islamabad, Pakistan, 2008. [Google Scholar]
- Zhang, X.; Srinivasan, R.; Hao, F. Predicting hydrologic response to climate change in the Luohe river basin using the SWAT model. Trans. ASABE 2007, 50, 901–910. [Google Scholar] [CrossRef]
- Singh, A.; Gosain, A. Climate-change impact assessment using GIS-based hydrological modelling. Water Int. 2011, 36, 386–397. [Google Scholar] [CrossRef]
- Sood, A.; Muthuwatta, L.; McCartney, M. A SWAT evaluation of the effect of climate change on the hydrology of the Volta river basin. Water Int. 2013, 38, 297–311. [Google Scholar] [CrossRef]
- Akhtar, M.; Ahmad, N.; Booij, M. The impact of climate change on the water resources of Hindukush–Karakorum–Himalaya region under different glacier coverage scenarios. J. Hydrol. 2008, 355, 148–163. [Google Scholar] [CrossRef]
- Pradhanang, S.M.; Anandhi, A.; Mukundan, R.; Zion, M.S.; Pierson, D.C.; Schneiderman, E.M.; Matonse, A.; Frei, A. Application of SWAT model to assess snowpack development and streamflow in the Cannonsville watershed, New York, USA. Hydrol. Process. 2011, 25, 3268–3277. [Google Scholar] [CrossRef]
- Khan, A.; Ghoraba, S.; Arnold, J.G.; Di Luzio, M. Hydrological modeling of upper Indus basin and assessment of deltaic ecology. Int. J. Mod. Eng. Res. (IJMER) 2014, 4, 73–85. [Google Scholar]
- Shrestha, M.; Koike, T.; Hirabayashi, Y.; Xue, Y.; Wang, L.; Rasul, G.; Ahmad, B. Integrated simulation of snow and glacier melt in water and energy balance-based, distributed hydrological modeling framework at Hunza river basin of Pakistan Karakoram region. J. Geophys. Res. Atmos. 2015, 120, 4889–4919. [Google Scholar] [CrossRef]
- Immerzeel, W.W.; Van Beek, L.P.; Bierkens, M.F. Climate change will affect the Asian water towers. Science 2010, 328, 1382–1385. [Google Scholar] [CrossRef] [PubMed]
- Konz, M.; Uhlenbrook, S.; Braun, L.; Shrestha, A.; Demuth, S. Implementation of a process-based catchment model in a poorly gauged, highly glacierized Himalayan headwater. Hydrol. Earth Syst. Sci. Discuss. 2007, 11, 1323–1339. [Google Scholar] [CrossRef] [Green Version]
- Akhtar, M.; Ahmad, N.; Booij, M. Use of regional climate model simulations as input for hydrological models for the Hindukush-Karakorum-Himalaya region. Hydrol. Earth Syst. Sci. 2009, 13, 1075–1089. [Google Scholar] [CrossRef]
- Neitsch, S.L.; Arnold, J.G.; Kiniry, J.R.; Williams, J.R. Soil and Water Assessment Tool Theoretical Documentation Version 2009; Texas Water Resources Institute: College Station, TX, USA, 2011. [Google Scholar]
- Wake, C.P. Glaciochemical investigations as a tool for determining the spatial and seasonal variation of snow accumulation in the central Karakoram, Northern Pakistan. Ann. Glaciol. 1989, 13, 279–284. [Google Scholar]
- Tachikawa, T.; Kaku, M.; Iwasaki, A.; Gesch, D.; Oimoen, M.; Zhang, Z.; Danielson, J.; Krieger, T.; Curtis, B.; Haase, J. ASTER Global Digital Elevation Model Version 2—Summary of Validation Results; NASA: Washington, DC, USA, 2011.
