The Hindu Kush Himalaya (HKH) has some of the largest glaciers in the world [1
]. They act as natural water storage—reservoirs that store precipitation in the winter in the form of snow and release it in the summer as meltwater [2
]. A major part of the flow in the Indus river system in the western Himalaya comes from snow and glacier fed river catchments in the Karakoram region [3
]. Modeling studies suggest that the contribution of snow and glacier melt in the Indus river is as high as 50–80% [4
Climate change is expected to affect various components in the hydrological cycle [8
]. Recent changes identified at high elevations in the Karakoram include shifts in seasonal temperatures, snowfall, and snow cover [9
]. Temperature change is expected to adversely affect the glacier and ice reserves of the Himalayan region with a potential shift in the equilibrium line altitude (ELA), recession of glacier termini to higher elevations, and reduction in glacier area and ice volume [10
]. Khattak, et al. [11
] reported an increase in winter maximum temperatures between 1976 and 2005 in the upper, the middle, and the lower parts of the Indus basin of 1.79, 1.66, and 1.20 °C, respectively. Nepal and Shrestha [12
] also reported an overall gradual increase in temperature in the Indus basin but with some differences in the reported seasonal trends. Bolch, et al. [13
] project that HKH glaciers will lose more than one-third of their volume, even if warming is kept to 1.5 °C. In the first instance, rising temperatures are likely to lead to an increased melt rate and runoff [14
]. Archer [15
] suggests that a 1 °C rise in mean summer temperature would result in a 16% increase in summer runoff into the Hunza and the Shyok rivers due to accelerated glacier melt. However, as the glacier storage is reduced, the runoff is expected to decrease [14
]. The changes in glacier mass and groundwater storage are likely to impact water resources at a regional scale. Reduction in runoff has serious implications for the extensive downstream areas of the Indus river and especially for the people who rely on meltwater for their water supply [16
]. Climate change is projected to compound the pressure on natural resources and the environment associated with rapid urbanization, industrialization, and economic development [19
] and interact with these resources in a complex way across the HKH [20
]. Better understanding of the potential impact on downstream water availability resulting from changes in snow and glacier reserves in the high mountain catchments of the Indus will be crucial for future planning in the region.
The impact of potential changes in glaciers on water resources can be assessed using glacio-hydrological simulation [21
], but developing reliable models is difficult in high mountain areas due to the lack of available input data and the extreme topography. Precipitation data are the most important input in distributed hydrological modeling of mountainous river basins [24
], and uncertainty in spatial distribution and amount can strongly affect the results. Precipitation in the Himalayas remains poorly defined due to remoteness and the lack of reliable rainfall networks [25
]. There are very few measuring stations in high mountain areas in the Himalayas, and even the available stations are mostly valley based [26
], while the satellite derived precipitation products are generally of insufficient resolution and quality to capture the spatial variation and magnitude of mountain precipitation, especially at the basin scale [26
The Hunza basin, a glaciated sub-catchment of the Indus river, was selected for a study of the impact of potential glacier recession on river discharge and indicator of potential downstream changes in the Indus river system. The flow regime of the Hunza river is dominated by snow and glacier melt runoff [4
]. Various studies suggest that snow and glacier melt contribute 83–90% of flow in the basin, and that glacier ice melt alone contributes 33–85% [4
]. The variation is in part due to the different way in which glacier ice melt processes are treated in the modeling context. Very few hydrological models have incorporated glacier mass change processes. These include the Spatial Processes in Hydrology (SPHY) cryosphere-hydrological model, which was used to calculate the change in glacier area resulting from climate change in the Hunza basin [30
], and a glacier mass balance and ice redistribution model applied by Shea, et al. [31
] in the Mount Everest region for historic and future periods.
The J2000 hydrological model was selected to simulate snow cover area and runoff in the basin. The model is designed for use at different catchment scales and has been used for simulations of flow elsewhere in the Himalayan region—in the eastern catchment of the Ganges [32
] and the Tibetan Plateau [35
]—as well as other parts of the world. The study had three main objectives: (1) to assess the capability of the J2000 model to perform the simulations of snow cover and river flow, (2) to simulate the contribution of snowmelt and different components of glacier melt to total river discharge, and (3) to test the potential effects of different scenarios of glacier recession on ice melt and river discharge. The novelty of the study lies in the application of the J2000 model to simulate hydrological processes in the western Himalaya using snow cover and discharge data and to assess the potential effects of glacier recession on water availability downstream.
3. Results and Discussion
3.1. Snow Cover Simulation
The J2000 model’s ability to simulate seasonal snow cover was assessed by comparing the simulated snow cover area (calculated using the corrected precipitation values) with MODIS snow cover values (Figure 4
). In J2000, the maximum snow cover area of the 8-day interval matching the MODIS time period was chosen. The snow cover area simulated by the model was in good agreement with the area inferred from the MODIS snow cover data (R2
= 0.65; positive bias of 6%).
The MODIS data for the Hunza basin show a maximum average snow cover area of 81% (11,213 km2) in March and a minimum average snow cover area of 42% (5835 km2) in August for 2000–2010. The simulated values from the J2000 model for maximum snow cover area (11,904 km2) in March were similar to the observed values; those for the minimum snow cover area (4128 km2) in August were lower than the observed values, but there was good agreement in the period of snow melt. In some years (2002, 2005, 2008), the snow accumulation was underestimated by the J2000 model. Overall, the snow cover area was simulated well by the J2000 model, but snow cover variability was less well represented.