- Nachtergaele, F.; van Velthuizen, H.; Verelst, L.; Batjes, N.; Dijkshoorn, K.; van Engelen, V.; Fischer, G.; Jones, A.; Montanarella, L.; Petri, M. Harmonized World Soil Database; Food and Agriculture Organization of the United Nations: Rome, Italy, 2008. [Google Scholar]
- Arnold, J.G.; Srinivasan, R.; Muttiah, R.S.; Williams, J.R. Large area hydrologic modeling and assessment part I: Model development 1. Jawra J. Am. Water Resour. Assoc. 1998, 34, 73–89. [Google Scholar] [CrossRef]
- Abbaspour, K.; Vejdani, M.; Haghighat, S. SWAT-CUP calibration and uncertainty programs for SWAT. In Proceedings of the MODSIM 2007 International Congress on Modelling and Simulation, Christchurch, New Zealand, 10–13 December 2007; Oxley, L., Kulasiri, D., Eds.; Modelling and Simulation Society of Australia and New Zealand: Christchurch, New Zealand, 2007; pp. 74–80. [Google Scholar]
- Winchell, M.; Srinivasan, R.; Di Luzio, M.; Arnold, J. Arc SWAT Interface for SWAT2005 User’s Guide; Texas Agricultural Experiment Station and United States Department of Agriculture: Temple, TX, USA, 2007.
- Gitau, M.W.; Chaubey, I. Regionalization of SWAT model parameters for use in ungauged watersheds. Water 2010, 2, 849–871. [Google Scholar] [CrossRef]
- Abbaspour, K.C. SWAT-CUP4: SWAT Calibration and Uncertainty Programs—A User Manual; Swiss Federal Institute of Aquatic Science and Technology, Eawag: Dübendorf, Switzerland, 2011. [Google Scholar]
- Abbaspour, K.C.; Faramarzi, M.; Ghasemi, S.S.; Yang, H. Assessing the impact of climate change on water resources in Iran. Water Resour. Res. 2009, 45. [Google Scholar] [CrossRef]
- Nash, J.E.; Sutcliffe, J.V. River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol. 1970, 10, 282–290. [Google Scholar] [CrossRef]
- Krause, P.; Boyle, D.; Bäse, F. Comparison of different efficiency criteria for hydrological model assessment. Adv. Geosci. 2005, 5, 89–97. [Google Scholar] [CrossRef]
- Soncini, A.; Bocchiola, D.; Confortola, G.; Bianchi, A.; Rosso, R.; Mayer, C.; Lambrecht, A.; Palazzi, E.; Smiraglia, C.; Diolaiuti, G. Future hydrological regimes in the Upper Indus basin: A case study from a high-altitude glacierized catchment. J. Hydrometeorol. 2015, 16, 306–326. [Google Scholar] [CrossRef]
- Qin, D.; Plattner, G.; Tignor, M.; Allen, S.; Boschung, J.; Nauels, A.; Xia, Y.; Bex, V.; Midgley, P. Summary for Policymakers. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2013. [Google Scholar]
- Van Vuuren, D.P.; Edmonds, J.; Kainuma, M.; Riahi, K.; Thomson, A.; Hibbard, K.; Hurtt, G.C.; Kram, T.; Krey, V.; Lamarque, J.-F. The representative concentration pathways: An overview. Clim. Chang. 2011, 109, 5–31. [Google Scholar] [CrossRef]
- Groppelli, B.; Bocchiola, D.; Rosso, R. Spatial downscaling of precipitation from gcms for climate change projections using random cascades: A case study in Italy. Water Resour. Res. 2011, 47, 201–209. [Google Scholar] [CrossRef]
- Groppelli, B.; Soncini, A.; Bocchiola, D.; Rosso, R. Evaluation of future hydrological cycle under climate change scenarios in a mesoscale alpine watershed of Italy. Nat. Hazards Earth Syst. Sci. 2011, 11, 1769–1785. [Google Scholar] [CrossRef] [Green Version]
- Ouyang, F.; Zhu, Y.; Fu, G.; Lü, H.; Zhang, A.; Yu, Z.; Chen, X. Impacts of climate change under CMIP5 RCP scenarios on streamflow in the Huangnizhuang catchment. Stoch. Environ. Res. Risk Assess. 2015, 29, 1781–1795. [Google Scholar] [CrossRef]
- Ahl, R.S.; Woods, S.W.; Zuuring, H.R. Hydrologic calibration and validation of SWAT in a snow-dominated rocky mountain watershed, Montana, USA 1. JAWRA J. Am. Water Resour. Assoc. 2008, 44, 1411–1430. [Google Scholar] [CrossRef]
- Rahman, K.; Maringanti, C.; Beniston, M.; Widmer, F.; Abbaspour, K.; Lehmann, A. Streamflow modeling in a highly managed mountainous glacier watershed using SWAT: The upper Rhone river watershed case in Switzerland. Water Resour. Manag. 2013, 27, 323–339. [Google Scholar] [CrossRef]
- Xue, C.; Chen, B.; Wu, H. Parameter uncertainty analysis of surface flow and sediment yield in the Huolin basin, China. J. Hydrol. Eng. 2013, 19, 1224–1236. [Google Scholar] [CrossRef]
- Lemonds, P.J.; McCray, J.E. Modeling hydrology in a small rocky mountain watershed serving large urban populations. J. Am. Water Resour. Assoc. (JAWRA) 2007, 43, 875–887. [Google Scholar] [CrossRef]
- Noor, H.; Vafakhah, M.; Taheriyoun, M.; Moghadasi, M. Hydrology modelling in Taleghan mountainous watershed using SWAT. J. Water Land Dev. 2014, 20, 11–18. [Google Scholar] [CrossRef]
- Kim, N.W.; Lee, J. Temporally weighted average curve number method for daily runoff simulation. Hydrol. Process. 2008, 22, 4936–4948. [Google Scholar] [CrossRef]
- Choi, J.Y.; Engel, B.A.; Chung, H.W. Daily streamflow modelling and assessment based on the curve-number technique. Hydrol. Process. 2002, 16, 3131–3150. [Google Scholar] [CrossRef]
- Fontaine, T.; Cruickshank, T.; Arnold, J.; Hotchkiss, R. Development of a snowfall–snowmelt routine for mountainous terrain for the soil water assessment tool (SWAT). J. Hydrol. 2002, 262, 209–223. [Google Scholar] [CrossRef]
- Singh, P.; Bengtsson, L. Hydrological sensitivity of a large Himalayan basin to climate change. Hydrol. Process. 2004, 18, 2363–2385. [Google Scholar] [CrossRef]
- WWF-Pakistan. Hydrological Assessment and Bathymetric Mapping of Attaabad Lake, Hunza; World Wide Fund for Nature: Islamabad, Pakistan, 2011. [Google Scholar]
- Fadil, A.; Rhinane, H.; Kaoukaya, A.; Kharchaf, Y.; Bachir, O.A. Hydrologic modeling of the Bouregreg watershed (Morocco) using GIS and SWAT model. J. Geogr. Inf. Syst. 2011, 3, 279–289. [Google Scholar] [CrossRef]
- Sathian, K.; Syamala, P. Application of GIS integrated SWAT model for basin level water balance. Indian J. Soil Conserv. 2009, 37, 100–105. [Google Scholar]
- Kazmi, D.H.; Li, J.; Rasul, G.; Tong, J.; Ali, G.; Cheema, S.B.; Liu, L.; Gemmer, M.; Fischer, T. Statistical downscaling and future scenario generation of temperatures for Pakistan region. Theor. Appl. Climatol. 2015, 120, 341–350. [Google Scholar] [CrossRef]