3.2. Hydrograph Analysis during Calibration and Validation
A split sample procedure was used for calibration and validation of the simulated discharge values against observed discharge [63
]. The period for which data was available was not very long, thus a larger part (2000–2004) was used for calibration to ensure calibration was meaningful and the remainder [2008–2010 (no data available for 2005–2007)] was used for validation. Figure 5
shows the simulated and the observed daily discharge values for the calibration and the validation periods together with the corrected precipitation, and Figure 6
shows the average monthly simulated and observed discharge values and a scatter plot of the daily observed and simulated discharge values. Table 5
shows the values of the different efficiency criteria derived from comparison of the simulated daily discharge values with observed values over the calibration and the validation periods using the observed precipitation values and the corrected precipitation values. For the water balance assessment (discussed in the later sections), we took the data from the period of 2000–2004 and 2008–2010 (a total of eight years).
The graphical and the statistical evaluation showed that the J2000 model was able to reproduce the overall hydrological dynamics fairly well. The base flow was well simulated during both the calibration and the validation periods. The rising and the falling limbs were also well simulated by the model. However, there were some high peaks shown in the simulated discharge in April 2002 and 2003 that were not identified in the observed discharge. Both visual inspection and the efficiency criteria values indicated that the simulated discharge values (especially base flow and discharge peaks) were considerably closer to the observed values when using the corrected precipitation in both the calibration and the validation periods.
3.3. Contribution of Ice Melt to Total Discharge
In the model, glacier melt runoff (melt from the glacier area) is the sum of snowmelt runoff (seasonal snowfall), ice melt runoff, and rain runoff (from rainfall over the glacier area). Glacier ice melt begins in a glacier HRU after the seasonal snowfall has melted and the seasonal snow storage on that HRU is zero. Figure 7
shows the simulated monthly average contribution of the ice melt component to total simulated discharge from the basin. The monthly values of proportional contribution to basin total discharge for the individual components are shown in Table 6
Melt from the glacier area contributed 60% (on average) to the total discharge from the basin during the modeling period; 47% of the total from ice melt, 12% from seasonal snowmelt, and 1% from rain runoff. The maximum average contribution of glacier melt to total discharge was in August (73%); there was no contribution in December, January, or February. The seasonal contribution of ice melt was particularly significant at 52% of the average monthly discharge in the summer period (June to October).
Snowmelt from outside of the glacier area comprised 39% of total runoff in the basin, but a part of this infiltrated to soil and evaporated (about 6%), thus the total contribution of snowmelt from outside the glacier area to discharge was 33%. The contribution of all snowmelt to total discharge was 45% (33% snowmelt from outside the glacier area and 12% snowmelt from the glacier area).
shows the contribution of snowmelt, glacier ice melt, and total snow and glacier melt to discharge identified in various modeling studies in and close to the Hunza basin. Glacier ice melt is given separately to meet the different definitions used in the studies. For example, Refs. [14
] do not include snowfall or rainfall on the glacier surface and only consider glacier ice melt, whereas the present study used seasonal snowfall and rainfall as well as glacier ice melt to assess the contribution to discharge from the glacier area (contribution to glacier ice melt alone was 47%, and both snow and ice melt was 59%). Our results suggest that snow and glacier melt from the whole basin contribute 92% of total discharge, which is close to the values given by [4
], the studies that provide the most direct comparison in terms of area and basin size. The approach used by [4
] was similar to that used in the J2000, with seasonal snowfall on the glacier taken into consideration, although the value calculated for glacier ice melt in our study was slightly higher. The values calculated for glacier ice melt by [14
] were higher again than those calculated using our approach because of the different methods used to realize glacier melt.
3.4. Water Balance Analysis
The results of the overall water balance analysis for the Hunza basin are shown in Figure 8
. The average annual input to total water during the modeling period was 731 mm (70%) from precipitation and 315 mm (30%) from glacier ice melt, giving a total of 1046 mm. Actual evapotranspiration (actEt) returned 162 mm (16%) of total input to the atmosphere, 672 mm (64%) left the basin as total river discharge, and 20% remained in different forms of storage, such as snow, soil, and groundwater.
3.5. Impact of Different Scenarios on Ice Melt Runoff and River Discharge
shows the results of the analysis of the impact of glacier recession on ice melt runoff as well as the resultant total discharge calculated using the J2000 model. The analysis focused on the impact on ice melt, as this is likely to be the component most affected. The snowfall and rainfall components are not affected by the change in glacier area (the only component changed in the scenario), as they will simply runoff from bare land instead of the glacier surface. In a warming world, the change in temperature will also affect snowfall distribution and other hydrological processes, but in this study, we only looked at the glacier recession scenario.
The simulated monthly average ice melt runoff during the modeling period was used as the baseline for the glacier shrinkage scenarios. Total ice melt declined by 55%, 81%, and 96%, respectively, under the scenarios of recession to 4000, 4500, and 5000 masl; the average annual contribution to total discharge declined from 47% at the baseline to close to zero (29%, 14%, and 4%, respectively), and the total average annual river discharge declined by 28%, 40%, and 46%, respectively. The reduction was mainly in summer discharge. Discharge actually increased very slightly in some months during the accumulation period (December, January, February, and March) because, as glaciers recede, the snow falls on bare land, infiltrates after melt, and contributes to river discharge through interflow and groundwater.
3.6. Uncertainties and Limitations
Climate data quality plays a very crucial role in hydrological modeling. The climate stations in the Hunza basin are valley-based and do not sufficiently represent the spatial distribution of precipitation in the mountain range. The precipitation in this study was corrected using a precipitation gradient, but these were calculated values, and some uncertainty remains. The validation of the J2000 model simulated snow cover area using MODIS snow cover data helped to constrain the snow and glacier related parameters in the model, which reduced the parametric uncertainties. However, the MODIS snow cover data also contained some uncertainty, as they were limited to 500 m resolution over an 8-day period. Finally, both accuracy and length of discharge data are essential to calibrate and validate hydrological models, but limited data availability meant that the typical length of the modeling period in this study was only eight years.