- Islam, S.; Rehman, N.; Sheikh, M.; Khan, A. Assessment of Future Changes in Temperature Related Extreme Indices over Pakistan Using Regional Climate Model Precis; Research Report GCISC-RR-05; Global Change Impact Study Centre: Islamabad, Pakistan, 2009. [Google Scholar]
- Rajbhandari, R.; Shrestha, A.; Kulkarni, A.; Patwardhan, S.; Bajracharya, S. Projected changes in climate over the Indus river basin using a high resolution regional climate model (PRECIS). Clim. Dyn. 2015, 44, 339–357. [Google Scholar] [CrossRef]
- Forsythe, N.; Fowler, H.; Blenkinsop, S.; Burton, A.; Kilsby, C.; Archer, D.; Harpham, C.; Hashmi, M. Application of a stochastic weather generator to assess climate change impacts in a semi-arid climate: The Upper Indus basin. J. Hydrol. 2014, 517, 1019–1034. [Google Scholar] [CrossRef] [Green Version]
- Laghari, A.; Vanham, D.; Rauch, W. The Indus basin in the framework of current and future water resources management. Hydrol. Earth Syst. Sci. 2012, 16, 1063–1083. [Google Scholar] [CrossRef]
- Lutz, A.; Immerzeel, W.; Shrestha, A.; Bierkens, M. Consistent increase in high asia’s runoff due to increasing glacier melt and precipitation. Nat. Clim. Chang. 2014, 4, 587–592. [Google Scholar] [CrossRef]
Data Type | Data Source and Range | Scale | Description | ||
---|---|---|---|---|---|
DEM | ASTER-GDEM (30 m) | Grid Cell: (30 × 30 m) | ASTER-GDEM of the USGS [20] | ||
Land-use | Landsat TM data (2009 and 2010) | Grid Cell: (30 × 30 m) | Landsat TM 30 × 30 m-resolution data sets from the years 2009 and 2010 were used to extract land-cover for study area | ||
Soil | FAO Soil Map | 1:5,000,000 | FAO digital soil map downloaded from [21] | ||
Stream and River network | Survey of Pakistan (Toposheets) | 1:500,000 | Extracted from survey of Pakistan Toposheet maps | ||
Weather | Khunjerab, Ziarat and Naltar Automatic Weather Stations of Water and Power Development Authority (1995–2010) | Daily basis | Daily weather datasets (temperature, precipitation, wind speed, solar radiation and humidity) | ||
River flow | Surface water Hydrology Project of Water and Power Development Authority (1980–2010) | Daily basis | Mean daily discharge (m3/s) of Danyore gauge station | ||
List of Hydrological and meteorological stations | |||||
Station Name | Calibration | Validation | Latitude (DD) | Longitude (DD) | Elevation (m) |
Khunjerab | 1998–2004 | 2008–2010 | 36.8500 | 75.4000 | 4730 |
Ziarat | 1998–2004 | 2008–2010 | 36.8333 | 74.4333 | 3669 |
Naltar | 1998–2004 | 2008–2010 | 36.2167 | 74.2667 | 2810 |
Danyore flow gauge station | 1998–2004 | 2008–2010 | 35.9278 | 74.3764 | 1370 |
List of meteorological stations used for control period | |||||
Station | Available data period | Latitude | Longitude | Elevation (m) | |
Gilgit | 1980–2010 | 35.9167 | 74.3333 | 1460 | |
Gupis | 36.1667 | 73.4000 | 2156 | ||
Skardu | 35.300 | 75.6833 | 2317 |
Model | Research Center | Location | Grid Size |
---|---|---|---|
EC-EARTH | EC-Earth Consortium | European Union | 1.25° × 1.25° |
ECHAM6 | Max Planck Institute for Meteorology | Germany | 1.875° × 1.875° |
CCSM4 | National Center for Atmospheric Research | United State | 1.25° × 1.25° |
Parameter Name | Description | Range/Percentage | Adjusted Value |
---|---|---|---|
ESCO | Soil evaporation compensation factor | 0–1 | 0.52 |
SMFMX | Melt factor for snow on 21 December (mm H2O/°C-day) | 0–10 | −0.08 |
SMFMN | Melt factor for snow on 21 June (mm H2O/°C-day) | 0–10 | 0.82 |
SFTMP | Snowfall temperature (°C) | −5, +5 | 6.25 |
SMTMP | Snow melt base temperature (°C) | −5, +5 | 1.91 |
TIMP | Snowpack temp lag factor | 0.01–1 | 2.57 |
TLAPS | Temp lapse rate (°C/km) | 0, −10 | −6.07 |
PLAPS | Precipitation lapse rate (mm H2O/km) | 0–100 | 22.38 |
SNOCOVMX | Areal snow coverage threshold at 100% | 0–650 | 1.26 |
SNO50COV | Areal snow coverage threshold at 50% | 0–1 | 0.48 |
CN2 | Initial Soil Conservation Service (SCS) curve number II for moisture | −5, +5 | 1.96 |
CH_N2 | Manning’s “n” for main channel | 0.01–0.059 | 0.015 |
CH_K2 | Hydrologic conductivity for main channel (mm/h) | 0–150 | 124.12 |
ALPHA_BF | Base flow recession constant | 0–1 | 0.47 |
GW_DELAY | Groundwater delay time (days) | 0–100 | 0.37 |
RCHRG_DP | Deep aquifer percolation fraction (%) | 0–1 | 0.22 |
GWQMN | Threshold depth in shallow aquifer for return flow (mm) | 0–5000 | 1.58 |
GW_REVAP | Groundwater re-evaporation coefficient | 0.02–0.2 | 0.31 |
SOL_AWC | Soil available water capacity (mm) | ±50% | −0.30% |
SOL_K | Saturated hydraulic conductivity (mm/h) | ±50% | 18% |
SOL_BD | Soil bulk density (g·cm−3) | 1.40–1.73 | 1.23 |
Coefficients | Calibration Period (1998–2004) | Validation Period (2008–2010) | ||
---|---|---|---|---|
Observed | Simulated | Observed | Simulated | |
Mean | 300.46 (m3/s) | 277.46 (m3/s) | 324.46 (m3/s) | 312.24 (m3/s) |
r-factor | 0.76 | 0.88 | ||
p-factor | 0.79 | 0.85 | ||
R2 | 0.82 | 0.91 | ||
NS | 0.80 | 0.87 | ||
PBIAS | 3.9 | 1.8 | ||
RSR | 0.45 | 0.59 |
Component of Water Balance (mm) | Calibration (1998–2004) | Validation (2008–2010) |
---|---|---|
Yearly precipitation | 388.6 | 325.4 |
Potential evapotranspiration (PET) | 77.6 | 67.10 |
Actual evapotranspiration (AET) | 43.7 | 44.5 |
Water yield (WYLD) | 57.39 | 53.40 |
Surface runoff (Sur_Q) | 176.92 | 145.83 |
Soil water (SW) | 34.71 | 30.20 |
Lateral flow (Lat_Q) | 125.87 | 122.67 |
Contribution of groundwater to stream flow (Gw_Q) | 31.44 | 27.54 |
Average Monthly Values (mm) for the Hunza River Basin | |||||||
---|---|---|---|---|---|---|---|
Month | Precipitation (mm) | Snow Fall (mm) | Surf Q (mm) | Lat Q (mm) | Water Yield (mm) | ET (mm) | PET (mm) |
1 | 19.03 | 18.66 | 0.55 | 0.6 | 1.8 | 0.01 | 0.02 |
2 | 33.21 | 26.66 | 12.75 | 6.75 | 20.02 | 0.02 | 0.02 |
3 | 32.6 | 15.07 | 34.02 | 21.25 | 57.39 | 0.7 | 0.97 |
4 | 61.46 | 7.19 | 32.45 | 17.16 | 54.27 | 3.4 | 5.39 |
5 | 34.32 | 3.89 | 27.19 | 11.24 | 43.68 | 5.45 | 10.07 |
6 | 38.04 | 2.82 | 18.08 | 12.51 | 34.4 | 8.06 | 14.06 |
7 | 39.11 | 0.75 | 13.83 | 15.92 | 32.63 | 8.81 | 16.97 |
8 | 45.2 | 0.84 | 16.09 | 18.19 | 37.65 | 8.45 | 14.93 |
9 | 32.74 | 2.48 | 11.29 | 11.52 | 26.32 | 5.66 | 9.75 |
10 | 20.24 | 4.63 | 5.88 | 6.19 | 15.01 | 2.36 | 4.17 |
11 | 15.35 | 6.44 | 4.39 | 3.74 | 10.18 | 0.67 | 1.08 |
12 | 18.07 | 16.34 | 0.71 | 0.98 | 3.09 | 0.11 | 0.17 |
Stations | Mean RG | St. Dev RG | Mean RSi | St. Dev RSi |
---|---|---|---|---|
Khunjerab | 3.66 | 11.33 | 3.38 | 19.84 |
Ziarat | 3.96 | 10.23 | 3.38 | 18.59 |
Naltar | 3.96 | 10.76 | 3.38 | 22.55 |
Gilgit | 4.13 | 10.55 | 3.24 | 17.14 |
Gupis | 3.74 | 8.85 | 3.20 | 15.89 |
Skardu | 3.22 | 10.76 | 3.47 | 13.49 |
2030–2059 | GCMs | January | February | March | April | May | June | July | August | September | October | November | December | Annually |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RCP2.6 | EC-EARTH | 1.32 | 1.02 | 0.5 | 1.4 | 1.32 | 1.3 | 1.46 | 1.39 | 0.82 | 1.27 | 2.59 | 2.3 | 1.39 |
CCSM4 | 1.39 | 0.25 | −0.33 | −0.22 | 0.45 | 0.79 | 1.49 | 1.73 | 21.76 | 2.89 | 2.75 | 2.19 | 2.93 | |
ECHAM6 | 1.52 | 0.61 | 1.59 | 1.31 | 0.74 | 1.28 | 2.36 | 1.14 | 1.56 | 1.52 | 2.39 | 1.31 | 1.44 | |
RCP4.5 | EC-EARTH | 1.32 | 1.02 | 0.5 | 1.4 | 1.32 | 21.3 | 1.46 | 1.39 | 0.82 | 1.27 | 2.59 | 2.3 | 3.06 |
CCSM4 | 1.39 | 0.25 | −0.13 | −0.22 | 0.45 | 0.79 | 1.49 | 1.73 | 2.76 | 2.89 | 2.75 | 2.19 | 1.36 | |
ECHAM6 | 1.52 | 0.61 | 1.59 | 1.31 | 0.74 | 1.28 | 2.36 | 1.14 | 1.56 | 1.52 | 2.39 | 1.31 | 1.44 | |
RCP8.5 | EC-EARTH | 1.52 | 1.65 | 1.75 | 2.28 | 1.89 | 1.73 | 0.97 | 0.89 | 2.4 | 2.46 | 2.04 | 1.57 | 1.76 |
CCSM4 | 2.35 | 1.31 | 0.76 | 0.98 | 2.66 | 1.89 | 2.09 | 2.35 | 2.83 | 3.65 | 3.89 | 2.34 | 2.26 | |
ECHAM6 | 1.05 | 0.97 | 1.28 | 1.53 | 1.14 | 1.33 | 2.28 | 1.85 | 3.2 | 2.57 | 2.09 | 1.45 | 1.73 | |
2070–2099 | GCMs | January | February | March | April | May | June | July | August | September | October | November | December | Annually |
RCP2.6 | EC-EARTH | 1.54 | 0.67 | 1.04 | −0.46 | 1.98 | 0.75 | 0.65 | 1.13 | 0.67 | 0.88 | −0.3 | 2.09 | 0.89 |
CCSM4 | 0.45 | 0.63 | 0.3 | 0.32 | −0.18 | −0.51 | 0.74 | 1.27 | 2.63 | 2.2 | 2.09 | 0.95 | 0.91 | |
ECHAM6 | −0.45 | 0.75 | 1.36 | 1.23 | 0.76 | 0.37 | 1.24 | 0.89 | 0.66 | 1.76 | 0.72 | 0.18 | 0.79 | |
RCP4.5 | EC-EARTH | 3.75 | 3.16 | 2.44 | 2.78 | 3.01 | 2.67 | 3.24 | 3.54 | 3.45 | 3.59 | 2.67 | 2.09 | 3.03 |
CCSM4 | 2.65 | 2.24 | 1.03 | 0.83 | 1.1 | 1.05 | 2.65 | 2.52 | 3.84 | 3.64 | 2.09 | 2.35 | 2.17 | |
ECHAM6 | 3.5 | 1.97 | 3.05 | 2.07 | 1.45 | 2.34 | 3.23 | 2.38 | 3.26 | 2.55 | 2.76 | 2.96 | 2.63 | |
RCP8.5 | EC-EARTH | 4.06 | 3.96 | 5.78 | 5.97 | 5.44 | 4.65 | 5.02 | 5.74 | 5.67 | 6.66 | 6.25 | 5.57 | 5.40 |
CCSM4 | 4.78 | 4.39 | 4.06 | 4.16 | 4.91 | 3.29 | 5.47 | 6.02 | 6.61 | 9.98 | 6.43 | 5.81 | 5.49 | |
ECHAM6 | 5.76 | 4.83 | 5.29 | 5.09 | 4.65 | 6.08 | 6.76 | 5.46 | 5.33 | 6.45 | 6.46 | 6.34 | 5.71 |
2030–2059 | GCMs | January | February | March | April | May | June | July | August | September | October | November | December | Annually |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RCP2.6 | EC-EARTH | 13.75 | 10.65 | 23.7 | −10.9 | 10.67 | 24.04 | 6.59 | 16.69 | 25.89 | −15.43 | 5.88 | −19.5 | 7.67 |
CCSM4 | −26.9 | 15.03 | −32.9 | 24.95 | −5.83 | −8.43 | 2.54 | 38.14 | −21.07 | −67.3 | 75.37 | −14.0 | −1.70 | |
ECHAM6 | 13.0 | −34.5 | 25.8 | −12.8 | 38.8 | 0.4 | −6.2 | 25.0 | 18.0 | −48.4 | −23.2 | 22.0 | 1.49 | |
RCP4.5 | EC-EARTH | 10.8 | 46.5 | 4.9 | 43.2 | −13 | −7.4 | 16.5 | 3.1 | −2.2 | 50.5 | −27.6 | −4.4 | 10.08 |
CCSM4 | 66.9 | 10.54 | −7.7 | 25.3 | −20.43 | 38.9 | −10.99 | −43.8 | −27.2 | −49.5 | −49.6 | −22.2 | −7.48 | |
ECHAM6 | 10.56 | 20 | −15.3 | 3.87 | 35.5 | −41.6 | −20.7 | 18 | 12.2 | −12.4 | −6.2 | 35.4 | 3.28 | |
RCP8.5 | EC-EARTH | 19.0 | 9.2 | −16.4 | 49.03 | 7.6 | 12.7 | 77.2 | 20.2 | 11.3 | 34.5 | −40.8 | 44.2 | 18.98 |
CCSM4 | 4.97 | 14.8 | −25.5 | 5.5 | −3.18 | 49.3 | −19.34 | −20.93 | −23.3 | −29.56 | −34.54 | 0.1 | −6.81 | |
ECHAM6 | −17.6 | −13.4 | −16.9 | −17.0 | 23 | −29.3 | −8.5 | 13.6 | −9.7 | 20.13 | −27.4 | 29 | −4.51 | |
2070–2099 | GCMs | January | February | March | April | May | June | July | August | September | October | November | December | Annually |
RCP2.6 | EC-EARTH | 12.0 | 22.43 | −8.9 | 21.0 | −45.5 | 47.3 | −2.7 | 11.5 | 1.03 | 22 | −31.8 | −14.2 | 2.85 |
CCSM4 | 38.43 | 2.67 | −18.7 | 57.6 | 12.1 | −20.23 | 2.3 | 2.7 | −12.45 | 30.76 | −34.5 | 15.4 | 6.34 | |
ECHAM6 | 11.01 | −24.1 | 18.6 | −14.6 | 12.0 | −18.4 | 7.65 | 6.33 | 3.3 | 107 | −7.4 | 67.55 | 14.08 | |
RCP4.5 | EC-EARTH | −23.3 | 32.1 | −18.0 | 13.6 | 17.4 | 39.65 | 23.02 | −10.8 | −17.0 | 35.2 | −34.7 | 86.4 | 11.96 |
CCSM4 | 10.1 | 16.6 | 24.4 | 24.2 | 21.9 | 24 | 25.4 | 27.7 | 21.3 | 17.9 | 16.5 | 12.1 | 20.18 | |
ECHAM6 | −32.5 | −0.5 | 23.45 | 25.43 | 57.1 | −11.4 | 2.04 | 9.56 | −7.7 | −6.7 | −0.4 | 5.2 | 5.30 | |
RCP8.5 | EC-EARTH | 52.8 | 23.7 | 72.0 | 42.0 | 8.0 | 38.9 | 37.1 | −27.9 | −11.5 | 43 | −1.4 | 95.2 | 30.99 |
CCSM4 | 21.8 | −39.7 | −19 | 27.0 | 55.8 | 102.8 | −30.9 | 17.4 | −6.2 | −6.1 | −27.3 | 8.5 | 8.68 | |
ECHAM6 | 23.55 | −34.3 | −3.5 | −17.3 | 27.4 | −1.99 | −13.6 | −1.45 | 65.5 | 56.4 | −85.7 | 22.8 | 3.15 |
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Garee, K.; Chen, X.; Bao, A.; Wang, Y.; Meng, F. Hydrological Modeling of the Upper Indus Basin: A Case Study from a High-Altitude Glacierized Catchment Hunza. Water 2017, 9, 17. https://doi.org/10.3390/w9010017
Garee K, Chen X, Bao A, Wang Y, Meng F. Hydrological Modeling of the Upper Indus Basin: A Case Study from a High-Altitude Glacierized Catchment Hunza. Water. 2017; 9(1):17. https://doi.org/10.3390/w9010017
Chicago/Turabian StyleGaree, Khan, Xi Chen, Anming Bao, Yu Wang, and Fanhao Meng. 2017. "Hydrological Modeling of the Upper Indus Basin: A Case Study from a High-Altitude Glacierized Catchment Hunza" Water 9, no. 1: 17. https://doi.org/10.3390/w9010017